biomedical engineering - ingegneria biomedica

L'obiettivo dell'insegnamento è quello di fornire al futuro ingegnere biomedico le conoscenze sulle principali misure e indicatori di salute e di sanità, sui principali modelli di valutazione, su alcune problematiche emergenti e sull'organizzazione dei sistemi sanitari nei principali peasi industrializzati

Conoscenza e capacità di comprensione:
Conoscenze delle principali misure e indicatori di sistema (demografia, salute, stile di vita, personale sanitario, strutture e attrezzature, funzionamento delle strutture, spesa sanitaria)
Conoscenza sui principali modelli dei determinanti della salute e sulla rappresentazione e valutazione di sistemi sanitari
Conoscenza sui sistemi sanitari in Italia e nei principali paesi industrializzati (USA, Francia, Germania, Regno Unito, Spagna, Svizzera, Giappone e Cina)
Conoscenza sulle problematiche emergenti in sanità
Conoscenza e capacità di comprensione applicate:
Capacità di comprendere i principali modelli dei determinanti della salute e sulla rappresentazione e valutazione di sistemi sanitari
Capacità di comprendere gli adeguamenti, le riforme e i sistemi di classificazione dei sistemi sanitari
Autonomia di giudizio:
Capacità di valutare le principali misure e indicatori di sistema
Capacità di valutare in senso critico i modelli dei determinanti della salute
Capacità di valutare le criticità e i punti di forza di un sistema sanitario contestualizzato nella specifica realtà nazionale
Capacità di mettere a confronto le performance di diversi sistemi sanitari
Capacità di inquadrare nella dimensione europea dell’assetto istituzionale e gestionale del settore dei servizi sanitari in Italia
Abilità comunicative:
Capacità di esporre le principali misure e indicatori di sistema
Capacità di esporre i modelli e la rappresentazione dei sistemi sanitari
Capacità di descrivere i sistemi sanitari, i punti di forza e i limiti
Capacità di apprendere:
Abilità di comprendere e argomentare in senso critico la letteratura sull’argomento

Sanità e Salute
Evoluzione, misura e rappresentazione della salute e dei sistemi sanitari
Sanità e salute: due storie diverse. Organizzazioni internazionali di misura, controllo e intervento sulla salute. Storia della medicina ed evoluzione della sanità Sistemi di governo nazionali e sopranazionali. Grandezze per la quantificazione della salute e della sanità. Misure, indicatori, indici. Spesa sanitaria. Metodi di rappresentazione dei sistemi. Nuove emergenze in sanità
Il rapporto Sanità e Salute
I determinanti della salute. I determinanti non medici. Quanto pesa la sanità sulla salute? Modelli di salute. Modelli qualitativi. Modelli quantitativi:il modello di Dever, il modello Crivellini-Ronchi. Sanità indotta: il modello di Domenighetti. Modelli di valutazione dei sistemi sanitari: i modelli WHO, modelli Oecd, Euro Health Consumer Index, Indice Sanitario, studio Cerm, Sistema Bersaglio, Indice di Bloomberg, Commonwealth Fond, Meridiano Sanità Index, Programma Nazionale Esiti, Valutazione LEA, valutazioni CREA. Valutazione-Informazione-Scelta-Governo. Indici di disuguaglianza di salute e di accesso ai servizi.
I sistemi sanitari dei principali paesi industrializzati
Il sistema sanitario negli Stati Uniti. Modello organizzativo. La riforma Obama del 2010. Spesa sanitaria. Assicurazioni private. Medicare. Medicaid. Chip ed altri programmi pubblici. Piani Sanitari e organizzazioni di gestione della salute. Strutture sanitarie e dati di sistema.
Il sistema sanitario nel Regno Unito. Organizzazione del National Health Service. Leggi di riforma: da Thatcher a Blair e a Cameron. Spesa sanitaria. Strutture ospedaliere e assistenza di base. Settore farmaceutico. Health Service Commissioners. Dati di sistema. Valutazioni e informazione.
Il sistema sanitario in Francia. Organizzazione e schema istituzionale. Programmazione. Settore pubblico e settore privato. Spesa sanitaria. Il sistema assicurativo obbligatorio ed assicurazioni integrative volontarie. Strutture sanitarie. Dati di sistema.
Il sistema sanitario in Germania. Cenni storici. Modello organizzativo. Riforme del 2003 e del 2007. Spesa sanitaria. Strutture sanitarie e ruolo delle organizzazioni mediche. Settore farmaceutico. Dati di sistema e tendenze.
Il sistema sanitario in Spagna. Organizzazione generale. Spesa sanitaria. Dati di sistema.
Il sistema sanitario in Svizzera. Modello organizzativo. Il sistema assicurativo. Spesa sanitaria. Dati di sistema.
Il sistema sanitario in Giappone. Modello organizzativo. Il sistema assicurativo. Spesa sanitaria
Il sistema sanitario in Cina. Storia e organizzazione attuale. Dati e tendenze.
Il sistema sanitario in Italia
1. Cenni storici: dalla riforma del 1888 all'istituzione del Servizio Sanitario Nazionale. La riforma del 1992-93 e le sue integrazioni. Federalismo fiscale e sanità. Il Servizio Sanitario Nazionale come sistema. Ruolo delle Regioni e organizzazione nel territorio. Strumenti di governo. Piani Sanitari Regionali e modelli organizzativi. Accreditamento.
2. Le Aziende Sanitarie.Nuovi criteri di gestione. Il sistema di rimborso a prestazione (DRG). Aziende ospedaliere. Controllo di gestione. Centri di costo. Assetto organizzativo e strategie di gestione.
3. Spesa sanitaria e finanziamento. Strutture e prestazioni. Mobilità sanitaria. LEA. Il sistema dell' Emergenza-Urgenza.
4. Diversità regionali, deficit e debito. Piani di Rientro e Commissariamento. Appropriatezza.
5. Qualità in sanità. Tutela degli utenti. Carta dei servizi. URP. Sicurezza. Sistemi di valutazione di sistemi e di servizi sanitari. Informazione. Ruolo degli utenti e strumenti a loro disposizione.
6. Italia vs resto del mondo: pregi e limiti del sistema italiano.
Problematiche emergenti
Valutazione delle tecnologie sanitarie.
Esplosione della spesa e necessità di scelta. Valutazioni economiche. Health Technology Assessment. Definizioni, centri, associazioni internazionali e nazionali. Esempi.
Disuguaglianze di salute e politiche sanitarie.
Strutture e servizi sanitari
Evoluzione dei modelli strutturali e organizzativi ospedalieri. Centri extra ospedalieri. Assistenza domiciliare. Cure primarie. Demografia, salute e nuove emergenze organizzative. Il problema della regolazione degli accessi e delle liste di attesa.
Gli errori in sanità. Risk management
Definizioni e classificazioni. Stima e quantificazione degli Eventi Avversi. Errori medici, infezioni ospedaliere. Gestione del rischio clinico. Unità di Gestione del Rischio. Tecnologie e riduzione del rischio.
Il settore farmaceutico
Spesa farmaceutica. Il mercato farmaceutico e le regole di vendita dei farmaci. Compartecipazione alla spesa. Farmaci generici e politiche conseguenti. Il mercato parallelo.
Il ruolo dell'informazione
Libertà di cura e informazione. Consumo sanitario e informazione. Conflitti di interesse e informazione. L'informazione come strumento di regolazione del sistema sanitario.

Prova scritta obbligatoria, senza prove in itinere
La valutazione avverrà tramite una prova scritta sugli argomenti trattati nel modulo.
La prova scritta prevede sia domande aperte sia domande a risposta multipla, relative agli argomenti trattati durante le lezioni.
Non sono previste prove in itinere.

Marcello Crivellini, Manuela Galli, Sanita' e salute: due storie diverse, Editore: Franco Angeli, Anno edizione: 2016

Bioinformatics and Genomics represent the evolution of modern biology through the integration of information science and computational methods. Students will acquire a fundamental understanding of genome biology and the principal computational approaches for the analysis of biomolecular and omics data generated by current technologies in molecular biology and medicine.

Upon successful completion of the course, students will have acquired:
Knowledge and understanding of the fundamental concepts of functional genomics, the data generated by major biotechnological platforms, and the most appropriate computational methods for their analysis. (DD1)
Ability to apply suitable computational methods and algorithms for the analysis of sequence data, gene expression data, and genetic variation. (DD2)
Ability to select appropriate computational tools for addressing the main problems encountered in bioinformatics. (DD3)
Mastery of the terminology required to accurately describe different types of biological data and the analytical methodologies employed. (DD4)
An appreciation of the role of information bioengineering in modern biology, together with the ability to understand advanced topics by building on the knowledge and analytical tools acquired during the course. (DD5)

DNA: structure and function. Review of the main biotechnological techniques. Access to specialized biological databases and integrated analysis tools. Sequence analysis (nucleotide and amino acid): similarity and homology. Sequence alignment algorithms: global and local alignment strategies, statistical significance, implementation, and use of heuristic methods. Comparison of nucleotide and protein sequences: substitution matrices and evolutionary models. Definition and application of Hidden Markov Models (HMMs) for sequence modeling. Molecular evolution and phylogenetic tree reconstruction: phenetic and cladistic approaches; assessment of phylogenetic tree reliability. Information theory in molecular biology: entropy and mutual information; applications to the identification of potential transcription factor binding sites and RNA secondary structure prediction. Bioinformatics approaches for protein characterization. Gene expression data analysis: normalization, differential expression analysis, data visualization, and clustering methods. Functional annotation, dedicated biological resources, and enrichment analysis. Multiple testing correction methods. Systems biology approaches for gene network characterization. Biological networks and network motifs. Mechanisms regulating gene expression. MicroRNAs: structure, function, and prediction methods. Fundamentals of epigenomics. Next-generation sequencing (NGS). Large-scale and multi-omics data analysis for medical genomics and precision medicine. Basic concepts of genetics: principles and methods of linkage analysis for disease gene identification and genetic association studies.
Guided computer practical lessons: the Bioinformatics and Functional Genomics course includes practical computer-based laboratory sessions designed to complement theoretical lectures. Topics covered in the guided laboratory exercises include: the MATLAB environment and dedicated functions; access to biological databases and the use of online bioinformatics tools; application of information theory to identify potential transcription factor binding sites; examples of Hidden Markov Model applications in bioinformatics; gene expression data analysis, including preprocessing, differential expression analysis, visualization of results, and clustering of expression profiles; survival analysis.

Prova scritta obbligatoria, senza prove in itinere
The assessment consists of a written examination designed to evaluate students' understanding of the theoretical concepts, their ability to apply these concepts to specific problems, and their ability to write code for the analysis of the types of data covered in the course. The exam lasts one and a half hours.

Pevsner Jonathan, Bioinformatics and Functional Genomics, Editore: Wiley-Blackwell, Anno edizione: 2015, ISBN: 978-1-118-58178-0

Fornire gli elementi conoscitivi e metodologici per affrontare in modo propositivo e progettuale le vaste problematiche del sistema motorio, sia per quanto riguarda le basi fisiologiche del movimento umano, sia per quanto riguarda le tecniche di analisi quantitativa del movimento, la modellistica, la simulazione e la stima delle grandezza biomeccaniche di interesse, e le tecniche di recupero della funzione motoria.

Ci si aspetta che lo studente, al termine del corso, abbia acquisito competenze adeguate relative alla biomeccanica del movimento umano, sappia descrivere il sistema muscolo scheletrico e conosca le sue rappresentazioni mediante modelli, conosca le basi di cinematica, statica e dinamica per la rappresentazione del movimento umano, sia competente nella misura delle variabili biomeccaniche mediante strumenti avanzati.
In particolare ci si aspetta che lo studente al termine del corso abbia:
a) DdD 1 Conoscenza dei principi fondamentali del sistema motorio e comprensione della biomeccanica del movimento umano.
b) DdD 2 Capacità di applicare e realizzare modelli biomeccanici per la rappresentazione delle strategie relative al controllo posturale e alla locomozione
c) DdD 4 Abilità comunicative e proprietà di linguaggio nella trattazione di tematiche inerenti la biomeccanica del movimento.

Programma:
1) L’uomo come macchina automotrice: organizzazione delle varie componenti funzionali; postura e movimento, sistemi di controllo, concetti di sinergia e di programma motorio, considerazioni energetiche;
2 )La struttura di sostegno: architettura dello scheletro; descrizione anatomo-funzionale dei principali complessi articolari;
3) Il sistema motore: il muscolo come attuatore; architettura del sistema muscolare e del sistema di trasmissione meccanica; modalità di contrazione e di controllo motorio; schemi dei principali circuiti riflessi;
4) Conversione di energia metabolica in meccanica; il lavoro muscolare; rendimento meccanico dell'esercizio fisico;
5) Metodi per l’analisi biomeccanica del sistema motorio: le rilevazioni e le stime antropometriche; la misura cinematica (tecniche e strumentazioni); la misura dinamometrica; misurazioni sperimentali invasive (forze intra-articolari, forze tendinee, pressioni intradiscali, pressione intra-addominale, cinematica di segmenti ossei); la registrazione elettromiografica; l’approccio modellistico; l’analisi della funzionalità locomotoria;
6) Modelli biomeccanici per lo studio del sistema motorio: modelli delle articolazioni (sistemi di riferimento, gradi di libertà, assi funzionali); modelli del muscolo (modelli per lo studio della contrazione, modelli della struttura, modelli per lo studio della cinematica muscolare); modelli del sistema di controllo motorio (modelli ‘alfa’, ‘gamma’, ‘lambda’); modelli segmentari (arto superiore, spalla, arto inferiore, il modello a pendolo inverso, il modello ‘total body’);
7) Il problema dinamico inverso: descrizione cinematica di sistemi articolati; sistemi di riferimento e matrici di rototraslazione; ricerca di assi funzionali; equazioni di equilibrio dinamico. Stima di forze muscolari e intra-articolari e approcci al superamento dell’indeterminatezza: metodi e criteri di ottimizzazione; ipotesi sulle strategie di coordinamento motorio; uso di informazioni elettromiografiche (modelli ‘EMG driven’); definizione delle situazioni di carico; modellizzazione delle strutture intra-articolari (vincoli, superfici coniugate);
8) Cenni alla soluzione del problema dinamico diretto: metodi approssimati di soluzione; simulazione del movimento umano; esempi di applicazione (con riferimento anche a software di simulazione, tecniche di ‘rendering’, grafica 3-D, animazione);
9) Applicazioni cliniche:ortopedia e chirurgia funzionale (endoprotesi, mezzi di sintesi ossea, trasposizioni muscolari); riabilitazione (ortesi e sistemi ibridi)
Esercitazioni:
Le esercitazioni sono dedicate all’approfondimento dei temi trattati durante il corso e comprenderanno dimostrazioni con l'utilizzo di tecnologia traspotabile in aula ed esercitazioni al computer.
Laboratorio:
Verranno svolte dimostrazioni relative all'utilizzo delle strumentazioni più significative per l'analisi del movimento
Bioengineering of the motor system
Automotive properties of the human motor system. Energetics: mechanical work, efficiency, methabolic to mechanic energy conversion. Functional properties of the muscle, in relation to structure, fibre types, anatomical arrangement. The motor control system: spinal reflexes, synergies, motor programmes, models. Methods for experimental investigation with and without perturbation, invasive/non invasive measurements. Kinematic description of multiple joint system: reference frames, roto-translation matrices, functional axes. The inverse dynamics problems: dynamic equilibrium equations, inertia and visco-elastic parameters estimation, internal forces estimation. Principles of the forward dynamics approach. Musculo-skeletal models for the analysis and simulation of human movement. Clinical applications (functional orthopaedic surgery, prostheses).
Testi consigliati:
L'intero contenuto del corso è disponibile on line. Inoltre si consiglia la consultazione dei testi riportati in bibliografia

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
La prova d’esame consiste in una prova scritta, della durata di 75 minuti, articolata in domande a risposta aperta ed esercizi, seguita da un’eventuale prova orale facoltativa, alla quale è possibile accedere solo previo superamento della prova scritta. In caso di svolgimento della prova orale, il voto finale sarà determinato dalla media dei voti conseguiti nelle due prove.
La conoscenza e la comprensione degli argomenti del corso sono valutate mediante domande specifiche ed esercizi applicativi.
La capacità di comprensione e applicazione delle conoscenze acquisite è verificata attraverso quesiti relativi agli argomenti affrontati durante le esercitazioni.

Carlo Albino Frigo, Bioingegneria del sistema motorio, Editore: Aracne Editore edizione: 2018, ISBN: 978-88-255-1573-2
J.A. Kapandji, Fisiologia articolare: schemi commentati di biomeccanica umana (Vol. 1-2), Editore: DEMI Roma, Anno edizione: 1974
Giovanni Legnani e Giacomo Paglieri, Fondamenti di Meccanica e Biomeccanica del Movimento, Editore: CittàStudi Edizioni, Anno edizione: 2016, ISBN: 978-88-251-7407-6

The course is aimed at providing the students with the proper methodologies to design and evaluate the performances of heart valve prostheses and extracorporeal circulation devices. After a short introduction recalling the basic physiology and energetics of the heart and the circulatory system, the main causes of blood hemolysis and coagulation will be discussed. Criteria about how to optimise, adjust and evaluate the performances of the devices will be given. The students will acquire the ability to solve problems related to the interaction of the devices with blood. Moreover, the course aims at bringing the students to make autonomous judgements about the possible solutions to be adopted.
Along the course, clinical applications/implantation procedures and devices will be shown to the students either in the form of videos/slides or by showing real commercial devices.
Students will be brought in small groups to the Laboratory to observe and understand how experimental testing of the devices focused during the course is performed, so as to enhance understanding of the taught theory.

At the end of the course, the students are expected to possess both know-how and know-why about the blood contacting devices and prostheses working conditions, constraints and design specifications.
With reference to the Dublin Descriptors listed herunder:
DD1 - Knowledge and understanding;
DD2 - Applying knowledge and understanding;
DD3 - Making judgements;
DD4 - Communication skills;
DD5 - Learning skills;
the students are expected to develop and acquire abilities to meet all the Descriptors, namely applied to:

• knowledge of the main concepts about the design constraints and specifications of the artificial internal organs or life support systems focused during the course;
• knowledge and understanding of the main physical, mechanical, and chemical phenomena taking place in the devices;
• knowledge to perform calculations to design, quantify, and evaluate, the performances of the devices;
• ability to develop, judge and apply patient specific approaches to the design of artificial systems and know how to optimize the interaction with the living organism;
• development of skills to face and communicate/promote innovation in this field of biomedical engineering.

The course is aimed at teaching the design and evaluation criteria of artificial heart valve prostheses and extracorporeal circulation devices.
More specifically, the main subjects are the following:
-Survey about cardiovascular system's functional anatomy and physiology;
-Interaction between the cardiovascular and the artificial systems: hemolysis and coagulation;
-Design and evaluation criteria of mechanical and biological prosthetic heart valves;
-Design criteria for extracorporeal circuit devices and choice of the optimal vascular access;
-Layout of the extracorporeal circuit;
-Design and optimization of extra-corporeal blood pumps;
-Design of blood heat exchangers;
-Design of extracorporeal blood oxygenation devices;
-Design of blood filters.
International standards prescription related to the testing and the production of prosthetic heart valves and extracorporeal circulation devices will also be presented.
Practical class activities will deal with the solution of problems related to the design and setting of the studied Biomachines. Laboratory activities will be planned to allow the students to focus the experimental evaluation of the studied devices and the related instrumentation.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
The final exam will consist in a written test which, in specific cases, can be followed by an oral exam (if grade > 25/30).
The students will be evaluated based on their capacity to answer theoretical questions about the subjects taught during the course and to frame them in a wider scenario. More specifically, skills in knowledge understanding and communication will be evaluated. Moreover, the student will be evaluated based on his/her capacity to establish connections among the different subjects dealt with during the course, and among the other courses attended. More specifically, students' skills in applying knowledge and understanding to problem-solving, in making judgements about the possible technical choices, and in communication modalities to explain and assess the validity of the chosen methods, will be evaluated (as per DD1, DD2, DD3, DD4, DD5).

Maria Laura Costantino, Notes from the Course of Biomachines
Note: The notes are available in pdf format on the Beep page of the course

Obiettivi dell'insegnamento
L’insegnamento integrato di Biomateriali ha l’obiettivo di fornire agli studenti gli strumenti per analizzare criticamente la selezione, il comportamento e la progettazione dei biomateriali impiegati nella realizzazione di dispositivi medici per applicazioni a contatto con tessuti duri e molli. Partendo da casi clinici e applicativi, l’insegnamento guida gli studenti nel collegare i requisiti funzionali e biologici dei dispositivi alle proprietà dei materiali, alla loro struttura e storia di processo, ai fenomeni di degradazione nell’ambiente fisiologico e alle interazioni tra materiale e organismo. Il percorso didattico è organizzato secondo una struttura comune ai due moduli, articolata in quattro blocchi tematici: i) relazione tra struttura e proprietà dei materiali, ii) effetti dell’ambiente fisiologico sui materiali, iii) risposta biologica e integrazione, iv) strategie ingegneristiche e tecnologie di fabbricazione per controllare proprietà, degradazione e risposta dell’organismo. Il modulo modulo Biomateriali [2] è dedicato prevalentemente ai biomateriali polimerici e ai dispositivi per applicazioni a contatto con tessuti molli, con particolare riferimento agli ambiti cardiovascolare, oftalmologico, della chirurgia ricostruttiva e del wound healing. L’insegnamento mira a sviluppare la capacità di interpretare criticamente dati sperimentali e letteratura scientifica del settore, confrontare soluzioni alternative e formulare scelte motivate di materiali, processi, trattamenti e metodi di caratterizzazione in relazione a specifici problemi applicativi.
Learning Objectives
The integrated Biomaterials course aims to provide students with the knowledge and analytical tools required to critically evaluate the selection, performance, and design of biomaterials used in the development of medical devices intended for applications involving contact with hard and soft tissues. Drawing on application-oriented case studies, the course guides students in establishing connections between the functional and biological requirements of medical devices and the properties of biomaterials, their structure and processing history, degradation phenomena occurring in the physiological environment, and the interactions between materials and the host. The course is organized around a common framework shared by both modules and structured into four thematic blocks: i) the relationship between material structure and properties ii) the effects of the physiological environment on materials iii)biological response and tissue integration iv) engineering strategies and manufacturing technologies for controlling material properties, degradation, and the biological response. Biomaterials [2] is primarily devoted to biomaterials and medical devices for applications involving soft tissues, with particular emphasis on the cardiovascular, ophthalmic, reconstructive surgery, and wound care fields. The course aims to develop students ability to critically interpret experimental data and relevant scientific literature, compare alternative solutions, and make well-reasoned decisions regarding the selection of materials, manufacturing processes, surface treatments, and characterization methods in relation to specific biomedical applications.

Risultati di apprendimento attesi
A seguito del superamento dell’esame, lo studente:
conosce e comprende le principali classi di biomateriali impiegate in dispositivi per applicazioni a contatto con tessuti molli ed è in grado di spiegare come composizione, struttura, processo produttivo e ambiente fisiologico ne determinino proprietà, degradazione, risposta biologica e prestazioni; (DD1)
è in grado di analizzare specifici problemi applicativi, individuando i requisiti clinici, meccanici e biologici rilevanti e correlando tali requisiti alla selezione dei materiali, ai processi di fabbricazione, ai metodi di caratterizzazione e alle strategie utilizzate per controllare degradazione e interazione con l’organismo; (DD2)
è in grado di interpretare criticamente dati sperimentali e letteratura scientifica, formulare conclusioni proporzionate alle evidenze disponibili e sviluppare, individualmente o in gruppo, proposte motivate per problemi applicativi nel settore dei biomateriali, selezionando le informazioni rilevanti, organizzando risorse e tempi e comunicando in modo chiaro ed efficace il razionale, i vantaggi e i limiti delle soluzioni considerate. (DD2–DD3–DD4–DD5)
Expected learning outcomes
Upon successful completion of the course, the student will:
know and understand the main classes of biomaterials used in devices for soft-tissue applications and explain how composition, structure, manufacturing processes and the physiological environment determine their properties, degradation, biological response and performance; (DD1)
be able to analyse specific application problems by identifying the relevant clinical, mechanical and biological requirements and relating them to material selection, manufacturing processes, characterization methods and the strategies used to control degradation and host interaction; (DD2)
be able to critically interpret experimental data and scientific literature, draw conclusions proportionate to the available evidence, and develop, individually or in groups, justified proposals for application problems in the biomaterials field, selecting relevant information, organizing resources and time, and clearly and effectively communicating the rationale, advantages and limitations of the considered solutions. (DD2–DD3–DD4–DD5)

Argomenti trattati
Progettazione di polimeri stabili nell' ambiente biologico: evoluzione, composizione, caratterizzazione. Polimeri biodegradabili di sintesi: composizione, lavorazione, sterilizzazione e meccanismi di degradazione. Caratterizzazione della degradazione in vitro e in vivo. Studio delle proprietà dinamico-meccaniche dei biomateriali polimerici. Caratterizzazione reologica. Tecniche di caratterizzazione di superficie. Soluzioni bioispirate e applicazione di smart materials per dispositivi impiantabili.
Le nozioni teoriche saranno sempre applicate a diversi casi studio e casi applicativi relativi a materiali impiantabili a contatto con tessuti molli (in particolare in chirurgia cardiovascolare, oftalmologica, ricostruttiva e wound healing) che consentano di apprezzare vantaggi e limiti delle diverse strategie progettuali, nonchè delle potenzialità e delle limitazioni delle tecniche di caratterizzazione dei materiali e dell'interazione con l'ospite.
Sono anche previsti alcuni seminari per approfondire, grazie al contributo di ospiti esperti, alcune delle tematiche affrontate durante le lezioni.
Course Content
Design of polymers stable in the biological environment: evolution, composition, and characterization. Synthetic biodegradable polymers: composition, processing, sterilization, and degradation mechanisms. Techniques for the characterization of in vitro and in vivo degradation. Dynamic mechanical properties of polymeric biomaterials. Rheological characterization. Surface characterization techniques. Bioinspired approaches and application of smart materials for implantable medical devices.
The theoretical concepts will be applied to a range of case studies and practical applications involving implantable materials in contact with soft tissues, particularly in cardiovascular, ophthalmic, and reconstructive surgery, as well as wound healing. These case studies are intended to illustrate the advantages and limitations of different design strategies, as well as the strengths and constraints of material characterization techniques and the assessment of host–material interactions.
The course also includes a number of seminars delivered by invited experts, providing students with the opportunity to explore selected topics covered during the lectures in greater depth.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
Modalità di valutazione
La valutazione dell’insegnamento prevede il superamento di una prova scritta obbligatoria per ciascuno dei due moduli, secondo le modalità illustrate all’inizio del corso. Di norma, non è prevista una prova orale.
Le prove scritte comprendono quesiti a risposta multipla e domande aperte, finalizzati ad accertare la conoscenza e la comprensione degli argomenti trattati, nonché la capacità di applicare le conoscenze acquisite alla risoluzione di problemi specifici, interpretare criticamente dati sperimentali e formulare conclusioni motivate.
Gli studenti possono inoltre partecipare ad attività di gruppo facoltative, comuni ai due moduli, dedicate all’analisi critica della letteratura scientifica e di casi studio. Tali attività richiedono di reperire e selezionare informazioni da fonti scientifiche pertinenti, organizzare il lavoro di gruppo e le risorse disponibili, rispettare le scadenze previste, predisporre una presentazione ed esporla nella data concordata, comunicando in modo chiaro, efficace e rigoroso gli aspetti più rilevanti. Le presentazioni sono seguite da una discussione e da un commento da parte del docente. La valutazione di queste attività facoltative può concorrere alla determinazione del voto finale, secondo i criteri illustrati all’inizio del corso e indicate nel materiale didattico fornito.
Assessment Methods
Assessment for this course consists of a compulsory written examination for each module, according to the procedures communicated at the beginning of the course. An oral examination is not normally required.
Each written examination includes multiple-choice questions and open-ended questions designed to assess student knowledge and understanding of the course contents, as well as their ability to apply acquired knowledge to specific problems, critically interpret experimental data, and draw well-supported conclusions.
Students may also participate in optional group activities shared by both modules, focusing on the critical analysis of scientific literature and case studies. These activities require students to identify and select relevant information from appropriate scientific sources, organize group work and available resources, meet assigned deadlines, and deliver an oral presentation on an agreed date while communicating the key findings clearly, effectively, and rigorously.
Each presentation is followed by a discussion and a feedback from teachers. The assessment of these complementary activities may contribute to the final course grade, according to the criteria communicated at the beginning of the course and described in the uploaded materials.

The primary objective of the course is to provide students with the knowledge required to understand and apply the latest electronic technologies employed in the design and assessment of widely used biomedical instrumentation. In particular, the course will provide an in-depth study of their operating principles, architectures for data processing and transmission, and selected examples of diagnostic and therapeutic applications. (DD1, DD2, DD3, DD5)

Through the study of the topics covered in this course, students will:
Acquire specific knowledge of the general architecture of electronic systems for biomedical devices (DD1);
Gain an in-depth understanding of the operation of the most common electronic devices used for signal acquisition in analog and digital systems for biomedical applications (DD1);
Become familiar with the structure and operating principles of microprocessors and master the basic concepts underlying the design of embedded systems (DD1, DD2);
Study selected representative architectures of commercial microcontrollers (DD1);
Acquire basic knowledge of the data transmission systems commonly used by biomedical devices (DD1);
Learn how to design both the hardware and software of simple biomedical devices, also acquiring practical development skills through the planned experimental laboratory activities (DD2, DD3, DD5);
Be able to analyze more complex devices by identifying their main components and critically evaluating their key characteristics in relation to the implementation choices adopted by the manufacturer (DD2, DD3).

Characteristics and architectures of electronic instrumentation in Biomedical Engineering: General architecture of a measurement system. Signal acquisition chain. Analog and digital signal systems. Signal conditioning and the analog front-end: operational amplifiers, active filters, A/D and D/A converters, protection and isolation circuits. Introduction to signal interfacing and conditioning issues. Review of bioelectrical signal amplification. Digital processing systems: fundamentals of digital electronics, microprocessors, microcontrollers, and DSPs. Study of selected microcontrollers used as examples. Firmware development and debugging. Development environments. Introduction to embedded operating systems. Architectures for real-time processing and DSP. Integrated-sensor systems and smart-sensor systems. Introduction to power electronics for power supply circuits and actuator control.
Data transmission in biomedical applications: Data transmission systems: encoding, bandwidth requirements, and transmission channels. Integration between instruments: communication architectures and protocols. Data transmission between biomedical devices. Network architectures and protocols. The ISO/OSI model. RS-232 and Ethernet interfaces. TCP/IP communication protocols. Telemetry and wireless communication. IoT technologies for biomedical applications.
Instrumentation for critically ill patients: Monitoring systems for vital functions in intensive care: ECG, blood pressure, and blood oxygen saturation. Study of an intensive care monitor. The mechanical ventilator: operating principles and technological challenges. Study of the architecture of an intensive care mechanical ventilator.
Instrumentation for diagnostic imaging: The ultrasound scanner: techniques, technological challenges, and visualization methods. Echo generation and detection devices based on piezoelectric transducers. Applications in interventional procedures.
Laboratory: Laboratory activities will be organized using microcontroller boards and related development systems, and by implementing simple electronic circuits for interfacing with sensors and actuators. In addition, selected devices, such as a mechanical ventilator, an intensive care monitor, and an ultrasound scanner, will be studied in detail by analyzing their electrical schematics and performing measurements at the input and output of the different stages of the control electronics.

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
The assessment will consist of a written test and an oral examination. The written test, lasting 3 hours, will be divided into two sections. The first section, designed to evaluate the design skills acquired during the course, will provide the specifications of a simple electronic device for biomedical applications. Students will be required to design the hardware and firmware needed to implement its main functions. The second section will include questions aimed at assessing students’ understanding of the theoretical content of the course.

Biomedical imaging is one of the fastest-growing application areas of artificial intelligence, enabling automated diagnosis, precision medicine, image-guided interventions, and quantitative clinical decision support. Developing these technologies requires a solid understanding of the mathematical principles that underpin modern artificial intelligence. This course introduces those principles through the study of biomedical image processing, providing students with the theoretical foundations and practical skills required to understand, design, implement, and critically evaluate computational methods for extracting meaningful information from biomedical images.
Adopting a unified mathematical perspective, the course integrates classical image processing, statistical learning, optimization, and machine learning into a common computational framework that provide the foundation for both classical methodologies and contemporary AI-based approaches.
At the end of the course, students will be able to work on biomedical image processing problems using appropriate mathematical models, select suitable computational methodologies, implement complete biomedical image processing pipelines, and critically evaluate both classical and AI-based solutions in terms of accuracy, robustness, interpretability, computational complexity, and generalization capability.

Knowledge and understanding
Students will learn:
the mathematical foundations of image representation, feature extraction, and biomedical image processing;
the principles of statistical learning, probabilistic modeling, dimensionality reduction, and latent-variable methods for biomedical image analysis;
the role of optimization, regularization, inverse problems, and variational methods in biomedical image processing;
the theoretical foundations of machine learning and deep learning, including neural networks, convolutional neural networks, and representation learning;
the mathematical principles underlying representative biomedical image processing tasks, including segmentation, registration, and motion analysis.
Applying knowledge and understanding
Given a biomedical image processing problem, students will be able to:
formulate the problem using appropriate mathematical models and image representations;
select suitable statistical, optimization-based, or learning-based methodologies according to the characteristics of the data and the application;
implement and evaluate biomedical image processing algorithms using modern software tools;
design complete biomedical image processing pipelines;
quantitatively assess the performance, robustness, and generalization capability of image processing methods.
Making judgements
Given a biomedical image processing problem, students will be able to:
critically compare classical model-based, statistical, and learning-based methodologies;
evaluate the impact of modeling assumptions, data quality, and computational constraints on algorithm performance;
identify sources of error, overfitting, and limited generalization in image processing systems;
assess the trade-offs between accuracy, robustness, interpretability, and computational complexity when selecting an appropriate methodology.
Communications skills
Students will learn to:
communicate biomedical image processing methodologies using appropriate scientific and technical terminology;
present experimental methodologies and quantitative results in a clear and structured manner;
critically discuss the strengths and limitations of different image processing approaches.
Lifelong learning skills
Students will learn to:
read and critically interpret the scientific literature in biomedical image processing, computer vision, and artificial intelligence;
independently explore emerging methodologies, software tools, and computational frameworks;
adapt their mathematical knowledge to new developments in machine learning and biomedical imaging;
develop biomedical image processing solutions through independent study and continuous learning.

Biomedical image processing is a multidisciplinary field that combines image processing, statistical learning, optimization, and artificial intelligence to extract meaningful information from biomedical images. The course presents the mathematical foundations and computational methodologies underlying modern biomedical image processing, emphasizing the progression from classical model-based approaches to contemporary deep learning methods.
The main topics covered in the course are organized as follows.
1. Image Representation and Feature Extraction
Digital image representation and sampling.
Convolution, filtering, and image enhancement.
Edge detection and feature extraction.
Texture descriptors and shape representations.
2. Statistical Learning for Biomedical Images
Principal Component Analysis.
Gaussian Mixture Models.
Maximum Likelihood estimation.
Expectation-Maximization.
Latent-variable models and representation learning.
3. Optimization and Variational Methods
Least-squares formulations and inverse problems.
Regularization techniques.
Gradient-based optimization methods.
Variational methods for biomedical image processing.
Active contours, level-set methods, and the Chan-Vese model.
4. Machine Learning and Deep Learning
Fundamentals of machine learning.
Neural networks and backpropagation.
Representation learning.
Convolutional Neural Networks.
Encoder-decoder architectures.
U-Net for biomedical image segmentation.
Image registration (overview)
Object tracking (overview)

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), individuale, senza revisioni periodiche, con presentazione/discussione finale
The course assessment consists of a mandatory written examination and an optional practical project.
Written Examination (up to 100%): Assesses the students' understanding of the theoretical foundations and computational methodologies presented during the course, including theoretical questions and algorithmic analyses related to image processing, optimization, statistical learning, machine learning and deep learning.
Optional Project (+30% bonus): Consists of the design, implementation, and experimental evaluation of a biomedical image processing pipeline using modern software frameworks (e.g., PyTorch). Students opting for the project will apply course methodologies to a realistic biomedical imaging problem and present their results.

Rafael C. Gonzalez, Richard Eugene Woods, Digital Image Processing, Editore: Pearson
Christopher M. Bishop, Pattern Recognition and Machine Learning https://www.microsoft.com/en-us/research/wp-content/uploads/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
Christopher M. Bishop, Hugh Bishop, Deep Learning: Foundations and Concepts, Editore: Springer, Anno edizione: 2024 https://link.springer.com/content/pdf/10.1007/978-3-031-45468-4.pdf

The course provides a comprehensive overview of the structural, biochemical, and functional properties of the major classes of biomolecules, including proteins, lipids, and carbohydrates. Students will acquire a thorough understanding of the molecular architecture of biological macromolecules and of the relationship between their chemical structure, biological function, and involvement in cellular pathways. Particular emphasis is placed on proteins and enzymes, focusing on their structural organization, catalytic mechanisms, physiological functions, and pathological alterations, as well as on their potential applications in biotechnology and biomedicine.

Students will:
acquire a comprehensive understanding of the structural and functional properties of biomolecules and of their role in cellular and metabolic processes (DdD1 – Knowledge and Understanding);
understand the correlation between protein structure and biological function, recognizing how structural alterations, genetic mutations, and metabolic dysregulation contribute to human diseases (DdD1 – Knowledge and Understanding);
apply biochemical and structural knowledge to identify, describe, and analyze the properties of known and unknown biomolecules, relating molecular characteristics to biological function and biomedical applications (DdD2 – Applying Knowledge and Understanding);
critically evaluate biochemical mechanisms and interpret experimental and scientific data related to biomolecular structure and function (DdD3 – Making Judgements);
communicate scientific concepts effectively using appropriate biochemical terminology in both written and oral presentations (DdD4 – Communication Skills);
develop autonomous learning skills through literature analysis and collaborative project work, strengthening their ability to investigate emerging topics in biochemistry and molecular medicine (DdD5 – Learning Skills).
As part of the course, students will work in teams to develop and present a project on an advanced topic of their choice related to biochemistry, molecular biology, or biomedical research. This activity is intended to foster teamwork, critical thinking, independent learning, scientific communication, and public speaking skills.

The course begins with an introduction to the fundamental principles of chemistry, biochemistry, and thermodynamics, including the properties of water, pH, biological buffers, and the thermodynamic principles governing biological systems. The chemical structure and biological significance of the principal classes of biomolecules are then introduced.
The structure and properties of amino acids are presented as the basis for peptide bond formation and protein biosynthesis. Protein architecture is discussed in detail, covering primary, secondary, tertiary, and quaternary structures, together with the molecular forces responsible for protein folding and stability. Students are introduced to the main analytical methodologies employed for protein purification and characterization, including chromatographic techniques, electrophoresis, X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and mass spectrometry. The course addresses the application of Artificial Intelligence to the investigation of protein structure, with particular emphasis on structure prediction and biomedical research.
Protein classification is addressed through the study of representative fibrous and globular proteins. Fibrous proteins include keratins, silk and spider fibroins, and collagen, highlighting their structural organization and mechanical properties. The course also examines muscle proteins and their role in the molecular mechanisms of muscle contraction. Among globular proteins, particular attention is devoted to immunoglobulins, myoglobin, and hemoglobin, emphasizing the relationship between molecular structure and biological function.
The organization and function of biological membranes are discussed, including membrane composition, membrane proteins, and the molecular mechanisms underlying passive and active transport processes. Enzyme structure and function are then examined from both kinetic and thermodynamic perspectives, with particular attention to catalytic mechanisms, enzyme regulation through competitive and non-competitive inhibition, allosteric regulation, and covalent modifications. Relevant examples, including serine proteases, are analyzed to illustrate enzyme function in physiological and pathological contexts.
The course provides a detailed overview of cellular metabolism and metabolic thermodynamics. The principal biochemical pathways of carbohydrate metabolism are described, including glycolysis, the tricarboxylic acid (Krebs) cycle, glycogenesis, and glycogenolysis. Lipid metabolism is addressed through the structure and biological function of triglycerides and the mechanisms of fatty acid degradation. Protein metabolism is also discussed, including proteolysis and amino acid catabolism.
A dedicated section focuses on the molecular basis of human diseases associated with protein dysfunction, genetic mutations, and altered metabolic pathways. Current therapeutic strategies targeting proteins and metabolic processes are presented, together with an introduction to the emerging role of artificial intelligence in biomedical research, diagnostics, drug discovery, and precision medicine.
Learning methodology
The course is delivered through a combination of traditional lectures and interactive learning activities designed to promote both theoretical understanding and the practical application of biochemical concepts.
Lectures are supported by PowerPoint presentations and interactive digital tools, including Wooclap, to encourage active student participation, facilitate real-time assessment of learning, and promote classroom engagement. Interactive sessions are integrated throughout the course to consolidate the concepts presented during lectures, allowing students to apply theoretical principles to the solution of exercises, discuss case studies, and clarify any questions arising from the course material.
To further encourage active learning and the development of transversal skills, students are offered the opportunity to participate in an optional team-based project during the semester. Working in small groups, students investigate an emerging topic in the field of biomolecules, biochemistry, or molecular medicine through independent literature research. The activity is designed to foster critical thinking, autonomous learning, scientific reasoning, teamwork, and peer-to-peer learning. Project assessment is based on both the quality of the presentation materials (PowerPoint slides) and the oral presentation delivered by each group.
Biomolecules materials
All teaching materials, including lecture slides and supporting resources used during lectures and interactive sessions, are made available through the BEEP online learning platform prior to each class, enabling students to prepare in advance and facilitating continuous review throughout the semester.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
Final exam and evaluation:
Student learning will be assessed through a final written examination administered during the official examination sessions. The examination will be delivered using Microsoft Forms and will consist of a combination of multiple-choice questions and open-ended questions requiring detailed, reasoned responses. The assessment is designed to evaluate not only the acquisition of theoretical knowledge but also the students' ability to integrate concepts, apply biochemical principles to specific problems, and demonstrate critical thinking.
The examination aims to verify the achievement of the intended learning outcomes by assessing students' capacity to understand, interpret, and correlate the structural and functional properties of biomolecules, metabolic pathways, and their relevance in physiological, pathological, and biomedical contexts.
The following aspects will be considered in the evaluation:
the completeness and accuracy of the acquired knowledge;
the ability to establish logical connections among different topics covered during the course;
the appropriate and accurate use of scientific terminology;
the ability to apply theoretical knowledge to the analysis and solution of biochemical problems;
the capacity for critical reasoning and independent evaluation of biochemical concepts and biological processes;
where applicable, the ability to investigate an advanced topic independently and communicate it effectively through a clear and well-structured oral presentation.
The written examination consists of questions assigned different weights according to their complexity and relevance to the learning objectives. The final grade is determined by the overall performance across all examination questions. Students who choose to participate in the optional group project may receive additional evaluation based on the quality of the submitted presentation materials and the effectiveness of the oral presentation, according to the assessment criteria communicated during the course.

David Whitford, Proteins: Structure and Function, Editore: John Wiley & sons,, ISBN: 978-0471-49894-0
Nelson & Cox, Introduzione alla biochimica di Lehninger, Editore: Zanichelli, ISBN: 8808064131
D. Voet, JG Voet, CW Pratt, Fondamenti di Biochimica, Editore: Zanichelli, ISBN: 880806879X

The course aims at introducing and understanding some important activities, processes, and tools characterizing business organizations.
The course bases on the framework of the Business Plan that is able to synthetize all the most important challenges that managers have to face and is particularly useful for entrepreneurs (or would-be entrepreneurs). It analyzes more in depth the topics of Strategy and Marketing. As far as Strategy is concerned, it presents the main concepts and theories characterizing the corporate strategy both concerning the specific Strategic Business Unit and the Corporate level. As far as Marketing is concerned, it deals with the implementation of a Marketing Analysis, it defines the main concepts of Marketing Strategy, it shows the key issues influencing Marketing Mix Decisions and, finally, it explains how to implement and control a marketing plan. The final part of the course presents the basics of project management.
The course will be supported by examples, case studies, exercises, and a project work in the context of biomedical industries that will allow students to put into practice what they have learnt in theory.
The project work is organized as a sort of hackathon (we call it a Canvasthon) that allows student to work in class during class hours to develop the Business Model Canvas for a business idea proposed by a MedTech company or an hospital or another organisation working in the HealthTech industry.

DdD1. Knowledge and understanding
By the end of the course, the students will:
understand the meaning of strategic decisions;
understand the main concepts of corporate strategy;
understand the basic concepts characterizing project management;
understand when it is necessary to develop a Business Plan, who are the potential targets of the document and which tools could be used in order develop innovative and creative solutions;
DdD2. Applying knowledge and understanding
By the end of the course, the students will:
be able to interpret and analyze a market and business environment;
be able to analyze a company's business model and, business strategy;
be able to develop a business plan.

Business Planning
The Business Plan: What, When, How and Why;
Executive Summary: The main topics of the Business Plan;
Introduction: the Business Idea, the entrepreneurial team, the Company, Value proposition canvas;
Products and services: a short presentation, main strengths and weaknesses;
Strategic plan: internal and external analysis, SWOT analysis, main aims;
Marketing plan: Market segmentation and strategic positioning, 7Ps;
Operating, organizational and human resource plan.
Business Processes;
Financial Plan;
Risk analysis.
Business Model Canvas
Business Model Canvas;
Value Proposition Canvas;
Corporate strategy
Strategic decisional process;
Strategic Business Units (SBUs) vs. Corporate;
Internal and external analysis;
Models supporting the SBU strategy: Porter’s Five forces, Porter’s Value Chain, PESTLE analysis; resource/competence theory;
Marketing
Marketing Analysis;
Marketing Strategy;
Marketing Mix Decisions;
Implementation and Control.
Basics of Project Management
Main components of project management: scope, time, progress, cost, risk, stakeholders, value

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
The exam consists of two parts:
Part 1: Group Project work (60%):
Groups of students will participate in a sort of Hackathon (Canvasthon) aimed at developing a draft of Business Plan, focusing in particular on the Business Model Canvas. Such a Business Plan will concern a business idea provided by the professor on a topic of bio-engineering. The Canvasthon will take place in class, but homework is still necessary.
Part 2: individual oral exam (40%):
An individual oral exam on the course topics and the business plan will complete the exam.

Vaughan Evans, Writing a Business Plan: How to win backing to start up or grow your business, Editore: Editore: FT Pr, Anno edizione: 2022, ISBN: 978-1292416175

The course provides students with the fundamental knowledge required to understand the structure, function, and mechanical behaviour of cellular and subcellular structures, with particular emphasis on the cell membrane and cytoskeletal filaments.
The course addresses the molecular and biophysical mechanisms underlying key cellular processes, including cell adhesion, locomotion, intracellular transport, and cell division. Particular attention is devoted to the relationship between molecular structure and cellular function, as well as to the mechanical properties of biomolecules and biopolymers.
Students will acquire the theoretical background required to understand and interpret analytical and mathematical models describing molecular and cellular mechanisms. The course also introduces the main experimental approaches and technologies used for the visualization, manipulation, and mechanical characterization of biomolecules, cells, and subcellular structures at the molecular and cellular scales.
Lectures are complemented by tutorials and laboratory activities aimed at applying theoretical concepts to practical and quantitative problems, including molecular modelling, cell imaging, image analysis, mechanical characterization, and the design and experimental characterization of in vitro cell culture systems.

Upon successful completion of the course, students will be able to:
Knowledge and Understanding (DD1)
describe the structure, function, and mechanical properties of the main biomolecules and biopolymers involved in cellular and cytoskeletal structures;
explain the fundamental principles governing the mechanical behaviour of cell membranes and cytoskeletal filaments;
describe the molecular mechanisms underlying cell adhesion, locomotion, intracellular transport, and cell division;
understand the fundamental principles of mathematical and mechanical models used to describe and interpret molecular and cellular mechanisms.
Applying Knowledge and Understanding (DD2)
relate molecular and structural features to the functional mechanisms underlying cellular behaviour;
apply analytical and modelling approaches to describe the mechanical behaviour of cellular and subcellular structures;
determine relevant biophysical parameters from experimental data and quantitative models;
identify appropriate experimental techniques, instrumentation, and experimental setups for the visualization, manipulation, and mechanical characterization of biomolecules, cells, and subcellular structures;
analyse microscopy images and perform quantitative or semi-quantitative measurements of cellular and morphological parameters using dedicated image-analysis software.
Making Judgements and Communication Skills (DD3)
identify appropriate experimental approaches and define suitable protocols for the characterization of biomolecules and cellular structures;
critically evaluate experimental observations and quantitative results in relation to the underlying molecular and cellular mechanisms;
integrate information from different disciplines, including molecular biology, cell biology, biophysics, mechanics, and bioengineering, to interpret complex cellular phenomena;
communicate scientific and technical concepts using appropriate terminology and formalism.

Molecular and Cellular Foundations
Introduction to the cell: DNA, RNA, and proteins; DNA packaging and organization; protein structure and function; molecular recognition mechanisms; protein folding and misfolding; molecular mechanisms associated with protein misfolding diseases, including prion diseases and amyloid fibril formation in neurodegenerative disorders.
Cell Membrane Mechanics and Transport
Structural and mechanical models of biological membranes; membrane composition and organization; membrane mechanical behaviour; lipid rafts and membrane fluidity; passive, active, and protein-mediated membrane transport; bacterial cell membranes.
Application of membrane biophysics to extracorporeal devices for sepsis treatment: drug–lipopolysaccharide interactions analysed through molecular mechanics approaches; multiscale analysis of device performance and efficiency.
Cytoskeleton Mechanics and Cellular Dynamics
Structure and function of the cytoskeleton; intracellular filaments; microtubules, actin filaments, and intermediate filaments; filament polymerization and depolymerization; structure and function of cilia, flagella, and microvilli.
Intracellular transport along cytoskeletal filaments; mechanisms of mitotic spindle formation; role of the cytoskeleton in mitosis and cell division.
Cell adhesion mechanisms; cell–cell adhesion and intercellular communication, including gap junctions; cell–substrate adhesion; mechanisms of cell locomotion and cytoskeletal remodelling during cell migration.
Mechanical Characterization of Cytoskeletal Filaments and Biopolymers
Experimental approaches for the direct and indirect characterization of filament mechanical properties, including tensile testing using atomic force microscopy (AFM), bending stiffness measurements, and estimation of Young's modulus.
Fundamentals of biopolymer mechanics: filament bending behaviour; persistence length; elastic energy and deformation; filament fluctuations; elastic constants; mechanical behaviour of short and long filaments.
Molecular Motors and Active Cellular Processes
Structure and function of molecular motor proteins, including myosins, kinesins, and dyneins; mechanisms of active intracellular transport; directional movement and force generation; mechanical models describing molecular conformational changes and motor activity.
Experimental Technologies for Biomolecular and Cellular Characterization
Overview of the principal experimental techniques used for the visualization, manipulation, and characterization of biomolecules, cells, and subcellular structures, including optical microscopy, electron microscopy, optical and magnetic tweezers, and atomic force microscopy (AFM).
The fundamental operating principles and applications of these techniques are introduced, with particular emphasis on their use in quantitative biophysical and biomechanical investigations.
Optical Tweezers and Single-Molecule Manipulation
Fundamental principles of optical trapping; experimental setup and instrumentation; calibration procedures; force measurement; applications of optical tweezers to the manipulation and mechanical characterization of single molecules and biological structures.
Tutorials and Laboratory Activities
Lectures are complemented by tutorials and laboratory activities designed to reinforce theoretical concepts through practical applications, analytical calculations, numerical analysis, and the use of dedicated software.
Activities include:
fundamentals of molecular modelling and analysis of molecular structures;
retrieval of molecular structures from the Protein Data Bank (PDB) and visualization and analysis using VMD;
mechanical characterization of cell membranes using micropipette-based techniques, with applications to erythrocytes and leukocytes;
analytical exercises addressing membrane mechanics and cellular mechanical properties;
mechanotransduction mechanisms and the role of mechanical stimuli in regulating cellular responses and cytoskeletal organization, with a specific application to endothelial cells;
design of a cell culture chamber for the application of controlled shear stress using Autodesk Inventor CAD software;
cell imaging and semi-quantitative analysis of cellular morphological parameters using histological and fluorescence microscopy images and FIJI software;
experimental fabrication and characterization of the designed cell culture chamber;
analysis of cell and subcellular structure images acquired following controlled mechanical stimulation;
quantitative analysis and modelling of cytoskeletal filament mechanics through dedicated exercises.

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
Attendance at lectures, tutorials, and laboratory activities is not compulsory.
No midterm examinations or early examination sessions are scheduled. The examination is conducted during the official examination sessions.
The final assessment consists of a written examination followed by an oral examination.
Written Examination
The written examination is conducted on the date of the official examination session and is used to determine admission to the oral examination.
The written test generally consists of:
five multiple-choice questions;
five open-ended questions.
The written examination accounts for one-third of the final grade.
Oral Examination
Students admitted to the oral examination take an in-depth oral examination during the days immediately following the official examination session, with the date agreed upon with the instructor.
The oral examination assesses:
theoretical knowledge, including definitions and conceptual descriptions;
quantitative descriptions and mathematical formulations;
understanding and interpretation of mathematical and mechanical models;
ability to solve analytical and quantitative problems;
ability to critically discuss course topics;
ability to establish connections between different concepts and mechanisms addressed throughout the course.
The oral examination accounts for two-thirds of the final grade.
Optional Group Project
Students are encouraged to participate in a group project carried out during selected tutorial sessions and to prepare a final report to be submitted at the end of the course.
The project is optional and is assessed on a scale from 0 to 1 point. The resulting score is added to the weighted average obtained from the written and oral examinations.
Assessment Criteria
The overall assessment of the student's learning outcomes is based on the following criteria:
ability to describe and quantitatively formalize the main molecular and cellular phenomena using physicochemical, biophysical, and engineering approaches;
ability to integrate knowledge from different areas and critically analyse the mechanisms underlying molecular and cellular behaviour;
depth of understanding of the scientific and technological challenges associated with the use and characterization of biomolecules and cells in research and clinical applications;
ability to describe and critically discuss analytical and mathematical models used to predict, describe, or interpret molecular and cellular mechanisms;
ability to critically interpret and discuss the results obtained through analytical and mathematical approaches;
ability to communicate scientific concepts clearly and effectively using appropriate technical and scientific terminology.

L’insegnamento ha lo scopo di fornire agli allievi le basi per la comprensione dei processi e dei requisiti regolatori che governano la certificazione di un dispositivo medico necessaria per la sua immissione in commercio. L’insegnamento intende sviluppare negli allievi le capacità dapprima di identificare i requisiti regolatori applicabili ad una qualsiasi tipologia di dispositivo medico, quindi comprendere il ruolo di tali requisiti all’interno dei processi di qualità aziendale e infine di elaborare una strategia specifica per un dato dispositivo in termini di compilazione di documenti tecnici da sottoporre agli enti preposti alla certificazione.

Lo studente deve:

• conoscere in modo critico i concetti fondamentali degli aspetti regolatori per la certificazione di prodotto e quelli relativi alla certificazione dei sistemi di qualità aziendale (DD1);
• conoscere in modo critico le tecniche di analisi dei rischi e di valutazione clinica di un nuovo dispositivo (DD2);
• essere in grado di identificare per un particolare dispositivo medico i requisiti regolatori applicabili ad esso per la sua certificazione, in relazione alla sua particolare interazione con il sistema vivente (DD3);
• essere in grado di identificare una serie di azioni di valutazione della soluzione progettuale adottata per il dispositivo che dimostrino la conformità dello stesso ai requisiti regolatori precedentemente individuati (DD4);
• essere in grado di stilare i principali documenti tecnici da utilizzare per la presentazione di una richiesta di certificazione di un nuovo dispositivo (DD5);
• essere in grado di pianificare le eventuali attività di valutazione del dispositivo in studi clinici (DD6);
• essere in grado di rapportarsi con i singoli attori del percorso di certificazione, conoscendo le peculiarità di ciascuno e le modalità di comunicazione (DD7).

• Aspetti generali di progettazione in bioingegneria e collocazione di questa all’interno del percorso regolatorio e di qualità aziendale.
• Legislazioni di riferimento relative a dispositivi medici principalmente in ambito europeo (Regolamento UE 2017/745) con accenni a procedure internazionali (legislazione FDA e derivate).
• Gestione della Qualità aziendale per un Fabbricante di dispositivi medici e relativi requisiti normativi (norme ISO 9001 e ISO 13485).
• Normativa internazionale di prodotto: ruolo della normativa all’interno del percorso per la dimostrazione della conformità del dispositivo ai requisiti regolatori.
• Normativa a supporto dell’analisi di rischio (ISO 14971) e della valutazione clinica (Regolamento UE 2017/745) di un dispositivo medico.
• Individuazione dei requisiti regolatori applicabili ad un dispositivo medico.
• Individuazione delle attività di verifica e validazione di un dispositivo medico per la dimostrazione di conformità ai requisiti regolatori e di qualità aziendale.
• Costruzione della documentazione a supporto della certificazione di prodotto: contenuto e stesura di un Fascicolo Tecnico di prodotto.
• Pianificazione e costruzione di uno studio clinico a supporto della dimostrazione di conformità ai requisiti regolatori.
  Le esercitazioni consistono nell’approfondimento mediante esame di casi reali delle problematiche legate agli aspetti di certificazione di particolari classi di dispositivi ( es. impiantabili, software, attivi…)

Prova scritta obbligatoria, senza prove in itinere
Gli esami consisteranno in un esame scritto diviso in una parte di domande a risposta multipla e in una parte di domande aperte.
In sede di esame finale lo studente deve dimostrare di essere in grado di:

• rispondere correttamente alle domande proposte (già discusse nel corso dell’insegnamento) e contestualmente individuare ed escludere quelle non corrette;
  -utilizzare un linguaggio formalmente corretto ed adeguato alla materia d'esame.
  Nella valutazione dell'esame la determinazione del voto finale tiene conto dei seguenti elementi:
• conoscenza e comprensione degli argomenti;
• capacità di analisi dei quesiti posti;
• correttezza formale e sostanziale delle risposte fornite alle domande aperte.

The main objective of this course is the detailed study of the mathematical models describing the electric activity of the heart. The course adopts a multiscale approach, starting at the single ionic channel and moving to the ionic currents, the action potential of the different cells of the heart, up to the torso. The course looks in detail at the numerical solution of the resulting equations as well as their implementation on the computer, favoring the interaction of the student with computer codes implemented in a user-friendly environment. These modeling and simulation tools are then applied to the study of arrhythmias, such as those associated with acute regional ischemia, and other pathologies.

Lectures and exercise sessions will allow students to:
Understand and describe complex engineering problems in the field of biomedical engineering (DD1)
Set up a model to simulate the electric activity of the heart, identifying the right elements composing the model and their characteristics (DD2)
Interpret the results of an electrophysiological simulation and the interactions/synergies between the different elements of the model (DD1)
The project laboratory will allow students to:
Perform continuous learning and develop strategies for autonomous learning (DD5)
Analyze software code written by others (DD2)
Summarize and present the results achieved during the analysis and specification activities (DD3 and DD4)
Communicate the results of their activity in a clear and concise manner (DD3 and DD4)

Basic physiology of the heart: functioning and structure. Modeling cardiac excitation and excitability: the cardiac action potential. Models of cardiac action potential. Numerical integration of models of action potential. The concept of stability and requirements of the time step. The muscle structure of the heart: mathematical models of cardiac anatomy. Modeling propagation in excitable media: monodomain and bidomain equations. The extracellular potential and the model of the torso. The finite element method for modeling impulse propagation: algorithms, numerical implementation, convergence, stability and performance. Influence of space and time discretization. Cardiac arrhythmias: types of arrhythmias and mechanisms causing reentries. The concept of vulnerability window. The acute ischemic heart. Study of other cardiac pathologies.
Course material:
Handouts, presentations, (accessible) papers on computational biology of the heart. For model implementation, the software OpenCore is used for single cell models, and the software package Matlab will be used for modeling the electric and electromechanical models in 1D and 2D cases.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
Students will be evaluated at the end of the course on the basis of a written examination on the theoretical parts of the course, the reports from practical sessions, and the presentation of the course project.
Final exam: 80%
Report of practical sessions and project presentation: 20%
Oral exam: Students with a final mark of 30/30 can opt for an oral exam to achieve the final mark 30 with honors

Sundnes J, Lines G T, Ca X, Nielsen B F, Mardal K A, Tveito A, Computing the Electrical Activity in the Heart, Editore: Springer, Anno edizione: 2006, ISBN: 3-540-33432-7
Panfilov AV, Holden AV (Editors), Computational Biology of the Heart, Editore: John Wiley and Sons, Anno edizione: 1997, ISBN: 0-471-96020-9
Keener J, Sneyd J, Mathematical Physiology, Editore: Springer Verlag, Anno edizione: 1994
Jaakko Malmivuo and Robert Plonsey, Bioelectromagnetism http://www.bem.fi/book/

L'insegnamento di Costruzioni Biomeccaniche si propone di illustrare criteri e strumenti di base per la progettazione di sistemi biomeccanici, con particolare riferimento ai dispositivi protesici per il sistema muscolo-scheletrico, dentale e cardiovascolare.
Sulla base dell’analisi funzionale dei sistemi studiati, vengono definite le caratteristiche dei singoli componenti, per i quali viene impostata la verifica e il dimensionamento secondo le diverse esigenze, con particolare attenzione alle problematiche di affidabilità meccanica e resistenza a fatica; viene anche discusso l'uso delle norme tecniche nella progettazione e la verifica di dispositivi biomeccanici.
Sono inoltre previste esercitazioni di laboratorio progettuale al fine di permettere agli allievi di sperimentare alcune metodologie di progettazione di sistemi e componenti biomeccanici.

Ci si aspetta che alla fine dell'insegnamento di Costruzioni Biomeccaniche gli allievi siano in grado di:

• formulare un percorso efficace per la progettazione di una costruzione biomeccanica (dispositivo o set-up di prova), identificandone gli aspetti fondamentali e specifici e definendo in autonomia le possibili soluzioni progettuali (DD1)
• applicare in modo corretto gli strumenti di base per la progettazione di sistemi biomeccanici (disegno costruttivo, modelli analitici, modelli numerici, test sperimentali), scegliendo lo strumento più adatto alle specifiche problematiche e fasi della progettazione (DD2)
• svolgere la verifica di resistenza e il dimensionamento di un dispositivo biomeccanico, con specifica attenzione alle problematiche di fatica, usura e tenuta dei collegamenti (DD2,DD3)
• utilizzare in modo critico le norme tecniche in fase di progettazione e verifica di dispositivi biomeccanici (DD3)

• Aspetti introduttivi: progettazione meccanica e biomeccanica; le fasi di un progetto; gli strumenti del progettista. Uso integrato di analisi teorica, prove sperimentali e modelli computazionali. Norme tecniche. Prove a norma. Prove tramite simulatori.
• Disegno meccanico e progettazione: quotatura e processo tecnologico; quotatura e funzione del pezzo. Esempi applicativi. Disegno e realtà: errori di lavorazione; rugosità; tolleranze dimensionali; accoppiamenti con gioco o interferenza; tolleranze geometriche; calcolo di accoppiamenti; catene di tolleranze.
• Cedimento meccanico e verifica di resistenza: sforzo nominale, limite e ammissibile; coefficienti di sicurezza; resistenza a carichi statici; verifica in stato di sforzo semplice; effetto di intaglio sui diversi materiali; richiami ai criteri di resistenza in stato di sforzo composto; dimensionamento e verifica di un dispositivo.
• Calcolo di azioni interne e sforzi in strutture biomeccaniche 3D. Calcolo degli sforzi nominali in dispositivi biomeccanici.
• Resistenza a fatica nelle costruzioni biomeccaniche: generalità; esperienza e diagramma di Wöhler; rottura a fatica di elementi biomeccanici; tipi di sollecitazione ciclica; metodi per la determinazione del limite di resistenza a fatica di un materiale; costruzione del diagramma di Wöhler di un materiale o di un dispositivo biomeccanico; prove a fatica su protesi ortopediche: norme e problematica; fatica ed affidabilità di un dispositivo biomeccanico; fattori che influenzano la resistenza a fatica; sollecitazioni medie non nulle; diagramma di Haigh; margine di sicurezza a fatica; influenza di intaglio, dimensione pezzo, rugosità; verifica di resistenza a fatica in stato semplice; accumulo del danno a fatica; legge di Miner; verifica a fatica in stato di sforzo composto; dimensionamento a fatica di una protesi d’anca; utilizzo dei metodi FEM per la verifica di resistenza di una protesi di ginocchio. Leghe metalli che a memoria di forma e loro comportamento a fatica. Diversi approcci per la previsione della vita a fatica di una costruzione biomeccanica. Damage tolerant analysis di uno stent vascolare.
• Elementi di collegamento in biomeccanica: collegamenti fissi e mobili; collegamenti forzati; calcolo del forzamento di un mozzo su un albero. Protesi modulari e protesi custom. Dimensionamento di un cono Morse di una protesi d'anca: modello analitico, rischio di disaccoppiamento dei collegamenti, micromovimenti e fretting. Rottura per fatica di protesi modulari. Collegamenti a vite negli impianti dentali; effetto dell'applicazione del carico masticatorio. Viti di pressione per il bloccaggio di snodi nei fissatori. Viti di trasmissione (bioreattore).
• Collegamenti mobili e usura in biomeccanica: tribologia delle protesi articolari; attrito, lubrificazione, usura; diagramma di Stribeck; tipi di usura; valutazione dell'usura in protesi articolari; modelli di usura.
  Esercitazioni e attività di laboratorio
  Alle lezioni teoriche verranno affiancate attività pratiche, utili alla comprensione degli aspetti teorici presentati:
  i) s volgimento di temi d’esame; ii) analisi in aula di costruzioni biomeccaniche; iii) analisi di norme tecniche: iv) analisi di lavori scientifici; v) descrizione di esempi applicativi e “case histories”; vi) laboratorio progettuale di modellazione CAD di componenti biomeccanici.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
L’esame finale consiste in una prova scritta nella quale gli studenti verranno valutati rispetto alla loro capacità di risolvere problemi quantitativi relativi alla progettazione di costruzioni biomeccaniche e di rispondere a quesiti teorici relativi a quanto visto durante il corso, dimostrando anche di saper applicare gli strumenti appresi a nuovi problemi di progettazione biomeccanica.
Più precisamente la prova scritta verificherà il livello di conoscenza e comprensione, la capacità di utilizzo degli argomenti dell'insegnamento, e la capacità di esposizione con linguaggio appropriato tramite:

• un esercizio di dimensionamento o verifica a fatica di un dispositivo biomeccanico
• alcuni quesiti teorici che possono essere relativi a: i) una problematica di progettazione biomeccanica discussa in aula, focalizzando uno dei seguenti aspetti: disegno costruttivo, modelli analitici e numerici, test sperimentali, norme tecniche, confronto di metodologie; ii) la formalizzazione e risoluzione di una problematica di progettazione biomeccanica, diversa da quelle viste in aula
  Inoltre, potranno essere proposti alcuni lavori di gruppo in aula che, se ben svolti, concorreranno alla valutazione finale.

The course aims to provide students with the theoretical tools required to undertake the design process of an implantable medical device (endoprosthesis). The course is intended to develop students’ ability to first identify the design requirements of an endoprosthesis, then find a technological solution that meets clinical needs, and finally verify that the proposed solution ensures the functionality of the prosthesis.
This second part of the course will cover current solutions available for prosthetic replacements of parts of the musculoskeletal and cardiovascular systems, including invited seminars from representatives of biomedical companies.

The student must:
critically understand the fundamental concepts of endoprosthesis design techniques (DD1);
be able to define the design requirements of an endoprosthesis, taking into account its interaction with living tissues, and identify a technological solution for its manufacturing (DD2);
be able to formulate and solve engineering problems for the validation of the identified design solution (DD3);
be able to interpret design results and, where necessary, develop new design solutions consistent with the identified requirements (DD4).

The topics covered in the first part of the course are:
compatibility and reliability of endoprostheses;
design process stages;
manufacturing technologies and prototyping;
technical drawing as a means of transferring design requirements to production;
construction materials for endoprostheses
example of design of endoprostheses: hip prostheses
example of design of endoprostheses: knee prostheses
Practical exercises consist of in-depth analyses of real case studies concerning the design aspects of specific prostheses, with the application of analytical, computational, and experimental methods.
The topics covered in the second part of the course are:
prostheses for the musculoskeletal system, including pelvic reconstruction prostheses, devices for osteosynthesis and spinal surgery, dental implants;
prostheses for the cardiovascular system, including valve prostheses, vascular prostheses, endovascular stents, complex prostheses for the cardio- (i.e., stent grafts, percutaneous valve prostheses, left atrial appendage closure devices) and neuro-vascular systems (i.e., clips, coils, webs, flow diverter stents).
For each category, the specifications of the endoprostheses are defined in terms of anatomical, functional, biological and surgical compatibility, with the different clinical indications for the various models clearly set out. Furthermore, the main technological solutions are illustrated, highlighting aspects relating to materials, and the primary causes of failure encountered in clinical practice are analysed, leading to measures to improve the device through design feedback.

Prova scritta obbligatoria, senza prove in itinere
The exams are conducted through a written test on the scheduled examination date. The examination includes:
multiple-choice theoretical questions on the topics covered in the first and second parts of the course;
one open question on a topic covered in either the first or second part of the course;
a short numerical exercise in biomechanics applied to an endoprosthesis.
The examination does NOT include an oral component.
During the final examination, students must demonstrate that they are able to:
correctly answer the proposed theoretical questions (previously discussed during the course) while identifying and excluding incorrect alternatives;
identify and solve the proposed problem using the appropriate methods and tools taught during the course (problem analysis, identification of the solution strategy, and execution of calculations).
The final grade is determined based on the following criteria:
knowledge and understanding of the subjects covered;
ability to analyze engineering problems;
formal and substantive correctness of the procedures adopted for problem solving;
accuracy of the results obtained.

This is an equalization course offered at the master students of Telecommunications Engineering for filling gaps in their background on communications networks. The goal of the course is to teach students the basic concepts for understanding, modelling, and analyzing computer communications networks and the Internet. Moreover, the course provides the students with the instrument for understanding common applications based on data exchange between remote hosts.
Lectures provide some highlights of the main course topics and it is expected that the students go more in depth in studying the teaching material and books suggested according to the guidelines provided. Exercises will be proposed during the course on all topics.

1. Knowledge and understanding (DD1)
Students will learn how to:

• Identify the main components of the Internet infrastructure and understand a network protocol
• Describe a computer network system in terms of functions and relations between network layers
• Describe the information transfer modes in terms of packet switching, circuit switching, datagram and virtual circuits
• Understand the main known algorithms for routing, flow and congestion control, error recovery
• Use some basic tools to analyze Internet traffic and simulate a computer network
  2. Applying knowledge and understanding (DD2)
  Students will be able to:
• Model packet switching networks so to analyze simple transfer of packets through simple networks, to calculate and predict paths and arrival and transfer times
• Understand how remote applications exchange data across the Internet exploiting the transport protocols
• Use a layered approach to analyze and design a system
• Understand simple configuration and management operations of basic equipment, such as routers, switches and hosts
• Configure IP addressing schemes of public and private networks
  3. Making judgement (DD3)
  Students will be able to:
• Appreciate the importance of the layered approach in network design and standardization
• Identify the most critical parameters for the correct operation of net applications, according to their quality-of-service requirements
• Understand the relations between service providers and network operators and the main players of the Internet economy
  4. Communication (DD4)
  Students will learn to:
• Write and describe basic network technologies at different levels of detail, also by comparing them with respect to specific aspects
• Master the main representations techniques (time diagrams, flow charts, etc.) used to analyze networks
• Present ideas and topics to their colleagues, so to work in groups to the solution of exercises and application examples during the course

Basics of communication networks
Functional and layered models (services, protocols, primitives, TCP/IP model)
Application layer (examples of application protocols)
Transport layer (ARQ protocols, socket interface, UDP and TCP, flow and congestion control)
Network layer (Network function and routing, addressing, forwarding, IP, ARP, routing protocols)
Data link layer (framing, error control, multipoint networks, ALOHA and CSMA protocols, Ethernet, WiFi, ARP protocol, LAN interconnection)
Exercises will be presented for all the topics. For selected topics, some software tools for analysis and simulation will be presented during lectures in short lab sessions. It is expected that students will make more experience about the use of the tools at home.

Prova scritta condizionata (a scelta del docente), senza prove in itinere
Valutazione continua condizionata (a scelta del docente)
The assessment is based on a written exam at the end of the course, comprising 2–4 practical exercises and 2–4 theoretical open questions covering all topics. The exam evaluates the student’s ability to:
1) understand and describe the main concepts, protocols, and architectures of communication networks ( DD1 );
2) apply theoretical knowledge to analyze and configure basic network scenarios ( DD2 );
3) critically assess network performance and design choices ( DD3 );
4) clearly explain concepts using appropriate representations and technical language ( DD4 ).
A final mark of 30 cum laude will be assigned when the total score is ≥ 31.5. An additional oral exam may be required at the discretion of the teacher in exceptional cases.

Achille Pattavina, Internet and Computer networks. Introduction, Editore: Independently published - Textbook available in Amazon webstore, Anno edizione: 2020, ISBN: 979-12-200-6574-0

L'obiettivo dell'insegnamento è quello di fornire al futuro ingegnere biomedico le conoscenze di base e operative per la redazione, comprensione e utilizzo della documentazione a supporto delle apparecchiature e dei dispositivi medici, sia in qualità di progettista/produttore sia in qualità di utente in ambito sanitario.

A seguito del superamento dell’esame, lo studente:
Conosce le problematiche, soprattutto di natura elettrica, connesse ad un dispositivo elettromedicale. (DD1)
Comprendere la documentazione e i manuali tecnici relativi alla sicurezza elettrica forniti con l'apparecchiatura elettromedicale sulla base delle normative valide sul territorio nazionale. (DD1)
E’ in grado di gestire la fase di ricevimento/accettazione di un'apparecchiatura elettromedicale all'interno di un ambiente medico (DD2)
E’ capace di a pplicare il concetto di sicurezza ai dispositivi medici e alle strutture. (DdD 2)
E’ in grado di valutare la correttezza della documentazione implementata. (DdD 3)

Problematiche inerenti alla sicurezza in ambiente sanitario: cenni sui fenomeni di tetanizzazione, fibrillazione ventricolare, folgorazione, microshock e macroshock. Rischi di natura elettrica ed elettromagnetica. Altri rischi: meccanici, termici, chimici.
Sistemi di classificazione delle apparecchiature elettromedicali in base al rischio (modalità e grado di protezione dal punto di vista elettrico).
Effetti biologici dei campi elettromagnetici: radiazioni ionizzanti e non ionizzanti. Principi di funzionamento e problematiche inerenti alla sicurezza nelle apparecchiature elettromedicali: sistemi radiografici, TAC, Risonanza Magnetica Nucleare, laser, elettrobisturi, adroterapia.
La gestione dei dispositivi medici in ambito sanitario: normativa di riferimento nazionale ed internazionale e sua evoluzione; organismi di normazione, di ispezione e vigilanza; esempi di applicazione in ambito ospedaliero delle norme CEI per le principali apparecchiature elettromedicali.
La gestione della qualità in sanità: applicazione dei principi della certificazione ISO 9001:2015 in Ingegneria Clinica ed esempi pratici di processi certificati.
Indagini cliniche.
Il processo di sterilizzazione.
Il rischio biologico.

Prova scritta obbligatoria, senza prove in itinere
La valutazione avverrà tramite una prova scritta, nelle date degli appelli pubblicati. Trattandosi di un Corso Integrato la votazione riportata nella prova scritta di ciascun modulo concorrerà al voto finale (nel caso che sia stata raggiunta la sufficienza in entrambi i moduli). E' necessario ottenere la sufficienza in entrambe le parti del corso integrato nello stesso appello. Non sono previste prove in itinere.

S. Fonda, L. Mariani, A. Sargentini, Tecnologie biomediche e sanitarie, Editore: Pàtron, Bologna
John G. Webster, Medical Instrumentation Application and Design

The goal of the course is to provide the background for advanced modelling and data analysis, together with Kalman Filter techniques for parameters and virtual sensors estimation. The course is also intended to illustrate data mining concepts and methods, and to provide an introduction to optimization theory. The course has both a theoretical and a practical flavour, and is focused on the following topics: Stationary stochastic processes generated as output of dynamic systems. ARMA and ARMAX models. Prediction. Non-parametric models based on the spectral characteristics of a process. Estimation methods based on minimum prediction error. Model complexity analysis and parameters identification. Virtual sensors: Kalman Filter; Extended Kalman Filter for gray-box parameters identification. Data mining process. Exploratory data analysis, data preparation and feature selection techniques. Classification methods. Clustering. Association rules. Introduction to linear optimization.

We present some classical differential mathematical models, developing and analyzing analytical methods for the existence and the computation of solutions.

The lectures and the laboratories will provide students with:
i) knowledge and understanding of (DD1)

• the main properties of the solutions to some linear partial differential equations;
• the basic tools used for solving linear partial differential equations;
• the modeling of some real world problems;
  ii) the ability to apply the previous knowledge to simple examples on the calculator (DD2); in particular, the student is required to
• solve some linear partial differential equations;
• discuss the properties of some mathematical models.
  The instructor expects a broad comprehension of the subjects which should not be limited to the statement of theoretical results. Instead, the acquired knowledge should enable students to express
  critical judgment and make informed choices on some analytical methods for partial differential equations (DD3). The students are expected to express their answers in a clear and rigorous way.

Review: Differential calculus for functions of several real variables; series of functions.
Transport equation: method of characteristics; Duhamel formula.
Heat equation: separation of variables; maximum principles; fundamental solution; Cauchy problems with different types of boundary conditions.
Laplace-Poisson equation: harmonic functions; mean value properties; maximum principles; well-posed problems; Poisson’s formula for the disk.
Wave equation: D’Alembert formula; separation of variables.
Basics of Functional Analysis: Lebesgue integral; Lebesgue spaces; weak derivatives; Sobolev spaces; Hilbert spaces; projection theorems; Riesz representation theorem; Lax-Milgram representation theorem; weak formulation of elliptic boundary value problems.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
There are five examination dates (two in January-February, two in June-July, one in September).
The exam consists of a written test which contains questions and exercises on the topics of the course; the amount of points for each correct answer will be specified on the text; an oral exam is at the instructor's discretion.
It is possible to redo one of the two parts (Mathematical or Numerical), while keeping valid a positive mark obtained in the other part.
A positive mark in one of the two modules remains valid up to and including the last examination session, unless a student takes another exam for that module (even in case of withdrawing).

The purpose of this course is to provide the students with an approach to the tissue mechanics aimed at obtaining the relationship between function-property-microstructure. On the basis of the experimental observations of the mechanical response of the tissues, predictive models allowing for the knowledge of the constituents properties are formulated.

DD1-Knowledge and comprehension: through the Lectures, the students will learn the modeling approach for the basic mechanical response of biological tissues with reference to both soft and hard tissues. This capability will be evaluated though the oral session of the exams.
DD2-Capability to apply the knowledge: through the excercize and practical sessions in the classroom, the students will learn how to formulate and solve simple boundary value problems in which specific biological structures are subjected to loads: governing equations, boundary conditions and solution strategies. Representative examples are: simplified model of an artery under pressure, biaxial loading of thin anistoropic membranes; This capability will be evaluated through assigned problems and/or projects during the exam
DD3 Indivisual judgement: the students will learn how to critically discuss the results of the boundary value problems with specific reference to the physiological and pathological conditions of the tissues. This capability will be evaluated thorugh a deep discussion of the obtained results in a problem or a project during the exam session.

The lectures will follow a logical sequence on the basis of the modeling approaches. In particular, hard tissues like bone and soft tissues like arteries and ligaments will be selected as representative examples.
Cortical bone:
anisotropy of hard tissues: modeling material symmetries;
bone tissue as a composite material;
property-structure relationship at nano- and microscopic level;
brief introduction to homogenization methods for heterogeneous materials.
Trabecular bone tissue:
brief introduction to mechanical behaviour of cellular solids; simple mechanical models for elastic and post-elastic response of trabecular bone;
soft tissues: (vascular walls, tendons and ligaments, cartilage)
Brief description of tissue composition;
Kinetics and kinematics of tissues subjected to large deformations;
Constitutive relationships for isotropic and anisotropic tissue reponse;
Simple boundary value problems with specific reference to tissues;
Modeling of time dependent response of tissues: viscoelasticity;
Mechanics of cartilage: cartilage poroelasticity.
In addition to theoretical lectures, exericises on selected boundary value problems will be solved. Furthemore, practical lectures in which application of the finite element method to specific tissue mechanics problems will be given.
In this course, a "collaborative class" with the University of Liegi will be active. In this initiatives, students may take part to working group with students from the university of Liegi with the purpose to develop joint projects that will be object of the final exam. Project description and specific tutorials will be given during the course.

Prova orale obbligatoria
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The student may choose one of the two examination modes, as follows:
1. Oral exam;
2. Group project
____
1. ORAL EXAM
In the Oral examination the students should show:

• their capability to clearly describe the theoretical approaches to the mechanics of biological tissues;
• their capability to write governing equations for a specific problem with non linear anisotropic material properties (examples are: uniaxial tension of non linear materials like soft biological tissues; biaxial stretching, inflation of soft tubes, inflation of arteries...);
• their capability to solve the above mentioned problems.
  2. GROUP PROJECT
  The students may create working groups (max 2 students) to carry out a project (assigned in agreement with the students). The project will consist in the development of an original numerical model on a specific tissue/biomaterial mechanics problem. Finite elements or other numerical methods are available. Tutorial and assistance to the project development will be provided. The project will be summarized in a final presentation in which motivation, objectives, materials and methods, results and accurate discussion will be described.

Pasquale Vena, Lecture Notes

The goal of the course is to enable students to acquire expertise in the engineering methodologies and processes required to manage biomedical and clinical data for healthcare, research, and application development purposes. The main characteristics of biomedical and clinical data, as well as the standards for their storage, transmission, management, and sharing, are described. In addition, the basic processes of healthcare systems are illustrated to help students understand and work in clinical environments, manage and process data from health information systems, and work for companies developing medical devices, software, and related services.
Specifically, the Medical Informatics [2] module introduces primary healthcare processes involving the generation or management of health data. It discusses methodologies for designing, representing, and documenting such processes, focusing on both clinical ( primary use ) and biomedical research ( secondary use ) settings.

The lectures and exercise sessions will allow students to:

• Analyze and understand the goals, assumptions, and requirements associated with the design of database systems for health applications (DD1);
• Model a relational database properly from a formal viewpoint, describing its architecture and structure using appropriate design tools (DD1, DD2);
• Model healthcare processes through established design workflows, from understanding the context to representing the activities and the interacting objects with widespread modeling languages (DD1, DD2, DD3);
• Describe the main standards for medical data archiving, management, and sharing (DD1);
• Identify some of the main healthcare applications of Medical Informatics and explain their characteristics (DD1).
  The project and the laboratories will allow students to:
• Acquire hands-on experience with tools for database design and management (DD2);
• Define, understand, evaluate, and represent requirements, processes, and activities for a given application; investigate these aspects independently during exercises, laboratory sessions, and project development (DD2, DD3, DD5);
• Write design specification reports, document the developed applications, and present the results of their work with a critical approach (DD3, DD4);
• Develop applications in a collaborative setting using widely adopted programming languages, libraries, and tools in the data management and analysis fields, such as Python and SQL (DD2); cooperation within teams and discussion with project tutors are expected to foster critical thinking (DD3), communication skills (DD4), and encourage students to broaden and independently develop knowledge by exchanging perspectives and solutions with their peers (DD5).
  (DD: Dublin Descriptors)

Healthcare processes, their design, representation, and documentation

• Definition of clinical and healthcare processes, including information and telecommunication requirements: Definition of a “process”, with examples on the basic processes of the healthcare setting; overview of the technical and organizational processes to support clinical and research pathways, including international guidelines. Typical scenarios from public health organizations and research institutions, illustrating the needs and main strategies of information exchange and communication.
• Healthcare process modeling: Importance of process modeling to monitor, control, and continuously improve e-health processes. Object-oriented modeling is presented with its basic elements and concepts. A comprehensive methodology to model e-health processes by means of domain analysis (i.e., context analysis), conceptual, and logical modeling.
• Unified Modeling Language (UML): UML is introduced as a tool to design, represent, and document healthcare processes and related software. In addition to describing the main diagrams and requiring students to practice with them, the course will teach strategies and approaches for leveraging UML as a powerful reasoning tool to understand requirements and solve software issues on paper, before software implementation. Examples and exercises will include basic healthcare processes, such as the Italian process for booking medical examinations and patient reception in the emergency department (“triage”).
• Examples from Medical Informatics applications: Various applications of the above topics are presented, showing how to identify the different process phases and the actors involved, define their roles and responsibilities, and model activities (seen as components of the whole process) and process exceptions.
  Protection, sharing, and transmission of health data
• Basic concepts on security and privacy for clinical data: Motivations and objectives of data protection in the healthcare domain; real-world examples of data breaches and vulnerabilities targeting healthcare systems and medical devices; the Big Data era and the associated new risks in healthcare; cybersecurity and information security fundamentals, including description of the CIA triad; data protection requirements of secure systems, privacy and ethics, legal aspects and consent to personal data usage for research; introduction to the General Data Protection Regulation (GDPR); basics on security management (data access policies, definition of users’ roles and permissions, defense against attacks); preservation of data integrity and data availability.
• Health data sharing for research: Motivations and objectives of data sharing for research; architectures of federated local-cloud platforms; methods for semi-automated data integration, standardization, and structuring of semi-structured (biosignals, bioimages) or unstructured (free-text documents) data and related metadata; data harmonization, integration, and interoperability.
• Basic concepts of data transmission and networking: Development of telecommunication systems; basics of computer networking, including the ISO/OSI and TCP/IP models; main network topologies; applications of networking to the healthcare domain.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, con revisioni periodiche, con presentazione/discussione finale
Active and regular participation in lessons and laboratory sessions is strongly recommended.
The written final exam is mandatory and based on the topics of the syllabus. The ability to discuss the course topics comprehensively in written form, using appropriate terminology and paying attention to relevant conceptual aspects, is a fundamental element for a positive evaluation.
The laboratory project presentation, as well as the delivery of the other project artifacts (i.e., project report and code), is part of the final evaluation. As the project is an integral part of the course, which should be developed primarily during laboratory sessions, students who cannot participate due to conflicts with other classes must inform the professors at the beginning of the course. As a replacement for the project evaluation, these students will take a second part of the written exam to be evaluated on the technical aspects assessed by the project, including all topics addressed during laboratory and exercise sessions.

Pinciroli Francesco, Masseroli Marco, Elementi di Informatica BioMedica, Editore: Polipress, Anno edizione: 2005, ISBN: 88-7398-0171
Pinciroli Francesco, Combi Carlo Pozzi Giuseppe, Basi di dati per l'informatica medica. Concetti, linguaggi, applicazioni, Editore: Pàtron,, Anno edizione: 1998, ISBN: 8855524623
Fowler Martin,: A Brief Guide to the Standard Object Modeling Language, Editore: Addison-Wesley Professional, Anno edizione: 2003, ISBN: 978-0321193681
Shortliffe Edward H. and Cimino James J., Biomedical Informatics: Computer Applications in Health Care and Biomedicine, Editore: Springer, Anno edizione: 2006, ISBN: 978-0387289861
Elmasri Ramez, Navathe Shamkant, Fundamentals of Database Systems, Editore: Addison Wesley, Anno edizione: 2010, ISBN: 978-0136086208
Naiburg Eric J., Maksimchuk Robert A, UML for Database Design, Editore: Addison-Wesley Professional, Anno edizione: 2001, ISBN: 978-0201721638
Kurose James F., Ross Keith W, Computer Networking. A top-down approach featuring the Internet, Editore: Addison-Wesley Professional,, Anno edizione: 2005

The course aims to provide a theoretical and practical overview of the methods and tools used for the analysis of the functionality of the main systems with particular attention to the motor, metabolic and respiratory systems, integrating brief notes on cardiac function.
The main objectives of the course are:
Description of the main parameters that characterize some human functions: motor, muscular, respiratory and metabolic;
Description of the physical principles and the instrumentation used to measure the parameters of interest.
Description of the methods adopted for the indirect estimation of parameters that cannot be measured directly;
Description of operational protocols illustrating applications.

It is expected that the student, at the end of the course, will have acquired a)adequate skills to be able to interact with clinical staff in functional evaluation and rehabilitation environments, b) the ability to use innovative tools and c) the ability to propose new solutions in the field of biomechanics of functional evaluation. More specifically, the student is expected to acquire:

• Knowledge and understanding of the functional assessment of the motor, respiratory and cardiac systems (DD1);
• Applied understanding skills in the selection of the most appropriate instrumentation for the evaluation of the functionality of systems (DD2);
• Making judgements in the evaluation of issues related to methods and technologies in the field of functional evaluation of both the healthy subject and the pathological subject (DD3);
• Communication skills and language properties in the treatment of issues related to functional assessment in the context of evidence based medicine (DD4).

Principles of Functional Assessment
1.1 The Role of Measurement in Functional Assessment
Importance of quantitative assessment in clinical practice, rehabilitation, and research
Functional outcome measures and performance indicators
Principles of measurement: accuracy, precision, repeatability, validity, and reliability
Instrumental versus observational assessment
Selection of appropriate measurement methods according to the clinical question
Methods and Technologies for Human Movement Assessment
2.1 Functional Anatomy of the Neuro-Musculoskeletal System
Functional organization of the neuro-musculoskeletal system
Principles of human movement
2.2 Biomechanical Variables for Movement Assessment
Kinematics of body segments, joints, and the center of mass
External forces: ground reaction forces, interaction forces, and pressure distribution
Joint kinetics: moments and powers
Muscle activation: surface electromyography (sEMG) and force–EMG relationships
2.3 Motion Capture Technologies
2.3.1 Optical Motion Capture Systems
Principles of stereophotogrammetry
Marker-based and markerless motion capture systems
Calibration procedures and measurement accuracy
Clinical and research applications
2.3.2 Wearable Motion Analysis Systems
Inertial Measurement Units (IMUs)
Accelerometers, gyroscopes, and magnetometers
Electrogoniometers
Sensor fusion techniques
Wearable technologies for movement monitoring
2.4 Force and Pressure Measurement Systems
Force platforms
Pressure platforms and plantar pressure measurement
Dynamometers
2.5 Timing and Performance Measurement Systems
Photocell systems for timing analysis
Reaction time measurement systems
Assessment of speed, agility, and motor performance
2.6 Biomechanical Modelling and Functional Assessment
Inverse dynamics
Estimation of joint moments and reaction forces
Instrumented gait analysis
Clinical protocols for functional movement assessment
Methods and Technologies for Postural Assessment
3.1 Functional Anatomy and Physiology of Balance
Sensory contributions to postural control: visual, vestibular, and somatosensory systems
Motor strategies for balance control
Models of postural regulation
3.2 Variables for Postural Assessment
Static and dynamic balance
Center of Pressure (CoP) measures
Stabilometric parameters
3.3 Measurement Technologies
3.3.1 Static Postural Assessment
Computerized stabilometric analysis
Craniocorpography
3.3.2 Dynamic Posturography
Principles of Computerized Dynamic Posturography
Sensory Organization Test (SOT)
Motor Control Test (MCT)
Adaptation Test (ADT)
Clinical interpretation of dynamic posturography outcomes
Methods and Technologies for Respiratory and Muscle Metabolic Assessment
4.1 Functional Physiology
Energy metabolism: aerobic and anaerobic pathways
Respiratory mechanics and gas exchange
4.2 Respiratory Function Assessment
Spirometry
Body plethysmography
Flow measurement techniques
Breath-by-breath metabolic analysis
4.3 Functional Exercise Testing
Cardiopulmonary exercise testing
Evaluation of aerobic capacity
Indirect assessment of muscle metabolism
Overview of Cardiovascular Assessment
Functional anatomy of the cardiovascular system
Principles of electrocardiography (ECG)
Holter ECG and ambulatory blood pressure monitoring
Practical Activities
Practical sessions will complement the lectures through hands-on demonstrations of the measurement technologies and assessment methods presented during the course. Students will actively participate in laboratory activities involving data acquisition and functional assessment using the instrumentation. Whenever feasible, the equipment will be brought into the classroom; otherwise, practical sessions will be conducted in the laboratory.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
A written test (with open questions and exercises) followed by an optional oral test possible only in case of sufficiency of the written test (which will be averaged with the mark of the written test).
Knowledge and understanding of the subject are assessed through specific questions and application exercises.
Comprehension is assessed through questions based on the topics covered during the exercises.
Making judgements and communication skills are assessed by means of open questions and any oral exams.

Materiale didattico preparato dal docente- dispense del corso pdf

This course aims at providing essential knowledge on human neurosensory systems from a biomedical engineering point of view. Methods focus on physiological and anatomical studies of the mammalian nervous system as well as behavioral studies in animals and humans. The material is expected to provide a concise, solid platform of knowledge on specific sensory pathways of the human neural system, and to provide the student with the basic tools to begin the journey of discovery and learning into the field of Computational Neuroscience. The course will further provide benchmark knowledge on state-of-the art technology aimed at replacing specific sensory mechanisms and at diagnosing pathologies related to their functions.
The course is divided in two parts:
PART 1: Methods. The first lectures present the general structure and functional anatomy of the neurosensory systems, including the neuron, neural networks and brain organization. A second section highlights methodological representations of the basic physiology of the ear, as well as transduction, transmission and neural processing of the auditory stimulus. The course further presents a methodological portrayal of the basic physiology of the eye, as well as transduction, transmission and neural processing of the visual stimulus. Basic mechanisms related to visual pattern, color and depth perception, and spatial localization are also introduced. Finally, a brief overview of the somatosensory, olfactory, and gustatory systems is provided.
PART 2: Technologies. The first lectures of this part provide basic concepts on advanced signal processing techniques. Then, basic concepts in psychoacoustics, including speech fundamentals, and visual psychophysics, including color coding and color perception, are introduced. The course further highlights critical elements that have contributed to the technological knowledge towards the realization of diagnostic devices and auditory aids, including recording evoked potential techniques. Technological advances towards the realization of diagnostic devices and visual aids are also introduced.

Students are expected to gain a solid platform of knowledge on how the human neural system is organized and how it functions, on the basic mathematical principles underlying the system’s physiology, and on state-of-the art technology aimed at replacing specific sensory mechanisms and at diagnosing pathologies related to their functions.
More specifically, students will be able to understand basic principles on:
Understanding the brain: from the neuron to the central nervous system. (DD1)
Physiology of the ear and transduction, transmission and neural processing of the auditory stimulus. (DD1)
Physiology of the eye and transduction, transmission and neural processing of the visual stimulus. (DD1)
Key concepts related to visual pattern, color and depth perception, and spatial localization. (DD1)
Physiology of the somatosensory, olfactory, and gustatory systems. (DD1)
How to apply technological concepts and advanced signal processing techniques towards the realization of diagnostic devices and auditory aids. (DD2)
How to apply technological concepts and advanced signal processing techniques towards the realization of diagnostic devices and visual aids. (DD2)
How to identify specific classes of common problems in the different systems or organs, at various scales of investigation. (DD3)

PART 1
General structure and functional anatomy of neurosensory systems. The Neuron and Neural Pathways. Functional investigation methods. Introduction to modeling of neurosensory systems.
Highlights on the auditory system. Sound and speech. The external and middle ear. Cochlea, basilar membrane and ciliate cells. The spiral ganglion and the VIII nerve. Brain stem nuclei, middle geniculate, and auditory cortex.
Highlights on the visual system. Remarks on optics. Structure and function of the retina. Chiasm and lateral geniculate body. Structure and feature extraction in the primary and secondary visual cortex. Dorsal and ventral integration areas.
Brief overview of the somatosensory, olfactory, and gustatory systems.
PART 2
Basic elements of neurosensory signal processing.
Auditory psychophysics and audiology. Evoked potentials and otoacoustic emissions. External prostheses, implantable devices and cochlear implants. Speech processing: brief remarks. eHealth and Hearing: novel solutions and mobile apps. Auditory diagnostics and hearing effort assessments.
Visual psychophysics. Color perception and coding. Visual illusions. Visual evoked potentials. Ophthalmological instrumentation. Eye tracking and Pupil Dynamics Analysis. Aids for visual impairment. Echography. Basic principles of Laser surgery. Overview on retinal implants.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente) o condizionata (a scelta del docente)
Students are expected to pass two written tests, focused on both open and multiple-choice questions related to PART 1 and PART 2 respectively. At each call, the student may choose to take the exam relative to either both Parts of the Integrated Course or one of the two Parts, provided that he/she passes both Parts in one of the sessions of the academic year of attendance. The exam can be finalized only if BOTH written exams are passed, and the final grade will be the average of the two grades. A further Oral Exam is OPTIONAL, unless specifically required by the teacher. If taken, the oral will contribute to 30% of the final grade.

Riccardo Barbieri, Giuseppe Baselli, Ferdinando Grandori, Alessia Paglialonga, Bioengineering of Neurosensory Systems, Editore: Pàtron Editore, Anno edizione: 2023, ISBN: 9788855535977

This course gives an overview of techniques and algorithms in machine learning and pattern recognition. It provides students with the basic ideas and intuition behind modern machine learning methods as well as a more detailed coverage of most techniques.
The course fits into the overall program curriculum pursuing some of the defined general learning goals. In particular, the course contributes to the development of the following capabilities:
Understand context, functions, processes in a business and industrial environment and the impact of those factors on business performance
Identify trends, technologies and key methodologies in a specific domain (specialization streams)
Design solutions applying a scientific and engineering approach (Analysis, Learning, Reasoning, and Modeling capability deriving from a solid and rigorous multidisciplinary background) to face problems and opportunities in a business and industrial environment

By the end of the module, students should:
Have a good understanding of the fundamental issues and challenges of machine learning: data, model selection, model complexity (DD1).
Develop an appreciation for what is involved in learning from data.
Understand a wide variety of machine learning algorithms (DD1).
Understand how to apply a variety of learning algorithms to data (DD2).
Understand how to perform evaluation of learning algorithms and model selection (DD3).
Have an understanding of the strengths and weaknesses of many popular machine learning approaches (DD1).
Appreciate the underlying mathematical relationships within and across machine learning algorithms and the paradigms of supervised and unsupervised learning (DD1).
Apply the algorithms to a real-world problem, optimize the models learned and report on the expected accuracy that can be achieved by applying the models (DD2,DD4).
Be able to design and implement various machine learning algorithms in a range of real-world applications (DD2).
Be able to write code in Python programming language to use machine learning algorithms (DD2).

Introduction to Machine Learning
Motivations of machine learning. Machine learning, artificial intelligence and big data. Applications of machine learning. Representation of input data. Machine learning process.
Exploratory data analysis
Data validation and cleansing, outlier and missing values detection. Data transformation. Data reduction. Sampling. Feature selection. Features extraction by filtering. Principal component analysis. Data discretization. Univariate analysis: graphical analysis, measures of central tendency, dispersion, relative location, heterogeneity, analysis of the empirical density. Bivariate analysis: graphical analysis, measures of correlation, contingency tables. Multivariate analysis: graphical analysis, measures of correlation.
Supervised learning: classification and regression
Taxonomy of supervised methods. Evaluation of classification models: holdout, cross-validation, confusion matrix and derived metrics, ROC curve, cumulative gain and lift. Treatment of categorical attributes. Nearest neighbor. Classification and regression trees: splitting, stopping and pruning. Bayesian methods: naive methods, Bayesian networks. Logistic regression. Neural networks: Rosenblatt perceptron, multi-level feed-forward networks. Support vector machines: structural risk minimization, maximal margin hyperplane for linear separation, nonlinear separation. Simple and multiple linear regression. Assumptions on residuals. Least square regression: normality and independence of residuals, significance of coefficients, analysis of variance, coefficients of determination and linear correlation, multicollinearity, confidence and prediction limits. Selection of predictive variables. Ridge regression. Generalized linear regression.
Association rules
Motivation and evaluation of association rules. Single-dimension association rules. Apriori algorithm. Generation of frequent itemsets, generation of strong rules. General association rules.
Clustering
Taxonomy of clustering methods. Affinity measures. Partition methods: K-means, K-medoids. Hierarchical methods: agglomerative methods, divisive methods. Evaluation of clustering models.
Applications and use cases
Introduction to Python programming language and its main libraries for machine learning (Scikit-learn, Keras). Applications in relational marketing using Python: lifetime value analysis, acquisition, retention, cross-selling, market basket analysis. Web mining. Social market analysis. Speech recognition. Text mining. Fraud and anomaly detection. Bioinformatics.

Prova scritta obbligatoria, senza prove in itinere
The final mark is determined by a written test based on open and closed form answers. Theoretical questions aim at assessing knowledge acquisition with respect to tasks, methods and algorithms. Applied questions aim at assessing the ability to apply methods and algorithms, to properly understand the outputs and derive the implications for the application domain, to show programming skills in Python.
Notice that only students officially registered for a given session will be allowed to take the examination in that session.

C. Vercellis, Business intelligence: data mining and optimization for decision making, Editore: Wiley, Anno edizione: 2009
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Editore: Springer, Anno edizione: 2011
E. Alpaydin, Introduction to Machine Learning, Editore: MIT press, Anno edizione: 2014
A. Geron, Hands-On Machine Learning With Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems, Editore: O'Reilly, Anno edizione: 2017

The Motor System Rehabilitation course is designed to provide students with a comprehensive understanding of human movement mechanisms, the principles of neural plasticity, and their relevance in rehabilitation. The course offers a structured framework on technologies and methodologies used in the multidisciplinary rehabilitation process, focusing on three key strategies: functional replacement, functional restoration, and neuromodulation of the central and peripheral nervous systems.

It is expected that, by the end of the course, the student will have acquired adequate skills to interact with clinical staff in a rehabilitation environment, and the ability to use innovative tools and propose new solutions in the field of rehabilitation biomechanics. More specifically, it is expected that the student will acquire:
Knowledge and understanding of motor rehabilitation, functional electrical stimulation, prosthetics, orthoses, assistive devices, and neuromodulation (DD1).
Applied comprehension skills regarding issues related to motor rehabilitation, functional electrical stimulation, prosthetics, assistive devices, and orthoses (DD2).
Autonomy in evaluating topics related to methods and technologies in the rehabilitation field (DD3).
Communication skills and language proficiency in discussing topics related to motor rehabilitation (DD4).

Programme:
Introduction to motor system rehabilitation
Definitions of ICDIH and ICF classifications
Main pathologies leading to sensorimotor limitations
Phenomena of plasticity and recovery of biological damage:
Plasticity at the central nervous system, peripheral, and muscular level.
Methodologies and technologies to measure plasticity induced by rehabilitation at cortical, cortico-spinal and spinal level.
Motor System Rehabilitation through function restoration
Functional Electrical Stimulation (FES)
Hybrid FES (robotic + FES)
Biofeedback
Virtual reality (XR, VR, AR)
Motor System Rehabilitation through function replacement
Prosthetics
Orthosis
Assistive technologies
Motor System Rehabilitation through neuromodulation
Invasive Technologies (e.g. Deep Brain Stimulation, Implanted Spinal Cord Stimulation)
Non Invasive Technologies (e.g. tDCS, rTMS, Paired Associative Stimulation, Operant conditioning of spinal reflexes)
Motor System Rehabilitation through physical therapies
Tecar therapy
Laser therapy
Shock waves
Magnetic therapy
Ultrasound therapy
Motor System Tele-rehabilitation
Teleconsultation
Televisit
Digital tools integrated in televisit
Technologies for active and healthy ageing
Pediatrics rehabilitation and habilitation
The program includes theoretical lectures, seminars held by clinical experts and interactive laboratory experiences

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
The exam will consist of a written test with open and multiple choice questions lasting 1 hour and 30 minutes followed by an optional oral exam, possible only if the written exam is sufficient.

The course aims to introduce the fundamental analytical techniques of control theory, systems analysis, and model identification, and to demonstrate their application to the study of physiological regulation. Students will explore the dynamic behaviour of physiological systems, with particular reference to examples such as the cardiovascular system. Alongside qualitative descriptions, the course provides a rigorous introduction to mathematical modelling, enabling students to formulate, analyse, and validate models through computer-based simulations.

Upon successful completion of this course, students should be able to:
Knowledge and Understanding
Understand the application of mathematical techniques to the modelling of biological control systems.
Explain the fundamental concepts of systems theory and the differences between engineering and physiological control systems.
Applying Knowledge and Understanding
Develop simple mathematical models of physiological control systems.
Apply basic analytical techniques from control theory to determine the dynamic characteristics of linear closed-loop systems.
Apply basic system identification methods to estimate the parameters of physiological models.
Analyse and simulate the dynamics of simple models of physiological systems (e.g., cardiovascular regulatory mechanisms).
Use MATLAB and Simulink to analyse and simulate models of physiological systems.
Making Judgements
Select appropriate modelling approaches for a given physiological system.
Evaluate and compare different modelling techniques and their suitability for specific applications.
Identify the most appropriate modelling strategy based on the characteristics of the physiological system under study.
Lifelong Learning Skills
Recognise the competencies and knowledge required to address modelling problems involving physiological dynamical systems.
Independently acquire and apply new methods and tools for the analysis and modelling of physiological systems.

Course Contents
This course provides students with the theoretical foundations and practical tools needed to model, analyze, and control physiological systems using engineering approaches. By combining concepts from systems and control theory with real-world biomedical applications, students will learn how mathematical models can be used to understand physiological regulation, interpret clinical data, and support the design of medical technologies.
Foundations of Physiological Control Systems
Introduction to physiological control systems and their fundamental role in maintaining homeostasis.
Comparison between engineering control systems and physiological regulatory mechanisms, highlighting similarities that enable the application of engineering methods to biological systems.
Dynamic Modelling of Physiological Processes
Modelling of dynamic systems using distributed-parameter and lumped-parameter approaches.
Development of mathematical models of physiological systems through electrical and mechanical analogies, including applications such as the Windkessel model for cardiovascular dynamics.
Translation of complex physiological phenomena into computational models suitable for analysis, simulation, and prediction.
Analysis of Physiological Regulation
Static analysis of feedback-controlled physiological systems and determination of steady-state operating conditions, with applications to cardiovascular regulation and cardiac output control.
Time-domain analysis of physiological control systems, including the characterization of transient responses to physiological perturbations and clinical interventions.
Evaluation of system behavior through impulse and step response analysis to understand adaptation, recovery, and regulation mechanisms.
Frequency-Domain Analysis and Signal Interpretation
Frequency-domain characterization of physiological systems and biological signals.
Applications to respiratory sinus arrhythmia, cardiac baroreflex function, and non-invasive estimation of central blood pressure.
Extraction of clinically relevant information from physiological signals through spectral and frequency-response analysis.
Computational Modelling and Simulation
Computational simulation of open-loop and closed-loop physiological dynamics.
Analysis of physiological regulation under normal and pathological conditions.
Application to clinically relevant systems such as glucose–insulin regulation, providing insight into mechanisms underlying metabolic disorders and diabetes management technologies.
Stability and Performance of Physiological Systems
Stability analysis of linear physiological models and assessment of system robustness.
Applications to respiratory control and pathological phenomena such as Cheyne–Stokes breathing.
Frequency-response methods for evaluating physiological performance, resilience, and susceptibility to instability.
System Identification from Physiological Data
Data-driven modelling techniques for extracting physiological information from experimental and clinical measurements.
Least-squares parameter estimation and optimization-based identification methods.
Closed-loop identification of autonomic cardiovascular regulation using multivariate ARX models.
Modelling and identification of cardiac baroreflex dynamics from physiological recordings.
Biomedical Engineering and Medical Device Applications
Throughout the course, students will work with examples drawn from major physiological regulatory systems, including autonomic cardiovascular control, baroreflex regulation, blood volume control, chemoreceptive mechanisms, and metabolic regulation.
Special emphasis is placed on the practical use of modelling and control techniques in biomedical engineering. Students will learn how these methods are employed in:
Physiological monitoring and interpretation of biomedical signals;
Development of patient-specific computational models;
Design of monitoring systems;
Assessment of cardiovascular, respiratory, and metabolic function;
Analysis of physiological responses to therapies and clinical interventions;
Advanced technologies for automated physiological regulation, including closed-loop medical systems.
By the end of the course, students will be able to formulate, analyze, simulate, and identify physiological control systems, gaining skills that are directly applicable in biomedical engineering, medical technology development, physiological data analysis, and translational healthcare research.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
Assessment is based on a written examination with open-ended questions. A distinction ( cum laude ) may be awarded for outstanding performance. An optional oral discussion may be taken after passing the written examination and within the same examination session.
The examination aims to evaluate students' understanding of the mathematical tools and physiological models presented in the course and their ability to apply them to the modelling and analysis of physiological systems.

Michael C. K. Khoo, Physiological Control Systems: Analysis, Simulation, and Estimation, 2nd Edition, Editore: Wiley-IEEE Press, Anno edizione: 2018, ISBN: 978-1-119-05533-4
WeBeep https://webeep.polimi.it/login/index.php

Struttura e funzione del sistema respiratorio e delle sue componenti. Statica, cinematica, dinamica ed energetica della ventilazione. Stima delle azioni muscolari e delle proprietà meccaniche (resistive, elastiche ed inerziali). Modelli del sistema respiratorio: morfometrici e funzionali, a parametri concentrati e distribuiti. Metodi di misura delle variabili primarie (volumi, flussi, pressioni e concentrazioni di gas).
Metodi di indagine clinica. Ventilazione meccanica. Interazioni con il sistema cardiovascolare. Controllo della ventilazione

L'insegnamento ha l'obiettivo di fornire allo studente le conoscenze e gli strumenti metodologici necessari per comprendere i principali approcci e le problematiche connesse alla progettazione e realizzazione di strutture destinate alla sostituzione e alla rigenerazione dei tessuti del corpo umano nell'ambito della Medicina Rigenerativa e dell'Ingegneria dei Tessuti. Il corso affronta inoltre l'impiego di tali strutture per lo sviluppo di modelli in vitro destinati allo studio di tessuti fisiologici e patologici. Lo studente acquisirà inoltre conoscenze relative alle principali tecnologie avanzate di additive manufacturing e biofabrication, alle tecniche di modifica fisico-meccanica dei biomateriali e ai metodi di caratterizzazione morfologica, chimico-fisica, meccanica e biologica. Tali competenze gli consentiranno di progettare e sviluppare scaffold per l'ingegneria tissutale, modelli 3D in vitro e sistemi innovativi per il rilascio controllato di farmaci e cellule.
The course aims to provide students with the knowledge and methodological tools required to understand the main approaches and challenges involved in the design and fabrication of structures for the replacement and regeneration of human tissues within the fields of Regenerative Medicine and Tissue Engineering. The course also addresses the use of these structures for the development of advanced in vitro models of healthy and pathological tissues. In addition, students will acquire knowledge of advanced additive manufacturing and biofabrication technologies, techniques for the physico-mechanical modification of biomaterials, and methods for the morphological, physicochemical, mechanical, and biological characterization of biomaterials and engineered constructs. These competencies will enable students to design and develop scaffolds for tissue engineering, 3D in vitro models, and innovative drug and cell delivery systems.

A seguito del superamento dell'esame, lo studente:
Conosce e comprende le principali classi di biomateriali polimerici impiegati nella Medicina Rigenerativa e nell'Ingegneria dei Tessuti, le loro proprietà, i criteri di selezione e i metodi di caratterizzazione. Conosce inoltre le principali tecniche di modifica superficiale, con particolare riferimento ai metodi di microstrutturazione mediante tecniche soft-litografiche, e le tecnologie di biofabrication e additive manufacturing utilizzate per la realizzazione di scaffold e costrutti 3D. (DdD 1)
È in grado di applicare le conoscenze acquisite alla progettazione e allo sviluppo di scaffold, sistemi per il rilascio controllato di farmaci e cellule e modelli 3D in vitro che riproducano tessuti fisiologici e patologici, selezionando materiali e tecnologie di fabbricazione adeguati alle specifiche applicazioni biomedicali. (DdD 2)
È in grado di analizzare criticamente le soluzioni proposte nella letteratura scientifica, motivare le scelte progettuali sulla base di criteri scientifici e ingegneristici, comunicare efficacemente i risultati utilizzando un linguaggio tecnico-scientifico appropriato e approfondire autonomamente temi avanzati della Medicina Rigenerativa e dell'Ingegneria dei Tessuti. (DdD 3, DdD 4 e DdD 5)
Upon successful completion of the course, the student:
Knows and understands the main classes of polymeric biomaterials used in Regenerative Medicine and Tissue Engineering, their properties, selection criteria, and characterization methods. The student also understands the main surface modification techniques, with particular emphasis on soft lithography-based microstructuring methods, as well as the biofabrication and additive manufacturing technologies used to produce scaffolds and 3D tissue-engineered constructs. (DdD 1)
Is able to apply the acquired knowledge to the design and development of scaffolds, controlled drug and cell delivery systems, and 3D in vitro models that reproduce healthy and pathological tissues, selecting the most appropriate biomaterials and fabrication technologies for specific biomedical applications. (DdD 2)
Is able to critically analyze the solutions proposed in the scientific literature, justify design choices based on scientific and engineering principles, effectively communicate technical results using appropriate scientific terminology, and independently deepen knowledge of advanced topics in Regenerative Medicine and Tissue Engineering. (DdD 3, DdD 4 and DdD 5)

Argomenti trattati
Materiali per la Medicina Rigenerativa e l'Ingegneria dei Tessuti. Polimeri naturali: principali caratteristiche e applicazioni come biomateriali (fibroina della seta, alginato, chitosano, gelatina e acido ialuronico). Idrogeli stimuli-responsive. Metodi di caratterizzazione morfologica, chimico-fisica, meccanica e biologica di biomateriali e costrutti bioingegnerizzati. Tecnologie convenzionali e avanzate di fabbricazione, con particolare riferimento alle tecniche di additive manufacturing e biofabrication per la realizzazione di strutture bidimensionali e tridimensionali. Tecniche di modifica superficiale per applicazioni biomimetiche e biointegrative, con particolare attenzione ai metodi di micro- e nanostrutturazione. Approcci alla Medicina Rigenerativa: sistemi per il rilascio controllato di farmaci, biomolecole e cellule, sistemi iniettabili, microcapsule, Cell Sheet Engineering e strategie di vascolarizzazione. Applicazioni nell'Ingegneria dei Tessuti e nella Medicina Rigenerativa, con esempi relativi a tessuto muscolare, tessuto osseo, vasi sanguigni e tessuti molli (in particolare tessuto adiposo). Sviluppo di modelli 3D in vitro basati su scaffold per lo studio di tessuti fisiologici e patologici.
Attività didattiche integrative
Durante il corso saranno organizzati seminari tenuti da esperti provenienti dal mondo della ricerca e dell'industria, finalizzati ad approfondire specifici argomenti affrontati durante le lezioni.
Le esercitazioni, svolte in piccoli gruppi, saranno dedicate all'applicazione delle conoscenze acquisite durante il corso mediante la discussione di casi di studio, la risoluzione di problemi e lo svolgimento di esercizi relativi agli argomenti trattati a lezione. Le esercitazioni saranno distribuite lungo tutto il semestre e saranno oggetto di autovalutazione.
Le attività progettuali prevedono il coinvolgimento attivo degli studenti, organizzati in gruppi, nello sviluppo di un progetto assegnato dal docente. Il progetto richiederà l'applicazione integrata delle conoscenze acquisite durante il corso e comprenderà una significativa componente di lavoro autonomo.
Le attività progettuali contribuiranno alla valutazione finale dell'insegnamento, mentre le esercitazioni avranno finalità esclusivamente formative e saranno oggetto di autovalutazione.
Topics
Materials for Regenerative Medicine and Tissue Engineering. Natural polymers: main characteristics and applications as biomaterials (silk fibroin, alginate, chitosan, gelatin, and hyaluronic acid). Stimuli-responsive hydrogels. Morphological, physicochemical, mechanical, and biological characterization methods for biomaterials and tissue-engineered constructs. Conventional and advanced fabrication technologies, with particular emphasis on additive manufacturing and biofabrication techniques for the production of two- and three-dimensional structures. Surface modification techniques for biomimetic and biointegrative applications, with particular focus on micro- and nanopatterning methods. Regenerative medicine approaches, including controlled drug, biomolecule, and cell delivery systems, injectable systems, microcapsules, Cell Sheet Engineering, and vascularization strategies. Applications in Tissue Engineering and Regenerative Medicine, including skeletal muscle, bone, blood vessels, and soft tissues (with particular emphasis on adipose tissue). Development of 3D scaffold-based in vitro models for the study of healthy and pathological tissues.
Complementary Learning Activities
Throughout the course, seminars delivered by experts from academia and industry will provide students with the opportunity to explore selected topics covered during the lectures in greater depth.
Tutorial sessions, carried out in small groups, will focus on applying the knowledge acquired during the course through case studies, problem-solving activities, and exercises related to the topics discussed in class. These sessions will take place throughout the semester and will be self-assessed.
Project activities will actively engage students, working in small groups, in the development of an engineering project assigned by the instructor. The project will require the integrated application of the knowledge acquired during the course and will include a substantial component of independent work.
The project activities will contribute to the final course assessment, whereas the tutorial sessions are intended exclusively for formative purposes and will be self-assessed.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
Descrizione modalità di valutazione
L'esame consiste in una prova scritta della durata di 75 minuti, relativa all'intero programma dell'insegnamento, composta da due parti. Lo studente dovrà rispondere a domande a risposta multipla e a domande aperte sui concetti fondamentali trattati durante il corso, utilizzando il formalismo e la terminologia tecnico-scientifica appropriati. L'esame riguarda gli argomenti sviluppati durante le lezioni, le esercitazioni e i seminari tenuti da docenti invitati. Inoltre, durante la prova potranno essere richiesti approfondimenti relativi a specifici aspetti o applicazioni dei concetti trattati, anche attraverso la discussione di casi analoghi a quelli affrontati durante il corso. In particolare:
Prima parte: 10 quesiti a risposta multipla. Punteggio minimo per l'accesso alla seconda parte: 7/10 (+1 risposta corretta, 0 risposta non data, −0,5 risposta errata).
Seconda parte: domande a risposta aperta.
Il voto della prova scritta corrisponde al 50% della valutazione finale.
Il progetto, sviluppato durante il semestre, sarà valutato sia in itinere sia al termine delle attività e contribuirà per il restante 50% alla valutazione finale.
La valutazione è finalizzata a verificare il raggiungimento dei risultati di apprendimento attesi, in coerenza con i Descrittori di Dublino. In particolare:
la prova scritta verifica l'acquisizione delle conoscenze e della comprensione dei biomateriali, delle tecnologie di biofabrication, dei metodi di caratterizzazione e degli approcci propri della Medicina Rigenerativa e dell'Ingegneria dei Tessuti (DdD 1 – Conoscenza e comprensione);
il progetto valuta la capacità di applicare le conoscenze acquisite alla progettazione e allo sviluppo di scaffold, sistemi di rilascio di farmaci e cellule e modelli tridimensionali in vitro per specifiche applicazioni biomedicali (DdD 2 – Capacità di applicare conoscenza e comprensione);
la valutazione complessiva, attraverso la prova scritta, il progetto e la relativa discussione, verifica la capacità dello studente di analizzare criticamente le soluzioni proposte, motivare le scelte progettuali sulla base di criteri scientifici e ingegneristici, comunicare efficacemente utilizzando un linguaggio tecnico-scientifico appropriato e operare in gruppo sviluppando autonomia di giudizio e capacità di apprendimento (DdD 3 – Autonomia di giudizio, DdD 4 – Abilità comunicative e DdD 5 – Capacità di apprendimento).
Ai fini della determinazione del voto finale saranno presi in considerazione i seguenti elementi:
conoscenza e comprensione degli argomenti trattati;
capacità di analisi critica e di collegamento tra i diversi contenuti del corso;
capacità di applicare le conoscenze acquisite alla discussione di casi e problematiche specifiche;
correttezza e appropriatezza del linguaggio tecnico-scientifico utilizzato;
autonomia di giudizio e capacità di motivare le scelte progettuali;
qualità del progetto sviluppato, inclusa la capacità di organizzare il lavoro e collaborare efficacemente all'interno del gruppo.
The assessment consists of a 75-minute written examination covering the entire course syllabus. The examination is divided into two parts. Students are required to answer multiple-choice and open-ended questions addressing the fundamental concepts covered during the course, using appropriate scientific terminology and engineering formalism. The examination covers the topics presented during the lectures, tutorial sessions, and seminars delivered by invited speakers. In addition, students may be asked to further discuss specific aspects or applications of the concepts covered, including the analysis of case studies similar to those addressed during the course. Specifically:
Part I: 10 multiple-choice questions. The minimum score required to access Part II is 7/10 (+1 for each correct answer, 0 for unanswered questions, −0.5 for incorrect answers).
Part II: open-ended questions.
The written examination accounts for 50% of the final grade.
The project, developed throughout the semester, will be assessed both during its development and at the end of the course and will contribute the remaining 50% of the final grade.
The assessment is designed to verify the achievement of the intended learning outcomes in accordance with the Dublin Descriptors. In particular:
the written examination assesses the acquisition of knowledge and understanding of biomaterials, biofabrication technologies, characterization methods, and the fundamental principles of Regenerative Medicine and Tissue Engineering (DdD 1 – Knowledge and Understanding);
the project evaluates the ability to apply the acquired knowledge to the design and development of scaffolds, controlled drug and cell delivery systems, and three-dimensional in vitro models for specific biomedical applications (DdD 2 – Applying Knowledge and Understanding);
the overall assessment, including the written examination, the project, and its discussion, evaluates the student's ability to critically analyze proposed solutions, justify design choices based on scientific and engineering principles, communicate technical concepts effectively using appropriate scientific terminology, and work collaboratively while demonstrating independent judgment and lifelong learning skills (DdD 3 – Making Judgements, DdD 4 – Communication Skills, and DdD 5 – Learning Skills).
The final grade will be determined by considering the following criteria:
knowledge and understanding of the course topics;
ability to critically analyze and integrate the different course contents;
ability to apply the acquired knowledge to the discussion of specific case studies and engineering problems;
correctness and appropriate use of scientific and technical terminology;
independent judgment and ability to justify design choices;
quality of the project, including the ability to organize the work effectively and collaborate within a team.

Slides delle lezioni https://beep.metid.polimi.it
Buddy D. Ratner, Allan S. Hoffman, Frederick J. Schoen and Jack E. Lemons, Biomaterials Science (Third Edition) An Introduction to Materials in Medicine, Editore: Elsevier, Anno edizione: 2013, ISBN: 978-0-12-374626-9
Note: Il libro e' presente tra gli e-book della biblioteca del Politecnico
Articoli vari su riviste
Maria Cristina Tanzi, Silvia Farè, Gabriele Candiani, Foundations of Biomaterials Engineering 1st Edition, Editore: Academic Press

Caratteristiche e principi di funzionamento dei sensori per strumentazione clinica. Tecnologie a semiconduttore. Sensori di forza, pressione, movimento, accelerazione, temperatura, umidità, concentrazione di gas, radiazione elettromagnetica. Misure ottiche (fotodiodi, LED, CCD, CMOS). Sensori piezoelettrici, piroelettrici ed elettrochimici FET. Sensori a fibra ottica. Condizionamento dei sensori.
Sistemi per la diagnosi basata su immagini (endoscopia, radiografia computerizzata, tomografia computerizzata, risonanza magnetica, medicina nucleare, ecografia ed ultrasonografia). Sistemi per il monitoraggio emodinamico e respiratorio. Metodi e strumenti per laboratorio di analisi clinica (spettrometria, analisi elettrochimica ed elettroforesi). Microscopia. Strumenti per la chirurgia e la terapia.

The Technology Management in Hospital course provides students with instruments and tools to:
•critically review literature (including systematic review and meta-analysis)
•properly design clinical trials
•model decision making processes
•assess medical technology in clinics developing a small project

Knowledge and understanding: provides the basis and opportunities for clinical trials organization and evaluation, technology assessment, decision making modelling.
Applying knowledge and understanding: problem solving abilities, in particular for literature assessment and technology cost/ benefit assessment
Making judgements: case-based analyses and group discussions
Communication: projects results presentation (flipped/ blended)
Lifelong learning skills: self-assessment tools and interaction tools.

• Review literature and implement meta-analysis
• Properly design a clinical trial
• Properly report clinical trials findings
• Implement economic evaluations
• Model the decision making process
• Healthcare technology managemente
• Perform a case-base evaluation for medical technology management in clinics.

Prova orale obbligatoria
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, con revisioni periodiche, con presentazione/discussione finale
Small project related to the technology management chapter.
Discussion on the project and interview with teacher/ teaching assistants. Students can bring literature to start the interview.

The course aims to provide students with the theoretical and numerical tools for describing and understanding the engineering aspects of transport phenomena in biological systems (with particular emphasis on the cardiovascular, renal, and respiratory systems of the human body), in the main systems and devices that operate on biological fluids, and in various biotechnological applications. The course also aims to develop students' ability to identify, correctly formulate, and solve problems encountered in both ordinary and innovative biomedical applications. Finally, applications to some medical devices and biological systems will be presented to illustrate more specific design aspects.

The student:

• critically understands the fundamental concepts of mass, momentum, and energy transport that govern many phenomena at the interface between engineering, medicine, and biology (DD1, DD3);
• is able to formulate and solve engineering problems related to the design of medical devices and their interaction with living tissues, as well as understanding their behaviour (DD2);
• is able to interpret the results of the aforementioned calculations and, if necessary, refine the problem formulation (DD1);
• possesses methods and tools for tackling transport problems in artificial and biological systems, even those not explicitly covered in the course, arising from innovation in biomedical engineering applications (DD3).

The main topics covered in the course are:

• General transport phenomena. Steady and unsteady conservation of mass, momentum, and energy; dimensionless conservation equations.
• General fluid dynamics. Rheology of non-Newtonian fluids; flow in porous media; turbulence models; efflux phenomena; energy losses in ducts.
• Fluid dynamics in large blood vessels. Pulsatile flow; blood flow at bifurcations and major curvatures; pressure wave propagation; wall shear stress in blood vessels; fluid dynamics in collapsible vessels.
• Microcirculation. Blood rheology in the microcirculation; red blood cell motion in capillaries.
• Fluid dynamics in the upper airways.
• Convective transport phenomena in blood vessels and airways. Dispersed-phase transport in fluid flows (cells, atmospheric particulate matter, aerosols).
• Diffusive and convective mass transport in continuous media; steady and unsteady conservation of chemical species.
• Active and passive mass transport through porous and continuous membranes (biological and synthetic, thin and thick); controlled drug release; facilitated diffusion through membranes via specific carriers.
• Osmotic and electrochemical flows and equilibria.
• Microfluid dynamics and microfluidic devices for biomedical and biotechnological applications.
  The exercise sessions will consist of the numerical solution of problems calculating transport quantities that represent the main types encountered in biomedical engineering applications. A few exercise sessions will illustrate examples of biotechnological problems solved by using commercially available computational fluid dynamics software packages.

Prova scritta obbligatoria, senza prove in itinere
The exam will consist of a written test, approximately one and a half to two hours long, requiring students to solve problems (using calculations performed with a standard scientific calculator) and answer questions on the topics covered in lectures and exercises. The exam will not include an oral part.
During the exam, students must demonstrate their ability to:

• apply the subject methods and tools to address and solve the proposed problems (problem analysis, proposed solution schemes, justification of hypotheses and choices, and calculations)
• answer the proposed questions on theoretical and design aspects discussed during the course.
  The final grade will take into account the following elements:
• ability to analyze problems;
• formal and substantive correctness of the problem-solving procedures;
• accuracy of the results obtained;
• knowledge and understanding of the topics;
• appropriateness of the technical and scientific language used;
• independent judgment.

G. Dubini, Course notes, Anno edizione: 2026
R.B. Bird, W.E. Stewart, E.N. Lightfoot, Transport Phenomena, 2nd ed., Editore: John Wiley & Sons, Inc, Anno edizione: 2007, ISBN: 978-0470115398
E.L. Cussler, Diffusion: Mass Transfer in Fluid Systems, 3rd ed., Editore: Cambridge University Press, Anno edizione: 2009, ISBN: 978-0521871211
Y.C. Fung, Biomechanics: Circulation, 2nd ed., Editore: Springer Nature, Anno edizione: 1996, ISBN: 978-0387943848

Management sanitario
Il corso fornisce i concetti fondamentali del management applicato nell'ambito sanitario e socio-sanitario. Gli studenti saranno esposti alle sfide che l'ecosistema sanitario e socio-sanitario sta affrontando e apprenderanno gli approcci manageriali più recenti per l'erogazione di servizi sanitari e socio-sanitari efficaci e sostenibili. Il coinvolgimento nelle lezioni di senior manager offrirà agli studenti la possibilità di apprendere direttamente "dal campo" e di comprendere la complessità nella pratica reale. Il corso affronta tre temi principali: la struttura e il finanziamento dell'ecosistema sanitario e socio-sanitario, i principali processi e unità organizzative nelle organizzazioni sanitarie, la valutazione delle tecnologie e la gestione dell'innovazione in ambito sanitario e socio-sanitario. Le competenze acquisite saranno utili per tutti gli studenti interessati ad operare in contesti professionali e/o ad alta intensità di conoscenza.

The course deals with the development and use of the latest engineering methods for high-fidelity simulations of surgical and interventional treatments of the cardiovascular system. Applications are related to real-world goals that arise in the clinical scenario, and are mainly related to the optimization of surgical/interventional procedures, the evaluation of surgical/prosthetic device performances and, importantly, the training of clinicians towards a better understanding and execution of complex procedures.
The aim of the course is to provide attendees with the knowledge needed to understand or design and implement computational and experimental models, and with an integrated vision, encompassing advanced experimental methods, advanced computational methods, and their combination.

By the end of the course, students are expected to:
i. learn and handle with confidence the concepts of solid mechanics that are relevant to modeling the mechanical response of cardiovascular tissues (DD1)
ii. be familiar with the basic concepts of medical imaging and image processing that are needed to reconstruct 3D geometrical models of cardiovascular structures and for image-based biomechanical analyses (DD1)
iii. understand how a finite element model works and what are the implications of different choices when defining a model of this kind (DD1)
iv. develop a critical and integrated mindset, allowing them to identify suitable modeling strategies to simulate complex problems in the context of cardiovascular solid mechanics, cardiac surgery and interventional cardiology (DD2,DD3)

Section 1. Introductory concepts and motivations.
1.1. General requirements of a simulation environment for procedure optimization, device testing and/or training.
1.2. Traditional in-vivo animal testing and related limitations.
1.3. The current regulatory scenario.
Section 2. Experimental laboratory platforms. (subject of the experimental module)
Section 3. In-silico virtual phantoms.
3.1. Introduction to numerical modelling approaches.
3.2. Brief review of large strain theory and constitutive modeling for cardiovascular tissues.
3.3. Overview of clinical imaging technologies a s a source of data for computational modeling
3.4. Geometrical reconstruction and biomechanical analysis of cardiovascular structures from medical imaging
3.5. Finite element modeling: theory, implementation, and introductory hands-on sessions.
3.6. Realistic modeling of boundary conditions.
3.7. Criteria for the implementation of numerical models of surgical devices and procedures: matching high-fidelity with practical goals.
3.8. Cutting edge and alternative methods for computational modelling and biomechanical analyses
Section 4. Integration of experimental and computational methods.
4.1. Criteria for setting up integrated studies exploiting the complementarities of the two approaches.
4.2. Techniques for data integration: critical matching of quantitative information from different approaches.
4.3. The use of experiments for validation of the models: analysis of real-world case studies.
4.4. The use of models for interpreting experimental data: analysis of real-world case studies.
Tutorials and other activities
Class tutorials deal about the application to cases belonging to the real clinical world, including the simulation of recent catheter-based, minimally invasive and reparative cardiovascular procedures.
If compatible with the course timeline, seminars will be given by professionals from the industrial and clinical arena, and laboratory visits will be organized, with practical demonstrations of real experimental and computational procedures.

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
The final exam will consist in a written examination encompassing both modules and in an interview for each module. The written examinations and the interviews will all be in English.
The written examination will consist in ten multiple-choice questions on the basic concepts taught during the course. A mark of at least 6/10 is required to access the interviews.
Candidates will be interviewed separately by the teachers of both modules. In each interview, the answers to the written text will be discussed to subsequently move to more complex topics. The goal of the interview will be twofold: on the one hand, assessing whether candidates have learned the theoretical and technical concepts taught in the classes; on the other hand, testing their capability to apply these concepts to tackle new problems.
The final evaluation will be the results of a discussion between the teachers, comparing the Candidate's knowledge in the topics of both modules. An insufficient evaluation in one module will preclude passing the exam.

Jay D. Humphrey, Cardiovascular Solid Mechanics, Editore: Springer-Verlag New York, Anno edizione: 2002, ISBN: 978-0-387-95168-3
MIT Open Courseware - Finite Element Procedures for Solids and Structures https://ocw.mit.edu/resources/res-2-002-finite-element-procedures-for-solids-and-structures-spring-2010/
Note: This item is not a textbook, but rather a series of classes from the MIT in Boston. Videos, notes and textbooks are freely available for each class.

The course aims at enabling students to understand, design and implement advanced signal and data processing algorithms, as well as the mathematical and statistical modeling paradigms used in medicine and biology. The course covers the fundamentals of non-traditional and advanced analytical approaches to improving medical diagnosis, treatment, and rehabilitation. Each topic is covered both theoretically and practically, with hands-on sessions and key examples relating to the cardiovascular, respiratory and neural systems, focusing on the central and peripheral autonomic nervous system. The course alternates interactive and participative lessons and seminars with training and practical sessions. The aim is to equip students with the technical competencies of an expert biomedical data scientist, an increasingly sought-after professional profile in biomedical engineering.

Students will learn how to:

• Identify a specific problem in dealing with targeted medical information and apply the most suitable algorithm from those explained in the course and presented in the literature. (DD1, DD2)
• Evaluate the pros and cons of alternative approaches in relation to more established benchmarks.
• Identify specific classes of common problems in biomedical information processing in a wide range of applications for different systems or organs, at various spatial and temporal scales of investigation (DD3).
• Properly manage and apply advanced methods and algorithms like time-frequency and time-scale distributions, chaotic time series analysis, higher order analysis. (DD2)
• Properly manage and apply advanced methods and algorithms of statistical analysis and modeling, multivariate regression, clustering, and classification from evenly and unevenly sampled time series. (DD2)

Methods.

• Introduction to Information Theory.
• Highlight of the neuron and neural coding
• Statistical modeling: Bayes' rule, State-Space Models, Point Process Models.
• Blind source separation: Use of principal component analysis (PCA) and independent component analysis (ICA) for filtering.
• Multivariate Causality and Higher Order Spectra.
• Highlights on Statistical Learning and Data Mining.
• Regression and Classification.
• Machine Learning: Graph Search, Nearest Neighbors, Decision Trees, Neural Networks, Support Vector Machines.
• Deep and Reinforcement Learning.
• Unsupervised Learning.
  Applications. Unraveling the Neural Code. Neural and Cardiovascular Point Process Models. EEG and MEG Blind Source Separation. Higher order analysis of cardiorespiratory signals and EEG recordings. Physiological Brain Correlates: Combining Brain Imaging techniques with Physiological variables. Classification: USA arrest data, the Heart Data example, gene expression data, breast cancer microarray study.

Prova orale obbligatoria
This second course module [2] will be evaluated on the basis of an oral exam covering the topics addressed in Lessons and Exercise hours.
Of note, the first course module [1] will be evaluated on the basis of a written exam covering the topics addressed in Lessons and Exercise hours. The exam will include open questions and a practical project consisting of designing an analysis framework suitable for a given research question.
At each call, students may choose to take the exam for only one module of the integrated course, provided they commit to completing the other part of the course during the academic year in which they are enrolled.
The integrated exam is passed if the student achieves a pass mark in both parts. The final grade is the average of the grades obtained in the two parts.

L’insegnamento, composto da due moduli integrati, intende introdurre gli elementi di base per lo studio di sistemi sperimentali di interesse biotecnologico (Applicazioni Biotecnologiche [1]) e dei Bioreattori per la medicina rigenerativa (Bioreattori [2]).
L’insegnamento del modulo di Applicazioni Biotecnologiche [1] si propone di fornire allo studente gli elementi conoscitivi a livello molecolare e cellulare dei sistemi biologici eucarioti e procarioti e le basi teoriche delle biotecnologie nei campi della diagnostica, della farmacologia a livello profilattico e terapeutico, nonché la conoscenza delle tecnologie più rilevanti per l'analisi degli acidi nucleici e delle proteine in generale.

A seguito del superamento dell'esame, lo studente:
avrà acquisito nozioni dettagliate e sarà in grado di spaziare tra struttura e funzione di acidi nucleici e proteine (DD1);
avrà acquisito nozioni dettagliate sull’organizzazione delle (macro)molecole a costituire cellule e tessuti (DD1);
conoscerà e saprà spiegare il principio di funzionamento e l’ambito applicativo, nonché pregi e difetti delle principali tecniche biotecnologiche utilizzate a livello laboratoriale (DD1);
avrà acquisito i fondamentali per poter applicare con profitto le tecniche trattate a problematiche laboratoriali (DD2);
sarà in grado di affrontare problemi teorico/pratici inerenti alle Biotecnologie, anche non esplicitamente trattati nell’insegnamento, utilizzando i metodi e le tecnologie apprese (DD3);
avrà acquisito un lessico scientifico corretto e adeguato per intavolare discussioni scientifiche con bioingegneri, biologi, biotecnologi e medici (DD4).

L’insegnamento del modulo di Applicazioni Biotecnologiche [1] si propone di introdurre gli elementi di base per lo studio di sistemi sperimentali di interesse biotecnologico.
Differenze tra eucarioti e procarioti: la compartimentazione. Il dogma centrale della biologia molecolare: cenni sulla trascrizione e la traduzione. Struttura e funzione dei principali biopolimeri cellulari eucariotici quali RNA (mRNA, tRNA, rRNA), DNA (genoma, cromosomi, cromatina e geni), proteine (struttura I°, II°, III°, IV°). Sintesi, folding e degradazione delle proteine. Il genotipo e il fenotipo cellulare. La segnalazione cellulare. Il metabolismo cellulare. Il ciclo cellulare (fasi G1, S, G2, M) e le cellule quiescenti/senescenti (G0). La mitosi. I fattori di crescita. Sincronizzazione del ciclo cellulare. Cenni sulla meiosi. Esempi pratici tratti dalla bibliografia scientifica.
Cenni sui principali sistemi sperimentali di interesse biotecnologico: cellule eucarioti in coltura. Cellule primarie e linee cellulari. La morte cellulare: l'apoptosi e la necrosi. Tecniche di fusione cellulare per generare ibridomi e principali applicazioni: la produzione di anticorpi monoclonali (mAbs). Differenze tra mAbs e anticorpi policlonali e tra anticorpi I° e II°. L’utilizzo degli anticorpi nelle tecniche di: Western Blot, Saggio Immuno-Adsorbente legato ad un Enzima (ELISA), Lateral Flow Assay (LFA), citofluorimetria (FCM) e immunofluorescenza (immunoistochimica – IHC e immunocitochimica – ICC) per la rilevazione dei marcatori cellulari (Cluster di Differenziamento - CD). Procedure di estrazione delle proteine da campioni biologici. Tecniche cromatografiche e elettroforetiche (SDS-PAGE) per la separazione e/o purificazione delle proteine. Il sequenziamento peptidico. Esempi pratici tratti dalla bibliografia scientifica.
Estrazione purificazione degli acidi nucleici, PCR, PCR in tempo reale e metodi di sequenziamento. Enzimi di restrizioni, ligasi e fosfatasi. Metodi per il trasferimento genico (trasfezione e trasduzione) in cellule eucarioti e terapia genica. Over-espressione genica e silenziamento. I vettori di clonaggio e i vettori di espressione. I geni reporter. La trasfezione transiente. Metodi chimici (polimeri e lipidi cationici) e fisici (microiniezione, magnetofezione, sonoporazione, elettroporazione e gene gun) per trasfettare. Valutazione della trasfezione: efficienza di trasfezione e citotossicità. Parametri chimico-fisici rilevanti per la valutazione di un trasfettante: il rapporto di carica, le dimensioni e la carica delle particelle. Esempi pratici tratti dalla bibliografia scientifica.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente) o condizionata (a scelta del docente)
Prima prova scritta della durata di 45 min (15 quesiti a risposta multipla, punteggio minimo per il superamento della prima prova: 9/15. +1 riposta corretta; 0 nessuna risposta; -0.5 risposta sbagliata), seguita da una seconda prova scritta a domande aperte della durata di 45 min (punteggio minimo per il superamento della seconda prova: 9/16) su aspetti teorici e pratici del corso. Condizione necessaria per l’ammissione al secondo scritto è il superamento della prima prova. Entrambe le valutazioni si considerano superate con punteggio minimo di 9. Il voto finale del modulo Applicazioni Biotecnologiche [1] è dato dalla somma della valutazione delle due prove. Se entrambe le prove hanno avuto esito positivo, lo studente può (facoltativo) sostenere la prova orale con attribuzione di un punteggio di +/- 2 punti sul voto finale della valutazione scritta.
Non è previsto lo svolgimento della prova in itinere.
In sede di esame finale lo studente deve dimostrare di:
conoscere i fondamenti teorici degli argomenti trattati durante il corso;
conoscere e saper applicare le tecniche e tecnologie oggetto del corso a problematiche laboratoriali;
saper gestire risorse e tempi per il completamento del compito assegnato.

The course is focused on methods, technologies and operating principles that enable the manipulation and control of biological entities at the micro- and nano-scale. Topics include microdevices for the control of the environment at the scale level of cells (lab-on-chips, bioMEMS, organs-on-chips) and molecular-level nanostructures (quantum wires, quantum dots, graphene or carbon nanotubes, biological nanostructures obtained e.g. from DNA).
Systems at the micro- and nano-scale are ubiquitous in a wide range of technological innovations; they nowadays represent enabling tools for the advancement of fundamental research and it is believed that, in the near future, they will invade the biotechnological/biomedical market.

Students must acquire specific competences according with the following Dublin Descriptors:
A: KNOWLEDGE AND UNDERSTANDING
Knowledge and ability to discuss and present the main theoretical aspects presented in the course.
B: APPLYING KNOWLEDGE AND UNDERSTANDING
Resolving problems and exercises at the nanoscale applying the correct mathematical models.
C: INDIPENDENCE IN JUDGEMENT
Critically analyzing the recent literature on nanotechnology and discussing results.
D: COMUNICATION SKILLS
Ability to present a paper of the most recent literature, the course is in english and students are encouraged to ask questions and discuss in English.

STRUCTURE OF THE COURSE:
The course is organized in lessons which focus on different theoretical aspects at the micro- and nano -scale levels. Within the course an integrated perspective of the design of bioartificial devices will be provided, deriving from the interaction of bioengineering approaches acting at these scale levels. Practicals and tutorials will cover important topics at both scalses:
Practicals and tutorials will teach how to approach the design of simple microdevices for cell culturing, from their dimensioning to their realization with microtechnologies and testing with current laboratory techniques.
TOPICS:
The first part of this integrated course will regard sytems at the microscale:

• The course will first deal with the design principles of small-scale devices enabling the culture of living cells, from cell monolayers to organized proto-tissues.
• On the ground of the design principles, the course will deal with the basic theoretical laws concerning transport phenomena at the microscopic scale. In particular, micro fluid-dynamics, particle transport, solute transport, etc. will be treated.
• In parallel to the theoretical aspects, the microfabrication technologies used for manufacturing and the techniques for manipulating fluids and particles at the microscopic scale will be reviewed, and their implications for microdevice design will be integrated in the general vision.

Prova scritta obbligatoria, senza prove in itinere
The Micro and Nano modules will be evaluated in separate tests. The exams consist in quiz + open questions/exercises

Albert Folch, Introduction to BioMEMS, Editore: CRC Press, Anno edizione: 2012, ISBN: 1439818398
Note: The book explains how certain problems in biology and medicine benefit from the miniaturization of devices. It covers the whole breadth of this dynamic field, including classical microfabrication, microfluidics, tissue engineering, cell-based and non cell-based devices, and implantable systems.

The objectives of the course are the guided attainment, by a strongly experimental approach, of the techniques and issues concerning the development of structures and constructs for biomedical application. The course aims to teach a methodological approach by using specific protocols as examples from the field of biomaterials. The specific goals of the course are:
to introduce the students to the techniques and methodology typical of biomaterial science, where the preparative stage is related to the characterization of biomaterials, by a hands-on approach.
to guide the students to acquire a preliminary knowledge about planning a laboratory activity regarding biomaterials and their characterization, through some selected examples of the preparation and characterization of different types of biomaterials, surface modification, processing techniques.
to teach how to discuss the rationale of an activity and its relation to the experimental results
to induce the students to use the acquired knowledge to develop autonomous projects regarding the subjects of the course
to produce an effective teamwork

At the end of the course, the students will

• know the basic methodology of the biomaterials preparation and characterization and the related scientific lexicon (DD1)
• improve the ability to critically use the scientific literature regarding the preparation of biomaterials and their characterization (DD3)
• be able to set up experimental activities and report them according to an appropriate scientific format (DD2,DD4)
• be able to apply strategies for an effective teamwork (DD2)

The Laboratory of Biomaterials provides students a thorough knowledge of experimental methodologies to manage the preparation of materials and smart surfaces. In the part of Laboratory of Instrumental Analysis the most used investigation techniques for chemico-physical, morphological and mechanical characterization of materials are described, with particular emphasis to the techniques used in the biomedical field. As processing is a specific aspect in biomaterials science, some simple examples of processing and surface modification will be addressed. The deep analysis of some examples from scientific literature will contribute to the critical knowledge.
The students have the possibility to use some of the techniques in an experimental and guided path, with a laboratory part fundamental to reach the objective of the course. The methodology of active learning and flipped classroom will be adopted, with ad hoc practicals. The practicals/activities will be chosen as explicative examples of the methodology underlying the principal concepts in the biomaterial field, such as:
1. Preparation and characterization. As case studies, different techniques will be applied to biomedical hydrogels to prepare/synthesise and characterize microspheres, macrogels and biomedical composites.
2. Manufacturing advanced materials for specific applications and surface modification techniques. 3D printing of polymeric substrates and surface modification of metallic biomaterials will be used as examples.
3. Characterisation of biomaterials. Introduction to instrumental analysis and the main analytical techniques, such as the different types of microscopy, spectroscopy, and chromatography, differential scanning calorimetry, XRD, surface analsys techniques.
3. Design of biomaterials. At the end of the course, the students will be challenged to develop a research protocol for the production and characterisation of a scaffold for a specific application. The different approaches will be discussed by peer review within the groups, than applied during the experimental lab.
From the explicative examples described above, the general concepts will be cooperatively derived, stimulating an active participation of the students.
NOTA IMPORTANTE
L'accesso al corso è a numero limitato. La procedura obbligatoria di richiesta accesso si trova qui https://www.ccsbio.polimi.it/corso/laurea-magistrale/#Corsi_Laboratorio
It should be noted that this is a limited number access course. The mandatory procedure for access request can be found at https://www.ccsbio.polimi.it/corso/laurea-magistrale/#Corsi_Laboratorio

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
Valutazione continua obbligatoria
The students will work in small groups, and the team's success will affect the grade, with group evaluations included in the final mark.
Experimental lab and practicals are the prevalent part of the course. Appropriate theoretical knowledge is fundamental to following the course's experimental part. For this reason, it will be assessed by pre-lab questions to be answered by the groups. Pre-lab questions and active participation in the class activity will contribute to the final score. Evaluated pre-lesson questions (individual) will be applied when the flipped classroom is applied.
(10% of the final score)
One team-report regarding an experimental project activity will be considered part of the evaluation. The project will be guided and conducted in the lab.
(30% of the final score)
An individual oral exam will complete the assessment of the learning.
(60% of the final score)

Scientific articles and review will be suggested as well as links http://webeep.polimi.it/login/index.php

The course exposes participants to design and implementation of biomedical signal processing methods in Matlab/Phyton. The objective is to provide tools and programming skills through the realization of basic signal processing algorithms, working in teams under the supervision of the lecturer. Advanced topics of biomedical signal processing will be exploited through specific project assignment. Project topics range from multi-variate parametric estimate, optimal filters, time-frequency transform, to non-linear analysis, synchronization and coupling. Innovative didactic schemes will include: learning-by-doing, bleanded learning and flipped classroom.
It should be noted that this is a limited number access course. The mandatory procedure for access request is here (http://www.ccsbio.polimi.it/)

The expectations of achievements and abilities are described in the following table
Dublin Descriptors
Expected learning outcomes
Knowledge and understanding

• Understand the basics of biomedical signal processing in Matlab
• Knowledge of digital filter requirements for biomedical applications
• Knowledge of algorithms for waveform detection in Biosignals.
• Knowledge of spectral estimation methods.
• Understanding of noise redaction methods for biomedical signal enhancements
  Applying knowledge and understanding
• Design and implement biomedical signal algorithms and tools
• Tune algorithms and methods to optimize their performance.
• Implement algorithm and tools in Matlab
  Making judgements
• Evaluate the correctness of the implemented algorithms.
• Select the best options and implementation schemes.
• Compare and evaluate the performance of different solutions.
  Communication
• To be able to describe signal processing methods, its advantages and performances.
• To be able to defend the implementation of a certain algorithm in front of an audience and the lecturer.
  Lifelong learning skills
• To be able to understand and implement any biomedical signal processing algorithm published in literature.

Introduction to software tools for scientific computing and their use for the design of biosignal processing algorithms.
Data Analysis: Data fitting, criteria for model selection and identification, LMS estimation. Interpolation.
Signal analysis: FFT and harmonic analysis of biosignals. Design of FIR and IIR filters for biomedical applications, detection of waves and patterns. Spectral estimate based on parametric and non-parametric methods, application to EEG and Heart Rate Variability signals. Enhancement of evoked potentials by averaging and single sweep approach.
It should be noted that this is a limited number access course. The mandatory procedure for access request is here (http://www.ccsbio.polimi.it/)

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
During the course students will work in small teams under the supervision of the lecturer. The teams will be asked to implement biomedical signal processing algorithms (DdD2) and discuss with the Lectruer impementation options (DdD3,DdD4,DdD5)
A final project will be assigned to each team. During the oral examination, the team will present and discuss the project, its implementation, and the results achieved (DdD4). The examination will assess each team member individually, taking into account their understanding of the problem and problem-solving abilities (DdD2), their contribution to the algorithm implementation and the rationale behind the adopted design choices (DdD2, DdD3), their ability to discuss and justify the selected implementation strategies (DdD4), the overall quality of the work and the performance of the proposed algorithms (DdD5), as well as their knowledge and understanding of the topics covered during the course (DdD2).
Final score will be based on laboratory works (10%), Project delevolpment (70%) and Oral session (20%).

Richard Shiavi, Introduction to Applied Statistical Signal Analysis, Editore: Academic Press, Anno edizione: 2006, ISBN: 978-0120885817
Virginia Stonick & Kevin Bradley, Labs for Signals and Systems Using MATLAB, Editore: CL-Engineering, Anno edizione: 1995, ISBN: 978-0534938086

The course provides the theoretical and practical foundations of biomolecular modelling, with particular emphasis on molecular dynamics simulations as tools for the study, analysis, and design of biomolecular systems. The course connects molecular-scale structure and dynamics with problems such as protein stability, molecular recognition, protein engineering, biomaterials, nanobiotechnology, drug delivery, and biomedical or biotechnological applications.
Students learn how biomolecular structures are obtained, predicted, represented, simulated, and critically interpreted. The laboratory-based structure of the course allows students to move from theoretical principles to computational practice: selecting molecular models, preparing simulation systems, running simulations, analyzing trajectories, and evaluating the reliability and limitations of the results. Through journal club and group project activities, students develop the ability to use computational modelling in applied research and design workflows, integrating scientific literature, quantitative analysis, and technical communication.

At the end of the course, the student:
Knows and understands the molecular basis of biomolecular structure, dynamics, and interactions, and can explain the theoretical principles of molecular mechanics, force fields, molecular dynamics, enhanced sampling, coarse-grained modelling, and protein design.
Is able to apply biomolecular modelling methods by setting up, running, visualizing, and analyzing molecular simulations of proteins and related biomolecular systems using common computational tools.
Is able to extract and interpret molecular descriptors such as structural stability, flexibility, secondary-structure changes, hydrogen bonds, salt bridges, molecular contacts, diffusion, binding-related interactions, and effects of mutations.
Is able to critically assess the assumptions, approximations, strengths, and limitations of computational models, and to judge whether a modelling strategy is appropriate for a given biomolecular or biomedical question.
Is able to analyze scientific literature, work in a team on a computational project, and communicate modelling methods, results, uncertainty, and conclusions clearly in English.

Role of biomolecular modelling in the molecular-level understanding, prediction, design, and interpretation of biomolecular systems.
Protein structure and function: amino acids, folding, stability, structure-function relationships, and relevance to protein engineering and biomedical applications.
Experimental and computational biomolecular structures: X-ray crystallography, NMR, cryo-EM, Protein Data Bank, homology modelling, AlphaFold and its limitations.
Molecular mechanics and force fields: bonded and non-bonded interactions, electrostatics, van der Waals interactions, atom types, parametrization, transferability, and empirical approximations.
Molecular dynamics workflow: system preparation, solvation, ions, periodic boundary conditions, energy minimization, equilibration, production simulations, and trajectory generation.
Running MD simulations: Newtonian dynamics, numerical integration, time step selection, thermodynamic ensembles, temperature and pressure control.
Analysis of molecular simulations: RMSD, RMSF, secondary structure, hydrogen bonds, salt bridges, native contacts, molecular interactions, diffusion, self-assembly, and stability indicators.
Advanced modelling approaches: enhanced sampling, steered MD, metadynamics, free-energy concepts, alchemical transformations, mutation and binding free-energy estimates.
Beyond atomistic MD: coarse-grained modelling, MARTINI, Monte Carlo sampling, Rosetta, protein design, and modelling strategies for larger time and length scales.
Practical computational laboratory: molecular visualization, Linux/virtual-machine environment, VMD, MD setup, simulation execution, and trajectory analysis.
Journal club and group project on biomolecular modelling topics with application-oriented relevance.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The final grade is based on the evaluation of both theoretical understanding and practical modelling skills:
Journal club presentation: 10%. Students present and discuss a scientific paper related to biomolecular modelling and its application-oriented relevance.
Group project development and presentation: 30%. Students carry out a computational modelling project, analyze the results, discuss the relevance of the findings, and answer questions.
Final written exam: 60%. The exam includes multiple-choice and open questions on the theoretical and practical contents of the course.
The assessment verifies the student’s ability to understand modelling principles, apply computational workflows, interpret molecular simulation data, evaluate model reliability and limitations, and communicate results in a scientifically sound and application-oriented way.

The course is aimed at teaching students how to be critical thinkers able to apply their knowledge to solve a real-problem coming from an external stakeholder such as a company.
Students will work in groups under the joint supervision of a company and an academic tutor.
The proposing company defines specific objectives and expected results (project briefs) that should be achieved by the group during the course. The students will learn how to identify needs, define functional and technical specifications, design prototypes and validate their design. Special attention will be devoted to develop in students: i) a sense of personal responsibility with respect to the project goal, ii) the ability to collaborate in team iii) the communication ability required to present their project.
Indeed, students will attend project-advancement meeting to present project deliverables and milestones specifically suited for each individual project. A final dissemination meeting will be held where every team will present their achievements.
The course project activity could be held within Politecnico and company laboratories, if necessary and according to companies’ availability.
The course involves 2 innovative teaching CFUs.

Knowledge and understanding
Students will gain knowledge about:

• How to solve a problem starting from real needs, identifying requirements, and designing prototypes (TRL 3/4) within a fixed timeframe.
• Basic principles concerning how to give technical presentations
  Applying knowledge and understanding
  Given a specific project, students will be able to:
• gather experience from industry professionals
• plan the activity of the team to efficiently reach the project aims
• define functional requirements and technical specification
• design and eventually test an effective solution (prototype)
  Making judgements
  Given a relatively complex problem, students will be able to:
• develop self-assessment abilities
• improve collaboration skills and responsibility
• estimate the resources needed for the proposed solution, identify risks and define mitigation actions
  Communication
  Students will learn to:
• improve communication skills by presenting their work to their colleagues and to both the company and academic tutor
• improve skills in disseminating the final results to the general public
  Lifelong learning skills
  Students will learn:
• how to develop a real project proposed by an external stakeholder;
• how to work in team according to a precise gantt of activities.

The lecturer will assign every team with a specific project. Proponent companies will identify the specific tasks and aims. Teams will be advised by a company-based supervisor and an academic tutor.
Students will be provided with skills focused on prototype design, such as work organization, communication and dissemination. Lessons will be focused on project planning, team work, privacy, risk analysis and regulations concerning biomedical devices.
Meetings will be held with every single group to assess project advancement. Groups will discuss their advancements with the lecturer, the company advisor (if available) and other students to provide an active and participated discussion to promote constructive criticism skills.
Every group will discuss their final design in a plenary final presentation.
Further details on the course organisation will be provided during the first day.

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
Final mark will be composed 80% on the team project development and presentations and 20% on an individual evaluation.
Type of assessment
Description
Dublin descriptor
Assessment of laboratory project
Assessment of the design and experimental project developed by students in groups
2, 3, 4, 5
Oral presentation
Assessment of the presentation of the project developed by students in groups
2, 3, 4, 5
Oral exam
Theoretical questions on the course topics applied in the context of the project developed
1,4,5

The course introduces to the numerical techniques currently adopted to compute the mechanical response of solids and the fluid dynamics in complex systems. Within the course, the student will practice with numerical solvers aimed at structural and fluid-dynamics analysis. After theoretical and lab-training lectures, the students will work in small groups and apply the acquired knowledge and competences by developing numerical projects in the context of biomedical applications.
Important Note
This is a limited number access course. The mandatory procedure for access request is here:
www.ccsbio.polimi.it/laurea-magistrale/?lang=en#Laboratory_Courses_LAB_courses

Knowledge (DD1): students are expected to acquire notions on continuum mechanics, on numerical techniques to solve partial differential equations. They are also expected to understand the impact of modeling hypotheses and to use their knowledge on real problems.
Competences (DD2): by the end of the course, students are expected to be able to i) formulate and solve complex problems in the field of biomedical engineering, ii) apply engineering methods to the field of biology and biotechnology, use of two software packages that well represent the standard commercial solvers in the field of structural mechanics and fluid dynamics, respectively.
Autonomy and communication abilities: after the course, thanks to the experience of the proposed project, the students are expected to operate autonomous decisions about the definition of modelling strategies and design of devices (DD3) and to communicate the results of their activity in clear and concise manner (DD4).

The course is organized in two parts, focused on the analysis of structural mechanics through finite element modeling and on computational fluid dynamics through finite volume modeling, respectively. In both parts, students will be introduced to the computational methods implemented in a commercial code with a mix of lectures and tutorials. At the end of each part, students will develop a project working in small teams.
Part one: finite element modeling and structural mechanics
Solid mechanics and introduction to the finite element method: the governing equations of solid mechanics will be overviewed. Their weak form and the corresponding discretization and solution will be introduced. In details, standard displacement-based approach will be introduced together with discretization strategies of governing equations, numerical integration issues and algorithms for solution of linear and nonlinear problems.
Principles of solid mechanics simulations: the commercial package Abaqus will be introduced, the main steps for the problem solution including the boundary conditions, meshing algorithms and solution strategies will be reviewed for linear and nonlinear simulations.
Tutorials: by using Abaqus, students will train in teams on tutorials following a step-by-step guide.
Project: working again in team and using Abaqus, students will have to set and run an analysis to tackle a real-world problem. The problem to be simulated will be exhaustively described in terms of design specifications, but students will have to independently choose how to set their model. At the end of the project, each team will have to produce a report.
Part two: finite volume modeling and fluid dynamics
Introduction to fluid dynamics: the equations governing the conservation of mass, of linear momentum and of energy will be derived and discussed. Their weak form, its discretization and solution will be introduced based on the general scheme of convection-diffusion equations.
Principles of fluid mechanics simulation: lectures will describe:

• how to obtain the linear algebraic form of the field equations
• the iterative methods for algebraic system solution, the upwinding schemes, the pressure-velocity coupling algorithms
• how to check convergence and result accuracy.
  Tutorials: the commercial package Ansys-Fluent will be introduced. The team of students will work on tutorials, devoted to introducing progressively more complex features of the software package, including the use of user-defined functions.
  Project: working again in team and using Ansys-Fluent, students will have to set and run an analysis to tackle a real world problem. The problem to be simulated will be exhaustively described in terms of design specifications, but students will have to independently choose how to set their model. At the end of the project, each team will have to produce a report.
  Important Note
  This is a limited number access course. The mandatory procedure for access request is here:
  www.ccsbio.polimi.it/laurea-magistrale/?lang=en#Laboratory_Courses_LAB_courses

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
Students will be evaluated at the end of the course on the basis of the two reports (one for solid mechanics FEM group-project and one for CFD finite volumes group-project) produced within the end of the course and of a personal oral examination (presentation&discussion of the projects considering connections with the theoretical aspects of methods) during the exam session. The final mark will be the average of the marks of each report and of the oral examination (note that all three marks must be greater than or equal to 18/30).

e-Health is defined as the use of Information and Communication Technologies (ICT) for health. In the context of ageing population and increase in chronic patients-related healthcare expenditure, ICT represents a key component for ensuring quality, safety, access and efficiency, also stimulating new paradigms of care, from a paternalistic to a collaborative model, from acute care to prevention.
The goal of the course is to enable students with relevant tools and concepts that are needed to master the complexity of ICT in healthcare in this continuously evolving field. Through the course, structured in two parts, Methods and Applications, which are interleaved throughout the semester, also practical projects will be proposed to groups of students, to stimulate their problem-solving, and giving an opportunity to present and discuss their work to an audience.
Innovation in teaching modality will be also utilized during the class.
Starting A.A.2021/22, the course "E-Health: Methods and Applications" will contribute for 5 CFU to the achievement of the certification titled "Interdisciplinary Programme in Smart Infrastructures" (https://www.ingindinf.polimi.it/en/education/interdisciplinary-programmes/translate-to-english-programma-interdisciplinare-in-smart-infrastructures).

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will learn how to:
Understand basic and advanced concepts of e-health
Use different taxonomies in the healthcare field
Face with different ICT tools according to the required situation
Consider the concept of ethics, privacy, behavioral changes from an engineering point of view
Use some medical informatics tools for data analysis
Applying knowledge and understanding
Given specific project cases, students will be able to:
Detail the corresponding requirements
Analyze and comment on specific design choices
Develop and test code fulfilling the requirements
Making judgements
Given a relatively complex problem, students will be able to:
Evaluate correctness of proposed solutions
Understand which factors are important to avoid failures when implementing eHealth
What are the usable tools for care professionals, the role of patient and of the engineer
Communication
Students will learn to:
Write a report to describe the design and implementation choices
Present their work in front of their colleagues during project labs
Lifelong learning skills
Students will learn:
how to develop a realistic project
how to use ICT in the context of healthcare
useful concepts to help them interpreting the surrounding everyday healthcare reality with a different perception

Please note that this list is purely indicative and class arguments may change due to teaching needs in this highly evolving field.
Overview of e-health data: Data flow in healthcare, Classification of Data type, Data collection and recording, Use of e-health data, From data to information to knowledge based system in healthcare. Interoperability and the importance of standards in healthcare
E-health terminologies: Definitions of terminologies, classifications and onthologies; some relevant examples: Systematized Nomenclature for Medicine (SNOMED CT), Unified Medical Language System (UMLS) and Metamap.
Natural Language Processing and Large Language Models in health care
Digital medicine and digital health, digital biomarkers, and digital therapeutics
Methods to design, develop and evaluate e-health technologies: the holistic user centered approach starting from the contextual inquiries, and the identification of functional requirements and technical specifications, towards the development of prototypes, and the iterative and final testing.
The design, development and testing of serious games for the screening and training of learning disabilities.
Digital medicine for Ageing in place: smart Internet of thing objects for remote monitoring; transparent daily life monitoring as a frontiers towards early diagnosis and a new model of care. The role of Artificial Intelligence in the context of Ageing in place.
Decision making in e-health: Biomedical Decision Making: probabilistic clinical reasoning; Management of uncertainties for decision making; Clinical Decision support system; Mining hospital data to predict patient outcomes.
E-health for clinical trials: the use of computerized systems in clinical investigations: electronic report form, electronic patient reported outcome measures, e-health technologies to support randomized clinical trials, decentralised clinical trials.
A project laboratory is mandatory and is an integral part of the course. The objective of projects is to give the students the opportunity to tackle a real problem using concepts learned in class, working in small groups and organizing their work. Projects will be assigned at the beginning of the semester and are expected to be given in at fixed deadlines defined by the time the project will be assigned. The evaluation of projects will be based on the produced reports and on a final presentation.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
The assessment will be based on a written exam, covering the two parts of the integrated course (Methods and Applications) at the end of the course, and on the projects developed in the laboratory part of the course. These projects will be presented to the instructor and colleagues at the end of the course. The scores of the written exam and of the project part, weighting each for 50% of the final score, will be summed to compute the total score. In addition, optional oral exam is allowed with a +/- 3 points range modification on the total score.
Type of assessment
Description
Dublin descriptor
Written test
Theoretical questions on all course topics with open answer
1,4,5
Assessment of laboratory project
Assessment of the design and experimental work developed by students either individually or in groups
2, 3, 4, 5
Oral presentation
Assessment of the presentation of the work developed as part of laboratory activities developed by students either individually or in groups
2, 3, 4, 5
Optional Oral exam
Theoretical questions on the course topics
1,4,5

Lisette van Gemert-Pijnen, Saskia M. Kelders, Hanneke Kip, Robbert Sanderman, EHealth Research, Theory and Development: A Multi-Disciplinary Approach, Editore: Routledge, Anno edizione: 2018, ISBN: 978-1-138-23043-9
Shortliffe Edward H. and Cimino James J., Biomedical Informatics: Computer Applications in Health Care and Biomedicine, Editore: Springer, ISBN: 978-0387289861

The course is designed to equip students with the skills needed to develop a technological project in the biomedical field, guiding them through the following key stages:
Defining Application Requirements: Identifying specific needs of the biomedical project, such as relevant variables, signal acquisition, processing, and electronic design.
Component Selection: Choosing suitable components, whether custom or commercial, to meet the project's needs.
System Architecture Design: Planning the overall system structure, including sensors, front-end circuits, and data processing and visualization.
Hardware and Software Implementation: Developing the system using appropriate development tools and techniques for practical implementation.
Verification and Performance Evaluation: Applying suitable procedures to test the functionality and assess the performance of the developed prototype.
Technical Report Writing: Documenting the project details, methodologies, and outcomes in a comprehensive technical report.

Students will gain experience with key electronic laboratory tools (including the soldering iron, multimeter, oscilloscope, and power supply) and will learn essential engineering methods (such as application analysis, requirement definition, system design, and component selection) to develop biomedical technology projects. (Lectures and practical exercises). (DD 1)
Students will develop skills in designing electronic boards, writing firmware for microcontrollers, and building hardware prototypes using discrete components and devices for acquiring and processing biomedical signals. (Exercises and laboratory practice) (DD 1.2)
Through practical application, students will learn to apply their knowledge to define project requirements, outline key development phases, and select appropriate tools and components, with a focus on biomedical applications of interest. (Development of educational projects) (DD 3.4)
Students will learn how to collaborate in groups on project development and present a selected topic to the class in a flipped classroom format. (Development of educational projects) (DD 4.5)

The course covers the methodologies and electronic technologies for the development of systems dedicated to: i) the use of sensors and actuators in the medical field; ii) acquisition, conditioning, and processing of biomedical signals; iii) monitoring of parameters related to the person and the surrounding environment. The course is structured in two main phases.
The first phase includes lectures and exercises where the instructor presents the following topics: i) Measurement tools typically used in an electronic laboratory, such as testers/multimeters, power supplies, oscilloscopes, and signal generators; ii) Technologies for hardware and firmware design, including circuit design, PCB design, and platforms for microcontroller-based design and development; iii) Basics of analog and digital sensors and actuators; iv) Environments and platforms for software interface development; v) 3D printing technologies for the design of mechanical supports.
The second phase of the course involves group project development. Students are divided into groups and guided through the specific project topics assigned. Students will tackle the entire chain of activities required, starting from conceptual design and requirements definition, component selection, circuit design and implementation, microcontroller firmware programming, and concluding with the final testing phase of the developed prototype.
To complement the project activities and promote active learning, structured thinking, knowledge acquisition, and peer discussions, each student group will be responsible for exploring a specific topic related to the ongoing project, which will be presented and discussed in a flipped classroom format.
Interaction between the instructor and students is supported by the WeBeep platform, which provides course slides, documents such as tutorials and development examples, datasheets of general interest for HW/SW components, and submission of project reports for the final presentation.
The examination consists of evaluating a final project report and an oral presentation.
Important Note
This is a limited-access course. The mandatory procedure for access requests is here:
www.ccsbio.polimi.it/laurea-magistrale/?lang=en#Laboratory_Courses_LAB_courses

Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
The final evaluation consists of assessing a group project report and an oral presentation conducted after the course concludes. The final grade will also consider additional factors, including student commitment, attendance and participation in laboratory activities, teamwork skills, and overall mastery of the course content.

The objective of the course is to provide students with the knowledge and skills required to address research questions in the field of functional motor assessment. These competencies may be applied to support the diagnosis of motor disorders, assess athletes’ performance, plan rehabilitation treatments, evaluate the effectiveness of interventions, support workplace risk assessment, and monitor motor activity in daily life.
Throughout the course, students will experience all the main steps involved in generating new knowledge in this field, including the identification of a research question, the design of an experimental protocol, an introduction to Ethics Committee procedures, the preparation of the experimental set-up, data collection, the implementation of data-analysis algorithms, and the interpretation of results in relation to the relevant scientific literature.
The course is organised into two parts which run in parallel:
a lecture-based component, including lectures and laboratory sessions aimed at introducing the fundamental concepts, measurement instruments, and data-analysis methods;
a practical component, in which students will work in small teams on a specific project agreed upon with the lecturer. The projects will be introduced on the first day of the course.
Part of the course will also be devoted to visits and meetings with key stakeholders working in the field of functional assessment.

The expected learning outcomes are summarized in the following table:
Dublin Descriptors
Expected learning outcomes
Knowledge and understanding

• Knowledge of the main equipment which can be used for functional evaluation of motor skills.
• Knowledge of algorithms for data analysis of kinematic, dynamics and EMG signals.
• Understand the technical requirements and the return of investment of a Motion Analysis Lab.
  Applying knowledge and understanding
• Design and implement experimental protocols in the field of functional evaluation.
• Set-up the equipment for data acquisition.
• Implement algorithms and tools to derive outcome measures.
  Making judgements
• Select the best options for data collection and analysis.
• Compare and evaluate the performance of different solutions.
• Evaluate the results derived from small sets of collected data with respect to the literature.
  Communication
• To be able to work in teams of peers under the supervision of the lecturer.
• To be able to present intermediate and final results of the assigned project.
• To be able to prepare the documents for requesting Ethical Committee approval.
• To be able to defend implementation choices and discuss the achieved results in comparison with the literature.
  Lifelong learning skills
• To be able to identify research questions and to carry out all the steps needed to build new knowledge in the field of functional evaluation.

This course focuses on quantitative movement analysis and its applications, with a particular emphasis on neurological rehabilitation.
Course Topics
Introduction to Research Design and Ethical Principles in Research Students will learn how to design and conduct a research study, covering key aspects such as study design selection, procedures for obtaining ethical approval for research involving human participants, data collection and analysis, definition of performance indicators, introduction to statistical analysis, and interpretation of results.
Tools, Protocols, and Data Analysis for Functional Assessment of Motor Skills The course provides an overview of state-of-the-art technologies and methods for the quantitative evaluation of motor performance, including:
Marker-based motion capture systems: standard protocols, data processing and analysis, extraction of biomechanical parameters, generation of reports, and identification of typical movement patterns for gait and other tasks (e.g., sit-to-stand, arm reaching, sports movements, and ergonomics).
Markerless video-based motion capture systems: various configurations (RGB and RGB-D cameras), data acquisition protocols, analysis pipelines, and application examples.
Wearable inertial sensors: for kinematic assessment in real-world conditions.
Surface multi-channel electromyography (EMG): electrode placement, signal processing algorithms, and analysis of muscle synergies.
Different technologies and methods are first introduced through lectures and then explored through hands-on laboratory sessions, reinforcing learning-by-doing.
Case Studies and Expert Contributions The course includes presentations from professionals in the field, such as industry experts, specialists in sports biomechanics, and clinicians in neurological rehabilitation. When possible, on-site visits to relevant institutions or companies are organized.
Additional Information
Please note that this is a limited-enrollment course. Instructions for submitting an access request are available at http://www.ccsbio.polimi.it.
The course includes 1 CFU of innovative didactic activity, based on learning-by-doing methodologies.

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
During the course, students will work in small teams under the supervision of the lecturer and a designated tutor. Each group will be assigned a project in the field of functional movement assessment. Possible projects may include, for example, the validation of a new measurement tool against a reference standard, the analysis of EMG signals during a specific motor task, or the design of a clinical study to assess the effectiveness of an innovative rehabilitation treatment.
Each team will be required to:
define specific project objectives based on the general research aim assigned (DdD5);
design an experimental protocol for data collection (DdD2, DdD3);
prepare the experimental set-up, including the required equipment and testing environment (DdD2, DdD3);
collect a pilot dataset to assess the feasibility of the proposed protocol;
implement the algorithms required for data processing and analysis (DdD2, DdD3);
interpret and critically discuss the results in relation to the relevant scientific literature (DdD3, DdD4).
Throughout the course, each team will:
deliver two interim presentations, focusing respectively on the experimental protocol and the data-analysis pipeline;
One week before the selected exam date, each team will:
submit a final written report in the format of a scientific journal paper, describing the background, methodology, results, and discussion of the project;
submit the developed code and the collected dataset as part of the final project deliverables.
The project and its outcomes will be discussed during an individual final oral examination (DdD4). The assessment will consider:
understanding of the research topic and problem-solving skills (DdD2, DdD5);
quality and justification of the methodological and implementation choices (DdD2, DdD3);
ability to critically discuss and defend the decisions made during the project (DdD4);
individual contribution, overall commitment, and quality of the work performed (DdD5).
Grading breakdown
Group assessment: 50%
Two interim presentations: 12.5%
Final written report: 25%
Quality, clarity, and reproducibility of the submitted code: 12.5%
Individual assessment: 50%
Oral discussion of the project: 12.5%
Oral examination on the course contents: 37.5%

Il Laboratorio di Biocompatibilità e Colture Cellulari si propone di presentare le tecniche di coltura cellulare ed i principali protocolli e saggi per la validazione biologica in vitro di materiali e dispositivi biomedicali, nonché di illustrare in linea generale le normative vigenti in materia.

Al termine del corso di Laboratorio lo studente dovrà aver familiarizzato con gli aspetti teorici e sperimentali relativi alla validazione biologica in vitro di materiali e dispositivi biomedicali (DD1).
In particolare, dovrà saper prevedere ed affrontare le principali problematiche legate allo svolgimento delle prove e dei saggi in presenza di uno scaffold/dispositivo (DD2, DD3).
Dovrà saper utilizzare le conoscenze apprese per affrontare problemi complessi nell’ambito della validazione biologica in vitro ed essere in grado di discutere criticamente protocolli e risultati relativi a prove di biocompatibilità in vitro (DD3).
Dovrà aver raffinato le proprie capacità di progettazione dell'attività sperimentale, e di organizzazione, analisi e presentazione dei dati ottenuti (DD4).
N.B: Il laboratorio è a numero chiuso, il collegamento al bando online per l'assegnazione dei posti è disponibile sul sito del corso di studi.

Contenuti delle lezioni
Interazione biomateriale-ambiente biologico;
Classificazione dei laboratori e degli agenti biologici; valutazione di rischi; regole di sicurezza; buona pratica di laboratorio;
Validazione biologica dei biomateriali: modelli e saggi in vitro ed in vivo; normative;
Colture cellulari: tecniche, apparecchiature; saggi per lo studio della risposta cellulare.
Attività di laboratorio (in squadre)
Utilizzo di strumentazione e tecniche comunemente impiegate in laboratori di coltura cellulare;
Avvio della coltura cellulare;
Studio della risposta cellulare in presenza di scaffold/dispositivi.
Le attività di laboratorio si svolgeranno in squadre.
Attività di progetto (in squadre)
Ogni squadra sarà responsabile di affrontare durante il corso un selezionato "case study" assegnato dalla docente, attinente agli argomenti dell’insegnamento, al fine di stimolare l’apprendimento attivo, le capacità di strutturazione del pensiero e della conoscenza, il confronto tra colleghi.
Il case study sarà svolto e discusso in modo articolato in più sessioni con tutti gli studenti del corso in modalità flipped classroom ed anche con docenti/ricercatori ospiti, responsabili di aziende, esperti di proprietà intellettuale e trasferimento tecnologico.

Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
La valutazione terrà conto principalmente della capacità di programmazione/elaborazione e di analisi critica dell'attività sperimentale e di progetto.
Dal punto di vista organizzativo, verrà richiesta la consegna di un lavoro di gruppo relativo alla attività di laboratorio che sarà oggetto di una presentazione/discussione in occasione dell'esame e di una presentazione relativa al "case study" assegnato che verrà presentato in una data fissata durante il corso da svolgersi entro la fine del corso stesso.
La valutazione individuale finale terrà conto della qualità di tali elaborati e della capacità individuale di presentazione e discussione dei relativi contenuti.

L'insegnamento è incentrato sulle tecniche per lo studio in laboratorio di problemi di biofluidodinamica di interesse per la fisiopatologia cardiovascolare, la chirurgia e i dispositivi biomedici.
Nelle lezioni si esaminano i metodi per riprodurre in laboratorio condizioni fluidodinamiche di interesse biomedico, gli strumenti e le tecniche di misura, le procedure di elaborazione dei dati sperimentali. Durante le attività di laboratorio questi temi vengono applicati a casi pratici, affrontati mediante la stesura dei protocolli di prova, la scelta o la realizzazione degli apparati sperimentali, l'esecuzione dei test, l'analisi dei dati e la stesura di un report finale.
L'insegnamento sarà parzialmente erogato in modalità di didattica innovativa.

Come risultato dello studio delle tematiche trattate nelle lezioni, ci si aspetta che lo studente:. maturi una buona conoscenza dei criteri per progettare un'attività sperimentale, specificatamente nel campo della biofluidodinamica (DdD1);. apprenda la natura degli strumenti sperimentali, le tecniche per il loro utilizzo, la logica con cui elaborare i dati sperimentali grezzi (DD1).
A seguito dello svolgimento del progetto sperimentale a squadre, ci si attende che lo studente:. sia in grado di applicare le nozioni teoriche apprese, per progettare con approccio critico una campagna sperimentale nel campo della biofluidodinamica (DdD2);. si ponga obbiettivi motivati e fattibili, sviluppi senso critico nelle scelte, riconosca i limiti degli approcci utilizzati ai fini della progettazione sperimentale (DdD3);. acquisisca o sappia migliorare la proprietà di linguaggio bioingegneristico (DdD4).

Criteri di progetto di esperimenti di biofluidodinamica. Richiami sugli aspetti salienti della fluidodinamica nel sistema cardiovascolare. Criteri generali per riprodurre in vitro condizioni biofluidodinamiche di interesse. I modelli e il loro utilizzo nel progetto sperimentale. Modelli idraulici a parametri concentrati. Similitudine idraulica.
Strumentazione di misura e di attuazione. Strumenti e metodi per la misura e l'acquisizione di grandezze fluidodinamiche caratteristiche del sistema cardiocircolatorio. Problematiche di misura e loro implicazioni pratiche. Sistemi pompanti stazionarida laboratorio, cenni a sistemi pompanti pulsatili.
Protocolli di laboratorio per sperimentazioni di biofluidodinamica in vitro. Scelta della strumentazione. Cenni alle problematiche relative al trattamento dei fluidi. Fluidi di lavoro alternativi al sangue. Metodi per il progetto e la realizzazione di circuiti idraulici di simulazione.
Elaborazione e analisi dei dati. Procedure di analisi dei risultati sperimentali. Modelli interpretativi. Estrazione di indici sintetici a partire dai dati sperimentali grezzi. Indicazioni sulla stesura di report tecnico-scientifici relativi ad attività sperimentali.
Attività di laboratorio
Svolgimento di attività pratiche a squadre, consistenti nello studio sperimentale di casi emblematici scelti in diversi ambiti di interesse per la biofluidodinamica.
Schema delle attività:

• Analisi e discussione del caso proposto: impostazione ragionata del problema e individuazione dei quesiti bioingegneristicamente significativi;
• Progetto dell'esperimento: individuazione delle caratteristiche fluidodinamiche di interesse e analisi critica della letteratura; stesura dei protocolli di prova; scelta della strumentazione; progetto e realizzazione del set-up sperimentale;
• Esecuzione degli esperimenti in laboratorio;
• Discussione dei risultati ottenuti; elaborazione e interpretazione dei dati sperimentali; estrazione di indici sintetici dai dati;
• Stesura di una relazione sull'attività sperimentale svolta; presentazione collegiale da parte della squadra.
  L'insegnamento prevede che la maggior parte del tempo-studente sia speso per lo svolgimento di attività pratiche in laboratorio.
  Sono previste presentazioni, anche in itinere, dell'attività sperimentale realizzata.
  L'accesso all'insegnamento è a numero limitato. La procedura obbligatoria di richiesta accesso si trova qui http://www.ccsbio.polimi.it/?page_id=27

Prova orale obbligatoria
La valutazione avviene mediante esame orale individuale.
La relazione scritta prodotta dallo studente durante le attività di laboratorio (in collaborazione con la sua squadra) è oggetto del colloquio orale, ma non è pregiudiziale ai fini della valutazione.
Lo studente dovrà, in sede di esame:. dimostrare di conoscere i criteri per progettare un'attività sperimentale in biofluidodinamica;. dimostrare di conoscere strumenti e tecniche sperimentali e per l'elaborazione dei dati;. saper impostare in autonomia una campagna sperimentale biofluidodinamica su un nuovo tema proposto dal docente;. mostrare senso critico nella scelta degli obiettivi e delle metodologie, riconoscendone i vantaggi e i limiti;. trattare verbalmente il tema sperimentale con chiarezza e proprietà di linguaggio bioingegneristico.

L'insegnamento si articola in lezioni di teoria ed esercitazioni pratiche di laboratorio. Nelle lezioni di teoria, dopo aver fornito le nozioni fondamentali di metrologia e illustrati i principali strumenti di misura, si descrivono i modelli costitutivi ed i metodi di caratterizzazione della meccanica dei tessuti, con principale riferimento ai tessuti biologici.
La parte sperimentale è volta all’apprendimento guidato delle fasi della caratterizzazione meccanica di tessuti biologici: a) progettazione dell’esperimento; b) preparazione dei campioni; c) utilizzo delle macchine e delle attrezzature di prova; d) esecuzione dell’esperimento; e) analisi dei dati e identificazione delle proprietà costitutive.

Al termine dell'insegnamento, lo studente dovrebbe aver acquisito la capacità di:

• comprendere la risposta meccanica dei tessuti (DD1)
• progettare ed analizzare esperimenti per caratterizzare meccanicamente i tessuti (DD1, DD2)

Nota Bene: l’accesso al corso è a numero limitato in base alle indicazioni della scuola sulla procedura di scelta e selezione dei corsi di Laboratorio.
Programma delle lezioni
a) Fondamenti di misure meccaniche e termiche
Principi della misura. Caratteristiche degli strumenti di misura. Misure di lunghezza e spostamento. Misura di deformazioni. Misure di forza, coppia e pressione.
(b) Meccanica dei tessuti biologici (richiami)
Struttura e composizione dei tessuti biologici; classificazione ed analisi dei principali tessuti. Caratteristiche meccaniche dei tessuti e risposta alle sollecitazioni: funzione, struttura e caratteristiche meccaniche in condizioni fisiologiche e patologiche (cenni); elasticità, viscoelasticità e poroelasticità; anisotropia; resistenza meccanica e trauma; riparazione e rimodellamento.
(c) Metodi sperimentali per la caratterizzazione meccanica dei tessuti biologici
Problemi nella sperimentazione. Conservazione e preparazione dei campioni. Afferraggi per prove uniassiali e biassiali. Misura di deformazione su tessuti ad alta deformabilità. Prove di caratterizzazione su tessuti e organi. Prove meccaniche su tessuti ricchi di collagene (legamenti, membrane biologiche) e su tessuti mineralizzati (osso). Prove su materiali bifasici (cartilagine). Misura di viscosità di fluidi biologici. Metodi di caratterizzazione in vivo (cenni).
Attività di laboratorio
(a) Materiali e metodi di prova
Macchine di prova: dispositivi uniassiali e biassiali; attuatori; sistemi di controllo in forza, spostamento e deformazione. Esecuzione di prove meccaniche (trazione, compressione, flessione, torsione, creep e rilassamento, isteresi, viscosità, durezza). Taratura di trasduttori.
(b) Caratterizzazione di tessuti soffici
Preparazione dei campioni. Afferraggio e misura di deformazione (macroscopica vs. microscopica con metodo di contattto vs. microscopica con metodo ottico). Prove di caratterizzazione viscoelastica (precondizionamento, trazione, creep, rilassamento, isteresi). Prove uniassiali (es. tendini, legamenti) e biassiali (es. membrane). Identificazione dei parametri di modelli tempo-dipendenti (es. viscoelastico e/o poroelastico).
(c) Caratterizzazione di tessuti mineralizzati
Preparazione dei campioni. Afferraggio e misura di deformazione. Prove di trazione, compressione e flessione (trazione indiretta) su osso.
(d) Caratterizzazione di tessuti poroelastici
Preparazione dei campioni. Prove di compressione e creep confinato e non confinato su tessuto cartilagineo e/o scaffold polimerici. Prove di permeabilità diretta (Darcy). Determinazione dei parametri del modello poroelastico.

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, senza presentazione/discussione finale
Gli studenti, suddivisi in gruppi (scelti dal docente in modo casuale e a rotazione per ogni esperienza di laboratorio in modo da evitare ridondanze), dovranno redigere delle relazioni sulle attività sperimentali svolte durante le ore di laboratorio e consegnarle secondo scadenze concordate in anticipo. Tutti i membri di ciascun gruppo sono ritenuti interamente responsabili della qualità dei manoscritti presentati.
Concorrono alla valutazione finale:

• l e relazioni presentate dagli studenti in gruppo,
• un esame orale individuale che verterà su tutti gli argomenti affrontati durante il corso,
• una valutazione fra gli studenti dello stesso gruppo (anonima e nota solo al docente, da giustificare in modo opportuno).

Luigi La Barbera, Federica Boschetti, Virginio Quaglini, dispense del corso
Note: le dispense saranno distribuite durante il corso
A. Capello, Misure Meccaniche e Termiche, Editore: Casa Editrice Ambrosiana, ISBN: 88-408-0072-7
Y.C. Fung, Biomechanics: Mechanical Properties of Living Tissues, Editore: Springer, ISBN: 978-0387979472

L'insegnamento di Laboratorio di Costruzioni Biomeccaniche si propone di fornire agli studenti occasioni in cui sperimentare, sotto la guida dei docenti, gli strumenti di base per la progettazione biomeccanica, con particolare riferimento ai set-up e protocolli sperimentali.
Con riferimento ad alcuni esempi applicativi di interesse, verrà mostrato come integrare i diversi strumenti teorici, computazionali e sperimentali nelle diverse fasi della progettazione biomeccanica.

Al termine dell'insegnamento, lo studente dovrebbe aver acquisito la capacità di sviluppare set-up e protocolli sperimentali per la progettazione biomeccanica e la misura di grandezze biomeccaniche (DD1,DD2)

NB L’accesso all'insegnmentoo è a numero limitato. La procedura obbligatoria di richiesta accesso si trova qui http://www.ccsbio.polimi.it/?page_id=27
Gli strumenti della progettazione biomeccanica. Prove sperimentali. Modelli computazionali. Analisi teorica. Generalità sulle misure. P rove secondo norma. Progettazione di sistemi di misura.
Gli argomenti specifici trattati a lezione sono di supporto ai laboratori sperimentali (vedi elenco).
Laboratorio sperimentale:

• Misure di viscosità su fluidi newtoniani e non-newtoniani
• Misure di permeabilità idraulica di tessuti e materiali duri e soffici
• Misure di permeabilità ai gas di membrane
• Misure di pressioni di contatto in biomeccanica
• Caratterizzazione degli elementi (R,C) di un banco prova idraulico
• Esperimenti su tubi collassabili e distributori di portata
• Effetto di intaglio su materiali duttili e fragili
  Modalità di esame: relazioni sui laboratori a gruppi oltre ad uno scritto individuale. Non sono previste verifiche in itinere

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
Durante le ore di laboratorio verranno svolte attività per le quali è richiesta una relazione. Le ralzioni sono a gruppi di 4 persone. La verifica consiste in un esame scritto individuale, che verterà sugli argomenti trattati a lezione, seguito da una discussione delle relazioni svolte.

Presentare allo studente gli aspetti fondamentali dell'elaborazione delle immagini, applicati alle immagini biomediche (pre-elaborazione e filtraggio, segmentazione, rappresentazione, registrazione). Il connubio tra teoria e pratica laboratoriale consentirà di comprendere pienamente le nozioni presentate, con un approccio "language agnostic", per sviluppare algoritmi ad hoc per la soluzione di problemi specifici dell'elaborazione delle immagini, approfondendone i relativi aspetti teorici e pratici. Tecniche di didattica innovativa ("flipped classroom" e "learn-by-doing") verranno utilizzate per stimolare la partecipazione degli studenti al corso e lo sviluppo di capacità di studio autonomo. E' previsto anche l'utilizzo di ChatGPT nel processo di apprendimento.

Declinati sulla base dei Descrittori di Dublino (DdD)
1) Conoscenza e comprensione (DdD1)
A seguito del superamento dell'esame, lo studente conosce:

• i principi alla base del saper affrontare un problema di elaborazione di immagini biomediche
• - i principi alla base della verifica di correttezza dei risultati ottenuti
  2) Capacità di applicare conoscenza e comprensione (DdD2)
  Attraverso sia gli esempi affrontati in classe che gli esercizi svolti a casa, al seguito del superamento dell'esame lo studente acquisisce le capacità di applicare le tecniche e la teoria apprese a casi reali, per quanto di complessità limitata.
  3) Autonomia di giudizio (DdD3)
  A seguito del superamento dell'esame lo studente:
• è in grado di affrontare problemi di elaborazione di immagini biomediche anche al di fuori dei casi visti durante il corso
• è in grado di sviluppare in modo autonomo software ad hoc per la soluzione di problemi specifici, valutando le sue prestazioni in merito al problema in oggetto
• è in grado di valutare le caratteristiche e comparare prodotti software per l'elaborazione di immagini
  4) Abilità comunicative (DdD4) e Capacità di apprendimento (DdD5)
• E’ in grado di comunicare i risultati della propria attività in modo chiaro e convincente
• In autonomia, opera e comunica le scelte progettuali effettuate
• Anche attraverso l'uso di tecniche di "learn by doing" e "flipped classroom", lo studente acquisisce la capacità di apprendere in maniera autonoma argomenti legati alle tematiche generali affrontate durante il corso.

Questo è un elenco di massima degli argomenti che verranno trattati durante il corso, declinati ad applicazioni aventi per oggetti immagini biomediche:
1) Introduzione alla elaborazione della immagini biomediche
2) Dicom, spazio dei colori, multiframe array e filmati, image enhancement
3) Caratteristiche delle bioimmagini, compressione
4) Convoluzione e filtraggio 2-D
5) Thresholding e Clustering
6) Segmentazione con edge detection e region growing
7) Machine learning: applicazione alla classificazione ed estrazione di informazioni dalle immagini
8) F ilter design
10) Morphological image processing per immagini binarie e a livelli di grigio
12) Interpolazione spaziale e registrazione di immagini
13) Image representation
Essendo un corso di laboratorio, la frequenza al corso è caldamente consigliata per il superamento dell'esame.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
Durante il corso, verrà assegnato un progetto da svolgere in gruppo, avente per oggetto la risoluzione pratica di un problema di elaborazione di immagini, da consegnarsi nei tempi indicati dal Docente, e che prevede una presentazione finale nell'ultima settimana di lezioni. Tale progetto peserà per il 50% del voto, mentre il restante 50% sarà costituito dalla prova scritta, riguardante aspetti teorico-pratici della elaborazione delle bioimmagini. E' possibile poi integrare il voto finale con una prova orale (facoltativa) con incremento massimo di 3 punti.

slides and distributed material WeBeep
Note: Slides delle singole lezioni saranno messe a disposizione
Gonzalez/Woods, Digital image processing using Matlab, 4th edition, Editore: Pearson, Anno edizione: 2018, ISBN: 9780982085400 https://www.vitalsource.com/en-uk/referral?term=9781292223070
Ravishankar Chityala, Sridevi Pudipeddi, Image processing and acquisition using Python - second edition, Editore: Taylor and Francis, Anno edizione: 2021

Il Laboratorio di Micro- e Nano-strutture è focalizzato sull'analisi del comportamento cellulare in risposta alla morfologia superficiale del supporto su cui esse aderiscono. Anche la morfologia superficiale, infatti, ha un ruolo importante nel determinare molti comportamenti cellulari, inclusi la proliferazione, la migrazione e il differenziamento.
Specifiche morfologie su scala micro- e nano-metrica, pertanto, possono essere sfruttate per la realizzazione di scaffold "istruttivi", capaci di indirizzare il destino cellulare.
Con l'obiettivo di comprendere questo legame tra comportamento e morfologia superficiale, verranno inizialmente inzialmente illustrate le tecniche di preparazione e caratterizzazione di superfici strutturate, alcune delle quali saranno utilizzate per la realizzazione di scaffold nella parte laboratoriali.
Saranno quindi illustrati i principali metodi per la coltura e l'analisi delle cellule in vitro, da utilizzare per la valutazione del comportamento di cellule di linea seminate sui substrati preparati.

Al termine del laboratorio lo studente dovrà aver familiarizzato con le principali tecniche di fabbricazione di superfici micro- e nanostrutturate, e con alcune delle più diffuse tecniche di caratterizzazione cellulare (DD1).
In particolare, dovrà saper individuare potenziali aspetti critici legati alla fabbricazione rispetto alla biocompatibilità ed anche prevedere le principali problematiche legate alla caratterizzazione biologica in presenza di uno scaffold (DD2, DD3).
Dovrà saper utilizzare le conoscenze apprese per affrontare problemi complessi di valutazione dell'influenza della morfologia superficiale sul comportamento cellulare ed essere in grado di discutere criticamente protocolli e risultati relativi alla sperimentazione in vitro in quest'ambito (DD3).
Dovrà, inoltre, aver raffinato le proprie capacità di progettazione della attività sperimentale, e di organizzazione, analisi e presentazione dei dati ottenuti (DD4).
N.B: Il laboratorio è a numero chiuso, il collegamente al bando online per l'assegnazione dei posti è disponibile sul sito del corso di studi.

Contenuti delle lezioni
Parte I - Tecniche di fabbricazione e caratterizzazione delle superfici
Microscopia ottica ed elettronica (SEM, ESEM, TEM...)
Microscopia a foza atomica (AFM) e profilometria
Tecniche per la preparazione di substrati micro- e nano-strutturati (litografia ottica, soft lithography, hot embossing...)
Applicazione di materiali micro- e nano- strutturati nell'ingegneria dei tessuti
Parte II - Metodi di coltura e analisi cellulare in vitro
Introduzione alla coltura in vitro e laboratori biologici
Tecnica sterile e metodi di coltura cellulare
Metodi di valutazione della morfologia, della vitalità e della attività cellulare
Attività di Laboratorio
Preparazione di scaffold micro- e nano-strutturati
Caratterizzazione morfologica degli scaffold
Semina cellulare e mantenimento in coltura
Valutazione della attività cellulare
Analisi della morfologia cellulare

Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
La valutazione terrà conto principalmente della capacità di programmazione e soprattutto di analisi critica dell'attività sperimentale.
Dal punti di vista organizzativo, nel corso del laboratorio verrà richiesta la consegna di un lavoro individuale (di analisi della letteratura) e di un lavoro di gruppo (relativo alla attività sperimentale di laboratorio). La valutazione terrà conto della qualità di tali elaborati, e della capacità di presentazione ed discussione dei relativi contenuti, sia in momenti dedicati durante il corso che al termine, in un esame orale.

Il corso ha l’obbiettivo fare acquisire allo studente in grado la capacità di impostare un progetto di sviluppo tecnologico in ambito biomedico attraverso attività quali:
1) Definizione dei requisiti dell’applicazione specifica (per esempio, misurare segnali biomedici, rimuovere degli artefatti da movimento);
2) Definizione e selezione dei componenti necessari (custom/commerciali);
3) Progettazione dell’architettura (acquisizione, invio e salvataggio dei dati; front-end; elaborazione ed interpretazione);
4) Implementazione hardware/firmware/software tramite l'utilizzo opportuno di strumenti di sviluppo e realizzazione pratica;
5) Selezione di procedure opportune per la verifica della funzionalità e la valutazione delle prestazioni del prototipo sviluppato;
6) Scrittura di un report tecnico del progetto affrontato.

Lo studente prenderà confidenza con strumenti tipici del laboratorio elettronico, tra cui il saldatore, il multimetro, il generatore di segnale, l’oscilloscopio e l’alimentatore e da bando, e approfondirà i principali metodi ingegneristici (analisi dell’applicazione, definizione dei requisiti, design del sistema, selezione dei componenti più adatti con requisiti specifici in funzione dall’applicazione) per affrontare lo sviluppo di un progetto tecnologico in ambito biomedico (didattica trasmissiva/frontale e laboratoriale). DD1
Lo studente acquisirà capacità di progettare schede elettroniche, sviluppare firmware per microcontrollore, implementare prototipi HW con componenti discreti e dispositivi per l’acquisizione e l’elaborazione di segnali biomedici (didattica laboratoriale e progettuale). DD1,2
Lo studente applicherà la conoscenza maturata alla definizione dei requisiti per il progetto didattico, alla definizione delle fasi principali di sviluppo, e all’individuazione dei principali strumenti e componenti necessari, approfondendo le tematiche biomedicali di interesse specifico all’applicazione (didattica progettuale). DD3,4
Lo studente imparerà a collaborare in gruppo per lo sviluppo del progetto didattico (didattica progettuale). DD4,5

Il corso inquadra le metodologie e tecnologie elettroniche per lo sviluppo di sistemi dedicati a:
a) utilizzo di sensori e attuatori in ambito medicale;
b) acquisizione, condizionamento e elaborazione di segnali biomedici;
c) invio dei dati mediante protocolli di trasmissione wireless;
c) monitoraggio di parametri relativi alla persona e all'ambiente circostante.
Il corso si sviluppa in due fasi principali.
La prima fase prevede erogazione di didattica frontale/trasmissiva e laboratoriale in cui la docente presenta i seguenti argomenti:
strumenti tipici di un laboratorio elettronico quali saldatore, multimetro, alimentatore, oscilloscopio, generatore di segnale
tecnologie per progettazione hardware e firmware, quali progettazione circuitale e design di circuiti stampati, piattaforme per progettazione e sviluppo su microcontrollore
basi di sensoristica analogica e digitale
protocolli di comunicazione
ambienti e piattaforme per sviluppo di interfacce software
tecnologie di stampa 3D e realizzazione di case.
La seconda fase del corso si articola attraverso lo sviluppo di un progetto di gruppo. Gli studenti sono suddivisi in gruppi e assistiti nell'affrontare le specifiche tematiche del progetto, assegnato nelle prime settimane del corso. Gli studenti affrontano tutta la catena di attività richieste a partire dalla progettazione concettuale e definizione dei requisiti, la selezione dei componenti e eventuali acquisti, lo sviluppo e l’implementazione di una soluzione circuitale, la programmazione firmware del microcontrollore, fino alla prototipazione della soluzione reale attraverso schede breadboard o kit integrati di sviluppo e alla fase finale di testing.
Ad integrazione delle attività progettuali, ciascun gruppo di studenti dovrà inquadrare lo stato dell’arte relativo al proprio progetto in fase di sviluppo, con l’obiettivo di stimolare l’apprendimento attivo, la strutturazione critica delle conoscenze e il confronto tra colleghi. Questo approfondimento sarà presentato e discusso durante la prima presentazione intermedia.
L’accesso al corso è a numero limitato. La procedura obbligatoria di richiesta accesso si trova nel sito web del Corso di Studi di Ingegneria Biomedica.
L’interazione tra docente e studente è supportata dalla piattaforma WeBeep (slide del corso, documenti come tutorial e esempi di sviluppo, datasheet di interesse generale per componenti HS/SW, consegna dei report didattici per la presentazione finale).
La modalità di esame prevede la valutazione delle presentazioni intermedie, del report finale di progetto e di una presentazione orale conclusiva del progetto stesso.
Alla costruzione del voto finale partecipano inoltre criteri aggiuntivi quali l’impegno, il livello di presenza in laboratorio, la capacità di lavorare in team e il grado di apprendimento raggiunto.

Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
La modalità di esame prevede la valutazione delle presentazioni intermedie, del report finale di progetto e di una presentazione orale conclusiva del progetto stesso.
Alla costruzione del voto finale partecipano inoltre criteri aggiuntivi quali l’impegno, il livello di presenza in laboratorio, la capacità di lavorare in team e il grado di apprendimento raggiunto.

The course is aimed at providing the students the proper methodologies to design and evaluate life support systems and artificial internal organs, namely artificial lung/ventilation systems, artificial kidney/dialysis therapy, artificial pancreas, artificial heart/ventricular assist devices, being them either implantable, or para-, or extra-corporeal. The machine to patient interaction will be also focused and discussed. After an introduction of the basic physiology of the native internal organs, the design of life support systems will be focused based on the mechanical and physico-chemical phenomena taking place in them. Mass transfer, energy supply and converters, will be studied and optimized as a function of the specific application. Criteria on how to optimize and adjust the performance and efficiency of the systems will be given also paying attention to the specific life-style of the patient.
Moreover the course aims at giving the students hints about ethical approaches to the design of life saving devices and to bring students to make autonomous judgements about the possible solutions to be adopted.
Along the course clinical applications/implantation procedure and devices will be shown to the students either in the form of videos/slides or by showing real commercial devices.

At the end of the course the students are expected to possess both know-how and know-why about the artificial internal organs or life support systems devices working conditions, constraints and design methods.
With reference to the Doublin Descriptors listed herunder:
DD1 - Knowledge and understanding;
DD2 - Applying knowledge and understanding;
DD3 - Making judgements;
DD4 - Communication skills;
DD5 - Learning skills.
the students are expected to develop and acquire abilities to meet all the Descriptors, namely applied to:

• knowledge of the main concepts about the design constraints and specifications of the artificial internal organs or life support systems focused during the course;
• knowledge and understanding of the main physical, mechanical, and chemical phenomena taking place in the devices;
• knowledge to perform calculations to design, quantify, and evaluate, the performances of the devices;
• ability to develop, judge and apply patient specific approaches to the design of artificial systems and know how to optimize the interaction with the living organism;
• development of skills to face and communicate/promote innovation in this field of biomedical engineering.

The course is aimed at teaching design and evaluation criteria of artificial internal organs and life supprot systems devices.
More specifically, the main subjects are the following:

• Survey about functional anatomy and physiology of the internal organs possibly needing assistance or substiitution;
• Design criteria and optimisation of life support systems devices/artificial internal organs;
• Materials choice;
• Adjustment methods and criteria to optimise the machine to patient interface;
• Optimization criteria for device setting;
• Mass transfer in solutions and through artificial membranes;
• Design and optimisation of dialysis filters and oxygenators;
• Design and optimization of blood pumps;
• Energy sources and converters;
• Efficiency evaluation and optimisation criteria of the systems;
  International standards prescription related to the testing and the production of life support systems will also be presented.
  Practical class activities will deal with the solution of problems related to the design and evaluation of the studied life support system/arytificial organs.

Prova scritta obbligatoria, senza prove in itinere
The final exam will consist in either an oral or a written test (which in specific cases can be followed by an oral exam) based on the number of students attending the course.
The students will be informed about the exam modalities at the very beginning of the course.
The students will be evaluated based on their capacity to answer theoretical questions about the subjects taught during the course
and to frame them in a wider scenario. More specifically skills about knowledge and understanding and communications, will be
evaluated. Moreover, the student will be evaluated based on his/her capacity to establish connections among the different subjects
dealt with during the course, and among the course and the other courses attended. More specifically, students' skills in applying
knowledge and understanding to problem solving, in making judgements about the possible technical choices, and in communication
modalities to explain and assess the validity of the chosen methods, will be evaluated (as per DD1, DD2, DD3, DD4, DD5).

Maria Laura Costantino, Notes from the Course of Life Support Systems
Note: The notes are available in pdf format on the Beep page of the course.

The course “Medical Robotics and Technologies for Computer Aided Surgery Laboratory” provides the students with knowledge on image-guided surgery and medical robotics, starting from basis on robotics (kinematics, motion planning, dynamics, control, human robot interfaces). Laboratory activities are aimed at providing the students with skills and capabilities for designing and implementing the surgical planning and the proper control for executing the robotic assisted surgical intervention.

Knowledge and understanding
Knowledge of basics of robotics
Knowledge of requirements of a surgical robotic assistant
Knowledge of development tools for medical robotics
Applying knowledge and understanding
Problem solving abilities to aspects related to robotic surgery planning and execution
Ability of autonomously carry on a project related to image guided surgery and robotics (supervised by tutors)
Making judgement
This capability will be elicited by autonomous decision making during problem solving and project design and development
Communication skills
The project results will be demoed or presented during the exam
Learning skills
Hands-on activities during the practice part of the course will allow testing of learning skills. Tutors will closely supervise.

The course includes the following topics:
1) intro on medical robotics
2) introduction on spatial transformations/ registration
3) robot kinematics
4) robot dynamics
5) path planning
6) basics of position/ force control and human-robot interaction

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
The exam includes 1) the implementation of small projects for surgical task executions (homework), 2) the presentation/ discussion of a lab project, supervised by one of the lab tutors and discussion on the course content with the teacher/ teaching assistants

Nowadays, deep neural networks can outperform traditional hand-crafted algorithms, achieving human performance in solving many complex tasks, such as natural language processing, text modeling, gene expression modeling, and image recognition. The course describes the applications of these tools to the analysis of biomedical signals, image and data, learning how to cope the peculiar needs of these applications. The current healthcare practice including AI will be described, together with issues related to security, ethical and legal aspects. The current AI developments in different areas will be covered: precision medicine, diagnostic image processing and radiomics, biosignal classifications, staging and patient risk stratification, surgical planning, treatments strategy selection for oncology patients.

The expectations of achievements and abilities are described in the following table
Dublin Descriptors
Expected learning outcomes
Knowledge and understanding

• Understand the peculiarity of medical data
• Understand the ethical and legal aspects related to AI applied in medicine
• Knowledge of algorithms for big data analysis
  Applying knowledge and understanding
• Design and implement AI tools for biomedical applications
• Tune algorithms and methods for any specific biomedical problem.
  Making judgements
• Evaluate the correctness of the implemented algorithms.
• Select the best options and implementation schemes.
• Compare and evaluate the performance of different solutions.
  Communication
• To be able to describe AI methods, advantages and performances.
• To be able to defend the implementation of a certain algorithm in front of an audience and the lecturer.
  Lifelong learning skills
• To be able to understand and implement any AI algorithm published in literature.

Current healthcare and big data in medicine
Glossary in AI
From machine learning to deep
Explainable AI
Security and privacy applying AI
Ethical and legal aspects
Practical aspects related to biomedical data
AI in drug discovery, drug delivery, cancer detection, radiomics in oncology, image segmentation, image classification, medical devices, monitoring.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The written exam will contain questions about the course topics (DdD2, DdD3, DdD4, DdD5).
A project will be assigned to small teams: the project, its implementation and the related results will be discussed (DdD2, DdD3, DdD4, DdD5) by the team during the oral exam. The Oral session will assess the team in terms of: i) level of understanding of the problem and problem solving skills (DdD2); ii) Algorithm implementation and related options (DdD2, DdD3); iii) capability to discuss/defend the selected implementation options (DdD4); iv); overall quality of the work and algorithm performances (DdD5).
Final score will be based on the written exam (60%) and project development and discussion during the oral presentation (40%).

Immagini biomediche - Introduzione: importanza dell'imaging, 3D, multimodale e funzionale. Metodi di ricostruzione da proiezioni di immagini 3D, numerici, statistici. Principi e strumentazione per immagini di risonanza magnetica nucleare (MRI). Non idealità ed artefatti MRI. Spettroscopia MRI in vivo. Angiografia MRI. Immagini MRI pesate in diffusione e trattografia. MRI funzionale e mappe parametriche statistiche. Ecografia non lineare e 3D. Immagini cardiache.
Chirurgia assistita - Struttura dei sistemi di Computer Aided Surgery (CAS). Fase pre-operatoria: imaging 4-D; metodi di segmentazione 2D e 3D; modellazione e rappresentazione di superfici e volumi. Simulazione e pianificazione chirurgica: convenzioni di rappresentazione di posizione e orientamento; mapping e trasformazioni. Cenni di robotica in chirurgia: catene cinematiche, problema cinematico diretto e inverso. Fase intra-operatoria: chirurgia stereotassica e chirurgia guidata da immagini. Il problema della registrazione: metodi per punti corrispondenti; registrazione di superfici; acquisizione e registrazione di immagini intra-operatorie; Target Registration Error. Navigazione in chirurgia. Gestione dei fenomeni di organ motion; metodi di registrazione non rigida. Esempi di applicazione: neurochirurgia, chirurgia ortopedica, radiochirurgia.

The NEUROENGINEERING course introduces engineering methods, computational models, and enabling technologies for understanding, modelling, interacting with, restoring, and augmenting nervous-system function.
Through a transdisciplinary approach integrating neuroscience, biomedical engineering, robotics, artificial intelligence, and data science, the course addresses the relationships between biological and artificial information-processing systems. Particular attention is given to the development, implementation, validation, and critical evaluation of computational and technological solutions for neuroengineering applications.
Neuroengineering [1] focuses on computational neuroscience, brain simulation, and neuroscience-inspired artificial intelligence (NeuroAI). Students will study models of neurons and neuronal networks, methods for simulating brain circuits, and the principles through which knowledge of biological intelligence can inform the design of artificial learning systems. Neuroscience-inspired deep-learning architectures will be introduced and analysed in relation to representation, generalization, learning, and biological plausibility.
The course aims to enable students to move from the formulation of a neuroengineering problem to the identification of user and system requirements, selection of appropriate computational or technological methods, implementation of a solution, and critical evaluation of its performance, reliability, limitations, and feasibility.

Upon successful completion of the course, students will be able to achieve the following learning outcomes, organized for Dublin Descriptors (DD).
Knowledge and understanding - DD1
Students will be able to:
describe the principal computational models of neurons, neuronal populations, and brain circuits;
explain the main methodologies used for multiscale brain simulation and for validating computational models against experimental and physiopathological data;
explain the relationships between biological and artificial intelligence, including representation, learning, generalization, architectural priors, and biological plausibility;
describe major neuroscience-inspired deep-learning architectures and their operating principles.
Applying knowledge and understanding - DD2
Students will be able to:
formulate and implement simplified computational models of neurons and neuronal networks;
configure, train, and evaluate artificial neural-network models for neuroengineering and biomedical applications;
analyse and compare representations and computations in biological and artificial neural systems;
select appropriate neural-network architectures according to the characteristics of the data, task, and application context;
implement simple projects tailored to specific research or clinical scenarios, starting from a proper understanding of the current state of the art (scientific literature).
Making judgements - DD3
Students will be able to:
critically examine the assumptions underlying computational and technological neuroengineering solutions;
evaluate the suitability of experimental data, computational models, validation procedures, and performance metrics;
compare alternative solutions in terms of accuracy, generalization, computational complexity, robustness, interpretability, fairness, safety, and feasibility;
evaluate the transferability and scalability of a proposed solution across research, clinical, and operational contexts;
Communication skills - DD4
Students will be able to:
communicate neuroengineering concepts and project results using appropriate scientific and technical terminology;
present computational, experimental, or technological solutions clearly to both specialist and interdisciplinary audiences, exploiting video-based media too;
document methods, design decisions, validation procedures, results, and limitations;
collaborate effectively within interdisciplinary groups and contribute to technical discussions.
Learning skills - DD5
Students will be able to:
independently consult and critically interpret scientific literature in neuroengineering, neuroscience, robotics, and artificial intelligence;
learn and apply new computational tools, simulation environments, and experimental methodologies;
identify the knowledge and technical resources required to address an unfamiliar neuroengineering problem;
autonomously extend the methods introduced during the course to new clinical, technological, and research applications.

Neuroengineering part [1] will address three main topics:
Computational Neuroscience and Brain Simulation
This topic introduces the mathematical and computational principles used to describe neural activity at different spatial and temporal scales.
The main contents include:
biophysical and phenomenological models of neurons;
membrane potential, action-potential generation, synaptic transmission, and neuronal excitability;
models of neuronal populations and networks;
emerging properties of interconnected neuronal systems;
neural coding and information processing in neuronal circuits;
simulation of brain microcircuits and large-scale neuronal networks;
modelling of physiological and pathological neural dynamics;
parameter identification and validation of computational models against experimental data;
analysis of model assumptions, complexity, biological plausibility, and computational cost;
examples of brain-circuit simulation, including computational models of the cerebellum, the thalamus, the hippocampus, and multi-area multi-scale models.
Students will learn how to select an appropriate modelling scale, implement simplified neuronal and network models, analyse simulation results, and evaluate the correspondence between model behaviour and neurophysiological observations.
NeuroAI
This topic introduces NeuroAI as the study of the reciprocal relationships between neuroscience and artificial intelligence. It examines how artificial-intelligence methods can support the investigation of brain function and how principles of biological intelligence can guide the development of more adaptive and generalizable artificial systems.
The main contents include:
definitions, objectives, and methodological foundations of NeuroAI;
common concepts and terminology across neuroscience, cognitive science, and machine learning;
levels and scales of analysis of biological and artificial intelligence;
comparison between biological neural networks and artificial neural networks;
generalization as a common challenge for natural and artificial intelligent systems;
methods for comparing internal representations in biological and artificial systems;
advantages and limitations of using artificial neural networks as models of brain computation;
practical implementation, analysis, and critical interpretation of simplified NeuroAI models.
Artificial Neural Network Architectures
This section examines how organizational and computational principles observed in nervous systems can inform artificial neural-network design.
The main contents include:
artificial neurons and multilayer neural networks;
supervised, unsupervised, and representation-learning paradigms;
error-backpropagation and its relationship with biologically plausible learning;
convolutional neural networks and hierarchical sensory processing;
recurrent neural networks and temporal information processing;
neural mechanisms of memory and their computational counterparts;
attention mechanisms and attentional modulation;
Students will implement and compare selected architectures, analyse their internal representations, and select appropriate models according to the task, data, and application constraints.
Course organization
The overall course is organized into two phases:
Phase 1 (weeks 1-7): The professors will provide frontal classes and practical lessons to explain all six course topics.
Phase 2 (weeks 8-15): Project workshops (Innovative Teaching Credit), "meet the expert" seminars, and journal clubs.
Project workshops
The project workshop involves various project topics, each supervised by a PhD student or postdoctoral researcher (Tutor). Students indicate their preferences and are assigned to Topic Teams (TT) of approximately 20 students. After the second meeting, each Tutor will divide the TT into multiple smaller groups (4-5 students each). Each group will work independently on the same topic.
Each group attends seven meetings with the tutor:
Meeting 1: analysis of the scientific literature, discussion of the state of the art, and definition of a project objective aimed at improving existing approaches;
Meeting 2: a tailored laboratory activity providing the practical tools and knowledge required for the project;
Meetings 3–7: weekly progress reviews, discussion of intermediate results, technical issues, validation methods, and next steps.
Between meetings, students work autonomously to design, implement, and evaluate their proposed solution.
The final deliverable is a short video presenting the problem, proposed approach, implementation, results, limitations, and feasibility.
A board composed of the professors and tutors evaluates each project, considering scientific quality, originality, feasibility, validation, teamwork, and communication skills.
At the end of the course, the best video for each of the project topics is presented during the final lecture.
If there are exceptional, sensible reasons not to participate in the Project workshop, the student will take an oral exam.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
Valutazione continua facoltativa (a scelta dello studente)
The final assessment consists of:
Written examination — up to 21 points: questions covering the entire course syllabus, without separate assessments for Neuroengineering [1] and [2]. In-line evaluation can substitute for the written test.
Project workshop — up to 11 points: evaluation of the proposed solution, methodology, results, originality, feasibility, teamwork, and final video presentation.
Journal Club quizzes — up to 2 bonus points: awarded through quizzes associated with the Journal Club activities.
No connected or connectable devices, including smart glasses, may be used during the written examination. At the beginning of each examination, all such devices must be switched off and placed in the location indicated by the instructor.
The instructor may request, at any time, an oral discussion to supplement or complete the written examination.
An oral exam will substitute for the Project workshops for students who cannot (for exceptional, sensible reasons, previously discussed with the professors) attend the Project workshops.

Course powerpoint slides
Course note compendium
Source codes for exercise laboratory

The course provides theoretical and practical training on the principal analytical techniques employed for the extraction, isolation, purification, identification, and characterization of proteins. Particular emphasis is placed on the selection of appropriate analytical methodologies according to the characteristics of the biological sample, the experimental objectives, and the specific biomedical application. Students acquire the theoretical foundations of the analytical techniques and develop an understanding of their advantages, limitations, and fields of application. The course is strongly laboratory-oriented and is designed to integrate theoretical knowledge with hands-on experimental experience.

During laboratory sessions, students apply analytical protocols introduced during lectures, gaining direct experience with the experimental workflow for protein extraction, separation, and identification. Working in small groups (approximately 10 groups), students perform laboratory activities using dedicated instrumentation, including protein electrophoresis systems and mass spectrometry platforms for protein identification and characterization. Throughout the laboratory activities, students are guided in the interpretation of experimental results, the identification of potential sources of error, and the implementation of appropriate troubleshooting strategies under the supervision of the instructor.
Upon successful completion of the course, students will be able to:
DdD1 – Knowledge and Understanding: demonstrate knowledge of the principles underlying the main analytical techniques used for protein extraction, purification, separation, identification, and characterization, as well as their applications in biomedical and biochemical research.
DdD2 – Applying Knowledge and Understanding: apply appropriate experimental methodologies for protein analysis, operate the laboratory instrumentation introduced during the course under supervision, and interpret experimental data obtained through electrophoretic and mass spectrometry analyses.
DdD3 – Making Judgements: critically evaluate experimental procedures and analytical results, identify methodological limitations and experimental errors, and propose appropriate solutions to common laboratory problems through scientific reasoning and data interpretation.
DdD4 – Communication Skills: effectively communicate experimental procedures, analytical results, and scientific conclusions using appropriate technical terminology in written format.
DdD5 – Learning Skills: develop autonomous learning abilities through critical analysis of scientific literature, independent investigation of advanced analytical methodologies, and reflection on laboratory practices.

The course is subject to limited enrollment. Admission is regulated according to the procedures established by the Degree Programme Board and published on the official programme website: http://www.ccsbio.polimi.it.
Introduction to proteomics and to protein structure
The course combines theoretical lectures with extensive laboratory activities to provide students with both the scientific background and the practical skills required for modern proteomic analysis.
The theoretical component introduces the fundamental principles of proteomics and protein structural organization, providing the knowledge necessary to understand the experimental approaches employed in protein analysis. The complete experimental workflow for protein extraction, purification, characterization, and identification is presented, together with the methodologies commonly adopted in biomedical and biochemical research laboratories.
The course covers methods for protein extraction and purification from biological samples, including protein precipitation techniques and quantitative determination of protein concentration through colorimetric protein assays.
Chromatographic techniques are introduced by discussing the principles of chromatographic separation, stationary and mobile phases, and the main experimental parameters governing chromatographic performance, including retention time, selectivity, and resolution. Particular attention is devoted to the operating principles, applications, and limitations of the principal chromatographic methods used in proteomics, including reverse-phase chromatography (RPC), affinity chromatography (AC), and immunoaffinity chromatography (IAC).
Electrophoretic techniques are presented as essential tools for protein separation and characterization. The course addresses the principles of polyacrylamide gel electrophoresis (PAGE), highlighting the differences between native PAGE and sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), together with the applications of Western blotting for protein detection. Two-dimensional gel electrophoresis (2-DE) is also discussed, including the principles of isoelectric focusing (IEF) and its integration with SDS-PAGE for high-resolution protein separation.
The principles of mass spectrometry (MS) are examined in detail. Students are introduced to the main instrumental components and operating parameters, the characteristics of different mass analyzers, including ion trap and quadrupole instruments, and the interpretation of mass spectra. The course also presents the bioinformatic tools and dedicated software employed for mass spectrometry data processing and protein identification.
Advanced quantitative proteomic methodologies are introduced, including isobaric tags for relative and absolute quantification (iTRAQ), and stable isotope labeling by amino acids in cell culture (SILAC), highlighting their applications in comparative proteomic studies.
The theoretical lectures provide the scientific foundations required for the subsequent laboratory activities.
To further promote independent learning and scientific reasoning, students may optionally undertake a group project aimed at deepening a topic related to analytical protein technologies. Depending on the proposed activity, the project may consist of the preparation of a scientific-style report, a critical review of laboratory methodologies and experimental results, or a concise manuscript structured according to the conventions of the scientific literature. This activity is intended to strengthen students' critical thinking, and scientific writing, while fostering greater autonomy in the analysis and interpretation of experimental data.
Laboratory activities
The laboratory component represents an integral part of the course and enables students to apply the theoretical concepts through hands-on experimental experience. Laboratory sessions are carried out in small groups of two to three students to ensure active participation and direct interaction with the instrumentation and analytical procedures.
During the laboratory sessions, students perform the complete experimental workflow of a proteomic analysis, including:
protein extraction from biological samples and purification through precipitation protocols;
quantitative determination of protein concentration using appropriate protein assays;
protein separation by SDS-PAGE;
protein separation by two-dimensional gel electrophoresis (2-DE);
excision of protein bands or spots and sample preparation for mass spectrometric analysis;
liquid chromatography coupled with ion trap mass spectrometry (nanoLC–XL ion trap MS) for peptide analysis;
acquisition and processing of raw mass spectrometric data using dedicated software for protein identification;
critical interpretation, validation, and presentation of the experimental results obtained throughout the proteomic workflow.
The laboratory activities provide students with direct experience in the use of analytical instrumentation, the execution of standardized experimental protocols, the interpretation of experimental data, and the critical evaluation of analytical results, thereby strengthening the practical competencies required for experimental research in proteomics and biomolecular analysis.
Learning methodology
The course combines theoretical lectures with laboratory-based practical activities to ensure the integration of fundamental knowledge and experimental skills in proteomic analysis.
Lectures are delivered using PowerPoint presentations and are supported by interactive digital tools, including videos and Wooclap, to promote active student participation, facilitate formative assessment, and encourage discussion of the theoretical concepts presented during the course.
The laboratory component constitutes the core of the educational experience. Prior to accessing the teaching laboratories, all students are required to complete the mandatory Departmental training course on laboratory safety and health regulations. Access to laboratory activities is permitted only after successful completion of the associated safety assessment, in accordance with the Politecnico's regulations governing laboratory teaching.
Laboratory sessions are conducted under the continuous supervision of the professor and teaching tutors. Students perform the experimental activities following standardized protocols provided during the course and acquire practical experience in the use of analytical instrumentation and proteomic methodologies. Throughout the laboratory sessions, students are guided in the execution of experimental procedures, the interpretation of analytical results, and the identification and resolution of common experimental issues, thereby reinforcing their practical competencies and critical approach to laboratory work.
To further encourage independent learning and scientific reasoning, students are offered the opportunity to undertake an optional project based on the critical analysis of a proteomic case study. The project consists of preparing a scientific-style manuscript or report based on experimental data provided by the instructor. This activity is intended to strengthen students' ability to analyze scientific data, critically interpret experimental findings, and communicate scientific results according to the conventions of the biomedical literature.
Proteomics materials
All teaching materials, including lecture slides, laboratory protocols, and supplementary resources, are made available through the BEEP online learning platform before each lecture or laboratory session. The online material supports students in preparing for classroom activities, reviewing course contents, and consolidating the knowledge and experimental skills acquired throughout the course.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
Student learning is assessed through a final written examination held during the official examination sessions. The examination is administered using Microsoft Forms and consists of a combination of multiple-choice questions and open-ended questions requiring structured and reasoned answers. The assessment is designed to verify both the acquisition of theoretical knowledge and the students' ability to apply analytical principles, interpret experimental data, and critically evaluate proteomic methodologies.
The examination evaluates the achievement of the intended learning outcomes, with particular emphasis on the understanding of the analytical techniques presented during the course and their application to laboratory-based proteomic investigations.
The following aspects are considered in the evaluation:
completeness and accuracy of the knowledge acquired regarding the principles, methodologies, and applications of protein extraction, purification, separation, and identification techniques;
ability to apply theoretical knowledge to the interpretation of experimental procedures, analytical workflows, and proteomic data, as well as to solve problems related to laboratory activities;
ability to establish logical connections among different topics, critically evaluate experimental methodologies and analytical results;
appropriate use of scientific terminology and ability to communicate analytical procedures and experimental findings in a clear, accurate, and scientifically rigorous manner;
where applicable, ability to independently elaborate a proteomic case study through the preparation of a scientific-style manuscript or technical report based on the experimental data provided during the course.
The written examination consists of questions assigned different weights according to their complexity and relevance to the learning objectives. The final grade is determined by the overall performance achieved in the examination. Students who successfully complete the optional project may receive additional evaluation based on the scientific quality, critical analysis, and clarity of the manuscript or report produced, according to the assessment criteria communicated during the course.

Jan-Christer Janson, Protein Purification, Editore: Wiley, Anno edizione: 2011
Hamdan, M, Righetti, P.G, Proteomics Today: Protein Assessment and Biomarkers Using Mass Spectrometry, 2-D Electrophoresis and Microarray Technology, Editore: Wiley-VCH, Hoboken, Anno edizione: 2005

Goal of 'technologies for motor behaviour analysis and virtual modelling 2' is focused at providing the student with the knowledge about advanced techniques for motion capture and exploitation of human motion for simulation and virtualization with application in functional surgical planning, human factors ergonomics. In the module 2, a full panorama of paradigmatic and modern techniques is presented and the assignment of practical projects requiring the experimental set-up of human motion collection and complete the pipeline for importing this data in to human motion virtualization environment will allow students to familiarize with the problematics of experiments organization and simulation potentials.

The first result expected is the deep interdisciplinary knowledge about the techniques for motor behaviour analysis, simulation and their biomedical related requirements (DD1). The student will be, with the acquired knowledge, able to organize meaningfully experimental campaign for human motion capture and to design appropriate data analysis for their exploitation in the frame of human motion virtualization software tools. (DD2). The course is mainly thought to allow the student to gain the knowledge of the aspects of the description of each method and/or system at the detailed mathematical approach underlying the models implemented for motion simulation and virtualization. The interview focused on their experimental experience and on the concepts provided during the lectures will expand students’ focused communication skills (DD4)

The course describes in detail the core techniques for the measurement of human motor acts. The lectures and other activities include the following topics:
1) Methods for human motor behaviour simulation and virtualization; human body modelling;
application of captured human motion data to virtual models and scene to simulate interaction;
2) Advanced camera calibration techniques; technologies for capturing and rendering human body surfaces;
3) Human factors; human machine interface; functional surgery planning, learned through the application of specific sotware applications for human motion simulation

Prova orale obbligatoria
Evaluation will be carried out through an interview which will assess the level of participation of each student to the practical assigned project by discussing the critical aspects of the experience and the obtained results. Further preparation assessment will be focused during the interview on the concepts provided during the frontal lectures to the students.

L'obiettivo del corso integrato è quello di presentare allo studente la progettazione di tecnologie nel campo della medicina rigenerativa, come i sistemi di coltura cellulare automatizzati e substrati innovativi per la coltura cellulare. Il corso si propone di affrontare gli aspetti formativi relativi all'applicazione clinica di tali tecnologie. Inoltre, il corso si concentra sugli aspetti fondamentali della modellizzazione del microambiente cellulare e sulla progettazione di sistemi avanzati per lo sviluppo di organi bioartificiali.

Dd1: dopo aver superato l'esame, lo studente avrà acquisito la conoscenza e la comprensione di (DD1,DD2):
1) i rischi clinici associati a una terapia rigenerativa specifica;
2) le fonti cellulari che possono essere potenzialmente utilizzate sull'uomo in una specifica terapia rigenerativa;
3) trasporto di massa in un prodotto cellulare e le relative condizioni al contorno;
5) i risultati clinici di tutte le fasi di sviluppo di una specifica terapia rigenerativa.

Introduzione alla medicina rigenerativa.
Razionale per l'uso di materiale biologico per sostituire la funzione dei tessuti e degli organi. La crisi dei trapianti. Esempi pionieristici di terapie rigenerative. Schematizzazione dei processi nelle terapie rigenerative. Etica e regolamentazione per lo sviluppo di nuovi prodotti commerciali per una terapia rigenerativa.
I componenti fondamentali di una terapia rigenerativa: biomateriali, cellule e segnali.
Biomateriali per la medicina rigenerativa. Manipolazione cellulare, fonte cellulare, cellule staminali e cellule differenziate, tecniche di isolamento e separazione cellulare, espansione cellulare. Trasporto di massa e condizionamento di costrutti cellularizzati. Strategie per modellare e ingegnerizzare il microambiente cellulare.
Dinamica dei tessuti.
Morfogenesi. L'omeostasi. Riparazione. Tecniche di bioingegneria per modellare e ingegnerizzare stati dinamici dei tessuti. Sistemi corporei: morfogenesi di base, macro anatomia, micro anatomia e popolazioni cellulari, fisiologia, patologia e terapia convenzionale.
Applicazione clinica.

Prova scritta obbligatoria, senza prove in itinere
L'esame finale consisterà di un elenco scritto di domande aperte relative a tutti gli argomenti trattati dal corso integrato. Lo studente sarà invitato a:
1) individuare i rischi clinici associati a una terapia rigenerativa specifica;
2) individuare le fonti cellulari che possono essere potenzialmente utilizzate sull'uomo in una specifica terapia rigenerativa;
3) scrivere l'equazione che descrive il trasporto di massa in un prodotto cellulare e le relative condizioni al contorno;
4) calcolare e rappresentare graficamente il profilo di concentrazione di un soluto di interesse all'interno di un prodotto cellulare utilizzato in una specifica terapia rigenerativa;
5) descrivere e discutere i risultati clinici di tutte le fasi di sviluppo di una terapia rigenerativa specifica.

Dispense del corso in lingua inglese
Mantero S, Remuzzi A, Raimondi MT, Ahluwalia A., Fondamenti di ingegneria dei tessuti per la medicina rigenerativa, Editore: Pàtron Editore, Bologna, Italia, Anno edizione: 2009, ISBN: 9788855530392
Tanzi MC, Bianchi A, Farè S, Mantero S, Raimondi M.T., Visai L., Approccio integrato per la medicina rigenerativa., Editore: Pàtron Editore, Bologna, Italia, Anno edizione: 2013, ISBN: 978-88-555-3241-9

Il corso affronta la modellazione e la mappatura 3D utilizzando tecniche fotogrammetriche. Particolare attenzione è dedicata all'elaborazione di immagini digitali acquisite da droni. All'inizio del corso verranno forniti i fondamenti analitici della fotogrammetria per l'orientamento del sensore e per la ricostruzione 3D dell'oggetto. Successivamente, verranno introdotte le immagini digitali e gli algoritmi per il miglioramento e la corrispondenza delle immagini. Infine, verranno illustrati i prodotti della fotogrammetria, come ortofoto, DTM/DSM, modelli 3D, e i loro processi produttivi. Verranno discussi anche i sensori per l'acquisizione dei dati. Attività di laboratorio complementari integreranno le altre classi.

Goal of this course
The Advanced Circuit Theory course is designed to complement the basic students' knowledge on electrical circuits obtained in the course of the Junior Electrical Engineering degree, with more "in depth" or complementary topics that are typical of a rigourous and scientifical approach to the study of circuits. In particular, the analysis of linear and non - linear circuits, purely resistive or dynamic is formalised using general methods such as modified nodal analysis and state equations. The basic framework of circuit simulators is presented, along with explanations related to possible simulation failures. Finally, network functions of linear dynamic circuits are introduced in order to present some simple examples of one-port synthesis and filter design. In all the topics covered, specific attention is given to the application of general methods.

Knowledge and understanding (Dublin Descriptor 1)
At the end of the course and after successfully passing the exam, the student:

• knows the fundamental principles of graph theory applied to electrical circuits
• has knowledge of the state space representation of nonlinear dynamic circuits, of the linearisation methods and of energy based stability properties of circuits
• knows how to apply the standard modified nodal analysis method for the solution of general circuits (nonlinear, dynamic etc.) including integration methods and nonlinear solvers.
• Understands the basic properties of linear passive one-ports and knows the standard methods for their realization
• Moreover, as a more general and methodological goal, a rigorous, math based approach to circuits.
  Applying knowledge and understanding (Dublin Descriptor 2)
  At the end of the course and after successfully passing the exam, the student:
• is able to write solving equations for general circuits and analyzing their solutions both using numerical methods and using closed form solutions
• is able to write, using standard programming languages, the basic structure of a circuit simulator
• knows how to synthesize a passive linear one port using Cauer or Foster methods

1 Fundamental laws of electrical networks
Topological properties. I mplicit and explicit Kirchhoff's laws. Graph matrices and their relations. Power and energy. I mplicit and explicit Constitutive formulations of one-port and two-port components. Unidirectional ity, ' reciprocity', symmetry and other properties of linear two-ports. Special two-ports: transformer, gyrator and operational amplifiers. Connection of two-ports.
2 Analysis of linear and nonlinear resistive networks
General methods of analysis. Nodal analysis (NA ) and Modified Nodal Analysis (MNA ). Numerical methods for solving linear algebraic systems. Sparsity. Characterization of nonlinear elements and their linearization. Examples: diode, BJT and MOS transistors, OP-AMP. Small-signal analysis. Graphical and numerical methods for solving systems of nonlinear algebraic circuit and their interpretation. Properties of circuits composed of strictly passive non-linear and strictly monotonous non-linear bipoles.
3 D ynamic circuit a nalysis
Formulation of state equations in the time domain. Solution of the state equation of linear dynamic circuits in the time domain: free and forced components of the solution. Natural frequencies and stability. Second-order linear circuits. Qualitative study of simple dynamic nonlinear circuits: oscillators and bistable. MNA formulation for general circuits. Simple numerical methods for the solution of the MNA problem.
4 Network Functions
Analysis of linear dynamic circuit s in sinusoidal steady state. Network functions in the phasor domain. Frequency response. Analysis of linear dynamic circuits using the Laplace transform. State equations and their solution in the Laplace domain. Immittances and general network functions H(s), poles and zeros.
5 Synthesis of one- ports and passive filter design
Properties of immittance functions Z(s ) and Y( s) of RLC passive one-ports. Properties and synthesis of one-ports with only two elements (LC, RC, RL ) according to the canonical forms of Foster and Cauer. Design of a low pass filter. Sensitivity analysis with respect to a parameter of the circuit and method for that network.

Prova scritta condizionata (a scelta del docente), con prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), individuale, senza revisioni periodiche, senza presentazione/discussione finale
Valutazione continua facoltativa (a scelta dello studente)
The exam is composed of two parts, a first "practical" written test, followed by a second written test related to the theory. The goal of the practical part is assessing the student's capability of application of the main topics of the course with a direct approach to simple problems (Dublin descriptors 1 and 2), the theory part to test the knowledge and understanding of the main methods used in the course aiming at the self-sufficiency in the solution of problems not considered in this course (Dublin descriptor 1).
The date of the practical part is published online on the POLIMI webpage, students can access the theory part either immediately after the practical part (and will be corrected only if the practical part is sufficient), or (in this case only if the practical part is sufficient) in any other following exam session (in this case the student will be temporarily marked as non-passed and will have to enroll to the chosen session).
The practical part lasts 2h15' and consists in the solution of three problems covering the main areas of the course, the students must work autonomously but can have access to a single self produced A4 sheet containing formulas or anything else the student believes can be useful to pass this part of the exam. Clarity in the description of the methods used and in the results obtained and terseness of language will be evaluated positively. The practical part is marked from 0/30 to 30/30; access to the theory part is reserved to students with a mark greater or equal to 16/30. The theory part lasts 30' and consists in two questions that must be answered in written form each question taking no more than one A4 page and where terseness of language and a quantitative approach where possible are encouraged. The theory part, if not sufficient, invalidates the whole exam, if sufficient it can increment or decrement the practical part mark usually in the range -4/+3.
The practical part can be substituted by a project whose contents are discussed and agreed by both parts and must be submitted at most 7 days before the date for the theory part.
When possible a non mandatory midterm, possibly online, will be issued and will give the students a chance to increment the final mark by at most 2 points.
Readability and compliance to the instructions, including requests to fill online forms, is part of the evaluation of all parts.
Communication of results will be via mail using the official POLIMI student mail address.

Leon O. Chua, Charles A. Desoer, Ernest S. Kuh, Linear and Nonlinear Circuits - International Edition, Editore: McGraw (but now in non defined international reprint), Anno edizione: 1987, ISBN: 9780070108981
Advanced Circuit Theory - Lecture Notes https://webeep.polimi.it
Note: Lecture Notes available on the POLIMI webeep platform.

Obiettivo del corso è introdurre lo studente alla teoria e alla tecnologia della micro e nanofabbricazione, fornendogli le conoscenze essenziali e le capacità sperimentali sulle tecniche di lavorazione di base e avanzate. Una parte del corso è dedicata alla realizzazione di film ed eterostrutture, partendo dalle conoscenze di base del vuoto e delle proprietà delle superficie, passando alle tecniche di deposizione (evaporazione termica, ablazione laser, sputtering, deposizione chimica), mentre una seconda parte è incentrata sulla realizzazione di dispositivi mediante litografia convenzionale (ottica, a fascio di elettroni) e avanzata (maskless, UV, raggi X), nonché tecniche di etching. L'attività sperimentale è svolta presso il centro di micro e nanotecnologie Polifab del Politecnico di Milano. L'obiettivo principale del corso è fornire allo studente la capacità di progettare e realizzare un flusso di processo convenzionale per la fabbricazione di semplici dispositivi elettronici e spintronici.

"Metodi numerici basati su Decomposizione dei Domini per problemi eterogenei
I.1 Metodi di Decomposizione and formulazione del problema di Poisson
I.2 Equazione di interfaccia
I.3 Formulazione debole e approssimazione Elementi FIniti
I.4 Estenzione alla legge di Hooke e di Stokes
I.5 Esempi di Decomposizione di Domini Eterogenea
II Approssimazione numerica del problema di interazione fluido-struttura
II.1 Introduzione al sistema cardiovascolare e esempi di applicazione clinica
II.2 Preprocessing: segmentazione di immagini cliniche e generazione della mesh computazionale
II.3 Un breve richiamo sull'approssimazione delle equazioni di Navier-Stokes e dell'elastodinamica
II.4 Il problema accoppiato
II.5 Formulazione Arbitrary Lagrangian-Eulerian
II.6 IL trattamento dell'accoppiamento geometrico
II.7 Algoritmi numerici basati su schemi partizionati. Effetto di massa aggiunta
II.8 Strategie monolitiche e precondizionamento III Metodi numerici per l'accoppiamento fra modelli geometricamente eterogenei
III.1 Condizioni al bordo deficitarie
III.2 Il modello 1D per emodinamcia
III.3 Mmodelli 0D e condizioni di outlets
III.4 Modelli multiscala geometrici 3D-1D e 3D-0D
IV Modellazione della funzione cardiaca
IV.1 Breve intoruzione alla fisiologia cardiaca
IV.2La propagazione elettrica e le equazione bidominio
IV.3 Stretgie numeriche per il problema di elettro-fisiologia
IV.4 La contrazione meccanica attiva
IV.5 Algoritmi per il problema elettro-meccanico
IV.6 Numerical strategies for the Electro-mechanical coupling
IV.7 Modelli di dinamica valvolare
IV.8 Verso la modellazione della funziona cardiaca completa"

Course held by a visiting professor, the program can change every academic year

The course aims at providing students with the critical and conceptual tools to reflect upon
some of the main ethical and social issues raised by the development of Artificial Intelligence
(AI). The course will be articulated in three modules, deeply interconnected to provide students
with a coherent narrative. The course will start with the analysis of the problems related to the
definition of "AI", starting from the first characterizations of AI dating back to the 1950s and
comparing them with the way AI is typically discussed nowadays. In the second module, the
topic of AI-related risk will be expanded so as to include aspects of uncertainty. In the last
module, the ethical framework of Trustworthy AI will be presented as a way to deal with AIrelated uncertainty. After a general presentation of the Trustworthy AI framework, specific
issues in AI ethics will be discussed, also depending on the students' interests.

Holography is a technology allowing the recording and re-projection of both the intensity and phase of a propagating wavefront of light. It has vast applications in metrology, biomedical research, and digital displays. However its requirement of sub-wavelength light detector and modulator, makes it one of the few technologies in which an analog implementation vastly outperforms its digital counterpart. As a consequence, further development of digital holography technologies is an important field of both academic and industrial research.
This course covers methods of holographic recording and projection of light. In the introductory part of the course, the physics of light propagation is discussed, with a particular focus on interference and diffraction phenomena. Traditional analog holography is then introduced, both in its theoretical interpretation and experimental implementation. The course then covers the acquisition of holograms using digital sensors, highlighting the existing advantages and limitations, and its applications in metrology and microscopy. Finally, existing methodologies for the calculation and experimental projection of arbitrary holograms are introduced, with particular emphasis on the potential of this technology in augmented reality applications.
The course is complemented by laboratory sessions. Most laboratory sessions consist in computer simulations of propagation of light, and in processing of existing digital holography datasets. In the final laboratory sessions, the students will prepare code for computer generated holography, and will have the opportunity to perform a practical experiments projecting computed holograms using phase and intensity spatial light modulators.

The lecture session will allow the students to fully comprehend the principles of analog and digital holography, including their features and limitations. They will be capable to accurately describe and discuss the working principle of any practical implementation of holographic technology, reasoning on the importance and the role of the design parameters of the systems (e.g. resolution, wavelength, time constraints).
Students will also be able to critically examine proposed real-world photonics applications and propose an appropriate design of an holographic recording or measurement experiment optimizing the performance of the system, and discuss the expected limitations in the results imposed by the physics of the system.
The laboratory sessions will allow the students to independently develop simple software for the simulation of holography systems, for the computer generation of holograms and for the data processing of digital holography datasets.
The exam modality is intended as an opportunity to practice soft skills such as preparation of presentations and public speaking

Lectures topics:
Analog holography (2 CFU):
Wave propagation of light
Interference phenomena
Hologram registration and reprojection
Practical implementations
Digital holography (1 CFU):
Hardware limitations
Data processing and reconstruction
Metrology applications
Microscopy applications
Computer generated holography (1 CFU):
Fourier transform based methods for image projection
Primitives based methods for structured illumination
Hologram design for phase modulators and intensity modulators
Applications in Microscopy
Applications in Lithography/manufacturing
Applications in Augmented reality devices
Laboratory topics (1CFU):
Simulation of wave propagation of light in three dimensions
Processing of recorded digital holograms
Computer generation of holograms and projection experiments

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, individuale, senza revisioni periodiche, con presentazione/discussione finale
The students will be evaluated through an oral exam. The dates of the oral exams will be decided based on the teacher’s and the students’ schedule, at least one exam date will be made available for each of the exam sessions.
During the exams the students will be asked to provide a 15 minutes presentation regarding a topic of the course of their choosing. The students are encouraged to present a topic based on their literature review which should provide more detailed information and insight compared to what has been covered in the lectures. At the end of the presentation, the oral exam will continue with questions about the presentation topic and the rest of the course's program.
The oral exam is expected to last approximately 40 minutes per each student.

Kostuk, Raymond K., Holography: Principles and Applications, Editore: Taylor & Francis, Anno edizione: 2024

This laboratory course has the aim to let students practice the design and implementation of optical microscopy for biomedical and pharmacology applications, including methods for cell manipulation and live imaging of 3D biological samples at subcellular resolution.

The expectation is to provide students with applied and practical skills on optical microscopy methods to be used independently in the investigation of complex biological samples and cell models developed in the field of bioengineering and biophysics. During the course students will work in group.
At the end of the course will have students acquired the following ability.
Applying knowledge and understanding: Design and execute experiments to explore biophysical and bioengineering challenges, using specialized optical microscopy methods.
Knowledge and understanding: Gain a foundational comprehension of optical fluorescence microscopy and related techniques, and their applicability in bioengineering contexts.
Applying knowledge and understanding: Identify the critical parameters to be investigated and the best techniques to be applied for the investigation of biological tissues.
Communication: Articulate the principles, advantages, and performance metrics of fluorescence processing methods and associated techniques in both written and verbal forms.
Lifelong learning skills: Acquire a level of expertise that empowers communication about biophotonics with peers, academic professionals, and the broader community.

Topic lectures and laboratoury activity
Conventional optical microscopy methods (brightfield, darkfield, phase, DIC)
Fluorescence labelling (antibodies, gene reporter, live cell staining)
Processing of microscopy datasets
Image analysis quantification for biomedical applications
Acquisition of phase contrast images of cultured cells
Acquisition of cells and tissues with widefield, confocal and light sheet fluorescence and multiphoton microscopes.
Segmentation, tracking, classification
Drug testing, tissue formation/regeneration, embryonic
Imaging in optically-accessible culture devices (cell-seeded scaffolds, microfluidic bioreactors, etc.), in vivo imaging in bioengineered cell models (organoids, cellularised constructs, gene edited cells, etc.), advanced imaging technique (FRAP, FRET) for protein diffusive model generation and protein-protein interaction.

Prova orale obbligatoria
The final assessment consists of an oral presentation of a scientific paper related to the topics covered in the course. Students are expected to present and critically discuss the paper, demonstrating their understanding of both the specific work and its connection to the experimental apparatus utilized during the course.

Goals
The objective of the course is to present to the students, approaching the end of their M.Sc. educational carrier, an overview of the field of biochips and analytical lab-on-chip microsystems (whose recent advances are relevant both from the scientific and industrial point of view) with specific insights on the technological aspects of the interface between (micro)electronics and electrochemistry and biology, both at cellular and molecular levels. The content of the course reflects the multidisciplinarity of the nano-bio-tech field and is mainly focused on the different manipulation and detection approaches of biochemical target analytes on chip. The main goal is to provide the students with the fundamental theoretical tools for the design of active microfluidic systems (basics of fluid dynamics at the microscale), and of the engineering of the detection systems (optical and electrochemical transducers). At the same time, several practical examples are discussed in order to illustrate design choices and trade-offs. Starting from 2020, a part of the course will be taught by means of innovative learning techniques. The topics of microfabrication (hard and soft-lithography) will be addressed by means of MOOC videos recorded in actual clean-rooms and fabrication facilities. Finally, a "blended" and "flipped-classroom" approach will be adopted in the last part of the course including a project-based and a laboratory activities where student will fabricate real devices and boards: at first they will be grouped into small teams (3-4 persons max.), will design at home the layout of the microchannels and electrodes on a PCB including a transimpedance amplifiers, according to some design challenges posed by the teacher, and then will fabricate them in the lab with 3D printing and soft-lithography (PDMS elastomer by means of replica molding). A characterization with fluids, electrolytes. digital microscopes, pumps and virtual instrumentation (oscilloscope, network analyzer used for impedance measurements) will conclude this experimental activity. Prior to fabrication of the PCB, each team will present their design in the class and feedback from a general discussion will be collected.

Expected Learning Outcomes
Knowledge and understanding (Dublin Descriptor #1)
At the end of the course the students are expected to:
· possess a good understanding of lab-on-chip systems (needs, applications, advantages and limits) and bio-electronics convergence
· possess a basic knowledge of hard and soft lithographic microfabrication approaches
· possess a basic knowledge of the laws describing the flow of liquid in microchannels and the forces to actuate particles in liquid
· presses a good knowledge of the electrochemical interface and instrumentation for biosensing
· possess a good knowledge of the DNA macromolecule and the instrumentation for its analysis
· understand the potentials and challenges of CMOS/biochips integration
Applying knowledge and understanding (Dublin Descriptor #2)
At the end of the course the students are expected to:
· be able to analyze and design a microfluidic system in laminar regime (fluidic sizing, choice of components such as pumps/valves), starting from the sample preparation and manipulation
· be able to design an analytical assay (for instance for molecular recognition or DNA analysis)
· be able to design integrated detectors (both optical and electrochemical)
· be able to design current and impedance sensing circuits for biochips
Making judgements (Dublin Descriptor #3)
Students will be able to:
· evaluate the most appropriate approach (materials, fabrication) to address an analytical need
· apply the knowledge to different application areas
· apply the knowledge to design a novel micro-device
Communication (Dublin Descriptor #4)
During the work in teams for the experimental laboratory, students will be able to communicate among each other (in the first phase at home, when the microfluidic channels are designed upon technological specifications set in the classroom). Then, during the lab. and the oral exam their communication skills are deployed to motivate their design choices and comment the experimental results.

Topics
The course is articulated in theoretical lessons, combined with quantitative exercises and with case studies oriented to the analysis and design of microsystems. An experimental laboratory consisting of: a design phase at home in teams with a propor design software and two days of experimental activity in the lab. (fabrication and characterization of a PDMS microfluidic device). Experts in the biochip field are also invited during the course to lecture seminars on specific topics, such as micromagnetism and DNA analysis, or to present the point of view of companies carrying on R&D in the biochip field (STMicroelectronics, Flextronics, Dianax etc…). A visit to the renewed clean-room and microfabrication facility of Politecnico di Milano (PoliFab) concludes the course.
1. Introduction to biochips: architecture of lab-a-chip micro-analytical systems, motivations and impact, design paradigms, market projections, basic biological macromolecules used for as receptors in affinity biosensors.
2. Introduction to microfluidics: fluid dynamics, Reynolds number, electrical equivalent of hydraulic circuits (fluidic capacitance) and electro-osmotic flow. Actuation forces for particle manipulation within biochips: diffusion, electrophoresis, dielectrophoresis, electrowetting and droplet-based digital microfluidics. Active and passive microfluidic components: valves, mixers and pumps.
3. Introduction to microfabrication: review of Silicon CMOS-MEMS processes, lithography, thin film deposition and patterning, as compared to elastomer (PDMS) and plastic based soft lithography (replica molding).
4. Optical detection: fluorescence (strengths and limitations, Atomic Force Microscopy ), source-filter-detector architectures, integrated photonics for biosensing based on evanescent field (waveguides, Mach-Zehnder interferometers, ring resonators and contactless light monitors). Electrochemical detection: the electrochemical interface with physiological ionic solutions (charge transfer and induction).
5. Electrochemical instrumentation: analytical techniques (cyclic voltammetry, amperometry and impedance spectroscopy) and the potentiostatic workstations. Micro and nano-scale electrochemistry: effects of electrode scaling.
6. Low-noise current measurements: review of noise in electronic devices, the transimpedance amplifier (working principle, properties and limitations) and advanced circuit topologies (integrator-differentiator schemes with different CMOS-compatible bias resetting solutions).
7. Impedance measurements and instrumentation, comparison of sensing configurations: bridge, ratiometric, current sensing, lock-in detection, resonant circuits, FFT-based and time-domain approaches.
8. Detection of DNA: structure and properties, microarrays (spotting and probe synthesis), electrophoresis, amplification with Polymerase Chain Reaction and quantitative detection with real-time PCR and approaches for sequencing.
9. Biochips for molecular detection: resonating cantilevers for molecular weighing, amperometric detection of neurotransmitters, redox cycling, glucose sensors based on enzymatic mediators, and nanopores. Biochips for cellular detection: interface with electrogenic cells, equivalent model of passive cells, label-free impedance monitoring a cell colony dynamics and single cell counting and sizing with microfluidic impedance flow cytometry (design case study).
10. Sample preparation (cell lysis, sample mixing, concentration and purification). Microelectronics CMOS biochips: state of the art performance in CMOS detection, design criteria, advantages and limits, the critical role of the substrate, packaging and interfacing issues.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente) o condizionata (a scelta del docente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
Assessment
Exams are held at the end of the course (no intermediate tests). The exam consists of a written test (of a duration of 2 hours) followed by an oral exam. The written test (assessing Dublin Descriptors 1 and 2) encompasses the solution of numerical exercises focusing on 2 types. The first type concerns the microfabrication and the fluidics (and components) of micro-channels and devices. The second type mostly focuses on electrochemical detection. The students will be asked to analyze circuits and systems, as well as to design them. The maximum evaluation of the written test is 30. Two bonus points are added for those who attended the laboratory activity.
The outcome of the written tests is communicated to the students by email. Then, a meeting in the classroom is scheduled for the students to vision and discuss the correction of the written test. They can accept or refuse the evaluation, either by email or in-person communication. They can undertake the oral test in the following days, by agreeing a calendar with the teacher.
The oral discussion (assessing Dublin Descriptors 1,2,3,4) is mandatory for students achieving top marks in the ranking (> 27/30) in the written test and it is optional and open for all students with a mark above 17/30 in the written test. In the oral test (lasting 15 to 40 minutes) students will be asked discuss the ultimate limits of the different technologies, pros and cons, compare them, and to design complete systems to address novel design cases proposed during the oral exam. The maximum evaluation is 30 cum laude.

Patrick Tabeling, Introduction to Microfluidics, Editore: Oxford University Press, Anno edizione: 2010, ISBN: 9780199588169
Eugenio Iannone, Labs on Chip: Principles, Design and Technology, Editore: CRC Press, Anno edizione: 2014, ISBN: 978-1466560727
Albert Folch, Hidden in Plain Sight: The History, Science, and Engineering of Microfluidic Technology, Anno edizione: 2022

Aims
Bioinformatics and Computational Biology are new disciplines born from the simultaneous progress of information technologies and bio-technologies and their application to the study of biological phenomena. They leverage on the increasing growth of available biomolecular information and bio-medical-molecular knowledge, which they significantly contribute to enhance and help to apply also in the clinic.
The course aims to illustrate how computer science principles, technologies, methods and instruments can be profitably used for the computational analysis, information content increment and interpretation of biological data produced by genome sequencing, gene expression measurements and proteomics. It will be highlighted as the application to biological data of the engineering themes of data bases, information theory, data and text mining and others can contribute to increasing biomedical knowledge and improving health care.
Students will develop biological, bioinformatics and computational skills to manage and process bio-medical-molecular data and knowledge, as well as they will acquire knowledge of the instruments needed to tackle various issues in computational biology; this will make the students able to take advantage of the opportunities offered by the increasing bioinformatics development and relevance.
Obiettivi
La bioinformatica e la Biologia Computazionale sono nuove discipline nate dal concomitante progresso delle tecnologie dell’informazione e delle bio-tecnologie e dalla loro applicazione allo studio dei fenomeni biologici. Queste discipline traggono vantaggio dal crescente aumento di informazioni biomolecolari e conoscenze bio-medico-molecolari sempre più disponibili, che esse contribuiscono significativamente a migliorare e aiutano ad applicare anche all'ambito clinico.
Il corso si prefigge di illustrare come principi, tecnologie, metodi e strumenti informatici possono essere proficuamente utilizzati per l'analisi computazionale, l’accrescimento del contenuto informativo e l'interpretazione di dati biologici generati dal sequenziamento dei genomi, dalle misurazioni dell’espressione genica e dalla proteomica. Verrà messo in evidenza come l’applicazione ai dati biologici dei temi ingegneristici di basi di dati, teoria dell’informazione, data e text mining, e altri possa contribuire all’accrescimento delle conoscenze biomediche e al miglioramento della cura della salute.
Gli studenti svilupperanno competenze biologiche, bioinformatiche e computazionali per gestire ed elaborare dati e conoscenze bio-medico-molecolari, e acquisiranno conoscenza degli strumenti necessari per affrontare vari problemi di biologia computazionale; questo permetterà agli studenti di trarre vantaggio dalle opportunità offerte dal crescente sviluppo e rilevanza della bioinformatica.

Learning outcomes
Dublin descriptors
Expected learning outcomes
Knowledge and understanding
Knowledge of main genomic and molecular biology concepts
Knowledge of genomic biomolecular data and information
Comprehension of typical computational approaches and techniques used to quantitatively evaluate genomic and transcriptomic data and information in order to extract biomedical-molecular knowledge
Comprehension of the systems for the encoding, structuring and storing of biological and biomedical information and knowledge
Applying knowledge and understanding
Development of computational abilities about the management and quantitative evaluation of heterogeneous biomedical-molecular data, information and knowledge
Ability in leveraging the acquired competences to take advantage of the opportunities offered by the increasing bioinformatics development and relevance
Making judgements
Evaluate comparatively the advantages and disadvantages of the different approaches and techniques for understanding biomolecular phenomena
Identify the most suitable approaches and techniques for specific bioinformatics problems
Communication
Learning the use of proper terms to address specific data and activities in genomics
Lifelong learning skills
Learning how engineering methods of data bases, information theory, data and text mining and others can contribute to increasing biomedical knowledge and improving health care
Learning how to effectively use computer science approaches to tackle complex problems successfully, in an adequate way for the biological domain
Be able to learn and use new computational approaches and techniques, and to apply them profitably in the biological domain
Be able to use the acquired knowledge and competences to tackle methodologically complex problems
Risultati di apprendimento attesi
Descrittori di Dublino
Risultati di apprendimento attesi
Conoscenza e comprensione
Conoscenza dei principali concetti genomici e di biologia molecolare
Conoscenza dei dati e informazioni genomiche biomolecolari
Comprensione dei tipici approcci e tecniche computazionali usate per valutare quantitativamente dati e informazioni genomiche e trascrittomiche al fine di estrarre nuova conoscenza biomedica-molecolare
Comprensione dei sistemi per la codifica, strutturazione e memorizzazione delle informazioni e conoscenze biologiche e biomediche
Capacità di applicare conoscenza e comprensione
Sviluppo di abilità computazionali relative alla gestione e valutazione quantitativa di dati, informazioni e conoscenze biomediche-molecolari eterogenee
Capacità di sfruttare le competenze acquisite per trarre vantaggio dalle opportunità offerte dal crescente sviluppo e rilevanza della bioinformatica
Autonomia di giudizio
Valutare comparativamente benefici e svantaggi di diversi approcci e tecniche per la comprensione di fenomeni biomolecolari
Individuare gli approcci e le tecniche più adatti a specifici problemi bioinformatici
Comunicazione
Apprendere a usare termini appropriati per riferirsi a specifici dati e attività relative all’ambito genomico
Capacità di apprendimento
Apprendere come metodi ingegneristici di basi di dati, teoria dell’informazione, mining di dati e testi, e altri possono contribuire ad accrescere la conoscenza biomedica e migliorare la cura della salute
Apprendere come usare efficacemente approcci informatici per affrontare problemi complessi con successo, in modo adeguato per il dominio biologico
Essere in grado di apprendere e usare nuovi approcci e tecniche computazionali, e applicarle proficuamente nell’ambito biologico
Essere in grado di utilizzare le conoscenze e competenze apprese per affrontare in maniera metodologica problemi complessi

Syllabus
Seminar lectures and practices in informatics room on the following topics compose the course; in case, at the end of the course, an external technical visit to an experimental research laboratory, or a seminar lecture by a field expert, will take place.

• Introduction (2 hours): definitions, methodologies and motivations
• Genetic and molecular biology concepts (8 hours): organisms, cells, biological molecules and their structure, duplication and expression of genetic information, protein synthesis, structure of genes and transcripts, hints of protein structure, genome, transcriptome, proteome, hints of hereditary pathologies
• Techniques of biomolecular sequence analysis (8 hours): importance of biological sequence comparison, local or global alignment of two biomolecular sequences, sequence similarity search
• Technologies for gene expression measurement and analysis (3+6 hours): biotechnologies for gene expression measurement, computational methods for gene expression data analysis, data mining of gene expression data
• Biological network analysis (3 hours): main characteristics of a biological network, mining and visualization of complex network features, computational methods for gene network extraction and analysis
• Bio-terminologies, bio-ontologies and methodologies for their analysis (2+2 hours): functional and phenotypic annotations of genes and proteins, controlled vocabularies for genomic and proteomic annotation, Open Biomedical Ontologies: the Gene Ontology and other bio-ontologies, enrichment and similarity analysis of annotations
• Genomic and proteomic databanks (2 hours): databank types and access methodologies, main databanks and their relations, provided data and formats, search methods in databanks, integration and update of data and information
• Seminar on opportunity and challenges of modern computational genomics (3 hours): next generation sequencing technologies and multimodal data, and their potential in answering to the actual biological questions
• Examples of available bioinformatics tools and their use (24 hours): main software tools available as Web applications, Web services and freeware and open source programs
  Programma delle lezioni e delle esercitazioni
  Il corso si compone di lezioni frontali ed esercitazioni in aula informatizzata sui seguenti argomenti; alla fine del corso potrà essere organizzata una visita tecnica esterna a un laboratorio sperimentale di ricerca, o una lezione seminariale di un esperto del settore.
• Introduzione (2 ore): definizioni, metodologie e motivazioni
• Elementi di genetica e biologia molecolare (8 ore): organismi, cellule, molecole biologiche e loro struttura, duplicazione ed espressione dell’informazione genica, sintesi proteica, struttura di geni e trascritti, cenni di struttura di una proteina, genoma, trascrittoma, proteoma, cenni di patologie a base ereditaria
• Tecniche di analisi di sequenze biomolecolari (8 ore): importanza del confronto di sequenze biologiche, allineamento (locale o globale) di due sequenze biomolecolari, ricerca di similarità tra sequenze
• Tecnologie per la misurazione e l’analisi dell’espressione genica (3+6 ore): biotecnologie per la misurazione dell'espressione genica, metodi computazionali per l’analisi dei dati di espressione genica, data mining dei dati di espressione genica
• Analisi di reti biologiche (3 ore): principali caratteristiche di una rete, reti di regolazione genica, metodi computazionali per l’estrazione e l’analisi di reti geniche, mining di caratteristiche di reti
• Bio-terminologie, bio-ontologie e metodologie per la loro analisi (2+2 ore): annotazioni funzionali e fenotipiche di geni e proteine, vocabolari controllati per l’annotazione genomica e proteomica, le Open Biomedical Ontologies: la Gene Ontology e altre bio-ontologie, analisi di arricchimento e di similarità di annotazioni
• Banche dati genomiche e proteomiche (2 ore): tipi di banche dati e metodologie di accesso e interrogazione, principali banche dati e loro relazioni, dati e formati forniti, metodi di ricerca in banche dati, integrazione e aggiornamento di dati e informazioni
• Seminario su opportunità e sfide della moderna genomica computazionale (3 ore): tecnologie e dati multimodali di sequenziamento di nuova generazione e loro potenzialità per rispondere alle attuali domande biologiche
• Esempi di strumenti bioinformatici disponibili e loro utilizzo (24 ore): principali strumenti software disponibili come applicazioni web, servizi web e programmi freeware e open source
  Laboratory activities
  During course practices, all given in an informatics room, several bioinformatics technologies, databanks and publicly available tools will be illustrated and used.
  Attività di laboratorio
  Durante le esercitazioni del corso, tutte svolte in aula informatizzata, saranno illustrate e utilizzate diverse tecnologie, banche dati e strumenti bioinformatici pubblicamente disponibili.
  Lecturer notes
  Other teaching material
  The slides presented during the course and the estimated detailed schedule of lectures and practices are available on the "We Be e-Poli" (WeBeeP), the portal for the network activities of students and professors at the Politecnico di Milano, accessible from the Politecnico di Milano Web site; students registered to the course for the current academic year can access it.
  Note Docente
  Altro materiale didattico
  Le diapositive presentate durante il corso e il calendario preventivo dettagliato delle lezioni ed esercitazioni sono disponibili in "We Be e-Poli" (WeBeeP), il portale per le attività in rete di docenti e studenti del Politecnico di Milano, accessibile dal sito web del Politecnico di Milano; vi hanno accesso gli studenti iscritti al corso nell'anno accademico corrente.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Assessment
The assessment is based on a written exam at the end of the course, with exercises and open questions on all the topics presented during the course lectures or practices.
At the instructor's discretion, an oral exam may also be required after the written exam to verify its content and the student's knowledge of the subject.
Competence in discussing and correlating together the course topics, with appropriate terms and attention to the relevant conceptual aspects, is a fundamental element for a positive evaluation.
The following table provides a detailed overview of the different types of assessments that contribute to verify the expected learning outcomes.
Type of assessment
Description
Pursued learning outcome (Dublin descriptor)
Written test
Exercise resolution
Theorical questions with open answer about all course topics
Questions aimed at highlighting the student ability in elaborating relations between the various topics of the course and with other course topics
1, 2, 3, 5
1, 2, 3, 5
1, 2, 3, 4, 5
Modalità di valutazione
L'esame consiste in una prova scritta alla fine del corso, con esercizi e domande aperte che coprono tutti gli argomenti trattati durante le lezioni o le esercitazioni del corso.
A scelta e discrezione del docente può essere richiesta anche una prova orale dopo la prova scritta per verificarne i contenuti e la conoscenza della materia da parte dello studente.
La capacità di discutere e relazionare tra loro gli argomenti oggetto del corso, con l’impiego dei termini appropriati e l’attenzione agli aspetti concettuali importanti, costituisce elemento fondamentale per una valutazione positiva.
La tabella che segue dettaglia come le diverse forme di valutazione contribuiscono alla verifica dei risultati di apprendimento attesi.
Modalità di verifica
Descrizione
Risultato di apprendimento perseguito (descrittore di Dublino)
Esame scritto
Risoluzione di esercizi
Domande di carattere teorico a risposta aperta su tutti gli argomenti del corso
Domande volte a evidenziare la capacità dello studente di elaborare collegamenti fra i vari argomenti del corso e con quelli di altri insegnamenti
1, 2, 3, 5
1, 2, 3, 5
1, 2, 3, 4, 5

A.M. Lesk, Introduction to Bioinformatics. 4th edition, Editore: Oxford University Press, New York, NY, Anno edizione: 2014, ISBN: 978-0-19-965156-6
P.M. Selzer, R.J. Marhöfer, A. Rohwer, Applied bioinformatics: An introduction. 2nd edition., Editore: Springer, Berlin, D, Anno edizione: 2018, ISBN: 978-3-319-68299-0
Helmer Citterich, Ferrè, Pavesi, Pesole, Romualdi, Fondamenti di bioinformatica, Editore: Zanichelli, Anno edizione: 2018, ISBN: 9788808621122
A.A. Lizabeth, Fundamental Molecular Biology, Editore: Blackwell publishing, Oxford, UK, Anno edizione: 2007, ISBN: 978-1-4051-0379-4
A.A. Lizabeth, Fondamenti di biologia molecolare, Editore: Zanichelli, Bologna, IT, Anno edizione: 2008, ISBN: 9788808166227
D.W. Ross, Introduction to molecular medicine. 3rd edition, Editore: Springer, New York, NY, Anno edizione: 2002, ISBN: 0-387-95372-8

Questo corso si propone di introdurre gli studenti di ingegneria a questioni filosofiche ed etiche che emergono con l'uso delle tecnologie biomediche. Ad esempio, gli ingegneri spesso si avvalgono di simulazioni informatiche di modelli idealizzati dei loro oggetti di studio. Tuttavia, tali procedure sono affette da incertezze rappresentazionali e computazionali che sono ineliminabili. Si presentano quindi questioni filosofiche circa l'attendibilità delle predizioni risultanti, oltre che questioni etiche riguardanti la responsabilità morale dell'uso delle tecnologie utilizzate. Allo stesso modo, l'inedita abilità a trattare un'enorme quantità di dati apre scenari affascinanti, soprattutto nel campo della medicina di precisione. Eppure, anche qui si devono affrontare domande aperte sulla capacità predittiva degli algoritmi impiegati nel trattamento dei Big Data, oltre che problemi morali concernenti la privacy e il controllo delle informazioni personali.

This course provides the principles of biotechnology processes, with particular reference to the drug product (DP) manufacturing. The analysis starts with an introduction covering the industrial processes for the synthesis of biopharmaceuticals, including therapeutic proteins, peptides and oligonucleotides. After having clarified how a drug is manufactured, all the aspects related to drug formulation aimed at improved stability and therapeutic index will be addressed. This discussion will specifically refer to the concept of controlled drug delivery. The main objective here is to prevent or at least moderate the side effects related to systemic delivery, as it is the case for cancer chemiotherapies. The fundamental principles of the design of a drug delivery carrier are discussed and various options are presented in connection to different administration processes. Next, the treatment focuses on the use of polymer nanoparticles for the delivery of various drugs, including the case of gene therapies, as well as for diagnostic applications, such as imaging through different physical properties. The synthesis of various nanoparticles, with different size and surface properties (e.g., charge and chemical functionalities), and their efficiency in penetrating the target tissue are discussed. The discussion will follow on the fill and finish operations required to the preparation of the DP to distribution and administration. The main post-processing operations such as terminal sterilization, lyophilization, vial filling, capping and inspection will be discussed in order to understand the hurdles and critical steps required for bringing a drug formulation to hospitals.
The course combines frontal lectures with laboratory experiences, providing the students with an applied understanding of the technological challenges and practical issues arising during the manufacturing of colloidal drug formulations.

The lectures will allow the students to:

• understand the issues (efficacy, safety and cost) related to the use of biopharmaceuticals for therapeutic applications
• understand the role of organic and inorganic nanoparticles in biomedical appliacations
• understand the polymer reaction and colloidal engineering aspects related to the use of nanoparticles in medicine
• design and synthesize polymers and polymer nanoparticles for therapeutic and diagnostic applications
• design the main unit operations for fill&finish
• understand the main challanges related to operate in sterile conditions and ensure durg safety during the whole supply chain
  The laboratory experiments will allow students to:
• design and synthesize in a chemical laboratory drug carriers based on polymers and polymer nanoparticles for specific therapeutic indications.

Lectures
The lectures describe the physicochemical fundamentals of processes for the production of drugs and devices for biomedical applications.
The topics covered can be organized in three main parts.
In the first, introductory part the background related to biopharmaceuticals, including definition, main features and market considerations are addressed. The main aspects related to the manufacturing of therapeutic proteins, such as monoclonal antibodies, and oligonucleotides through recombinant technology will be discussed. The main aspects of the design of cell cultures and subsequent purification of the product through various steps, referred to as capture and polishing, usually based on chromatographic techniques will be covered.
The second part refers to the synthesis of polymers and polymer nanoparticles for the delivery of active therapeutic principles. We begin from general considerations about different polymerization chemistries and processes, in order to provide the necessary background to all students. In particular we discuss: the kinetics of free radical polymerization, emulsion polymerization processes; living polymerization processes: ROP, RAFT and ATRP and finally methods for the synthesis of polymeric nanoparticles. Colloidal stability is discussed with reference to the shelf drug stability and its formulation depending on the type of drug administration. These topics will be covered in an applied way, integrating the lectures with practical examples of applications as well as with laboratory experiments (as detailed in the following).
The third part covers the processes for fill and finish operations. A perspective on good manufacturing practices (GMP) and regulatory landscape will be provided first. Then, following a typical process flowchart, the main unit operations will be described, including freeze/thaw, filtration, pooling and mixing, filling and aseptic processes, lyophilization, capping and inspection. An outlook of the current technologies as well as of the future trends for this drug product manufacturing section will finally be provided.
Laboratory Project (Laboratorio Sperimentale)
After the introductory lectures, the students move to the polymer laboratory where they are divided in groups of 4 members and conduct selected experiments for the synthesis of polymer nanoparticles and gels for localized and controlled drug delivery.
In particular, the topics covered in the laboratory experiences include:
Synthesis of polymer nanoparticles
Magnetic and fluorescent nanoparticles for medical imaging
Hydrophilic gels for localised delivery of antibiotics
Smart polymer excipients able to respond to temperature stimuli

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, senza presentazione/discussione finale
The final evaluation consists of two contributions: oral exam on the content of the lectures (60%), and laboratory reports (40%).
In particular, in the oral examination, the student should demonstrate to have acquired the understanding and knowledge about the various aspects discussed in the lectures, about the physical chemistry of drug delivery vehicles and devices for imaging as well as the processes for producing biopharmaceuticals, with particular attention to fill and finish operations. The oral exam accounts for 60% of the final grade.
The reports for each laboratory experiment, prepared by the work team, will be evaluated and credits will be given based on the team understanding of the obtained results and of the related critical issues. This implies that unsuccesfull results are accepted provided that the team can explain them and indicate possible alternatives. Attendance to the laboratory experiments is required, in order to collect the data necessary for the final written reports. The evaluation of the laboratory reports accounts for 40% of the final grade.

D. Pfister, L. Nicoud and M. Morbidelli, Continuous Biopharmaceutical Processes, Editore: Cambridge University Press, New York, Anno edizione: 2018
N. Isaacs, Physical Organic Chemistry, 2nd Edition, Editore: Addison Wesley Longman, Anno edizione: 1995, ISBN: 0-582-21863-2
Nicholas W. Warne, Hanns-Christian Mahler, Challenges in Protein Product Development, Editore: Springer Nature

Insegnamento della Facolta' di Medicina e Chirurgia dell'Universita' degli Studi di Milano aperto a un numero limitato di studenti di Ing. Biomedica. Lezioni frontali introducono i principali temi di clinica ortopedica e traumatologica. Queste vengono completate da studio integrativo sulle nozioni di base di anatomia e fisiologia necessarie, visite a laboratori ed approfondimenti sulle indagini strumentali.

Insegnamento della Facolta' di Medicina e Chirurgia dell'Universita' degli Studi di Milano aperto a un numero limitato di studenti di Ing. Biomedica. Lezioni frontali introducono i principali temi di clinica ostretica e ginecologica. Queste vengono completate da studio integrativo sulle nozioni di base di anatomia e fisiologia necessarie, visite a laboratori ed approfondimenti sulle indagini strumentali.

Objective of the course is to introduce the basic principles and design instruments for collaborative robotic devices, when coupled with a human being, to assist, rehabilitate, or augment movement capabilities. The main steps of the design of collaborative robotics in the healthcare domain are the definition of functional and technical requirements following a user-centered approach, the identification of a proper mechatronic framework, the identification of a sensor network for proprioception, and the definition of low level and high-level collaborative control framework.
A technical and functional evaluation with tests on a testbench and human subjects are finally needed.
Specific objectives of the course are:
to learn the methodology to define user specifications and technical requirements;
to learn the knowledge needed to understand and design the mechatronics system;
to learn the knowledge needed to understand and design the cooperative control framework;
to learn the knowledge needed to understand and design the sensor network for user’s intention detection and proprioception layer;
to understand how to perform Verification and Validation (V&V) of the integrated system.
The course has a strong practical and laboratory-based approach.

Lectures will be delivered by the lecturers followed by practical guided examples and open discussions. This will allow students to (DdD 1):
Analyze and understand the goals, assumptions and requirements associated to a collaborative robot, and model them formally;
Define the architecture of a collaborative robot mechatronic and control framework, and model them formally;
Identify and define the verification and validation activities needed for a collaborative robot.
The laboratory activities and the project work will allow students to (DdD2 and DdD3):
Simulate in Matlab environment kinematic, dynamic and path planning of a collaborative robot;
Design, build and control a simple collaborative robot;
Learn how to solve problems not strictly faced within the Course.

Introduction to the collaborative robotics problem - what is a collaborative robot for human augmentation; typical applications and trend for collaborative robots in healthcare (e.g., assistive robots, rehabilitation robots, service robots); a Short History of collaborative robots; robot classification (interaction modalities, end-effectors/rigid exoskeletons/soft exoskeletons, active/passive, etc.); different types of joints.
Definition of technical and functional requirements following a user-centered approach.
Kinematics of collaborative robots - forward and inverse kinematic problem; position, velocity and acceleration, joints and task space description.
Dynamics of collaborative robots, specifically related to robots worn by human subjects.
Basics of hardware-level designs for compliance.
Basics of low-Level Robot-Assisted Control Strategies (e.g. impedance, admittance).
Introduction to high-Level interaction Modalities (e.g. passive, active-assisted, active-resisted, error-augmentation).
Trajectory/motion planning for a collaborative robot.
Overview of strategies for user’s intention detection, including the selection of proper sensors to detect user’s intention of movement (e.g. inertial sensors, EMG sensors, eye tracking, Brain-computer interface) and basics of signal processing of biological signals.
Verification and Validation process.

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
During the course, the students will work in small teams under the supervision of a lecturer and of a tutor.
Each group will be assigned with a specific assignment involving the definition of the functional specifications of a collaborative robot for healthcare applications and the use of a dedicated hardware. Students will be required to define the technical requirements, design the mechatronic system, integrate the sensors needed to detect the user’s intention, develop the control system, and perform preliminary functional tests.
A one-degree-of-freedom exoskeleton for educational purposes will be used to to carry out the assignment (EduExo Pro, AUXIVO).
The two course modules share a single final assessment, consisting of the following components:
Interim project presentation (6%): each group will present the progress of the project according to the schedule introduced by the lecturers during the first class and published on the course WeBeep page.
Project submission and demonstration (25%): during the final class of the semester, each group will submit the project code and present a practical demonstration of the developed system, according to the schedule published on WeBeep.
Individual examination (69%): during the official examination sessions, each student will be assessed individually through:
a written exercise covering the course topics (19%);
an individual discussion of the project (37.5%);
an oral examination on the course content (12.5%).
Please note that the grade awarded for the project component cannot be rejected or repeated within the same academic year.

Etienne Burdet, David W. Franklin, Theodore E. Milner., Human Robotics: Neuromechanics and Motor Control., Editore: The MIT Press, Cambridge, Massachusetts, London (UK).
Books of industrial robotics

L'obiettivo del corso è duplice:
1. Rendere gli studenti consapevoli delle complesse dinamiche della comunicazione e della persuasione, con un'enfasi speciale sulla componente personale (mittente/destinatario).
2. Equipaggiare gli studenti con abilità comunicative perché siano in grado di produrre comunicazioni orali, scritte e in forma multimediale che siano chiare e persuasive.
Il corso adotta tre strategie:

• Lezioni frontali
• Modalità "blended" e "flipped" (gli studenti accederanno in maniera autonoma a risorse online quali esempi di discorsi - in forma scritta o di video -, articoli, casi di studio, lezioni in video)
• Tutoring in presenza o in remoto (attraverso collaborazione "cloud") sui compiti da svolgere e sul progetto finale

The course is intended to provide students with the knowledge and tools necessary to design communication networks. In the first part of the course the different segments of networks (access, metro and core), with the role of optical-network technologies. Practical network design problems will be solved thorugh mathematical formulations (integer linear programming) and heuristic approaches, also with the help of a laboratory activity based on the open-source software Net2Plan. Various techniques to ensure network survivability against failures will be described. In the second part of the course, fundamental concepts of queuing theory will be introduced to solve capacity planning an network dimensioning problems (based on, e.g., Erlang and Engset formulas) considering different users' behavior.

Knowledge and Understanding (DD1)

• Students will learn how to:
  -- Use Integer Linear Programming to solve routing and capacity assignment problems
  -- Identify the right protection technique given the network characteristics
  -- Master fundamental tele-traffic formulas as Erlang B, C and Engset
  Applying Knowledge and Understanding (DD2)
  Given specific network characteristics, students will be able to:
  -- Model and implement network routing and capacity assignment problems
  -- apply fundamental tele-traffic formulas in realistic case-studies
  Making judgements (DD3)
  Students will be able to:
  -- understand the fundamental principles that govern the design of communication networks
  -- identify which protection strategy shall be appied based on service and network characteristics
  -- identify which tele-traffic fundamental formula shall be applied in practical capacity planning cases
  Communication (DD4)
• Students will learn how to present to a network engineer, in written and oral form, the actual design and operation problem that is facing in a communication network

1 – Introduction on Optical Networks
Optical networking principles and challenges: telecom network overview, business models, traffic engineering vs network engineering vs network design. Enabling Technologies: optical fiber, optical transmitters, optical receivers, optical amplifiers, switching elements, Wavelength Division Multiplexing (WDM).
2 – Exact and Heuristic (Optical Core) Network design methodologies
Network design based on mathematical modelling: flow formulation, route formulation. Modelling of network protection: dedicated protection, shared protection. Network design by heuristic approaches: greedy, local search. Lab activity with Net2Plan: a free and open-source Java tool for the design of communication networks
3 – Protection techniques
Network survivability: objectives and protection techniques. Single-layer and multi-layer protection techniques: protection at IP layer, protection at physical layer. Protection techniques in SONET/SDH: line and section protection, point-to-point and ring structures, dedicated and shared protection. Protection in the optical layer: solutions for ring networks and for mesh network, dedicated-vs-shared protection, ring cover and p-cycle techniques.
5 – Queueing theory primer
Markov, birth death and Poisson process.
6 – Capacity dimensioning in communication networks
Introduction on traffic modeling: single and multiple source model. Analysis of multiple-server systems with assumption BCC, BCH, BCD. Evaluation of congestion and statistics of carried/lost traffic (Erlang B and C, Engset). Capacity dimensioning with Wilkinson, Fredericks and Lindberger approaches.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, con revisioni periodiche, con presentazione/discussione finale
The verification of knowledge for the course content consists in a test at the end of the course that comprises a written test and, if required, an oral examination on the subjects covered in the course (DD1). Projects, to be defined with the professor, can be used to substitute the oral examination.
The written exam (DD2, DD3) is based on numerical exercises (up to 5) and/or open question (1 or 2) on the topics such as protection, network design thorugh Integer Linear Programming and capacity dimensioning using queueing theory formulas. the written exam is used to assess the capability of the students to manage theoretical and technological tools to design communication networks. The laboratory activity using the software Net2Plan and NetworkX Python Libraries can be used to complement the final written score.
The oral exam (DD4) will be used by the instructor to verify student' understading of the fundamental concepts such as enabling technologies for networking, queuing theory behind formulas for capcity dimensioning, protection at different network layers.
In case of negative evaluation the student is admitted to the following tests of the academic year.

Materiale corso https://webeep.polimi.it/course/view.php?id=21359

The course provides the theoretical tools and practical methods that are needed for the basic design of structural elements made of composites materials, both from the structural and manufacturing point of view. The course will discuss the peculiar mechanical properties of composite materials and how these are influenced by the microscopic material structure. It will provide insight on how the strengths and weaknesses of composite materials should be exploited or dealt with during the design of structural elements. In addition, the students will be able to manage theoretical and pratical design tools to strengthen existing structural elements with composite materials.
Case studies dealing with the design and strengthening of simple structural elements will be also presented.

The course will allow students to:
Understand the mechanical and physical properties of composite materials (DdD1);
Define the layout of the composite material to meet specific performance demands (DdD2);
Understand the stress-transfer mechanism within the composite material and between a structural element and the composite material bonded to it (DdD1).
Manage different design guidelines to strengthen existing structural elements (DdD2).
The project laboratory will allow students to:
Exploit the advantages of composite materials and provide proper solution to manage their weaknesses (DdD1 - DdD2);
Design simple structural elements using composite materials (DdD2);
Design strengthening solutions of existing structural elements using composite materials (DdD2).

Composition, microstructure and properties of the main types of fibers and matrices used for the manufacture of composite materials
Manufacturing techniques for composite materials and structures
Experimental characterization of the properties of composite materials with reference to current standards
Elastic properties of composite materials
Composites plate mechanics (Classical Lamination Theory) and its applications
Failure criteria for composite materials and laminates
Examples of applications of composite materials in the various fields of structural engineering
Applications of the finite element method to composite structures: outlines
Bond behavior of composite material applied to existing structures
Bending and shear strengthening of existing structural elements with composite materials
Confinement of existing structural elements with composite materials

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
Evaluation is based on the homeworks assigned during the course and on a final project. The final project will involve either the design of a simple composite structure decided by the students or the design of a strengthening solution of an existing structural element using composite materials.
The students will be required to produce a report of the homeworks and of the final project to show that they are able to design a lamination sequence optimal with respect to the stated design goals.
The project will be presented during an oral examination; the project should demonstrate that the student understands and is able to manage the course topics. The oral examintaion will be based on a discussion of the final project; however, questions on all the course topics may be asked.

Barbero, E.J., Introduction to Composite Materials Design, Editore: Taylor & Francis Group, ISBN: 9781420079159
V.V. Vasilev, E. V. Morozov, Mechanics and Analysis of composite materials, Editore: Elsevier, ISBN: 978-0-08-098231-1
J.N. Reddy, Mechanics of Laminated Composite Plates and Shells: Theory and Analysis, Editore: Taylor & Francis, Anno edizione: 2003, ISBN: 9780849315923

The course introduces the students to the most important methods for the numerical computation of electromagnetic fields. Typical applications of the presented methods can be found in electrical and biomedical engineering.
Each method will be introduced theoretically and then applied to canonical problems using Matlab/Octave.
Challenging problems taken from real applications (some of them suggested by private companies or research centers) will be proposed to the students for their final project work.

Lectures sessions will allow students to know:
the most important differential and integral formulations and discretization methods for the numerical solution of quasi-stationary Maxwell equations;
one of the most common discretization methods for the numerical solution of the equations describing the propagation of an electrical signal along a transmission line;
the vectorial basis functions for the discretization of electromagnetic fields;
one of the most common discretization methods for electromagnetic wave propagation problems
how to implement the presented numerical methods in Matlab/Octave;
Laboratory sessions will lead the students to perform numerical experiments using existing codes and electromagnetic software packages.
The final project work will stimulate the ability to apply the previous knowledge to a real application, choosing the most suitable method for the electromagnetic problem at hand.
The instructor expects a deep comprehension of the subjects which should not be limited to the statement of theoretical results.
Instead, the acquired knowledge should enable students to express critical judgment.

Formulations for quasi-stationary electromagnetic problems
The Method of Moments (MOM) for magnetostatic and electrostatic problems
The Boundary Element Method (BEM) for the numerical solution of Laplace equation in two and three dimensions
BEM for eddy current problems
The Finite Element Method (FEM): theoretical background
FEM for electrostatic and eddy current problems
Vectorial basis functions for FEM discretization of electromagnetic field problems
FEM/BEM coupling
The Partial Element Equivalent Circuit (PEEC) method
The Finite Difference Time Domain (FDTD) method for transmission lines
FDTD for electromagnetic wave propagation problems

Progetto/i - Elaborato/i obbligatorio, individuale, con revisioni periodiche, con presentazione/discussione finale
Students will be evaluated at the end of the course on the basis of the development and oral presentation of a project in Matlab/Octave in order to show their ability in implementing an electromagnetic numerical formulation.

S. N. Makarov, G. M. Noetscher, A. Nazarian, Low-frequency Electromagnetic Modeling for Electrical and Biological Systems Using MATLAB, Editore: John Wiley & Sons, Anno edizione: 2016, ISBN: 978-1-119-05256-2
J. T. Katsikadelis, The Boundary Element Method for Engineers and Scientists, Editore: Elsevier, Anno edizione: 2016, ISBN: 9780128044933
T. Rylander, P. Ingelström, A. Bondeson, Computational Electromagnetics, Editore: Springer, Anno edizione: 2014, ISBN: 1489986022
Özlem Özgün, Mustafa Kuzuolu, MATLAB-based Finite Element Programming in Electromagnetic Modeling, Editore: CRC Press, Anno edizione: 2019, ISBN: 978-1-4987-8407-8

General description and motivation:
Nature is the ultimate engineer — its principles underpin both the human body and the most advanced electronic systems. In this course, we will explore the deep and fascinating connections between Life Sciences and Electronics, using the language of mathematics and scientific computing to understand and model complex real-world phenomena. From charge transport in nanoscale transistors to ion flows across biological membranes, and from electrical signaling in neurons to the physics of semiconductor devices, we will discover how core principles transcend disciplines. By applying mathematical modeling and numerical methods, we will develop stable and accurate numerical simulations that bridge biology and electronics at both the systems and molecular levels.
The aim of the course is to provide students with the basic tools required to construct mathematical models of realistic problems and the theoretical foundations needed to develop and analyze numerical methods and algorithms for their stable and accurate solution on a computer. The course focuses on mathematical modeling using differential equations—both Ordinary Differential Equations (ODEs) and Partial Differential Equations (PDEs)—with applications in Electronics and Biomedical Engineering. Numerical methods will be presented, emphasizing stable approximations of ODEs and PDEs, particularly through the Finite Element Method and its variants. Additionally, the course will introduce a novel computational paradigm in Scientific Machine Learning: Physics-Informed Neural Networks (PINNs).
The course features hands-on computer lab sessions where students will extensively use MATLAB and python, a multi-paradigm programming language and numerical computing environment developed by MathWorks, to solve mathematical problems and perform simulations. Students are required to install and use the software on their own laptops.
The full course yields 8 CFUs and is associated with the following other two courses, each one yielding 5 CFUs:
050492 - COMPUTATIONAL MODELING IN ELECTRONICS AND BIOMATHEMATICS
096660 - NUMERICAL METHODS IN MICROELECTRONICS
The course pursues three specific objectives:
1. Provide a unified mathematical modeling framework for:
a) Cellular biology
b) Solid-state electronics
2. Provide numerical methods for the simulation of:
a) Cellular systems
b) Electronic devices
3. Develop critical skills for the analysis and design of complex systems in:
a) Life Sciences
b) Electronics
For students enrolled in course 050492: objectives 1.a, 2.a and 3.a.
For students enrolled in course 096660: objectives 1.b, 2.b and 3.b.

Lectures, laboratory activities and projects will:
1) Provide students with the knowledge required to formulate, analyze, and simulate mathematical models arising in engineering and life-science applications.
a) For students in Electronics Engineering (course 096660), this includes the acquisition of the fundamental principles of mathematical modeling and numerical approximation and its application to the simulation of semiconductor devices.
b) For students in Mathematical and Biomedical Engineering (course 050492, 096659, referring specifically to Module 2, see below), the course leverages these foundations to introduce advanced computational paradigms based on hybrid physics-based and data-driven models, such as Physics-Informed Neural Networks (PINNs), with perspectives towards digital-twin frameworks for engineering and biomathematical applications.
2) Enable students to quantitatively assess the physical accuracy of models and the numerical performance of computational methods through the solution of exercises and project assignments of increasing complexity.
3) Develop the ability to critically analyze, design, and validate computational models through the combined use of mathematical modeling, numerical simulation, and (for the courses 050492, 096659) modern scientific machine learning tools.
The theoretical concepts, methods, and algorithms will be validated during laboratory sessions through numerical experiments, project assignments, and critical discussion of results.
Project assignments will contribute to the final course grade.
The expected learning outcomes (according to the Dublin descriptors) are in the following.
Knowledge and understading:

• develop knowledge and understanding of the mathematical models and numerical methods for their approximation.
  Applying knowledge and understading:
• apply the numerical methods to the solution of mathematical problems arising in different applications in Engineering;
• implement numerical algorithms;
• effectively use the software library MATLAB to implement the algorithms, solve the numerical problems, and perform numerical simulations.
  Making judgement:
• choose the most appropriate numerical algorithm to solve a mathematical problem based on theoretical properties and computational aspects;
• critically interpret the numerical results based on the properties of the mathematical problem and of the numerical method.
  Communication skills:
• present a project.
  Ability to learn:
• elaborate on the material provided during lectures and laboratory sessions to choose the most appropriate numerical algorithms and solvers;
• develop projects on numerical problems that are relevant in Engineering and Life Sciences, in particular for Electronics and Biomedical Engineering.
  Students are expected to develop a deep understanding of the models and methods discussed, going beyond theoretical results and standard exercises, and applying their knowledge effectively. They should critically assess different situations, make informed decisions, and justify their reasoning. Accuracy in calculations and a well-structured presentation of the theory are also required.

The course is subdivided into three main parts or modules.
Module 1 – Foundations of Computational Modeling
a. Mathematical models based on ordinary and partial differential equations (ODEs and PDEs).
b. Numerical approximation of ODEs and PDEs through finite element methods.
c. Finite element methods for balance laws. Discretiztion of diffusion–advection–reaction models in conservative form. Discussion of stability, conservation, and convergence properties.
Module 2 – Hybrid Physics-Based and Data-Driven Models in Biomathematics
a. Review of cellular and tissue models in electrophysiology, including ion transport, membrane dynamics, action potential propagation, and cardiac electrophysiology and their approximation using the finite element method.
b. Hybrid physics-based/data-driven modeling. Artificial Neural Networks (ANNs): training, testing, validation, and learning paradigms. Physics-Informed Neural Networks (PINNs) as surrogate solvers for differential equations, inverse problems, and data assimilation.
c. Applications to cardiac electrophysiology and personalized medicine. Digital-twin perspectives for personalized modeling and simulation in life sciences.
Module 3 – Computational Models for Microelectronics
a. Drift–Diffusion and Poisson–Drift–Diffusion models for semiconductor devices.
b. Conservative numerical approximations, including finite-volume and Scharfetter–Gummel methods.
c. Nonlinear solution strategies, including Gummel iterations and Newton methods.
d. Applications to semiconductor devices, with emphasis on p–n junctions.
For students enrolled in course 096659: Modules 1, 2 and 3.
For students enrolled in course 050492: Modules 1 and 2.
For students enrolled in course 096660: Modules 1 and 3.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
Progetto/i - Elaborato/i obbligatorio, individuale, senza revisioni periodiche, con presentazione/discussione finale
The exam consists of project evaluations, a written test, and an optional oral examination, with the final grade based on the combined results.
Project assignments. Evaluation of 2 project assignments during the semester, cosen among modules 1,2,3. Students attending modules 1,2 or 1,3 will take a project for each module, students taking modules 1,2,3 can choose two projects on two distinct modules. Projects will be assigned with mandatory deadlines set by the lecturer along the semester. The maximum score for each projects is 5 for a total of 10 points.
Written Test. A mandatory written test will be held during one of the 5 exam sessions scheduled by the School of Industrial and Information Engineering. The test includes theoretical and practical exercises covering all topics from the lectures and lab sessions. The use of the computer, algorithms and scripts is required for the test. Students must answer theoretical questions, formulate and solve mathematical and numerical problems, implement algorithms and critically interpret the results based on the theoretical properties of the numerical methods. The written test yields a maximum score of 30; a minimum score of 15 is required to pass the written test and the exam.
Oral Exam. It is optional and it covers all the topics of the course. It is available only if the written test is passed with a minimum score of 15. It affects only the written test score of +/- 3 points, not exceeding a total of 30 points.
The total score of the exam is the sum of the project score and the written test score, each multiplied by 0,825. The exam is passed only if the total score is 18 or higher. Cum laude is granted if the total score is larger than or equal to 32.

R. Sacco, G. Guidoboni, A. G. Mauri, A Comprehensive Physically Based Approach to Modeling in Bioengineering and Life Sciences, Editore: Elsevier Inc. 50 Hampshire St., Cambridge MA 02139, USA, Anno edizione: 2019
A. Quarteroni, Numerical Models for Differential Problems, Editore: Springer, Anno edizione: 2017
P. Colli Franzone, L. Pavarino, S. Scacchi, Mathematical Cardiac Electrophysiology, Editore: Springer, Anno edizione: 2014
R. Muller and T. Kamins, Device Electronics for Integrated Circuits, Editore: John Wiley and Sons, Anno edizione: 2003
J. Keener and J. Sneyd, Mathematical Physiology, Editore: Springer-Verlag, Anno edizione: 2009
I. Rubinstein, Electrodiffusion of Ions, Editore: SIAM Philadelphia, Anno edizione: 1990

The course deals with the application of ethical frameworks, notions, and concepts to problems created, aggravated or transformed by digital technologies. The main aim of the course is to offer theoretical and methodological tools to analyze the increasingly complex scenario in which digital technologies are created, deployed, adopted, and used. While the scope of the course include digital and computer technologies in general, special attention is devoted to Artificial Intelligence (AI) and, in particular, to some of its discontinuities with traditional computer technologies. Each session of the course focuses on a specific topic that is analyzed through the ethical lens provided by normative reasoning, that is reasoning about normative matters, such as obligations, permissions, and the rights of individuals or groups. In addition, in order to raise the critical attitude of participants, the standard theoretical tools of normative reasoning are critically evaluated against some of the current issues of digital ethics.
The course is intended to give students a chance to reflect on the ethical and social impact of digital technologies by focusing on some issues of the digital age. No specific prerequisites are needed. At the end of the course students will:
acquire a broad perspective on the ethical and social implications of computer technologies and learn how to recognize and analyze ethical and social issues inherent in computer technologies;
be acquainted with normative ethics and normative argumentation and use critical skills in ethically evaluating cases-studies involving computer technologies;
apply ethical theories in a critical way to problems created, aggravated or transformed by computer technologies.
The course includes lectures by the instructor and invited lecturers; class participation will be expected, and students should apply what they learn through readings and lectures by looking at current events through an ethical lens.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will:
Acquire a broad perspective on the ethical and social impacts and implications of information technologies;
Be acquainted with normative ethics and normative argumentation;
Learn how to recognize and analyze ethical and social aspects and issues inherent in technology;
Be able to understand how technical problems are inherently connected to a social dimension within a socio-technical perspective.
Applying knowledge and understanding
Students will:
Be able to use critical skills in clarifying and ethically analyzing cases-studies involving information technology;
Be able to apply ethical theories to problems created, aggravated or transformed by computer technology;
Be able to explore and assess possibilities for solving or diminishing existing and emerging ethical and social problems that attach information technology.
Making judgements
Students will be able:
To autonomously analyze the ethical theories to problems created, aggravated or transformed by computer technology;
To evaluate and select the appropriate knowledge in the effort of elaborating and justifying a philosophical argument on a topic autonomously selected.
Communication
Students will learn to:
Exercise and improve their skills in critical writing;
Present in an effective way the results of their independent research, being able to justify their choices.
Lifelong learning skills
Students will:
Be better prepared to their future professional life in an ethically and socially responsible way;
Be able to analyze problems through an ethical lens.

The course will cover different topics both from a theoretical and a more practical point of view. We will start with a broad analysis of the concept of responsibility, in particular in an engineering perspective, and of normative ethics and its tools, including normative reasoning. We will discuss ethical questions in the design of technology with a focus on the moralization of technologies. Then ethics in IT-configured societies will be discussed and technology as the instrumentation of human action will be presented. Within this context we will focus on: a socio-technical approach to AI and its ethics, machine decisions and their ethical issues, information flow, privacy, and surveillance, digital intellectual property and digital order. Specific topics will be covered by invited lecturers.
Students will be supervised in the development of the final project (group presentations in class) in order to meet the standards required by scientific publications.
Additional information can be found on WeBeep.
Required bibliography
Readings (papers and book chapters) made available on WeBeep.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, con revisioni periodiche, con presentazione/discussione finale
Grading will be on the following basis: 50% final project (group presentations in class); 50% written test on the course topics.
Type of assessment
Description
Dublin descriptor
Final project
(class presentation)
Students are required to:
Develop a philosophical argument in which the presented claims are justified by the use of ethical argumentation and the relevant literature;
To present this argument either in an oral presentation in class in front of the instructor and their student colleagues.
1, 3
4, 5
Written test
Questions on the course topics that require to have carefully studied the suggested bibliography and the ability to apply it to case-studies;
Questions on the ethical issues associated to information technologies that require to have studied the suggested bibliography and to have developed critical skills in analyzing technical problems with the tools provided by ethical theories and argumentation.
1, 2
3, 4, 5

I. van de Poel, L. Royakkers, Ethics, technology and engineering: An introduction, Editore: Wiley-Blackwell, Anno edizione: 2023
Carissa Veliz, Oxford Handbook of Digital Ethics, Editore: Oxford University Press, Anno edizione: 2023, ISBN: 9780198857815

Modern computer systems routinely process high-value information, including financial and personal data, economic transactions, and intellectual property. At the same time, they are increasingly pervasive, continuously available, and interconnected, making information security a major engineering challenge.
Security engineering is the discipline of designing and building secure systems. It is inherently interdisciplinary, combining cryptography, software engineering, network security, and political and social considerations.
This course provides a broad introduction to cybersecurity and to the methods used to design, validate, and ethically test security mechanisms. Lectures examine the main components of computer and information systems, with particular attention to their security subsystems. Students will analyze vulnerabilities, understand how they can be exploited, and learn how to prevent similar design and implementation errors.

Expected learning outcomes
Knowledge and understanding
Students will acquire broad knowledge of fundamental security technologies, their limitations, and common vulnerabilities and exploitation techniques.
Applying knowledge and understanding
Students will be able to identify and assess basic security issues in systems, networks, and applications; evaluate and improve proposed solutions; design high-level solutions to security problems; exploit simple application vulnerabilities in controlled environments; and develop basic patches.
Making judgements
Students will be able to comparatively evaluate security threats, vulnerabilities, mitigation mechanisms, and alternative solutions; reason about trade-offs; and assess risks in realistic scenarios.
Communication
Students will use appropriate terminology to explain security issues and justify security choices clearly.
Lifelong learning skills
Students will be able to update their knowledge independently and apply risk and security assessment principles to complex systems, including non-digital contexts.

1. Introduction to Information System Security

• Security properties (confidentiality, integrity, and availability), threats, attacker models, and security-engineering methodology.
  2. Fundamentals of cryptography
• Overview of cryptographic primitives (symmetric and asymmetric ciphers, signature schemes, message authentication codes, cryptographic hashes) and summary of their functionality
  3. Authentication, authorization, and access control
• Authentication and authorization mechanisms; discretionary (DAC), mandatory (MAC), and role-based (RBAC) access-control models.
  4. Application and web security
• Software exploitation: buffer overflows (in-depth) and other memory-corruption vulnerabilities (concepts, e.g. format string bugs, Return-Oriented Programming, Return to Lib C), stack protections, and mitigations (*)
• Web application security: major OWASP vulnerability classes, including injection and broken access control, with their exploitation and mitigation (*)
  5. Network security
• Stateful and stateless packet filtering
• Software-Defined Networking (SDN) and its security implications
  6. Malware
• The evolution of malware
• Botnets and the underground economy (essential concepts)
• Malware analysis
• Anti-analysis techniques
• Rootkits
• Mobile malware (essential concepts only)
  The course follows a strong hands-on approach. Practical exercises are provided for all topics marked with (*) in the syllabus. A virtual hacking lab allows students to practise exploiting and securing computer applications in a controlled environment.
  The course complements, rather than replaces, dedicated cryptography courses such as “095947 - CRYPTOGRAPHY AND ARCHITECTURES FOR COMPUTER SECURITY”. The overlap with these courses is minimal.
  The course also provides the foundation for the more advanced security courses “056896 - OFFENSIVE AND DEFENSIVE CYBERSECURITY” and “062113 - HUMAN AND PHYSICAL ASPECTS OF SECURITY”.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The assessment consists of a mandatory written test, graded out of 34 points, and, at the instructor’s discretion, a confirmatory oral test.
The written test consists of open questions and exercises that require students to apply theory and reasoning to use cases, code samples, network diagrams, and similar material. Students may answer in English or Italian. A large collection of past exams, many with solutions, is available on WeBeep.
The written test is graded on a 34-point scale. A score below 12 points is a failing grade («riprovato») under the exam regulations, and the student is not admitted to the following exam call. When required, the oral test is confirmatory and is used to verify the written-test result.
Optional homework and challenges, including activities in the virtual hacking lab, are intended solely for practice and self-assessment. They do not contribute additional points to the final grade. The maximum grade, including cum laude, can be obtained from the written test alone.
The written test is closed-book. Students may not consult course materials, notes, or other aids, and electronic devices are prohibited. Using or accessing unauthorized materials or devices, or communicating with other students, results in immediate removal from the exam and exclusion from the following exam call.
Type of assessment
Description
Dublin descriptor
Written test
Exercises requiring the analysis or design of security solutions for proposed use cases, including the exploitation of specific vulnerabilities. Graded out of 34 points; a score below 12 is a failing grade («riprovato») under the exam regulations.
1, 2, 3, 4, 5
Oral test (confirmatory)
Held at the instructor’s discretion to verify and confirm the result of the written test.
1, 2, 3, 4, 5

Dieter Gollmann, Computer Security - 3rd edition, Editore: Wiley, Anno edizione: 2011, ISBN: 978-0-470-74115-3
Note: It is vital that you get the 3rd edition if you choose this book!
William Stallings, Lawrie Brown, Computer Security: Principles and Practice, Editore: Pearson
Ross Anderson, Security Engineering, Editore: Wiley, ISBN: 0-471-38922-6 http://www.cl.cam.ac.uk/~rja14/book.html
The Joy of Cryptography by Mike Rosulek https://joyofcryptography.com/
Note: An introductory cryptography textbook (freely available)
C. Anley, J. Heasman, F. Linder, G. Richarte, The Shellcoder's Handbook, Editore: Wiley
M. Howard, D. LeBlanc, Writing Secure Code, Editore: Microsoft Press
M. Mitchell, J. Oldham, A. Samuel, Advanced Linux Programming
Note: Chapter 10

Modern large-scale datacenters require the seamless integration of different components - applications, computation nodes, storage devices, and networks - into one computing infrastructure. The course covers the basics of current datacenters architectures, ranging from the analysis of the single components to the global infrastructure. The focus is on the foundations required to understand the design of computing infrastructures that are scalable, available, secure and flexible at the same time. Virtualization, container-based technology, cloud computing and storage systems are analyzed in depth to show how they can be used to support challenging tasks such as machine-learning applications and high-performance computing.

Knowledge and understanding (DD1):

• The student will acquire knowledge about the Computing Infrastructure field. In particular, the students will learn the essential elements that are part of a scalable data center and the basic concepts of reliability, performance evaluations, virtualization, and cloud computing.
  Applying knowledge and understanding (DD2):
• The student will be able to understand the design choices done within a large-scale computing infrastructure.
• The student will be able to design both computational and storage components of a data center, to satisfy specific service-level requirements
• The student will be able to apply performance and reliability analysis to the data center and to other complex systems, even outside the computer engineering domain
  Making judgements (DD3):
• The student will be able to explore alternative solutions within a data center, in terms of computing architectures and service models.
• The student will be able to analyze and discuss the trade-offs between reliability, performance, and costs of different hardware and software technologies within a data center.
  Communication skills (DD4):
• The student will be able to use the data center terminology appropriately.
• The student will be able to illustrate the characteristics and functionality of a data center infrastructure and related service models.
  Learning skills (DD5):
• The student will develop learning skills necessary to autonomously extend performance and reliability analysis to other types of components in a data center, and to autonomously understand novel technologies within computing infrastructures together with their impact.

Hardware infrastructure of datacenters:

• Basic components, Rack structure, Cooling
• Hard Disk Drive and Solid State Disks
• RAID architectures
• Hardware Accelerators
  Software infrastructure of datacenters:
• Virtualization: basic concepts, technologies, hypervisors and containers
• Computing Architecture: Cloud, Edge and Fog Computing
• Infrastructure-, platform- and software-as-a-service
  Methods:
• Scalability and performance of datacenters: definitions, fundamental laws, queuing network theory basics
• Reliability and availability of datacenters: definitions, fundamental laws, reliability block diagrams
  Exercises to apply the knowledge in simple examples or to more complicated scenarios will be used especially considering the parts related to reliability, storage system design, and performance analysis.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The evaluation consists of a written exam on the topics covered in the course. The exam will include both theoretical questions and numerical problems.
More in detail the written exam includes:

• Close-ended questions on course topics to verify the capacity to recognize the basic concept behind the design of a large-scale computing infrastructure; (DD1, DD2)
• Open-ended theoretical questions on course topics to verify the capacity to describe the key concepts related to the design of a large-scale computing infrastructure; (DD1, DD2, DD3, DD4, DD5)
• Numerical problem resolutions regarding analysis and design of data center and data center components, virtualization, storage systems, dependability, and performance modeling. (DD1, DD2, DD3)
  An oral test could be requested by the lecturer to deepen specific questions of the written test and further verify the capacity of the student to describe the key concepts related to the design of a large-scale computing infrastructure and develop links between the various topics of the course (DD1, DD2, DD3, DD4, DD5)

Course Web Site webeep.polimi.it
Luiz André Barroso and Urs Hölzle, The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines
Edward D. Lazowska, John Zahorjan,G. Scott Graham, Kenneth C. Sevcik, Quantitative System Performance: Computer System Analysis Using Queueing Network Models
Note: (Open Access Book) (chapters 1-4)
James Smith and Ravi Nair, "Virtual Machines" Versatile Platforms for Systems and Processes
Caesar Wu and Rajkumar Buyya, Cloud Data Centers and Cost Modeling

The goal of this course is to present current and advanced methodologies for the control of robotic manipulators. The course covers selected topics ranging from kinematic and dynamic modelling of an industrial robot, to advanced motion planning and control, to control of the interaction of the robot with the environment based on force and vision feedback. The goal of the course is fully aligned with the overall goals of the Automation and Control Engineering Program, while being an excellent complement for students enrolled in other Programs (Biomedical Engineering, Computer Science and Engineering, Electronics Engineering, Engineering Physics, and others).
A mix of theoretical and industrially relevant topics characterizes the course, where extensive use of software for simulation and offline programming of robots will be made.

The expected learning outcomes of the course can be classified according to the Dublin descriptors.
Dd1 (Knowledge and understanding)
Students will understand:

• the role of industrial robots in the factory, why and where they should be used in the production systems;
• the dynamic model of a robot;
• the advanced tools for planning and control of the motion of the robots;
• the interaction of the robot with the environment;
• the role and importance of collaborative robots.
  Dd2 (Applying knowledge and understanding)
  Students will learn how to:
  -use mathematics to describe the motion of a robot, in terms of both kinematics and dynamics;
  -plan a suitable motion for the robot both in free environment and in presence of obstacles;
  -tune an industrial motion control system and understand the rationale and potentialities of advanced nonlinear model based control strategies;
  -master the control of the interaction of the robot with the environment, either with force or with vision sensors;
  -master software programs to simulate and to offline program the robots.
  Dd3 (Making judgement)
  Students will be able to decide which modelling, planning and tracking technique to adopt and how to apply them for the motion planning and control of industrial robotis

1. Introduction
Industrial robots: basic concepts and examples.
Market of industrial robotics.
Trends in industrial robotics.
2. Advanced robot kinematics
Review of direct, inverse and differential kinematics.
Kinematic calibration.
Kinematics of redundant manipulators: methods for redundancy resolution.
3. Robot dynamics
Dynamic models of robot manipulators.
Euler-Lagrange and Newton-Euler formulations: main properties.
Identification of dynamic parameters.
Direct and inverse dynamics.
4. Advanced motion planning
Review of motion generation in joint space and in operational space.
Path planning in joint space and in the operational space.
Path planning with obstacle avoidance.
Kinematic and dynamic scaling of trajectories.
Interpolation of points (splines).
5. Control of robot manipulators
Review of independent joint control methods.
Centralized model-based controllers: computed torque feedforward control, PD control with gravity compensation, inverse dynamics control, robust and adaptive control.
Operational space control.
6. Interaction with the environment
Force sensors.
Impedance and admittance control.
Hybrid position/force control.
7. Control with vision sensors
Image processing.
Camera calibration.
Image-based and position-based control systems.
Interaction matrix and image Jacobian.
8. Collaborative robotics (notes)
Human-robot interaction.
Collaborative applications.
Some of the practice sessions will make use of computer simulation tools and of commercial tools for robot offline programming.

Prova scritta obbligatoria, senza prove in itinere
The final assessment will be a written exam, consisting of both numerical exercises and theoretical questions.
In the written exam the student should be able to:
-compute the dynamic model of simple two degrees of freedom robots (Dd1, Dd2);
-discuss the properties of a kinematically redundant robot (Dd1);
-compute or rescale a trajectory in joint space or in the operational space (Dd1, Dd2, Dd3);
-discuss the main properties of centralized model-based controllers and solve simple numerical related design problems (Dd1, Dd2. Dd3);
-discuss the main properties of the control with force and vision sensors and solve simple numerical related design problems (Dd1, Dd2).

Bruno Siciliano, Luigi Villani, Giuseppe Oriolo, Alessandro De Luca, Foundations of Robotics, Editore: Springer, Anno edizione: 2025, ISBN: 978-3-031-85522-1 https://doi.org/10.1007/978-3-031-85523-8
B. Siciliano, L. Sciavicco, L. Villani, G. Oriolo, Robotica: modellistica, pianificazione e controllo, 3a Ed., Editore: McGraw-Hill Italia, Anno edizione: 2008, ISBN: 9788838663222 http://www.catalogo.mcgraw-hill.it/catLibro.asp?item_id=2317
G. Magnani, G. Ferretti, P. Rocco, Tecnologie dei sistemi di controllo, 2a Ed., Editore: McGraw-Hill Italia, Anno edizione: 2007, ISBN: 9788838672750 http://www.catalogo.mcgraw-hill.it/catLibro.asp?item_id=2141

See the program of the module in the master course

See the program of the module in the master course

L'insegnamento affronta il tema della creatività, in particolare nel campo della scienza e della tecnologia, con l'obiettivo di stimolare negli studenti la consapevolezza del fatto che esistono alcuni fattori chiave per creare ambienti di lavoro e modi di agire e pensare maggiormente creativi. Dopo una base teorica sulla creatività in generale e della creatività negli ambiti della scienza e della tecnologia, con particolare riferimento alle opere di Kuhn e Popper, vengono proposte una serie di metodologie da sperimentare in maniera laboratoriale, ispirate degli studi di Resnick, De Bono, Kelley e altri autori. L'approccio viene validato praticamente attraverso lo svolgimento di progetti di gruppo su temi dati, da affrontare in gruppi interdisciplinari.

Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
Valutazione continua obbligatoria
See the program of the module in the master course

The Critical Thinking course aims to provide, in an innovative manner, the methodological tools necessary to reason and argue soundly under conditions of risk and uncertainty in science and technology. The tools used will be those belonging to logic, philosophy, argumentation, and the psychology of thought, as applied to uncertain reasoning. The course content will be presented and discussed interactively with students. The course is intended to give students the opportunity to reflect critically on the cultural, social, and ethical impacts of science and technology

( Knowledge and understanding )
Students will:

• be acquainted with different forms of scientific reasoning
• acquire methodological skills in order to deal with risk and uncertainty
  ( Applying knowledge and understanding )
  Students will:
• foster their ability to solve problems and cope with uncertainty
• apply critical thinking to complex problems
• integrate their technical knowledge with humanistic, cognitive and ethical perspectives on science and technology
  ( Making judgements )
  Students will be able to:
• use methods and imagine scenarios in order to deal with (severe) uncertainty
• enhance creativity in thinking and reasoning
  ( Communication )
  Students will learn how to
• improve their skills in a writing an academic paper
• make academic presentations
• evaluate different forms of (risk) communication
  ( Lifelong learning skills )
  Students will
• be ready to evaluate and take decisions under conditions of uncertainty
• be better prepared to their future professional life in a socially responsible way
• be able to propose innovation solutions to complex and uncertain problems.

The course will cover different topics both from a theoretical and a more practical point of view.
The list of topics includes:

• scientific reasoning: deduction, induction, and abduction
• elements of logic and argumentation
• probabilistic reasoning: biases and paradoxes
• risk theory
• risk perception
• risk communication
• risk and moral argumentation
• decision making under severe uncertainty
• technological innovation and uncertainty
• scenario planning
• sustainability and scientific values

Progetto/i - Elaborato/i obbligatorio, individuale, senza revisioni periodiche, con presentazione/discussione finale
An individual final essay
Students are required to develop an argumentative thesis in an individual essay in which the specific claims are justified by the use of critical thinking and the relevant literature.
Students are required to read and carefully study the suggested bibliography and to apply it to case-studies.
Collective presentations
Students are encouraged to give collective presentations in order to reduce the length of the individual final essay (this option is available only for some study programs). All detailed information and the pdfs of the scientific papers that we will be used will be made available.
The individual final essay will be evaluated considering the following criteria:
1) originality of the main thesis
2) knowledge of the scientific and technological background
3) argumentative skills
4) ability to integrate technical knowledge with critical reflection
5) compliance with academic standards

Damiano Canale, Roberto Ciuni, Aldo Frigerio, Giovanni Tuzet, Critical Thinking. An Introduction, Editore: Bocconi University Press - Egea, Anno edizione: 2021, ISBN: 9788899902858
Daniele Chiffi, Stefano Moroni, Uncertainty, Innovation and the City, Editore: Edward Elgar, Anno edizione: 2025, ISBN: 9781035342662 https://www.e-elgar.com/shop/gbp/uncertainty-innovation-and-the-city-9781035342662.html?srsltid=AfmBOoooZkaZdrxskmSY2ckahsDBQ2HOVkhHF7S-AU-24J5_HcpovGK-
Mattia Andreoletti, Daniele Chiffi, Philosophy of Risk, Editore: Cambridge University Press, Anno edizione: 2027
Note: If not yet published, an advanced draft of the book will be made available to students.

The goal of the course is
to enable students to deepen the knowledge of the architecture and functionalities of relational database management systems (internal structure, modularity, capabilities,...);
to achieve a well-established awareness of the implications of the transactional ACID properties on concurrency and reliability;
to show to the students how relational data are stored within a DBMS and how the availability of suitable indexing structures and data statistics profoundly affect query execution;
to learn how a rule-based computational paradigm can help to incrementally build additional features for a DBMS such as business rules, checking of general constraints, view maintenance of derived data, etc. via the concept of trigger;
to learn how to bridge the gap between relational and object models with the help of Object/Relational Mapping systems;
to understand how data can be ranked and restricted to a representative subset that reveals the most promising part of a dataset, thus enabling subsequent analysis tasks.
The main purpose of the course is to build on consolidated data management technologies to provide the learners with the ability to understand the opportunities and limitations of the plethora of new emerging technologies for data management.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will learn:
The internal structure of the "black-box" that implements the SQL language
New language paradigms (data-driven rule-based programming)
How abstract ACID transactional properties translate into system requirements
How object-oriented data models can be mapped onto relational systems declaratively
The geometrical interpretation of data attributes and its importance for ranking data
Applying knowledge and understanding
Given practical scenarios, students will be able to:
Undertake strategical suitable actions to improve the performances of a database management system w.r.t. a specific goal (e.g., speed-up of a specific family of queries)
Tune relational database system requirements to the actual needs of a data-centric application
Bridge the gap between the relational and the object model
Extract the potentially most interesting objects from a dataset
Making judgements
Students will be able to:
Evaluate whether a specific application or scenario requires the power and complexity of a full-fledged relational DBMS or can instead benefit of simpler, light-weighted alternative solutions
Analyse and understand the probable causes of inefficiency in a complex multi-layered application built on a relational system (identifying bottlenecks and critical configurations)
Compare and balance pros and cons of different solutions for the same problem
Lifelong learning skills
The course:
Provides a solid, time-tested theoretical foundations that capture the essentials of many technological solutions given over time to classical data management problems
Develops the ability to understand the opportunities and limitations of the plethora of new emerging technologies for data management
Contributes to broaden the mental agility and flexibility that is required by a data engineer to envision and design alternative solutions to a given problem based on the constraints of the environment in which the system is expected to be deployed

Transactional Systems. Why transactional systems are relevant. Notion of transaction. ACID properties: atomicity, consistency, isolation, durability.
Concurrency control theory. Histories (or schedules), serializability, various notions of equivalence, complexity of testing. View-serializability, conflict-serializability. Two-phase locking. Hierarchical locking. Deadlock analysis and resolution. Timestamp-based concurrency control. Multi-version concurrency control. Implementation of locking in commercial systems. Concurrency control in distributed databases.
Reliability control theory. Notion of: stable storage, logging, checkpointing, write-ahead log rule, commit rule. Recovery protocols: warm restart, cold restart. Implementation of reliability control in commercial systems. Commit protocols, theory of two-phase-commit, presumed-abort and read-only optimisations, non-blocking protocols (3 and 4 phase commit protocols). Implementation of commit protocols with heterogeneous DB servers in the X-open standard.
Internal structure of a database server. Buffer Management. Page management. Data organisations according to the sequential, direct, and indexed data structures. B and B+ trees. Hashing functions. Access methods: scans, ordering, joins. Join methods. Query optimisation fundamentals. Cost models and optimal query plan selection (branch and bound method for execution plan selection). Database administration in commercial systems. Hints to physical database design (index selection, primary storage method selection).
Active databases: The ECA Paradigm (event/condition/action) and data management. Execution methods for active databases. Trigger languages and systems. Formal properties of active rule sets. Termination, confluence, observable determinism. Rule analysis. Design of active rules for integrity maintenance, automatic data derivation, application business rules, etc.
The concept of Object Relational Mapping: mapping entities, relationships and hierarchies; lifecycle of managed objects, transactional support in the business tier. Example of ORM data connector: Java Persistence API
Ranking: geometrical interpretation of data attributes, scoring functions, access modes, the middleware scenario, algorithmic approaches to ranking, optimality and instance optimality, the notion of dominance, skylines, k-skybands.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The assessment will be based on a written exam to be taken in the exam sessions defined by the school and covering all aspects in the syllabus. The exam is closed book. It will assign up to 32 points. 30 cum laude will be assigned when the total score is equal to or higher than 31.
The following table provides a detailed overview of the elements that will be considered in the various assessment activities.
Type of assessment
Description
Dublin descriptor
Written test
Exercises focusing on comprehension
Estimation of query execution costs
Concurrency control techniques
Theoretical open question
Exercises focusing on design aspects
Design of trigger-based systems
Formulation of ORM data mapping
Design of ranking strategies
1,2,3
1, 2, 3, 5

P. Atzeni, S. Ceri, P. Fraternali, S. Paraboschi, R. Torlone, Basi di dati. 6th edition, Editore: McGraw-Hill Italia, Anno edizione: 2023, ISBN: 978-8838656545
Note: Students may use also previous editions of the book.
P. Atzeni, S. Ceri, S. Paraboschi, R. Torlone, Database systems, Editore: McGraw Hill, Anno edizione: 1999, ISBN: 978-0077095000
Note: English version of an older edition of the Italian textbook.
PDF available at http://dbbook.dia.uniroma3.it/
R. Elmasri, S. Navathe, Database systems, Editore: Pearson, Anno edizione: 2010, ISBN: 978-0132144988
Note: General database textbook, for students who are not able to read the textbook in Italian. Many other textbooks can be used.
Mike Keith Merrick Schincariol, Pro JPA 2 in Java EE 8: An In-Depth Guide to Java Persistence APIs, Editore: Apress, Anno edizione: 2018, ISBN: 1484234197 https://www.apress.com/gp/book/9781484234198
Note: Code available

Given the always increasing diffusion of mobile and wearable devices (e.g., smartphones, tables, and watches), the course aims to present the main techniques and technologies to design and implement applications for these devices. More specifically, the course will address the design of "mobile" applications by considering both the problem of designing the user experience, that is, the screens used to interact with the user, and the problem of understanding the actual distribution of the components that constitute the application and their interactions.
As for technologies, the course will present the main solutions that are available today. It will cover both iOS-based (iPhone/iPad) and Android-based applications. It will also present cross-platform options as the emerging solution for the realization of cross-platform "mobile" applications.

DdD1: Knowledge and understanding
Students will learn how to:

• Identify the key elements of a modern mobile application
• Design appropriate GUIs
• Design and implement complete mobile applications using different technologies
• Select among different architectural and technological options
  DdD2: Applying knowledge and understanding
  Given specific applications, students will be able to:
• Detail concrete and efficient GUIs
• Identify and select among different architectural and technological choices
• Apply theory to assess designed solutions and available technological alternatives
• Design, document, develop and test complex mobile applications.
  DdD3: Making judgements
  Given relatively complex mobile applications, students will be able to:
• Analyze and understand the requirements associated with the mobile applications and the devices of interest
• Define the architecture of the corresponding mobile application using proper styles and patterns
• Identify and define the needed external elements, libraries, and services
  DdD4: Communication
  Students will learn to:
• Write a design document
• Conceive a technical presentation
• Present their work in front of the professor and their colleagues
  DdD5: Lifelong learning skills
  Students will learn how to
• Develop mobile applications
• Identify alternatives and select the proper technologies
• Frame and evaluate new, future technologies.

The course is roughly divided in five parts:

• The first part frames the problem and the new/many opportunities these devices offer. It also gives a brief survey of the different alternatives and "competing" solutions.
• The second part is about the design of mobile applications. It tries to identify some guidelines and recurring patterns to "ease" the design of these applications and also to help obtain quality solutions.
• The third part introduces the main novelties and characteristics of some key solutions (Flutter and React Native) for the development of cross-platform applications.
• The fourth part is is about Android and about the development of applications for this operating system, and for the family of devices (phones, tablets, watches, TVs) that use it, by means of Kotlin and Jetpack compose.
• The fifth part introduces the development of applications for iOS-based devices by using Swift and SwiftUI.

Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The exam consists of a project that must be developed by a group of up to 3 students (max). The project must be documented, presented, demoed, and discussed with the professor.
Type of assessment
Description
Dublin descriptor
Assessment of project artifacts
Assessment of the design and experimental work developed by students, either individually or in groups, as part of the project
1, 2, 3, 4, 5
Oral presentation
Assessment of the project presentation
2, 3, 4, 5

Flutter official documentation https://reactnative.dev
React Native official documentation https://reactnative.dev
Android official documentation https://www.android.com
iOS official documentation https://developer.apple.com/ios/planning/

Insegnamento della Facolta' di Medicina e Chirurgia dell'Universita' degli Studi di Milano aperto a un numero limitato di studenti di Ing. Biomedica. Lezioni frontali introducono i principali temi di radiologia e medicina nucleare ed alle relative applicazione diagnostiche e terapeutiche. Particolari approfondimenti sono dedicati alla strumentazione.

The course Digital Security Management addresses the fundamental management and technical aspects of security and privacy in organizations, emphasizing the need for good security management practices. The goal of this course is to provide concepts and practical methods about the management of security policies, methods, tools, services, and responsibilites in organizations.
The knowldege that will be acquired can enable an aware communication between those who speak the language of security and those who are acquainted with the language of enterprise, so supporting informed conversations between technology teams and organization managers.
More precisely, the course aims are to: i) identify the problems and concepts of security and privacy management in organizations; ii) show how these problems can be solved through organizational and technical measures. Therefore, it describes the policies, methods, tools, and resources to set in place in private and public organizations towards these aims.

The course is organized in two parts: 1) an introduction to the definitions and problems of security, fundamentals and management, in particular; 2) the description of technical measure that can be adopted to deploy security management.
Security and privacy of enterprise systems is focused, with a view of new applications, such as data science and big data, streaming from IoT systems, Datawarehouse systems, and cybersecurity.
The GDPR and the legislation at the Europena level for security, privacy and AI an its application are analyzed through descriptions of guidelines to security plans formulation. Testimonials of security management will give cases and experiences in practical application of measures for data privacy, security plans, management of security services for privacy protection, and other security management in enterprises.

PART I SECURITY MANAGEMENT CONCEPTS
Introduction to Security Management Concepts.

• Fundamentals of security: Physical, Organizational and Technical Security, threats, attacks, defenses,
  principles of cryptography, network security, audit, definition of Cybersecurity.
• Security Properties (Confidentiality-Privacy, Integrity, Authenticity, Availability)
• Attacks, vulnerabilites, countermeasures.
  Security as a Non-Functional Requirement.
• Security as a Quality of Service (QoS) parameter: information security and security information (costs of maintenance and
  hidden costs).
• Security specifications: description of what/where/when/how to monitor and control, languages and tools for specification.
• Security impact on organization structures: analysis methods (business, organization, architecture, technology cycle.
• Resource Management and Security: Security by Design, organization and technical measures. Security Policies.
• The GDPR for privacy and its application.
• Policies in Europe, US and other Countries (DoD, NIST Criteria, Standardization Agencies).
  PART II TECHNICAL ISSUES FOR SECURITY MANAGEMENT
  Services for Security Management.
• Smart access and authentication model and services.
• Organizational services (policies, plans, security charts, etc.).
• Access, authorization, user profiles management.
• Decision Making in dynamic, agile and distributed organizations.
• Security services for Certification, Authorities, trust systems, PKI, evolution of cryptography management.
  Data Security
  Policies and models.
  Security management in the DBMS, role of the Security Officer.
  New trends: security of Big Data, security of the Data Warehouse, Cybersecurity.
  Security Plans Management
  Data Protection Plans.
  Guidelines to formulation, continuous monitoring and adequacy.
  Ethics, privacy, governance
  Examples in several domains: Banking, Healthcare, Insurance, Industry, Manufacturing, Government
  Security and AI: issues, legislation, technical aspects, ethical aspects.

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, individuale, con revisioni periodiche, con presentazione/discussione finale
Valutazione continua obbligatoria
Presentation of a project developed in class. The project (by individual or groups of 2 students) is developed during the teaching time in class, and through home work. A set of project themes is proposed by the teacher. The students can also propose a case they know or they are already working at in other classes or in their personal life.
The project develops the description of an anlyzed AS-IS enterprise security status, and proposes a set of guidelines to improve security be putting into evidence the security lacks in data, machines, networks, organigram and so on. The ISO 27001 standars is the guide during risk analysis and security measures deployment. The case is illustrated in the final classes as a presentation given by the student in simulation of a company presentation. The project documentation (between 40 and 60 pages, formatted according to a teachedr-defined index) is to be uploaded on WEBEEP 10 days before the exam. The evaluation of the project sums up to 16 points.
Written or oral exam on security issues presented in class is the second part of the exam, grading to a maximum of 16 points.
Additional points can be gained by the students by selecting a reading set on topics proposed by the teacher. A summary of reading work is presented in class using slides prepared by the student. This part is optional and gives 1 or 2 additional points.

Stallings, William, and Lawrie Brown., Computer security: principles and practice., Editore: Pearson, Anno edizione: 2015
Castano, S., Fugini, M., Martella, G., Samarati, P, Database Security, Editore: Addison-Wesley, Anno edizione: 1995
M.FUGINI, Security Management Notes, Anno edizione: 2024 https://fugini.faculty.polimi.it/
Note: avalable as pdf
Trim, P. R., & Lee, Y. I. (2023), Managing cybersecurity threats and increasing organizational resilience., Anno edizione: 2023 Big Data and Cognitive Computing, 7(4), 177

The course fits into the overall program curriculum pursuing some of the defined general learning goals. In particular, the course contributes to the development of the following capabilities:
Understand context, functions, processes in a business and industrial environment and the impact of those factors on business performance
Identify trends, technologies and key methodologies in a specific domain (specialization streams)
Interact in a professional, responsible, effective and constructive way in a working environment, also motivating group members

Students will be able to:
Understand and describe the different measures of inputs, outputs and outcomes related to the activities of the healthcare sector, the purposes of each type of measure and how performance measurement can be a useful information tool for healthcare policies and managerial decisions
Critically analyze and appraise the results of performance measurement for improving the outcome of policy and managerial decision-making, at the healthcare system level as well as at the healthcare providers level, understanding the key success factors and the challenges to its successful use

SETTING THE CONTEXT OF PERFORMANCE MEASUREMENT: A BRIEF INTRODUCTION TO THE FUNCTIONING OF HEALTHCARE SYSTEMS
The architecture of healthcare systems
Organization, financing, and delivery of healthcare services
ISSUES, METHODOLOGIES AND USE OF PERFORMANCE MEASUREMENT IN HEALTHCARE
A framework for the measurement of performance in healthcare
The different dimensions of performance
Health
Clinical quality and appropriateness
Financial protection
Equity
Efficiency
To provide students with the necessary skills to operationalize the theoretical concepts related to performance measurement, their presentation in the lectures will be followed by a practice class discussion of applied work related to the different measures. Special attention will be devoted to the data to be used, to their sources and to their appropriate treatment for the application of the different methodologies.
A detailed schedule with the dates and the topics dealt with in the lectures and in the practice classes will be provided before the beginning of the course on the WeBeep platform.

Altro
1) Students regularly attending the course will be assessed as follows:
Essay 65%
Oral exam 35%
Essay
Each student will be assigned a topic related to a specific area of healthcare performance measurement or a specific indicator, selected from those discussed during lectures. Based on relevant academic and policy literature, the student will write an individual essay critically evaluating the strengths and weaknesses of the measurement approach and discussing its main implications for policy and managerial decision-making.
The essay aims to assess the student’s ability to critically interpret and appraise performance measurement results. Guidelines for the structure and content of the essay will be provided in advance.
Oral exam
The oral examination will cover all the topics presented during the lectures. It is designed to assess students’ comprehension and integration of the course material.
A detailed schedule, including key deadlines for all assignments and the date of topic assignment for the essay, will be published on the WeBeep platform before the start of the course. The full assessment process—including the submission of the essay and the oral exam—will be completed within approximately two weeks after the end of the teaching period, and before the official exam dates.
2) Assessment for Non-Attending Students
Students who do not attend classes will be assessed through a written examination (open-ended questions) during the regular exam sessions. The written exam will have a duration of 90 minutes.
At the instructor’s discretion, an oral exam may be required in addition to the written test, particularly when the written responses do not provide sufficient evidence of mastery of the course content. When applicable, the oral exam will take place within seven days of the written test.

Irene Papanicolas and Peter C. Smith (eds), Health system performance comparison, Editore: Open University Press McGraw-Hill Education, Anno edizione: 2013, ISBN: 978-0-33-524726-4 https://www.euro.who.int/__data/assets/pdf_file/0009/244836/Health-System-Performance-Comparison.pdf
Note: Relevant chapters: 5, 6, 7, 8, 9, 10

Main goal of the course is to enable students to analyse and to design electronic circuits and systems.
The course applies the concepts of frequency-domain and time-domain analysis, noise investigation, and performance assessment to electronic components and circuits, for designing and developing electronic boards and products. The course presents and exploits both analog (transistors and operational amplifiers) and mixed-signal (samplers and ADC/DAC converters) electronic components, in either general-purpose configurations (e.g., voltage-mode operational amplifiers and basic ADCs) or advanced implementations (e.g. current-feedback amplifiers, multi-step ADCs, sigma-delta modulators, etc.).
The course provides the theoretical methodology and the practical skills to understand how a circuit works (moving from the electronic schematics to hand computations of frequency-domain and time-domain performance) and vice versa from high-levels specifications and requirements, to component selection, schematics design, and component sizing. Each step is explained both theoretically (“lesson” classes) and practically (“exercise” classes), through the study of real circuits and case-studies.

Knowledge and understanding (Dublin Descriptor 1)
At the end of the course and after successfully passing the exam, the student:
knows how to analyse analog and mixed-circuit electronic circuits, and to understand the impact of component sizing on performance;
knows how to design feedback circuits and to compute frequency response, impedances, stability, and noise performance;
understands how to sample, hold, and convert analog signals to digital samples, for proper static and dynamic performance, and how to select the suitable ADC and DAC components.
Applying knowledge and understanding (Dublin Descriptor 2)
At the end of the course and after successfully passing the exam, the student:
is able to apply the acquired understanding to assess operation and performance of given electronic circuit schematics;
is able to apply the acquired knowledge and skills to design electronic circuits from specifications and block diagrams;
is able to discuss pros and cons and performance trade-offs among different circuital implementations.

Noise
Basics of noise: distribution, spectrum, power, and effective value.
Electronic noise: shot, thermal, flicker, and burst noise.
Noise Equivalent Generators, equivalent Bandwidth, Noise Figure and Temperature.
Case-studies of noise analysis in electronic circuits.
Amplification
Operational Amplifiers: parameters, feedback and virtual ground, main configurations.
Frequency-domain response and stability of circuits: analysis, diagrams, compensation methods.
Advanced components: instrumentation (INA), isolation (ISO), current-feedback (CFA), current-mode (CMA, Norton), transconductance (OTA), and Fully-Differential (FDA) amplifiers.
Component data-sheets and case-studies of circuits employing amplifiers.
Sampling
Basics of sampling: time-domain and frequency-domain analysis, aliasing, Shannon theorem.
Sample-and-Hold circuits: static and dynamic errors, compensation techniques, performance.
Advanced components: role of feedback, stability and precision issues, speed-accuracy trade-offs.
Analog multiplexers and digital potentiometers: parameters, timing, acquisition techniques.
Conversion
Digital-to-Analog converters (DAC) and Analog-to-Digital converters (ADC): basic architectures, conversion time, speed/accuracy trade-off, timings.
Dynamic performance: FFT spectra, SNR, SiNAD, ENOB, THD and IMD distortions.
Advanced ADCs: interpolation, folding, half-flash, multistep, time-interleaving architectures.
Over-sampling Sigma-Delta modulators: noise-shaping, SNR and bit improvements, components.
Components data-sheets and case-studies of circuits employing S&Hs, muxes, ADCs, and DACs.

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
Students are required to pass a WRITTEN test, followed by a final VIVA (oral) test. The written test aims at assessing student’s understanding of circuit analysis methodologies and design criteria provided through the course (Dublin Descriptors 1 and 2). The viva test aims at evaluating the student’s knowledge and understanding about the overall course topics (Dublin Descriptors 1 and 2). The date of the written test will be published on-line on the POLIMI webpage. The viva test will be held usually within 1-2 weeks after the written test, once the teacher will have evaluated the written tests and informed the students.
The WRITTEN test lasts 3 hours, during which students work autonomously (no chat, no smart-phones, no text books, no lesson slides) on 5 different circuits, to be either studied, sized, or designed; each circuit has two questions to answers with computations, graphs and text. Soon after the written test, the teacher will send the detailed solutions to students by email, for self-assessment. Those students wishing to withdraw their test may send an email to the teacher, asking not to evaluate it (e.g., if she/he realises that the test does not reach her/his target quality). Then, the teacher evaluates all written tests, fills in an excel file with a grade (from 0.0 to 1.0) per each question, and the final mark (from 0 to 30 out of 30) for the written test, and emails it to all students. Only students with a ranking higher than 17/30 can attend the viva test. The email also provides dates and locations among which the student can choose from to give the final viva test.
At the VIVA test, each student will be asked with questions covering all course topics, mainly focused on assessing the theoretical know-how acquired (Dublin Descriptor 1) and how to apply it to actual electronic circuits (Dublin Descriptor 2) for both analysis and design. The viva usually lasts 20-30 minutes per student. The viva ends with a final mark (from 18 to 30 cum laude out of 30) if passed, or a fail (the student has to redo the complete exam). Usually, the viva test results in an increment or decrement of the written test’s mark up to +/- 4 (out of 30). Therefore, students may consider to withdraw after the evaluation of the written test or during the viva test, if the grade is far away from their expectations (e.g., with a written test grade of 22/30, when the desired mark is higher than 28/30).

Franco Zappa, Electronic Systems, Editore: Esculapio, Bologna, Anno edizione: 2012, ISBN: 9788893850858 www.editrice-esculapio.com/zappa-electronic-systems/

Goal of the course is to provide a basic knowledge of the most significant use of sensors, circuits, electronics systems in the biomedical instrumentation, starting from recalling the basic principles of the instrument/technique and then analysing and designing the sensors and electronics circuits employed in the instrument. The tipologies of instruments considered in the course are: conventional and advanced instrumentation for the analysis of signals of biological origin, systems for medical imaging based on the use of radiation (X and gamma rays). Special focus of the course will regard application of microelectronic circuits, as prosthetic devices (cochlear implant, artificial retina) and integrated readout circuits for signal processing in medical imaging. The course includes also two laboratories, one concerning the implementation of neural networks for processing detector signals in medical imaging, the second concerning the experimental measurements of gamma-ray coincidences (PET principle).

Knowledge and understanding
After passing the exam, the student:
knows the fundamental principles, concepts and basic implementations of sensors, circuits and electronic systems used in biomedical instrumentation
knows how to define specifications of sensors, circuits and electronic systems to be used in biomedical instrumentation
knows the main guidelines to design electronics circuits to be used in biomedical instrumentation, including attention to electronics noise performances, bandwidth and power requirements
knows the main potential and limitations of using microelectronics design to realize electronics circuits for biomedical applications
Applying knowledge and understanding
After passing the exam, the student:
is able to define the specifications of sensors, circuits and electronic systems to be used in biomedical instrumentation
is able to select technologies and methodologies to obtain solutions to problems regarding sensors, circuits and electronic systems to be used in biomedical instrumentation
is able to design electronics circuits or systems for processing signals of biological origin or for medical imaging
is able to evaluate measured performances of an electronic circuit or system used in biomedical instrumentation

I part – Electronics for processing biological signals
1) Origin of biological signals. Principle of electrocardiography (ECG) and requirements for the electronics. Electrodes.
2) Circuits for the amplification and filtering of biological potentials. Application to the electrocardiography: basic parameters and design issues (noise, insulation and protection techniques, common-mode and interferences rejection). The Instrumentation Amplifier: application to ECG.
3) The electronics of the pacemaker: basic functionalities, batteries, amplification circuits, pulses generators, telemetry. Generation of pulses larger than the battery voltage. Charge pumps. Amplifiers based on switched-capacitor topologies.
4) Design example of an ultra low-noise Instrumentation Amplifier for biosignals: architecture and noise analysis.
5) Microelectronics prostheses: cochlear implant and artificial retina.
II part – Electronics for medical imaging systems
1) Basics on medical imaging systems based on the use of X and gamma rays. Digital radiography, computed tomography (CT), SPECT and PET. Figures of merit: efficiency, resolution, signal/noise ratio. The PET time-of-flight (TOF).
2) Fundamentals of detectors for medical imaging. The Anger Camera. Scintillators coupled to photodetectors. Estimation methods for reconstructing the event in the detector (centroid, maximum likelihood, neural networks). The Silicon Photomultipliers (SiPMs) and their readout electronics. Gamma cameras for SPECT and PET. Potential and challenges of PET-TOF.
3) Integrated circuits for medical imaging. Electronics noise and its representation by means of the equivalent noise charge. The charge preamplifier and the optimization of the input transistor. The shaping amplifier. The peak stretcher. The baseline holder.
4) Timing techniques: leading edge, constant fraction discriminator, zero-crossing discriminator. Time-to-amplitude converter (TAC) and Time-to-digital converter (TDC).
5) Digital Radiography and Photon Counting Computed Tomography.
Laboratories
1) Design and optimization of neural networks for processing detector signals in medical imaging (3 hours)
2) Experimental measurements of gamma-ray coincidences (PET principle) (3 hours).

Prova orale obbligatoria
The examination is based on a oral exam only. The exam will consist in a discussion on two topics, one chosen about the first part of the course and one about the second part. After a general exposition of the topic requested, further questions may be more focused on specific details in order to better evaluate the depth of knowledge of the student about the topic under discussion. Schematics of the main circuits explained during the lessons are supposed to be described autonomously by the student.
A list of possible dates (about six per call) are provided in advance to the students to facilitate the choice of the day for the oral exam. Only one date can be chosen for each call (appello), but with possibility to modify the choice. The student can decline the proposed mark, if considered not satisfactory, without any penalty for the next calls. Tutorials to clarify course topics and individual student questions will be also organize during each call session.

J.G.Webster, Medical Instrumentation: Application and Design, Editore: John Wiley and Sons, Anno edizione: 2010
J.G.Webster, Design of Cardiac Pacemakers, Editore: IEEE
Smith and Webb, Introduction to medical imaging, Editore: Cambridge, Anno edizione: 2011
Krzysztof Iniewski, Medical Imaging: Principles, Detectors and Electronics, Editore: Wiley, Anno edizione: 2009
M.Nakhostin, Signal processing for radiation detectors, Editore: Wiley, Anno edizione: 2017
Angelo Rivetti, CMOS: Front-End Electronics for Radiation Sensors, Editore: CRC Press, Anno edizione: 2015

The course “Embedded and Edge Artificial Intelligence” is designed to provide students with the algorithmic and technological foundations and practical skills needed to design, optimize, and deploy artificial intelligence systems specifically tailored for embedded and edge computing platforms. It focuses on the unique challenges of bringing intelligence to the edge — where memory, energy, and processing power are highly constrained — and explores how AI models can be adapted to operate efficiently on microcontrollers and low-power devices. Students will explore the full stack, from hardware architectures and sensor-level data acquisition to signal processing, model design, quantization, and inference. Particular emphasis is placed on embedded and edge machine and deep learning models, approximate and adaptive computing, early-exit strategies, and on-device learning in non-stationary environments. By the end of the course, students will be able to engineer intelligent, real-time, and context-aware AI systems directly on embedded and edge devices.

DdD1 - Knowledge and understanding
Students will acquire:

• Knowledge and understanding of the hardware architectures for embedded and edge artificial intelligence
• Knowledge and understanding of the main programming models, frameworks and toolboxes for embedded and edge artificial intelligence
• Knowledge and understanding of the distributed and federated frameworks and toolboxes for pervasive applications based on embedded and edge artificial intelligence
  DdD2 - Applying knowledge and understanding
  Given specific problems and project cases, students will be capable to:
• Design and implement pervasive applications based on embedded and edge artificial intelligence
• Apply the most suitable machine and deep learning algorithms for a given family of hardware architectures
  DdD3 - Making judgements
  Given specific problems and project cases, students will be capable to:
• Evaluate the effectiveness and efficiency of hardware architectures for embedded and edge artificial intelligence
• Compare advantages and disadvantages of the different families of machine and deep learning algorithms in pervasive applications based on embedded and edge artificial intelligence
  DdD 5 - Learning skills
  Students will be capable to learn autonomously:
• New frameworks and toolboxes for embedded and edge artificial intelligence
• How to exploit acquired knowledge and skills to consider hardwer architectures for new complex problems

01 – Intro to the course
Introduction to the course, application context of Embedded and Edge AI, teaching structure, and related research areas.
02 – Embedded and Edge Hardware
Overview of embedded system architectures, including sensors, memory, interfaces, and practical memory footprint estimations.
03 – Algorithms for Embedded and Edge AI
End-to-end data processing pipeline: preprocessing, feature extraction, sensor fusion, and trade-offs in latency and accuracy.
04 – Machine Learning for Embedded and Edge AI
Fundamentals of supervised learning for embedded and edge AI, generalization errors, model evaluation methods, and basic neural network concepts.
05 – Deep Learning for Embedded and Edge AI
Computational complexity of deep networks, CNN structures, MAC calculation, and memory analysis for embedded and edge AI
06 – Architectures for EEAI
Lightweight architectures for embedded and edge AI (e.g., SqueezeNet, MobileNet) and design strategies to reduce parameters and operations.
07 – Approximate Computing for EEAI
Quantization and approximate computing techniques to lower memory usage and energy consumption in deep models.
08 – Early Exit Neural Networks
Conditional inference strategies to reduce latency through intermediate classifiers and tailored training approaches.
09 – On-device Learning
Personalized and continuous learning directly on the device, with focus on memory constraints, ethics, and adaptability.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
The exam consists of a written test and a project.
Written test — open questions and exercises on the algorithmic and methodological topics of the course. It assesses DdD1 (knowledge of hardware architectures, programming models, frameworks and toolboxes for embedded, edge and federated AI), DdD2 (application of the course methodologies — memory footprint and MAC estimation, quantization, lightweight architectures, early-exit strategies — and selection of the algorithms most suitable for a given hardware family) and DdD3 (critical comparison of alternative architectural and algorithmic solutions). Criteria: correctness and rigour of the answers, appropriateness of the solution with respect to the stated constraints, ability to argue the trade-offs among accuracy, latency, memory and energy.
Project (mandatory, group work — up to two students, no periodic revisions, no final presentation/discussion) — design and development of an "embedded and edge AI system" for selected case studies, delivered as code and a technical report. It assesses DdD2 (implementation of a pervasive embedded/edge AI application on a real target platform under memory, energy and latency constraints), DdD3 (justification of the design choices and critical evaluation of the results in the report) and DdD5 (autonomous adoption of frameworks, toolboxes and technical documentation not covered in class). Criteria: correctness and completeness of the implementation, soundness of the experimental evaluation, degree of autonomy in the use of new tools.

Il corso affronta le principali questioni teoriche relative all'innovazione socio-tecnica (quali i concetti di base dell'innovazione tecnologica, le dinamiche di diffusione delle innovazioni, i limiti del determinismo tecnologico, l'agency degli oggetti tecnologici, la flessibilità interpretativa della tecnologia, genere e tecnologia) e analizza esempi rilevanti di tecnologie d'avanguardia che hanno un impatto sulla società e sulla vita umana, come l'Internet delle cose, la fabbricazione digitale, la realtà aumentata e l'intelligenza artificiale. Anziché focalizzarsi sul loro funzionamento, le prestazioni e i problemi tecnici, il corso affronterà le questioni etiche che esse sollevano e gli impatti sociali che producono.

This course deals with the application of ethical theories to problems created, aggravated or transformed by technology. This course aims at showing how the decision to develop a technology (meant at large), the processes of its design, development, management, control, and production are inherently moral. It is intended to give students a chance to reflect on the ethical, social, and cultural impact of technological applications (in particular AI).
The course includes lectures by the instructor and invited lecturers; class participation will be expected, and students should apply what they learn through reading, lectures, and class discussions by looking at current events through an ethical lens.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will:
Acquire a broad perspective on the ethical and social impacts and implications of science and technology;
Be acquainted with normative ethics and normative argumentation;
Learn how to recognize and analyze ethical and social aspects and issues inherent in some technological applications;
Be able to understand how technical problems are inherently connected to a social dimension within a socio-technical perspective.
Applying knowledge and understanding
Students will:
Be able to use critical skills in clarifying and ethically analyzing case-studies involving technology;
Be able to apply ethical theories to problems created, aggravated or transformed by current technologies;
Be able to explore and assess possibilities for solving or diminishing existing and emerging ethical and social problems.
Making judgements
Students will be able:
To autonomously analyze the ethical issues of a technology and to develop a critical analysis on that;
To evaluate and select the appropriate knowledge in the effort of elaborating and justifying a philosophical argument on a topic autonomously selected.
Communication
Students will learn to:
Exercise and improve their skills in a critical writing;
Present in an effective way the results of their research, being able to justify their choices.
Lifelong learning skills
Students will:
Be better prepared to their future professional life in an ethically and socially responsible way;
Be able to analyze problems through an ethical lens.

The course will cover different topics both from a theoretical and a more practical point of view. We will start with a broad analysis of the concept of responsibility, in particular in an engineering perspective, and of normative ethics and its tools. We will discuss ethical questions in the design of technology with a focus on Design Ethics and its Social Ethics paradigm. Then ethics in IT-configured societies will be discussed and technology as the instrumentation of human action will be presented. Topics will include, for instance, the novel framework of Responsible Research and Innovation, Value-Sensitive design, the ethical aspects of technicolgical risks, the relationship among sustainability, ethics and technology, information flow, privacy, and values and uncertainty
Students will be supervised in class sessions in the development of the individual final essay in order to meet the standards required by scientific publications.

Progetto/i - Elaborato/i obbligatorio, individuale, senza revisioni periodiche, con presentazione/discussione finale
Type of assessment
Description
Dublin descriptor
Collective presentations
Students are encouraged to:
Develop an argument in which the presented claims are justified by the use of ethical argumentation and the relevant literature;
To present this argument in an oral presentation in class in front of the instructor and their student colleagues.
1, 3
4, 5
Individual written essay
The individual final essay must show that the student has the capacity to read and carefully study the suggested bibliography and the ability to apply it to case-studies;
The individual final essay will be about the ethical issues associated to technologies that require to have developed critical skills in analyzing technical problems with the tools provided by ethical theories and argumentation.
1, 2
3, 4, 5

Ibo van de Poel, Lambèr Royakkers, Ethics, Technology, and Engineering: An Introduction (second edition), Editore: Wiley-Blackwell, Anno edizione: 2023, ISBN: 978-1-119-87943-5
Mattia Andreoletti, Daniele Chiffi, Philosophy of Risk, Editore: Cambridge University Press, Anno edizione: 2027
Note: If not yet published, an advanced draft of the book will be made available to students.
Behnam Taebi, Ethics and Engineering. An Introduction, Editore: Cambridge University Press, Anno edizione: 2021, ISBN: 9781316628409

L’insegnamento mira ad integrare ed approfondire la conoscenza di tematiche proprie della fisica nucleare mediante un’ottica ed un approccio prevalentemente sperimentali. A tal fine è previsto un ampio spazio dedicato alle attività di laboratorio, gestite in presenza con una opportuna remotizzazione digitale di alcune esperienze e/o strumentazione nucleare per una miglior fruizione da parte dello studente. All’introduzione e discussione delle principali quantità fisiche d’interesse fa seguito la classificazione e rappresentazione dei radionuclidi, la caratterizzazione sperimentale dei processi dinamici di decadimento radioattivo, l’analisi dettagliata delle evidenze sperimentali e del confronto con la previsione teorica. L’insegnamento prevede inoltre l’introduzione di concetti fondamentali nell’ambito della fisica delle radiazioni ionizzanti, in particolare della dosimetria delle radiazioni, mediante la definizione delle quantità fisiche più significative e lo studio del loro impiego nella caratterizzazione e valutazione della sicurezza di campi di radiazione.

Lo studente:

• riconosce, distingue e sa analizzare i principali aspetti e problematiche associati alla fisica nucleare sperimentale e gli strumenti fisico-matematici ed operativi necessari per la caratterizzazione delle proprietà fisiche dei nuclei
• conosce i concetti e le quantità fisiche di base della fisica e della dosimetria delle radiazioni, alcuni elementi utili alla descrizione degli effetti biologici delle radiazioni, ed aspetti fondamentali di radioprotezione
• è in grado di applicare i metodi della fisica nucleare sperimentale, elaborare i dati prodotti da un esperimento ed analizzare le informazioni ottenute al fine di ottenere una descrizione il più possibile dettagliato dei principali processi fisici di decadimento
• è in grado di selezionare e condurre un’attività sperimentale di fisica nucleare in sostanziale autonomia, rappresentare i dati prodotti in modo appropriato e discutere criticamente i risultati ottenuti, utilizzando un linguaggio e una terminologia corretta ed appropriata dal punto di vista scientifico.

L’insegnamento, mediante lezioni teoriche e laboratori pratici di caratterizzazione ed analisi, tratta i seguenti argomenti:
Concetto di sezione d’urto. Introduzione al concetto di sezione d’urto totale e differenziale, impiego di tale quantità nella fisica del nucleo e delle radiazioni. Descrizione delle principali tipologie di sezione d’urto.
Modello atomico nucleare e scattering coulombiano. Descrizione dell’esperienza di Geiger e Marsden. Analisi quantitativa dello scattering coulombiano mediante il modello non relativistico di Rutherford. Impiego dello scattering di Rutherford negli esperimenti di fisica nucleare. Scattering di Rutherford nella fisica delle radiazioni.
Il decadimento radioattivo. Caratteristiche del processo stocastico di decadimento radioattivo. Legge fondamentale del decadimento. Definizione delle quantità principali definite per descrivere il decadimento. Branching di decadimento. Radioattività per attivazione e catene di decadimenti radioattivi. Radioattività naturale. Tecniche di analisi basate sul processo di decadimento radioattivo.
Decadimenti alfa, beta e gamma. Richiami alla teoria e analisi della sistematica degli radionuclidi emettitori. Principi di conservazione applicati ai processi di decadimento. Analisi delle catene di decadimento. Processi di decadimento alternativi. Analisi approfondita di schemi di decadimento radioattivo.
Tavola dei nuclidi. Descrizione della tavola di Segre e sue applicazioni. Rappresentazione dei radionuclidi di vario genere. Analisi ed impiego di tavole dei nuclidi complesse.
Campi di radiazioni. Introduzione alla spettrometria, spettri differenziali e spettri integrali. Applicazione dello spettrometria all’analisi nucleare. Principali grandezze radiometriche per la caratterizzazione di campi di radiazioni.
Elementi di dosimetria. Effetti dell’interazione radiazione-tessuto. Introduzione al concetto di dose assorbita, definizione e limiti. Diversa radiosensibilità dei tessuti. Concetto e definizione di dose efficace. Limiti di dose in campo lavorativo.

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
L'esame consiste in una presentazione individuale dell'attività sperimentale svolta in gruppo durante l'ultimo laboratorio, nel corso della quale vengono rivolte anche domande di carattere teorico. La presentazione individuale è finalizzata ad accertare il contributo personale all'attività di laboratorio e il livello di raggiungimento degli obiettivi formativi, indipendentemente dal fatto che l'esperienza sperimentale sia stata condotta in gruppo.
La presentazione dell'attività sperimentale è finalizzata a valutare la capacità di:
selezionare e condurre, nell'ambito dell'attività di gruppo e in sostanziale autonomia, un esperimento di fisica nucleare finalizzato alla caratterizzazione di un radionuclide emettitore alfa, beta e/o gamma;
elaborare e analizzare dati sperimentali derivanti da un esperimento di fisica nucleare mediante strumenti di analisi e di calcolo appropriati;
discutere criticamente e con rigore i risultati sperimentali ottenuti, valutandone l'affidabilità, le incertezze sperimentali e le principali fonti di errore, applicando in modo approfondito i concetti di fisica nucleare appresi;
motivare le scelte sperimentali e metodologiche adottate, dimostrando autonomia di giudizio nell'interpretazione dei risultati e nella valutazione dei limiti dell'esperimento;
presentare in modo chiaro ed efficace l'intera attività sperimentale mediante una relazione tecnica, utilizzando un linguaggio scientifico appropriato;
utilizzare in autonomia le conoscenze e le competenze acquisite per affrontare problemi sperimentali analoghi, individuando le procedure di misura e gli strumenti di analisi più idonei.
L'integrazione con domande di carattere teorico è finalizzata a valutare la capacità di:
cogliere e interpretare il significato fisico delle principali grandezze utilizzate nell'ambito della fisica e della dosimetria delle radiazioni;
applicare correttamente le grandezze proprie della radioprotezione, conoscendone il significato fisico, gli ambiti di applicazione e i limiti operativi associati;
collegare le conoscenze teoriche ai risultati sperimentali e applicarle in contesti analoghi, dimostrando capacità di approfondimento e di apprendimento autonomo.

Dispense e materiale forniti dal docente tramite il sito Beep dell'insegnamento
K.S. Krane, Introductory Nuclear Physics, Editore: Wiley, ISBN: 978-0471805533

Lo scopo dell'insegnamento è di fornire una valida conoscenza sui fondamenti della misurazione (inquadramento storico e istituzionale, Sistema Internazionale di unità di misura, regole operative) e sugli strumenti specifici di analisi utilizzati nelle misure (calcolo dell’incertezza di misura e corrispondente analisi statistica, regressione dei dati sperimentali, elaborazione del segnale di misura).
Si illustreranno principi e modalità di funzionamento dei sistemi di acquisizione dati (DAQ) e della strumentazione digitale di misura. Nel corso delle attività di Laboratorio si insegnerà ad utilizzare le schede DAQ e il software LabVIEW per la realizzazione di strumenti virtuali utili per le misure automatizzate e la rappresentazione dei risultati.
Si offrirà agli studenti una descrizione in dettaglio dei principi di funzionamento e delle prestazioni di strumenti di misura quali voltmetri numerici, oscilloscopi digitali, e analizzatori di spettro.

A seguito del superamento dell’esame, lo studente:
-conosce i termini e i principi di base della metrologia (descrittore di Dublino 1, DdD1);
-conosce l'attuale organizzazione del sistema internazionale di unità di misura (DdD1);
-è in grado di stimare l'incertezza di misura e la sua propagazione, attraverso tecniche statistiche (DdD2);
-conosce i principi di funzionamento della principale strumentazione elettronica di misura (DdD1);
-è in grado di gestire problemi non strettamente inerenti alle problematiche affrontate nel Corso di laurea assumendo decisioni motivate (DdD2 e DdD3);

• acquisce le competenze necessarie ad effettuare misure con la moderna strumentazione ed interpretarne correttamente i risultati (DdD2 e DdD3). Tali competenze saranno consolidate ulteriormente dalle attività di laboratorio sperimentale proposte nel corso.
  -migliora la sua capacità di apprendimento (DdD5).

La misura e le principali caratteristiche metrologiche. Accuratezza, incertezza, risoluzione, sensibilità, ripetibilità, riferibilità, etc. Il sistema internazionale di unità (SI), regole e simboli del SI, vari tipi di campioni. Definizioni e operazioni su unità logaritmiche (dB, dBm, dBc, Np). Blocchi essenziali di un sistema di misura. Caratteristiche dei segnali (continui e discreti in ampiezza e tempo, periodici, aperiodici) e parametri di interesse (valor medio, efficace, di picco).
Elementi di statistica e incertezza di misura. Significato di valor medio, varianza, varianza del valor medio e numero di gradi di libertà. La miglior stima del risultato di misura e della sua incertezza, attraverso misure ripetute (A) o altri metodi (B). Stima dell’incertezza in misure dirette. Propagazione dell'errore e calcolo dell'incertezza in misure indirette. Indipendenza e correlazione statistiche. Compatibilità fra misure e medie pesate.
La rappresentazione dei risultati sperimentali. Diagrammi cartesiani e polari. Scale lineari e logaritmiche. Interpolazione e regressione dei dati sperimentali.
Strumentazione digitale. Richiami sul campionamento e conversione analogico/digitale (A/D). Voltmetri digitali e multimetri: errore di quantizzazione, risoluzione, numero di bit e numero di cifre in un convertitore A/D. Bit equivalenti. Esempi di voltmetri differenziali (approssimazioni successive e flash). Principio di integrazione e voltmetro integratore a doppia rampa.
Sistemi di acquisizione dati. Schede di acquisizione. Linguaggio di programmazione LabVIEW e strumenti virtuali (VI). Protocolli di comunicazione tra strumenti (RS232, IEEE488, USB).
Strumentazione generale di laboratorio. Oscilloscopio analogico: funzionamento, comandi, blocchi circuitali essenziali, sonde, prestazioni. Oscilloscopio digitale: campionamento in tempo reale e in tempo equivalente; modalità di trigger avanzato; misure automatiche. Esempi di misure con l'oscilloscopio (ampiezza, fase, frequenza, tempo di salita, etc.). Analizzatore di spettro (AS): sistemi analogici per l'analisi simultanea e sequenziale, strumenti digitali con analisi numerica (FFT). Risoluzione in frequenza e sensibilità di un analizzatore di spettro. L'AS ad eterodina: reiezione della frequenza immagine, vincoli tra risoluzione e velocità di scansione dell'asse delle frequenze. Esempi di misura con AS sui segnali modulati e misure di rapporto segnale-rumore.
Attività di laboratorio
L'attività di laboratorio, essenzialmente di tipo sperimentale, ha lo scopo di istruire lo studente nell’uso della moderna strumentazione digitale di misura. In particolare, saranno insegnati i fondamenti di programmazione del linguaggio LabVIEW e quanto appreso sarà direttamente verificato mediante la preparazione di esperimenti di misura basati su schede di acquisizione dati. Si sperimenteranno in concreto le problematiche connesse con il condizionamento e l’elaborazione del segnale di misura.
Ogni studente svolgerà 8 ore di laboratorio suddivise in 2 sessioni di 4 ore. Ai sistemi DAQ e al software LabVIEW saranno dedicate le prime 4 ore di laboratorio e all’impiego degli altri strumenti di misura le rimanenti 4 ore di laboratorio. I dettagli organizzativi relativi alle attività di laboratorio e alle corrispondenti valutazioni saranno presentati a esercitazione comunque resi consultabili alla pagina WEBEEP del Corso.
Prerequisiti
Costituiscono utile propedeuticità agli argomenti del corso le materie di Elettrotecnica, Fondamenti di Elettronica, e Informatica 1.

Prova scritta obbligatoria, con prove in itinere
Prova orale facoltativa (a scelta dello studente)
La valutazione dello studente è effettuata in base all'esito della prova d'esame e dei laboratori. La prova d'esame consiste in una verifica scritta (DdD1 e DdD2) con eventuale discussione orale (DdD1 e DdD4) a discrezione del docente o su richiesta dello studente.
La prova scritta consiste in una serie di domande di teoria a risposta multipla (DdD1), a cui seguono degli esercizi da svolgere (DdD2). Nelle domande di teoria è inclusa qualche domanda sulle attività di laboratorio.
E' prevista nel corso una prova in itinere sugli argomenti trattati nella prima parte del corso. Il risultato di tale prova, se positivo, consentirà di non svolgere una parte del compito scritto gli appelli successivi, mantenendo per tale parte il risultato conseguito nella prova in itinere. Per tutti gli appelli dell'anno sarà possibile mantenere il risultato della prova in itinere.

M. Norgia, R. Ottoboni, A. Pesatori C. Svelto, Misure: dai fondamenti alla strumentazione, Editore: Società Editrice Esculapio, Anno edizione: 2022, ISBN: 978-88-9385-304-0
M. Norgia, A. Pesatori, C. Svelto, Esercizi di Misure, Editore: Società Editrice Esculapio, Anno edizione: 2006

The goal of the course is to introduce the students to basic problems, models, and techniques of Artificial Intelligence (AI), and to enable them to model and solve specific AI problems. The course covers the most fundamental concepts, modelling approaches, and resolution methods of classical AI, and provides an introduction to the history of the discipline and to some philosophical issues involved. The course also briefly overviews some AI topics that are discussed in depth in other courses. The teaching method is traditional (classroom lessons).

Dublin descriptors
Expected learning outcomes
Knowledge and understanding
Students will become acquainted to:
· the types of problems that are tackled in AI
· the types of applications that can profit from the use of AI-based solutions
· basic AI modelling and problem-solving techniques
· crucial moments in the history of AI
· critical issues involved in the effort of mechanising intelligent processes
Applying knowledge and understanding
Students will learn to:
· analyse and model problems according to different AI approaches
· design specific problem-solving techniques as instances of general classes of techniques
· evaluate the complexity of different modelling and problem solving techniques in connection with particular problems
Lifelong learning skills
Students will become able to appreciate the relevance and possibility of developing and applying AI methods in different application fields of industrial relevance
Students will be able to understand and critically evaluate AI systems deployed in different application scenarios

Introduction to AI
overview of the problems tackled in AI
main research areas and application fields
State space and related problem solving methods
state spaces and search methods
non-informed and informed search methods
adversarial search: minimax, alfa-beta pruning, and Monte Carlo tree search
constraint satisfaction problems
Logic and reasoning
recalls of propositional logic
recalls of resolution theorem proving
model-checking methods for propositional logic, SAT solvers
Planning
plan formation and execution
the STRIPS/PDDL model
planning as a search problem
satisfiability-based planning (SATPlan)
Uncertainty in knowledge representation and reasoning
Overview of AI topics
AI vs. Machine Learning (ML)
supervised, unsupervised, and reinforcement learning
Deep Learning (DL)
History and foundations
historical outline of the discipline
critical concepts of AI and their philosophical implications

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The assessment is based on a written, closed-book test at the end of the course, taken in person, in class. It typically consists of questions on the course topics. Any test can include conceptual questions and exercises requiring a significant modeling effort. The test awards a maximum of 32 points (30 cum laude when the total score is 32). Students have approximately 1.5 hours to answer the test. Students can take the test at any exam session during the year. The written test can be taken only in person. The discussion of the graded exams is also in person only. Instructors can summon students who passed the written test for an additional (in-person) oral exam that will cover the entire course program.
Type of assessment
Description
Dublin descriptor
Written test
Solution of numerical problems
· solution of problems involving the application of known methods and techniques
Exercises focusing on design aspects
· solution of problems involving original modeling
Theoretical questions on all course topics with open answers:
· questions concerning fundamental conceptual aspects of AI models and methods
1,2
1, 2, 5
1, 5

S. Russell, P. Norvig, Artificial Intelligence: A Modern Approach (4th edition), Editore: Prentice Hall, Anno edizione: 2021, ISBN: 978-0134610993 https://aima.cs.berkeley.edu
Note: Available at https://resolver.vitalsource.com/9781292401171 (from the Politecnico's network).
Stuart Russell and Peter Norvig, Intelligenza artificiale: Un approccio moderno, volume 1 (4a edizione), Editore: Pearson, Anno edizione: 2021, ISBN: 9788891904454 https://aima.cs.berkeley.edu
Note: Italian translation of the first part of the textbook (which covers almost all content of the course).

Operations Research (O.R.) is the branch of applied mathematics dealing with quantitative methods to analyze and solve complex real-world decision-making problems. The course covers some of the fundamental concepts and methods of O.R. pertaining to graph optimization, linear programming and integer linear programming. The emphasis is on optimization models and efficient algorithms with a wide range of important applications in engineering and management.

Knowledge and understanding (DdD 1)
The students will learn:

• the mathematical language of optimization models and some important classes of models
• the main properties and algorithms (including the worst case complexity) of/for the following graph optimization problems: minimum cost spanning tree, shortest path and maximum flow
• the critical path method for project scheduling
• the geometrical and algebraic aspects of Linear Programming (LP), and an LP algorithm
• the main properties of LP duality, including weak/strong duality and the complementary slackness optimality conditions
• the economical interpretation of the dual of some LP problems
• the main characteristics of Integer Linear Programming (ILP) problems
• the cutting plane method with fractional Gomory cuts
• the Branch-and-Bound method for ILP and other combinatorial optimization problems.
  Applying knowledge and understanding (DdD 2)
  The students will be able to:
• build optimization models starting from the decision problem descriptions, solve them using a modelling language based on Python and a state-of-the-art solver, and interpret the output
• identify an appropriate graph optimization algorithm to tackle a given decision problem and apply it to small examples
• apply the LP method to textbook examples
• construct the dual of LP problems in explicit and parametric form
• apply the studied ILP methods (Branch-and-Bound method, cutting plane method with fractional Gomory cuts) to small examples
• leverage on the acquired theoretical and algorithmic knowledge to answer simple questions.
  Learning skills (DdD 3, DdD 5)
  The students will learn how to tackle complex decision-making problems with a solid mathematical and algorithmic approach, which can be adapted and extended to deal with a variety of real-world problems.

1. Introduction
Decision-making problems. Main steps of an O.R. study. Different types of optimization (mathematical programming) problems. Optimization models (decision variables, objective function, constraints) and modeling techniques.
2. Graph and network optimization
Optimum spanning trees. Shortest path problems: algorithms for the main variants (including Dynamic programming for acyclic graphs). Application to project scheduling: critial path method and Gantt diagrams. Maximum flows: Ford-Fulkerson algorithm and maximum flow-minimum cut theorem. Minimum cost flows, matching in bipartite graphs. An example of NP-hard network optimization problems: the Traveling salesman problem.
3. Linear Programming (LP)
LP models. Geometric aspects (vertices of the feasible region) and algebraic aspects (basic feasible solutions). Fundamental properties of linear programming. The Simplex method. LP duality theory: pair of dual problems, weak and strong duality, complementary slackness conditions. Economic interpretation. Sensitivity analysis. Special cases: assignment and transportation problems.
4. Integer Linear Programming (ILP)
ILP models for, among others, transportation, routing, scheduling and location problems. Formulations and linear programming relaxation. Exact methods: Branch-and-Bound algorithm, cutting plane method with fractional Gomory cuts.
Time permitting we will review some basics of computational complexity (recognition problems, classes P and NP, polynomial-time reductions, NP-complete and NP-hard problems) and introduce basic heuristic algorithms (greedy and local search).
A couple of topics will be assigned for independent study (e.g. LP sensitivity analysis) prior to sessions devoted to exercises or computer laboratory activities. There will be computer lab meetings and computer lab assignments.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Valutazione continua facoltativa (a scelta dello studente)
A written exam which covers all the material presented in the lectures, the sessions devoted to the exercises, and the computer laboratory sessions and assignments. The first part of the written exam will be closed book and the last part will be open book. A few points will be assigned based on some formative assesments during the semester, further details will be provided at the beginning of the course.
The exam consists of several questions aimed at evaluating the level of (applying) knowledge and understanding of the main concepts, models, results and methods covered in the course as specified in the expected learning outcomes. In particular, it includes modeling questions, the solution of numerical problems using appropriate algorithms, and theoretical questions related to the main concepts, methods and results. (DdD 1, DdD 2, DdD3)

All the course information and material will be available on WeBeep.
F. Hillier, G.J. Lieberman, Introduction to Operations Research, Editore: McGraw-Hill, Anno edizione: 2021
Note: 11th Edition
R.K. Ahuja, T.L. Magnanti, J.B. Orlin, Network flows. Theory, algorithms and applications, Editore: Prentice Hall, Anno edizione: 1993
D. Bertsimas, J. Tsitsiklis, Introduction to Linear Optimization, Editore: Athena Scientific, Anno edizione: 1997
M. Fischetti, Introduction to Mathematical Optimization, Anno edizione: 2019

The main objective of the course is to introduce the fundamentals of electromagnetic fields. We start from the fields and the sources of the electric and magnetic fields in the time-invariant case, up to the electromagnetic fields in the time varying domain. This course is intended to lay solid foundations for the follow-up courses investigating more in detail the role of electromagnetic waves in ICT applications (e.g. guided and free-space transmission of information). Some elements of further courses are already preliminarily introduced in this course (e.g. transmission lines and distributed circuits and plane waves).

After passing the exam, the student:
knows the basic principles and laws regulating electromagnetic fields, both in the static domain and in the time varying domain, and their interaction with the environment (e.g. metals, dielectric media, magnetic media, …). Descriptor DD1;
knows the main features and working principles of systems and applications based on electromagnetic fields, DD1;
can solve simple problems involving electromagnetic fields, DD2;
possesses the knowledge necessary to define the main specifications of a simple transmission line based system, DD2.

Theory
Basics of scalar and vector fields and vector differential operators. Review of electrostatics and magnetostatics in free space and in materials.
Maxwell’s Equations. Definition of time-varying regime. Integral and differential forms of Faraday's law, Ampere-Maxwell’s law and Gauss laws. Poynting’s theorem. Maxwell’s equation in sinusoidal steady state regime.
Plane waves. Definition and main properties of plane waves. Plane wave solution of Maxwell’s equations in lossless and lossy media. Polarization of plane waves. Propagation in dielectrics and good conductors. Orthogonal incidence of plane wave on double-layer structure: reflection and transmission.
Transmission lines. Transmission line equations and their solution in terms of voltage and current waves and associated fields. Characteristics and input impedances. Incident and reflected waves. Reflection and transmission coefficients and standing wave ratio. Power balance. Lossy transmission lines.
Practice
Examples of calculation of elettrostatic and magnetostatic fields with elementary sources.
Examples of calculation of the electromagnetic field in the time varying domain.
Simple problems involving the propagation of plane waves in free space and in different materials.
Calculation of the main parameters characterizing tranmission lines.
Calulation of the impedance and of power transfer along a transmission line (also lossy ones).

Prova scritta obbligatoria, senza prove in itinere
The exam includes a written test of 2 hours (DD1, DD2). The written test consists in the solution of 4 problems respectively on: electrostatic, magnetostatic, propagation of plane waves, transmission lines. This test aims at assessing the ability of the student to solve simple problems using the tools and concepts presented during the lectures. The test is open book: the student can use any material (e.g. personal notes, books) during the written test, but electronic devices that can connect to the web are not allowed. The student is asked to numerically solve the excercises with some explanation on the method used and the work hypothetis (DD2).

The course Fundamentals of Photonics: the science of light is tailored to provide students with the basic concepts required to understand Smart Eyewear technologies.
It begins with an introduction to radiometry and photometry, also covering the fundamentals of optics. It explores natural and artificial light sources, detectors (e.g. photographic cameras) and displays. The human visual system and the basis of colorimetry are also addressed. Finally, it examines smart eyewear, focusing on augmented reality concepts and devices.
The course includes lectures in the class, seminars and interactive activities.

• Knowledge and understanding – DD1
  Students will learn and understand the basic concept of Photonics, including the nature of light and the properties of radiation fields. Additionally, the main components present in augmented reality eyewear will be discussed.
• Applying knowledge and understanding – DD2
  Students will be able to understand the opportunities and the critical issues that make the design of augmented reality glasses the new technological challenge that large IT/eyewear companies will face in the coming years.
• Making judgements – DD3
  Students will acquire the ability to understand the role of the different components that transform ordinary glasses into smart glasses.
• Communication Skills - DD4
  During the evaluation phase, students will be asked to present additional material to the class on a specific topic covered in the course.
• Learning skills – DD5
  Students will develop the skills to design smart eyewear concepts.

Introduction
In this intruductory module the basic concepts of smart eyewear are presented
Topics:
Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) eyewear
The concepts of eyebox, angular field of view, contrast and spatial resolution
Elements of Radiometry (8+2 h)
Radiometry: the sciences of measuring light. In this module, the basic concepts of optics are introduced. Then, the quantities of Radiometry are presented, highlighting their properties through examples and applications.
Topics:
What is light: its wave and corpuscular nature
The properties of light in the classical framework: wavelength, frequency and polarization
Basic concepts of geometrical optics: the lens and its imaging capability
Stops, pupils and windows in an optical system
Definition of the radiometric quantities and their properties
Light sources (6+2 h)
In this module different light emission mechansms are presented, considering natural and artificial light sources and their spectral features. Monocromaticy and coherence are briefly discussed.
Topics:
Primary and secondary light sources
Lambertian sources and their properties
The sun and other thermal sources
The sources of monocromatic light: the Laser and the LED
Radiation detectors (2+2 h)
In this module the basic principles of light-matter interaction are introduced. The most important classes of detectors are presented, considering both point-like and imagind detectors.
Topics:
Figures of merit of radiation detectors: the responsivity and the sensitivity
General properties of quantum detectors
Junction detectors: the photodiode
CMOS imaging detectors: the operating mechanism and their features
The human visual system (4 h)
In this module a simple model of the human eye is introduced, considering the mechanism of vision and the property of the eye as a primary detector of light.
Topics:
Cones and rods: the photopic and scotopic vision
The logaritmic response of the human eye: the Weber’s law
The different regions of the retina and the visual acuity
The depth clues in the human visual system
Introduction to Photometry and its link with Radiometry
Introduction to colorimetry (4+2 h)
This module explains the principles of colourimetry, linking the human eye’s sensitivity to light in the visible spectrum with the main colour spaces.
Topics:
The CIE color matching functions
The colorimetric spaces RGB and XYZ
The L*a*b* color space
The Correlated Color Temperature (CCT) of a light source
Display systems (2+2 h)
In this module the main display types are presented showing pros and cons
Topics:
Liquid crystals and ligth modulators
Liquid Crystals Displays (LCD)
LEDs, OLEDs and self luminescent panels
Laser scanners
Smart eyewear (2+2 h)
In this module the main features of Augmented Realy glasses are considered.
Topics:
Waveguide based AR Eyewear
Holographic AR Eyewear
Holographic optical elements
Teaching consists of lectures, interactive activities and simple laboratory experiments.

Altro
Students will choose, in agreement with the lecturer, a specific topic covered in the course on which to carry out further research ( Applying knowledge and understanding – DD2 ). The material used for this research may be drawn from academic articles or other sources of proven quality. Each student will give a presentation on their chosen topic in front of the class ( Communication Skills - DD4). At the end of the presentation, the lecturer may ask the student questions, which may refer to topics other than the one chosen ( Knowledge and understanding – DD1). For this questions the lectturer will take into account the students’ background.

Gianluca Valentini, Photonics: Measuring Light
Note: The book can be dowloaded from the WeBeeP website
Gianluca Valentini, Exercises on Photonics
Note: The book can be dowloaded from the WeBeeP website
Frank L. Pedrotti, Leno M. Pedrotti, Leno S. Pedrotti, Introduction to Optics, Editore: Cambridge University Press

The course focuses on developing students' knowledge of geographic data analysis concepts, methods, and tools. The course aims to provide an exhaustive frame of geographic information: from acquisition to validation, processing and web publishing. Authoritative data, as well as geo-crowdsourced data, are both parts of the course. The Geospatial Web and Spatial Data Infrastructures are also taught. An introduction to Digital Twin Earth and Geospatial Artificial Intelligence, including Geospatial Foundation Models, will be provided. Students are asked to learn the most used desktop and web, proprietary and free open-source software packages (QGIS, ArcGIS, PostresSQL, PostGIS, GeoServer, OpenLayers, OSGEOLive).

Students will learn how to:
Design and develop a Geographic Information System, taking into account the different components
Explore geospatial data and create corresponding thematic maps by using GIS software
Manage geospatial data with advanced GIS functionalities
Design and develop a WebGIS
Given specific project cases, students will be able to:
Detail the corresponding requirements
Manage the geospatial data in proprietary and open source GIS applications
Develop the related webGIS for showing the results of their work
Given a relatively complex problem, students will be able to:
Analyze and understand the goals, assumptions, and requirements associated with that problem and model them
Define the data and architecture for fulfilling the problem
Develop partially or totally the GIS solution
Students will learn to:
Write a document with the specifications of their project
Present their work
Publish their result through a webGIS
Students will learn how to develop a realistic project in the GIS domain.

Introduction to GIS;
Reference and coordinate systems; their transformations.
Map projections: distortions; conformal and equal area maps.
Geographic data models and structures.
Fields: exploring data, spatial statistics, geostatistics
Introduction to Geospatial Artificial Intelligence
GIS and the Internet
WebGIS
Meta information, interoperability
Web Geo Services
The Geospatial Web
Spatial Data Infrastructures and the Digital Twin Earth
Participatory web GIS: citizen science, VGI, geo-crowdsourcing
Applications of GIS in various domains, especially connected with SDGs (webinars also involving external experts, like Heads of GIS Centers of different UN Agencies)
Exercise sessions and a project laboratory are integral parts of the course.
A project will be assigned during the Laboratory hours at the end of the practice. It will be developed by students, under the supervision of academic tutors in small teams of 2-3 members. Project artifacts (requirement analysis, design document, test report, code) are expected to be released at the exam. The evaluation of projects will be based on the produced artifacts and a presentation.

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
Written examination covering the theoretical part of the course. Oral/practical examination covering Desktop and WebGIS parts of the course, along with a discussion of the laboratory project.
During the exam, students will be evaluated on their understanding of theory, practical skills, and the laboratory project. Scores from these three components will be weighted and averaged to calculate the final score. Theory will account for 1/2, practical skills for 1/3 (Desktop and WebGIS), and the laboratory project for 1/6 of total score. Each part of the exam can be taken separately during different exam sessions. A grade of "30 cum laude" will be awarded if the total score is 31 or higher.

Slides of the course https://webeep.polimi.it/
Note: Geographic Information Systems (GIS)
Pandey, Jatin, Energy and Resources Institute Content Provider, and Darshana Pathak, Geographic Information System, Editore: New Delhi, India: The Energy and Resources Institute, Anno edizione: 2014

La protezione dei sistemi digitali richiede la comprensione delle minacce che li possono affliggere. Tale comprensione non può derivare esclusivamente dall'analisi dei componenti IT del sistema, ma deve anche prendere in considerazione altre minacce che possono sorgere a causa degli elementi umani e fisici del sistema in esame. L'obiettivo del corso è presentare la mentalità necessaria per identificare le minacce umane e fisiche e i loro percorsi di attacco, fornendo allo studente le conoscenze su come mitigarle.
Per raggiungere questo obiettivo, il corso inizia introducendo il concetto di modello di minaccia. Successivamente, ci concentreremo sulla valutazione di una vasta gamma di casi d'uso che evidenziano l'impatto degli elementi umani e fisici sulla sicurezza: una sezione del corso esplorerà l'aspetto umano dell'ingegneria sociale, il phishing, l'impersonificazione e i fondamenti della sicurezza fisica. Un'altra sezione del corso si focalizza sui Cyber-Physical Systems (CPS), comprendendo la differenza tra CPS e sistemi IT standard, esplorando il campo della sicurezza dei sensori e presentando attacchi noti.

Hence the relationship between environmental factors and human health has been known for years, there is now more attention from both the general public and different stakeholders (industry, healthcare professionals, policy makers) in addressing this matter. This fact generates the need to provide multidisciplinary competences, related to both environmental and biomedical engineering, to collect, organize, analyze, and visualize the multisource data available to generate quantitative data and actionable information that could serve as basis for decision makers.
This Lab module aims course is intended to provide the student with competences about how the environment may affect human well-being and health, and to provide the skills needed to access and process available geo-localized multi-source environmental and health data to support public health management decision-making.
Students will also learn the principles and concepts of air quality and dispersion and will be able to implement and perform air dispersion modelling, presenting appropriately the air pollution results and understanding their implications in terms of exposure and potential health effects.
The course is structured in two modules:
Through Module 1, students will acquire the basic knowledge to retrieve and manage open access environmental data, collected both through measuring stations and satellite imagery, allowing to generate scenario analysis to assess the exposure to environmental pollution. Students will also learn how to retrieve geo-localized disease-related data and how to develop descriptive and predictive epidemiological data-driven models to ultimately support decision-making.
Through Module 2, students will learn the principles and concepts of air quality and atmospheric dispersion modelling, presenting appropriately the air pollution results and understanding their implications in terms of exposure and potential health effects.

Module I
Knowledge and understanding
Students will learn how to:
Understand basic principles of data geo-localization, and the usage of Geographic Information Systems
Interpret the theories and principles of the impact of environmental factors on human health
Applying knowledge and understanding
Given specific project cases, students will be able to:
Detail the corresponding requirements
Analyze and comment on specific design choices
Project spatially explicit models for analysis of georeferenced data
Making judgements
Given a relatively complex problem, students will be able to:
Evaluate correctness of proposed solutions
Understand which factors are important and which limitations the proposed solution could have bases on temporal and spatial resolution of the input data
What are the usable tools for care professionals, the role of patient and of the engineer
Communication
Students will learn to:
Build maps for effective visualization of georeferenced information with Geographic Information Systems (GIS)
Present their work in front of their colleagues during project lab
Write a report to describe the design and implementation choice
Lifelong learning skills
Students will learn how to be able to:
Manage the execution of group projects related to coding with the support of informatics tools (GitHub)
Query and pre-process georeferenced data
Implement frameworks for the execution of modelling tasks with python programming language
Module II

• knowledge on air quality issues and human health-related implications (DD1)
• knowledge on air quality data sources and features (DD1)
• basic knowledge on air quality modelling (DD1 & DD2)

Module I covers the following topics:
· Geo-referenced data and Geographic Information Systems (GIS): coordinates reference systems, management of geographic data in vector format through GIS (QGIS), management and conversion of raster files, basics of geo-processing functions
· Most commonly used open-data sources: how to access, download and process geo-referenced data such as Copernicus, OpenStreetMap, ISTAT, regional geo-portal, ARPA, land-use database (DUSAF), Milan topographic database (etc.)
· Health and health-care related data, contextualization with environmental perspective
· Python programming language, PyCharm IDE, QGIS console, QGIS-based programming in Python
· Geo-referenced custom structured databases (focus on data types)
· Data pre-processing and features selection: missing data, outliers, principal component analysis
· Explorative analysis: variables distribution, correlation analysis, logistic regression, geographically weighted regression
· Explanatory/predictive modelling through machine learning techniques (interpretable machine learning)
· Coding projects with GitHub
Module II covers the fundamentals of atmospheric pollution, with particular reference to:

• Air quality: pollutant features, air quality indicators and reference levels, adverse effects on human health
• Air quality phaenomena and related spatial scales: global, regional and local air quality issues • Air quality data: spatial and temporal resolution, averaging time, regulatory limits.
• Atmospheric pollution sources: primary and secondary pollutants.
• Features of air quality models: scope of air quality modelling, local scale models, regional scale models, statistical models.

Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The achievement of the expected learning outcomes of both Module 1 and 2 will be evaluated through the assignment of a project work to be developed and discussed in its conceptual scheme and operational implementation. Along with a final report and presentation, the evaluation process will also take into account the activities held during laboratory hours.

Modulo 2: materiali a cura del docente disponibili sul portale WeBeep www.webeep.polimi.it
Note: I materiali forniti coprono tutti gli argomenti trattati nell'ambito dell'insegnamento. Gli altri testi di seguito elencati possono fornire ulteriori approfondimenti e possono costituire riferimenti utili per l'attività professionale.
C. D. Cooper, F.C. Alley, Air Pollution Control, Editore: Waveland. Press Inc., Anno edizione: 2011, ISBN: 1-57766-678-X
Theodore L., Air pollution control equipment calculations, Editore: Wiley-Interscience, Anno edizione: 2008, ISBN: 0470209674

Aims
Information and communication technologies (ICT) are having a tremendous impact on health care research and clinical practice, and on the development of healthcare systems.
The course presents computer science principles, technologies, methods and instruments for the structured collection, integration, sharing and computational analysis of clinical information and biomedical knowledge. It will point out as information and knowledge engineering – in particular the areas regarding knowledge description, data bases, semantic web, workflows, web services and service oriented architectures – can profitably support the effective management of medical, clinical and biomolecular data, and contribute to extract their valuable information content toward improving biomedical knowledge and health care.
Students will build knowledge on issues related to bio-medical-molecular data and clinical information, and will develop skills on ICT instruments enabling the interoperability and development of applications to manage and process such data and information; they will learn how to effectively use the ICT to efficiently tackle the mentioned issues in an adequate and sustainable way for the biomedical domain.
After the course, students will be able to leverage the acquired competences to take advantage of the opportunities offered by the increasing ICT development and relevance in the health care.
Obiettivi
Le tecnologie dell’Informazione e della comunicazione (ICT) stanno avendo uno straordinario impatto sulle scienze della vita e sulla cura della salute, sia nella ricerca che nella pratica clinica, e sullo sviluppo dei sistemi sanitari.
Il corso presenta principi, tecnologie, metodi e strumenti informatici per la raccolta strutturata, integrazione, condivisione e analisi computazionale di informazioni cliniche e conoscenza biomedica. Verrà evidenziato come l’ingegneria dell’informazione e della conoscenza – in particolare le aree riguardanti la descrizione della conoscenza, le basi di dati, il semantic web, i workflow, i servizi web e le architetture orientate ai servizi – possano proficuamente aiutare la gestione efficace di dati medici, clinici e biomolecolari, e contribuire a estrarre il loro prezioso contenuto informativo per il miglioramento della conoscenza biomedica e la cura della salute.
Gli studenti acquisiranno conoscenza dei problemi relativi ai dati bio-medici-molecolari e alle informazioni cliniche, svilupperanno abilità relative agli strumenti dell’ICT che permettono l'interoperabilità e lo sviluppo di applicazioni per gestire ed elaborare tali dati e informazioni; impareranno come usare efficacemente l’ICT per affrontare efficientemente i problemi citati, in modo adeguato e sostenibile per l’ambito biomedico.
Dopo il corso gli studenti saranno capaci di sfruttare le competenze acquisite per trarre vantaggio dalle opportunità offerte dal crescente sviluppo e rilevanza dell’ICT nella cura della salute.

Learning outcomes
Dublin descriptors
Expected learning outcomes
Knowledge and understanding
Knowledge of clinical data and information
Comprehension of the heterogeneity of ICT systems in the health care domain
Comprehension of the problems related to the heterogeneous bio-medical-molecular data
Knowledge of the different technological approaches adopted to obtain the interoperability of clinical/healthcare data and systems
Knowledge of the systems for the collection, encoding and structuring of information and knowledge, particularly in the health care domain
Applying knowledge and understanding
Development of abilities about the ICT instruments that allow the interoperability and the development of applications for the management and elaboration of heterogeneous biomedical data and information
Ability in leveraging the acquired competences to take advantage of the opportunities offered by the increasing ICT development and relevance in the life sciences and health care
Making judgements
Evaluate comparatively the advantages and disadvantages of the different ICT approaches and technologies for solving a clinical/healthcare problem
Identify the most suitable approaches and technologies for specific problems in the health care domain
Communication
Learning the use of proper terms to address specific data and activities in health care
Lifelong learning skills
Learning how to effectively use ICT to tackle problems successfully, in an adequate and sustainable way for the biomedical domain
Be able to learn and use new ICT approaches and technologies, and to apply them profitably in the clinical-healthcare domain
Be able to use the acquired knowledge and competences to tackle methodologically complex problems
Risultati di apprendimento attesi
Descrittori di Dublino
Risultati di apprendimento attesi
Conoscenza e comprensione
Conoscenza di dati e informazioni cliniche
Comprensione dell’eterogeneità dei sistemi di ICT nell’ambito della cura della salute
Comprensione dei problemi relativi agli eterogenei dati bio-medici-molecolari
Conoscenza di diversi approcci tecnologici proficuamente adottati per ottenere l’interoperabilità di dati e sistemi clinico-sanitari
Conoscenza dei sistemi di raccolta, codifica e strutturazione delle informazioni e conoscenze, in particolare nell’ambito della cura della salute
Capacità di applicare conoscenza e comprensione
Sviluppo di abilità relative agli strumenti dell’ICT che permettono l’interoperabilità e lo sviluppo di applicazioni per la gestione e l’elaborazione di dati e informazioni biomediche eterogenee
Capacità di sfruttare le competenze acquisite per trarre vantaggio dalle opportunità offerte dal crescente sviluppo e rilevanza dell’ICT nelle scienze della vita e nella cura della salute
Autonomia di giudizio
Valutare comparativamente benefici e svantaggi di diversi approcci e tecnologie di ICT per la soluzione di un problema clinico-sanitario
Individuare gli approcci e le tecnologie più adatti a specifici problemi nell’ambito della cura della salute
Comunicazione
Apprendere a usare termini appropriati per riferirsi a specifici dati e attività relative alla cura della salute
Capacità di apprendimento
Apprendere come usare efficacemente l’ICT per affrontare i problemi con successo, in modo adeguato e sostenibile per l’ambito biomedico
Essere in grado di apprendere e usare nuovi approcci e tecnologie dell’ICT e di applicarli proficuamente nell’ambito clinico-sanitario
Essere in grado di utilizzare le conoscenze e competenze apprese per affrontare in maniera metodologica problemi complessi

Syllabus
Seminar lectures and practices in informatics room on the following topics compose the course; in case, at the end of the course, an external technical visit to an experimental research laboratory, or a seminar lecture by a field expert, will take place.

• Introduction (2 hours): Definitions, methodologies and motivations
• Medical information and medical record standards for interoperability and sharing (2+8 hours): Taxonomy of medical information and examples of medical records, concepts of biomedical data security and privacy, standards for biomedical information structuring, interoperability and sharing
• Biomedical terminologies and ontologies (4 hours): Controlled vocabularies for digital medical records, interoperability of biomedical nomenclature systems
• Databanks of bio-medical-molecular information (2 hours): Bio-medical-molecular databanks and web applications to use them, integration and analysis of the provided information
• Biomedical web services and workflows (4+2 hours): Hints of Service Oriented Architectures, technologies for programmatic access to biomedical-molecular distributed resources, web service composition and orchestration, workflow execution and sharing
• Semantic web for health care (6 hours): Introduction and main technological features, biomedical data in RDF, SPARQL for data integration and querying, biomedical ontologies in RDF-S/OWL, automatic reasoning and its requirements
• Examples of some available software tools for healthcare and biomedicine (20 hours): Software tools available as web applications, web services, freeware and open source programs
  Programma delle lezioni e delle esercitazioni
  Il corso si compone di lezioni frontali ed esercitazioni in aula informatizzata sui seguenti argomenti; alla fine del corso potrà essere organizzata una visita tecnica esterna a un laboratorio sperimentale di ricerca, o una lezione seminariale di un esperto del settore.
• Introduzione (2 ore): definizioni, metodologie e motivazioni
• Standard, interoperabilità e condivisione di informazioni mediche e cartelle cliniche (2+8 ore): Tassonomia di informazioni mediche ed esempi di cartelle cliniche, elementi di sicurezza e riservatezza dei dati biomedici, standard per la strutturazione, interoperabilità e condivisione di informazioni biomediche
• Terminologie e ontologie biomediche (4 ore): Vocabolari controllati per cartelle cliniche digitali, interoperabilità di sistemi di nomenclatura biomedici
• Banche dati di informazioni bio-medico-molecolari (2 ore): Banche dati bio-mediche-molecolari e applicazioni web per il loro utilizzo, integrazione e analisi delle informazioni fornite
• Servizi web e workflow biomedici (4+2 ore): Elementi di Architetture Orientate ai Servizi, tecnologie per l’accesso programmatico a risorse biomediche-molecolari distribuite, composizione e orchestrazione di servizi web, esecuzione e condivisione di workflow
• Semantic web per la cura della salute (6 ore): Introduzione e principali caratteristiche tecnologiche, dati biomedici in RDF, SPARQL per l’integrazione e l’interrogazione dei dati, ontologie biomediche in RDF-S/OWL, reasoning automatico e suoi requisiti
• Esempi di alcuni strumenti software disponibili per la sanità e la biomedicina (20 ore): strumenti software disponibili come applicazioni web, servizi web e programmi freeware e open source
  Laboratory activities
  During course practices, all given in an informatics room, several information and communication technologies, data banks and public available tools will be illustrated and used in the health care and life science context.
  Attività di laboratorio
  Durante le esercitazioni del corso, tutte svolte in aula informatizzata, varie tecnologie dell’informazione e della comunicazione, banche dati e strumenti pubblicamente disponibili saranno illustrati e utilizzati nel contesto della cura della salute e delle scienze della vita.
  Lecturer notes
  Other teaching material
  The slides presented during the course and the estimated detailed schedule of lectures and practices are available on the "We Be e-Poli" (WeBeeP), the portal for the network activities of students and professors at the Politecnico di Milano, accessible from the Politecnico di Milano web site; students registered to the course for the current academic year can access it.
  Note docente
  Altro materiale didattico
  Le diapositive presentate durante il corso e il calendario preventivo dettagliato delle lezioni ed esercitazioni sono disponibili in "We Be e-Poli" (WeBeeP), il portale per le attività in rete di docenti e studenti del Politecnico di Milano, accessibile dal sito web del Politecnico di Milano; vi hanno accesso gli studenti iscritti al corso nell'anno accademico corrente.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Assessment
The assessment is based on a written exam at the end of the course, with exercises and open questions on all the topics presented during the course lectures or practices.
At the lecturer's choice and discretion, an oral exam may also be required after the written exam to verify its content and the student's knowledge of the subject.
Competence in discussing and correlating together the course topics, with appropriate terms and attention to the relevant conceptual aspects, is a fundamental element for a positive evaluation.
The following table provides a detailed overview of the different types of assessments that contribute to verify the expected learning outcomes.
Type of assessment
Description
Pursued learning outcome (Dublin descriptor)
Written test
Exercise resolution
Theorical questions with open answer about all course topics
Questions aimed at highlighting the student ability in elaborating relations between the various topics of the course and with other course topics
1, 2, 3, 5
1, 2, 3, 5
1, 2, 3, 4, 5
Modalità di valutazione
L'esame consiste in una prova scritta alla fine del corso, con esercizi e domande aperte che coprono tutti gli argomenti trattati durante le lezioni o le esercitazioni del corso.
A scelta e discrezione del docente può essere richiesta anche una prova orale dopo la prova scritta per verificarne i contenuti e la conoscenza della materia da parte dello studente.
La capacità di discutere e relazionare tra loro gli argomenti oggetto del corso, con l’impiego dei termini appropriati e l’attenzione agli aspetti concettuali importanti, costituisce elemento fondamentale per una valutazione positiva.
La tabella che segue dettaglia come le diverse forme di valutazione contribuiscono alla verifica dei risultati di apprendimento attesi.
Modalità di verifica
Descrizione
Risultato di apprendimento perseguito (descrittore di Dublino)
Esame scritto
Risoluzione di esercizi
Domande di carattere teorico a risposta aperta su tutti gli argomenti del corso
Domande volte a evidenziare la capacità dello studente di elaborare collegamenti fra i vari argomenti del corso e con quelli di altri insegnamenti
1, 2, 3, 5
1, 2, 3, 5
1, 2, 3, 4, 5

T. Benson, Principles of health interoperability HL7 and SNOMED, Editore: Springer, New York, NJ, Anno edizione: 2009, ISBN: 978-1-84882-802-5
Note: Health informatics series.
C.J.O. Baker, K-H. Cheung, editors, Semantic web. Revolutionizing knowledge discovery in the life sciences, Editore: Springer, New York, NJ, Anno edizione: 2007, ISBN: 978-0-387-48836-5
F. Pinciroli, M. Masseroli, curatori, Elementi di informatica biomedica, Editore: Polipress, Milano, IT, Anno edizione: 2005, ISBN: 88-7398-0171
Note: Collana Scienze e tecnologie per la salute.
E. Della Valle, I. Celino, D. Cerizza, Semantic web. Dai fondamenti alla realizzazione di un'applicazione, Editore: Pearson Education Italia, Torino, IT, Anno edizione: 2009, ISBN: 978-8-87-192578-3

This course will introduce principles and methodologies for the design of digital technologies that can grant diversity and accessibility to all. Students will learn a methodological framework that can help them analyze the system users and context of use, recognize specific needs, e.g., related to physical abilities, gender, and socio-economic status, and consider these needs in the design of digital systems that behave respecting such a diversity. This design approach is particularly relevant when designing the user interfaces of digital systems; but it can also be critical when considering algorithms that produce unfair predictions due to the adoption of incomplete, faulty, and prejudicial data sets. Overall, the goal is to lead students to learn how to put the users in the center from the very beginning of the design process, to recognize exclusion and learn from diversity, solve for one and extend to many, thus reaching designs that can benefit people universally.
The main topics that will be covered are:
Human-centered design: user research, participatory design and co-design, user-based evaluation
Principles and guidelines for the accessibility and usability of interactive digital systems and visual communication artifacts
Interface Desing: principles and methods for the design of inclusive, accessible and usable interfaces
Emerging technologies, e.g., Conversational AI, IoT and tangible computing, AR/VR, enhancing digital inclusion for underserved populations (people with disabilities, children and older adults)
Inclusive AI: recognizing bias and exclusion in AI
Human-centered AI

L'obiettivo del corso è l'integrazione di conoscenze, strumenti e metodi acquisiti durante il percorso formativo, applicati alla progettazione di prodotti indossabili innovativi per lo sport, sia nel campo tecnologico sia nei tessuti ultra-tecnici che potrebbero agire in forte integrazione.
Questo corso offrirà l'opportunità di applicare le conoscenze pregresse, da integrarsi con principi e metodi per la progettazione di prodotti industriali, principi di ergonomia, apprendimento di tecniche e metodi per lo sviluppo di concetti innovativi e sviluppo del prodotto, metodi di rappresentazione e comunicazione del progetto, nella progettazione e realizzazione di un sistema proof-of-concept.
L'insegnamento sarà svolto con il contributo di aziende del settore sportivo e il supporto di centri di ricerca e sviluppo con l'obiettivo di trasferire conoscenze applicabili nel campo dei prodotti sportivi indossabili di media-alta complessità.

This course provides basic knowledge for choosing and functionally designing industrial robots. Great attention is given to manipulators and a brief overview is given on service robots. Topics covered include mathematical analysis of principal kinematic chains and dynamic analysis, an introduction to path planning theory, an overview of the mechanical devices or components used in this field and touches on the criteria in the use of industrial robots. The emphasis is not on the control algorithm structures or complicated robot mathematical modelling, but on learning the fundamental concepts and core principles useful in the design and use of robots, especially from a mechanical point of view. The intent is to help students acquire a basic set of analytical tools and expertise for working in the robotic field. The lessons are complemented by exercises based on industrial cases. The exercises could be carried out in groups.

Knowledge and understanding.
After the exam the student:
Has an overview of the robotic field and the use of robots in industrial applications
understands how to model a robot from a kinematic and dynamic point of view
knows the principal mechanical components used in the robotic field
knows the principal methodologies and approaches used to program an industrial robot
knows the basis of the path planning problem for robotic applications
has an overview of the performance of a robot with particular reference to the calibration problem and reference to harmonized technical standards
Applying knowledge and understanding
After the exam the student will be able to:
solve the kinematic and dynamic problem of a robot
plan the trajectory of an end effector in the joint space as well in the working space
design a simple 3dof industrial robot
design the layout of a robotic cell and its production cycle
use an industrial manipulator for simple handling tasks

Origins, history and description of robots: origins and modern trending in robotics. Robot classification. Industrial robotic objectives and issues. Description of basic structure of a robot system: controllers, actuators, transducers, sensors and the like. General characteristics of an industrial robot.
Manipulators analysis: References to general methodologies for direct and forward kinematic analysis and dynamics of multi-degree of freedom systems (Jacobian matrix, singularities, …). Matrix methods for the analysis of 3D systems. Position and orientation of a body in the space (reference systems, position, velocity and acceleration matrix, …). Manipulator dynamics, lumped parameter models for dynamic simulation.
Reference to path planning and control: References to trajectory descriptions in the joint space and in the workspace. Mentions of control of the manipulators in position, force and hybrids. Language and robot programming systems.
Components for industrial robotics: Electric actuators. Speed reducers, remote transmissions. Gripping systems. Position, speed, acceleration and force sensors.
Robot performance: Effects of structural errors, elasticity, backlash and friction in mechanical transmissions. Kinematic and dynamic calibration of the robots. The international regulation for the definition and measurement of robot performance.
Overview of the uses of industrial robots. Principal technical standards: nomenclature, presentation of characteristics, performance criteria and related test methods, safety, coordinated systems and movements, mechanical interfaces (Italian and European legislation, machine directives, harmonized technical standards). Reference to security in the robotic cells and use of a robot in a production line. Applications, case description in assembly, handling and processing operations.

Prova orale obbligatoria
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The exam consists of an optional project developed during the practice sections and evaluated during the last period of the course and an oral exam on the main subjects treated during the lessons.
The project mostly aims at checking the acquired capabilities of applying knowledge and understanding, while the oral part of the exam is focused on checking also knwowledge and understand of the student.

B. Siciliano, L. Sciavicco, L. Villani, G. Oriolo, Robotics: Modelling, Planning and Control, Editore: Springer-Verlag London, ISBN: 978-1-84628-642-1
G. Legnani, Robotica Industriale, Editore: Casa Editrice Ambrosiana, ISBN: 88-408-1262-8

L'insegnamento "Interazione luce materia" ha l’obiettivo di insegnare allo studente i concetti di base dell’interazione tra luce e materia nell’ambito dell’elettromagnetismo classico e dell’ottica lineare.
L'insegnamento è propedeutico ad alcuni insegnamenti della Laurea Magistrale (es. “Nanoscopy and Optical Tomography”/”Biophotonics”, “Physics of ultra fast processes”, “Micro and Nano Optics”, “Physics of photovoltaic processes”).

Conoscenza e comprensione (DdD 1): Lo studente acquisisce gli strumenti teorici e applicativi per comprendere i concetti alla base dei fenomeni di interazione tra luce e materia.
Autonomia di giudizio (DdD 3): Lo studente sarà in grado di distinguere in modo critico la complessità dell'interazione tra luce e materia avendo acquisito l'abilità di riconoscere e analizzare i fenomeni di assorbimento, diffusione da particelle e da fluttuazioni, diffusione singola e multipla, fluorescenza e fosforescenza, effetti fotochimici fototermici e fotomeccanici.
Capacità di apprendimento (DdD 5): Le competenze acquisite permetteranno allo studente di comprendere in modo autonomo le recenti e diverse applicazioni dei fenomeni di interazione tra luce e materia in Biomedicina e Nanotecnologie.

Nel'insegnamento vengono illustrati i principali fenomeni fisici legati all’interazione luce-materia. Nella prima parte si considerano i fenomeni che sono utilizzati per caratterizzazione e diagnostica dei materiali (es. assorbimento, diffusione, fluorescenza), mentre nella seconda parte vengono mostrati i fenomeni di interazione che possono comportare modifiche irreversibili nei materiali per effetti fotochimici, fototermici e fotoablativi. Si pone inoltre risalto sulle applicazioni in diversi campi quali Biomedicina e Nanotecnologie. L’approccio adottato è quello dell’elettromagnetismo classico e dell’ottica lineare in cui verrà considerato l’intervallo dello spettro elettromagnetico dall’ultravioletto all’infrarosso caratteristico dell’emissione delle sorgenti laser. La trattazione classica dei fenomeni considerati consente di fornire soluzioni accurate per gran parte delle applicazioni di interesse pratico.
Programma dettagliato
Introduzione ai fenomeni di diffusione e assorbimento. Assorbimento e dispersione nei dielettrici. Legge di Beer-Lambert, attenuazione e densità ottica. Diffusione e assorbimento da singola particella dielettrica: teoria di Mie e Rayleigh. Collezioni di particelle in approssimazione di diffusione singola. Teoria del trasporto radiativo per diffusione multipla. Approssimazione di regime diffusivo. Diffusione da fluttuazioni termodinamiche. Strumentazione per misure di assorbimento e diffusione.
Introduzione ai fenomeni di luminescenza e fotoluminescenza. Diagrammi di Perrin-Jablonski. Caratteristiche dell’emissione di fluorescenza: Stokes shift, spettri di emissione, tempo di vita ed efficienza. Spettroscopia di fluorescenza: misure di spettri, misure di tempo di vita risolte nel tempo o in frequenza. Strumentazione per spettroscopia di fluorescenza. Effetto del solvente, della temperatura e dell’ambiente, quenching, anisotropia.
Effetti fotochimici, fototermici, e fotoablativi. Densità di potenza e tempo di esposizione. Applicazioni in biologia e nanotecnologie.

Prova scritta facoltativa (a scelta dello studente), senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
Per la verifica dell'apprendimento sono previsti 6 appelli di cui 1 immediatamente al termine dell'insegnamento (es. Aprile), 3 nella sessione di esame estiva (Giugno, Luglio, Settembre) e 2 nella sessione d'esame invernale (Gennaio, Febbraio).
Ad ogni appello la valutazione verterà su tutto il programma. Non sono previste prove in itinere su parti del programma.
In ogni appello lo studente può scegliere se sostenere una prova scritta o una prova orale.
La prova d’esame scritta ha una durata di 1,5 ore e consiste in 3 quesiti nella forma di domande aperte. Il docente si riserva la possibilità di integrare il voto della prova scritta con una prova orale per accertare o confermare l'autonomia di giudizio raggiunta dallo studente.
In alternativa alla prova scritta lo studente può scegliere di sostenere una prova orale che tipicamente si svolge in piedi alla lavagna, con una durata di circa 30 minuti e 2-3 quesiti nella forma di domande aperte.
Lo studente può sempre decidere di rifiutare la verbalizzazione di un voto positivo. In tal caso il voto non viene perso ma viene conservato finché lo studente non si presenta ad un nuovo appello e consegna una nuova prova scritta da correggere (o in alternativa si presenta ad una nuova prova orale).

Dispense a cura del docente webeep.polimi.it
Craig F. Bohren, Donald R. Huffman, Absorption and Scattering of Light by Small Particles, Editore: John Wiley & Sons, Anno edizione: 2008, ISBN: 9783527618163
Dennis Goldstein, Polarized light, Editore: Marcel Dekker Inc., Anno edizione: 2003, ISBN: 0-8247-4053-X
Martelli-Del Bianco-Ismaelli-Zaccanti, Light Propagation Through Biological Tissue and Other Diffusive Media: Theory, Solutions, and Software, Editore: SPIE Press, Anno edizione: 2010, ISBN: 9780819476586
Joseph R. Lakowicz, Principles of Fluorescence Spectroscopy, Editore: Springer Science & Business Media, Anno edizione: 2007, ISBN: 9780387463124
Markolf H. Niemz, Laser-Tissue Interactions, Editore: Springer-Verlag, Anno edizione: 2007, ISBN: 3540405534

At the end of the course the students will be able to:
-anlyze and design complex telecommunication systems based on Internet of Things
-understand and apply complex communication technologies at the very heart of IoT systems
-design communication networks of tiny and constrained devices to enable IoT applications and services
-design IoT systems interconnected with the Internet

1 - Knowledge and understanding
Students will learn how to:
-Analyze complex communication technologies for IoT systems
-Analyze application layer protocols for the IoT
-Analyze management platforms for the IoT
-Identify IoT system components and their relations
-Use operative systems for the IoT
-Use prototyping platforms for the IoT
2 - Applying knowledge and understanding
Students will be able to:
-Dimension and optimize the energy consumption of sensor nodes
-Evaluate the performance of communication technologies for the IoT
-Evaluate the performance of application layer protocols for the IoT
-Evaluate the performance of management platforms for the IoT
-Develop simple applications based on IoT
3 - Making judgements
Students will learn to:
-Understand the fundamental tradeoffs that govern the energy management of battery-operated sensor nodes
-Identify how the fundamental system limitations impact the design of the IoT systems
-Recognize the design space and its degrees of freedom that can be exploited to define communication technologies for the IoT
4 - Communication
Students will learn to:
-describe complex network technologies at different levels of detail

The course will provide the basics on the four major building blocks of the Internet of Things: the reference hardware, the communication technologies, the management platforms and the data storage/processing solutions. The course will start off by overviewing the most common hardware components of sensor nodes, and will then move to addressing the challenges which have to be faced to interconnect and manage such tiny devices. In this field, low power short rannge and long range communication technologies will be reviewd. Finally, the course will describe the main features and requirements of management platform commonly used to orchestrate IoT systems.
Program at a Glance
1-Introduction: the vision of ambient intelligence, application examples, enabling Technologies (Sensor Networks, RFID), networking Building Blocks and Abstractions
2-Hardware Components & Abstractions: sensor node hardware architectures, basics of ADC, energy consumption models for sensor nodes
3-Application layer protocols for the IoT: networking basics, the Constrained Application Layer Protocol (COAP), the Message Queuing Telemetry (MQTT)
4-Communication technologies for the Internet of Things long range solutions: evolution of the mobile radio network to support IoT traffic (LTE-CatM, NB-IoT), examples of Low Power Wide Area Networks (LoraWAN),
5-Communication technologies for the Internet of Things - short range solutions: introduction to capillary network infrastructures, the ZigBee protocol stack (IEEE 802.15.4 PHY/MAC layer, ZigBee Network layer, ZigBee application layer and profiles), Bluetooth and BLE, overview of THREAD and MATTER
6-Radio Frequency Identification: application Scenarios, the Physical Layers of RFID (operation band, transmission fundamentals), Collision Arbitration Standards and Solutions (Tree-based arbitration, frame-aloha based arbitration, the Q-Algorithm, performance evaluation of arbitration protocols)
7-Hands-On Activities: management and prototyping platforms (ThingSpeak, NodeRed, WokWi), analysis of IoT traffic via Wireshark

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
The students will be evaluated through two mutually exclusive ways:
(i) in-course challenges
or
(ii) a final written exam on the topics of the course
The students can decide which type of evaluation is the most appropriate for them,
The written exam includes three exercises and a set of questions on the topics of the course. The students may refer to the course web site to check for templates and sample exams from past year's editions of the course.
The in-course challenges consist in developing mini-projects with fixed delivery deadlines throughout the course. The mini-projects will be organized as follows: one written homework with quantitative/qualitative exercises (similar to those discussed during exercises sessions), three practical mini-projects on the development of IoT applications by leveraging the software tools presented during lectures and lab sessions.
The final grade is
-the grade obtained in the final exam, for those students attending the final exam
-the average gade of the 4 in-course challenges, for those students clearing the course with the challenges. In this case, a short final written exam up to 3 additional points can be given to increase the final grade
The mini-projects can be developed by groups of up 2 students.
The written homework must be done singularly
The students will have to prove in the written exam and in the in-course challenges:
-to have clearly understood and acquired to the topics of the course
-to be able to qualitatively assess all the building blocks of a IoT system (hardware, communication technologies, management platforms and data storage/processing)
-to be able to assess the energy consumption of IoT devices
-to be able to dimension and optimize IEEE 802.15.4 networks
-to be able to evaluate the performances of RFID arbitration systems based on Frame ALOHA and binary trees
-to have acquired and understood the working details of short range (ZigBee, Bluetooth/BLE) and long range (LoraWAN, NB-IoT) communication technologies for the IoT
-to have acquired and understood the working details of IoT application layer protocols (COAP, MQTT)
-to be able to manage the reference software (Node-Red, Thingspeak)
-to be able to analyze IoT traffic
-to be able to build up working IoT application or networking solutions leveraging the aforementioned software
-to be able to produce clear and comprehensive documentaion describing the developed project

INTRODUZIONE ALLA FISICA QUANTISTICA
Partendo dalla crisi della fisica classica il corso si propone di fornire i fondamenti di fisica quantistica necessari per una descrizione microscopica del comportamento della materia e per la comprensione della manifestazione di nuovi fenomeni. La descrizione include concetti fondamentali (dualismo onda-particella, funzione d'onda, indeterminazione e principio di esclusione), l'equazione di Schroedinger, operatori quantistici, stati stazionari, stati legati e non legati, statistiche quantistiche, densità degli stati e cenni alla fisica atomica. Particolare rilevanza viene data al cambiamento delle proprietà quando un sistema viene confinato alla scala nanometrica.

Dopo una breve introduzione dei materiali strutturali e funzionali, Partendo dalla descrizione della struttura atomica e delle proprietà periodiche degli elementi, si descriverà il legame chimico presente nelle diverse classi di materiali, comprendenti i solidi ionici, molecolari, covalenti e metallici. Si discuteranno poi le varie forze intermolecolari per descrivere i solidi cristallini ed amorfi, considerando quindi i passaggi di stato in alcuni materiali molecolari o metallici in termini dei diagrammi di stato a uno e a due componenti. Si mostrerà infine come le proprietà generali di varie classi di materiali tradizionali o avanzati (semiconduttori, materiali polimerici, ceramici, metalli) possono essere spiegate tramite la struttura, i legami chimici e/o le forze intermolecolari.

This soft-skills course aims to introduce foreign students to the fundamentals of Italian culture within the broader context of European history and culture. The course is divided in two units.
In Unit 1 (3 credits) students will learn about the main events in the history of Europe and Italy and about their socio-cultural implications, with special attention to contemporary issues. Students will also learn about key artistic and literary movements within this broad historical context.
In Unit 2 (2 credits), the focus will be on Italian contemporary literature, focusing on the potential intersection of literary research with pressing contemporary issues, such as ecology, mental health, and human and civil rights.
The goal of the course is to provide students with a deeper understanding of the social and cultural context in which they are studying and living and in which they might work in the future. Given the variety of topics and subjects, which will span art, literature and cinema, lectures will make considerable use of multimodal resources.

At the end of the course students will have developed an understanding of Europe's and Italy's cultural past and present, and will be able to use these competences to interpret current socio-cultural issues. A special focus on Italian history of the 19th-20th centuries will enable students to understand Italy’s cultural landscape and the basic structure of Italian society. They will also be able to understand and appreciate cultural artefacts in a culturally competent manner.
In Unit 2, the course will explore Italian contemporary literature, using debates and literary objects as a way to face pressing contemporary issues, such as ecology, mental health, and human and civil rights.
During the course, team-work will be frequently used to improve students' communication skills, especially in relation to topics they are not familiar with, from both a disciplinary and cultural viewpoint. Most lectures include an analysis of a specific cultural object: students will learn to apply analytical techniques to a variety of artefacts, and will eventually be able to use them autonomously. At the end of the course, students will be able to discuss in detail a single cultural object, discussing and assessing its main historical and formal features.

Unit 1 (3 credits)
A first series of lectures will focus on European history, from Ancient Grece up to the 19 th century; by learning about the foundations of European civilisation, focusing especially on its early times, international students will gain a better understanding of the contemporary western world. This historical background is a prerequisite for approaching the study of culture: the course will deal with the fundamentals of western literature, religion, art and cultural imaginaries. Moreover, the historical part will provide an overview of contemporary Europe both as an institution and a cultural entity.
The second part of this Unit is mainly dedicated to Modern Italy. Lectures will mainly deal with the history of Italy from unification (1861) to the present day. Students will have an opportunity to discuss crucial cultural problems, spanning politics, literature, cinema and local customs. Special attention will be paid to social imaginaries and narratives, which will be used as an entry point for a critical approach to the investigation of identities and cultural trends.
Unit 2 (2 credits)
In the second part, the course will explore Italian literature—with forays into cinema and the arts—focusing particularly on the thematic aspects of the reflections of major authors: Calvino’s ecological anxieties, Primo Levi’s unique blend of science and literary inquiry, and the distinct ways Ginzburg and Morante retell History. In short, the second part of the course will provide an opportunity to delve deeper into events and themes highlighted in the first part, using different approaches and languages.

Prova scritta facoltativa (a scelta dello studente), senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
The examination for 5-credit students is composed of three parts: a group presentation, a written test about unit 1 and an interview about the contents of the whole syllabus.
3-credit students will give the group presentation and sit the written test about unit 1 or, alternatively, an oral interview about the sylllabus of the same unit.
Attending students will study the content of the lectures based on the power point presentations and other materials uploaded on Beep, as well as on the compulsory references books (see references section). In gr
Non-attending students will study the recordings of the lectures, the power point presentations, the compulsory reference books and the other materials uploaded on Beep. Additionally, they are required to read the book Modern Italy. A Very Short Introduction by Anna Cento Bull (see references section).

Ernst Hans Gombrich, A Little History of the World, Editore: Yale University Press, Anno edizione: 2008, ISBN: 978-0-300-14332-4
Christopher Duggan, A Coincise History of Italy, Editore: Cambridge University Press, Anno edizione: 2014, ISBN: 978-0-521-74743-1
Note: Second Edition
Anna Cento Bull, Modern Italy. A Very Short Introduction, Editore: Oxford University Press, Anno edizione: 2016, ISBN: 978-0-19-872651-7
Jeremy Black, A Brief History of Italy, Editore: Robinson, Anno edizione: 2018, ISBN: 978-1-47214-088-3
John Foot, Modern Italy, Editore: Palgrave Macmillan, Anno edizione: 2003, ISBN: 978-0-333-66905-1
Zygmunt G. Baranski, Rebecca J. West, The Cambridge Companion to Modern Italian Culture, Editore: Cambridge University Press, Anno edizione: 2001, ISBN: 9780521559829

L’insegnamento ha l’obiettivo generale di f ornire conoscenza, anche operativa, sulle nuove metodologie dette NAM in vitro (es. substrati miniaturizzati 3D, bioreattori millifluidici, niche -on-chip) per la modellazione sperimentale di fisiologia, patologia, e terapia, utilizzate nella sperimentazione preclinica di prodotti per terapie avanzate e biofarmaci. Il programma di questo corso è organizzato in lezioni teoriche e attività pratiche di laboratorio. Il corso trasmette le conoscenze sviluppate nell’ambito di vari progetti ERC.
Gli obiettivi specifici dell’insegnamento sono:
fornire conoscenza su progettazione e sull’impiego di dispositivi NAM finalizzati alla sperimentazione farmacologica, soprattutto di prodotti per terapie avanzate e farmaci biologici (cellule geneticamente modificate, vaccini a mRNA, ecc);
fornire conoscenza sulla modellazione di microambienti umani presenti n el sistema immunitario, nel sistema respiratorio e nei tumori solidi;
fornire competenza operativa su simulazioni computazionali ed esperimenti di laboratorio, finalizzati alla sperimentazione preclinica di prodotti per terapie avanzate e biofarmaci, utilizzando piattaforme NAM esistenti.

A seguito del superamento dell’esame, lo studente:

• Conosce i termini e i principi di base della modellazione cellulare (DdD 1)
• E' in grado di applicare la conoscenza a specifici problemi del settore della modellazione cellulare (DdD 2)
• E’ in grado di gestire problemi non strettamente inerenti alle problematiche affrontate nel Corso di
  laboratorio di modellazione cellulare, assumendo decisioni motivate (DdD 3)

Richiamo sui processi di sviluppo di prodotti per terapie avanzate e biofarmaci (es. terapie cellulari, cellule geneticamente modificate, immunoterapie, vaccini a mRNA). Progettazione di piattaforme sperimentali in vitro, che riproducano la fisiologia, la patologia e la risposta umana a prodotti per terapie avanzate e biofarmaci. Progettazione e realizzazione di esperimenti di laboratorio con piattaforme esistenti, per la sperimentazione preclinica di prodotti per terapie avanzate e biofarmaci in settori di applicazione relativi al sistema immunitario, al sistema respiratorio, e al cancro. Il programma di questo corso è organizzato in lezioni teoriche e pratiche di laboratorio.
LEZIONI DI TEORIA
Introduzione al campo della sperimentazione preclinica di prodotti per terapie avanzate e biofarmaci.
Cenni di farmacocinetica, farmacodinamica, tossicità e parametri per la loro misura in vitro (C50, ecc.).
Riprogrammazione cellulare e modifica del genoma.
Dall'ingegneria degli organi agli organoidi.
Terapie avanzate: regolazione ed etica.
PRATICHE DI LABORATORIO SPERIMENTALE
Revisione delle tecniche di coltura cellulare di base: cellule vascolari, immunitarie e tumorali.
Uso di fattori biologici per la regolazione della permeabilità capillare.
Realizzazione di micro scaffold polimerici tramite tecniche di micro-stereolitografia (quali la fotopolimerizzazione laser a due fotoni).
Approcci per l'ingegneria della reazione immunitaria: modellazione della fibrosi da corpo estraneo.
PRATICHE DI LABORATORIO INFORMATICO
Analisi bioinformatica di banche dati genomiche per l’individuazione di mutazioni genetiche.
Modellazione multifisica di reti microvascolari per la previsione della diffusione di farmaci.
Scrittura di codici CAD/CAM per fabbricazione tramite tecniche di micro-stereolitografia.
Modellazione multifisica di sistemi cellulari ingegnerizzati.

Prova orale obbligatoria
La valutazione consiste in una prova orale sui risultati delle attività di laboratorio e sugli argomenti spiegati a lezione.

The "Laboratory of Internet of Medical Things" course is designed to equip students with the interdisciplinary skills required to develop, implement, and validate connected medical devices. The course is designed for students from all backgrounds that are related to Information Technology, in particular Biomedical Engineering, Telecommunications Engineering, Electrical and Electronics Engineering, Computer Sciences, and Automation and Control Engineering, but is also open to other backgrounds.
A key objective of the course is to enable students to design and prototype a functional medical device starting from commercial microcontrollers. This involves interfacing these microcontrollers with analog and digital sensors commonly employed in medical applications, creating circuit schematics, developing printed circuit board layouts, and designing enclosures for the devices. By mastering these skills, students will be prepared to tackle the hardware challenges inherent in IoMT device development.
In addition to hardware design, the course emphasizes the importance of sensor data acquisition and basic on-board processing. Students will learn to capture data from various sensors and perform basic processing at the firmware level. This foundation is essential for ensuring reliable data collection in real-world applications.
Wireless connectivity is another critical component of the course, where students will explore wireless IoT communication protocols. Understanding these technologies will enable students to effectively transmit sensor data to external devices like smartphones and laptops, facilitating real-time monitoring and data analysis.
The course also covers the development of applications for receiving, displaying, and storing sensor data. Students will gain hands-on experience in creating mobile or desktop applications that integrate with cloud services, providing solutions for data visualization and storage.
As the course deals specifically with IoMT, so with medical applications, students will design and implement basic validation protocols, using appropriate gold standard instruments for comparison. This practical approach to validation will allow the students to comprehend and fully appreciate the challenges and complexity of validation of devices when the final goal is a clinical-grade application.
Additionally, the course fosters transversal and soft skills. Students will work in groups to manage their projects, prepare technical reports, and present their findings, thereby honing their ability to communicate complex technical information effectively.
By the end of the course, students will have a thorough understanding of the theoretical and practical aspects of end-to-end IoMT devices development.

The expected learning outcomes are the following:
Hardware design and prototyping: the students should be able to design a simple device using a commercial microcontroller, interface the microcontroller with analog and/or digital sensors commonly used in medical applications, design a schematic of the circuit, create a printed circuit board layout and develop a case to host the device (DD1, DD2).
Sensor data acquisition and on-board processing: the students should be able to acquire data from sensors and implement a basic processing at the firmware level (DD1, DD2).
Wireless connectivity: the students should be able to implement wireless IoT communication protocols (e.g., Bluetooth/BLE, Wi-Fi) to transmit the acquired data to an external device; the student should be able to configure and deploy message application protocols for the IoT (e.g., MQTT) (DD1, DD2).
App development and data storage: the students should be able to design and develop an application (mobile or desktop) to receive and display sensor data in real-time and save such data locally. The app should also include features to visualize the history of the collected data (DD1, DD2).
Validation protocol: the students should be able to design and carry out a basic validation protocol of the biomedical signal or measurement acquired with the device they have developed. The students must choose an appropriate gold standard instrument for the validation protocol (DD1, DD2).
Presentations to the rest of the class: the students should be able to prepare intermediate project presentations for the lectures and their peers during which they explain in detail what they have done during the practical project and the relevant background/state of the art (DD4, DD5).
Project management and collaboration: the students should be able to develop project management skills to design, implement, and test the IoMT devices and applications (DD2, DD3, DD4).
Documentation and reporting: the students should be able to document the design, development, and testing processes clearly and concisely, preparing both a written technical report and an oral presentation to communicate project outcomes effectively to the class (DD3, DD4).

The topics of the classroom-taught lectures and guided practical activities will be the following:
Introduction to the course
Hardware + printed circuit boards tutorial
Laboratory instrumentation + practical session
Wireless protocols and Internetworking + BLE tutorial
Firmware
The rest of the activities is focused on the development of the practical group project, which consists in developing a connected IoMT device from all aspects (hardware, firmware, data acquisition, data storage, processing), according to what is described in the “Expected Learning Outcomes” section, and specifically:
Hardware design and prototyping
Sensor data acquisition and on-board processing
Wireless connectivity of IoMT devices
App development and data storage
Design of a validation protocol

Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
The students will be divided into groups and the evaluation of the lecturers will be based on the following activities:
Group project, which will be presented orally by the group in one of the exam calls and reported in written form (the template is provided at the beginning of the course). The project is assigned at the beginning of the course based on expressed preferences within some options provided by the lecturers.
Intermediate presentations, which will be presented orally by the group to the rest of the class. The topic of the presentations is an update on the project status.
Additional oral exam upon request of the lecturer.

J.G. Webster, A.J. Nimunkar, Medical instrumentation: application and design, Editore: Wiley, Anno edizione: 2020
R. Melen, V. Trecordi, IoT Networking, Editore: Pearson, Anno edizione: 2023

This Laboratory course is aimed at providing theoretical and experimental knowledge about radiation therapy and its fundamental technological and clinical aspects. The course consists of a theoretical part illustrating the basic principles of radiobiology applied to radiotherapy, for better understanding the main relations with the related computational and experimental dosimetry. The fundamental principles of Monte Carlo simulations are illustrated, together with computational and experimental dosimetry techniques applied in conventional radiotherapy and hadrontherapy. Technologies and methods for clinical treatment planning will be described along with the issue of the transfer of the plan to the reality of each irradiation session, including personalized therapies also designed for mobile tumors.
***Access to the course is limited and subject to a maximum number of participants***
Admission procedure
Students enrolled in Biomedical Engineering: for information on the application and admission procedure, students enrolled in Biomedical Engineering should refer to the dedicated section of the Biomedical Engineering Programme website (https://www.ccsbio.polimi.it/laurea-magistrale/?lang=en#TRANSVERSAL_LAB_and_Capstone_Projects_courses)
Students enrolled in Nuclear Engineering: the student must select this course by submitting an autonomous study plan. The student must send an email to [email protected] and to the Study Plans Committee ([email protected]) to express their interest in attending the course. After the deadline for study plan submission, the Study Plans Committee will define the list of admitted students and will confirm or reject each request based on the number of available places. Students who are not admitted to the course must propose an alternative 5-CFU course to the Study Plans Committee.

Expected learning is described according to the Dublin Descriptors (DD): “DD1 Knowing, DD2 applying, DD3 evaluating, DD4 communicating, DD5 learning to learn”.
The student will learn:
-the fundamental aspects of radiobiology for radiotherapy (DD1);
-the main dosimetric quantities related to radiotherapy (DD1);
-the principles of Monte Carlo simulations (DD1 and DD2);
-the main experimental and computational dosimetry techniques in conventional radiotherapy and hadrontherapy (DD1 and DD2);
-the main aspects related to quality assurance, patient positioning and image guidance in conventional radiotherapy and hadrontherapy (DD1);
-technologies and methods for 4D treatment planning and dose delivery (DD1);
-the use of advanced imaging techniques and deep learning for patient stratification and therapy personalization (DD1 and DD2).
The student will be able to:
-elaborate and apply computational and experimental techniques for patient radiotherapy treatments (DD2 and DD3);
-compare experimental data and simulation outcomes in a critical way (DD3);
-exhibit fluency in technical communications in the field of engineering dosimetry techniques for radiation therapy with use of accurate scientific terminology (DD4);
-assess critically different approaches to the treatment plan transfer in the reality of each irradiation session (DD3);
-apply conventional and advanced indices for patient selection and treatment personalization (DD2 and DD5).

Radiobiology for radiotherapy: dose-response curves and radiation-induced DNA damage; quantification of cell kill and cell survival curve; target theory and the assocoiated models; the linear-quadratic model; the isoeffect dose and the nominal standard dose; the clinical use of the the linear-quadratic model in fractionated radiotherapy; early- and late-responding tissues and alpha/beta ratio; equivalent dose in 2 Gy fractions; the relative biological effectiveness and the oxygen effect; tumour volume and tumour growth.
Computational dosimetry: definition of main dosimetric key quantities; cavity theory; introduction to Monte Carlo simulations for calculation of dosimetric quantities.
Computational dosimetry applications for patient treatment: numerical simulation of an alpha depth-dose profile and of a photon depth-dose profile; numerical simulation of a simplified treatment planning on a tumor mass based on the spread out of Bragg peak of a proton beam.
Experimental aspects for verifying computational dosimetry: radiation dosimeters and dosimetry techniques; reference dosimetry and traceability; outline of ion chambers and calorimeters; non-reference dosimetry and Quality Assurance; measurement of an alpha depth-dose profile; measurement of a photon depth-dose profile.
Image guidance for treatment geometry verification, correction and adaptation: Optical tracking technologies; image guidance technologies; Cone-beam CT; QA procedures in radiation therapy.
Treatment planning and delivery in 4D: Motion management strategies; point-based and image based rigid registration.
Models for dynamic targeting of moving tumor volumes: Deformable image registration (DIR); strategies for DIR quality assessment; internal-external correlation strategies for gated irradiation and tumor tracking; global image based modeling in hadrontherapy.
Advanced multiparametric imaging and deep learning for patient stratification and treatment personalization: MRI anatomical and functional imaging for treatment planning; Virtual CT generation; deep learning approaches for CBCT application in adaptive therapy strategies; radiomics and dosiomics for patient stratification and treatment personalization.
Several laboratories will be offered aiming at gaining experience with both computational and experimental features of dosimetry and treatment planning applied to radiotherapy. Moreover, a final project work, focused on a specific application of the topics and tools encountered during the course, will be assigned to students subdivided in small groups.

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The evaluation consists in an oral examination aimed at verifying the knowledge of the course topics. In particular, the ability in understanding and describing the fundamental theoretical aspects illustrated during the course and the ability in analyzing and discussing experimental and simulated results will be verified. In addition, the examination requires the preparation and the discussion of a presentation regarding the final project work, in order to test the ability to discuss and communicate results in a professional way.

This course introduces the principles and practical applications of wearable biosignal processing. Students will gain hands-on experience with wearable devices, data acquisition, signal analysis, and the implementation of algorithms for real-time biosignal interpretation. The topics will cover: an introduction to the recording of ECG, EEG, EMG, PPG and IMU through wearables. Noise sources characterization in wearable signals (motion artifacts, interference) and algorithms for their reduction. Methods and algorithms for real-time signal processing and feature extraction, limits imposed by implementation in low-power systems. Case study development: algorithms for movement recognition, facial expressions, feature extraction from ECG/EEG recorded in non-conventional positions, cuffless BP estimation.

The course aims to provide students with both theoretical and practical competencies in the acquisition, processing, and interpretation of biosignals collected through wearable devices. Students will learn fundamental and advanced methods for handling signals such as ECG, EEG, EMG, PPG, and EOG, addressing the main challenges of real-world data such as noise, motion artifacts, and variability. Through lectures, laboratory work, and external seminars, students will develop the ability to design and implement signal processing pipelines, critically evaluate results, and understand current trends in wearable health monitoring.

1. Knowledge and Understanding (Dublin descriptor #1)
Understand the physiological origins and main characteristics of biosignals such as ECG, EEG, EMG, PPG, and EOG.
Comprehend fundamental and advanced methods for acquisition, preprocessing, and analysis of wearable biosignals.
Recognize challenges in real-world wearable signal analysis, such as motion artifacts, noise, and sensor variability.
2. Applying Knowledge and Understanding (Dublin descriptor #2)
Apply computational methods and algorithms to analyze biosignals using Python or MATLAB.
Design and implement complete signal processing pipelines for data acquired from wearable systems.
Evaluate data quality and select suitable preprocessing and feature extraction methods based on the signal characteristics.
3. Making Judgements (Dublin descriptor #3)
Critically assess the performance and limitations of different processing approaches.
Interpret results and justify methodological choices based on evidence and data characteristics.
Reflect on ethical and privacy issues related to the use of biomedical data from wearable devices.
4. Communication Skills (Dublin descriptor #4)
Present technical results clearly and effectively through oral presentations and written reports.
Collaborate efficiently in small research teams, sharing and discussing data and methodological decisions.
5. Lifelong earning Skills (Dublin descriptor #5)
Develop autonomous learning abilities for acquiring and integrating new signal processing techniques.
Critically engage with scientific literature and apply emerging methods to wearable biosignal analysis.

Introduction to biomedical and wearable signal processing
Overview of wearable systems: sensors, acquisition hardware, and data transmission
Physiological principles and signal characteristics of ECG, EEG, EMG, PPG, and EOG
Data acquisition and preprocessing techniques
Filtering, denoising, and motion artifact removal methods
Feature extraction, time–frequency analysis, and statistical signal modeling
Machine learning and data-driven methods for biosignal classification and interpretation
Multimodal data integration and synchronization in wearable systems
Laboratory sessions with real wearable data and project development
Seminars by external experts on emerging applications and research trends

Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
Valutazione continua obbligatoria
Evaluation is based on a group project involving the analysis of real biosignals acquired from wearable devices. Each group will design a complete processing pipeline and present the results through an oral presentation and a concise technical report. The assessment will consider methodological soundness, innovation, result interpretation, and clarity of presentation. Active participation in lectures, labs, and seminars will also contribute to the final grade, which reflects technical ability, analytical reasoning, and teamwork skills.

Location has become a crucial information for a wide range of services, many of which fall in the area of transportation. Modern transportation systems, often referred to as smart mobility or intelligent transportation systems (ITS), rely on information and communication technologies (ICT) to improve safety, sustainability and quality of mobility. Fundamental is the knowledge of position/velocity of vehicles, people and assets, either single entities or aggregates (i.e., traffic flows). Global navigation satellite system (GNSS) is the dominant technology in open sky, but severe propagation conditions (indoor, urban canyons) or critical performance requirements (accuracy, latency reliability) call for the integration with augmentation technologies, based on wireless networks, radar, and inertial systems.
The goal of this course is to teach students the fundamentals of localization, navigation and communication technologies for connected mobility. Specific objective are i) to provide a basic understanding of wireless technologies and processing methodologies for radio localization, including satellite navigation and wireless positioning based on cellular or short-range networks; ii) to investigate location-based services and technologies for smart mobility, focusing on communication systems for road safety and traffic efficiency, traffic monitoring systems, models and processing methodologies for traffic analytics, vehicular networks for cooperative intelligent transportation systems.
Lessons are integrated with seminars by experts from academia and industry. The course includes interactive activities and exercises carried out in Matlab environment with students organized in groups, aimed at the simulation and assessment of localization/mobility systems in practical use-cases. Laboratory activities will be carried out on use-cases and data defined in collaboration with companies of the sector.

Dublin Descriptors
Expected learning outcomes
1. Knowledge and understanding
Students will be able to:

• understand architectures, elements and signal processing methodologies of wireless positioning systems;
• understand sensing, processing and communication technologies for ITS;
• define mathematical models for formulation and analysis of a navigation problem;
• learn dynamic models and tracking filters for mobile scenarios.
  2. Applying knowledge and understanding
  Student will be able to:
• apply the mathematical models to design positioning algorithms and assess the related performance;
• apply dynamic models and tracking filters to practical scenarios of smart mobility.
  3. Making judgements
  Students will be able to:
• identify suited positioning technologies for different application scenarios;
• identify how the network deployment and measurement limitations impact on the system performance.
  4. Communication
  Students will be able to:
• describe the learned technologies, at different levels of details, also comparing them with respect to specific aspects;
• present a project consisting in the simulation of a localization/mobility problem, implementation of a selected methodology and evaluation of the performance (non mandatory);
• interact with other colleagues during laboratories and project discussion.
  5. Lifelong learning skills
  Students will be able to:
• learn the operating principles and potential of new emerging communication technologies for localization and connected mobility;
• learn how to develop a software prototype, assess the performance and validate it by numerical simulations.

Part I - Fundamentals of localization and navigation
Location-based services, location measurements (TOA, TDOA, AOA, RSS), infrastructures and protocols, spatial-temporal reference systems, positioning methods (multi-lateration/angulation, proximity sensing, fingerprinting, dead reckoning, hybrid approaches), navigation equations and algorithms, accuracy and geometric dilution of precision, dynamic models and filters for mobile positioning (Kalman, grid-based and particle filters).
Part II - Wireless technologies for indoor/outdoor applications
Positioning systems based on cellular and short-range networks (cellular, LPWAN, WiFi, Bluetooth, ZigBee, UWB, RF-ID), satellite navigation (GPS, Galileo, GNSS, Beidou) and augmentation systems, hybrid and advanced technologies (passive radio imaging, cooperative and distributed systems for multi-agent localization/tracking), examples and applications.
Part III - Smart mobility systems
Introduction to ITS, fundamentals of traffic modelling and control, technologies for traffic monitoring, processing methods for traffic analytics, communication systems for road safety and traffic efficiency, cooperative ITS (C-ITS), vehicle-to-anything (V2X) communication for assisted/automated driving, examples and applications.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The exam consists of a written test (2-3 numerical exercises and questions on the course contents). Students can integrate the written test result with either a project on a topic assigned by the teacher and her collaboratos, or an oral test on the course contents. In case of no project/oral, the final mark is the one obtained in the written test clipped to maximum 27/30. The project can add up to 4 points, the oral can add up to 2 points. Live question sessions (optional) are organized during the lessons, to facilitate self-assessment, contributing to up to 4 extra points; quizzes can be done only in presence.
The instructor reserves the right to require an oral examination.
Details on examination phases and modalities are in the table below.
Type of assessment
Description
Dublin descriptor
Written test (mandatory)
Numerical exercises.
Open-ended questions.
Close form question (true/false).
1, 2, 3, 4, 5
Project (optional)
The project consists in the development of a software prototype of a localization/mobility system in Matlab and assessment by simulations. The topic is assigned during the lab lessons and remains valid for the whole academic year (till Jan-Feb session included). It can be developed individually or in group.
Few days before the discussion of the projects with the teachers, students are required to submit their presentation file and source codes.
Software tools for project development will be acquired during the laboratory hands-on activities: students will have the opportunity to work on practical use-cases, practicing with real data acquired during experimental tests and/or projects in collaboration with companies of the sector.
2, 3, 4, 5
Quiz (optional)
Live question sessions are organized during the term for self-assessment. Quiz tests can be done only in presence.
1, 2
Oral test (optional)
Oral test on course contents.
3, 4, 5

Course website on the webeep platform of Politecnico di Milano: lecture notes; laboratory notes and codes; seminars by experts on positioning, navigation and smart mobility, from university/industry (approx. 2-3 seminars per year, since 2010-2011); projects by former students; examination papers; further material and information. www.polimi.it
A. Küpper, Location-based Services. Fundamentals and Operation, Editore: Wiley, Anno edizione: 2005
Note: Part 1 of the course - Fundamentals of localization and navigation
A. Bensky, Wireless Positioning Technologies and Applications, Editore: Artech House, Anno edizione: 2008
Note: Part 2 of the course - Terrestrial radio positioning
A. C. García, S. Maier, A. Phillips, Location-Based Services in Cellular Networks from GSM to 5G NR, Editore: Artech House, Anno edizione: 2020
Note: Part 2 of the course - Terrestrial radio positioning
R. Prasad e M. Ruggieri, Applied Satellite Navigation Using GPS, GALILEO, and Augmentation Systems, Editore: Artech House, Anno edizione: 2005
Note: Part 2 of the course - Satellite radio positioning
P. D. Groves, Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, Editore: Artech House, Anno edizione: 2007
Note: Part 2 of the course - Satellite radio positioning
E. D. Kaplan e C. Hegarty, Understanding GPS: Principles and Applications, Editore: Artech House, Anno edizione: 2005
Note: Part 2 of the course - Satellite radio positioning
B. Dalla Chiara, ITS nei trasporti stradali, Editore: EGAF, Anno edizione: 2013
Note: Part 3 of the course - Intelligent transportation systems
C. F. Daganzo, Fundamentals of Transportation and Traffic Operations, Editore: Pergamon-Elsevier, Anno edizione: 1997
Note: Part 3 of the course - Intelligent transportation systems
Claudia Campolo, Antonella Molinaro, Riccardo Scopigno, Vehicular ad hoc Networks - Standards, Solutions, and Research, Editore: Springer, Anno edizione: 2015, ISBN: 978-3-319-15497-8
Note: Part 3 of the course - Cooperative ITS
Markus Dillinger, Mikael Fallgren, Toktam Mahmoodi, Tommy Svensson, Cellular V2X for Connected Automated Driving, Editore: Wiley, Anno edizione: 2021, ISBN: 978-1119692645
Note: Part 3 of the course - Cooperative ITS

The main objective of the course is to provide the students with the necessary tools for the design and assessment of machine components, both under static and fatigue loading. The basics of mechanical design are illustrated in order to include capability to develop simplified structural models, basic theory of elasticity, design methods for static and fatigue loads (multiaxial and variable amplitude loading) and tools for assessing the influence of notches on static and fatigue strength.

Once the students has passed the exam, the students will:
possess the knowledge of the main tools (analytic approach) and methods for modelling structural behaviour of simple machine components/systems; (DdD1)
understand assumptions and limitations of the beforementioned tools and methods; (DdD1)
perform design and assessment of machine components/systems subjected to static and fatigue loading; (DdD2)
analyse and critically discuss the results. (DdD2)
Thanks to the practices that are focussed on case studies (basic optimization technique, bolted and welding joints, basic of gears, pressure vessels, power transmission shafts, static design of a prothesis) the students will improve the skills necessary for practical design applications. Moreover, the theoretical concepts and the case studies will provide together a more general capability to approach an engineering solution of simple problems related to structural design in several fields. These competences are therefore of benefit for a wide range of study courses.

The course is organized in lectures and tutorials. Theoretical concepts will be applied during application in order to show an engineering approach to the design and assessment of actual and relevant components.
Internal forces: definition of slender members, definition of internal forces and equations of equilibrium, frames;
Axial and bending problem in beams: hypotheses and basic assumptions for the Euler-Bernoulli beam; state of stress due to normal force and bending moment;
Shear in beams: relationship between bending moment and shear force; shear stress distribution in case of solid section; torsion in beam: distribution of torsional stresses for a circular solid beam;
State of stress: definition of equilibrium for deformable bodies; Cauchy tetrahedron; principal stresses and directions; stress invariants; hydrostatic stress tensor and stress deviator tensor; Mohr’s circles for plane stress; three-dimensional description of Mohr’s circles;
Material behaviour: tensile test devices and specimens; engineering stress-strain curve; true stress-strain curve; ductile and brittle failure mechanisms; ideal material models;
Influence of notch for ductile and brittle materials: phenomenological aspects; definition of Kt and Ks parameters.
Stress-strain relations: general expression of the elastic tensor; elastic tensor for isotropic materials; derivation of the generalised Hooke’s law; stress-strain relationships for plane stress and plane strain.
Failure criteria for isotropic materials: Galileo-Leibniz-Rankine-Navier criterion for brittle materials; Guest-Saint Venant-Tresca criterion for ductile materials; Huber-Hencky-von Mises criterion for ductile materials; general considerations about the definition of the safety factor;
Fatigue: fatigue phenomena; fatigue testing; definition of fatigue cycles; Wöhler diagram; finite and infinite life regime; relationship between fatigue limit and tensile strength; influence of mean stress with Haigh diagrams; notch effect on fatigue; influence of surface finishing; size effect and influence of stress gradient; damage accumulation with Miner-Palmgren rule; Gough-Pollard and Sines criteria for multi-axial loading condition.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
The exam is based on a written test followed by a voluntary oral one. Written exam is compulsory and cover all the theoretical and applicative topics of the course. The written test will include the solution of numerical exercises, aimed at assessing the capability of application of the design tools to practical cases and questions aimed at assessing the students’ capability of application of the design tools to simple practical cases regarding machine components and systems and/or to test students’ knowledge and understanding of the theoretical topics included in the program. Oral exam is voluntary and possible only in case written exam has been positively passed; oral exam can modify the score of +- 3 points and have to be carried out in the same exam session of the written one. The oral test will prove also the capability of critically discuss the design tools and their limits and or applicability conditions. Practical exercises can be also requested during oral exam.

Lecture notes by the teacher (english) Beep
N. E. Dowling, Mechanical Behavior of Materials, Global Edition 5th Edition, Editore: Person, Anno edizione: 2019
AA. VV, Costruzione di Macchine, Editore: McGraw Hill
Note: (italian language)

Machine learning deals with the problem of building programs that learn from experience. While artificial intelligence focuses on the representation of knowledge and the algorithms for reasoning about such knowledge, machine learning deals with the problem of extracting knowledge from experience gathered solving problems to be used to solve unseen instances of similar problems.
This course provides the theoretical and methodological foundation of machine learning methods and algorithms that are used in several application areas, including, Data Mining, Robotics, Autonomous Agents, Bioinformatics, Recommender Systems, Social Networks, etc.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will learn how to:
Model supervised learning and reinforcement learning problems
Train their models
Select the model complexity
Evaluate the performance of their models
Applying knowledge and understanding
Given specific project cases, students will be able to:
Model the learning problem
Select the most appropriate learning technique
Tune the hyperparameters of the studied techniques
Assess the performance of the learned model.
Making judgements
At the end of this course, students will be able to:
Correctly assess the performance of the proposed solutions
Be aware of fairness issues involved in the training of machine learning methods
Lifelong learning skills
Students will learn the trade-off between bias and variance and hot to manage it by selecting the proper complexity of the learning model
Students will learn to develop algorithms to solve relevant learning problems.

Introduction (1 class)
Basic concepts.
Learning theory. (4 classes)
Bias/variance tradeoff. Union and Chernoff/Hoeffding bounds.
VC dimension. Worst case (online) learning.
Practical advice on how to use learning algorithms.
Supervised learning. (8 classes)
Supervised learning setup. LMS.
Logistic regression. Perceptron. Exponential family.
Kernel methods: Radial Basis Networks, Gaussian Processes, and Support Vector Machines.
Model selection and feature selection.
Ensemble methods: Bagging, boosting.
Evaluating and debugging learning algorithms.
Reinforcement learning and control. (6 classes)
MDPs. Bellman equations.
Value iteration and policy iteration.
TD, SARSA, Q-learning.
Value function approximation.
Policy search. Reinforce. POMDPs.
Multi-Armed Bandit.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The assessment will be based on a written exam at the end of the course, where both theoretical competence and modeling skills will be tested.
Type of assessment
Description
Dublin descriptor
Written test
Solution of numerical problems: exercises on supervised learning and reinforcement learning problems
Solution of modeling problems: exercises where the student needs to properly model the machine learning problem and choosing the most appropriate solution technique
Theoretical questions with open answers.
1,2
1,2,3,5
1,2,5

Christopher M. Bishop, Pattern Recognition and Machine Learning, Editore: Springer-Verlag Berlin, Heidelberg, Anno edizione: 2006, ISBN: 0387310738 https://www.microsoft.com/en-us/research/people/cmbishop/prml-book/
Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, Editore: MIT Press, Cambridge, MA, Anno edizione: 2018, ISBN: 0262039249 http://incompleteideas.net/book/the-book-2nd.html
Murphy, K. P., Probabilistic Machine Learning: An introduction, Editore: MIT Press, Anno edizione: 2022 https://probml.github.io/pml-book/book1.html
Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, Mathematics for Machine Learning, Editore: Cambridge University Press, Anno edizione: 2020 https://mml-book.com/

Insegnamento della Facolta' di Medicina e Chirurgia dell'Universita' degli Studi di Milano aperto a un numero limitato di studenti di Ing. Biomedica. Lezioni frontali introducono i principali temi di clinica neurologica. Queste vengono completate da studio integrativo sulle nozioni di base di anatomia e fisiologia necessarie, visite a laboratori ed approfondimenti sulle indagini strumentali.

The goal of the course is to provide the engineering foundations and discuss the technological challenges related to the manufacturing of biotherapeutics, including proteins, peptides, monoclonal antibodies and oligonucleotides. The analysis starts from the processes for the drug substance production via fermentation. The different configurations of a bioreactor will be discussed, ranging from the most simple and established batch reactor to the perfusion bioreactor. Then, the purification and virus inactivation steps are considered, with particular reference to chromatographic processes. During the course, particular attention will be devoted to the ever-increasing effort towards process intensification. With this regard, a thorough insight into integrated continuous biomanufacturing and the quality-by-design principle will be provided.

The lectures will allow students to:

• understand the current processes and future needs for the GMP manufacturing of biopharmaceuticals.
• design and recognize the peculiar features of the different configurations of bioreactors for the manufacturing of biopharmaceuticals, including batch, fed-batch, chemostat and perfusion reactors.
• acquire the fundamentals of biopharmaceutical chromatography
• design chromatographic processes for capture and polishing of biopharmaceuticals
• critically discuss continuous countercurrent chromatographic processes
• contextualize the current trends of process integration and digitalization
  The project sessions, in turn, will allow students to:
• decline continuity equations to the design of specific unit operations used in the biopharmaceutical industry
• apply numerical methods to solve these equations and to verify the consistency of the results
• critically discuss the results achieved in the context of the process and the technoeconomical landscape specific for the sector

The lectures describe the physicochemical fundamentals of processes for the production of biopharmaceuticals. The focus will be on the manufacturing of these drugs through recombinant technology. In fact, these are typically expressed by cells cultured in suitable bioreactors and subsequently purified through various steps, referred to as capture and polishing, usually based on chromatographic techniques. The relevant engineering aspects of these processes are discussed with a focus on productivity, yield and purity of the final product, but also on the quality of the final drug in terms of aggregates, fragments and charge variant distributions and glycosylation patterns. The relationship between the process operating conditions and the drug efficacy and safety for the patient are discussed, also in connection with economical factors.
During the project, the students divided in groups of 4 people will deal with the development from the clinical to the commercial scale of a biopharmaceutical. The project is developed according to the following steps:

• literature search
• definition of the project strategy
• definition and planning of the project activity
• solution of the problem through suitable mathematical models
• delivery of the results through an oral presentation

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The final evaluation consists of two contributions: oral exam on the content of the lectures (60%) and oral presentation of the results obtained during the project (40%).
In particular, in the oral examination, the student should demonstrate to have acquired the understanding and knowledge about the various aspects discussed in the lectures. The principles of biopharmaceutical production, methods for purification with particular attention to chromatographic techniques are the main part of the exam. The oral exam accounts for 60% of the final grade.
For the research projects, each work team of 4 students is required to produce and discuss orally a report, in the form of a presentation, about the obtained results, in front of the class and the instructors. The students are required to have properly understood and master the research project they worked on, to critically analyze the obtained final results and to illustrate the required subsequent developments. The discussion of the results of the project accounts for 40% of the final grade.

M. Wolf, J.-M. Bielser and M. Morbidelli, Perfusion Cell Culture Processes for Biopharmaceuticals, Editore: Cambridge University Press, New York, Anno edizione: 2020
G. Carta, A. Jungbauer, Protein Chromatography: Process Development and Scale-Up, 2nd Edition, Editore: Wiley-VCH, Anno edizione: 2020, ISBN: 978-3-527-31819-3
D. Pfister, L. Nicoud and M. Morbidelli, Continuous Biopharmaceutical Processes, Editore: Cambridge University Press, New York, Anno edizione: 2018

Il corso si articola in lezioni frontali, ed esercitazioni pratiche di laboratorio, con l’obiettivo di fornire agli studenti i fondamenti teorico-pratici dei materia
quando utilizzati nell’ambito della salute. Durante le lezioni frontali, dopo aver fornito le nozioni fondamentali relative alle tecnologie di stampa 3D, si affrontano i temi più specifici legati ai materiali
bioinchiostri, e alla loro caratterizzazione, così come ad alcuni aspetti specifici legati alle applicazioni nell’ambito della salute, come ad esempio quelle in
La parte sperimentale è volta all’apprendimento guidato delle fasi di realizzazione di semplici disegni 3D, alla all’impiego di stampanti 3D fusione di filam
bioinchiostri, alla loro caratterizzazione reologica, meccanica, termica e biologica, alla progettazione ed esecuzione dell’esperimento di stampa 3D, a
stampati, alla analisi dei dati ottenuti.
*******
Il corso è tenuto secondo una modalità INTERCLASSE, ed è a numero chiuso per un massimo di 40 studenti, di cui 20 iscritti al corso di Laurea Magistral Nanotecnologie e 20 iscritti al corso di Laurea Magistrale in Ingegneria Biomedica.
Per quanto riguarda l'accesso al corso, per i soli studenti in Ingegneria dei Materiali e delle Nanotecnologie, si darà la precedenza a coloro che
giorno e l'ora di iscrizione.
Le lezioni, e le attività di laboratorio si svolgeranno congiuntamente per tutti gli studenti, divisi in gruppi eterogenei rispetto al corso di studi di provenienza.
I docenti e gli assistenti, che appartengono ai corsi di Studio In Ingegneria dei Materiali e delle Nanotecnologie, così come a quello di Ingnegneria Biomedica, s
svolgimento di lezioni, esercitazioni e attività laboratoriali in copresenza.

Lo studente:
conosce in modo critico i concetti fondamentali delle tecnologie manifatturiere additive (DD1);
è in grado di formulare e risolvere problemi ingegneristici relativi alla progettazione di bioinchiostri (DD2);
possiede metodi e strumenti per affrontare problemi relativi alla applicazione clinica del 3Dbioprinting (DD2);

Contenuti delle lezioni
Contenuto delle lezioni
Introduzione alle tecnologie di manifattura additiva
Introduzione al disegno 3D
Le tecnologie 3D Bioprinting
I bioinchiostri e la loro formulazione.
Applicazioni cliniche del 3D bioprinting
Applicazioni in ambito vascolare e cardiovascolare, e in oftalmologia
La sicurezza, gli aspetti etici e regolatori del 3D bioprinting Attività di laboratorio
Stampa 3D FDM e disegno 3D
Formulazione e caratterizzazione di bioinchiostri
Caratterizzazione termo-meccanica e biologica dei bioinchiostri
Stampa 3D dei bioinchiostri, con e senza cellule
Caratterizzazione termo-meccanica e biologica dei campioni stampati

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
L’esame è scritto ed è diviso in due parti. La prima parte consiste nella redazione di una relazione di gruppo relativa a contesto, metodologie, risultati, discussione. La seconda parte consiste in una prova scritta individuale costituita da 8 domande aperte e 8 domande chiuse a risposta multipla sugli argomenti del corso.
Nella valutazione dell’esame, la determinazione del voto finale tiene conto dei seguenti elementi:
Capacità di analisi dei problemi
Correttezza formale e sostanziale delle procedure individuate per risolvere i problemi
Correttezza dei risultati ottenuti
Conoscenza e comprensione degli argomenti
Adeguatezza del linguaggio tecnico scientifico utilizzato
Autonomia di giudizio

This course aims at providing the students with knowledge about radiation therapy and emission tomography and their fundamental technological aspects. The first part of the course deals with the interaction of radiation with the biological tissue for better understanding the mechanisms ruling dose delivering in radiation therapy. Moreover, some outlines about radiobiology are given, for understanding radiation interactions with the DNA and the cellular response against radiation. Microdosimetry is also introduced for focusing on measurement techniques, which are capable of giving deeper information about the quality of radiation fields. Two emission diagnostic techniques (SPECT and PET) are discussed by focusing on the detection systems and the phenomena affecting their response. The operation principles of particle accelerators applied to radiation therapy are given for a better understanding of the engineering aspects linked to the patient treatment. The final part of the course discusses electron and X-ray therapy, both conventional and conformal, and hadrontherapy with protons and carbon ions. Boron Neutron Capture Therapy (BNCT) is also treated, together with the problematics linked to the use of research nuclear reactors and particle accelerators as neutron sources.

The student:
knows the fundamental interactions of radiation with the biological tissue and the cellular response to radiation, the main emission diagnostic and radiation therapy techniques, the principles of operation of particle accelerators applied for radiation therapy and the detection techniques for dosimetry;
can apply her/his knowledge for assessing the quality of radiation fields and the radiation dose (laboratory sessions);
can judge critically the advantages and the problematics of different radiation therapy techniques;
acquires skills for communicating her/his knowledge in the field of engineering techniques for radiation therapy.

Principles of Radiation Physics. Definition of absorbed dose and dose prescription in radiation therapy. Interactions (ionization, excitation and Coulomb scattering) of charged particles with biological tissues. Production of Bremsstrahlung X-rays. Inelastic interactions of intermediate-energy hadrons with biological tissues. Photonuclear reactions. Interactions of electromagnetic radiation with biological tissues. Interactions of neutrons with biological tissues.
Outlines on radiation biology. Direct and indirect interactions of radiation with DNA. Survival curves. Relative biological effectiveness (RBE). The oxygen effect. Fractionation of radiation therapy.
Microdosimetry. Principles of microdosimetry. Microdosimetric spectra. The tissue-equivalent proportional counter (TEPC). Outlines on nanodosimetry.
Emission diagnostic techniques. Single Photon Emission Tomography (SPECT): collimation and detection of gamma rays, influence of Compton scattering. Positron Emission Tomography (PET): coincidence detection techniques, spatial resolution, random coincidence, attenuation and scattering, PET scintillators.
Principles of operation of particle accelerators. The Cockroft-Walton and Van de Graaff electrostatic accelerators. The betatron. The cyclotron. Resonant cavities (outlines). Linear accelerators for ions and electrons. The synchrotron (outlines).
Techniques for dose delivering. X-ray and electron radiation therapy: electron scatterers, Bremsstrahlung targets, flattening filters, collimators. Hadron-therapy: dual-scattering systems, modulators, spread-out of Bragg peak, active techniques. The gamma-knife and stereotactic techniques.
Boron Neutron capture Therapy (BNCT). Principles of BNCT. Neutron field tailoring for BNCT. Accelerator-based neutron sources for BNCT.
Course Organization
The course is structured in one semester with lectures of six hours per week. Seven experimental laboratories (duration about four hours per lab session) are offered, aiming at gaining experience with different types of radiation measurement techniques. Visits to radiation therapy, hadron therapy and BNCT facilities are organized in the framework of the course.

Prova orale obbligatoria
The evaluation consists in an oral examination. It aims at verifying the knowledge of the course topics, by focusing on the ability in understanding the physical and radiobiological aspects of different radiation therapy and diagnostic techniques. The student’s capability in evaluating the rationale of the engineering solutions to the main problematics of radiation therapy and dosimetry will also be verified. The first part of the exam foresees a critical discussion of one of the laboratory sessions. In particular, the student’s capability of carrying out autonomously a laboratory session will be verified. The student’s communication capabilities will be tested through discussions about the advantages of a given radiation therapy technique, the solutions adopted for dose delivery, the techniques for measuring the dose and for assessing the beam quality.

Notes about all the course topics and the laboratory sessions are available on the WeBeeP site.

The course will offer students basic and advanced knowledge in robotics, for design and programming in medical applications.
The course has the following objectives:
To make students familiar with kinematic structures of medical robots
To provide students with basics of medical robotics, in terms of general hardware (actuators and sensors) and software (planning and control)
To give students the knowledge on the basic design requirements for a robot to be used for medical applications
To make students able to design and test novel robotic structures for a medical-related application.
Each topic is treated both theoretically and practically through a project/ discussion work.

Students will gain knowledge and understanding of (Dublin Descriptor 1, DD1):
Elements included in a medical robot design and its control schema, including autonomous reasoning
Design requirements of a robot to be used in the medical sector
Design patient-centric control and planning with emphasis on safety, compliance, and delicate physical interaction.
Sensory information processing and context awareness, integrating medical perception (intra-operative imaging, tracking, force sensing) into closed-loop control.
Outcome measures to evaluate robotics in medical settings.
Students will acquire the capability to (DD2):
Design and implement novel solutions for medical robot
Model and simulate the robot behavior
Students will also develop skills relevant to (DD3, DD4 and DD5):
Apply an interdisciplinary approach for healthcare related applications
Benchmark robotics system in healthcare (accuracy, sterility, usability, risk).

Frontal lectures
History of medical robotics and taxonomy of medical robots
Design issues for robotics in healthcare:
Clinical constraints and metrics for validation
Sensors and actuators for medical applications,
Kinematic structures of robots in healthcare
Registration robot-to-imaging
Minimally invasive surgery: vision and control
Situation awareness in surgical robotics
Detection and classification of surgical tools and anatomical structures
Visual servoing
Teleoperation (scaling, clutching, tremor filtering, passivity and stability)
Autonomy in surgical applications
Interventional and continuum robots
Continuum robots modelling
Motion planning (trajectories for surgical)
Sensing and actuation for diagnosis/ therapies – soft catheters
Navigation and control with friction/ hysteresis compensation
Human robot interaction
Augmented reality
Haptics and force feedback
Virtual constraints
Humanoids for rehabilitation and Telepresence for remote diagnosis (examples)
Regulatory aspects, safety, ethical issues and certification
Hands-on / practice
Simulation environment for medical robotic applications
Computer vision tools for endoscopic image processing

Prova scritta condizionata (a scelta del docente), senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
Students will be assessed on the work produced during each stage of the practical exercises. Their knowledge of the topics covered in the lectures and included in the course bibliography will also be evaluated (DD1). Assessment will take into account students’ participation and interaction during the project activities (DD4), the outcomes of the design exercises (DD2 and DD3), their ability to work both independently and as part of a team (DD3 and DD4), and the quality of the representation and presentation of their design work (DD4).
The course will conclude at the end of the semester with the final evaluation (mandatory written test and compulsory interview) and the assignment of the final grade to all students. The final examination is intended to enable students to:
demonstrate that they have acquired a design approach that, through knowledge of technologies, enables them to identify technically feasible pathways for the development of medical robots (DD1 and DD2);
demonstrate the ability to respond effectively and promptly to design problem-solving challenges (DD2 and DD3);
demonstrate the ability to effectively solve problems related to the control of medical robots (DD2 and DD3);
communicate and justify design choices, technical solutions, and engineering decisions using appropriate scientific and technical terminology during the oral examination (DD4).

The intelligence of any autonomous system is strictly related to its capabilities to sense the surroundings. Sensors thus are becoming a fundamental building block in several societal fields, from personal hand-held devices (smartphone, cameras, wearables...) to autonomous mobility (car, planes, drones...), from biomedical smart tools (imaging, cardio, speakers...) to space exploration (satellites, rovers...), from Industry 4.0 (robotics, surveillance...) to health/fitness (step counter...).
The course aims thus at introducing the basic concepts of different microelectronic devices that allow acquiring information from the physical world around us (microsensors), and performing actions on it (microactuators) at the microscopic and nanoscopic level. The focus is centered on MEMS (Micro-Electro-Mechanical-Systems) transducers, on optical CMOS (complementary-metal-oxide-semiconductor) pixel image sensors, on related driving/readout electronics, and on system-level considerations (e.g. noise-consumption-area trade-off, bounded/unbounded performance optimization, definition of the specifications for the different applications...).
The course is thaught with a balanced mix of theoretical classes (with realistic numerical examples and case studies), project-oriented exercises, and software-assisted exercises. Through the teaching of these technologies, sensors, and circuits, the course provides consistent examples of electronic sensing systems applied in several fields of the modern and future society (Internet of Things, Autonomous Driving, Smart City, Industry 4.0...).

At the end of the course:

• the student will be aware of the increasing needs for sensors that the society is having in this era, their benefits and their prospected evolution;
• according to DdD1 for Master Level courses, the student will have a comprehensive knowledge, both from a top view (system-level) and in details (subsystems design), about the design strategies of sensing and transduction systems based on microelectronic sensors (MEMS and CMOS technologies, in particular) and on electronic circuits;
• according to DdD1 for Master Level courses, the student will know the theoretical basis and equations that lie behind the design of a sensing system, the trade-off between the electronic domain (power, noise) and the sensor domain (area, dynamic range, linearity, bandwidth...); he/she will know if, when and why the aid of CAD simulations is needed; and he/she will know how to use the approriate softwares to develop such simulations;
• according to DdD1 for Master Level courses, the student will also be able to discuss the physical principles at the basis of the discussed variety of sensors (in particular, MEMS and CMOS), and to identify the pros and cons of the different approaches;
• according to DdD2 for Master Level courses, the student will be able to tackle the design of a sensing system (e.g. accelerometer, clock, magnetometer, gyroscope, light sensor...) starting from the specifications given by the target application (consumer, automotive, medical, industrial...); to co-develop then the sub-specifications for the sensor part and for the electronic part; and finally to design the sensor and the electronics according to the technologies discussed in the course;
• acciording to DdD 2 for Master Level courses, the student wil be able to choose among different technologies to solve a specific problem related to a sensing system similar to those discussed in the course;
• according again to DdD2 for Master Level courses, the student will know the basics of advanced software used in the design of sensing systems (e.g. use of multiphysics finite element modeling for sensors design, use of Cadence for electronic design);
• according to DdD2 for Master Level courses, the student will know the most significant parameters that characterize the sensing system behavior (scale-factor and its drift, linearity, full-scale range, offset and its drift, noise, power consumption, bandwidth, area and - last but not least - cost). She/he will also know how to practically measure them;
• according to DdD3 for Master Level courses, the student will be able to extend the concept studied on the categories of sensors discussed in the course to other categories, not discussed in the course which share the same set of problems (e.g. to microphones, pressure sensors, ultrasonic transducers, IR sensors...). To this purpose, specific exercise will be given, where the student is request to design sensing systems not discussed in the course. To stimulate this ability, the students will be left time for autonomous solving before the exercise is solved by the lecturer.

The course discusses topics related mostly to MEMS and CMOS microsensors, and the related driving/readout electronics.

• MEMS sensors and transducers are probably the most disruptive technology of the 21st century. They are rapidly changing our everyday life and they are at the basis of the Internet-of-Things forthcoming revolution. MEMS allow acquiring - in ultra-compact dimensions and high-performance - several physical quantities like acceleration, rotational speed, magnetic field direction and pressure. These devices are nowadays widely applied in automotive, avionics, health care instrumentation and consumer applications (mobile phones, tablets, portable devices). Other kinds of MEMS allow generating and detecting ultrasound waves or producing video images by deflecting light beams.
• Optical and infrared image sensors, mostly CMOS Active Pixel Sensors, allow getting color and multispectral digital images both in still and video cameras. The applications of these devices range from consumer to scientific, surveillance, medical and technical fields. They pervade our everyday life in cameras and mobile phones, with a so far neverending performance improvement.
  The target audience of the Course is represented by students oriented towards either:
  (i) advanced design of innovative micro-sensors, or
  (ii) advanced design of their low-noise, low-power integrated electronics for sensors, or finally
  (iii) advanced design of electronic systems embedding these sensors.
  The Course is split into 3 main sections, whose main topics are summarized in the following.
• MEMS sensors (about 40% of the Course).
  Building technologies of MEMS and NEMS (Nano Electro Mechanical Systems). Sensing and actuation elements based on capacitive, piezoelectric and piezoresistive effects. Inertial sensors: accelerometers, gyroscopes. Springs configurations. MEMS resonators and resonant mass sensors. Magnetic field sensors: MEMS, Hall and AMR magnetometers. MEMS based on membranes: ultrasonic transducers (CMUT and PMUT) and microphones. Time sensors (resonators, oscillators, clocks). Optical MEMS (micromirrors, microbolometers). Aspects of MEMS relaibility (fracture, adhesion, fatigue).
• Electronics for MEMS driving and signal acquisition (about 25% of the Course).
  Low noise–low power front-end electronics for accelerometers. Oscillators and sensing electronics for gyroscopes. Amplitude-gain-control in oscillators. Resonant and off-resonance circuit operation. High-performance characterization setups (rate tables, shakers,...) and electronics for MEMS. Driving and sensing electronics for ultrasonic devices.
• Digital image sensors (about 35% of the Course).
  Basics about the interaction of light with the semiconductors. CMOS-APS pixel detectors. General architecture and sensor performances: noise, dynamic range, nonlinearities, frame rate. Main acquisition technques: 4T circuits and CDS (Correlated Double Sampling). HDR (High Dynamic Range) architecture. Backside illumination technologies.
  For each sensor topology, realistic case studies will be discussed through numerical exercises and comparisons to existing cutting-edge products. Some activities are also foreseen concerning the application of CAD software to the design of high-performance MEMS sensors. The software is licensed to PoliMi students. 2-3 seminars will be given by invited indusrial members about hot topics for the sensors community.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
Evaluation will be carried on so to understand whether the student has achieved the expected results, listed above. To this aim, the exam is formed by:
1 - a written exam, split into three parts
a) one theoretical question, whose aim is to verify the student comprehension of the physical principles behind the sensors and/or to verify her/his capability in making choices among different system topologies and/or to verify his capability in taking decisions and being convincing, and/or to verify her/his knowledge of the industrial state of the art and need (according to DdD1, DdD4 and DdD5);
b) one numerical exercises on design of sensing systems, whose aim is to verify how the student practically turns design considerations into a quantitative, optimized, system design, accounting for the trade offs and the boundary conditions (according to DdD2);
c) a second numerical exercises on design of sensing systems, whose aim is to stimulate the applications of the concept studied in the course to similar domains, e.g. other sensing systems, or systems for the same physical quantity but based on other technologies (according to DdD3);
The written exam duration will be 3 hours, and the maximum grade that can be achieved with the written exam is 30/30 (no cum laude).
2 - the discussion of the exam, and facultative oral exam
Students will be given the possibility to discuss the exam correction together with the lecturers. This will allow to understand more in depth the student awareness of the topics discussed in the written exam, and thus to deepen its evaluation. This procedure also aims at identifying possible misunderstandings, and thus may give rise to partial amendment of the grade.
There is then the possibility to take a facultative oral exam (approximate duration is 10-15 minutes) only for students with a grade equal or larger than 26. The oral exam can increase or decrease the grade, typically by 1 to 3 points. Oral questions often deal with problems that go beyond the course content, but that can be discussed by the students that have a comprehensive knoledge of the course topics.
The aim of the additional oral exam is to verify more in depth the knowledge acquired by the top-10% of the class, and gives the possibility to grant 30 cum laude for the students that really demonstrate advanced system design capabilities.
No intermediate (in itinere) exam sessions are foreseen. However, the new didactic approach stimulates a continuous self-judgment of the student preparation.

V. Kempe, Inertial MEMS: principles and practice
C. Akar, A. Shkel, MEMS vibratory gyroscopes
G. Langfelder, A. Tocchio, MEMS integrating motion and displacement sensors, Editore: Woodhead publishing
Note: Chapter 13 of the book: "Smart Sensors and Mems"

The course provides a view of how computational approaches combined with digital tools can extend design boundaries by enabling the creation of complex multifunctional shapes that can be manufactured through digital fabrication technologies. Students will be trained on algorithm-based modelling methods for designing complex, multifunctional and parametric shapes. By the end of the course, they will develop the capability to envision and create complex and multifunctional shapes to propose innovative engineering solutions. They will thus learn and use the main modelling techniques, approaches, and environments that support their creation. They will also understand how to properly create digital models of these complex shapes to guarantee their manufacturability.

By the end of the course, the students will be able to:
Knowledge and understanding
Understand the potential of complex shapes for developing cutting-edge engineering applications;
Know the main modelling methods and tools for creating complex, multifunctional and parametric shapes;
Discuss how these modelling methods and tools can support the development of advanced and novel engineering solutions;
Understand how to properly develop the digital model for the targeted design requirements and guarantee its manufacturability, particularly with digital fabrication technologies.
Apply the acquired knowledge and understanding
Formalise a design problem in terms of its main design variables and requirements;
Develop parametric modelling workflows for creating complex and multifunctional shapes targeted for specific engineering applications;
Evaluate the feasibility and, specifically, the manufacturability of a solution;
Develop and present a solution related to the analysed design problem.
Further developed skills
Develop a computational thinking mindset concerning how to exploit the potential of computation in product design;
Develop the capability to envision complex shapes and how they could be exploited for multiple engineering applications;
Develop learning capabilities by autonomously exploring how to adapt the modelling workflow for the specific solution under investigation;
Develop communication and collaboration skills to develop and present the ideated solution.

Methods and tools for product design
Computational thinking
Models and tools for computational modelling and form-finding strategies
Digital fabrication strategies
Experimental strategies for mechanical testing of complex shapes

Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
The exam will be an oral presentation focusing on discussing a project. This project can be developed individually or as a group and should be agreed upon with the course lecturer. Before the project discussion, the students will be asked to submit the project materials, which include a technical report, a presentation, a demo, and any other materials relevant to highlighting and discussing the developed work and related outputs. Further details about the material to be submitted will be discussed during the course.
The evaluation will consider the following aspects: the correctness of the terminology used and of the technical aspects presented; the rigorousness and effectiveness of the process followed to develop the project; ability and level of autonomy in solving the challenges encountered in developing the project; completeness and quality of the project material; quality and clarity of the oral presentation of the project; originality of the project in particular in terms of the ability to propose a design problem and find the related solutions by also implementing strategies not directly already discussed during the course; level of understanding of the main state-of-the-art with respect to the project's filed.

Tedeschi, A., AAD_ Alghorithms-Aided Design. Parametric strategies using Grasshopper, Editore: Le Penseur Publisher, Anno edizione: 2020, ISBN: 978-88-95315-30-0
Norman, Donald A., Design for a better world: meaningful, sustainable, humanity centred / Don Norman, Editore: The MIT press, Anno edizione: 2023, ISBN: 9780262047951

To give the students the knowledge of the main modalities for the functional investigation of the brain, ranging from the physical principles exploited for data creation and recording to methodologies for signal, image, and data analysis (feature extraction and classification). The students will be familiar with the main acquisition and processing tools and will be able to use them critically, according to different protocols and physiological/clinical needs.

• Knowledge and understanding: The students will acquire the theoretical tools to process signals and images with specific application to the problem of assessing the functionality of the central nervous system.
• Apply knowledge and understanding: The students will apply the tools for signal and image processing to real EEG, fMRI and fNIRS datasets.
• Making Judgements: The students will be able to judge the quality of an acquired dataset, to select the proper pipelines for signal and images analysis, and to critically evaluate the results of the processing.
• Lifelong learning skills: The students will face the key difference between theoretical models and practical implementation, as well as the ability of problem solving, team-working and workflow organization. The students will be invited to discuss scientific articles in the classroom, discussing their objectives, motivating the methodology and critically analyzing the results obtained. The students will be required to judge the plausibility of their own presentation as well as, in common discussions, of the ones of other teams.

Main processing methodologies for EEG signal (denoising, features in time, frequency and time-frequency domains; steady state and event-related responses; multichannel analysis; connectivity; source estimation). Main processing modalities for fNIRS signal (continuous wave and time domain modalities, photon sensitivity maps, estimation of oxygenated and de-oxygenated hemoglobin). Main processing modalities for fMRI analysis (analysis of the BOLD signal in resting state and event related). Examples of multimodal integration (EEG + fNIRS, EEG + fMRI).

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
The evaluation will be based on the creation and presentation of group project. The student has to demonstrate:
knowledge on theoretical tools to assess functional evaluation of the central nervous system.
Ability in using and managing EEG, fMRI, and fNIRS datasets.
Ability in processing EEG, fMRI and fNIRS datasets, and in critically discussing the results obtained.

The course illustrates the unit processes needed to realize an integrated circuit on silicon substrates. Beside acquiring familiarity with the unit processes physics and tools, the students will also learn how to arrange those basic steps into a process flow sequence. A simple CMOS circuit process flow will be analyzed.

Knowledge and undertsanding (Dublin descriptor 1):
After the course, the students will:
-Know the basic unit processes for an integrated circuit fabrication
-Understand the physics of the single process steps
-Understand the role of each process step in a CMOS and in a memory process flow
Applying knowledge and understanding (Dublin descriptor 2):
After the course, the students will:
-Be able to choose the best technique for each unit process step, based on the technology node and process flow requirements
-Understand how unit process properties must evolve as a function of technology node scaling
-Be able to predict the impact of a unit process step on the whole device process flow and on a device electrical performances

Introduction
-Semiconductor industry history
-Semiconductor physics review
Process flow (1)
-Process flow for a CMOS device
Silicon

• Crystallography review (Bravais lattice, crystalline structures)
  -Silicon lattice
• Defects and thermodynamics of point defects
  -Mono-crystalline silicon growth (CZ)
  -Silicon wafer fabrication process
  Silicon oxidation
• Silicon oxide structure
• Deal and Grove model
  -Oxidation kinetic dependence on substrate orientation and doping
• Oxidation and point defects
• Charged defects in silicon oxide and CV measurements
• Advanced silicon oxidation techniques
• High-k dielectrics
  Dopant diffusion in silicon
• Fick’s law
• Analytical solutions to second Fick’s law (Gaussian and ERFC profiles)
• Correction to Fick’s law
• Diffusion and point defects
  Ion implantation
• Ion implanter structure
• Implanted concentration profiles, channeling effects
• Nuclear and electronic ion stopping
• Crystallographic damage and TED
  Lithography
• Lithogrpahy basics concepts and main lithographic system architectures
  -Light sources
  -Reticles
  -Photo resist
  -Registration
• Projection lithography;
• Resolution and depth of focus
  -Advanced applications (immersion lithography, EUV, e-beam, self-aligned double patterning, self assembling block-co-polymers)
  · Thin films deposition: CVD and PVD techniques
  -Fluid dynamics basics
  -APCVD and LPDCVD basic models
  -Plasma-assisted CVD techniques
  -Evaporation
  -Sputtering
  Etching
  -Wet etching quick overview
  -Plasma ecthing
• Main properties
• Plasma etchers
• Etch chemistries
• Basic models
  Interconnects
• Local interconnects: silicides and salicides
• Contacts
  *Ohmic vs rectifying behavior
  *Main contact technologies
• Dielectrics: oxides and low-K materials
• Interconnects: subtractive and damascene approach
  Packaging and advanced packaging
  Text book
  D. Plummer and P. B. Griffin, Integrated Circuit Fabrication: Science and Technology. Cambridge University Press, 2023.

Prova orale obbligatoria
The final exam consists of an oral interview (NO WRITTEN TEST).
During the oral interview, every student will be asked three questions slected randomly among the course topics. The questions purpose will be to understand if the student has acquired the proper knowledge of signle process steps physics and their role in an integrated cirucit process flow (Dublin descirptor1), and to verify that the students can autonomously estimate the impact of each step on the entire process flow as welll as the challenges related to technology scaling.
A mark between 0/30 and 30/30 will be assigned for each question. If a 30/30 mark is assgned for each question and for the excercise, the final mark will be 30/30 cum laude.

Plummer, J. D.; Griffin, P. B., Integrated Circuit Fabrication: Science and Technology., Editore: Cambridge University Press., Anno edizione: 2023
James D. Plummer, Michael Deal, Peter D. Griffin, Silicon VLSI Technology: Fundamentals, Practice, and Modeling, Editore: Prentice Hall, Anno edizione: 2001

This course aims at providing students with the basic knowledge of mechanics of materials allowing for the micro-structural architectures and the constituents' properties. So the main objectives of the course will be the mechanics of composite materials. A second part of the course will be focused on the mechanics of thin films, and mechanical characterization testing at small length scale.
The lectures will focus mainly on the mathematical modeling of the mechanical behavior of the materials as well as to the mechanical experimental characterization techniques. To this purpose a brief summary on solid mechanics will be provided at the beginning of the course.
Applications to specific material systems will be treated in the practical lectures.
Two short visits at the Laboratory of Biological Structure Mechanics of Politecnico di Milano are planned with illustration of practical cases of the nanoindentation technique and of the micro-tensile testing.

The students will learn the modeling approaches for the basic mechanical response of micro and nanocomposite materials on the basis of the mechanical properties of the constituents (DdD1). The student should be able to effectively describe theoretical and numerical approaches (DdD1). Furthermore, the student should be able to effectively discuss literature papers describing theoretical and numerical approaches to micromechanics of materials and thin layers systems (DdD1). The student may also show their level of comprehension of the subjects by showing their capability to implement simple micromechanics problems using numerical codes or MATLAB environment (DdD2).

Basic equation of continuum mechanics (a brief summary)
Mechanics of composite materials, homogenization of elastic properties:
Concept of inclusions and inhomogeneities
Concept of eigenstrain
Definition of effective elastic properties
Bounds on effective moduli
Brief introduction to non linear homogenization approaches.
Mechanics of thin films
Motivation for thin films mechanics
Mechanical issues in thin films
Residual stresses in 1D and 2D problems
Curvature induced by residual stresses
Stress state at the interfaces
Experimental techniques for the Study of Mechanical Properties of Thin Films;
Practical lectures:
Applications to specific material systems: ceramic/ceramic composites; metal/ceramic composites; biological tissues (with specific reference to the bone tissue);
Nanoindentation experimental technique; Micro-tensile testing (an in-situ testing).

Prova orale obbligatoria
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
Each student will be called to make a critical review on literature papers focusing on a selected topic previously agreed with the lecturer. An oral presentation of such review with a deep discussion on the topic will constitute the oral exam.
As an alternative, the students may develop a simple project in which they implement a specific micromechanics problems using numerical codes or MATLAB environment.
The above mentioned examination modes will aim at:
1. showing his/her level of knowledge and comprehension of basic micromechanics.
2. showing his/her capability to apply basic micromechanics concepts to specific problems;
3. showing capability of critically review the works of others in the field of micromechanics;
4. showing his/her skill of communication.

By Jianmin Qu and Mohammed Cherkaoui, Fundamentals of micromechanics of solids, Editore: John Wiley & Sons, Anno edizione: 2006, ISBN: 978-0-471-46451-8
William D. Nix, Mechanical Properties of Thin Films http://imechanica.org/files/353%20Class%20Notes%202005.pdf
Note: Lecture notes

L'insegnamento fornisce una valida conoscenza sui fondamenti della misurazione (inquadramento storico e istituzionale, Sistema Internazionale di unità di misura, regole operative) e sugli strumenti specifici di analisi utilizzati nelle misure (stima ed espressione dell’incertezza di misura, corrispondente analisi statistica, regressione dei dati sperimentali, elaborazione del segnale di misura, elementi di affidabilità).
Saranno spiegati i principi e le modalità di funzionamento dei sistemi di acquisizione dati (DAQ) e della strumentazione digitale di misura.
Si offrirà agli studenti una descrizione con un certo dettaglio del funzionamento e delle prestazioni di strumenti di misura quali voltmetri numerici e oscilloscopi digitali.
Nel corso delle attività di laboratorio si insegnerà inoltre ad utilizzare le schede DAQ e il software LabVIEW per la realizzazione di strumenti virtuali (VI, software ) utili per le misure automatizzate e la rappresentazione dei risultati.

E' previsto che lo studente impari la terminologia delle misure e l'analisi dell'incertezza di misura in termini statistici. Lo studente acquisirà la conoscenza del funzionamento dei principali strumenti di misura (voltmetri, schede DAQ, oscilloscopi, analizzatori di spettro) e sarà in grado di progettare ed effettuare misure con la moderna strumentazione digitale di misura.
Le capacità di analisi e stima dell'incertezza di misura e le competenze di progettazione per l'impiego della strumentazione di misura saranno valutate attraverso domande ed esercizi presenti nel compito d'esame.
Risultati di apprendimento attesi al termine del corso e dopo il superamento dell’esame, con riferimento ai Descrittori di Dublino (DdD):
Conoscenza e comprensione (DdD 1) - Lo studente conosce le definizioni del Sistema Internazionale di unità di misura e il significato delle diverse grandezze fisiche. Ha acquisito le metodologie di analisi e stima dell’incertezza di misura nelle misure dirette e indirette. Viene introdotto ai concetti di base di affidabilità di componenti e sistemi. Conosce le strutture e i principi di funzionamento dei principali strumenti per misure elettroniche (voltmetri numerici, schede di acquisizione, oscilloscopi, analizzatori di spettro).
Capacità di applicare conoscenza e comprensione (DdD 2) - Anche grazie alle esercitazioni su problemi di misura, lo studente acquisisce la capacità di applicare i metodi, le tecniche, e le tecnologie apprese a casi reali semplici. In particolare, è in grado di analizzare il funzionamento di un sistema di misura, di analizzare le prestazioni metrologiche (anche in maniera comparativa) tra strumentazione diversa, è capace di progettare e/o effettuare misure elettriche ed elettroniche mediante la strumentazione studiata.
Autonomia di giudizio (DdD 3) - Lo studente è in grado di scegliere la corretta strumentazione di misura per una data applicazione. Lo studente sa individuare le corrette metodologie di analisi dei dati per una loro efficace rappresentazione in forma grafica.
Capacità di apprendimento (DdD 5) - Lo studente ha acquisito un bagaglio di concetti e strumenti sufficientemente generali da consentirgli di affrontare studi successivi nel campo dell’analisi avanzata dei dati di misura. Ha le competenze di base per approfondire gli studi sulla strumentazione elettronica di misura e per approcciare nuovi strumenti e metodi di misurazione. Sarà in grado di approfondire anche autonomamente le conoscenze acquisite nel campo delle misure, con la capacità di leggere e comprendere articoli di carattere scientifico e tecnico e manuali applicativi sempre inerenti alla strumentazione di misura. E' inoltre in grado di utilizzare le conoscenze e competenze apprese per affrontare in maniera strutturata problemi via via più complessi.

La misura e le principali caratteristiche metrologiche. Accuratezza, incertezza, risoluzione, sensibilità, ripetibilità, riferibilità, etc. Il sistema internazionale di unità (SI), regole e simboli del SI, vari tipi di campioni. Definizioni e operazioni su unità logaritmiche (dB, dBm, dBc, Np). Blocchi essenziali di un sistema di misura. Caratteristiche dei segnali (continui e discreti in ampiezza e tempo, periodici, aperiodici) e parametri di interesse (valor medio, efficace, di picco).
Elementi di statistica e incertezza di misura. Significato di valor medio, varianza, varianza del valor medio e numero di gradi di libertà. La miglior stima del risultato di misura e dell'incertezza statistica stimata attraverso la dispersione dei risultati (Cat. A) o con altri metodi (Cat. B). Stima dell’incertezza in misure dirette. Propagazione dell'errore e calcolo dell'incertezza in misure indirette. Indipendenza e correlazione statistiche. Compatibilità fra misure e medie pesate. Elementi di affidabilità.
La rappresentazione dei risultati sperimentali. Diagrammi cartesiani e polari. Scale lineari e logaritmiche. Interpolazione e regressione dei dati sperimentali.
Strumentazione digitale. Campionamento e conversione analogico/digitale (A/D). Voltmetri digitali e multimetri: errore di quantizzazione, risoluzione, numero di bit e numero di cifre in un convertitore A/D. Bit equivalenti. Esempi di voltmetri differenziali (approssimazioni successive, flash e pipeline ). Principio di integrazione e voltmetro integratore a doppia rampa.
Sistemi di acquisizione dati. Schede di acquisizione. Linguaggio di programmazione LabVIEW e strumenti virtuali (VI). Protocolli di comunicazione tra strumenti (RS232, RS422, IEEE488 e USB). Durante le esercitazioni di laboratorio saranno approcciati i fondamenti di programmazione del linguaggio LabVIEW e quanto appreso sarà direttamente verificato mediante la preparazione di esperimenti di misura basati su schede di acquisizione dati. Si sperimenteranno in concreto le problematiche connesse con il condizionamento e l’elaborazione del segnale di misura.
Strumentazione di base per misure. Oscilloscopio digitale: campionamento in tempo reale e in tempo equivalente; modalità di trigger avanzato; misure automatiche. Esempi di misure con l'oscilloscopio (ampiezza, fase, frequenza, tempo di salita, etc.).
Attività di laboratorio
Le attività di laboratorio sono di tipo sperimentale con l'obiettivo di familiarizzare gli studenti con i i programmi SW di acquisizione dei segnali e grandezze dal mondo analogico esterno. Il laboratorio ha lo scopo di avviare l’istruzione dello studente nell’uso della moderna strumentazione digitale di misura. In particolare, saranno accennati e messi in pratica i fondamenti di programmazione del linguaggio LabVIEW: quanto appreso sarà direttamente verificato mediante la preparazione di esperimenti di misura, su componenti e circuiti elettronici, svolti mediante schede di acquisizione dati. Si sperimenteranno in concreto le problematiche connesse con il condizionamento e l’elaborazione del segnale di misura. Gli studenti avranno la possibilità di cimentarsi anche nell’uso della strumentazione di misura (voltmetri numerici, generatori di funzioni, oscilloscopi digitali, e analizzatori di spettro) per la caratterizzazione dei segnali elettrici e elettronici. Ciascuno studente potrà svolgere 8 ore di laboratorio suddivise in 2 esercitazioni (di 4 ore ciascuna). Il calendario delle esercitazioni di laboratorio saranno indicati dal docente durante lo svolgimento del corso. I dettagli organizzativi relativi alle attività di laboratorio e alle corrispondenti valutazioni saranno presentati a esercitazione e comunque resi consultabili alla pagina Webeep del corso.

Prova scritta obbligatoria, con prove in itinere
Prova orale condizionata (a scelta del docente)
La valutazione dello studente è effettuata in base all'esito della prova d'esame
L'esame di Misure consta di due parti da svolgere individualmente
Un breve test a risposta multipla (circa 7-14 minuti di durata) con 5-10 domande a risposta multipla: 5 domande se è una prova in itinere o avete svolto proficuamente (voto >18/30) la prova in itinere e dovete svolgere solo la seconda parte, 10 domande se state svolgendo il compito intero.
Un esame scritto (circa 1-2 ore di durata) è costituito da 2-4 esercizi da svolgere su foglio: 2 esericizi se state svolgendo una prova in itinere o avete svolto proficuamente (voto >=18/30) la prova in itinere e dovete svolgere solo la seconda parte, 4 esercizi se state svolgendo il compito intero.
Il punteggio nel test a risposta multipla non costituisce impedimento allo svolgimento dell'esame scritto farà solo parte della valutazione totale (non saranno pubblicati alcuni test a risposta multipla ma saranno fatti esempi in classe).
La prova d’esame totale consiste in una verifica scritta, di durata circa 1.5-2 ore e da svolgersi il giorno dell'appello, seguita eventualmente da una breve interrogazione orale.
Nel compito, come certamente nel pre-compito, possono essere presenti anche quesiti teorici.
L’ordine logico e la chiarezza espositiva nel rispondere alle domande e nella soluzione dei problemi costituiscono elemento di valutazione. L’esame scritto, così come strutturato, consente di verificare i risultati di apprendimento attesi, anche già indicati secondo i Descrittori di Dublino.
Altre informazioni (anche sugli appelli e la logistica dell'insegnamento) sono reperibili alla pagina Weebeep dell'insegnamento.
Tipo di svolgimento
Descrizione
Descrittore di Dublino
Test scritto
Soluzione di problemi numerici:

• Calcolo dell'incertezza di misura in situazioni reali
• Calcolo delle grandezze nelle unità di misura adeguate al campo di misura (dB, dBm...)
• Esercizi di progettazione e dimensionamento e calcolo numerico:
• Interpolaizone e regressione su set di dati di misura
• Dimensionamento di una scheda di acquisizione dati per la misura di segnali di grandezze fisiche
• Progettazione e dimensionamento di voltmetri in base alle condizioni di misura
• Dimensionamento dei parametri di un oscilloscopio in base ai segnali di misura
  1,2
  1, 2, 3, 4, 5
  Partecipazione ai Laboratori
  Risposta a domande multiple sulle esperienze laboratoriali svolte
  2, 3, 4, 5
  Test orale (opzionale)
  Risposta a domande teoriche e svolgimento di semplici esercizi numerici
  2, 3, 4, 5

In many problems frequently encountered in engineering and science one has to estimate an unknown signal or an uncertain parameter. To this purpose, experimental data collected during a suitable experiment are processed. The aim of this course is to present the methods used for this transfer of the hidden information contained in data to the unknown. The basic technique is the Kalman filter, which is based on state space linear models and provides a recursive solution, suitable for real time implementation. The method is compared with other recursive estimation methods (RLS, etc.). Then the black box identification of state space models is studied. Finally, nonlinear identification methods are touched upon. The course is methodological in nature; however a significant test case from real data is presented.
In molti problemi dell'ingegneria e delle scienze occorre stimare un segnale ignoto o un parametro incerto. A tale scopo si ricorre alla osservazione di variabili misurabili nel corso di un dato esperimento. Lo scopo di questo corso e' di presentare i metodi che si usano per affrontare il problema del trasferimento dell'informazione nascosta nei dati all'incognita. La tecnica base di riferimento e' nota come Filtro di Kalman, che e' basata su modelli di stato, a matrici note, eventualmente ad eccezione di un parametro incerto. Una delle caratteristiche salienti di tale tecnica e' di essere ricorsiva, consentendo l'elaborazione dati in tempo reale. La tecnica viene confrontata con altri metodi di identificazione ricorsiva (RLS ecc.). Si studiano poi i problemi di identificazione "a scatola nera" dei modelli di stato (metodi a sottospazi). Infine si fa un cenno al problema della identificazione non lineare. Il corso ha carattere metodologico, ma significativi studi di caso vengono trattati.

The course aims to provide a general introduction to the computational methods which can be used to model the behaviour of molecular systems and materials. The lectures will introduce the theoretical concepts, first in qualitative terms and then in the form of equations and algorithms. The practical hand-on computational sessions will introduce the students to the usage of freely available programs for molecular simulation and visualization. The students will become aware of the meaning of "modelling", "simulation" and "computing", in a way which may be useful also in other, unrelated contexts.

The students will learn some theoretical principles underlying current molecular modelling methods. This will allow them to read an understand the scientific literature emplying these methods. (Dublin descriptor 1: Knowledge and insight).
They will learn how to use some common codes for the calculation and interpretation of the properties of molecules, liquids and materials, and specifically: VMD (visualization), ORCA (quantum chemistry), Tinker and LAMMPS (molecular dynamics and Monte Carlo). They will also learn some basics of scientific data analysis, using Python-based tools: Jupyter notebooks, numpy, scipy, matplotlib. (Dublin descriptor 2: Applying knowledge and insight).
They will be expected to gain some critical understanding of the methods, in order to choose the right method for a specific problem and assess the results of their calculations. (Dublin descriptor 3: Judgement).
The students will be expected to present the results of a computational project, both as a written report and during a subsequent oral discussion. (Dublin descriptor 4: Communication).

Introduction to molecular modelling: overview of problems and approaches.
Elements of molecular quantum mechanics: Schroedinger equation, Born-Oppenheimer approximation, electronic wavefunctions, independent-particle model, molecular orbital (MO) theory, basis sets. Molecular properties: the electron density and the molecular electrostatic potential. Electron correlation and the bond dissociation problem. Concepts from density functional theory (DFT): Hohenberg-Kohn theorem, Kohn-Sham scheme, exchange-correlation functionals. Semiempirical molecular orbital methods.
Empirical models: force fields for organic and biological molecules, shell model for ionic polarization, the embedded-atom model for metals, empirical "reactive" potentials for semiconductors and carbon-based systems. Coarse-grained models.
Exploring potential energy surfaces: stationary states, energy minimization and saddle point location methods. Conformations of large molecules.
Introduction to simulation methods: elements of statistical mechanics, ensembles and calculation of thermodynamic properties, boundary conditions, treatment of short- and long-range interactions.
Molecular dynamics (MD) simulations: integration algorithms, introduction of constraints. Choice of initial conditions. Controlling temperature and pressure in MD. Analysis of trajectories: pair distribution functions, analysis of molecular conformations, time correlation functions. Evaluation of transport properties. Non-equilibrium methods. Enhanced sampling for "rare" events.
Monte Carlo (MC) simulations: general principles (Markov processes, transition probabilities, detailed balance) and Metropolis Monte Carlo for the NVT ensemble. Generalization to other ensembles. Example: the Ising model.
Application examples:

• calculation of molecular energies, geometries, vibrational spectra, electron densities, interactions, etc.
• structure and properties of solids, liquids, soft materials, interfaces, etc.

Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The students will be evaluated on the basis of written reports on the practical modeling problems, followed by an oral examination covering the reports and the theoretical concepts underlying the empoyed methods. Projects can be carried out individually or in groups of two.
The final grade will be based on:

• understanding of the theoretical principles of the chosen computational model(s) (Dublin descriptor 1: knowledge and insight).
• capability to run the numerical simulation codes, extract and analyze the results from them (Dublin descriptor 2: Applying knowledge and insight).
• soundness of the computational method(s) for a specific problem, capability to interpret the results and identify possible problems or limitations (Dublin Descriptor 3: Judgement).
• presentation of the results, both in writing (report) and orally (final discussion) (Dublin Descriptor 4: Communication).

Frank Jensen, Introduction to computational chemistry (3rd. edition), Editore: JohnWiley & Sons, Ltd, Anno edizione: 2017, ISBN: 9781118825990
M. P. Allen and D. J. Tildesley, Computer Simulation of Liquids, 2nd edition, Editore: Oxford University Press, Anno edizione: 2017, ISBN: 9780198803201
Andrew R. Leach, Molecular Modelling: Principles and Applications (2nd ed.), Editore: Prentice Hall, Anno edizione: 2001, ISBN: 0582382106
Peter Atkins, Julio de Paula, ATKINS' PHYSICAL CHEMISTRY, Editore: W. H. Freeman and Company, New York, Anno edizione: 2006, ISBN: 0-7167-8759-8

This course provides an introduction to the emerging field of nanomedicine, including biotherapeutics and vaccines, at the interface of nanotechnology, pharmaceutics and medical science. A multidisciplinary overview of the research and development processes of innovative pharmaceutical products is provided, involving different subjects such as physics, chemistry, pharmacy, biology, medicine and engineering.
Lectures are designed to provide the student with state-of-the-art knowledge, skills and research experience of the principles, technology and applications within this area.

This course will allow students to:

• demonstrate knowledge and understanding in the field of nanomedicine.
• apply their knowledge and understanding, and their problem solving abilities, to the design of nanomaterials for biomedical applications.
• have the ability to integrate knowledge and handle complexity in this multidisciplinary field.
• have the learning skills to allow them to continue to study in a manner that may be largely autonomous.

Content includes:

• Design, synthesis and characterisation of bio-nanomaterials for drug delivery, imaging and diagnostics.​
• Biopharmaceutics formulation and delivery.​
• Physicochemical and biological interactions of nanoformulations with cells, tissues and organs. Pharmacokinetics, pharmacodynamics and biodistribution.​
• Applications, including cancer, gene delivery, vaccine delivery, cardiovascular diseases, infectious diseases, tissue engineering and cell encapsulation.​
• Nanotoxicology aspects and environmental monitoring.​
• Quality Assurance and regulatory aspects of drug development process. Translation of innovative products from bench to the clinic.​
  A laboratory teaching activity, comprising two two-hour sessions, is provided for Chemical Engineering students as a complementary component of the Pharma Track educational pathway.
  The laboratory experience involves the preparation of polymeric nanoparticles and the subsequent evaluation of the release profile of a tracer compound, enabling students to apply the theoretical concepts covered during the course in a practical setting.
  For students enrolled in the other Master's degree programmes in Engineering, the activity is delivered asynchronously through a video recording of the entire laboratory experience, accompanied by supporting teaching materials and explanatory slides to ensure effective achievement of the intended learning outcomes.

Prova orale obbligatoria
An oral examination will be carried out to assess the student’s knowledge and understanding on the nanomedicine topics. Specific questions will be formulated to evaluate problem solving abilities, as well as the capability to deal with multidisciplinary knowledge and handle problem complexity. Students must show the ability to cope autonomously with the design of a nanomaterial, depending on the application.

Lecture notes/handouts will be distributed. There are no specific textbooks for the course; assigned readings will be announced weekly based on topics being covered.

Interatomic forces; fundamentals of inter and intramolecular interactions. Bulk properties and properties of aggregates. Self assembly of molecules toward nanostructures. Nanometric architectures,nanocrysrtals,nanotubes,clusters and nanocomposites. Amorphous metals and quasi-crystals; Confinement effects.. Chemical and Physical properties of nanomaterials: nanomaterials in biology, biochemistry and chemical catalysis; pattern recognition, biosensors, biodevices. Nanomaterials for electronics and for data storage. Nanomaterials for energy saving. Manipulation of atoms and molecules. Preparation of films of nanoaggregates (Langmuir -Blodgett films, vapor phase deposition, laser ablation etc ).

Il corso "Natural Language Processing" introduce gli studenti ai problemi e alle metodologie relativi all'analisi e alla produzione di frasi di linguaggio naturale, sia scritte che parlate. Il corso presenta il ruolo attuale dei modelli stocastici e del Deep Learning, illustra nuove opportunità di combinare modelli tradizionali, basati sull'analisi formale, con modelli stocastici, per la morfologia, la sintassi, la semantica, la pragmatica, la voce, la prosodia, il discorso, il dialogo e la sentiment analysis. Il corso offre sessioni di esercitazione pratica, in cui gli studenti possono testare modelli e tecniche presentati durante le lezioni. Le applicazioni includeranno: l'analisi dell'interazione uomo-macchina e uomo-uomo, basate sul linguaggio; l'analisi linguistica e prosodica e la loro generazione per la riabilitazione; la ricerca e il riconoscimento di pattern per l'analisi del sentiment nell'interazione critica; l'analisi della complessità, nel testo e in generale nella comunicazione basata sulla parola; la definizione di profili utente che tengono conto delle preferenze in merito alle modalità di espressione (nel contesto forense, educativo e clinico).

The course, characterized by a strong experimental focus, aims to prepare a student to manage a measurement of the most common mechanical and, in general, physical quantities used in bioengineering, and used for the monitoring and control of biomechanical, bioinspired and bionic systems. A focus on the measurement of main physiological parameters will also be provided.
A relevant part of the course consists of experimental activities. Students will have the opportunity to practically work with sensors and measurement systems which are typically applied to the human body and bionic systems, and to autonomously perform data acquisition and analysis.
Specific attention will be devoted to dynamic measurements (acceleration, angular velocity, strain, physiological quantities) obtained with commercial inertial measurement units and lab prototypes and emphasis on the strategies adopted to monitor a bio-inspired process. The main requirements and working principles of sensors and devices for measurements applied to the human body and physiological quantities will be reviewed.
Practical aspects are strongly privileged: the student will learn to use a data acquisition system, to store and properly manage data. This will help develop the proper sensitivity to correct sensor use and a critical approach to data acquisition. The students will also learn to use systems engineering software for the mentioned applications, such as LabVIEW. This software, along with other programming languages (Matlab, Python), can be used to develop practical activities learnt during the course.
This course is based on the approach of “Flying Lab”, that creates practical labs in regular teaching rooms. Indeed, students are asked to carry their computers while the data acquisition boards, as well as the sensors and all that is needed, will be provided by the teacher so that they can carry out their experimentation everywhere. This of “Flying Lab” has been extensively tested, producing more than interesting results in the past years. The students will also be introduced to the mobile laboratory using smartphones.
The original modality of teaching and learning of this course has produced many successful results during the past years, with the large satisfaction and interest of all students.

Students are expected to develop specific knowledge and understanding of experimental issues (which will range from the to their use and choices for data acquisition, up to the final data management), as described in the following table:
Dublin Descriptors
Expected learning outcomes
DdD 1 - Knowledge and understanding
· Understand the basics of measurements of biomedical quantities
· Knowledge of the most common sensors and the criteria for their choice
· Knowledge of dynamic measurements
· Knowledge of methods for signal and data analysis in the time and frequency domains
DdD 2 - Applying knowledge and understanding
· Use sensors and devices for measuring biomedical parameters
· Develop and manage a complete measurement system
· Implement data analysis and visualization tools in LabVIEW and Matlab
DdD 3 - Making judgements
· Critical evaluation of the issues and correctness of the implemented tools
· Critical evaluation of the biomedical data/signals features, in both time and frequency domain
· Critical analysis of the state-of-the-art
· Peer-evaluation of methods and results of the experimental activity made by colleagues
DdD 4 - Communication
· To be able to present the results of the experimental work, in the form of a final report, to the teacher and the colleagues
· To be able to write a conference paper
DdD 5 - Lifelong learning skills
· To be able to plan an experimental case, based on the proper selection of the quantity to be measured, the sensor(s) and the implementation of the correct processing tool(s)
· To be able to understand state-of-the-art instrumentation, methods and results published in the literature
· To be able to transfer the methods and results of your work in a conference paper

The main topics are:
Metrology basics
Static and dynamic aspects in measurements
Data acquisition
Signal analysis, with specific emphasis on the analyses in the frequency domain
Sensors for the most common mechanical and thermal measurements applied to the human body and physiological parameters, e.g.:
-Strain measurements
-Acceleration and angular velocity measurements
-Temperature measurements
-Force and pressure measurements
-Measurement of physiological quantities
Fiber optic sensors and inertial sensors for biomedical and bioinspired applications will also be introduced and will be part of experimental labs.
The course will be completed with many practical examples or specific workshops, dealing with real examples from existing systems, mainly developed for biomedical quantities monitoring. During the practical classes, the students will be asked to perform data acquisition and analysis on their own, with the supervision and guidance of the teacher. One example among the different labs is the acquisition of acceleration during chosen physical activities (e.g., walking, running,..) and the analysis of the resulting data in both time and frequency domains.

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, individuale, senza revisioni periodiche, con presentazione/discussione finale
The exam consists of a discussion of the small projects developed during the semester. These small projects will be collected with a single-student report (DdD 3, DdD 4, DdD 5)
The most promising experimental projects will be selected to be the basis for the production of a paper to be submitted to an international conference, thus giving the students the precious and rare opportunity to enrich their CV (DdD 4 and DdD 5).
Every student is asked to bring her/his report to the final exam, where she/he will have to defend her/his activity or justify her/his choices individually. The student needs to show the good capability to present her/his job and to discuss it, after having shown good critical approaches to the problems she/he has faced, with proper technical language (DdD 1, DdD 2, DdD 3, DdD 4)
Students will be left free to develop a project as a final demonstration of their skills (DdD 1, DdD 2, DdD 5).
At the end of the semester, the students will be asked to study one state-of-the-art document (scientific article, patent,..) on a topic of their choice, and to critically analyze the technology and relevance of the measurement instruments/sensors involved in the study. The students will present the results of this analysis to their colleagues and to the teacher, in a sort of flipped classroom, to stimulate a peer-to-peer discussion. Feedback from the other students will be stimulated. This is an innovative evaluation modality (DdD 3, DdD 4).
During the past years, this personal involvement has produced excellent and valuable results, as well as great interest in the topics and development of critical thinking.

Introduce the student to the fundamentals of optical imaging from the cellular to the tissue level, presenting the physical basis, discussing instrumental implementations and describing examples of applications, with particular emphasis on biophysics and biology topics.

• Knowledge and understanding
  The students will learn the fundamentals of bio-photonics imaging technologies, that operate from the molecular level up to the tissue scale. The student will learn the major applications of optical microscopy in biology and in clinical diagnostics.
• Apply knowledge and understanding
  The students will be able to design autonomously an optical system for microscopy and imaging. The students will be able to evaluate the resolution and contrast in optical microscopy and nanoscopy systems.
• Making Judgements
  Given a specific optical imaging system, the students will have the ability to analyze the point spread function and compare the performances of different optical setups.
• Lifelong learning skills
  Students will be capable to understand how to take into account, and how to overcome the diffraction limit in an optical system, with particular emphasis on optical microscopy. Students will be capable to learn autonomously how to use an optical microscope and how to assess its performances.

Optical microscopy and nanoscopy:
Propagation of light in an optical microscope: point spread function in an optical microscope and 3D imaging theory.
Classical microscopy techniques for cellular and tissue imaging: bright-field, darkfield, phase contrast, fluorescence.
Three-dimensional imaging of tissues: confocal microscopy, structured illumination microscopy, 2-photon microscopy.
Three-dimensional imaging with interferometric techniques and imaging of the eye: Optical Coherence Tomography.
Imaging at the molecular level with optical microscopy, circumventing the resolution: Single Molecule Localization and Stimulated Emission Depletion Microscopy.
Applications of microscopy and super-resolution microscopy in cellular imaging, biophysics and biomedicine.

Prova scritta obbligatoria, senza prove in itinere
The final assessment will be based on a written test with open questions on the topics of the course.

Jerome, Introduction to Optical Microscopy, Editore: Cambridge University Press, Anno edizione: 2019, ISBN: 9781108552660

The objective of the course is to provide students with theoretical and practical knowledge on Organs-on-Chip platforms for advanced cell culture. The students will specifically receive a comprehensive overview of the OoC development process, including design criteria, fabrication methods, biological building blocks integration, system monitoring and qualification towards industrial/clinical application. The course imparts knowledge developed as part of an ERC project.

At the end of the course the student is able to:
Define the technological and biological requirements of an OoC model to respond to a specific context of use
Design OoC platform layouts and identify materials and fabrication methods
Select the proper biological components to be integrated in the system
Define the proper microenvironmental conditions and identify strategies to combine them
Identify the desired readouts and propose solutions for their integration in the system
Design an experimental plan to qualify the OoC model
Students are expected to acquire competences following the Dublin Descriptors reported below:
A: KNOWLEDGE AND UNDERSTANDING.
Knowledge and ability to discuss and present the main theoretical aspects presented in the course. Knowledge of Organs-on-chip and microfabrication concepts.
B: APPLYING KNOWLEDGE AND UNDERSTANDING
Resolving problems related to different context of use defining technical and biological requirements for OoC design. Design experimental plans for the system qualification.
C: INDIPENDENCE IN JUDGEMENT
Critically analyzing the recent literature on OoC and discussing results. Ability to select among available technologies the most suitable to respond to biological questions in specific context of use.
D: COMUNICATION SKILLS
Ability to present a paper of the most recent literature and to present own project results. T he course is in English and students are encouraged to ask questions and discuss in English.

Lectures
Introdution to Organs-on-Chip: definition, history, vision, conceptualization/design criteria; applications and contexts of use
Strategy and concepts for d evice fabrication: material and fabrication technique selection
OoC b iological building blocks: cells (including immune compartment and organoids as a brief overview); ECM compoments; Cell culture medium
Control of cell microenvironment in OoC: brief intro to microfluidic theoretical concepts; chemical conditioning; physical conditioning
Monitoring of OoC: offline, online (sensors), next generation readouts (omics, AI…)
Design of an experimental plan for the OoC qualification depending on context of use
Laboratory
Introduction to microfabrication procedures (e.g. visit to PoliFab facilities)
Hands-on in PDMS-based soft lithography techniques for OoC device production
Hands-on in cell culture applied to OoC
Hands-on in OoC readouts (e.g. microscopy based and sensors based)
During the course a project will be assigned aimed at designing an OoC model for a specific context of use, applying the knowledge acquired during classes and during visit to laboratories. The project results will be presented during the course and discussed in classroom.

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
The final grade is established based a report resulting from a group project undertaken during the course and on the final exam.
The project will consist in short group presentations and discussion that will take place during the semester. A final report will be requested by each group, which will be evaluated.
The final exam will consist in an oral exam where the content of the report will be discuss individually.

Modulo 1
Il ruolo dello sport nel processo di riabilitazione (1 ECTS) - (0,5 lezioni + 0,5 esercitazioni, discussioni e testimonianze)
basi del riapprendimento motorio
Il ruolo dello sport nel processo riabilitativo
Il ruolo delle tecnologie come fattore abilitante
Requisiti per una corretta progettazione tecnologica
Testimonianze cliniche e di atleti
Modulo 2 (1,5 ECTS)
Stimolazione elettrica funzionale per la riabilitazione sportiva (1,5 ECTS) (1 lezione + 0,5 laboratorio)
Fondamenti dei sistemi neuromuscolari
Principio neurofisiologico della FES
FES per il rafforzamento muscolare dopo infortuni sportivi
FES per la riabilitazione di pazienti neurologici
Modulo 3
Paraolimpiadi e Cybathlon (1 ECTS) (0,5 lezioni - 0,5 esercitazioni e testimonianze)
Discipline, sfide tecniche e testimonianze.
FES Bike per atleti SCI: l'esperienza del Cybathlon.
BCI per atleti con disabilità.
Le sfide tecniche delle Paralimpiadi: la biomeccanica per vincere (seminario e testimonianze della squadra paralimpica INAIL).
Modulo 4
Lavoro di progetto
Strumenti e ingegneria per le Paralimpiadi e il Cybathlon (1,5 ECTS) (0,2 lezione + 1,3 laboratorio di progetto)
Project work mirato alla progettazione di strumenti per paratleti e atleti di Cybathlon.

The course is designed to provide students of engineering with in-depth skills on Intellectual Property (IP) management, with a special focus on patents.
The students' learning goals are:
Understand challenges, functions, processes in a business and industrial environment and their mutual effects on business, economy, environment and society.
Identify trends, technologies, key methodologies and stakeholder needs in a specific domain.
Design solutions applying a scientific and engineering approach (Analysis, Learning, Reasoning, and Modeling capability deriving from a solid and rigorous multidisciplinary background) coupled with participatory approaches to face problems and opportunities in a business, industrial and societal environment.

Knowledge, applied knowledge and understanding (DdD1 and DdD2):

• Understand the economic and legal rationale of Intellectual Property rights.
• Understand the range of IP tools and the related procedures available to obtain the desired patent coverage based on the set objectives.
• Understand the functioning of international databases to carry out basic patent searches, for monitoring or determining the freedom of operation.
• Identify the content, duration and coverage of a patent document and the freedom to operate that it entails for future or adjacent inventions.
• Understand the sources of uncertainty regarding IPs and the limits and challenges of IP enforcement.
  Autonomy of judgment (DdD3):
• Design suitable strategies of IP coverage and management, taking into account the specific context of the company and its competitive environment.
• Design IP portfolio strategies that match the strategic objectives of the related organization.

The course includes three parts.
PART ONE focuses on the explanation of the economic and legal rationale of IP rights and describes the various protection mechanisms available at the Italian, European and extra-European level, highlighting the main international differences.
Topic 1: What are patents. The economic and the legal-procedural point-of-view.
Topic 2: The requirements of patents (novelty, inventive step, industrial applicability, sufficient description).
Topic 3: The full range of IP rights (copyrights, utility models, trademarks, design protection, and other IPs).
Topic 4: The patenting procedure (filing, examination, extension, granting, opposition, maintenance) of the UIBM, EPO, UPC, PCT and the main international differences.
Topic 5: Practitioner view (guest lecture)
Topic 6: Copyright, trademark
PART TWO focuses on the management of IPs, providing students with a current and far-reaching view on strategies for managing single IPs and portfolios of patents, discussing the strategies currently used by companies in different types of industries, including those that make an open and semi-open use of patents.
The lecutures will also highlight the global trends in infringement and counterfeiting.
Topic 7: Extracting value from IPs. Closed, semi-closed and open IP strategies and how/when to use them.
Topic 8: Patent portfolio management strategies with practical applications.
Topic 9: IP violations: counterfeiting, plagiarism, piracy and infringment. An equitable balance between IP protection and the human rights to a have affordable healthcare.
Topic 10: Methods, circumstances and reliability of IP assessments.
Topic 11: Open innovation
PART THREE focuses on training the students on the use of the databases.
This part includes a series of practical exercises, such as those to check the freedom to operate, the monitoring of competitors, and describes the main steps of prior art searches.
Topic 12: Patent Lab (part I). The patent databases. Search and read basic patent information.
Topic 13: Patent Lab (part II): Freedom to operate, monitoring, preliminary prior art search.
Topic 14: Groupwork: IP Portfolios

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, individuale, senza revisioni periodiche, senza presentazione/discussione finale
Student assessment will be based on:
Written homework assignment (10 points)
Written in-class essay exam (20 points)

Il Corso "Personalità Teambuilding e Leadership" introduce lo studente alla conoscenza delle strutture di personalità che orientano l'immagine di Sé, l'atteggiarsi, l'agire, il pensare, l'interagire, il comunicare e il cooperare: per lo studio, il lavoro e le relazioni interpersonali e con particolare riferimento allo sviluppo di competenze per il teambuilding e la leadership. Porta lo studente: - a valorizzare le proprie risorse e migliorare il proprio rendimento nell'acquisizione di nuove abilità e competenze; - a conoscere e ad affrontare meglio situazioni difficili a livello personale o di gruppo; - a prepararsi alla cooperazione e alla leadership; - ad affrontare al meglio le prime esperienze di inserimento lavorativo. Il corso offre laboratori esperienziali per lo sviluppo di soft skill nelle aree trattate, presenta i principi e modelli (di area costruttivista e relazionale) che guidano le esperienze di laboratorio e che possono essere adottati nella futura vita professionale e personale.

The course aims at providing the students with a critical understanding of the philosophical foundations of science and technology. But what is the relation between philosophy and science? On the one hand, the discoveries by scientists are often inspired by philosophical ideas. On the other hand, scientific achievements and technological advancements pose deep conceptual questions to philosophers. The connection between these disciplines is thus so tied that it becomes difficult to even draw a border between them. This course explores such a connection through the study of important episodes in the history of science. Outstanding issues of philosophy of science and technology, such as scientific progress, the method of science and the confirmation of theories and laws, will be addressed. Moreover, a particular focus will be devoted to philosophical questions concerning the nature of space as they emerge in the foundations of science, especially in relation to the famous debate between Leibniz and Newton.

The course aims at providing the students with a critical understanding of the philosophical foundations of science and technology.
But what is the relation between philosophy and science? On the one hand, the discoveries by scientists are often inspired by philosophical ideas. On the other hand, scientific achievements and technological advancements pose deep conceptual questions to philosophers. The connection between these disciplines is thus so tied that it becomes difficult to even draw a border between them. This course explores such a connection through the study of important episodes in the history of science. Outstanding issues of philosophy of science and technology, such as scientific progress, the method of science and the confirmation of theories and laws, will be addressed. Moreover, a particular focus will be devoted to philosophical questions concerning the nature of space as they emerge in the foundations of science, especially in relation to the famous debate between Leibniz and Newton. Finally, in the context of philosophy of technology, we will explore epistemic tools that are used to draw scientifically-informed inferences, such as computer simulations and thought experiments.
Here's below a list of the main topics:

• Method of science
• Demarcation between science and pseudo-science
• Confirmation
• Scientific Progress
• Philosophy of Space: Absolutism vs Relationism
• Scientific Reasoning
• Philosophy of Technology

Prova scritta obbligatoria, senza prove in itinere
Altro
Final written exam based on general questions about the topics of the lectures: specifically, the students are assigned to write several essays, which have to be emailed directly to the instructors at the following address: [email protected]. There are also individual class presentations scheduled during the term

Alan Chalmers, What is This Thing Called Science?, Editore: Hackett Pub Co Inc
Nick Huggett, Space from Zeno to Einstein: Classic Readings With a Contemporary Commentary, Editore: Bradford Books

Photonics is a pervasive technology and the development of advanced optical materials represents an active and vibrant field of research, at the boundary between condensed matter and photonics, with impact on many industrial sectors. In this course, students will develop the capacity to critically assess the optical properties of materials and their perspective use as building blocks for optical elements and photonic devices, supported by the knowledge that they will also develop on the techniques that can be used to grow and characterize such materials.

The goal of the course is to provide students with:
The tools for the analysis of the optical properties of materials, as described by their dielectric function, and the knowledge about the techniques that can be used to grow and characterize them (Dublin Descriptor 1, Knowledge and understanding);
The ability to apply the acquired knowkedge to the analysis of experimental data regarding the optical and structural properties of optical materials (Dublin Descriptor 2, Applying knowledge and understanding);
The ability to critically assess (at least qualitatively) the optical and structural properties of a certain material and to choose the most appropriate optical material for a given application (Dublin Descriptor 2, Applying knowledge and understanding, and Dublin Descriptor 3, Making judgements).

1 - Optical response of materials
This module covers the physical description of the optical response of materials, focusing on the modeling of the dielectric function and on the different light-matter interactions that characterize different classes of materials

• Transmission, reflection, and absorption of light; Kramers-Kronig relations
• The Drude-Lorentz material response
• Dielectric materials: dielectric function, absorption processes and color centers, amorphous glasses and crystalline materials, birefringent/anisotropic materials
• Semiconductors: dielectric function, absorption and emission processes, doping
• Metals: dielectric function, absorption processes
• Polymeric materials for optics
  2 - Growth techniques
  This module covers the different technologies available for the growth of optical materials, highlighting the advantages, disadvantages, and fields of application of each individual technique.
• Sputtering
• Chemical vapor deposition & Atomic layer deposition
• Electron-beam evaporation
• Sol-gel techniques
• Polymeric materials
• Glass processes
  3 - Structural characterization
  This module covers the main and most widespread techniques that are used to assess the crystalline structure and the surface morphology of optical materials.
• X-ray diffraction
• Scanning electron microscopy
• Atomic force microscopy
  4 - Optical characterization
  This module covers the main and most widespread techniques that are used to quantitatively characterize the optical response of materials, with emphasis both on their experimental implementation and on the fundamental aspects of data analysis and modeling.
• Reflection/transmission spectroscopy
• Ellipsometry and other polarization-resolved techniques
• Optical microscopy
  5 - Engineering of the optical response
  This module represents a brief introduction on the effect of nanoscale structuring can affect the optical properties of materials and can be used as a degree of freedom to achieve specific optical functionalities
• Multilayer and nanostructured coatings
• Effective medium approximation
• Photonic crystals and metamaterials
• Structural colors
  6 - Optical elements
  In this module we will discuss typical applications of optical materials in the design and realization of optical elements and devices
• Reflective and refractive optical elements: lenses and mirrors
• Diffractive optical elements: gratings, beam shapers, beam splitters, diffusers, waveplates, polarizers
• Integrated photonic circuits
• Light modulation technologies: liquid crystals, electro-optic effect, acousto-optic effect, phase-change materials
• Materials for solar cells and photodetectors
• Lasing materials
  7 - International standards for the quantification of the optical properties of materials
  In this module we will briefly cover the content of international ISO&ASTM standards that include the quantitative definition of the optical properties of materials and the protocols for the respective evaluation

Prova orale obbligatoria
The final evaluation will be done with an oral examination in which the students will discuss specific topics of the course to address their acquired knowledge, their understanding of the content, and their ability to apply and critically assess them.

Mark Fox, Optical Properties of Solids: 2ND EDITION, Editore: Oxford University Press, Anno edizione: 2010
The whole content of the course will be covered by the class lectures with slides made available on the WeBee portal webeep.polimi.it

This course provides the fundamentals of power electronics. Topics covered include: power semiconductor switches, drive and protection circuits; switching power converters; thermal design. The primary goal of the course is to provide students with the basic skills and knowledge required to analyze and design practical power electronic systems in the low-power range.

Knowledge and understanding (Dublin Descriptor 1)
On completion of this course, students will be able to:
comprehensively understand the principles and operation of modern power semiconductor devices, their limitations, and their driving and protection techniques;
have a sound knowledge and understanding of the operation of switch-mode DC-DC converters used in low power applications;
understand the dynamic performance of dc-dc converters and develop small-signal converter models;
understand and manage the thermal stress of active components in power electronic systems.
Applying knowledge and understanding (Dublin Descriptor 2)
Specific examples and case studies will be provided to make students able to:
select the active/passive components of a power electronic circuit on the basis of specifications;
apply appropriate techniques in the design of low-side and high-side MOSFET drivers;
analyze and design basic protection circuits for power semiconductor devices;
apply appropriate techniques in the analysis of different types of DC-DC converters;
develop and exploit the small-signal model of a DC-DC converter;
design different types of power converters to meet given specifications;
design simple closed loop regulators for DC-DC converters to meet functional objectives;
tackle with steady-state and dynamic thermal performance of power semiconductor devices and ICs.