Electronics And Communications Engineering

At the end of this course the student will have learned the basic concepts and techniques for the development of image processing and vision systems. He/she will be able to explain the models and algorithms behind the operation of medium to high complexity processing chains. He/she will be able to describe the processing chains of various vision and processing systems used in real-world applications, evaluating their effectiveness and possible limitations. The student will acquire the fundamental mathematical and algorithmic tools necessary to address most applications of image processing and vision, taking a rigorous approach to the methods used to conceive and develop reliable and effective solutions. He/she will know advanced techniques of computer vision and will acquire the ability to use them appropriately depending on the problem to be addressed. He/she will be able to read and understand scientific articles in the field, analyzing the topics and evaluating their actual reusability and validity. The application domains are very varied, among others: Medical Imaging, Video-surveillance, Computer Graphics, Video Compression, Industrial Inspection, Multimedia, Human-Computer Interfaces and Multimodal Signal Processing. The technological evolution of HW sensors and architectures for acquisition and processing has produced a large amount of data and today there is undoubted need for automatic tools that allow the analysis, annotation, interpretation and retrieval of the information available in them. In fact, image processing and artificial vision is one of the fastest-evolving ICT area, with applications spreading at a speed never seen in all areas of business and everyday life. The "mission" of this course is to motivate and provide students with that wealth of theoretical knowledge and mathematical tools that enable them to address various real-world issues in those areas where image processing and computer vision can provide adequate, robust and reliable responses. The course will enable them to gain in-depth knowledge of one of the most fascinating areas of Information and Communication Technology.

The course covers aspects from basics to advanced image processing techniques (P/O, a.k.o., Computer Vision) and focuses on hybrid processing aspects, using classical techniques and machine learning techniques. The main topics covered are: 1) Image formation process (2D and 3D transformations, 3D to 2D projection, color spaces, examples of application to design and sizing of capture systems from functional specifications) 2) segmentation (thresholding, clustering, Convolutional Neural Networks, detection of flat surfaces, examples of application to skin tumours and leaf volume of plants, to cell count and to license plates reading) 3) Introduction to Calibration techniques (Tsai grid, calculation of vanishing points, homography, examples of application: road accident analysis, distance measurements from monocular view) 4) Feature detection and Feature Engineering (cue points and surfaces, invariant descriptors, SIFT, SURF, FAST, HOGH, GIST, Shape features, features for tracking, examples of application to security: anti vandalism, monuments monitoring, people detection and tracking in indoor/outdoor areas) 5) Object Recognition, Statistical Learning (Bag Of Words), Deep Learning (CNN, Yolo, SSD, Visual Transformers) with application to: face detection, pedestrian identification, recognition of three-dimensional objects in stereo and range images, recognition of places 6) Deep Learning techniques for Computer Vision (shallow and deep learning, functional components of a Convolutional Neural Network, supervised training, major deep architectures: VGGNet, GoogleNet, ResNet, DenseNet, MaskNet, GAN, EfficientNet, SqueezeNet, PointNet, examples of application to vision) 7) Motion Estimation and Analysis (Cue tracking and Optical Flow, learning of typical trajectories, detection of anomalies, recognition of underparts, analysis and recognition of actions, fuzzy reasoning, Deep Learning, examples of application to the identification of aggressive acts and to ambient intelligence) The applications analysed refer to realizations actually developed in the Department's VIS-Lab some of which are implemented in market products. Students will thus be able to deal with real operative themes and corresponding implementation solutions.

Even if not mandatory, they are advisable: a basic mathematical knowledge (in particular about linear algebra and signal processing), a propensity towards reasoning and creativity, and good analytical skills

Basically, this course is based on front lessons. Important material will be covered during these lessons. The front lessons use Powerpoint presentations and some Matlab scripts specially prepared to help you understand specific topics. The entire Powerpoint slide set and Matlab scripts will be made available at the beginning of the course in the form of content to be copied to a Digital-Pen. The material provided includes documentary materials in pdf format linked to each course of lessons. Lessons are organized to engage students by encouraging critical interventions, evaluation skills, theoretical understanding, and by trying to stimulate their creative thinking and ability to solve problems. Students will learn basic concepts and principles by developing the ability to evaluate when, where and why a certain method is applicable to the problem they face.

One (optional) assignment involving both analytical and programming tasks: a list of assignments the students can choose from will be made available by the teacher. The students can also suggest a subject by themselves. In that case, they should send a proposal to the teacher with a title, a short description and a reference to a paper on the subject. The optional assignment can be fulfilled in groups of 2 or 3 or individually. The students must study the literature and implement (whole or part of) a method and test it on data/reproduce results available in the literature. The report should be in the form of a scientific report. The course concludes with an oral examination, during which the students will also present the results of their assignment. The purpose of the oral is to verify the level of knowledge that the student has acquired about the main content of the course and its skills in designing, applying knowledge, motivating and describing solutions based on artificial vision and image processing. Each student may decide not to perform the practical assignment and to take the oral exam alone. Students who carry out the assignment will be entitled to a reduction in oral subjects (the reduction will be in the sole judgement of the professor).

Recommended Books 1) R. Szeliski, “Computer Vision: Algorithms and Applications, 2nd ed.”, Springer 2022 2) David A. Forsyth and Jean Ponce, "Computer Vision: A Modern Approach", Pearson Education Limited, 2012. 3) John C. Russ, The Image Processing Handbook 7th Edition, CRC Press, 2016 4) E. R. Davies, "Computer Vision: Principles, Algorithms, Applications, Learning", ACADEMIC PRESS, 2018 5) I. Goodfellow, Y. Bangio, "Deep Learning", The MIT Press, 2016 6) M. Elgendy, “Deep Learning for Vision Systems”, Manning Publications Co., 2020 7) L. Lin, P. Luo, W. Zuo Eds., "Deep Learning for Human Centric Visual Analysis", Springer Nature Singapore Pte Ltd. 2020 8) X. Jiang, A. Hadid, Y. Pang, E. Granger Eds., "Deep Learning in Object Detection and Recognition", Springer Nature Singapore Pte Ltd. 2019 9) Olivier Faugeras, "Three Dimensional Computer Vision", MIT Press, 1993 10) J. F. Peters, "Foundations of Computer Vision: Computational Geometry, Visual Image Structures and Object Shape Detection", Springer Nature, 2018 11) L. Lu, X. Wang, G. Carneiro, L. Yang, "Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics", Springer Nature Switzerland AG 2019 Other interesting readings 1) Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, "Digital Image Processing Using MATLAB". 2) S. Mallat , "A Wavelet Tour of Signal Processing", Academic Press, 1999 3) H. Venkateswara, S. Panchanathan Eds., "Domain Adaptation in Computer Vision With Deep Learning", Springer Nature, Switzerland, AG 2020 4) Y. Ma, J. Tang, "Deep Learning on Graphs", Cambridge University Press, 2021 5) M. Leordeanu, "Unsupervised Learning in Space and Time: A Modern Approach for Computer Vision using Graph-based Techniques and Deep Neural Networks", Springer Nature Switzerland, AG 2020 6) Z. Wang, "Deep Learning Through Sparse and Low-Rank Modeling", Academic Press, 2019 7) X. Li, M. Wu, Z. Chen, L. Zhang Eds., "Deep Learning for Human Activity Recognition: Second International Workshop, DL-HAR 2020", Springer Nature Singapore Pte Ltd. 2021 8) S. Ansari, "Building Computer Vision Applications Using Artificial Neural Networks", Apress, 2020 9) S. Holden, "Computer Vision: Advanced Techniques and Applications", Clanrye Intl, 2019 10) M. Hassaballah, "Deep Learning in Computer Vision: Principles and Applications", CRC Press Taylor & Francis Group, New York, 2020 11) H. Dong, Z. Ding, S. Zhang, "Deep Reinforcement Learning: Fundamentals, Research and Applications ", Springer Nature Singapore Pte Ltd. 2020 12) N. Singh, P. Ahuja, "Fundamentals of Deep Learning and Computer Vision", BPB BOOK CENTRE, 2020 13) S. Khan, H. Rahmani, S. A. Shah, M. Bennamoun, "A Guide to Convolutional Neural Networks for Computer Vision", Morgan & Claypool 2018

The students will be introduced to the design of analog circuits, referring to standard CMOS technologies, adopting different levels of theoretical abstractions and circuit simulation techniques.

The course offers an introductory perspective on the analysis of fundamental CMOS circuit topologies used in analog Integrated Circuits, referring to industrial semiconductor fabrication process technologies.
Linear and nonlinear circuit analysis
Analysis and design of fundamental analog CMOS circuit topologies
CMOS Process Technologies
Computer Assisted Design Tools for Integrated Circuits Design
Basic analog CMOS layout design techniques

Electrical Engineering Basics (Circuit Theory, Electronics)

Lectures, lab activities

Oral Exam

Behzad Razavi, Design of analog COS Integrated circuits, McGraw Hill, 2018

Comprendere i principi fondamentali della interazione tra i campi elettromagnetici e i tessuti biologici e tra i campi elettromagnetici e le apparecchiature elettroniche. Apprendere semplici modelli di interazione ed imparare a valutare gli effetti delle loro predizioni. Conoscere le strategie di progettazione che portano a soluzioni affidabili, robuste e sicure dal punto di vista della compatibilità elettromagnetica per sistemi e dispositivi, in conformità con le normative nazionali e internazionali per la compatibilità elettromagnetica. Understanding the basic principles of the interaction of electromagnetic fields with both biological tissues and electronic equipment. Learning simple interaction models and how to assess the effect of their predictions. Learning the strategies to design electromagnetically reliable, robust and safe devices and systems, in accordance with national and international EMC regulation and directives.

• Interazione dei campi lettromagnetici con tessuti biologici: modelli ed effetti • Interazione dei campi elettromagnetici con le apparecchiature elettroniche: emissioni radiate e condotte, suscettibilità, interferenza, scarica elettrostatica • Panoramica sui requisiti e gli standard per la compatibilità elettromagnetica per i sistemi elettronici • Strategie di progettazione per la riduzione delle interferenze: schermature, cablaggi, filtraggi • tecniche di misure e strumentazione per la verifica del rispetto dei requisiti di compatibilità elttromagnetica • Interaction of electromagnetic fields with biological tissues: models and effects. • Interaction of electromagnetic fields with electronic equipment: radiated and conducted emissions, susceptibility, crosstalk, electrostatic discharge. • Overview of EMC requirements and standards for electronic systems • Design strategies for interference reduction: electromagnetic shielding, cabling, filtering. • EMC measurements and instrumentation for verification of compliance.

Sono auspicabili conoscenze di base di Fisica II e Campi elettromagnetici Knowledge of basic concepts of Physics II and electromagnetic fields are advisable

Lezioni frontali e esperienze di laboratorio Lectures and laboratory exercises

Esame orale e prova pratica in laboratorio. Lo scopo dell’esame è quello di verificare il livello di conoscenze che lo studente ha acquisito sui principali contenuti del corso e le sue abilità nell'applicarle a problemi pratici. Oral exam and laboratory practicals with the objective of assessing the knowledge of the subjects covered during the course and the capability to apply it to practical problems

Introduction to electromagnetic compatibility,
Paul, Clayton R
New York : Wiley ; 1992
Testo di approfondimento:
Noise reduction techniques in electronic systems / Henry W. Ott
Ott, Henry W
New York : Wiley ; c1988

Scopo del corso è fornire una panoramica sulle principali tecniche di misura in ambito biomedico e biologico e indagare le principali soluzioni strumentali disponibili

Il corso analizzarà le principali tecniche di misura in ambito biologico, clinico e medico con particolare attenzione alle misure relative al campionamento di fluidi biologico quali sangue e plasma. Si affronteranno le tematiche tipiche della progettazione elettronica della strumentazione in ambito medico

Elettronica I e II e Misure Elettroniche

Lezioni Frontali

La verifica sarà effettuata mediante un colloquio orale che verterà sulle principali architetture affrontate durante il corso

Biomedical Engineering Fundamentals di Joseph D. Bronzino (Autore)
Donald R. Peterson (Autore)
Taylor & Francis Ltd, 2018

Protecting the digital ecosystem that surrounds us and plays a more and more essential part in our lives is a pressing need that modern society can no longer ignore. The discipline studying the tools and technology that can be used to this aim is usually, and rather vaguely, referred to as cybersecurity. Such a term broadly encompasses a wide and diverse set of techniques including classical cryptographic tools, security protocols for user authentication, end-to-end communication, network monitoring and protection, intrusion detection, malware recognition, authentication and protection of multimedia contents.
In this framework, the goal of this course is to give a snapshot of some of the most common threats and security measures affecting end-to-end communications and networks, especially wireless networks. The first part of the course focuses on cryptography, since cryptographic tools are
the main ingredient behind most security protocols and information protection systems. Then the course passes to review the main threats to
cybersystems and present the main classes of countermeasures security engineers can take to defend against the cyber-threats.
The third and last part of the course will adopt a different perspective and introduce the students to some emerging security topcis including i) the protection of multimedia contents, including methods for copyright protection, media authentication and covert communication by means of
steganography, ii) the security of AI systems when they operate in adversarial setting

FOUNDATIONS OF CRYPTOGRAPHY This section aims at introducing the students to the basic concepts underlying modern cryptography Basic concepts and definitions Cryptanalysis and security models Symmetric encryption - Block ciphers - DES, AES - Stream ciphers - Key distribution Asymmetric cryptography - Basic concepts, trapdoor functions - Some popular public-key cryptosystems: RSA Key distribution: Diffie-Helman key exchange protocol Authentication - MAC functions - Hash functions - Digital signatures Random Number generators Signal and information processing in the encrypted domain - Application to privacy protection COMPUTER SECURITY This section puts in practice the cryptographic tools developed in the previous sections and enlarges the horizon to discuss several classes of threats against cybersystems and to present possible countermeasures User authentication - Password-Based Authentication - Token-Based Authentication - Biometric Authentication - Remote User Authentication Access control - Basic principles - Discretionary Access Control - Role-Based Access Control - Attribute-Based Access Control Malicious Software (Malware) - Classification of Malware - Propagation mechanisms - Payloads - Countermeasures Denial of Service (DoS) attacks - Classification of DoS - Distributed DoS attacks - Defenses Intrusion detection and prevention - Host-based intrusion detection - Network-based intrusion detection - Firewalls MULTIMEDIA SECURITY This section introduces some emerging topics dealing with the protection and authentication of multimedia contents and AI security. - Data hiding and watermarking - Steganography - Steganalysis - Multimedia Forensics - AI security: adversarial examples, backdoor attacks

Very basic elements of calculus, probability theory, digital networks,
image processing

Classes and home assignments

The exam consists of a multiple choice test and an oral exam

[1] W. Stallings, Cryptography and Network Security, Mc Graw Hill, 4-th edition
[2] Notes of the course available throigh the Moodle page of the course
[3] Additional notes provided during the course by the lecturers

To learn the theory and practice behind the transmission of digital information through bandpass channels, with a special focus on wireless communications. Moreover, the students will have the opportunity to acquire practical skills related to the design of matlab-based software defined radio solutions for the transmission of digital signals using the USRP SDR platform.

I. Digital communications through the Additive White Gaussian Channel: (i) digital modulations, (ii) demodulation and detection problem, (iii) probability of errors, (iv) symbol and frequency synchronization, (v) binary coded waveforms, linear block and convolutional codes, performance evaluations and Shannon bounds, (vi) Notes on Turbo and LDPC coding. II Digital communications through bandlimited channels: (i) spectrum of digital signals, (ii) Nyquist condition, pulse shaping, (iii) inter-symbol interference, MLSE and linear equalization, (iii) OFDM transmission scheme. III Wireless communications: (i) The wireless channel, path-loss, shadowing and multipath fading, (ii) diversity techniques, (iii) MIMO systems

Basic knowledge of signals theory.

Standard lectures in classroom and practical experience in the laboratory.

Small project using matlab and the SDR platform, and oral exam.

Communications systems Engineering, John G. Proakis Masoud Salehi 2nd Ed., Prentice Hall

This module gives the students the opportunity to get a professional edge by learning how to design industrial measurement systems (mixed-signal hardware + software) aiming at the following technical goals:

• design of automated and/or remotely controlled measurement systems;
• design of high-bandwidth signal processing units equipped with digital hardware accelerators (FPGAs SoCs), for real-time measurements and control applications;
• review of measurement data coding and transmission techniques.
  The course combines face-to-face teaching with hands-on lab exercises based on the National Instrument Laboratory Virtual Instrument Engineering Workbench (LabVIEW), a system-design platform and a development environment for a graphical programming language, used in a wide range of industrial fields, including robotics, industrial automation, electrical and electronic measurements, testing and validation, smart sensor networks and image processing.
  The students are guided through the lab experiences using hardware instrumentation, including data-acquisition systems (DAQs) and reconfigurable digital electronic devices (FPGAs).
  Students attending the course are prepared for taking a free of charge final examination to become a NI Certified LabVIEW Associate Developer (CLAD), obtaining a worldwide recognized certificate representing a key opportunity for their employment and future career advancements.
  The course is officially accredited to the National Instruments LabVIEW Academy Programme.

Design of real-time and non-real-time digital signal processing units; design of remotely controlled measurement systems; review of information coding and transmission methods suitable for the design of industrial distributed measurement systems; design of high bandwidth information processing digital hardware accelerators based on FPGAs, for real-time measurement and control applications. Introduction to Systems on Chips.
Graphical programming concepts: introduction to LabVIEW, design techniques and features. Virtual Instruments (VIs) and hardware interfaces to perform prototype testing, data acquisition, instrumentation control, datalogging, measurement analysis and report generation applications. Advanced VI design techniques: VI Server and object oriented programming, deterministic real-time systems. Introduction to LabVIEW FPGA to extend LabVIEW to field-programmable gate array (FPGA) applications that run on reconfigurable I/O hardware.

Introduction to computer science and electronics

Lectures and lab exercises

Final design project with oral discussion

The course material will be provided by the Lecturer during the course.

Fundamentals of modelling, design, analysis and manufacturing of mechanical
systems.

Fundamentals of modelling, design and manufacturing of mechanical
components and systems.
Mechanical systems: main definitions, classification, functions.
Mechanical system representation.
Computer Aided Engineering
Fundamentals of structural and multibody mechanics
Additive manufacturing technologies.

Physics and mathematics basics. Basic mechanics knowledge is also useful.

Lectures and laboratory exercises. Some of the lectures (about 40%) consist in simple design and analysis activities that are carried out by the students in the classroom or in laboratories, supervised by course lecturers.
Software resources for laboratory exercise will be available for students’
personal computers, too.

Oral exam consisting in an interview where the knowledge of course contents, ability to explain them and to apply them in engineering contexts are verified. A part of the exam will consist in the discussion of an essay
(synthesis of a project activity). The project is assigned during the course and can be developed individually or in small groups (2-3 people). In case of part-time students or students that cannot attend the course, the project can be assigned beyond course end, by contacting the lecturers.

For each of the topics, specific bibliography references will be provided
during the course. In some particular cases (e.g. part-time students or students with some specific learning needs) and/or for specific course topics (e.g. software tutorials), links to video-lectures will be provided.

Scritto e Orale Separati

LEARNING OBJECTIVES: The course provides students with the ability to understand, analyze, and design advanced computing systems and parallel applications. By the end of the course, students will be able to: - Acquire advanced knowledge of high-performance processor architectures, multiprocessor systems, and programming paradigms, including their theoretical foundations and practical implications. - Understand the hardware mechanisms that enable parallelism (e.g., dynamic scheduling, branch prediction, out-of-order execution, coherence protocols) and how they are applied in modern processors. - Analyze and design complex hardware/software systems in distributed and multicore environments, with particular attention to scalability, performance, and reliability. - Apply techniques for performance evaluation of high-performance systems, using both simulators and real programming paradigms. - Design parallel software in shared-memory and distributed environments, integrating knowledge from CUDA, OpenMP, MPI, and dataflow approaches. - Follow and critically assess technological evolution in architectures and parallel programming models, connecting state-of-the-art research to practical innovation. - Apply acquired knowledge to novel solutions presented in the scientific literature, and to the design of new data-intensive and parallel systems. - Evaluate trade-offs among different design alternatives in terms of performance, cost, and technological sustainability. - Communicate technical concepts clearly and rigorously to academic, professional, and industrial audiences, and interact effectively with the research community, through the execution of a small final group project. - Develop lifelong learning skills to continuously update knowledge in the rapidly evolving field of high-performance computing, including the ability to search, select, and study relevant literature, through the execution of a small final group project.

CONTENTS: Both High-Performance Computer Architecture (9 CFU) and Advanced Computer Architecture (6 CFU): - Introduction to performance evaluation: key metrics for performance, power, and reliability. - Pipeline review and hazards. - Dynamic instruction scheduling and Tomasulo’s algorithm for exploiting multiple arithmetic units. - Branch prediction: speculation of branch condition and branch target; predictor types (bimodal, BHSR, BHT, PHT, two-level adaptive) and advanced predictors (gshare, gselect). - Superscalar and out-of-order execution: processor renaming, instruction window, reorder buffer. - Software methods for extracting instruction-level parallelism. - Introduction to VLIW architectures. - Multiprocessor systems: Flynn’s taxonomy, UMA, NUMA, COMA organizations and associated programming models. - Cache coherence protocols: write update, write invalidate, hybrid protocols; snoopy-based MESI and DRAGON protocols. - Memory consistency models: sequential and relaxed models; ordering of memory accesses. - Vector and multimedia extensions. - Introduction to FPGA-based accelerators. Only High-Performance Computer Architecture (last 3 CFU): - Introduction to parallel programming. Parallelism and performance. - CUDA architecture and programming elements. - Introduction to OpenMP, Cilk, OpenMPI, and Dataflow programming models. - Advanced topics: reconfigurable computing, datacenters.

PREREQUISITES: Computer Architecture. Programming Fundamentals.

TEACHING METHODS: The teaching approach combines theoretical foundations with practical experience to foster knowledge, application, and critical skills. - Lectures: presentation of core concepts, algorithms, and architectural models (develops knowledge and understanding). - Problem-solving sessions: in-class exercises and guided solutions (develops application skills and quantitative evaluation ability). - Laboratory sessions: hands-on work with simulators and programming paradigms to design and test parallel applications (develops practical and design skills). - Group project: small teams design, implement, and document a parallel application, encouraging teamwork, communication, and synthesis skills. - Discussion and feedback: guided discussions on architectural trade-offs, state-of-the-art solutions, and research results (develops judgement and critical thinking). This blended approach ensures students achieve theoretical comprehension, problem-solving capability, critical evaluation skills, communication ability, and independent learning capacity.

DETAILED ASSESSMENT METHODS: Written examination followed by a programming project. The written part (2/3 of the final mark) consists of 4 open questions: 2 regarding the first 1/3 of the course and 2 regarding the second 1/3 of the course. The students are encouraged to take the written part in the midterm written part (given at the end of the first 1/3 of the course) and the final-term written part (given at the end of the second 1/3 of the course). The average of this two intermediate written exams substitutes the written part of the exams for the January-February session, otherwise the student can take the regular written part (4 questions) later. The evaluation includes a group project (1/3 of the final mark), since the last 1/3 of the course covers parallel programming aspects. The group should consist of two or three people maximum and involves programming by using one or more paradigms developed in that part of the course. The working source code and a short report are required to assess such project. The derived version of this course (Advanced Computer Architecture) is mapped on the first 2/3 of the content (6CFU) and the evaluation is the same as in High-Performance Computer Architecture (9CFU) for the written part and followed by an oral examination (still on the first 2/3 of the content). The written (and oral) parts consists of numerical exercises on the high-performance processors (simple pipe, superscalar, Tomasulo’s, vliw, branch-prediction) and on the basic mechanisms for multicore processors like coherence; both in the written and oral parts, there will be open questions on the topic of this syllabus. EVALUATION CIRTERIA: - Completeness and accuracy of theoretical knowledge in architectural concepts. - Ability to apply models and methods to solve quantitative problems and implement correct programming solutions. - Critical analysis of trade-offs between architectural and programming alternatives, with reference to performance, cost, and scalability. - Quality of project work: correctness of implementation, performance evaluation, clarity and rigor of the report. - Communication skills in presenting analyses, solutions, and project outcomes. - Autonomous learning and connection with literature: demonstrated ability to integrate and apply state-of-the-art techniques.

REFERENCE TEXTBOOK: * M. Dubois, M. Annavaram, P. Stenstrom, "Parallel Computer Organization and Design", Cambridge University Press, 2012, ISBN: 978-0-521-88675-8 OTHER TEXTBOOKS: * J.L. Hennessy, D.A. Patterson, "Computer Architecture: A Quantitative Approach" 6th Edition, Morgan Kaufman/Elsevier, 2019, ISBN: 978-0-12-811905-1. * D. Culler, J.P. Singh, A. Gupta, "Parallel Computer Architecture: A Hardware/Software Approach", Morgan Kaufman/Elsevier, 1998, ISBN-10 1558603433. * M.J. Flynn, "Computer Architecture: Pipelined and Parallel Processor Design", Jones and Bartlett Publishers, Inc., 1995, ISBN 0867202041 * David B. Kirk and Wen-mei W. Hwu, "Programming Massively Parallel Processors: A Hands-on Approach", Morgan Kaufmann 3rd ed. (2017) ISBN 978-0-12-811986-0.

Aim of the course and contribution to the programme. The Master's Degree in Electronics and Communications Engineering aims to educate graduates with advanced skills in information-processing and information-transmission systems, with specific competences in antenna and microwave systems, in analog and digital electronic design for electronics and telecommunications, and in the design of smart and wireless sensor systems. This course contributes directly to those objectives by providing the methodological and design competences required for the high-frequency section of such systems. Building on the electromagnetic and microwave background acquired in the previous courses, it moves the student from the analysis of individual devices to the design of complete RF subsystems, and it develops the ability to carry out a design using professional microwave CAD tools. The competences acquired are directly relevant to the professional roles indicated for the programme - the design, production and management of telecommunication systems and of wireless and smart sensor systems in the industrial, automotive, medical, environmental and space sectors - and provide a solid basis for the design activities of the final thesis work.
Expected learning outcomes. By the end of the course the student will be able to:

• LO1 - describe the operating principles, the performance figures of merit and the technological limits of the passive and active components used in RF, microwave and millimetre-wave subsystems (knowledge and understanding);
• LO2 - select and dimension impedance matching networks, bias networks and passive building blocks for an assigned specification (applying knowledge and understanding);
• LO3 - design a single-stage RF amplifier, choosing the operating point and the matching networks according to gain, noise and stability requirements, and discuss the trade-offs involved (applying knowledge and understanding; making judgements);
• LO4 - use a microwave CAD environment to simulate, optimise and verify a high-frequency circuit, and critically assess the reliability of the simulation results against theoretical predictions (applying knowledge and understanding; making judgements);
• LO5 - carry out the cascade analysis of an RF front end in terms of gain, noise figure, linearity and dynamic range, and relate the performance of the individual components to the performance of the overall subsystem (making judgements);
• LO6 - explain the operating principles of modern RFID subsystems and how they are integrated into high-frequency systems (knowledge and understanding);
• LO7 - present and document a design activity, motivating the design choices and the results obtained with appropriate technical language (communication skills; learning skills).

The course consists of 48 hours, approximately half of which are devoted to lectures and half to computer-laboratory sessions. In the laboratory sessions the topics introduced in the lectures are applied to the CAD design and simulation of simple RF and microwave devices, using a microwave CAD environment for circuit and electromagnetic simulation.
1. Introduction to high-frequency systems. Overview of RF, microwave and millimetre-wave applications. Frequency spectrum, bands and regulatory considerations. Distributed- versus lumped-element behaviour. Introduction to the microwave CAD environment used throughout the course.
2. High-frequency passive components. Behaviour of resistors, capacitors and inductors at high frequency; parasitics and self-resonance. Transmission lines and planar technologies (microstrip, coplanar waveguide). Impedance matching networks and Smith-chart based design. Laboratory: characterisation of high-frequency components and design of matching networks.
3. Bias networks and passive building blocks. DC returns and DC blocks, bias-injection circuits, bias tees. Outline of power dividers/combiners, couplers and filters as subsystem building blocks. Laboratory: design and simulation of a bias network and of a passive building block.
4. RF switches and control components. PIN-diode and transistor-based switches; insertion loss, isolation and linearity. Phase shifters and attenuators. Laboratory: design and simulation of an RF switch.
5. RF amplifiers. Two-port representation of active devices; scattering parameters and stability. Power gain definitions and gain circles. Low-noise design: noise figure, noise circles, gain/noise trade-off. Elements of power amplifier design and non-linear figures of merit (P1dB, IP3). Laboratory: complete design of a single-stage amplifier, from device data to a layout-oriented schematic.
6. Oscillators and mixers. Oscillation conditions, resonators, phase noise. Mixers: conversion gain, isolation, spurious response. Laboratory: simulation of an oscillator and of a mixer.
7. RF front-end system analysis. Cascade analysis of gain, noise figure and linearity; dynamic range; architecture of receiver and transmitter front ends. Laboratory: budget analysis of a complete front end.
8. Modern RFID systems. Basic operating principles, near-field and far-field links, tag and reader architectures, integration into high-frequency systems. Laboratory: link budget of an RFID system.

No compulsory prerequisite is set by the Study Programme Regulations for this course. However, the course is designed for students who have already completed the courses of Electromagnetic Fields, Microwave Engineering and Antennas and Propagation: previous attendance of these courses is strongly recommended, since the analysis and design methodologies developed here build directly on the field-theory background, on the scattering-parameter formalism and on the transmission-line, matching and radiating-system concepts introduced there. Students without this background will find it difficult to follow the course profitably.
More specifically, students are expected to possess the following knowledge and skills:

• Electromagnetics: Maxwell's equations, plane waves, propagation and reflection, guided propagation and transmission-line theory.
• Antennas and propagation: fundamental antenna parameters, radiation and link budget, elements of propagation in wireless links.
• Microwave engineering: scattering parameters, microwave network analysis, impedance matching and the Smith chart, planar transmission lines and elementary passive microwave networks.
• Circuit theory and electronics: frequency-domain analysis of linear circuits, two-port network representations (Z, Y, ABCD parameters), operating point and small-signal models of diodes and transistors, basic amplifier configurations, elements of noise in electronic circuits.
• Mathematical tools: complex numbers and complex functions, linear algebra and matrix calculus, Fourier analysis of signals.
  Students who have not attended the recommended courses are advised to review the corresponding chapters of the reference textbooks (Pozar: transmission lines, impedance matching, microwave network analysis; Ludwig: RF behaviour of components and amplifier fundamentals) before the beginning of the course; the relevant reading path is indicated on the USiena Integra page of the course.

Teaching activities. The course is organised in two closely integrated activities:

• Lectures (approximately half of the 48 hours), in which the theoretical background, the design methodologies and the figures of merit of high-frequency components and subsystems are presented, with worked examples and in-class problem solving and discussion. These activities mainly support LO1, LO2, LO3, LO5 and LO6.
• Computer-laboratory sessions (approximately half of the 48 hours), carried out in the computer laboratory using a microwave CAD environment for circuit and electromagnetic simulation. Each session follows the corresponding lecture topic and requires the student to design, simulate, optimise and critically discuss a simple RF or microwave device, starting from an assigned specification. These activities mainly support LO2, LO3, LO4, LO5 and LO7.
  The laboratory activity is an essential part of the course, since the design competences described in the expected learning outcomes are developed mainly there. There is no attendance requirement; students who take part in the laboratory sessions carry out a number of complete design activities and may, on a voluntary basis, write a short technical report on one of them, to be discussed at the final examination as described in the "Assessment methods" section.
  Student workload. Students are expected to review the corresponding chapters of the reference textbooks after each lecture and to complete the short design assignments proposed during the laboratory sessions.
  Non-attending students. Students who cannot attend the laboratory sessions are assessed on the design part of the course through a short design exercise at the examination, as described in the "Assessment methods" section. To make independent preparation possible, all the material needed to follow the course is published on the USiena Integra (Moodle) page: lecture slides, laboratory handouts describing each design activity step by step, the CAD project files and the device models used in the sessions, fully worked design examples with their expected results, additional exercises with solutions and guided reading paths on the reference textbooks. Non-attending students are encouraged to contact the lecturer during office hours, also remotely, to agree on an individual study path and to discuss the worked examples.
  The microwave CAD software is licensed for educational use through a licence server of the University and is therefore available only on the workstations of the Department computer laboratory, which cannot be reached from outside the University network: individual work with the CAD tool requires physical access to the laboratory. The laboratory is available to students of the course outside the scheduled sessions, during its opening hours and by arrangement with the lecturer, and students who cannot attend the scheduled sessions are strongly encouraged to make use of it. Students who wish to practise the circuit design activities on their own computer can use the free open-source simulator Qucs-S, which supports most of the circuit-level design tasks of the course although it does not replace the professional environment used in the laboratory sessions. Any change in the licensing conditions, including the possible availability of a student version installable on personal computers, is announced on the USiena Integra page of the course.

Format of the examination. The assessment consists of a single oral examination, held in the official examination sessions. There is no attendance requirement and no compulsory project work. The examination is composed of two parts: a part devoted to the design competences developed in the computer laboratory, which accounts for approximately 30% of the final mark, and a part devoted to the theoretical topics of the course, which accounts for approximately 70%.
Design part. Students may choose between two equivalent options.

• Optional technical report. Students who have taken part in the laboratory sessions may write a short technical report on one of the design activities carried out during the course and submit it at least one week before the examination date. In this case the examination starts with the discussion of the report: the student illustrates the specifications, the design choices, the simulation results obtained with the microwave CAD environment and their critical assessment, and answers questions on the alternatives that were discarded and on the limits of validity of the results (LO2, LO3, LO4, LO5, LO7).
• Design exercise at the examination. Students who do not submit a report solve and discuss a short design exercise of the same type as those developed in the laboratory sessions: typical examples are the design of a matching or bias network, the determination of the stability and gain conditions of an active two-port, or the noise and linearity budget of a receiving chain (LO2, LO3, LO5).
  The two options verify the same learning outcomes and are assessed with the same criteria and the same weight.
  Theoretical part. The examination continues with questions on the topics of the course, aimed at verifying the knowledge and understanding of the operating principles, of the performance figures of merit and of the technological limits of the passive and active components and of the RF subsystems covered in the lectures, as well as the ability to relate the behaviour of the individual components to the performance of the overall subsystem (LO1, LO5, LO6).
  Assessment criteria. The examination verifies: the correctness and completeness of the knowledge of the topics of the course (LO1, LO6); the ability to apply the design methodologies to an assigned specification and to justify the design choices and the trade-offs involved (LO2, LO3); the ability to interpret and critically assess design and simulation results (LO4, LO5); the clarity, rigour and appropriateness of the technical language (LO7).
  The final mark is expressed out of thirty. A mark of 18/30 requires a correct knowledge of the fundamental topics of the course and the ability to carry out an elementary design with guidance; the highest marks require autonomy in the design choices, a critical discussion of the results and full command of the technical language. Honours (lode) are awarded for an outstanding performance in both parts of the examination.
  Non-attending students. The examination is the same for attending and non-attending students. The design part is verified through the design exercise described above, for which the material published on the USiena Integra page (laboratory handouts, worked examples and exercises with solutions) provides the necessary preparation.

Main textbooks

• R. Ludwig, G. Bogdanov, RF Circuit Design: Theory and Applications, 2nd ed., Pearson/Prentice Hall, 2009.
• D. M. Pozar, Microwave Engineering, 4th ed., John Wiley & Sons, 2012.
  Suggested readings for further study
• I. J. Bahl, Fundamentals of RF and Microwave Transistor Amplifiers, John Wiley & Sons, 2009.
• G. Gonzalez, Microwave Transistor Amplifiers: Analysis and Design, 2nd ed., Prentice Hall, 1997.
  Additional material
  Lecture slides, laboratory handouts, worked design examples and the CAD project files used during the computer-laboratory sessions are made available to all students, attending and non-attending, on the USiena Integra (Moodle) page of the course (https://elearning.unisi.it). Datasheets, application notes and device models used in the laboratory activities are indicated during the course and posted on the same page.

Il corso avrà lo scopo di fornire agli studenti le conoscenze del problem solving nel contesto dell'affidabilità. Verranno sfruttati casi industriali e database più conosciuti e utilizzati per consentire allo studente di eseguire la progettazione della disponibilità e della sicurezza. Gli aspetti di affidabilità e disponibilità possono essere implementati sia durante la fase di progettazione che di valutazione. Gli studenti saranno in grado di definire criteri progettuali rispondenti ai requisiti di affidabilità e metodi di valutazione ai fini del retrofit.

Nel corso gli studenti verranno sfidati sulle più importanti funzioni di densità di probabilità per definire la legge fondamentale dell'affidabilità per qualsiasi tipo di componente. Verranno esaminate configurazioni singole e multiple e verranno fornite soluzioni analitiche. La modellazione della disponibilità sarà fornita in configurazioni complesse rappresentative di casi industriali. Verrà affrontato il progetto di sicurezza secondo le nozioni di base IEC61508.

Statistica di base, Fisica, Eletronica, Elettrotecnica.

Lezioni forntali e registrazioni streaming

Scritti o orali sul contenuto del corso

Marco Mugnaini Dependability in a Pocket S.S.Rao. Reliability Based Design J.W. McPherson. Reliability Physics and Engineering

Basis of Complex Analysis. Laplace Transform, with application to solution of linear differential equation.

Complex numbers.
Complex functions of complex variables.
Holomorphic functions, harmonic functions. Conformal mapping.
Complex exp, sin, cos and their inverse.
Complex integration. Cauchy formula.
Analytic functions. Taylor and Laurent power series expansion.
Residue Theorem and its application to the evaluation of integrals.
Laplace Transform and its application to differential equation solution.

Real Analysis and Calculus.

Theoretical lectures and exercises.

Written or Oral Exam

J. W. Brown, R. V. Churchill "Complex variables and applications", Mc Graw Hill, 9th ed (https://qcpages.qc.cuny.edu/~madrian/ChurchillBrown.pdf)
M. Beck, G. Marchesi, D. Pixton, L. Sabalka, "A first course in Complex analysis", Department of Mathematics, San Francisco State University, 2002-2012 (http://math.sfsu.edu/beck/papers/complex.pdf)

Fornire le conoscenze di base per la analisi el a progettazione di sistemi e circuiti a microonde passivi in guida d'onda e in tecnologia planare.

LINEE DI TRASMISSIONE E GUIDE D’ONDA
Soluzioni generali per modi TEM, TE e TM. Guida a piatti piani e paralleli, guida rettangolare, cavo coassiale, substrato dielettrico con piano di massa, stripline, microstriscia.
ANALISI DI RETI A MICROONDE
Tensioni e correnti equivalenti, concetto di impedenza.
Descrizioni matriciali di reti multiporta: matrice impedenza, matrice ammettenza, matrice di scattering, matrice ABCD.
Analizzatore di rete vettoriale.
Analisi modale di discontinuità.
RETI DI ADATTAMENTO DI IMPEDENZA
Carta di Smith.
Adattamento con elementi concentrate, adattamento tramite stub.
Trasformatore a lambda quarti.
DIVISORI DI POTENZA E ACCOPPIATORI DIREZIONALI
Proprietà fondamentali dei divisori e accoppiatori.
Divisore di potenza di Wilkinson.
Accoppiatori direzionali in guida d’onda.
Ibridi a 90° e 180°.
Uso di software di simulazione

Conoscenze di base sui campi elettromagnetici; operatori differenziali.

Lezioni frontali, esperienze di laboratorio, esercitazioni con un software di simulazione elettromagnetica

Come parte dell'esame finale, gli studenti dovranno portare a termine un progetto di un dispositivo a microonde (assegnato dal docente) tramite il software AWR.
L'esame finale è orale e comprende la discussione del progetto.

Testo di riferimento (parte di):
D. M. Pozar, Microwave Engineering, 3rd Ed., Wiley.
Testo di consultazione:
R. E. Collins, Foundations for Microwave Engineering, McGraw-Hill.

To deeply understand the main wireless and mobile network technologies, their functioning principles and related protocols. To achieve a good knowledge about the paradigm of Internet of Things and of their enabling technologies.
The student should acquire a deep knowledge of the main wireless and mobile telecommunication networks and how they work and interact.
She/he should be able to choose the right network solutions that can be adopted according to different requirements imposed by specific usage scenarios and applications.

Fundamentals on wireless and mobility
Widea area networking: 2G, 3G, 4G cellular systems
2G: details on GSM system
Network architecture, radio interface, functioning examples and signaling (user calls, localization,etc)
Security in GSM communications
Data communications with cellular systems, HSCSD
GPRS system: network architecture and radio channels
Evolution towards 3G systems
3G systems: UMTS
UMTS architecture, details on radio interface (UTRAN), CDMA
Evolution towards 4G systems: HSPA, LTE
4G systems, LTE-A (e-UTRAN)
Local area networks: Wireles LAN (WLAN)
IEEE 802.11 Standard
Architecture and services
Physical and MAC layer, access method to the wireless channel
Frame types and format
WLAN security: WEP, WPA and WPA2 (IEEE 802.11i)
Personal area networks: Wireless PAN (WPAN)
Bluetooth details
Mobility at network level: Mobile IP and micromobility (Cellular IP)
Internet of Things: introduction, smart objects, IoT technologies, architecture of IoT node, Machine to Machine (M2M) communication
Wireless technologies for local and wide IoT connectivity(Bluetooth Low Energy, WiFi, RFID, NFC, 802.15.4, Zigbee,...)
Low Power Wan Networks (LPWAN), LoRa/LoRaWAN
Laboratory activity, complete configuration and implementation examples of an IoT node and sensor data communication to an application server
(arduino board, LoRaWAN,..).

Basic knowledge of networking fundamentals and of the working principles of the Internet.

Frontal lessons with the involvement of students with questions and learning discussions.
Some lab and practical classes will also be held, in small groups of students who will interact with IoT devices and try to solve some problems related to IoT technologies.
The competences to be achieved consist mainly in theoretical understanding and problem solving skills.

Oral exam on the topics of the course.
The exam consists in several questions focusing on the main topics explained during the lectures. The discussion with the student should highlight her/his competence and ability to argue with mastery the topics of the course.

Reference Books

• Jochen Schiller: 'Mobile communications' - 2 ed., Addison Wesley, 2003.
  Other
• J. P. Vasseur and A.Dunkels, `Interconnecting Smart Objects with IP,`Morgan Kaufmann, 2010.
• William Stallings: "Wireless Communications & Networks" (2nd Edition), Pearson Education, 2011.
  Other references can be communicated during lectures

Understanding the evolution of cellular communications towards the 5G revolution, capable of providing an holistic vision of communications of the future, from the possibility of integrating billions of low energy consumption nodes in small areas, to the possibility of providing ultra low latency communications for emerging services, such as self driving cars. Apply basic techniques in modern digital communications and optimization to the problem of radio resource optimization in 5G new radio. Familiarize with real 5G scenarios

Introduction to wireless communication systems and cellular communications. The 5G revolution: holistic vision of digital communications. 5G scenarios, Massive Machine-to-Machine communications (smart cities, smart industry), Enhanced Mobile Broadband (gaming, UHD videos), Ultra-reliable Low Latency communications (self driving cars, tactile internet). 5G New-radio, Massive MIMO systems, Flexible OFDM, Interference cancelation. Radio Resource Management for 5G. Heterogeneous architectures, Macro-cells, Micro and Femto cells, Device to device communications, options for centralized and distributed resource allocations. Scheduling options for New radio with flexible frame structure. Study of the implementation of use cases and scenarios in a real 5G network. Massive MIMO: the transmission technology of 5G systems. Introduction to MIMO, MIMO capacity, MIMO cellular systems, the mMIMO effect, the problem of channel estimation, TDD vs FDD, pilot contamination and decontamination, open issues, a vision of the future: cell-free networks, Large Intelligent Surfaces.

Basic knowledge of digital communications

Frontal lessons, compilation of a final report with an analysis of the implementation of a use case in a real 5G

Written test with multiple choices and matlab project

5G NR: The Next Generation Wireless Access Technology Erik Dahlman Stefan Parkvall Johan Sköld

Protocolli rete, progetto di rete, ingegneria del traffico, esempi di configurazione

Questo corso è rivolto allo studio dei protocolli fondamentali di Internet quali: IP (indirizzamento e routing), TCP (Tahoe, NewReno e varianti) e UDP. Descrizione dei metodi analitici di base per l’ingegneria del traffico e la gestione della qualità del servizio: (i) Teoria delle code di tipo M/M/S e M/G/1 e reti di code; (ii) Token-bucket shaping e i meccanismi per la garanzia della qualità; (iii) Traffic offloading e multipath-routing. Cenni alla softwarizzazione e alla virtualizzazione delle reti in relazione alle reti 5G/6G. Descrizione delle tecnologie per l’Internet of Things (IoT) massivo. Una gruppo di lezioni sarà dedicato ad esempi pratici di configurazione delle reti sulla base di tool quali: ping, Iperf, Cisco packet tracer, Wireshark.

Conoscenze di fondamenti di telecomunicazione e teoria delle probabilità.

Lezioni teoriche, esercizi, esempi da laboratorio svolti in aula con portatili

Il corso prevede una prova scritta basata su esercizi numerici e una prova orale finale che serve a vagliare le conoscenze di base.
Inoltre è previsto un compitino intermedio e una relazione finale basata su attività individuale (network lab test, esercizio in matlab, o studio di una nuova tecnologia di reti).

G. Giambene, “Queuing Theory and Telecommunications: Networks and Applications”, Springer, N.Y., 3rd Ed. 2021.
J. F. Kurose, K. W. Ross, “Computer Networking: a Top-Down Approach Featuring the Internet”, 7a edizione, 2016.
Troy McMillan, "Cisco Networking Essentials", 2a edizione, 2015.
Jeremiah F. Hayes, “Modeling and Analysis of Computer Communications Networks”. Plenum Press, 1984.

Analysis of multirate systems and filter banks (time-frequency and time-scale representations). Knowledge of the fundamentals of the estimation theory. Detection theory and optimal signal detection. --- Analisi di sistemi multirate e di banchi di filtri (rappresentazioni tempo-frequenza e tempo-scala). Conoscenza dei fondamenti della teoria della stima. Conoscenza della teoria della rivelazione ottima di segnali.

Multirate signal processing. Frequency and z-domain analysis. Fractional sampling. Fractional sampling rate conversion. Polyphase representations and their applications. Filter banks. Perfect reconstruction conditions. Applications of filter banks. Subband analysis and synthesis of signals. Multiresolution analysis. Wavelet transform. Estimation theory: classical approach. Minimum variance unbiased estimators. Cramer-Rao lower bound. Best linear unbiased estimators. Maximum likelihood estimation. Linear models. Least squares estimation. Bayesian approach: MMSE estimation, scalar and vector MAP estimation. LMMSE. Sequential MMSE. Signal estimation: Wiener filter. Introduction to Kalman filtering. Signal detection. Statistical decision theory. MAP criterion. Simple hypothesis testing: Neyman-Pearson theorem. Bayes Risk. Matched filter. ---- Elaborazione dei segnali a frequenza di campionamento variabile. Analisi in frequenza e nel dominio Z. Variazione frazionaria del passo di campionamento. Rappresentazioni polifase e loro applicazioni. Banchi di filtri. Condizioni di perfetta ricostruzione. Applicazioni dei banchi di filtri. Analisi e sintesi dei segnali in sotto-bande. Analisi multirisoluzione. Introduzione alla trasformata wavelet. Teoria della stima. Approccio classico. Stimatori MVU. Limite di Cramer-Rao. Stimatori BLUE. Stima a massima verosimiglianza. Modelli lineari. Stima ai minimi quadrati. Approccio di Bayes: Stima MMSE, Stima MAP scalare e vettoriale. LMMSE. MMSE sequenziale. Stima di segnali: filtro di Wiener. Introduzione al filtro di Kalman. Teoria della rivelazione. Rivelazione di segnali. Teoria della decisione statistica. Criterio MAP. Test delle ipotesi semplice. Teorema di Neyman-Pearson. Rischio di Bayes. Filtro adattato.

Calculus Linear Algebra Signal Theory --- Analisi matematica. Algebra lineare. Teoria dei segnali.

Lectures. Practical classes. --- Didattica frontale. Esercitazioni. ---

Two (optional) midterm written exams (70% of the final mark, when passed). Final oral exam (30% of the final mark, or 100% without passing the midterm exams). All tests aims at verifying the knowledge of the fundamentals of multirate systems, estimation theory and detection theory. The final oral exam aims at verifying the problem-solving capability in estimation and detection. --- Due prove in itinere non obbligatorie. (70% del voto finale se superate). Prova orale finale (30% del voto finale con prove in itinere superate, o 100% del voto finale in assenza di prove in itinere). Le prove verificano la conoscenza di base dei sistemi a campionamento variabile, della teoria della stima e della rivelazione. La prova finale verifica la capacità di soluzione di problemi di stima e rivelazione.

P. P. Vaidyanathan, Multirate Systems and Filter Banks, Prentice Hall,1992. S.M. Kay, Fundamentals of statistical signal processing - Estimation theory, Prentice Hall, 1993 S.M. Kay, Fundamentals of statistical signal processing - Detection theory, Prentice Hall, 1993

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