Communications Engineering

At the end of this course, the students know the key characteristics of the considered communication systems, the most advanced techniques used for wireless and wired systems, and the practical scenarios and applications where they are used. In particular, they have an in-depth knowledge of the physical layer constituent blocks of: OFDM-based systems, 4G-LTE and 5G-New Radio systems. This way, the students acquire the ability to contribute to the design and the management of these state-of-the-art telecom systems.

The course is taught in English. This course introduces the most important state-of-the-art techniques used in modern wireless and wired communication systems. The focus is on the system physical layer of mobile networks, from digital modulation to beamforming. The key techniques of 4G and upcoming 5G are analyzed with the aim of understanding their impact on different applications, and their possible evolution. This course is useful for all students interested to digital transmission, both in the Communications and Computer Networks Engineering Degree and in the Electronic Engineering Degree.

Main topics of the course: - Review of wireless systems: Digital modulations (PSK, QAM), coding, multiple access schemes, channel models (10 h) - OFDM and modern techniques for wireless and wired systems (20 h) - 4G LTE mobile networks: physical layer (10 h) - New 5G systems physical layer: requirements, applications and technical solutions from modulation to MIMO and beamforming (20 h)

Modalita di esame: Prova scritta (in aula); Prova orale facoltativa; Elaborato scritto prodotto in gruppo;
Exam: Written test; Optional oral exam; Group essay;
... Written exam on course¿s topics (usually two hours and six questions, no books, no notes, highest mark = 30 cum laude). The assessment is made of six questions randomly extracted from a list prepared at the end of the course. Questions reflect key understanding the students should possess on the studied systems, the constituent blocks of their physical layer, and their most important applications.

Teacher¿s material, available on the course web portal. Suggested books: Proakis-Salehi ¿Communication Systems Engineering¿, Sesia-Toufik-Baker ¿LTE, The UMTS Long Term Evolution: From Theory to Practice¿, Erik Dahlman, Stefan Parkvall, Johan Sk¿ld, ¿5G NR The next generation wireless access technology¿ Presentations offered by Telecom companies.

The student will gain knowledge on algorithms and techniques needed to design, run and manage computer networks while providing a proper level of quality to network users. Besides the knowledge on specific algorithms, the students will gain the ability of selecting the proper algorithms depending on the proper networking framework, which includes the used technology, the user-service goals, the user traffic characterization and control, and the quality of service parameters. Different service and quality of service models will be discussed and analyzed. Lab experience will help students in applying concepts to a real, although emulated, network. The students will gain the ability to critically analyze standardization efforts, and to understand the reasons that drove the design choice in standards. A significant ability in discussing algorithm pros and cons is required in the final exam and will be gained during lectures through group discussions. The ability to apply the gained knowledge will be verified through class exercises and discussions, as well as during the oral examination. The oral examination will also help students in improving their communication skills. Abilities and knowledge that will be acquired: • Knowledge of the main methodological tools that can be used to design a networking system. • Knowledge of the main algorithms used to control the network and provide QoS. • Knowledge of the main techniques to manage a network. • Knowledge of the most significant architectures for data distribution. • Ability to understand the fundamental behavior of computer networking systems. • Ability to select the proper set of algorithms and technologies to reach a specific quality goal. • Ability to select the proper set of algorithms and tools to properly manage a network.

The course provides an overview of the main techniques used to design and evaluate the performance of telecommunication networks, focusing on data distribution, network slicing and virtualization, management, planning, design, and quality of service provisioning. Class lectures will introduce techniques to support heterogeneous traffic (voice, video, data) on a single network infrastructure and will discuss how to design computer network systems to reach a given target level of QoS and user satisfaction. Particular attention is devoted to algorithms on one hand and to standardization efforts on the other hand.

• Fundamental concepts of computer networks with a QoS perspective (16h) o Switching techniques properties (circuit, packet with datagram service, packet with virtual circuit service) (4h) o Topologies, routing and network performance (2h) o Introduction to quality of service techniques and traffic characterization: coding techniques and applications taxonomy (4h) o Recap on main architectures and protocols for quality of service (SDH and WDM, Frame relay, ATM, Ethernet, Internet, wireless LAN) (6h) • Quality of service: standardization efforts (10h) o QoS in Frame Relay and ATM (4h) o QoS in Internet: Intserv and Diffserv. RSVP. MPLS. RTP and multimedia traffic. (6h) • Algorithms to support QoS (20h) o Network planning (2h) o Connection Admission Control (CAC), routing and QoS routing (policy based routing, hierarchical approach) (4h) o Shaping and policing: leaky bucket, token bucket (2h) o Scheduling algorithms: FIFO, priority, RR, DRR, WRR, and WFQ (4h) o Congestion control and fair rate allocation (max-min fairness, buffer monitoring, TCP congestion control) (6h) o Dropping policies and AQM (Active Queue Management) techniques (2h) • Network management (20h) o SNMP protocol, MIB and ASN.1 (2h) o Capacity planning (2h) o Monitoring and measurements (2h) o The SDN (Software Defined Networking) approach (4h) o Network slicing and network function virtualization (2h) o Operational networks: case studies (6h) o Data distribution systems: P2P, CDN, cloud and data center (2h) Seminars given by network provider managers will be presented as concrete examples of operational networks.

Modalita di esame: Prova orale obbligatoria;
Exam: Compulsory oral exam;
... Oral examination on all the topics covered during lectures. The oral exam lasts roughly 15-30 minutes and is typically based on three-four questions on different topics discussed during lectures. Questions are both theoretical and based on short exercise/examples to check the student ability and knowledge to translate the theoretical concepts in practice. Answers are evaluated considering their correctness, the acquired level of knowledge, the ability to be precise and to put the topic in the proper context, the capacity to clearly communicate the technical content, and the ability to apply the acquired know-how to real, operative, networks.

The teaching material is made available in advance by the class teacher on the Didattica web portal. Useful references (books, scientific papers) are also listed for student convenience.

Knowlegde of the signal space representation and of the modulations for the additive Gaussian channel Knowledge of the optimum Bayesian receiver and its performance analysis Knowledge of the performance of standard digital modulations Knowledge of information theoretical metrics Knowledge of channel capacity and Shannon’s capacity formula Knowledge of block encoding techniques. Knowledge of convolutional encoding techniques. Knowledge of basic decoding algorithms for block coding. Knowledge of Viterbi algorithm and its application to convolutional decoding. Knowledge of key parameters dominating coding performance and providing coding gain. Knowledge of interleaving techniques for burst channels. Knowledge of most important application of coding to communication systems. Ability to design an optimum receiver over the AWGN channel Ability to evaluate the performance of digital modulations over the AWGN channel Ability to compare different digital modulations with different spectral efficiencies Ability to evaluate the capacity of some classes of discrete channels Ability to interpret the modulation performance in a Shannon diagram Ability to choose the coding parameters for a given communication system. Ability to evaluate a basic block and convolutional coding scheme. Ability to design a basic block coding scheme and a basic block decoding algorithm. Ability to design a convolutional coding scheme and a Viterbi decoding algorithm. Ability to design an interleaver to counter the effect of burst errors. Ability to understand the key properties of codes used in practical applications.

The goal of the course is providing a description of the fundamental features and technologies for digital communication systems. During the course, the basic characteristics of the additive Gaussian channel model will be described along with an information theoretical analysis and interpretation of the achievable transmission rate. Basic concepts on information and modulation theory will be illustrated in this framework. An introduction to coding will be provided, covering both block codes and convolutional codes. The key features of error correction and error detection will be studied. The advantages given by coding will be analysed, and applications to most important communication systems will be presented.

The course program is divided into two parts: 1. Digital modulations for the AWGN channel and basic concepts from Information Theory (4 credits) (prof. Taricco) • Analytic signal representation • Review of basic probability concepts • Introduction to signal spaces • Linear modulations for the AWGN channel • Digital receiver design • Baseband and pass-band modulations • Signal detection • Error probability • Standard digital modulations • Power density spectrum of linear modulations • Comparison of digital modulations: Shannon diagram • Information theory: entropy and mutual information • Definition of channel codes • Discrete channels • Discrete channel capacity • Continuous input-continuous output channels • Shannon capacity formula 2. Introduction to Channel Coding (4 credits) (prof. Garello) • Block codes o Generating matrix and parity check matrix o Hard and soft decoding o Error detection o Minimum distance, performance evaluation and coding gain o Interleaving for burst channels o Automatic Repeat Request o Communication systems applications • Convolutional codes o Convolutional encoder, trellis representation o Hard and soft decoding: the Viterbi algorithm o Minimum distance, performance evaluation and coding gain o Puncturing o Communication systems applications

Modalita di esame: Prova scritta (in aula);
Exam: Written test;
... A written exam, where six to eight exercises and/or theoretical questions on the two parts are proposed relevant to the course topics. The assessment will consider the correctness of the answers, the clarity and rigorousness of the development, and how exhaustive is the overall exam about the questions proposed. The questions aim at assessing the knowledge on the topics listed in the course program and the ability to apply the theoretical concepts for the solution of the exercises. Use only A4 white papers without stapling or foldings to simplify the scanning operations. Exam duration: 90 minutes. Books and lecture notes not allowed. Maximum grade: 30 LODE

1. For the first part, lecture notes handouts are provided to students. The following books represent useful supplementary reading: • S. Benedetto and E. Biglieri, Principles of Digital Transmission: With Wireless Applications. Kluwer. • A. Goldsmith, Wireless Communications. Cambridge University Press. • U. Madhow, Fundamentals of Digital Communication. Cambridge University Press. • A. Molisch, Wireless Communications. Wiley. J. Proakis and M. Salehi, Digital Communications (4th Edition). • McGraw-Hill. T. Rappaport, Wireless Communications: Principles and Practice (2nd Edition). Prentice-Hall. • D. Tse and P. Viswanath, Fundamentals of Wireless Communication. Cambridge University Press. 2. For the second part, • Stephen B. Wicker, Error Control Systems for Digital Communication and Storage, Prentice Hall • David J.C. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press

At the end of the course the students will - know and understand the basic principles of statistical machine learning applied to pattern recognition and classification; - know the principal techniques for classification, including generative linear Gaussian models and discriminative approaches based on logistic regression and support vector machines, among others; - understand the theoretical motivations behind different classification approaches, their main properties and domain of application, and their limitations; - be able to implement the different algorithms using wide-spread programming frameworks (Python) - be able to apply different methods to real tasks, to critically evaluate their effectiveness and to analyze which strategies are better suited to different applications; - be able to transfer the acquired knowledge and capabilities to solve novel classification problems, developing novel methods based on the frameworks that will be discussed during classes

The course aims at providing a solid introduction to machine learning, a branch of artificial intelligence that deals with the development of algorithms able to extract knowledge from data, with a focus on pattern recognition and classification problems. The course will cover the basic concepts of statistical machine learning, both from the frequentist and the Bayesian perspectives, and will be focused on the broad class of generative linear Gaussian models and discriminative classifiers based on logistic regression and support vector machines. The objective of the course is to provide the students with solid theoretical bases that will allow them to select, apply and evaluate different classification methods on real tasks. The students will also acquire the required competencies to devise novel approaches based on the frameworks that will be presented during the classes. The course will include laboratory activities that will allow the students to practice the theoretical notions on real data using modern programming frameworks that are widely employed both by research communities and companies.

Machine learning and pattern recognition - Introduction and definitions Probability theory concepts - Random Variables - Estimators - The Bayesian framework Introduction to Python - The language - Main numerical libraries Decision Theory - Inference, expected loss - Model taxonomy: generative and discriminative approaches - Model optimization, hyperparameter selection, cross-validation Model evaluation - Classification scores and log-likelihood ratios - Detection Cost Functions and optimal Bayes decisions Dimensionality reduction - Principal Component Analysis (PCA) - Linear Discriminant Analysis (LDA) Generative Gaussian models - Generative Gaussian classifiers: univariate Gaussian, Naive Bayes, multivariate Gaussian (MVG) - Tied covariance MVG and LDA Logistic Regression (LR) - From Tied MVG to LR - LR as ML solution for class labels - Binary and multiclass cross-entropy - From MVG to Quadratic LR - LR as empirical risk minimization - Overfitting and regularization Support Vector Machines (SVM) - Optimal classification hyperplane: the maximum margin definition - Margin maximization and L2 regularization - SVM as minimization of classification errors - Primal and dual SVM formulation - Non linear extension: brief introduction to kernels Density estimation and latent variable models - Gaussian mixture models (GMM) - The Expectation Maximization algorithm Continuous latent variable models: Linear-Gaussian Models - Linear regression - Linear regression and Tied MVG - MVG with unknown class means: Probabilistic LDA (PLDA) - Bayesian MVG - Factor Analysis: PLDA, Probabilistic PCA Approximated inference basics - Variational Bayes

Modalita di esame: Prova scritta (in aula); Elaborato progettuale individuale; Elaborato progettuale in gruppo;
Exam: Written test; Individual project; Group project;
... The exam will assess the knowledge of the course topics, and the ability of the candidate to apply such knowledge and the developed skills to solve specific problems. The exam will consist in two parts: - A project to be developed during the course. The students will be able to choose individual or (small) group projects among a set of possible choices (max. 18 points). - A written examination (max. 12 points). The final mark will be the sum of the report and written exam marks. To pass the exam, the report mark must be at least 9/18, the written exam mark must be at least 6/12, and the final mark must be at least 18/30. The projects will address machine learning tasks. For each project, a dataset will be provided, and the students will have to develop suitable models for the specific task based on the topics and tools presented during lectures and laboratories. Each candidate will have to provide a technical report detailing the employed methodology and a critical analysis of the obtained results. The report will assess: - The degree of understanding of the theoretical principles of different machine learning approaches - The ability of the student to analyze a specific problem, assessing which approaches, among those that have been presented, are more suited to solve the task - The ability of the student to apply the studied methods to devise suitable solutions for the specific case study - The ability of the student to critically evaluate the effectiveness of the proposed approaches. The written examination will consists of open questions covering the topics presented during the lectures. The written examination will assess: - The theoretical understanding of the basic principles of the presented machine learning approaches - The knowledge and understanding of the different approaches that have been presented during the lectures - The ability of the student to critically analyze and evaluate the different approaches.

[1] Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag, Berlin, Heidelberg. [2] Kevin P. Murphy. 2012. Machine Learning: A Probabilistic Perspective. The MIT Press. Additional material, including slides and code fragments, will be made available on the course website.

The student will acquire the knowledge of: • the reasons why optical fiber systems are today the backbone of fixed telecommunication networks • optical fiber transmission systems main features, performance, potential and impairments • the related transmitter and receiver technologies and algorithms needed to achieve reliable communication over fiber The expected outcome of the course is the ability for the student to: • understand the design and optimization process of practical optical transmitter and receiver systems, as well as of the fiber plant connecting them • apply such design techniques and optimization processes autonomously to practical transmission system examples.

The goal of this 6-credit course is to provide detailed knowledge about specific features, potential performance and design trade-offs of practical digital communication systems based on fiber-optic transmission. Optical fiber communications represent today the worldwide ubiquitous cabled infrastructure for the global Internet, from data-centers and server farms, through cities, countries and continents, all the way to the end users’ homes or working places. The course is fully given in English.

These are the main topics of the course: • Introduction to optical communication systems • System level description of the key optoelectronic components for optical fiber communications • semiconductor lasers • external modulators • optical filters • optical amplifiers • optoelectronic receivers based on photodiodes • Linear and non-linear propagation effects in single-mode optical fibers • chromatic dispersion • polarization linear effects • the Kerr non-linear effect • modeling of the interaction of linear and non-linear effects • Direct-detection systems based on PAM-M without optical amplification • practical non-amplified systems: typical structure, PIN+TIA or APD photodetection, modeling and performance calculation • design of systems using non-amplified systems: short and medium-haul systems, access and last-mile fiber-to-the-home (FTTH) systems based on PAM-M transmission • the ideal on-off optical transmission systems: an introduction to quantum-limited optical systems • Coherent transmission systems based on PM-QAM and optical amplification • generalities and description, block diagrams • ideal performance characterization in ASE noise • receiver DSP block diagrams and algorithms • coherent transmission systems and fiber nonlinearities: the Gaussian Noise model • Application scenarios and related design optimization: • data-center networks • access networks (fiber-to-the-home, or FTTH) • multi-thousand km submarine systems • optically-routed national and international backbone networks • a look towards the future: quantum cryptography

Modalita di esame: Prova scritta (in aula); Prova orale facoltativa;
Exam: Written test; Optional oral exam;
... The written exam is based on: - two numerical system design exercises, similar to those that will be solved during the course. - two-three theoretical questions, requiring a free-text answer. The exam will last two hours and it will be scored on a full scale up to 30. The evaluation of the written exam is based on the correct development of the proposed exercises from the description of the symbolic-formula solutions up to the numerical results. The theoretical questions will be judged according to the completeness of the answers, but also on the ability of the students to reply in a concise way. The written exam proposes exercises that allows to judge if the student knows the topic of the course and is able to apply this knowledge to solve some simplified design examples on modern digital transmission systems. The open questions allows to judge if the student has acquired the most relevant theoretical topic of the course. This written exam is a “closed-book exam�. During the written exam the student can use only: • A pocket calculator (NO laptop, tablets etc. Any type of cellphone should be switched OFF) • A 4 pages (max) summary of formulas written by the student herself/himself (4 pages total, meaning 2 sheets if one writes on the front and back of each sheet) • No other technical material is allowed (thus no books, handouts, old exercises, etc) The students who will get a score above 15/30 at the written exam can ask for an optional oral exam, where the questions will mostly regard the theoretical aspects of the course. The optional oral exam is always organized a few days after the written exams. It gives rise between -5 and +5 points that are added to the result of the written exam. During the semester, the Teachers will moreover propose some optional technical challenges/homeworks, that the students may try to solve and send to the Teacher. These optional technical challenges will be evaluated, giving a maximum score of up to 3 points. The final grade for Optical Communication is the sum of the aforementioned results of the written exam + optional technical challenges + optional oral exam. If this final sum is above 32, the student will get “30 cum laude�.

The optical system topics are fully covered by the material (handouts) provided by the teachers, both for the lectures and the design problems. All such material will be available on the website prior to classes. No specific extra material is needed. For personal further reading on optical fiber systems: • Optical Fiber Telecommunications Volume VIA and VIB: Systems and Networks (Optics and Photonics) by Kaminow, Ivan, Li, Tingye and Willner, Alan E., Academic Press; 6 edition (May 11, 2013). ASIN: B00CZANHGW • Xiang Zhou and Chongjin Xie, Enabling Technologies for High Spectral-efficiency Coherent Optical Communication Networks, John Wiley and Sons, Inc., 2015. • J.Proakis and M.Salehi, Digital Communications (5th ed). McGraw-Hill, 2008.

Students will first learn how to organize a full project working in a team. From a more technical perspective, they will learn how to design, develop and validate a numerical simulator starting from models applying an adequate method. Knowledge - Organization and management of a project - Model design and development - Simulation techniques - Critical thinking - Required technical competences Abilities - Ability to work in a team - Ability to apply and select the best simulation method - Technical communication - Dissemination of project progress and results

This course introduces Communication Engineering (CE) students to Project Based Learning (PBL). PBL is an innovative instructional approach that allows students to develop knowledge and skills engaging them in project where they face challenges and solve problems to develop a useful and “selllable� software for the communication industry. The students are responsible to plan the required learning activities, to manage the project by defining proper milestones and deadlines and to present their progress and final achievement. This first PBL course in the CE program will focus on simulation: students will build a digital twin of a real-world device, systems or network that allow to predict their behaviors. This is a fundamental step in the design process, but it can also be used in operational mode to optimize the control. This first PBL course in the CE program will focus on the design and implementation of a simulation model of a real-world communication device, system or network, in order to evaluate and predict its behavior and performance. This is a fundamental step in the design process, but it can also be used in live scenarios to optimize the control (e.g., digital twin). Based on the background acquired in other courses, students will develop a full-fledged simulator for a specific application. They will work in small groups to solve all problems that can arise, from the design to the final implementation of a simulator. We expect that students will learn new technical competences, improve the understanding of concepts learnt in previous course broadening their knowledge and improving also soft skills, like inter-personal communication, team-working and leadership.

Every academic year the course will be offered to students, a different set of projects will be proposed for the development in team of 3 or 4 students. Each project will fall in a specific area of Communication Engineering. This is a list of areas for the development of projects: - Ultra-high-capacity transcontinental optical links; - Communication- and energy-aware mission planning and control for cooperative UAVs; - Virtual services for autonomous mobile systems; - Satellite link for communications and ranging; - High-speed data-center links; - Application of probabilistic and statistical tools to detect network attacks; - Application of digital signal processing techniques to audio time series.

Modalita di esame: Prova orale obbligatoria; Elaborato scritto prodotto in gruppo; Elaborato progettuale in gruppo;
Exam: Compulsory oral exam; Group essay; Group project;
... Each team is required to deliver a final report on the project. Every student is required to take an oral exam. The final report must summarize all the work carried out through the project, highlighting main choices and describing all property of the developed simulator together with some practical use case. The oral exam is a discussion on the whole project and it will start from the delivered report. Periodical presentations must show the ability of the team to manage the project and the organization of the teamwork. The evaluation will consider the ability to use correct technical language, to focus on the core ideas and to clearly introduce concepts. The oral exam must show that the student: - understands the whole work developed by each student of the team; - contributed in a meaningful way to the team achievement; - learned the required competences and abilities to solve the addressed problem; - understand the achieved results. The exam grade is a weighted average of the following evaluations: quality of presentations (periodic ones and final one), final report, individual contribution within the team, individual oral discussion. The precise weighting criteria will be presented and discussed at the beginning of the course.

The teacher responsible of the course will provide slides and notes for the introductory classes addressing project management and dissemination of results. Every project will have a Reference Teacher who will follow the team: specific teaching material will be provided through the course website based on the learning plan agreed with each team.

Signal, image and video processing and learning (Image and video processing and learning)In general, the course is expected to provide the student with a solid background in three areas, namely signal compression, authentication and analysis, with an approach such that this information is “usable� in practical applications. For this purpose, each area is coupled with 6 hours of computer lab where students are expected to implement and test algorithms and learn the effect of various parameters. In particular, the course will allow the student to achieve the following results. 1. Knowledge of fundamentals of lossless and lossy compression and data compression techniques. 2. Knowledge of transform coding. 3. Knowledge of motion estimation techniques and their applications to video compression. This includes 4. Knowledge of a few reference standards for audio, image and video compression 5. Knowledge of feedforward and convolutional deep neural networks, and their application to image classification. 6. Knowledge of fundamentals of image forensics.Signal, image and video processing and learning (Signal processing: methods and algorithms)1. Knowledge of the foundations of discrete-time random processes 2. Knowledge of the basis of time-frequency analysis 3. Knowledge of the basis of estimation theory 4. Knowledge of the basis of Kalman filtering 5. Knowledge of the basis of detection theory 6. Ability to classify stationary and nonstationary random processes 7. Ability to design estimation algorithms for signals affected by noise 8. Ability to use the Kalman filter for the estimation of random processes and systems 9. Ability to design a detector Judgment and communication skills are strengthened during the laboratories thank to the continual interaction with the teacher. To improve the learning skill, we teach how to search scientific and tutorial references on the main online search engines, such as IEEE XPlore.

Signal, image and video processing and learning (Image and video processing and learning)The medium of instruction is English. This course addresses a few foundational aspects related to vision, and particularly compression of data, image and video sequences, including the related signal models, and processing of visual information for image interpretation and authentication purposes. The course will deal with compression starting from theoretical fundamentals and moving on to its application to the most important international standards, covering different “media� such as images and video. Image processing concepts will then be used to introduce more complex vision systems employing “deep� neural networks to extract and describe important image properties. Image classification will be considered as a use case of a realistic scenario. Finally, the image forensics problem will be introduced, aiming at authenticating the origin of an image.Signal, image and video processing and learning (Signal processing: methods and algorithms)The course gives the basis for the processing of random signals (random processes), which represent the most common type of signals in the fields of the Communication and Computer Networks Engineering, as well as, in general, in the fields of engineering where random quantities are measured. We consider both the case of deterministic signals affected by noise, generated for instance by the measurement system, as well as that of signals whose nature is inherently random, such as 1/f noise. The course begins by reviewing the foundations of discrete-time random processes, particularly by discussing the quantities that describe them, such as the autocorrelation function, the power spectrum, and the time-frequency spectrum, useful for signals whose frequency content changes with time. We consider both stationary and nonstationary random processes commonly encountered in nature. We then give the basis of estimation theory, and we derive and discuss the main estimators for the mean, variance, autocorrelation function, and power spectrum of stationary and nonstationary random processes. Furthermore, we introduce random dynamical systems and we derive the Kalman filter, which allows their optimal estimation. Finally, we introduce the basis of detection theory and we illustrate how to design a detector according to the Neyman-Pearson criterion. Half of the course takes place in the LAIB laboratories, where students implement and characterize in the Matlab environment all of the methods discussed during the lectures.

Signal, image and video processing and learning (Image and video processing and learning)Fundamentals of information theory and compression, data compression techniques (1.2 CFU). This includes: • Basics of information theory for lossless/lossy compression • Data compression techniques such as Huffman coding, arithmetic coding, dictionary coding and run-length coding Transform coding and prediction; the JPEG standard (1.5 CFU). This includes: • Separable extension of multidimensional transforms, the 2-dimensional Fourier Transform, discrete cosine transform, Karhunen-Loeve transform, signals over graphs and the Graph Fourier transform • Quantization techniques such as scalar quantization, robust quantization, Lloyd-Max quantization, entropy-constrained quantization and vector quantization. • Application to the JPEG image compression standard. Motion estimation and compensation techniques for video compression, and the H.264/AVC and H.265/HEVC standards (0.8 CFU). This includes: • 3D transforms versus temporal prediction for multidimensional data compression • Motion models • Definition a prototype video coder • Scalable video coding • The H.264/AVC video compression standard • The H.265/HEVC video compression standard Image forensics (1 CFU). This includes: • Photo response non uniformity • Application to detection of picture origin Feedforward and convolutional neural networks and their applications (1.5 CFU). This includes: • Neural networks architecture • Backpropagation algorithm • Cost functions, overfitting and regularization • Convolutional neural networks and deep learningSignal, image and video processing and learning (Signal processing: methods and algorithms)Introduction. Discrete-time random processes (15 hours) Nonstationary random processes (9 hours) Introduction to estimation theory (9 hours) Spectral estimation (6 hours) Time-frequency analysis (6 hours) The Kalman filter (9 hours) Introduction to detection theory (6 hours)

Signal, image and video processing and learning (Image and video processing and learning)Modalita di esame: Prova scritta (in aula); Prova orale obbligatoria; Prova orale facoltativa; Elaborato progettuale in gruppo; Signal, image and video processing and learning (Signal processing: methods and algorithms)Modalita di esame: Prova scritta in aula tramite PC con l'utilizzo della piattaforma di ateneo;
Signal, image and video processing and learning (Image and video processing and learning)Exam: Written test; Compulsory oral exam; Optional oral exam; Group project; Signal, image and video processing and learning (Signal processing: methods and algorithms)Exam: Computer-based written test in class using POLITO platform;
... Signal, image and video processing and learning (Image and video processing and learning)The exam aims at verifying the knowledge and understanding of the topics treated during the course, and the ability of the students to critically discuss such topics. The final exam will be a written exam. It lasts 1 hour and consists in discussing up to 2 topics, each topic discussion having limited size (one page of text). The written exam contributes to the final score for up to 24 points, and the discussion of each topic usually contributes equally to the score. The text of the questions may be provided through the Exam platform, but the answers have to be provided on paper. During the written exam, students are not allowed to use any books, lecture notes or any material other than a calculator. They must avoid having any active cell phone, tablet or other electronic means. The exam grade will be a weighted average of the score of the written exam and the report of the computer labs (up to 6 points). The exam is passed if the score is equal to or above 18/30. The computer lab reports will be scored according to technical quality of the work done and the understanding of the concepts learned during the course. The reports must be delivered to the course instructor within a deadline; the deadline, to be communicated during the course, will typically be the end of the course or a few days later. Student self-assessment will also be used to generate the computer lab score, i.e. students are going to score the contribution of other students in the same group towards achieving the objectives of the computer labs; sending self-assessments to the course instructor is mandatory in order to obtain a score for the computer labs. While the exam is typically written, the course instructor reserves the right to perform an oral examination in specific cases. The grade of this exam will be averaged with the grade of the first module of the course (Signal processing: methods and algorithms), yielding the final exam score.Signal, image and video processing and learning (Signal processing: methods and algorithms)The written exam with a duration of one hour is based on approximately 12-15 multiple choice questions which span the content of both the lectures and the laboratories. Every correct answer gives a positive score, which is identical for all of the questions. The final mark is the sum of all of the positive scores. During the exam it is not possible to use support material, such as notes or books. The highest mark which can be obtained at the written exam is 30 cum laude. If the number of students booked for the exam is smaller or equal than 10, the written exam can be replaced by an oral exam of approximately 30 minutes, focused on the topics taught both during the lectures and at the laboratories. The highest mark which can be obtained with the oral exam is 30 cum laude. The final mark for the Signal, image and video processing and learning course is the arithmetic average of the mark for this course (Signal processing: methods and algorithms) and of the mark for the Image and video processing and learning course. The highest final mark is 30 cum laude.

Signal, image and video processing and learning (Image and video processing and learning)Regarding compression techniques, the reference book is: K. Sayood, “Introduction to data compression, 3rd edition�, Kluwer Regarding neural networks, the reference book is the online book “Neural networks and deep learning� (2017), available at link: http://neuralnetworksanddeeplearning.com Signal, image and video processing and learning (Signal processing: methods and algorithms)[1] D. G. Manolakis, V. K. Ingle, and S. M. Kogon, Statistical and Adaptive Signal Processing, Artech House, 2011. [2] L. Cohen, Time-frequency analysis, Prentice Hall, 1995. [3] A. Gelb (Editor), Applied Optimal Estimation, The MIT Press, 1974. [4] Steven M. Kay, Fundamentals of Statistical signal processing: Estimation Theory, Prentice Hall,1993 [5] Steven M. Kay, Fundamentals of Statistical signal processing: Detection Theory, Prentice Hall,1993

Specific ability to design at least one type of antenna to be used in contemporary communication systems, employing antenna theory and computer-based procedures. Knowledge of the main classes of radiating systems currently in use in relevant wireless systems, for the most recent and significant applications. Ability to understand the requirements of radiating systems taking into account of the environment in whici it is embedded. Knowledge of techniques and dedicated tools for their design. Ability to understand and evaluate performance of commercial wireless access systems. Ability to apply the gained knowledges to the design of innovative systems for future scenarios.

Most of the present applications and future scenarios require a wireless access point, characterized by a proper radiating system, that it is called to meet the challenges of green systems with reduced power consumption, of intelligent transportation systems, high-performance space applications, or pervasive distributed-information and distributed-computing networks and systems, like Internet of Things. Considering different present and emerging applications (e.g. Broadcasting, Mobile communications, WiFi/WLAN, IoT), the suitable radiating systems for each of them will be discussed. Experience in designing antennas is important not only to carry out design tasks in a professional environment, but also to procure antennas and related components, as well as to conceive wireless systems as a whole in a multi-disciplinary environment.

• Introduction to antennas as system components - Antenna system parameters - Noiseless and Noise Link budget - Low and medium gain antennas for Broadcasting, Mobile communications, WiFi/WLAN, IoT (slot, patch, dipole) - UltraWide Band (UWB) antennas - High-gain, reconfigurable and MIMO antennas (Arrays) with beamforming - Fixed beam - Scanning beam - MIMO

Modalita di esame: Prova scritta (in aula); Prova orale obbligatoria;
Exam: Written test; Compulsory oral exam;
... A- Exam in standard form: written test on problem solving the test will propose problems similar to those assigned during the course, and questions on the lectures. It may encompass design of radiating elements, array factors, beam-forming networks or parts thereof. The test is closed-book (no material allowed), but useful formulas are provided with exam text (to avoid unnecessary mnemonic efforts); the formulas are taken from the course handouts. Duration: 2 hrs. B- Evaluation by grading of submitted assignments + oral discussion - Requires submission of all assignments, in complete form, by the indicated deadlines. The materials to be submitted include reports on the labs. - Assignments must be carried out individually (unless otherwise stated). - Oral discussion: discussion on the submitted assignments. In both cases A and B, the final score can be improved by the submission of a short report on the optional project activity 2c. One report for each group is required, to be delivered before the end of July's exam period. A maximum of 3 additional scores can be gained with this activity.

The learning will be supported by handouts made available by the instructors, via the didattica web portal. Other materials, and relevant sources will also be made available. The provided handouts are all that it is needed for preparing to the final exam. Further bibliographical indications will be given at the starting of the Course.

Students will acquire knowledge on wireless distributed network systems and the know-how to effectively apply these technologies to mobility services, system and environmental monitoring and data collection. In particular, students will develop competences on the medium access layer and the network layer of distributed wireless systems, and the ability to design a wireless network given the constraints that typically characterize practical smart city scenarios. In more detail, students will acquire knowledge and ability as reported below: 1. Knowledge of mobile network systems based on device-to-device communications and of sensor network applications. 2. Knowledge of protocols for radio channel access and traffic routing in distributed wireless networks. 3. Knowledge of channel access techniques, adaptive topology formation and traffic routing in wireless sensor networks. 4. Knowledge of the main performance metrics, which are relevant in wireless communication-based systems. 5. Ability to design a channel access protocol for wireless networks. 6. Ability to design a routing protocol for wireless networks under practical constraints and in relevant scenarios. 7. Ability to evaluate the performance of a distributed network system. 8. Ability to assess the suitability of a wireless technology to a given environment for application support.

The course introduces students to the fundamental aspects of communication networks for mobile users and of sensor networks, which represent the main ways to disseminate and collect information in smart cities, smart buildings, health care centers as well as for applications such as environmental monitoring. The course objectives are therefore to let students acquire (i) the necessary knowledge on wireless and mobile communication technologies, and (ii) the ability to apply such knowledge to the support of services for smart cities. Special attention will be given to WiFi-based technologies, device-to-device (or machine-to-machine) communication and to wireless sensor networks. For each application scenario (smart city, health, environmental monitoring), the main network technologies are discussed, along with their performance and the open problems for which a solution is still needed. The students will also learn the methodologies that can be used for modeling such systems and the main results that have been obtained. The knowledge and abilities that students will acquire through this course are relevant to create experts in the design and management of network services, and in the development for services in application domains such as transport, environmental monitoring, domotics, health.

The course includes both lectures and numerical exercises, which focus on wireless distributed systems. In particular, they describe the main existing and emerging technologies and highlight their features and challenges in different application scenarios. The topics covered by the course, and their weight expressed in hours, are as follows: - Mobile networks and device-to-device communication: generalities, classification and main issues (6 h) - Channel access techniques in distributed wireless networks (12 h) - Connected cars: services, network architecture, channel access techniques and main technical challenges (6h). - Performance of distributed wireless networks (4 h) - Proactive and reactive routing protocols for mobile networks (7 h) - The Bluetooth technology (4 h) - Sensor networks: generalities and applications; channel access techniques (5 h) - Network topology control and traffic routing for sensor networks (4 h). Numerical exercises and software demonstrations account for 12 h.

Modalita di esame: Prova scritta (in aula); Elaborato scritto prodotto in gruppo;
Exam: Written test; Group essay;
... The exam consists of a written test, which lasts about 1 hour and a half. No oral exam is foreseen. It is a closed-book exam, i.e., students cannot use textbooks, copy of the slides, or copy of the solutions of numerical problems. The test focuses on all course topics, specifically: WiFi/WiFi Direct, device-to-device networks, car-to-car communications, sensor networks, access control, topology formation and traffic routing. The test typically includes 4 questions, among which there is at least one numerical problem to solve. The remaining questions require the description and the analysis of system aspects. The descriptive questions aim at assessing the students' knowledge on mobile distributed network systems, channel access techniques, topology adaptation, and traffic routing mechanisms, as well as their understanding of protocols and algorithms to be used and configured in specific scenarios. Importantly, the test also aims to evaluate the students' ability to select a technological solution depending on the different practical conditions and applications. The numerical problems aim at verifying the competences acquired by the students on distributed channel access, sensor networks and traffic routing schemes, as well as their ability to correctly configure a network system. Each question is assigned a number of points, which reflects the level of difficulty of the question/problem. The answers to open questions are evaluated considering their correctness, the level of knowledge that the student has acquired on the topic, and the student’s ability to precisely answer the question and to clearly communicate the technical material with accurate terms. The solution of numerical problems is evaluated based on its correctness and technical rigor, on the rational followed by the student and on the student’s ability to apply the acquired know-how. The final mark is computed by summing the marks obtained in each question, with the final mark being 30/30 cum lode; the minimum mark students have to achieve in order to pass the exam is 18/30.

The teaching material consists of copy of the slides e during the course, text of numerical problems, and suggested reading. All the material is available on the web portal of the course. Useful references are as follows: - M. Gast, ed. (2002): "802.11 Wireless Networks: The Definitive Guide," O'Reilly (Networking), ISBN 978-0596001834. - M. Felegyhazi, J.-P. Hubaux, "Game Theory in Wireless Networks: A Tutorial," IEEE Communications Magazine. - C.S. Raghavendra, K.M. Krishna, M. Sivalingam, T. Znati, Wireless Sensor Networks, Kluwer Academic Publisher, 2004.

At the end of the course, students know the key characteristics and the most advanced techniques used for wireless communication systems. They have an in-depth knowledge of the physical layer constituent blocks of uplink and downlink wireless communications, of multiple-antenna communications, and of the advanced coding techniques required for the protection of data transmission from channel errors.

The course introduces several advanced techniques used in modern wireless communication systems, focusing on the physical layer. The course covers four main topics: 1) Uplink transmission techniques. 2) Downlink transmission techniques. 3) Multiple-antenna communications 4) Iterative receiver techniques. These techniques find application in modern 5G/6G communication networks, and their knowledge is a basic skill for modern communication engineers. This course is useful to all students who are interested in digital communications, both in the Communications Engineering and in Electronic Engineering MSc Degree.

Main topics of the course: • Multiuser techniques for the uplink channel (~20h) - Orthogonal techniques: FDMA/TDMA/CDMA - Multiuser detection techniques • Multiuser techniques for the downlink channel (~20h) - Information-theoretical broadcast channel - Nonorthogonal techniques: NOMA • Multiple-antenna communications (~20h) - Capacity of MIMO channels - Linear and orthogonal space-time codes - Optimum and linear receivers • Iterative receiver techniques (~20h) - Polar codes and their decoder - Coding for high spectral efficiency - Turbo equalization

Modalita di esame: Prova scritta (in aula); Elaborato progettuale in gruppo;
Exam: Written test; Group project;
... The goal of the exam is verifying that the students have acquired the basic notions presented during the course and can develop a Matlab application for a project. The maximum grade “30 e lode” is assigned if the final grade, rounded to the nearest integer, is at least 31. The grade is based on a weighted average of the assignment grade (60%) and of the written exam grade (40%). 1. The assignment grade is the arithmetic average of four project grades. Each project consists of the development of a Matlab application with a graphical user interface based on Matlab App Designer accompanied by a report illustrating the application, how to use it, and showing some results obtained by the application itself. The applications can be developed individually or in groups of up to 4 students, who share the same project grade. Projects are evaluated according to correctness, accuracy, user-friendliness of the interface, efficiency, and comprehensiveness with a maximum grade depending on the time of delivery. If the project is delivered within the end of the assignment period (from the first to the last lesson dedicated to a project) plus N weeks, the maximum grade reduces to 33 − 𝑁, for 𝑁 = 0,1,2,3. The project delivery is not accepted after the end of the assignment period plus 3 weeks or, if applicable, later than one week before the day of the exam. If one (or more) projects are not delivered (or delivered after four weeks), the corresponding grade(s) are 0 but the average grade is still used for the final grade [for example, project 1: 30, projects 2, 3 and 4 not delivered, average is (30+0+0+0)/4=7.5, 4.5 points (60% of 7.5) added to the grade]. 2. The written exam is based on the response to four theoretical questions (one for each of the four parts), taken from a list available to the students before the exam, and its overall duration is 90 minutes. The maximum grade of the written exam is 33. A student can opt out of the assignment grading scheme (with mandatory preliminary communication by institutional email to the lecturers) and take only the written exam. In that case, the final grade is the written exam grade and is upper limited to 25.

The course is based on lecture notes available to the students through the web site and more references are given during the course, as required. Book references: • S. Verdù, Multiuser Detection, CUP 1998 • T.M. Cover and J.A. Thomas, Elements of Information Theory. Wiley, 2006 • A. El Gamal and Y.-H. Kim, Network Information Theory. CUP, 2011 • R.W. Heath and A. Lozano, Foundations of MIMO Communication, CUP, 2019 • D. Tse and P. Viswanath, Fundamentals of Wireless Communication, CUP, 2004 • D.J.C. McKay, Information Theory, Inference, and Learning Algorithms, CUP, 2003 • T. Richardson and R. Urbanke, Modern Coding Theory, CUP, 2008

At the end of the course, students will acquire knowledge of the main cryptographic techniques used in security protocols and will understand how they satisfy usual security definitions. In detail: • Knowledge of basic security principles • Knowledge of common security definitions in modern cryptographic (perfect secrecy, computational secrecy) • Knowledge of main cryptographic techniques using symmetric and asymmetric keys • Knowledge of cryptographic hash functions and related authentication techniques • Knowledge of main security protocols for key management, authentication, message confidentiality Moreover, they should acquire the ability to choose and employ the most adequate cryptographic tools according to the security scenario and application needs, and the ability to evaluate the security of a specific protocol. In detail: • Ability to evaluate if a protocol satisfies a given security definition • Ability to design a security protocol providing confidentiality and authentication • Ability to assess the weaknesses of an existing protocol

This course introduces students to the main technologies dealing with information security. The objectives of the course can be summarized as: i) let the students be aware of the main objectives of information security, namely providing data confidentiality, integrity, and availability; ii) describe in detail the main cryptographic tools that can be used for achieving the above goals; iii) define what is considered secure in modern cryptography; iv) provide the students with the ability to analyze weaknesses in existing schemes and design adequate security solutions. One third of the course will take place in the LAIB laboratories, where the students will have the opportunity to implement and test a subset of the cryptographic techniques explained during lectures.

Security definitions and scenarios (0.3 CFU – 3h theory) • Confidentiality, integrity, availability, authentication, non-repudiation • Kerckhoff's principle • Attack models Security models (1.2 CFU – 9h theory + 3h lab) • Perfect secrecy, Shannon theorem, unicity distance • Computational secrecy • Examples on historical ciphers Symmetric key cryptography (0.9 CFU – 6h theory + 3h lab) • Pseudorandom generators and pseudorandom functions, stream ciphers and block ciphers • Modes of operation • Practical algorithms (DES, 3DES, AES, RC4) Asymmetric key cryptography (1.2 CFU – 9h theory + 3h lab) • Basic notions on number theory • One-way functions, trapdoor functions, factorization, discrete logarithm • Practical algorithms (RSA, El-Gamal, Elliptic Curve) Authentication and integrity (0.9 CFU – 6h theory + 3h lab) • Hash functions • Message authentication codes • Digital signatures Security protocols (1.5 CFU – 9h theory – 6h lab) • Key distribution, certification authorities, public-key infrastructures • Authentication protocols (passwords, second factor, webauthn) • End-to-end encryption

E' possibile sostenere l?esame in anticipo rispetto all?acquisizione della frequenza

Main textbook: • William Stallings, “Cryptography and Network Security: Principles and Practice�, Pearson, 2016. Additional readings: • Jonathan Katz, Yehuda Lindell, “Introduction to Modern Cryptography�, Chapman and Hall/CRC, 2014. • Alfred J. Menezes, Paul C. van Oorschot and Scott A. Vanstone, “Handbook of Applied Cryptography�, CRC Press, 2001. Other available material includes the slides used during the lectures and the material for the computer labs. The material will be available on the web.

• Knowledge of cloud computing and virtualization (computing, memory and network) - Knowledge of the theory of Clos topologies for data centers - Knowledge of different virtualization techniques (virtual machines, containers) - Knowledge of probabilistic data structures for fast packet processing - Knowledge of P4 abstraction for fast packet processing - Ability to design the interconnection network of a data center - Ability to design a software to compute the total number of devices in a data center - Ability to experiment Docker containers and configure the networking layer - Ability to design the power distribution network in a large data center - Ability to run an AI application on hardware accelerators

Data centers are the computing infrastructure supporting cloud computing applications. The class is focused on understanding the cloud computing approach and how the data centers are designed. The course focuses on cloud computing and on the different technologies for virtualization, from virtual machines to containers. Furthermore, it focuses also on the design of the data center, in particular on the computing resources, the interconnection infrastructure and the power management. State-of-the-art architectures for the design of large data centers will be considered, based on Clos topologies. Finally, the hardware architectures for advanced computing will be presented, highlighting the role of accelerators for practical relevant applications, as Artificial Intelligence (AI). A practical approach will be adopted, with virtual laboratories on containers, on data center design and on hardware acceleration. The involved professors are experts in computer networks and in electronic design, providing a complementary view of all the main technological issues involved in the design of data centers.

The class is divided in two parts, the first (Part A) on cloud computing and design of data centers (4CFU) and the second (Part B) on digital architectures and advanced computing architectures (2CFU). Lectures topics and corresponding credits: A.1 Cloud computing (2CFU) - Service models. Web services. - Virtual machines and containers. Docker engine and container networking - Lab on Docker engine and networking A.2 Design of data centers (2CFU) - Clos-based design of data centers. Recursive construction - Optical interconnections and switching - Lab on data center design - Software Defined Networking (SDN) and programmable data planes. P4 switches. - Traffic monitoring. Probabilistic data structures (hash tables, bloom filters, sketches) - Lab on probabilistic data structures B.1 Basic digital hardware architectures (0.68CFU) - Von Neumann architectures and fundamental components of a computing system - CPU/GPU architectures, pipelining and parallel processing, power consumption model and voltage scaling - Types of memories: static/dynamic/non-volatile - High-speed buses and transmission lines: the PCIexpress case - Power supply and power management in high-power data centers - Lab on practical optimization of a PCIexpress bus in a GPU-based server

Modalita di esame: Prova orale obbligatoria; Elaborato scritto individuale;
Exam: Compulsory oral exam; Individual essay;
... The final evaluation is based on the oral discussion on the delivered reports. The report will be evaluated based on correctness, original contribution and quality of presentation. The oral will be aimed at discussing the reports and at assessing the level of understanding of the theoretical topics covered in the class. Let GA be the grade of the oral of Part A and let GB be the grade of the oral of Part B, the final grade G of the exam will be computed based on the credit-weighted average of the two parts as follows: G=nearest-integer(4/6*GA+2/6*GB) Both GA and GB must be >=18 to be considered valid.

The teaching material (handouts and reference scientific papers) will be made available by the class teachers on the Politecnico courses web portal and is sufficient to cover all the topics taught in the class.

Students will mature a critical view and acquire the fundamentals of machine learning - applied to network measurements. In particular, students will get 1. the knowledge of the analysis of Internet configurations and misconfigurations, and protocols like IP, TCP, UDP, HTTP, DNS. 2. the knowledge of performance related problems in local and wide area networks via delay and throughput measurements 3. the ability to process real traffic traces and to extract information related to both performance and troubleshooting issues from real traffic. 4. the ability to use understand and use properly machine learning tools to solve some classification problems applied to internet traffic analysis. 5. the ability to use modern tools and programming languages like Python and Jupyter Notebook for quick prototyping of high level machine learning problems. The ability to apply the gained knowledge will be verified by the preparation of lab reports, and its discussion during an oral examination at the end of the course.

This is a laboratory and experimental course in the field of Internet traffic measurements. Most of the classes will be given in laboratories, with few introductory lessons for each field. Students will be guided to setup experiments, run active or passive measurement tools in real networks, and apply methodologies learned in previous classes. Students will use both passive traffic analysers, and active traffic generators to characterise and load the network. They will face lab sessions of increasing complexity, maturing a critical approach and scientific methodology toward the understanding of complex systems such as the Internet is. During the class, students will also learn basic machine learning methodologies to support the data analysis, following a data driven approach. In this second group of laboratories students will follow a hands-on approach, where the fundamentals of supervised machine learning for classification problem will be introduced and then applied in the context of Internet measurements.

Students will work in the Laboratory using PCs running Linux OS. Using Ethernet switches and WiFi access points, students will have to setup experiments configuring LANs. Each group of students will use 3 PCs. Real traffic traces will be used to allow students to deal with more realistic scenarios. Students will go though the following group of laboratories: • Configuration of hosts in Local Area Networks, IP addresses management, subnetting/supernetting (2 hours) • Traffic monitoring using sniffers in LAN: TCP, UDP and HTTP (6 hours) • Performance measurement for file transfers in wired networks (6 hours) • Performance measurement for file transfers in WiFi setup (6 hours) • Performance measurement for file transfers and impact of delay and packet losses (8 hours) • Analysis of traffic traces collected from real networks and development of post-processing tools to extract information out of the raw data (15 hours) • Usage of statistical methods and data mining techniques to find correlations and solve classification problems (15 hours)

Modalita di esame: Prova orale obbligatoria; Elaborato scritto individuale; Elaborato scritto prodotto in gruppo;
Exam: Compulsory oral exam; Individual essay; Group essay;
... During the laboratories, students are required to work in groups. At the end of the course, each group will have to deliver a written report on the laboratory experiences as indicated by the teacher during the course (group report). In total, the report focuses on 8 topics. The report must be delivered in a single PDF file, to be uploaded to the teaching portal by the end of the exam registration deadline. The report will be corrected to verify the correctness of the reported results, and the completeness in the description of the experiments performed. All students in the group will have the same mark, expressed in thirtieths, up to a maximum of 30/30. The group report mark will be valid for all members of the group, and will be valid for two years. Once corrected, the report can be changed only in case all students agree. Each student will also have to prepare and individual report, that have to be uploaded as a single PDF file on the teaching portal by the deadline for the student registration for the exam. The individual report will be discussed during oral examination. Each student will be asked to discuss the content of the reports (both group and individual) during the oral exam. The oral exam is individual, and students will be asked to answer questions about both the practical and theoretical part of the course. The grade for the oral exam must be sufficient to pass the exam, with minimum 18/30 and maximum 30 cum laude. The final vote will be a weighted average between the evaluations of the group laboratory report (70%) and oral examinations (30%). It is possible to get additional points with specific topics reports, or preparing lessons notes to be reused in subsequent years.

The teaching material will be made available by the class teacher on the Didattica web portal. Description of the lab experiments will be provided, and reference documentation will be made available to students. No textbook is available. Students must be familiar with Internet protocol and applications which can be found for example in A. Pattavina: Reti di telecomunicazioni, Mc.Graw-Hill (in Italian) or J.F. Kurose, K.W. Ross: Computer Networking: A Top-Down Approach Featuring the Internet, Pearson (in English)

The internship gives to the students the opportunity to experience professional development under an actual working environment. It enables the students to gain first-hand exposure of working in the real world. Through an internship, the students have the opportunity to apply the skills, knowledge and theoretical practice they learnt in university to challenging projects developed by the company. The internship allows to build connections with company professionals and to acquire new skills and abilities which can improve the student as a young professional, and furthermore enhance their resume.

Modalita di esame: Accertamento (esame senza voto);
Exam: Check;

The purpose of the course is to provide the basic knowledge of space missions and systems, helping the development of capabilities in formulation and solution of problems related to the design, realization and operation of space systems. Students will attain basic knowledge of the following topics: space system types, their application context, and ground support systems; technical features of on-board systems and subsystems, both in terms of operation tasks and necessary technologies. Specific objectives are knowledge of: specificities of space missions in terms of operational and functional characteristic; different types of space systems (both manned and unmanned) in orbit (satellites, probed, transfer vehicles, space stations) and on ground for planetary exploration (landers, rovers, and permanent habitats); space operational environment ad spaceflight mechanics most relevant operational orbits on board subsystems; relative dependence among mission requirements/constraints and features of the space systems, and relations at the system-subsystem lavel;

The main objective of the course is to contribute to the education of the mechatronic engineer, providing the basic knowledge on missions and space systems that represent an application field of relevant interest for the most innovative solutions of mechatronic engineering. The course offers a path aimed at learning knowledge and skills training in the field of Space Systems, i.e. complex innovative systems featuring novel technology, integrated within modern engineering systems for transport and telecommunications, and used for societal services like navigation, remote sensing and monitoring, and Internet of Things. The course therefore aims at developing in the student a multidisciplinary and critical mindset, providing the basic knowledge and tools necessary to understand Space Missions and the Systems that carry them out. Modern Space Systems are characterized by a high degree of complexity, mainly due to the multiple relationships and interactions between the elements that constitute the system itself and the different systems that contribute to the realization of the Space Mission. The skills acquired throughout the course are functional to support and encourage the student's inclusion in a high-profile professional context, in industry and in research centres in the aerospace domain and related fields, such as the automotive industry and robotics in general.

Space systems Introduction to the concept of system as integration of several systems and elements. System engineering as an approach and method for the design and operation of space systems and missions (3h). Introduction to the application context od space systems: challenges and opportunities of space exploration and exploitation(1.5h) Space environment and ots effect on space systems (3h). Type of space systems in the gereral context of space missions: orbital systems (satellites, probes, transport vehicles)m surface systems(landers, rovers), launchers, ground stations (4.5h). Payloads (3h). Electric power generation, storage, conditioning and distribution: electric power system (3h). Thermal protection and control (3h). Telecommunication system (3h). Data and command handling (3h) Astrodynamics principles of spaceflight mechanics: two-body problem, angular momentum and energy of orbits, keplerian orbits. orbital elements, reference systems, and ground track (5h). Geocentric orbits: classification, orbital transfers with impulses, orbital perturbations and their effect of orbital elements (5h). Relative motion and GNC: relative motion equations, exaples of proximity maneuvers, rendezvous and docking.equazioni del moto relativo tra due satelliti in orbita, esempi di manovre di prossimità , rendezvous and docking, strategie di Guidance, Navigation and Control (GNC)(5h). Rigid body rotational dynamics: Euler equations for rotational motion, Euler's angles and quaternions, torque-free motion and active attitude control (5h). Propulsion system Principles of space propulsion, thrust and specific impulse (3 h). Maneuvers, structural loads during launch (4.5 h). Chemical propulsion and electric propulsion: thrusters and operations, subsystems (7.5 h). Feed systems (3 h).

Modalita di esame: Prova scritta (in aula);
Exam: Written test;
... The exam is written, with multiple-choice questions and open-ended questions. The length is 1.5 hours. Maximum mark 30/30.

Cornelisse, J.W., Schoyer, H.F.R., and Wakker, K.F., Rocket Propulsion and Spaceflight Dynamics, Pitman, London, 1979.

• Knoweledges o Python language o State-of-the art transceivers for optical communications o Foundations of optical fiber propagation and modeling its impairments o Amplifiers and passive components o WDM spectral use and standards o ROADMs and node structure in general o YANG, Netconfig, GMPLS, OTN • Abilities o Python development within GitHub o Emulation of optical layer in photonic networks o Routing spectral and wavelength assignment o Multilayer orchestration o In general, ability to perform physical-layer-aware network analysis, design and optimization

The class of Open Optical Networks, OON in the following, aims at describing peculiarities of data networking based on the exploitation of photonic transmission on the optical fiber networks. With the specific purpose of multilayer optimization down from the IP layer, enabling full exploitation of the photonic transport layer either using the state-of-the art WDM fixed-grid, either the already standardized flex-grid. The network analysis will rely on the progressive abstraction of network elements and network subsystems to enable an open network management based on common APIs and data structures. The teaching method will follow an application-oriented introduction of concepts. To this purpose, students will be required to develop Phyton module performing simple network control operations, exploiting the open source library GNPy of the Telecom Infra Project. These will be the homeworks used to the student assessment. Coding will be addressed to the standard open-source procedure based on GitHub. Lectures on Python coding and use of Github will be part of the class. Seminars will be given by companies and operators of in the field. In particular, by Facebook, Cisco, SMOptics, Coriant Networks, OpenFiber and TIM. The final student assessment will be done through the discussion on the assigned homework. For OON students will be available a set of homework that may evolve into a Master thesis work, being its initial phase. The OON class will take advantage of the experience gained participating to the consortium Telecom Infra Project.

• Introduction to Python and Github • Introduction of optical communications and networking • Abstraction of disaggregated optical networks • Abstraction of data transport: fiber propagation and amplification • Optimization • Controlling

Modalita di esame: Prova orale obbligatoria; Prova pratica di laboratorio;
Exam: Compulsory oral exam; Practical lab skills test;
... Student assessment will be performed reviewing the Python coding homework, including the proper use of github and reports. This work can be a group work. This process will deliver a maximum of 25 points. The remaining 5 points – and possible laude – will be assigned during the individual final oral discussion.

Studying material will be available on “portale della didattica�. Books to deepen specific topics will be suggested as well.

The course aims at giving the students the correct methodologies to solve optimization problems. Linear Programming (LP) and Integer Linear Programming (ILP) will be the basic instruments to formulate constrained optimization problems. To solve the problems students will learn to use several different methodologies, considering both exact approaches and approximated approaches. The choice of the proper methodology will enhance students’ ability in making judgments. Particular attention will be devoted to local search heuristics as well as meta-heuristics and math-heuristics. In addition, flow problems in graphs will be analyzed. Finally, typical problems that are met when designing a computer network, such as: infrastructure capacity planning, the optimization of the set of routes, the definition of the maximum flow a network can support will be considered. Modeling and solving practical design problems will help students in increasing their ability in applying the acquired knowledge. In a nutshell the student will learn how: i) to formulate LP and ILP optimization problems; ii) to define and implement heuristic and meta-heuristic algorithms; iii) to select the most suited optimization technique; iv) to model and solve flow problems on networks.

The main aim of the course is to provide students with theoretical and operational tools for modeling and solving Operations Research and Optimization problems usually met in the design and the management of complex infrastructures such as computer networks. Operations Research deals with the mathematical modeling of complex problems and related solution algorithms. Modeling the problem means to write it in terms of mathematical programming, i.e. identifying an objective function (which has to be minimized or maximized) and a set of constraints to be satisfied. Solving the problem means to find a solution which maximizes or minimizes the objective function without violating the constraints, and this is accomplished by using appropriate solution algorithms. Most recent solution algorithms will be discussed for each computer network design/management problem presented in the course. During the course, students will be asked to practice the presented methodologies in laboratories.

• How to build mathematical models from real-life problems • Convex Analysis • Convex Optimization • Computational complexity of optimization problems • Elements of network flows • Local search heuristics • Meta-heuristics: Simulated Annealing, Genetic Algorithms, Tabu Search and others • Application to networks: o Topology and capacity planning problems in transport networks o Fairness issues

Modalita di esame: Prova scritta (in aula); Elaborato progettuale in gruppo;
Exam: Written test; Group project;
... Students are requested to accomplish a group assignment. Groups are asked to autonomously formulate and solve a specific optimization problem, using concepts and techniques learned during the course (the goal is to test skills/capabilities i)-v) described in the expected learning outcomes section). A report on the group activities must be then delivered and group members will be called for a final discussion about the assignment. At last, a brief oral exam will complete the assessment of individual knowledge/capacities (in particular skills/capability i)-iv) ). The exam consists of a couple of questions about concepts/optimization techniques discussed in class. The relative weight of the assignment and the oral exam will be respectively 50% and 50%. Students which have difficulties attending the labs can sustain a written test.

The material related to this course will be made available in electronic format through the didattica Web site. Students will receive also some research papers that describe selected networking problems and provide some solutions. The following books are suggested: • R. Tadei, F. Della Croce, Elementi di Ricerca Operativa, Progetto Leonardo, Editrice Esculapio, Bologna, 2010. • R. Tadei, F. Della Croce, A. Grosso, Fondamenti di Ottimizzazione, Progetto Leonardo, Editrice Esculapio, Bologna, 2005. • M. Ghirardi, A. Grosso, G. Perboli, Esercizi di Ricerca Operativa, Progetto Leonardo, Editrice Esculapio, Bologna, 2009. • D. J. Luenberger, Linear and Nonlinear Programming, Springer, 3rd ed., 2008. • M.S. Bazaraa, J.J. Jarvis. Linear Programming and Network Flows, Wiley, 3rd ed., 2005. • R.K. Ahuja, T.L. Magnanti, J.B. Orlin, Network Flows, Prentice Hall, 1993. • G.L. Nemhauser, L.A. Wolsey, Integer Programming, Wiley, 1998.

At the end of this course the following learning outcomes will be achieved, regarding Orbital Robotics (OR) and Distributed Space Systems (DSS). The student will: -know and comprehend, at a system engineering level, the fundamental elements pertaining to their Mechanics, and Guidance, Navigation & Control aspects; -know and comprehend the classification of several possible architectures; -know and comprehend the mathematical description of kinematics (for the translational, rotational and internal configuration), differential kinematics, and kinetics; -know and comprehend how to model the most common external actions (due to environment and actuators) as well as internal actions (due to internal dynamics and actuators); -know and comprehend selected analytical methods and techniques, based on Newtonian and analytical mechanics, for formulating mathematical models of the dynamics; -be able to apply the acquired methods for writing the equations of motion of specific instances of spacecraft-manipulator systems by leveraging symbolic computation; -know and comprehend selected method for the synthesis of autonomous Guidance, Navigation & Control logics to accomplish selected desire maneuvers; -be able to code prototype software to perform numerical simulation of their natural and forced motion; -be able to validate that software, i.e., to check whether it fulfills its intended purpose; -be able to use that software to preliminarily verify, via numerical simulation experiments, key performances (e.g., the satisfaction of selected requirements regarding guidance and control) by specific instances of OR or DSS systems, during sample maneuver scenarios; -be familiar, at a system engineering level, with the current technology pertaining to Orbital Robotics and Distributed Space Systems; -gain a general engineering intuition in terms of orders of magnitude of quantities involved, system capabilities, limitations, and possible innovative systems and applications.

Orbital robotics systems, that are covered in the first part of this course, are artificial satellite systems that can physically interact with another orbiting object by using actively controlled on board mechanisms that enable grasping, manipulation and mobility: a paradigmatic example is an artificial satellite equipped with an onboard robotic manipulator. Applications of orbital robotics systems include On-orbit Servicing, Assembly and Manufacturing, and space debris removal. Distributed Space Systems, that are covered in the second part of this course, are artificial satellite systems that occupy a region considerably larger than the one pertaining to a single traditional artificial satellite. Examples of Distributed Space Systems are Formation Flying Satellites, Tethered Satellites and Very Large Satellite Systems. Applications of Distributed Space Systems include: high-resolution Earth or astronomic observation, and collection and retransmission of solar power. This course will transfer knowledge of analytical methods and formulation practices for developing mathematical models of motion for classes of Orbital Robotics and Distributed Space Systems, under the effect of both environmental actions as well as actions by on-board actuators, such as thrusters and momentum exchange devices. Furthermore, the course will build comprehension of how these mathematical models of motion are used for the synthesis of autonomous guidance, navigation and control algorithms that enable the execution of translational, rotational and reconfiguration maneuvers to achieve specific mission capabilities. Finally, the course will develop comprehension on how software libraries are coded and used to perform numerical simulations to study the physical phenomenon of motion in specific instances of Orbital Robotics and Distributed Space Systems, and to assess the performance of selected guidance, navigation and control logics during specific maneuver scenarios. Examples will be considered of space systems encompassing current and emerging space technology solutions, and of maneuver scenarios pertaining to real space missions, either previously flown or planned.

20 hours each for Orbital Robotics Systems and Distributed Space Systems regarding: mathematical modeling of motion, synthesis of guidance, navigation and control methods, system engineering and technology 10 hour each for Orbital Robotics Systems and Distributed Space Systems regarding: coding of prototype simulation software, execution of numerical experiments of validation and verification

Modalita di esame: Prova scritta (in aula); Elaborato progettuale individuale;
Exam: Written test; Individual project;
... The exam is aimed at ascertaining knowledge of the topics listed in the official teaching program and the ability to apply the theory and related calculation methods to solving exercises. The evaluations are expressed out of thirty and the exam is passed if the score reported is at least 18/30.

The course reading materials will consist primarily of class notes developed by Prof. Romano, and distributed during the course. Other reference materials (as textbook excerpts and specialized papers) will be indicated during the course.

Knowledge and understanding of the functional principles of satellite-based positioning and of the factors affecting the performance; Knowledge of the current satellite navigation systems and understanding of the design criteria; Knowledge of the format of the signals used for positioning purposes and ability to design signals suited to radio-navigation applications (modulations, codes and multiplexing techniques); Knowledge error sources and of the effect of propagation in free-space and atmosphere; Understanding of the error sources effects on the positioning performance; ability to use models in order to mitigate the effects of the biases on the receiver measurements; Knowledge and understanding of the user receiver's architecture focusing on the base-band digital signal processing stages, of the acquisition stage and of the tracking stage (use of Delay Lock Loop and Phase Lock Loop); Ability to design simple algorithms for acquisition and tracking of GNSS signals; Knowledge of the main applications of satellite-based positioning;

The course aims at introducing the leading-edge radio-positioning and radio-navigation technologies, based on the use of Global Satellite Navigation Systems (GNSS), as for example, the American Global Positioning System (GPS) and the European system Galileo. The course introduces the basic principles of positioning, describes the satellite systems and signals and focuses on the design of user receivers and on the signal processing stages. The course is taught in English.

• Introduction to localisation and positioning techniques as well as radionavigation principles (6 hours): o Position estimation techniques based on direction of arrival, time of arrival, and differential time of arrival observations o Conical, spherical, hyperbolical methods o Positioning algorithms o The Geometrical Dilution of Precision • Uncertainty causes and error sources (6 hours) o Description of Error sources o Propagation issues o Link Budget and thermal noise o Error correction models • Basics of Reference Systems and Satellite Orbits (3 hours) o Reference frames o Satellite orbits o Time scales • Signal processing for radionavigation (6 hours): o Signal properties o Ranging codes generation o Signal modulations for radionavigation o Binary Offset Carrier modulation o Multiplexing techniques • System architcture of GPS and Galileo sysetms (3 hours) • GNSS receivers architectures (15 hours): o Front-end and A/D conversion o Acquisition of GNSS signals o Code and carrier tracking architectures o Pseudorange estimation o Multipath effects • GNSS Applications (4 hours): o Differential GNSS o Integration with communication systems and Assisted GNSS o Radio frequency interference issues • Classroom exercises on all the course topics (15 hours) • Laboratory activities for the implementation of a software GNSS receiver (20 hours)

Modalita di esame: Prova scritta (in aula); Prova orale facoltativa; Elaborato progettuale individuale; Prova scritta in aula tramite PC con l'utilizzo della piattaforma di ateneo;
Exam: Written test; Optional oral exam; Individual project; Computer-based written test in class using POLITO platform;
... The final exam aims at assessing both the achievement of the knowledge objectives and the ability to solve position estimation problems and to design algorithms for the GNSS signal processing. The final evaluation is based on an individual report of the lab activities, and on a written exam. An oral exam might be requested by the student or, in some cases as a free choice of the teacher; the oral exam can influence the grade achieved in the written exam in a positive or negative way. The final grade (on a scale of 32) is obtained as the sum of the evaluation of the lab report (up to 5 points) and the written exam (up to 27 points). Laude is achieved if the score of 32 is reached. The lab report must be delivered, following the modality that will be explained by the teacher during the classes and published on the course webpage, by the deadline associated with the exam booking on the web portal. The students shall deliver a written report (as a .pdf file) and the Matlab © code used to obtain the results. The written exam includes multiple answer questions on the theoretical subjects (up to 9 points) and exercises (up to 18 points). The use of books and notes is not allowed. The students shall bring their own calculator. The exercises must be solved on the sheets of paper provided by the teachers, where the students shall put their name and student number. The students shall write the solutions using pens and not pencils. The total time allocated to the written exam is 2 hours. The optional oral exam can be requested by the student if the sum of the grades achieved for the lab report and the written exam is larger than 15. The oral examination usually takes place the same day in which the written exam results are published, or at most few days later. The oral exam will measure the acquired knowledge and the ability to apply the concepts learned, and it covers all the course subjects (theory, exercises and lab activities).

Reference books are: 1. Misra P., Enge P. Global Positioning System: Signals, Measurements, and Performance (II edition), Ganga-Jamuna press 2. Kaplan, E. D., Hegarty C.H. Understanding GPS: principles and applications (III edition), Artech House, Norhood, MA, 2018 3. Zekavat R., Buehrer R. M., Handbook of Position Location: Theory, Practice and Advances, Wiley-IEEE Press, 2011 4. Parkinson B., Spilker J. J. , Global Positioning System: theory and applications, Vol. I e Vol. II, American Institute of Aeronautics, 1996. 5. European Space Agency: http://www.navipedia.net/ The teaching material (slides used during the lessons, solutions of exercises, examples of written exams) will be made available by the class teacher on the didattica web portal.

The course offers the student the possibility to deepen and put in practice the knowledge on physical layer functions of communication systems as well as the signal and image Processing techniques. The lab sessions will be preceded by lectures to describe the experiments that will be performed and the procedure to implement them. The course will provide knowledge of: - the methods for the experimental characterization of optical sources - the methods for the experimental characterization of active and passive optical devices - the methods for the experimental characterization of optical communications systems and subsystems - the architecture of Digital Signal processing (DSP) Systems - the fundamentals on signal and image compression - some algorithms of digital signal processing and their implementation methods The student will acquire the ability to: - characterize optical sources, active and passive optical devices, optical communications systems and subsystems - setup a laboratory experiment in the optical communication field - develop appropriate signal-processing algorithms and implement them on a DSP The ability to apply the gained knowledge will be verified from lab reports, and their discussion.

This is a laboratory and experimental course in the field of signal processing and optical communications. Most of the activity is performed in the laboratories, where students have the opportunity to setup experiments regarding signal analysis on a Digital Signal Processor (DSP) and optical communications. Few introductory lectures are given to introduce the lab experiences. One of the goals of the course is to foster the communication skills and the ability to work in groups.

The course covers three main areas of modern communications systems, and students take classes and laboratories focusing only on two of the two proposed areas: 1. Signal and Image Processing laboratory (3 CFU) 2. Optical Communications laboratory (3 CFU) for a total amount of 6 CFU. Theoretical Lectures: Optical communications (0.5 CFU) • Measures of optical powers, wavelength and corresponding instruments • Measure of optical spectrum and corresponding instruments • Methods for high resolution spectral characterization • Experimental modelling of optical transmission systems and corresponding instrumentation Signal Processing (0.5 CFU) • IReview of FIR and IIR filters • Fixed point implementation • Quantization • Source coding • DSP architecture and development tools Laboratories: Optical communications (2.5 CFU) • Spectral measures of laser sources through the “optical power meter� and the “wavelength meter� • Experimental procedure for the joint of optical fibers and corresponding measurement of the joint attenuation • Experimental characterization of passive optical devices • Realization of an optical amplifier and corresponding experimental characterization • Spectral characterization of optical sources through optical spectrum analyzers and high resolution methods Signal Processing (2.5 CFU) DSP implementation of linear filters and their application to lossless compression: - Program template and in/out template and in/out interfaces - basic FIR and IIR filtering structures - real-time filtering of audio signals - real-time lossless coding of audio signals - validation and testing

Modalita di esame: Prova orale obbligatoria; Elaborato scritto prodotto in gruppo;
Exam: Compulsory oral exam; Group essay;
... In order to verify the acquired laboratory experimental skills the students have to write reports and to discuss them during the oral examination. Students are required to write two reports on the laboratory experiences (one for every lab area). The report organization will be described during the lectures. Both reports have to be delivered 1 week before the exam day. Every lab report will have an independent evaluation (maximum mark 30/30). Each student will be asked to discuss the content of the report during an oral exam. The oral exam is individual, and students will be asked to answer questions about both the practical and the theoretical parts of the course (maximum mark 30/30) The final grade will be the average between the evaluations of the two laboratory reports and the oral examinations. The grading criteria are the following: - appropriate choice and application of the experimental methodology/algorithms - report clearness and completeness. - appropriate use of the technical terms - completeness of the discussion of the obtained experimental results

The teaching material will be made available by the instructors on the course web portal. Description of the lab experiments will be provided, and reference documentation will be made available to students.

The course offers the student the possibility to deepen and put in practice the knowledge on physical layer functions of wireless communication systems as well as the signal and image Processing techniques. The lab sessions will be preceded by lectures to describe the experiments that will be performed and the procedure to implement them. The course will provide knowledge of: - the architecture of Digital Signal processing (DSP) Systems - the fundamentals on signal and image compression - some algorithms of digital signal processing and their implementation methods - structure of Software Defined Radio (SDR) platforms - the methods for wireless communication systems design and prototyping The student will acquire the ability to: - develop appropriate signal-processing algorithms and implement them on a DSP - implement on suitable hardware/software platforms some of the blocks of the studied wireless communication standards The ability to apply the gained knowledge will be verified from lab reports, and their discussion.

This is a laboratory and experimental course in the field of signal processing and wireless communications. Most of the activity is performed in the laboratories, where students can setup experiments regarding signal analysis and wireless transmissions on Digital Signal Processors (DSP) or Universal Software Radio Peripherals (USRPs). Few introductory lectures are given to introduce the lab experiences. One of the goals of the course is to foster the communication skills and the ability to work in groups.

This lab course covers two main areas of modern communications systems: 1. Signal and Image Processing laboratory (3 CFU) 2. Wireless transmissions laboratory (3 CFU) for a total amount of 6 CFU. Theoretical Lectures: Signal Processing (0.5 CFU): • Introduction to compression • Decorrelation techniques for signals and images • Quantization • Source coding • DSP architecture and development tools Wireless communications (0.5 CFU): • Overview on the main components of the physical layer of broadband fixed and mobile radio-communication standards • Structure and main functions of conventional and software defined digital radio transceivers • Software defined radio (SDR) platforms Laboratories: Signal Processing (2.5 CFU): DSP implementation of a compression algorithm, including: - Program template and in/out interfaces - predictor - quantizer - lossless source coder - validation and testing Wireless communications (2.5 CFU): • Implementation on SDR platform (Ettus B210 and National Instruments USRP-2901) of some common transceiver blocks suitable for most wireless communication standards (WiFi, LTE, DVB) • Test and measurements of the performance of some implemented receiver algorithms

Modalita di esame: Prova orale obbligatoria; Prova pratica di laboratorio; Elaborato scritto prodotto in gruppo;
Exam: Compulsory oral exam; Practical lab skills test; Group essay;
... In order to verify the acquired laboratory experimental skills the students have to write reports and to discuss them during the oral examination. Students are required to write two reports on the laboratory experiences (one for every lab area). The report organization will be described during the lectures. Both reports must be delivered 1 week before the exam day. Every lab report will have an independent evaluation (maximum mark 30/30). Each student will be asked to discuss the content of the report during an oral exam. The oral exam is individual or in groups, and students will be asked to answer questions about both the practical and the theoretical parts of the course (maximum mark 30/30) The final grade will be the average between the evaluations of the two laboratory reports and the oral examinations. The grading criteria are the following: - appropriate choice and application of the experimental methodology/algorithms - report clearness and completeness. - appropriate use of the technical terms - completeness of the discussion of the obtained experimental results

The teaching material will be made available by the instructors on the course web portal. Description of the lab experiments will be provided, and reference documentation will be made available to students.

Al termine dell’insegnamento, studenti e studentesse avranno acquisito una conoscenza e una comprensione approfondita delle principali definizioni nonché dei dibattiti relativi alla diplomazia tecno-scientifica, così come dei processi storici che hanno caratterizzato le relazioni tra progetti tecnico-scientifici e dinamiche internazionali. Saranno inoltre in grado di applicare tali conoscenze per sviluppare una visione critica delle motivazioni complesse che guidano la cooperazione tecno-scientifica a livello globale, e di individuare le problematiche specifiche del settore tecnologico nei processi di diplomazia e nelle relazioni internazionali contemporanee.

La diplomazia tecno-scientifica è un ambito in crescente rilievo nel dibattito accademico e politico, che riguarda l’interazione tra scienza, tecnologia e relazioni internazionali. Enti nazionali e sovranazionali, come l’Unione Europea, la includono tra le priorità strategiche per affrontare sfide globali come il cambiamento climatico, la sicurezza energetica e la regolazione delle tecnologie emergenti. Il ruolo della cooperazione tecnico-scientifica si rivela centrale anche in situazioni di crisi geopolitica, come dimostra il caso del progetto ITER, in cui la collaborazione tra Unione Europea, Stati Uniti e Russia è proseguita nonostante le tensioni legate alla guerra in Ucraina. L'insegnamento introduce studenti e studentesse alle pratiche storiche e contemporanee della diplomazia tecno-scientifica, evidenziando come oggetti, infrastrutture, standard, progetti e conoscenze tecnico-scientifiche influenzino le relazioni tra Stati. Attraverso l’analisi di casi studio ed esempi concreti, l’insegnamento fornisce strumenti per comprendere criticamente il ruolo della tecnica e della tecnologia nelle dinamiche globali e per formare ingegneri, architette, architetti, e designer in grado di operare in contesti internazionali complessi.

1. Che cos’è la diplomazia tecno-scientifica (9 ore) La parte introduttiva dell'insegnamento è dedicata alla presentazione delle principali nozioni, definizioni e concettualizzazioni della diplomazia tecno-scientifica, con l’obiettivo di fornire una base teorica per l’analisi dei casi e delle pratiche contemporanee. 2. Critiche alla retorica della diplomazia tecno-scientifica (11 ore) A partire dall’analisi di esperienze e istituzioni spesso citate come esempi di diplomazia tecno-scientifica, si esamineranno le principali critiche a una narrazione idealizzata e apolitica del ruolo della scienza e della tecnologia nelle relazioni internazionali. 3. Storia delle relazioni tra scienza, tecnologia e relazioni internazionali (20 ore) Questa sezione esplorerà lo sviluppo storico del ruolo della scienza e della tecnica nello Stato e nella diplomazia, dalla seconda metà del XIX secolo fino all’epoca contemporanea. Saranno analizzate le trasformazioni politiche, istituzionali e culturali che hanno ridefinito queste relazioni nel tempo. 4. Analisi di casi studio (20 ore) Questa parte dell'insegnamento sarà dedicata a un’analisi approfondita di casi concreti di diplomazia tecno-scientifica, attraverso progetti internazionali o strategie statali. Saranno discussi esempi come: - il ruolo della scienza e della tecnica nella politica estera di vari stati, tra cui Cina e Stati Uniti; - la geopolitica dello smartphone e la guerra dei chip; - il ruolo del CERN nell’integrazione europea nel passato e nel presente; - il progetto ITER e la fusione nucleare come strumenti di distensione politica alla fine della Guerra Fredda; - intelligenza artificiale, interessi nazionali e diplomazia.

Modalita di esame: Prova scritta in aula tramite PC con l'utilizzo della piattaforma di ateneo;
Exam: Computer-based written test in class using POLITO platform;
... L’esame consiste in una prova scritta della durata di 2 ore, composta da: 10 domande a risposta multipla (ognuna con quattro opzioni, una sola corretta); 2 domande a risposta aperta. Ogni risposta corretta vale +2 punti. Le risposte errate o non date valgono 0 punti. Le domande a scelta multipla sono progettate per verificare la conoscenza e la comprensione da parte di studenti e studentesse delle principali definizioni e concettualizzazioni di diplomazia tecno-scientifica, delle critiche ad essa rivolte, nonché dei processi storici analizzati durante l’insegnamento. Esse contribuiscono alla valutazione dei primi due risultati di apprendimento attesi, ovvero: la comprensione dei dibattiti e delle definizioni chiave; e la conoscenza dei principali sviluppi storici del rapporto tra scienza, tecnica e relazioni internazionali. Ciascuna risposta aperta può ottenere un massimo di 5 punti. Le domande aperte valutano la capacità critica di applicare le conoscenze acquisite, discutendo in autonomia due dei casi studio affrontati nell'insegnamento. In particolare, misurano la capacità di: sviluppare una riflessione autonoma sulle motivazioni complesse che guidano la cooperazione tecno-scientifica internazionale; individuare e analizzare le problematiche specifiche del settore tecnologico nei processi diplomatici. Tali aspetti corrispondono ai restanti risultati di apprendimento attesi, relativi alla capacità di analisi critica, autonomia di giudizio e applicazione contestuale delle conoscenze. Durante la prova scritta non è consentito consultare testi, appunti o materiali didattici, né utilizzare dispositivi digitali con accesso al web (smartphone, smartwatch, tablet, ecc.). L’esame è considerato superato con un punteggio minimo di 18/30. Il punteggio massimo conseguibile è 30/30. L’esito della prova scritta sarà comunicato su portale della didattica entro una settimana dallo svolgimento dell’esame.

Bibliografia essenziale: - Ruffini, Pierre-Bruno. Science and Diplomacy: A New Dimension of International Relations. New York, NY: Springer Berlin Heidelberg, 2017. - Royal Society/AAAS. New Frontiers in Science Diplomacy. London: Royal Society, 2010 https://royalsociety.org/topics-policy/publications/2010/new-frontiers-science-diplomacy. - Royal Society/AAAS. Science Diplomacy in an Era of Disruption. London: Royal Society, 2025. https://www.aaas.org/sites/default/files/2025-02/Final_Science%20diplomacy_15%20years%20on_report_WEB.pdf - Directorate-General for Research and Innovation (European Commission), Rasmus Gjedssø Bertelsen, Laurent Bochereau, Effrosyni Chelioti, Ã�gota Dávid, Dovilė Gailiūtė-JanuÅ¡onė, Martina Hartl, et al. A European Framework for Science Diplomacy: Recommendations of the EU Science Diplomacy Working Groups. Publications Office of the European Union, 2025. https://data.europa.eu/doi/10.2777/9235330. - Turchetti, Simone, Matthew Adamson, Giulia Rispoli, Doubravka Olšáková, and Sam Robinson. “Introduction: Just Needham to Nixon? On Writing the History of ‘Science Diplomacy.’â€� Historical Studies in the Natural Sciences 50, no. 4 (September 23, 2020): 323–39. https://doi.org/10.1525/hsns.2020.50.4.323.

Modalita di esame: Accertamento (esame senza voto);
Exam: Check;
... The graduation grade for the Master's degree will be calculated starting from the average of the exams, to which a maximum of 8 additional points can be added. Points available to the Committee (maximum 6): - maximum 4 points for the Thesis; - maximum 2 points for the Thesis presentation. Points for the academic career (maximum 2): - max 1 point based on the number of honors (0.5 points for every 5 credits of exams with honors) - max 1 point based on the duration of the studies: * 1 point for graduating within the November/December session of the 2nd year of enrollment; * 0.5 points for graduating within the March/April session of the 3rd year of enrollment; * 0 points for graduating from the July session of the 3rd year. [Students with a path that requires an additional semester: 3rd year March/April and July - 1 point 3rd year October and November/December - 0.5 points Students with initial enrollment in March without advances ("anticipo crediti"): 3rd year March/April and July - 1 point 3rd year October and November/December - 0.5 points] If the final grade is equal to or greater than 112.51, the student will be awarded the maximum grade of 110 with honors (110 cum laude).

Modalita di esame: Accertamento (esame senza voto);
Exam: Check;
... The graduation grade for the Master's degree will be calculated starting from the average of the exams, to which a maximum of 8 additional points can be added. Points available to the Committee (maximum 6): - maximum 4 points for the Thesis; - maximum 2 points for the Thesis presentation. Points for the academic career (maximum 2): - max 1 point based on the number of honors (0.5 points for every 5 credits of exams with honors) - max 1 point based on the duration of the studies: * 1 point for graduating within the November/December session of the 2nd year of enrollment; * 0.5 points for graduating within the March/April session of the 3rd year of enrollment; * 0 points for graduating from the July session of the 3rd year. [Students with a path that requires an additional semester: 3rd year March/April and July - 1 point 3rd year October and November/December - 0.5 points Students with initial enrollment in March without advances ("anticipo crediti"): 3rd year March/April and July - 1 point 3rd year October and November/December - 0.5 points] If the final grade is equal to or greater than 112.51, the student will be awarded the maximum grade of 110 with honors (110 cum laude).