ICT Engineering for Smart Societies

1. Knowledge of the basics of statistical learning. 2. Knowledge of the theory and methods of classification for continuous and discrete data using maximum likelihood and Bayesian methods. 3. Knowledge of dimensionality reduction techniques and kernel methods. 4. Knowledge of neural networks: training/testing and applications. 5. Knowledge of tracking filters. 6. Ability to use Python programming frameworks, as well as the TensorFlow environment.

The medium of instruction is English. The course introduces statistical and machine learning methods for processing data and signals in order to extract information from them. Such methods are based on models that are typically learned from training data. In particular, the course covers statistical methods, where the data are modelled as a set of measured (or acquired) samples of a random process, and neural networks, where the model is a suitable combination of basic units (neurons) and the model parameters are found through numerical optimization. The first part of the course deals with statistical learning in maximum likelihood and Bayesian sense. The techniques presented during the course are applied to classification problems in several domains of ICT, including classification of continuous as well as discrete data, and dimensionality reduction using principal component analysis and kernel methods. In the second part of the course, feedforward and convolutional neural networks are introduced. Finally, statistical tracking filters are introduced. Throughout the course, the concepts above are illustrated with practical examples taken from ICT applications. A large part of the course takes place in the LAIB laboratories, where students implement and assess the methods discussed during the lectures. The lab activities are performed using the Python language, and possibly employing cloud-based environments such as Google CoLab. This course is designed jointly with the course “ICT for health�, with the objective to provide students with a coordinated “machine learning� approach that can be applied to several ICT problems; in particular, this course deals primarily with machine learning basics, classification and neural networks, while the “ICT for health� course addresses regression and clustering topics.

Discrete-time random signals and processes, probability distributions, model fitting (1.2CFU), including: • Introduction to machine learning; supervised vs. unsupervised learning, regression vs. classification, k-NN classifier, the curse of dimensionality, overfitting. • Review of probability, continuous vs. discrete random variables, Bayes’ rule, independence, common probability distributions, Gaussian multivariate distribution. Foundations of estimation theory (1.2CFU), including: • Maximum likelihood estimation • Bayesian and MAP estimation • Application to Gaussian processes Classification and latent linear models (0.6CFU), including: • Hypothesis testing • Bayesian decision theory and Bayesian classifiers • Performance assessment of classifiers • Principal component analysis and kernel methods Feedforward and convolutional deep neural networks (2.4CFU), including: • Perceptrons and architecture of a neural network • Backpropagation algorithm • Loss functions, overfitting and regularization • Convolutional networks and deep learning • Neural networks for classification, segmentation and object detection • Neural networks quantization • Transformers and generative adversarial networks Tracking filters (0.6CFU), including • Kalman filter

Modalita di esame: Prova orale obbligatoria;
Exam: Compulsory oral exam;
... The exam aims at assessing the knowledge and understanding of the topics described during the course, and the ability to critically discuss such topics. The exam is oral, and it may also use the report containing the description of the experiments carried out during the computer labs, including the obtained results. The exam typically starts from a discussion of one or more experiments done during the computer labs, and can extend to span the whole set of topics presented during the course. A lot of emphasis is given to the theory underlying each of the experiments. Possible topics addressed during the oral examination are: - a description of the considered system (or algorithm) and of its simulation model, - the theoretical background of the methods used in the Matlab-based experiments, - a discussion of the main parameters adopted in the experiments, - a clear presentation and discussion of the obtained results, - a discussion of the methods used to evaluate the performance of the algorithms, - a comparison with theoretical results (when applicable) The oral discussion is evaluated based on its correctness, the level of knowledge that the student has acquired on the topic, the ability to apply the acquired know-how, to clearly communicate the technical material with accurate terms and to correctly analyze, interpret, and comment the obtained results. During the oral examinations, the students may not consult any material,. The score of the oral examination is up to “30 e lode�, and the student passes the exam if they obtain at least 18/30.

The following texts will be used during the course: Regarding statistical models and machine learning, the reference book is: K.P. Murphy, “Machine learning – a probabilistic perspective�, MIT press, 2012. Regarding neural networks, there is no reference book.

Knowledge of: - basics in some health issues (management of elderlies, Parkinson's disease, EEG, ECG, dermatology, etc) - e-health and m-health applications - telemedicine applications - regression techniques - clustering techniques - classification techniques Ability to: - understand the issues of a telemedicine application - apply regression techniques in health problems - apply clustering techniques in health problems - apply classification techniques in health problems - use open-source machine learning software

The medium of instruction is English. The objectives of this course are to use machine learning in public health applications, in particular in the areas of basic research, prevention, diagnostic process, management of elderly people at home. The course is designed jointly with the course "Statistical Learning and Neural Networks", with the objective to provide students with a coordinated "machine learning" approach that can be applied to several ICT problems; in particular, "Statistical Learning and Neural Networks" deals primarily with machine learning in terms of classification and neural networks, while "ICT for health" addresses regression and clustering topics. Some classification techniques not analyzed in "Statistical Learning and Neural Networks" are analyzed in "ICT for health". The course is divided into two parts: 1) the description of some of the many health issues and 2) the description and use of the machine learning techniques that can be used to solve these issues. Several laboratory experiences are included, and the knowledge of the health issues from the medical point of view is fundamental for the correct system implementation. Python will be used as programming language (in particular Pandas and Scikit-learn ) and a learn-by-doing approach will be used.

• Description of some e-health, m-health, and telemedicine applications (2 CFU) on the following topics: smart aging, fitness, Parkinson's disease, EEG, ECG, dermatology, lean in health care (2.1 CFU). - Review of linear algebra and basics on optimization methods (0.6 CFU). - Introduction to Python (0.3 CFU). - regression techniques: linear regression, PCR, Gaussian processes for regression (0.9 CFU) - clustering techniques: k-means, hierarchical trees, DBSCAN (0.9 CFU) - classification techniques: decision trees and information theory, Hidden Markov Models (0.9 CFU) - Independent component analysis (ICA) applied to EEG (0.3 CFU)

Modalita di esame: Prova orale obbligatoria; Prova scritta in aula tramite PC con l'utilizzo della piattaforma di ateneo;
Exam: Compulsory oral exam; Computer-based written test in class using POLITO platform;
... The student must write two reports on the lab activity; together with the report the student must provide the zipped folder with the Python scripts. Each of these reports gets a grade between 0 and 5 (0 if the report is missing), for a total maximum grade equal to 10; the grade depends on the correctness of the results and on the completeness and clearness of the document. The mandatory oral exam consists of 3 questions, 1 question about the health issues, i.e. Parkinson's disease, dermatology, etc. as described in the lectures, 2 questions about the algorithms and methods described in the lectures and the lab activity. The student gets a grade from 0 to 7 on each question; the grade depends on the ability to describe and critically discuss the learned methods. The grades of the reports and the oral exam are added together to obtain the final grade. The "lode" is given to students with an overall grade 31. The ability of the student to apply the described machine learning techniques in Python will be checked through the analysis of the report and the Python scripts. The knowledge of the health issues with possible solutions and the knowledge of the regression, clustering and classification techniques will be checked during the oral exam. The student will improve his/her soft-skills related to the ability of writing a technical report, and the ability to discuss ideas during the oral exam.

• Class slides will be available on the portal - K. Murphy, "Machine Learning, a probabilistic perspective", MIT press, 2012 - Christopher M. Bishop, "Pattern Recognition and Machine Learning", Springer-Verlag New York, 2006 - David J.C. MacKay, "Information Theory, Inference and Learning Algorithms" Cambridge University Press 2003 - C.E. Rasmussen, C.K.I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006

Knowledge and abilities • Knowledge on smart network architectures • Knowledge on data distribution systems • Knowledge on algorithms and techniques needed to design and manage smart networks while providing a proper level of quality to users • Knowledge of the main methodological tools that can be used to design a networking system or evaluate its performance. • Knowledge of the main elements of a simulator. • Ability to evaluate the performance of a network through simulation • Ability to use basic results of random graph theory to understand some complex networks. • Ability to model traffic sources. • Ability to understand the fundamental behavior of a networking system in terms of its stability, presence of losses, bottlenecks. • Ability to select the proper set of algorithms and technologies to provide a given service with the desired level of quality of service. • Ability in identifying algorithm pros and cons.

The course is organized in three main parts: i) technologies for data transport and distribution in smart environments, ii) methodologies for the network design, planning and performance evaluation, and iii) laboratory experience. More in details, the course first provides a general overview of the technologies for data transport and distribution, with a particular attention to smart environments that require the management of large amounts of heterogeneous data. Then, the course focuses on some of the main methodologies used for the design, planning and performance evaluation of data distribution networks. Both analytical modelling (based on queuing theory and random graph theory) and simulation techniques are presented and applied to practical use cases. The lab experience is mainly devoted to simulation techniques (in python) and consists in a practical experience of design or performance evaluation of a smart network.

Basic concepts on computer networks (9h) • network elements, topologies, switching techniques, multiplexing & multiple access, network congestion • The architecture and protocol stack of the Internet • TCP and UDP Multimedia networking (3h) • RTC/RTCP • HTTP Live Streaming • Adaptive streaming Cloud computing (3h) • Introduction to cloud computing architectures • IaaS, PaaS, SaaS (Infrastructure, Platform, Software as a Service) models Data centers and CDNs (6h) • Data center and content distribution architectures • SDN and virtualization • Virtual machine migration Peer-to-peer systems (9h) • Structured vs unstructured systems • BitTorrent • P2P streaming Basics of Random Graph Theory (12 h) • Basic definitions • Erdos-Renyi random graphs • Random graphs with general distribution of the node degree • Small world effect and clustering coefficient • Watts-Strowgatz random graphs • Preferential attachment graphs Simulation (18 h) • Discrete event simulation • Fitting of empirical distribution • Analysis of simulation outputs • Understanding and identifying transients Fundamental concepts in queuing: (27h) • Markov chains (6h) • Birth-death processes (3h) • Elementary queuing systems (9h) • Queues with vacations (3h) • Erlang-n and hyper-exponential (3h) • M/G/1 queue (3h) Queueing networks (6h) An introduction to traffic measurements (3h) Laboratory (24h) • Performance evaluation of a networking system through simulation

Modalita di esame: Prova scritta (in aula); Prova orale obbligatoria; Elaborato scritto prodotto in gruppo;
Exam: Written test; Compulsory oral exam; Group essay;
... The exam is composed of three parts that reflect the three main topics of the course. i) The part on technologies is evaluated through a written examination of 1 h duration. The exam consists on 5 or 6 open questions. This part is evaluated with a score over 30 points. ii) The exam on methodologies is also a written exam, of 1.5h duration, and it includes 2 or 3 problems that require skills on the use of the methodologies for the network performance evaluation and design. This part is evaluated with a score over 30 points. iii) The lab experience will be evaluated through a report and, possibly, a short discussion on the report itself. The report leads to a score between 0 and 3. The final score is obtained by the mean of the first two scores to which the score of the lab report is added. All the three parts must be positively evaluated for passing the exam.

The teaching material will be provided by the teachers on the web portal.

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.

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: ì) 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 (9h) • Computational complexity of optimization problems (4,5 h) • Elements of network flows (4,5 h) • Local search heuristics (3h) • Meta-heuristics: Simulated Annealing, Genetic Algorithms, Tabu Search and others (7 h) • Application to networks: (15 h) o Routing problems (5 h) o Topology and capacity planning problems in transport networks (10 h)

Modalita di esame: Prova scritta (in aula); Elaborato progettuale in gruppo;
Exam: Written test; Group project;
... The exam will be written, and it will consists of two parts. Part 1 – theory – in which students will be asked to answer 3-4 questions or exercises dealing with the theoretical part of the course. This part permits to evaluate the student's level of mastering of concepts and methodologies. Part 2 – application to networking – in which students will be asked to answer 2-3 (open) questions or exercises dealing with the application part.. This part permits to evaluate the ability of the student to apply the OR methodologies to solve specific problems. No books and notes are allowed during the written exam, whose duration is about 1.5 h. Students will have to deliver a written report on their laboratory activities. The report will be per group of 3/4 students each. The report must be delivered before the written exam,.. The final grade (on a scale of 30) is obtained as a weighed average of the marks of each individual parts. The weight of each written parts is 0.35. The weight of the lab report is 0.30.

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.

The course aims to provide students with the knowledge of object oriented programming and tools for its implementation in Python. Knowledge of the protocols for implementing web service oriented communication paradigms. Knowledge of the main tools for programming environment in real-time and in the presence of sensors and actuators. The student will learn how to design a complete IoT system (microservice-based) and deploy it in a realistic application domain, starting data collection on IoT devices, data organisation on the host, data processing, visualisation and user interaction.

Internet of Things indicates a variety of devices connected via the Internet, including wearable devices, sensors deployed on buildings, industrial plants, production and distribution energy systems and in general on cyber-physical systems. IoT technology is widely adopted in many application domains, such as smart factories, buildings and districts for energy monitoring or surveillance, environment monitoring and control of natural phenomena, within transport systems for traffic management and finally within medical devices. In this context, the aim of the course is to give the student ability to program embedded devices connected to the network using programming paradigms for distributed, parallel and object oriented systems. The ultimate goal will be the ability of programming in the Internet-of-Things and Cyber-Physical-Systems world. Students must acquire the ability to deploy and use of web services and middleware tools and related protocols (MQTT, REST). The student will also acquire the ability to program according paradigms publish/subscribe and request/response. The student will explore the concepts and tools for interoperability between heterogeneous devices, protocols, Machine-to-Machine, operating systems-oriented smart objects and programming with real-time constraints and energy consumption. During the course will be presented case studies related to monitoring systems within smart cities and smart factories and includes a series of laboratory exercises where the student will implement the concepts of programming networked embedded devices equipped with sensors and actuators, and allowing remote interaction with these devices.

Introduction to object oriented programming and Python XML, JSON formats IoT devices and communication protocols Programming distributed objects using Web Services RestFULL Web Services (GET, PUT, POST, DELETE methods) Microservices and Middleware programming Middlewares Virtualisation (e.g. Dockers) Cloud, Edge and Fog Computing Cloud Models and Paradigms IoT communication protocols and IoT platforms Publish/subscribe and request/response communication paradigms MQTT protocol Examples of cloud systems in the market (e.g. Thingspeak, Amazon, IBM Bluemix) Labs Programming IoT devices and platforms (using RaspberryPI or similar)

Modalita di esame: Prova orale obbligatoria; Elaborato scritto prodotto in gruppo; Prova scritta in aula tramite PC con l'utilizzo della piattaforma di ateneo;
Exam: Compulsory oral exam; Group essay; Computer-based written test in class using POLITO platform;
... Written exam: - Written test on all the course topics (max score: 12/30) - Duration: 1h - Use of notes or course handbooks is forbidden Project report: - Project groups should be of 4 members - Oral presentation, given by all the team members, about the project developed during the course (max score: 18/30). This oral presentation will evaluate both the overall work done by the team and the knowledge of each individual team member on the course topics - Video material about the project (2 min presentation video and 10 min demo video) - No midterm deadlines for the project completion - It is mandatory to return all HW equipment provided by the teachers at the time of the oral exam The final score is given by adding both the written exam score and the final project evaluation score.

There is no official textbook. The handouts used in class and the documentation necessary for carrying out the exercises will be made available on the teaching web page. Additional material such as documents, scientific articles, links to websites, software and manuals will also be made available on the course web page.

The lectures cover theoretical principles of various measurement and spatial data acquisition techniques, statistical data analysis, and applications of Geomatics techniques in land surveying, building and civil engineering activities, mapping, monitoring, and land protection. The practical parts focus on developing operational skills based on theoretical knowledge. Students will learn to perform spatial measurements using the most up-to-date surveying techniques and manage various methods for acquiring positioning and navigation data while assessing their robustness against intrinsic impairments as well as unexpected intentional and unintentional disturbances. In this course, students will develop the ability to process on-field measurements. They will be presented with practical problems and required to autonomously develop computational procedures for implementing Geomatics applications, particularly those related to land and built area monitoring and representation. Students will acquire the skills to apply the knowledge gained in the ICT domain to navigation and mapping. They will develop the ability to implement calculation tools and procedures for positioning, representation, and visualization of spatial data in land and built area monitoring.

A significant portion of political decisions is made based on spatial data, which provide essential knowledge about the land and built areas, as well as support for urban development planning and engineering processes. Human activities require positioning information and navigation capabilities to acquire 3D data in real-time for location finding or route tracking. Even in daily life, popular products like Google Earth are increasingly used for activity planning and organization. In the last two decades, the acquisition, processing, and representation techniques for spatial data have undergone significant advancements, thanks to the increased use of Information and Communication Technologies (ICT). Methodologies involving 3D data acquisition and instrumentation have improved due to the adoption of Global Navigation Satellite Systems (GNSS) like GPS, integrated inertial systems, satellite remote sensing, and earth observation technologies such as those featured by the Copernicus program. Digital images, largely collected by aircraft and drones, have also contributed to these advancements. Furthermore, novel processing techniques allow for the optimal integration of sensors, resulting in unprecedented land representation and data management using Geographic Information System (GIS) techniques. The ICT field must interface with skilled experts who work with georeferencing, processing, and visualizing spatial data. It is often necessary to develop software or firmware that meets the requirements of positioning/georeferencing accuracy, representation, and database organization for georeferenced data. This course provides the fundamental principles of acquisition, processing, plotting, and representation of spatial data to create ICT professionals capable of interacting with operators in the Geomatics field, supporting their knowledge and control of land and building areas.

The course content encompasses two interconnected disciplines: • Satellites Systems for Positioning and Navigation • Surveying, Mapping and GIS Within these areas, lectures will cover the following topics: Fundamentals on Global Navigation Satellite Systems • Introduction and Fundamentals of Radionavigation • Error Sources and Modeling in GNSS State Estimation • Received Signal Power and Thermal Noise • Systems and Signals (Part I): Legacy Signal Architecture and GPS Signal Plan • Systems and Signals (Part II): Binary Offset Carrier Modulation and Galileo Signal Plan • Systems and Signals (Part III) • The GNSS Receiver: Acquisition and Tracking Stages • Pseudorange Measurement Computation at the Receiver Surveying, Mapping and GIS • GNSS Positioning: code and phase observations • GNSS Positioning methods in Surveying and Mapping • Network GNSS Augmentation • Introduction to Geodesy and Reference Systems • Cartography and Digital maps • Introduction to Remote sensing and Copernicus services • Innovative terrestrial and aerial technologies for geospatial data acquisition: • Geographic Information Systems (GIS)

Modalita di esame: Test informatizzato in laboratorio; Prova orale obbligatoria; Prova scritta in aula tramite PC con l'utilizzo della piattaforma di ateneo;
Exam: Computer lab-based test; Compulsory oral exam; Computer-based written test in class using POLITO platform;
... Compulsory oral exam; Individual project report; Computer-based written test in class using POLITO platform; The exam is organized into two parts: • A preliminary multiple-choice test (duration: 30 minutes) • An oral discussion about the course topics (duration: 20 minutes) The report on the lab activities contributes to the final grade of the exam. Multiple-Choice Test (mandatory) The multiple-choice test consists of 10 questions (5 on MODULE 1 and 5 on MODULE 2) covering the course topics. Each correct answer awards 3 points, with penalties for incorrect answers. A minimum of 18 points is required to proceed to the oral exam. The test is conducted using the student's laptop to access the online platform. Oral Exam (mandatory) The oral exam assesses knowledge of theoretical concepts in positioning, spatial data acquisition, and GIS, as well as the ability to apply these concepts to solve positioning and navigation problems. The oral exam includes at least two questions on MODULE 1 and two questions on MODULE 2. Both sections contribute equally to the final grade, and a positive mark (>18/30) is required in each section. Laboratory Report (mandatory) Each student must submit an individual report on the laboratory activities completed during the course. This report is scored on a scale from -1 to +2 points, which are added to the oral exam score. A score of -1 is given for non-submission or very poor quality, while higher scores consider the student’s ability to solve lab tasks, the completeness of the work, the quality of the report (organization, quality of figures, correctness of solutions, etc.), and the methodology used. Reports must be uploaded to the "portale della didattica" within one week after the lectures end, following the deadlines set by the teachers. The report is valid until the next academic year's course session. Final Grade Composition: • Multiple-choice test: 1/3 of the final grade • Oral exam: 2/3 of the final grade • Bonus: -1 to +2 additional points for the laboratory report Laude is awarded to students who achieve a final grade of 30 or higher and demonstrate excellent proficiency in the course disciplines and topics during the oral discussion Study materials such as books, slides, or other resources are NOT allowed during the exams.

Suggested books: • Hofmann-Wellenhof et al (2008) – GNSS Global Navigation Satellite system. Springer – New York. • Leick (2003) - Gps Satellite Surveying - J. Wiley – Canada. III Edizione. • Misra P., Enge P. Global Positioning System: Signals, Measurements, and Performance, Ganga-Jamuna press • F. Dovis, D. Margaria, P. Mulassano, F. Dominici, "Overview of Global Positioning Systems," in Handbook of Position Location: Theory, Practice, and Advances , IEEE, 2019, pp.655-705 doi: 10.1002/9781119434610.ch20 • L. Lo Presti, M. Fantino, M.Pini, "Digital Signal Processing for GNSS Receivers," in Handbook of Position Location: Theory, Practice, and Advances , IEEE, 2019, pp.707-761 doi: 10.1002/9781119434610.ch21 • N. Linty, F.Dovis "An overview on Global Positioning Techniques for Harsh Environments," in Handbook of Position Location: Theory, Practice, and Advances, IEEE, 2019, pp.839-881 doi: 10.1002/9781119434610.ch23 • Lecture notes and slides provided during the course, available on the website of teaching In-depth books • Cina, A. (2014). Dal GPS al GNSS per la geomatica. CELID, Torino. ISBN 978-8867890200 • Cina, A. (2002). Trattamento delle misure topografiche. CELID, Torino. ISBN 88-7661-534-2 • Comoglio, G. (2008). Topografia e cartografia. CELID, Torino • Global Navigation Satellite Systems: Signal, Theory and Applications, edited by Shuanggen Jin, ISBN 978-953-307-843-4, 438 pages, Publisher: InTech, (http://www.intechopen.com/books/global-navigation-satellite-systems-signal-theory-and-applications) • E. Falletti, G. Falco "Kalman Filter�based Approaches for Positioning: Integrating Global Positioning with Inertial Sensors," in Handbook of Position Location: Theory, Practice, and Advances, IEEE, 2019, pp.763-838 doi: 10.1002/9781119434610.ch22 • Satellite Positioning - Methods, Models and Applications, Edited by Shuanggen Jin, ISBN 978-953-51-1738-4, 212 pages, Publisher: InTech, (http://www.intechopen.com/books/satellite-positioning-methods-models-and-applications • Dovis F. (ed), "GNSS Interference Threats and Countermeasures", Artech House, 2014, ISBN: 978-1-60807-810-3

The course will provide students with a solid knowledge of state-of-the-art deep learning models in computer vision and their real-world adoption. The course will also provide students with practical abilities to design, use and customize such techniques. For this purpose, each course topic is coupled with computer labs 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 adaptive non-local models for vision and image processing (graph neural networks, Transformers) 2. Knowledge of modern multi-modal foundational models for visual understanding (CLIP, ...) 3. Knowledge of computational imaging principles and neural networks for solution of inverse problems (denoising, super-resolution, ...) 4. Knowledge of image generative models (GANs, denoising diffusion models) 5. Knowledge of continual learning techniques for personalization of generative models (finetuning, Adapters, LoRAs, ...)

The course addresses advanced topics in machine learning applied to the fields of the acquisition and processing of images and computer vision. The corse builds upon the machine learning fundamentals studied in the other courses to explore cutting-edge deep learning topics. This includes transformer-based foundational models for multimodal processing, and neural networks for computational imaging problems like super-resolution. It also delves into generative models for image synthesis, including the latest diffusion-based approaches, and continual learning techniques for their personalization.

• Review of neural networks (1h) - Graph signal processing and graph neural networks (6h) - Applications to sensor networks, IoT, image analysis - Transformers and transformer-based architectures for image, video, time series processing (9h) - Neural networks for computational imaging (9h) - Inverse problems in image processing (denoising, super-resolution, ...) - Generative models for image and video synthesis (11h) - Unconditional vs. text-conditional models - GANs, Diffusion models - Continual learning and personalization (Finetuning, Adapters, LoRA, ...)

Modalita di esame: Prova scritta (in aula); Elaborato scritto prodotto in gruppo;
Exam: Written test; Group essay;
... The exam aims at verifying the knowledge and understanding of the topics discussed during the course, and the ability of the students to critically discuss such topics. The final exam will consist of two parts. The first part will be a written exam. It lasts 1.5 hours and consists in discussing up to 3 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. During the written exam, students are not allowed to use any books, lecture notes or any material. They must avoid having any active cell phone, tablet or other electronic means. The second part will be the evaluation of the lab reports. This score will be up to 9 points and it will be awarded based on the technical merit of the work done, as well as the clarity and completeness of the report presentation (including the understanding of the concepts learned during the course as applied to each lab activity).

All topics will be covered by the course slides. Extra reading material will consist in research papers highlighted in course content.

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 and abilities: • (Knowledge) Familiarise with elements of transportation systems • (Knowledge) Understand the physics of the transport phenomena • (Knowledge) Illustrate ICT tools and methods for transport • (Knowledge) Compare transportation and traffic models • (Knowledge) Discuss techniques for transport management and control • (Knowledge) ITS applications to public transport, to fleets management, and to the transport demand management • (Ability) Critically analyse the existing transport systems • (Ability) Mobility data collection, cleaning, and value extraction • (Ability) Apply the fundamentals of transportation engineering in real case studies • (Ability) Use advanced statistical techniques and data mining to analyse (big) data • (Ability) Design "smart" transport systems with the help of the ICT • (Ability) Use models to identify the causes of congestion • (Ability) Propose traffic management strategiesÂ

The growth of information and communication technologies (ICT) affects all areas of society. This digital transformation has a disruptive nature in mobility and transport: Intelligent Transport Systems (ITS) is intended to improve the effectiveness and efficiency of transportation systems through advanced technologies in information systems, communications, and sensors. This course considers ITS as a lens through which one can view many transportation and societal issues. The subject should be of interest to students interested in the performance, control, and management of transportation systems and the deployment of advanced technology systems. The course will introduce the fundamentals of transport modelling and ITS, focusing on design and technological aspects. Traffic flow theory and traffic flow models in micro- and macroscopic levels are studied. Network-level aggregated modeling and control approaches are introduced. Advanced traffic management schemes (such as adaptive traffic signal control) are discussed. It also discusses current practices and new methods for data collection and analysis, performance monitoring, route design, frequency determination, and vehicle and crew scheduling. Finally, the course will assess the impact of recent trends such as environmental, social, and economic sustainability, the introduction of low emission zones, Mobility as a Service (MaaS), and the future of connected and autonomous vehicles.

Intelligent transport systems and ICT (1 CFU) – Definition of a transport system. Characteristics of transport demand and supply. Impacts of the transport systems (economic, social, environmental). Fundamentals of ITS (standard and architecture) and application to innovation for transport systems, in mobility management, and in the definition of mobility patterns. The relationship between transport systems and ICT. Advanced technologies for public transport management. Future evolution of transport systems. Modelling transport system and traffic flow (2 CFU) – Demand and Supply model. Definition of the study area and zoning. Graph of the transport network model. Trip generation and trip distribution modelling. Modal choice and assignment. Traffic variables to describe congestion and their relations among them. Review of the different families of traffic models (micro, meso, macro, network). Introduction to different car-following models. Description of space and time discretisation. Data collection and analysis (1.5 CFU) - Obtaining mobility data: from surveys to sensors. Open data and real-time data for management and planning of a transport system. Collect data from web systems. Analysis of mobility data: construction of O/D matrixes, definition of traffic zones, analysis of paths. Forecasting future scenarios. Transport management and control (1.5 CFU) - Control techniques. Traffic control for congested networks for single- and multi-region systems. Traffic signal control. Dynamic fixed-time signal plans. Discussion of real-time adaptive strategies. Route and departure time choice.Â

Modalita di esame: Prova orale obbligatoria; Elaborato scritto prodotto in gruppo;
Exam: Compulsory oral exam; Group essay;
... Students will work in groups of three, and they will be required to write a report on some of the laboratory assignments. The group reports must be uploaded to the Teaching Portal. The deadline for the upload is the same as the deadline for registering for the oral exam ("prenotazione esame"). The teachers will evaluate each report, and the grade (maximum 30 cum laude) will be proposed to the group. If the grade is insufficient (less than 18/30), the group must update the report. If sufficient, it can be improved only if all group students agree to do so. The report will be valid for 2 academic years so that students that did not pass the oral exam do not have to prepare another report. Reports will focus on specific topics assigned and discussed during the course. Students will need to prepare a textual document and share the developed code. Specifically, each group will prepare three reports as follows: • Data exploration of sharing systems. Extraction of data from MongoDB, the definition of filtering analytics and characterization of mobility patterns. • Forecasting future scenarios. Extraction of time series from real dataset, fitting and evaluation of regression algorithms for prediction. • Comparison of survey and data-based transport demand. Extraction of origin-destination matrices from data and survey, characterization of obtained demand models and quantitative comparisons of results. Examples of students' reports on similar topics will be made available for students on the Teaching Portal. Each student will have a compulsory oral exam (maximum 30 cum laude) on the topics discussed during the lectures and/or addressed during the exercises and laboratories. The oral examination must be sufficient (at least 18/30). In the oral exam, students will answer questions on the theoretical and practical parts. • For the theoretical part, students will be asked to discuss the methodologies learned during the course and their possible applications, with simple and practical example problems to discuss and solve. • For the practical part, students will discuss laboratories, including the developed code used for preparing the reports, and propose possible improvements and alternatives. The final grade will be given by the weighted average of the oral grade (70%) and the group's report grade (30%). The maximum grade will be 30 cum laude. • Group's report grade (30%, at least 18/30) • Individual oral exam (70%, at least 18/30)

Copies of the slides used during the lectures, examples of exercises, and manuals for the activities in the laboratory will be made available. All teaching material can be downloaded from the course page in the Teaching Portal. During each lecture, the teachers will also suggest additional reading material (books and research papers) for further deepening the subject.

Network design for Local, Personal, Body Area Networking, Antenna design in Tough Confined, Radioplanning in Tough, Dissipative, Constrained Environments

The course analyses the new technologies that are emerging for the implementation of the Internet of Things in a large variety of complex environments. The course addresses the Hardware Subsystem (dedicated design of antennas and radiofrequency circuits, choice of microprocessors and radios, study of intersystem compatibility) and the Physical Layer Subsystem (choice of the modulation scheme, the wireless standard and analysis of ad hoc dedicated communication schemes). As a case study, the techniques to deploy the of wireless sensor networks, RFID tags and Internet gateways in jet engines, snow, ice and water, ground and mines, human and animal bodies, dangerous environment (within fire, gassed, polluted premises) will be introduced.

Internet of Things: review of the most common field of applications Wireless Sensor Networks, RFIDs, Personal and Body Area Networks Main Internet of Things Technologies and Standards Hardware design in complex and constrained environments Network design in complex and constrained environments Physical Layer Design in complex and constrained environments Antenna Synthesis, Analysis and Realization in water, dissipative media, biological material The wireless communication channel in dissipative environments: Wide band vs Narrow Band Example of Application: Wireless Sensor Networks in Water Example of Application: Wireless Sensor Networks within Fire Example of Application: Wireless Sensor Networks and RFIDs inside Jet Engines Example of Application: RFIDs on and inside Human and Animal Bodies Example of Application: Internet of Things networking in Oil and Gas Industrial Premises

Modalita di esame: Prova orale obbligatoria; Elaborato progettuale individuale;
Exam: Compulsory oral exam; Individual project;
... One esperimental project assigned at the very beginning of the course, to be completed by the end Weight: 50% Public presentation of the project Weight: 20% Oral exam on the theoretical subjects Weight: 30%

Lecture Notes distributed by the lecturer

Students will gain skills and acquire knowledge concerning the relationship between ICT systems and: - main aspects connected to the principal building technological elements for further system integration; - main principles related to smart building and smart cities design with special regard to intelligent building optimisation; - resource-saving measures to be used in technical building systems (heating, ventilation, cooling, lighting); - indoor climate control, including natural ventilation; - comforts and indoor environmental quality in buildings (thermal, visual, IAQ) - methods, tools and procedures for assessing environmental and energy building performance; - concepts and basics to support dynamic/hourly building energy simulation; - white box, grey box and black box modelling for building performance analyses and digital twin applications; - Building Management System (BMS) and middleware platform development.

ICT knowledge and applications enable a better use of resources in buildings, hence allowing for optimising building indoor environmental quality while reducing energy needs during both building design and building intelligent operational phases, helping cities to reduce their carbon emissions. In the course Intelligent Building Optimization, we address this challenge, dealing with the integration of building systems, technology and energy systems within a smart control environment. This is done by describing building components and related technical systems, as well as digital architecture, climate-responsive building envelope, and kinetic façade. In this context, ICT is used for smart control of heating, ventilation, and air conditioning systems in order to provide thermal comfort and control air quality and illumination. White box models are adopted together with grey and black box ones to support the development of digital twins and optimise both building design envelope choices via surrogate modelling and building operational actions. The latter is performed by simulating an intelligent building management system (BMS) able to automatically set, by means of BMS control algorithms, building-related actuation parameters on the basis of measured real-time data, exploiting innovative approaches of the use of digital twins and advanced building modelling solutions.

Lectures and exercises regard the following topics: 1) smart building: concepts, scenarios and technological solutions 2) introduction to sustainable building: comfort domains (thermal, IAQ, visual) and energy performances 3) ICT optimisation techniques for building indoor environmental quality and energy savings 4) white box, grey box, and black box modelling for the built environment supporting digital twins 5) surrogate modelling production and applications 6) computing user comfort correlated KPIs, GUI, smart sensoring and actuating, data gathering and elaborations 7) Building Management System (BMS) and middleware platform development

Modalita di esame: Prova scritta (in aula); Prova orale obbligatoria; Elaborato scritto prodotto in gruppo;
Exam: Written test; Compulsory oral exam; Group essay;
... Learning target achievements will be reached via lectures, seminars and learning exercises through individual activity and teamwork in building design case studies. Deliveries are consistent with scheduled activities and final exam dates. The final exam with relevant scoring will include: - a test to assess theoretical knowledge acquired during the course (weight: 40% of the final score; duration: 1 h) consultation of supporting tools and material is not allowed; - an evaluation by the teachers of the final results of the exercises carried out during the course and presented in a book and a code repository one week before the final exam (weight: 40%); - a slide presentation of the results of the exercises carried out during the course and a discussion about them to check students comprehension of the learning process (weight: 20%; duration: 20 minutes per student team). The exercises are developed in teams supporting white box building energy and comfort modelling, surrogate modelling definition to optimise building design choices, and developing a simulated building control logic to support smart building operational management in semi-real-time. The laude will be assigned only if the answers to the different parts of the exam are not only correct from a numerical and content point of view but also exceptionally clear and complete in the exposition

Due to the innovative contents of this course no reference publications are given. Materials and documentation, such as slides, tables, graphs, software, printed handouts related to both lectures and exercises will be supplied during the course together with scientific papers. Nevetheless the following books may be considered: 1) Wang S (2010) Intelligent buildings and building automation, Spon Press. 2) Sinopoli J (2016) Advanced Technology for Smart Buildings, Artech House 3) Chiesa G (2020) Technological paradigms and digital eras, Springer 4) Sinopoli J (2009) Smart Buildings Systems for Architects, Owners and Builders, Butterworth-Heinemann 5) Pagani R, Chiesa G (eds) (2016) Urban data: Tools and methods towards the algorithmic city, FrancoAngeli 6) Hensen JLM, Lamberts R (eds) (2019) Building Performance Simulation for Design and Operation, 2nd edn, Routledge 7) Holzer P, Psomas T (eds) (2018) Ventilative Cooling Sourcebook, IEA EBC Annex 62, Aalborg Un. 8) Brackney L et al. (2018) Building Energy Modeling with OpenStudio, Springer

Students will learn problem-solving methodologies drawing on lessons from several disciplines and applying them for the development of a project aiming at solving a simple problem taken from a real case-study. Knowledge • Application of ICT technologies to other domains • Organisation and management of a project • Tools for project management Abilities • Ability to work in a heterogeneous team • Ability to apply and select the proper ICT technology in a context different from the one in which the technology was originally deployed • Technical and scientific communication • Dissemination of project results

This class will introduce the students to the project based learning approach. The student will learn how to apply the knowledge gained in the ICT area to the application domains characterizing the Master degree: health, intelligent transport systems, innovative building design, smart grids and geomatics for navigation based applications. Students will work together in groups to solve real-world problems in the above-mentioned application domains. Students are expected to deepen the concepts understanding, to broaden their knowledge base, to improved communication and interpersonal/social skills, to enhance their leadership skills, to increase their creativity, and to improve writing and communication skills.

Every year a specific set of interdisciplinary projects will be presented to the students, to be developed by teams of 3-4 students. A project in one of the area characterising the Master of Science (health, intelligent transport systems, innovative building design, smart grids and geomatics for navigation based applications) will be allocated to each team.

Modalita di esame: Prova orale obbligatoria; Elaborato progettuale in gruppo;
Exam: Compulsory oral exam; Group project;
... The exam grade is based for 1/3 on the quality of the progress presentations and the final presentation, and for 2/3 on the final oral exam. Each group is required to deliver the final report on the work performed at least one week before the exam’s date. The interim presentation must show the ability of the team to manage the project, the organization of the team work, and a proper delivery mode (correct technical language, clarity of presentation, ability to focus on the core ideas). The oral exam is a discussion on the whole project that has been developed and it will start from the delivered report, in order to test: - their understanding of the developed work of each student of the team - their acquired ability for the problem solving - the acquired knowledge of the ICT topics necessary for the project development - the actual results achieved

Teaching material will be provided to each team by the teachers tutoring the different projects, since it depends from the specific application area (health, intelligent transport systems, innovative building design, smart grids and geomatics for navigation based applications) as well as on the specific objective. The teacher responsible of the course will provide slides and notes on the web portal for the classes addressing the project management and communication of the results

Internships are offered to students as the first real chance to gain experience in the world of work. An internship gives you the opportunity to put into practice what you have learned during your studies and it will help you decide about your future professional career. Politecnico di Torino promotes internships and guarantees insurance coverage both for civil liability and occupational injuries. Before starting an internship, you need to include it in your Personal Study Plan/Annual Personal Study Plan in the academic year in which you would like to do your internship. The internship is part of a training project agreed among you, your academic tutor and your company tutor. The internship is worth 6 CFU and corresponds to a workload of 150 hours.

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

The students who pass the exam will be able to understand the basic concepts referring to the structure and operation of the electrical networks at the transmission and distribution levels; use the correct terminology to address power and energy systems problems referring to smart grids; interpret appropriately the role of smart grids in the current energy system context; formulate analysis and optimization problems referring to smart grid applications and choosing the suitable solver; understand the role of distributed generation and resources in active distribution network management and demand-side management, and their connections with ICT solutions for communication, control and data management.

This course is aimed at giving students with an ICT background a view on the structure and operation of modern and future electrical networks (smart grids), also pointing out the aspects referring to the interaction between power and energy systems and ICT contents. Starting from an overview on the evolution of the power and energy systems towards the construction of supergrids in high voltage bulk transmission systems and of microgrids in electrical distribution systems, the course addresses in a specific way the topics referring to the structure and operation of Medium Voltage and Low Voltage distribution systems, highlighting the contributions of the ICT sector to network modernization with increasing levels of automation, control, communication and data management. At the end of the semester, the student will have to gain knowledge on a set of aspects that enable him/her to dialogue with the operators of the electrical distribution system by using appropriate terminology and showing adequate competence in discussing about basic network-related problems. In addition, the student will have to gain competence in formulating optimization problems referring to the electrical distribution networks, choosing the most suitable methods to solve these problems. Finally, the student will be able to interact in an effective way with the network operators to discuss about the application of ICT solutions to the smart grid.

Introductory concepts: Structure of the electricity transmission and distribution systems. Emergent paradigms: smart grids, supergrids, microgrids. Energy and electricity supply. Capacity and energy provisions, supply adequacy, load balancing. Role of ICT for smart grids. Layer-based representation of power and information flows. Electricity transmission and distribution: Three-phase systems in balanced and unbalanced conditions. Characteristics and modelling of generators, lines, transformers, loads and storage systems. Technical evolution of the network infrastructure. Meshed, radial and reconfigurable weakly meshed network representations. Notes on service continuity and reliability. Smart distribution network analysis and optimization: Matrix-based representation of the network. Power flow calculations in radial and weakly meshed networks. Fault analysis, calculation of the short-circuit currents. Principles of network protection. Relays, switchgears, protective devices. Measurement technologies and applications. Distribution substations and distribution grid automation. Notes on the IEC Standardization. Optimal reconfiguration of distribution networks: objective function, constraints, solution methods and heuristics. Examples of optimal operational planning: problem formulation, solution methods and heuristics. Distributed generation and resources: distributed generation schemes, autonomous systems, grid-connected systems, islanding operation, microgrids. Notes on distributed generation (from renewable sources and combined energy production) and storage technologies. Distribution network models with distributed resources, active distribution network management. Probabilistic power flow calculations. Effects of the diffusion of the distributed resources. Evolution of the standards. Interface with the networks, local voltage control, frequency control. Virtual power plants. Integration of fluctuating renewable energy in transmission and distribution systems. Demand-side management: technologies for electrical load management. Customer interactions with smart metering systems, assessment of the controllable load. Thermostat-controlled loads. Aggregate residential load. Demand response concepts and programmes, demand flexibility, impacts of demand-side management on customer choices. Specific aspects of ICT applications for smart grid operation. Analysis of real cases.

Modalita di esame: Prova scritta (in aula);
Exam: Written test;
... The exam is composed of a mandatory written part (with maximum score 30/30) and of a non-mandatory oral part (accessible only is the score of the written exam is equal to or higher than 27/30. During the exam, the students have to respond a number of questions referring to the entire course programme, provided in a written way, with possible inclusion of numerical exercises. The maximum score assigned to each question is indicated after the text of the question. The minimum score to pass the exam (18/30) cannot be reached if one or more questions are not responded at all. The main competences evaluated are the ability of illustrating concepts, formulating problems, describing solution methods, and solving possible numerical exercises. The written test has duration of 2 hours. The time limit for completing the test is indicated on the blackboard at the beginning of the period. During the test the Commission identifies the location of the students. The students must exhibit a valid document with photo. During the test the students may exit from the room only if they withdraw from the test of deliver the final writing. In case of withdrawal the text of the exam has to be given back with the indication of the student’s name and surname on the first page and with the writing “WITHDRAWN� or “NOT DELIVERED�. When the writing is delivered, the text with the indication of the student’s name and surname must be included in the delivered material. The students may use only clean paper, pen and pocket calculator. Personal computers, laptops, tablets, or equipment for taking photos are not allowed. The clothes and the personal belongings must not remain on the work plan, nor under the desk, and must be closed and located in a position in which the contents relevant to the exam cannot be reached. Contacting other persons of material is not admitted. If a student is found with any material not allowed in accessible location, or contacting other persons, his/her test is immediately annulled and the student will have to leave the room. Any question concerning the test must be addressed to a member of the exam Commission. In case the question is of general interest, a member of the exam Commission will inform all the students participating in the exam. The results of the exam will be communicated through the web portal. The students will have the possibility of viewing the corrected test during the period of the current exam session.

There is no commercial book covering the contents of this course. The material (slides and handouts) used during the lectures and course activities will be available on the web portal.