telecommunication engineering - ingegneria delle telecomunicazioni

Main goal of the course is that the student understands all the major concepts used in modern microprocessors by the end of the semester.
The course will cover the different forms of parallelism found in applications (instruction-level, data-level, thread-level, gate-level) and how these can be exploited with various architectural features. It will cover pipelining, superscalar, speculative and out-of-order execution, vector machines, VLIW machines, multithreading, graphics processing units, multicore and parallel homogeneous and heterogeneous architectures. Final goal is to show how the software interacts with the hardware to provide performance and how trends in technology, application and economics have driven and drive continuing changes in the field.

Expected learning outcomes
DdD 1 (Knowledge and understanding): Students will learn how to:
Understand and analyze the most advanced microprocessors and computer architectures
Evaluate and compare the performance of computer architectures
Understand the most advanced techniques to optimize the memory hierarchy
Manage the parallelism at instruction-level, thread-level, multi-processor level
Understand the mechanisms of streaming and vector processing widely used in modern GP-GPUs.
DdD 2 (Applying knowledge and understanding): Given specific problems and project cases, students will be capable to:
Analyze and compare specific architectural choices of a computer system
Evaluate the performance of computer architectures by applying appropriate metrics
Exploit the parallelism at several levels from instructions to threads to cores to vectors
DdD 3 (Making judgments): Given specific problems and project cases, students will be capable to analyze the performance goals and compare autonomously different computer architecture choices in terms of performance metrics
DdD4 (Communication skills): Students will learn how to present in a technical text and how to discuss in front of their colleagues the benefits and drawbacks of advanced computer architecture techniques.
DdD 5 (Learning skills): Students will be capable to learn autonomously the most advanced research techniques related to state-of-the-art microprocessor and parallel computer architectures

Main lectures topics:
Review of basic computer architecture: the RISC approach and pipelining, the memory hierarchy
Basic performance evaluation metrics of computer architectures
Techniques for performance optimization: processor and memory
Instruction level parallelism: static and dynamic scheduling; superscalar architectures: principles and problems; VLIW (Very Long Instruction Word) architectures, examples of architecture families
Thread-level parallelism
Multiprocessors and multicore systems: taxonomy, topologies, communication management, memory management, cache coherency protocols, example of architectures
Stream processors and vector processors; Graphic Processors, GP-GPUs, heterogeneous architectures

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente) o condizionata (a scelta del docente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
NOTE: The course IS in ENGLISH, which means that the exam HAS TO BE PASSED in English, e.g. you HAVE to WRITE IN ENGLISH during the written test.
The final examination may be taken in one of the following ways:
Way 1. WRITTEN EXAM (closed book, no notes or usage of laptop/mobile, etc. allowed) consisting of:
1.1 The solution of some design problems based on the practical application of the course concepts and techniques -- Expected learning outcomes 1, 2, 3, 4, 5, 6 and 7.
1.2 Questions on the course concepts and techniques - Expected learning outcomes 1, 2, 3, 4, 5, 6 and 7.
WAY 2. PROJECT-BASED EVALUATION (valid only for the 1st session after the course):
2.1 (1/3 of the final grade) Evaluated through the course via short written tests - Expected learning outcomes 1, 2, 3, 4
2.2 (2/3 of the final grade) Evaluated, via a project assignment, done in team up to 2 members, demonstrating the mastering of the course concepts and techniques. During the final project presentation, each team will be asked to answer some questions on ACA topics, not only related to the project. - Expected learning outcomes 1, 2, 3, 4, 5, 6 and 7.

John Hennessy, David Patterson, Computer Architecture, A Quantitative Approach, Editore: Morgan Kaufmann, Fifth or Sixth Edition
Note: Additional information in slides and papers available through the course website.

Motivated by the increasing penetration of distributed energy resources (DERs) and the structuring of the electrical grid into multiple microgrids that enable the provision of new smart grid services, the goal of this course is to introduce students to strategies for the optimal coordination and control of complex systems coping with aspects like a multi-agent large scale structure, the presence of uncertainty affecting the system and of integer decision variables, and the need to guarantee privacy of local information.

The students will get some insight into the electrical grid evolution and learn methods for modeling typical energy management problems and mastering their complexity through optimization-based approaches for the operation of multi-agent possibly uncertain systems (DdD1 - Knowledge and understanding). Through the project work, they will not only apply the acquired knowledge (DdD2 - Capability to apply knowledge and understanding) but also improve their autonomy and critical thinking (DdD3 - Making judgements), their team working and communication skills (DdD4 - Communication skills), and their capability of devising ad-hoc solutions to the problem at hand (DdD5 - Learning skills).

1. Introduction to the electrical grid system and to the challenges and opportunities offered by its current evolution towards the smart grid concept. In particular, electrical energy production from traditional and renewable sources will be considered, as well as requirements in various use cases and scenarios, role of non-programmable sources and impact on the network infrastructure, with a special focus on technological trends and developments of DERs and microgrids, including storage, electric vehicles, and ICT technologies.
2. Description of some relevant case studies for the optimal management of the electrical grid that can be formulated in terms of constrained optimization problems possibly involving multiple agents and affected by uncertainty, calling for distributed and data-driven resolution approaches. In particular, we shall consider the provision of ancillary services to the grid by the aggregation of multiple prosumers, the scheduling of the charging of a fleet of electric vehicles, and the energy management of a building district.
3. Motivated by the above case studies, we shall present relevant algorithms for solving multi-agent convex optimization problems with coupling due to shared decision variables or resources (the distributed dual subgradient and alternating direction method of multipliers algorithms), and discuss their extension to address the case when integer variables and uncertainty known only from data are present.

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The final assessment will be based on

• an oral exam on the content of the course (DdD1, DdD4);
• a report and a presentation on a project work carried out by the students in small groups related to the presented case studies (DdD2, DdD3, DdD4, DdD5).

Slides of the course and some relevant papers https://webeep.polimi.it/

The course has two goals; the first is to foster a deeper understanding of fundamental operating systems topics by exposing students to modern operating systems’ internals, both general purpose and embedded*. The second but equally important goal is to provide practical experience with the design and development of OS components and/or system-level programs by means of a practical project proposed by the instructors. Depending on availability, some of the advanced course topics will be covered in seminars from industry experts.
[Note*: To build a broader knowledge and understanding of embedded systems it is suggested to follow also the Embedded Systems course]

Expected Learning Outcomes (ELO)
Upon successful course completion students will:
1. Knowledge and understanding
Know the mechanisms, policies and interfaces used by modern OSes to control and share, among running programs, resources such as processor, memory and other IO devices (through the device drivers).
Be aware of the main issues associated with concurrency that might arise in multi-process and multi-threaded system-level programs.
Know mechanisms, policies and interfaces adopted by modern OSes to manage modern storage peripherals ensuring proper access control to the data.
Know mechanisms and policies adopted by modern OSes to boot up, manage power consumption, virtualize resources, ensure security.
2. Application of knowledge and understanding
Be able to write multi-process, multi-threaded concurrent software for both embedded and general purpose multi-core systems.
Be able to write device driver software to interface with hardware peripherals.
Be able, depending on the choice of the project work, to design software using market-available technologies, possibly constrained by energy and performance requirements.
3. Making judgements
Be able to determine optimal policies for resource management given the requirements of real-world applications.
Be able to select the most suitable platform, operating system (or bare-metal programming) and development tools based on the application requirements and by adopting best industrial practices.
4. Lifelong learning skills
Be able to be follow and adopt future OSes technologies by adapting his/her knowledge with minimal effort in an ever-changing scenario.
Be able to undertake career paths in OS/embedded systems development.

The topics covered by the course will be addressed both theoretically and practically. For some of the course topics (related to the implementation of OS internals) we will mainly make references to the latest versions of the Linux source code although we will touch upon other Unix systems as well (e.g., MacOS’s XNU) and embedded OSes (FreeRTOS, Miosix). Part of the course will show practical examples on industrial boards (e.g., STM32/ARM) and, depending on availability, these boards can be borrowed by the students to finalise the course development project (which is optional). Besides, the course will cover practical use cases, tutorials and short introductions to the Linux Kernel Module APIs, POSIX C API, modern C++ threading API, GIT, GCC/CLANG, GNU Make etc. Concerning processor ISAs, the course will touch mainly Intel x86, ARM and RISCV. Depending on the availability of time, other modern system level languages (e.g., Rust) will also be touched upon.
The topics covered are the following:
Introduction and background
Main goals of an Operating System and its design patterns. Mechanisms and policies for mediating and regulating the access to system resources. Taxonomies of operating systems (monolithic, microkernel, hybrid, uni-kernels) with examples.
Resource Management and concurrency
Support for multi-process and multi-threaded execution. CPU scheduling for general purpose single and multiprocessors. Modern techniques for load-balancing processes on NUMA multiprocessors. Memory consistency basics and multi-threaded synchronization. Advanced locking techniques and lockless programming. Primitives for inter-process communication (IPC). Asynchronous programming. Deadlock and starvation. Virtual memory management. Basics of software and hardware virtualization, containers.
Peripheral and persistence management
Low-level access to peripherals (memory mapped vs port-based), communication buses (e.g. PCI), modern programmable interrupt controllers and interrupt management. Deferring interrupt work. Character based and block based IO. Development of modern device drivers. Overview of modern storage devices (HD, SSD) and file systems. The filesystem-centric view of peripherals.
Run-time support
Boot loaders and kernel initialization. Kernel device discovery (ACPI and device trees). The C runtime support, structure of dynamic libraries and linking. Overview of tools for development, analysis and profiling of embedded code.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
The evaluation of the expected learning outcomes will be carried out through a written exam during the official "appello," which will include open-ended and closed questions, as well as exercises (ELOs 1, 2, 3, 4).
Students will have the option, should they so choose, to supplement the exam evaluation with a development project on topics proposed by the instructors.

Slides, notes and other material available on the course's site https://www.vittoriozaccaria.net/#/r/aos
Remzi H Arpaci-Dusseau, Andrea C Arpaci-Dusseau, Operating Systems: Three Easy Pieces, Editore: CreateSpace Independent Publishing Platform; 1.00 edition, Anno edizione: 2018, ISBN: 198508659X https://pages.cs.wisc.edu/~remzi/OSTEP/
Note: Freely available on the web
Robert Love, Linux Kernel Development, Editore: Addison-Wesley Professional; 3rd edition, Anno edizione: 2010, ISBN: 0672329468
Daniel P. Bovet and Marco Cesati, Understanding the Linux kernel, Editore: O'Reilly, Anno edizione: 2006

This course is of 6 CFU and is associated with a version of 5 CFU named 097688 - ECONOMICS AND COMPUTATION (5 CFU). The current form specifies goals, program and expected learning results for both courses.
For both versions of the course (6 CFU and 5 CFU), the goal of the course is to provide the groundings of algorithmic microeconomics. More precisely, the course explores mathematical models, computational complexity, and algorithms for economic settings. The course focuses both on non-cooperative game theory, describing the models and the algorithms used in real-world applications in various settings (e.g., poker and security), and on mechanism design, describing economic mechanisms and algorithms used in auctioning settings (e.g., digital advertising and online auctions). The treatment of the topics will be both from a theoretical point of view, describing the models/algorithms and their properties, and from a practical point of view, discussing how the algorithms can be applied and applying them in practice during some laboratory sessions. Furthermore, the course aims at presenting some real-world applications of paramount importance. The difference between the two versions of the course (6 CFU and 5 CFU) is just in the depth with which some topics are discussed.

For both versions of the course (6 CFU and 5 CFU), we expect that the students learn:
how to model real-world applications by means of mathematical models from microeconomics,
how to identify the most suitable class of algorithms to solve a given problem,
how to use the tools already available to solve a given problem in practice,
how to design an algorithm to solve a given problem whenever no algorithm is known

We report the topics discussed in both versions of the course (6 CFU and 5 CFU). We use '(*)' for the topics that will not be discussed in the 5 CFU version.
PRELIMINARIES OF GAME THEORY
Definition of non-cooperative game
Representations of a game
ALGORITHMIC GAME THEORY
Solving maxmin/minmax problems
Computational complexity of finding a Nash equilibrium
Algorithms for finding a Nash equilibrium
Arrow-Debreu market model and equilibrium (*)
Algorithms for finding a Stackelberg equilibrium
Real-world application: security games
Congestion games
Price of Anarchy and Price of Stability
ECONOMIC MECHANISM DESIGN
Social choice function
Definition of economic mechanism
Gibbard-Satterthwaite theorem
Quasi-linear environment and linear environment
Implementation of a social choice function
Groves mechanisms
Green-Laffont characterization and Myerson characterization
Economic mechanisms for online advertising (real-world applications) (*)
ALGORITHMIC MECHANISM DESIGN
Non-scalability of Groves mechanisms (real-world examples: combinatorial auctions)
Developing computationally efficient mechanisms
Economic mechanisms for combinatorial auctions (real-world applications)
Economic mechanisms for double auctions (*)
Economic mechanisms for knapsack auctions
Automated mechanism design (*)

Prova scritta obbligatoria, con prove in itinere
Valutazione continua facoltativa (a scelta dello studente)
The exam is the same for both versions of the course (6 CFU and 5 CFU), except for the questions that, in the case of the 5 CFU version, may be different. More precisely, the exam can be of two forms:
standard: written exam (2.5 hours)
3 exercises (the first exercise is on noncooperative game theory models and on the adoption of operations research tools to solve a game, the second exercise is on algorithmic game theory or economic mechanism design, the third exercise is on economic mechanism design or algorithmic mechanism design) (max 27 points), --- here the students are required to solve exercises similar to those studied during the course or to answer some theoretical questions about topics discussed during the course; the capabilities the students must show are: modeling real-world applications by means of mathematical models from microeconomics, indentifying the most suitable class of algorithms to solve a given problem, using the tools already available to solve a given problem in practice ---;
1 question on advanced topics (on all the topics of the course) (max 5 points) --- here the students are required to answer a question that is beyond the topics discussed during the course; the capabilities the students must show are: generalizing the skills learned during the course to design an algorithm for a given problem that has not discussed during the course;
engaged: the course is divided into four parts (Part 1: noncooperative game theory models; Part 2: Maxmin and Nash equilibrium; Part 3: Stackelberg, Congestion games, social choice functions; Part 4: mechanism design and algorithm mechanism design), at the fourth week of each part of the course, students take part in a midterm exam (2.5 hours), composed as:
exercise/theory (1 exercise and 2 theory questions) (max 27 points) --- here the students are required to solve exercises similar to those studied during the course or to answer some theoretical questions about topics discussed during the course; the capabilities the students must show are: modeling real-world applications by means of mathematical models from microeconomics, identifying the most suitable class of algorithms to solve a given problem, using the tools already available to solve a given problem in practice ---;
challenge on scientific research problems (max 8 points); the challenge is carried on by teams, each of 2-3 students --- here the students are required to answer some questions that are beyond the topics discussed during the course; the capabilities the students must show are: generalizing the skills learned during the course to design an algorithm for a given problem that has not discussed during the course.
Each student can choose the form of the exam she/he prefers. If a student opts for the engaged form and she/he is not satisfied with the achieved grade, she/he can repeat the exam in the standard form.

Noam Nisan, Tim Roughgarden, Eva Tardos, V. Vazirani, Algorithmic Game Theory, Editore: Cambridge University Press
Kevin Leyton-Brown, Yoav Shoham, Essentials of Game Theory, Editore: Cambridge University Press

The course aims to provide the student with the basis of radiation theory and of the main concepts of antenna engineering for the design of complex telecommunication systems.
The course provides the students with instruments to understand and apply modern design technologies and methodologies. Some design examples are provided in order to teach the students to make autonomous judgements in the antenna design process.

Knowledge and understanding (Dublin descriptor DD1)
Students will learn how to make use of mathematical models for the understanding of the physics of electromagnetic radiation; define and use mathematical modesl for the analysis of the behavior of antennas at system level and at circuit level.
Applying knowledge and understanding (Dublin descriptor DD2)
Students will be able to compute the size of an antenna or array based on specific requirements; evaluate the performance of a designed antenna at system level; apply the mathematicl models using electromagnetic simulators.
Making judgements (Dublin descriptor DD3)

• Understanding the funtamental tradeoffs that govern the design of an antenna or array, identify the complexity of different solutions from a circuit point of view; choose a specific antenna type based on the desired radiation behavior.

Antennas
The course aims to provide the student with the basis of radiation theory and of the main concepts of antenna engineering. After introducing radiation theory, the main antennas topologies are introduced with a view to their application field (telecommunications, remote sensing, telemetry). We then discuss the effect of noise and finally show some measurement technique to characterize the main parameters of antennas.
Radiation theory. Maxwell equations in differential form, Poynting vector.
Radiation from electric hertzian dipole; computation of the near field, the induction field and the radiation field. Magnetic hertzian dipole: duality. The Huygens source.
The antenna as a transducer: main circuit parameters and radiation parameters (directivity, directivity function, gain, effective area, characteristic electric and magnetic length, efficiency, equivalent circuit, polarization).
Wire antennas (short dipole, half wavelength dipole and others).
Aperture antennas (horns, parabolic antennas)
Antenna arrays introduction (array factor, coupling)
Linear arrays (broadside, endfire, collinear, log-periodic)
Leaky wave antennas
Antennas measurements.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The examination will consist of a written test (2 hours) with 3 numerical exercises (DD1, DD2, DD3). No mobile phone are allowed in the test, but the student is allowed to bring his/her notes or books to consult. The student will be asked to:

• solve numerically some problems related to the topics of the course (knowledge and understanding with application, DD1, DD2, DD3)
• to design (numerically) an antenna or an array for a specific goal (making judgement, DD3).
  The student will be often asked to make some reasonable approximation when the solution appears too complex to solve exactly (making judgements, DD3).
  A supplementary and optional work is the study of some wire antennas by the use of electromagnetic simulators (DD1, DD2). The final result is then presented to the rest of the classroom (DD4). This optional work will grant up to three extra points in the final evaluation.
  30 cum laude will be assigned if the total score is greater than 31. The test will be evaluated in 1-2 weeks. Student can withdraw the final mark with no limitations on the number of tests they apply for.

S. J. Orfanidis, Electromagnetic Waves and Antennas
David M. Pozar, Microwave Engineering, Editore: Addison-Wesley
Robert E. Collin, Foundations for Microwave Engineering, Editore: McGraw-Hill
Notes for the course Webeep

Nowadays, deep neural networks can outperform traditional hand-crafted algorithms, achieving human-level performance on many complex tasks, such as natural language processing, text modeling, gene expression modeling, and image recognition. The course provides a broad introduction to neural networks (NNs), ranging from traditional feedforward (FFNNs) and recurrent (RNNs) to the most successful deep learning models, such as convolutional neural networks (CNNs) and long short-term memory (LSTM).
The course's primary goal is to provide students with the theoretical background and practical skills to understand and use NN, and to become familiar with Deep Learning for solving complex engineering problems.
This goal is pursued in the course by
Presenting major theoretical results underpinning NN (e.g., universal approximation, vanishing/exploding gradient, etc.)
Describing the most important algorithms for NN training (e.g., backpropagation, adaptive gradient algorithms, etc.)
Illustrating the best practices on how to successfully train and use these models (e.g., dropout, data augmentation, etc.)
Providing an overview of the most successful Deep Learning architectures (e.g., convolutional networks, autoencoders for embedding, long-short term memories for sequence-to-sequence learning, etc.)
Providing an overview of the most successful applications with particular emphasis on models for solving visual recognition tasks.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will learn:
· What are neural networks and the fundamental methods and techniques used for neural networks training
· What are the fundamental architectures used in deep learning, such as convolutional neural networks and long-short term memories, and when to use them
· What are the neural network architectures used in the different visual recognition tasks
· What are the main differences between machine learning and deep learning
Applying knowledge and understanding
Given a specific data analysis problem, the student will be able to:
· Identify which paradigm better describes a given problem
· Identify which technique to start from with the analysis, apply it to model the data and evaluate its outcome
· Implement fundamental deep learning algorithms for image and text analysis autonomously
Making judgments
Given a complex data analysis problem, students will be able to:
· Identify the most relevant model to be applied in the specific problem
· Identify the occurrence of overfitting by the model under analysis
· Iteratively refine the selected model in order to balance performance, computational complexity and overfitting
· Compare and select different models for the problem under analysis
Communication
The student will learn to:
· Discuss in written form the pros and cons of different machine learning techniques for a specific problem
Lifelong learning skills
The student will learn to:
· Face a real-life data analysis problem with a sound and complete methodological approach
· Understand complex machine learning techniques beyond the fundamental ones presented during lectures
· Develop new machine learning pipelines adapting to the specific problem at hand

Neural networks are mature, flexible, and powerful non-linear data-driven models that have successfully been applied to solve complex tasks in science and engineering. The advent of the deep learning paradigm, i.e., the use of (neural) network to simultaneously learn an optimal data representation and the corresponding model, has further boosted neural networks and the data-driven paradigm. These topics will be described in the course according to the following detailed program:
From the Perceptron to Neural Networks and the Feedforward architecture
Backpropagation and Neural Networks training algorithms, e.g., Adagrad, Adam, etc.
Best practices in neural network training: overfitting and cross-validation, stopping criteria, weight decay, dropout, data resampling, and augmentation.
Image Classification Problem and Neural Networks
Recurrent Neural Networks and other relevant architectures, such as (Sparse) Neural Autoencoders
Theoretical results: Neural Networks as universal approximation tools, vanishing and exploding gradients, etc.
Introduction to the Deep Learning paradigm and its main differences with respect to classical Machine Learning
Convolutional Neural Networks (CNN) architecture
The breakthrough of CNN and its interpretation
CNN training and data-augmentation
Structural learning, Long-Short Term Memories, and their applications to text and speech
Autoencoders and data embedding, word2vec, variational autoencoders
Transfer Learning for pre-trained Deep models
Extended models, including Fully Convolutional CNN, networks for image segmentation (U-net) and object detection (e.g., R-CNN, YOLO )

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
The course foresees a written exam and some homework assignments in the form of coding exercises/challenges. The written exam is aimed at verifying the acquisition of theoretical knowledge of the course subject, while the coding exercises aim at acquiring practical experience with the most widely used tools in the field.
The homework assignments contribute between 0 and 10 points to the final grade. The written exam is compulsory and will contribute between 0 and 22 points. The final grade is the sum of the grades for the homework assignments and the written exam.
Homework assignments need to be delivered during the course on fixed dates. The grade obtained on these assignments is valid for the entire academic year and can be combined with the written exam grade from any call in the same academic year.
Type of assessment
Description
Dublin descriptor
Written test
Solution of numerical problems
· Computation of simple backpropagation derivation
· Computation of simple sizing problems on CNN architectures
Answer to theoretical questions
· Formal derivation of learning techniques and models
· Comparison of models in terms of complexity, overfitting issues, and applicability in different cases of data analysis
1, 2
1, 2, 3, 4, 5
Assessment of practical homework
Execution of practical homework exercises
· Application of deep learning techniques to small dataset
· Application of model selection and validation techniques on simulated and real datasets
2, 3, 4, 5

Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, Editore: MIT Press, ISBN: 978-0262035613 http://www.deeplearningbook.org/

Visual information plays a central role in communication systems, multimedia applications, industrial monitoring, remote sensing, biomedical imaging and scientific instrumentation. Although images and videos are now commonly processed by complex software systems, their acquisition, representation and manipulation are based on fundamental principles of multidimensional signal processing.
“Video Signals” is a graduate-level introductory course devoted primarily to the fundamentals of digital image processing. No previous knowledge of image processing is assumed.
The course introduces the complete image-processing chain, from image formation and digital acquisition to enhancement, filtering, restoration, segmentation, geometric processing and compression. Particular attention is devoted to the mathematical and signal-processing principles underlying the different methods.
In the final part of the course, images are extended to time-varying visual signals. Basic concepts of video representation, motion estimation, optical flow and spatio-temporal compression are introduced without addressing advanced video communication protocols or machine-learning-based methods.
The goal of the course is to provide students with the theoretical and practical knowledge required to:
represent images as two-dimensional discrete signals;
understand the mechanisms of image formation, acquisition, sampling and quantisation;
analyse images in the spatial and frequency domains;
design deterministic and model-based image-processing procedures;
correct, enhance, restore and geometrically transform digital images;
extract meaningful structures from visual signals;
understand the fundamental principles of image compression;
understand how image-processing concepts can be extended to video sequences;
implement image-processing algorithms using MATLAB.
The course focuses on classical signal-processing, mathematical and model-based approaches. Artificial intelligence, machine learning and deep-learning methods are outside the scope of the course.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will learn how to:
model a digital image as a two-dimensional sampled and quantised signal;
understand the principal stages of image formation and digital acquisition;
describe the basic properties of camera models, optical systems and image sensors;
understand the main aspects of human visual perception relevant to image processing;
represent and process colour images;
analyse image signals in the spatial and frequency domains;
understand linear, non-linear and morphological image-processing operators;
model image noise, blur and geometric distortions;
understand the principles of image restoration and deconvolution;
understand the main approaches to image segmentation and feature extraction;
understand the fundamental principles of still-image compression;
extend image-processing concepts to video signals;
understand the basic principles of motion estimation, optical flow and temporal prediction.
Applying knowledge and understanding
Students will be able to:
implement image-processing algorithms in MATLAB;
design procedures for image enhancement and correction;
implement spatial-domain and frequency-domain filters;
apply morphological operators to binary and grayscale images;
perform image segmentation and feature extraction;
correct geometric and perspective distortions;
restore images degraded by noise or blur;
register and combine multiple images;
select appropriate algorithms and tune their parameters according to the application;
evaluate and compare different image-processing solutions;
implement simple procedures for motion estimation and temporal image processing.
Making judgments
Given an image-processing problem, students will be able to:
identify the objectives, assumptions and constraints associated with the problem;
formulate an appropriate mathematical or signal model;
select a suitable processing technique;
determine the sequence of operations required to solve the problem;
select and estimate the relevant parameters;
evaluate whether the obtained result is consistent with the problem requirements;
discuss the limitations and possible failure conditions of the selected method.
Communication skills
Students will learn to:
describe image-processing algorithms using appropriate mathematical and technical terminology;
explain the rationale behind a selected processing procedure;
represent an algorithm through equations, block diagrams, pseudocode or MATLAB instructions;
compare alternative methods by discussing their assumptions, strengths and limitations;
present the results of an image-processing procedure independently of its specific software implementation.

1. Digital image representation
Images as two-dimensional continuous and discrete signals.
Pixels, spatial resolution, dynamic range and bit depth.
Grayscale and colour images.
Image formats and numerical representations.
Basic MATLAB image representation and visualisation.
2. Sampling, quantisation and interpolation
Two-dimensional sampling and Nyquist criterion.
Spatial aliasing and moiré patterns.
Intensity quantisation and quantisation error.
Image resizing and resampling.
Nearest-neighbour, bilinear and bicubic interpolation.
Dithering and false contouring.
3. Image formation and acquisition
Pinhole and real camera models.
Perspective projection and homogeneous coordinates.
Focal length, field of view and magnification.
Lens distortion and camera calibration principles.
Image sensors, exposure and sensor noise.
Global and rolling shutter.
Basic digital-camera acquisition pipeline.
4. Human visual system, colour and HDR imaging
Brightness, contrast and spatial sensitivity of the human visual system.
Visual masking and perception of spatial details.
Tristimulus theory and colourimetry.
RGB, XYZ, YCbCr and perceptual colour representations.
White balance, colour correction and chroma subsampling.
Introduction to Wide Colour Gamut and High Dynamic Range imaging.
Exposure fusion, radiometric calibration and tone mapping.
5. Spatial-domain image processing
Point operations and intensity transformations.
Histogram computation, equalisation and matching.
Two-dimensional convolution and correlation.
Smoothing and sharpening filters.
Gaussian, Laplacian and unsharp-masking filters.
Median, order-statistics and edge-preserving filters.
Local statistics and adaptive filtering.
6. Edges, corners and parametric structures
First- and second-order image derivatives.
Gradient magnitude and orientation.
Sobel, Laplacian and Canny operators.
Corner and interest-point detection.
Hough transform for lines and circles.
Generalised Hough and Radon transforms.
Parameter selection and interpretation of the transformation spaces.
7. Frequency-domain and multiresolution processing
Two-dimensional discrete Fourier transform.
Magnitude and phase of the image spectrum.
Frequency-domain filtering.
Ideal, Butterworth and Gaussian filters.
Periodic-noise removal and notch filtering.
Relationship between spatial and frequency-domain processing.
Image pyramids, scale-space representations and wavelet fundamentals.
Applications to denoising and compression.
8. Mathematical morphology and segmentation
Binary and grayscale morphology.
Structuring elements, erosion and dilation.
Opening, closing and morphological gradient.
Connected components, hole filling and skeletonisation.
Global and local thresholding.
Otsu’s method.
Region growing, splitting and merging.
Watershed segmentation.
Morphological post-processing and evaluation of segmentation results.
9. Image degradation and restoration
Statistical models of Gaussian, impulse and Poisson noise.
Image blur and point spread functions.
Motion blur and defocus.
Spatial- and frequency-domain denoising.
Inverse and Wiener filtering.
Regularised restoration.
Deconvolution and blind deconvolution.
Iterative restoration methods.
Ambiguities and limitations of inverse problems.
10. Geometric processing, registration and stitching
Translation, rotation, scaling and affine transformations.
Projective transformations and homographies.
Forward and inverse geometric mapping.
Perspective correction and image rectification.
Feature-based and intensity-based registration.
Estimation of geometric transformations and outlier removal.
Basic principles of robust estimation and RANSAC.
Image mosaicing, panoramic imaging and blending.
Ghosting, exposure differences and parallax.
11. Image compression
Redundancy and information content in images.
Lossless and lossy compression.
Predictive and transform coding.
Discrete cosine transform and quantisation.
Run-length, Huffman and arithmetic coding.
JPEG coding architecture.
Compression ratio, quality and rate–distortion trade-off.
Compression artefacts.
Overview of JPEG 2000 and modern still-image coding approaches.
12. Introduction to video processing
Video as a spatial and temporal signal.
Frame rate, temporal sampling and temporal aliasing.
Spatial and temporal redundancy.
Frame differencing and temporal filtering.
Block-based motion estimation and motion vectors.
Brightness-constancy assumption and optical-flow equation.
Aperture problem.
Basic principles of Lucas–Kanade and Horn–Schunck methods.
Motion-compensated prediction.
Intra-frame and inter-frame coding.
General principles of spatio-temporal video compression.
Laboratory activities
Laboratory activities support the understanding of the theoretical concepts and are based primarily on MATLAB and the Image Processing Toolbox.
Indicative activities include:
image representation, sampling, quantisation and interpolation;
histogram processing and spatial filtering;
frequency-domain image analysis;
edge, corner and parametric-structure detection;
mathematical morphology and segmentation;
noise reduction, Wiener filtering and deconvolution;
geometric transformations and perspective correction;
image registration and panoramic image generation;
HDR image generation and tone mapping;
JPEG compression and analysis of compression artefacts;
block-based motion estimation and optical flow;
simple temporal processing of video sequences.
The activities will focus on understanding the behaviour of the algorithms, interpreting the results and evaluating the influence of parameter selection.
Innovative didactics
The flipped-classroom methodology will be applied to selected practical aspects of MATLAB-based image processing.
Students will receive preparatory material concerning MATLAB operations, image data structures and basic software procedures. Classroom activities will focus on:
comparing alternative algorithms;
predicting the effects of parameter changes;
identifying algorithmic limitations and failure conditions;
interpreting numerical and visual results;
designing complete image-processing procedures;
translating mathematical descriptions into MATLAB code.
Interactive activities may also include the discussion of multiple-choice questions designed to identify common conceptual errors and misconceptions.

Prova scritta obbligatoria, senza prove in itinere
The exam for the “video signals” module is a written test with 3 or 4 exercises to be solved in 2 hours. Two or three exercises require a numerical/procedural solution while the last one is a exercise requiring the definition of a Matlab procedure in order to solve the question and will focus on exercises exposed during laboratories. The Matlab code has to be written directly on the paper without any computer aid.
The exam for the first module will take place only in the written version; no oral exams, integrations or projects will be considered in order to increase the obtained grade.
Concerning the assessment of the whole exam (Audio and Video Signals)
In order to pass the whole “Audio and Video Signals” exam (first and second module) (for students that are not following just the Video module, a positive grade must be obtained in both modules and the final mark will be the average of the two marks rounded up the next integer.
In evaluating the average between the two modules a 30 cum laude mark in a module will be considered as 30; in order to get 30 cum laude as a final mark, the mark of at least one module must be 30 cum laude.
In every exam date the two modules will take place the same day one after the other (more details will be provided by the web poliself); however students can choose to take on a single module or both modules in the same day.
Once a student gets a positive mark in a module this mark will be automatically frozen until she/he takes again the same module in a following session; “taking again the exam on the same module” means that the student registers, participates and turns in his/her solution; while if the student simply participates to an exam but does not turn in his/her solution, this will not change the previous mark and the student will be considered as "asbsentee".
Once a student gets a positive mark in both modules the final mark will be evaluated and then published for the publishing period on the web poliself; at the end of this period it will be automatically recorded. If the student does not want to record that final grade he/she has to refuse it from the poliself: in that case both marks will be restored in a "frozen" state in order to allow the student to take on again a module (or both of them). However if the student has a frozen mark in both modules at the end of each exam to which his/her signed in, the final average mark will be automatically published and, after the publishing period, automatically recorded: so, if the student wants to improve the final mark he/she has to remember to refuse the published final grade.
Type of assessment
Description
Dublin descriptor
Written test
Numerical and analytical exercises
Exercises may concern:
sampling, quantisation and interpolation;
camera geometry;
spatial- and frequency-domain filtering;
colour and histogram transformations;
edge detection and morphology;
segmentation and Hough or Radon transforms;
noise reduction and restoration;
deconvolution;
geometric transformations and homographies;
image registration;
HDR imaging;
image compression;
basic motion estimation.
1,2,5
Algorithm-design and MATLAB-procedure exercises
Students may be required to define a complete procedure for:
image enhancement or restoration;
spatial- or frequency-domain filtering;
segmentation and morphological processing;
feature extraction and matching;
perspective correction;
image registration and stitching;
HDR image fusion;
image compression;
basic motion or optical-flow analysis.
The solution may be expressed through equations, block diagrams, pseudocode or MATLAB code.
1,2,3
Multiple-choice questions
Multiple-choice questions may cover all course topics, with particular emphasis on:
theoretical properties and assumptions of algorithms;
interpretation of spectra, histograms and transformation coefficients;
camera, sensor and colour-representation properties;
comparison among filtering, segmentation and restoration methods;
limitations of deconvolution and geometric processing;
compression principles and artefacts;
motion-estimation and optical-flow assumptions;
basic temporal-prediction principles.
1,3,4

R.C. Gonzalez, R. E. Woods, Digital Image Processing, Editore: Addison-Wesley Pub., Anno edizione: 2008
Nixon, Mark Aquado, Alberto S Book., Feature Extraction and Image Processing, Anno edizione: 2002, ISBN: 0750650788
Bankman, Isaac, Handbook of Medical Imaging, Editore: Elsevier Academic Press, Anno edizione: 2000, ISBN: 0120777908
Mallat, Stephane, Wavelet Tour of Signal Processing, Editore: Academic Press, Anno edizione: 1999, ISBN: 0-12-466606-X
Theodoridis, Sergios Koutroumbas, Konstantinos., Pattern Recognition, Editore: Elsevier Academic Press, 2003., ISBN: 0126858756

The goal of the course is to provide students with advanced audio signal processing skills and knowledge. In this course we first present the fundamental tools for analyzing, synthesizing and processing sounds (voice, music, acoustic signals). We then show how to use such tools for developing a wide range of applications, ranging from music information retrieval to acoustic array processing. Topics are treated both theoretically and practically.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students know the fundamentals of auditory perception and acoustics, and the theoretical tools for time-frequency analysis, processing and synthesis of audio signals, including their time/frequency resolution limits.
Applying knowledge and understanding
Students are able to implement advanced audio processing algorithms (denoising, dereverberation, audio effects, spatial analysis with microphone arrays) and to extract features and descriptors for acoustic characterization and music information retrieval.
Making judgements
Given a relatively complex audio problem, students are able to analyze its goals, assumptions and requirements and to define autonomously a suitable algorithmic procedure, motivating the choice of methods and the estimation of parameters.

Fundamentals
physiology of the hearing system and psycho-perception of sound
elements of acoustics
basic audio analysis: time-frequency analysis with Short-Time Fourier Transform or filterbanks
sound processing tools: filtering, nonlinear processing, auralization filters
adaptive filtering: Wiener-Hopf filter, steepest descent, LMS
space-time (array) processing: microphone arrays, beamforming
feature extraction and analysis
overview of sound synthesis tools: modulated delay lines, tunable delay lines, digital waveguides
Applications
harmonic analysis: pitch tracking, vocoder, envelope tracking
sound modification: time warping (resampling), time and pitch scaling (tonal and rhythmic corrector)
modulated delay lines and sound effects for musical applications: flanger, chorus, distortions, etc.
sound reverberation: perceptual methods, physics-inspired methods, geometric methods (acoustic ray and beam tracing)
music information retrieval: feature-based analysis and classification of musical excerpts, playlist generation, mood extraction, etc.
adaptive sound processing and applications: echo cancellation, noise reduction, dereverberation, etc.
array processing: beamforming, acoustic source localization and extraction (demixing), acoustic room compensation

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i condizionato (a scelta del docente), individuale, senza revisioni periodiche, con presentazione/discussione finale
The assessment will be based on a written exam at the end of the course. The written exam consists of numerical exercises and theoretical questions. This will assign up to 31 points. Students achieving a score greater than or equal to 28 can develop an optional project. The project must be planned with the teacher, and it can provide up to 4 additional points. 30 cum laude will be assigned when the total score is higher than 31.
Type of assessment
Description
Dublin descriptors
Written test
Solution of numerical problems
· Time and frequency audio resolution
· Wiener filtering estimation
· Microphone arrays for direction of arrival estimation
Exercises focusing on design aspects
· Optimal design of windowing solution for audio signal processing
· Design of acoustic restoration systems
Theoretical questions on all course topics with open answer
· Elements of acoustics and psychoacoustics
· Sound analysis and synthesis tools
· Audio effects and interpolated delay lines
· Acoustic restoration and reverberation
· Feature extraction and classification
· Microphone arrays
1, 2
1, 2, 3
1, 2

Lecture notes and slides https://beep.metid.polimi.it/

The objective of the course is the development of a general and specific know-how on the estimation and control methods for the autonomous navigation of road vehicles. The course focuses on ground vehicles and, more specifically, on passenger cars and alike. The course deals with the fundamental problems of trajectory and speed-profile generation, trajectory-tracking, positioning and obstacle-detection, and provides an overview of the main sensing and SW-sensing technologies in autonomous cars.
The course aims to illustrate the basic sub-problems and technologies needed to develop a completely-autonomous road vehicle, with special attention on the estimation issues, and on the control strategies and algorithms.
The main focus will be on fully-autonomous vehicles (L4 and L5), but also conditional autonomy (L3) will be considered, with attention on human-factors (disengagement and re-engagement of human driver).
The course will present the classical sub-topics of autonomous vehicle design:

• Sensing and perception, estimation, localization
• Global planning
• Local planning
• Trajectory tracking
  These topics will be developed at different levels of detail, where the emphasis will be on estimation and control algorithms specific for road vehicles (passenger cars or alike). The case of extreme performance (driving at the very limit of the car), in a in-circuit environment will be also considered.

Dublin Descriptor
Expected Learning Result (Specific to the Course)
Knowledge and understanding
Understand the fundamental problems, methods, and technologies for autonomous vehicle navigation (L3-L5), with a specific focus on estimation issues and control strategies (e.g., trajectory generation, localization, obstacle detection).
Applying knowledge and understanding
Design and apply control and estimation algorithms to solve specific sub-problems in autonomous navigation, such as trajectory-tracking, local planning, and vehicle control at the limits of handling.
Making judgements
Critically analyze and evaluate trade-offs between different design solutions, control algorithms, and sensing technologies for autonomous passenger cars, including considerations for human-factor issues in L3 systems.
Communication skills
Communicate a complex control-design solution for a specific autonomous vehicle application (e.g., path following, speed profiling) in a synthetic and technically precise way.
Learning skills
Autonomously read, interpret, and understand specialized industrial technical documentation and research papers in the field of autonomous vehicle control and estimation.

The course will cover the main areas of estimation and control technologies for autonomous cars (sensing and localization, global and local planning, trajectory-tracking and HMI). More specifically, the course sub-topics are the following:
Overview of “ego” sensing technologies; basics of their signal-processing
Road-sensing and vehicle-states sensing for control design: estimation of road-tire available friction, lateral and longitudinal slips (used for local planning and trajectory tracking)
Map-based positioning using GNSS, IMU and road-markers
Trajectory tracking (lateral, longitudinal, and joint lateral-longitudinal).
Local planning: control-oriented generation of trajectories (obstacle-avoidance trajectories)
Global planning: control-oriented generation of trajectories (in-circuit optimal trajectories) and optimal speed-profile (for energy, comfort, speed optimization)
Partially/conditioned autonomy and human factors

Prova scritta facoltativa (a scelta dello studente), senza prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The exam will be a written test in one of the official available dates or a project (group or single). The exam consists of questions on the topics presented during the course. The project requires the development of techniques starting from the one seen in class.
Grading will be on the following basis: either 100% final project (group presentations with discussion with instructor) or 100% written test on the course topics.
The instructor reserves the right to hold an oral examination in cases where the written test does is not definitive in assessing the student's knowledge.
Type of assessment
Description
Dublin descriptor
Final project
Students are required to:
show their understanding of the main problems (and the corresponding solutions) in the field of control-systems design in vehicles;
use the knowledge acquired in the course to solve problems that they have not be exposed to in class.
select and describe the most appropriate tools for a given application;
be able to communicate in a oral presentation the solution to a specific application problem (modelling or analysis or control problem).
implement the solution they propose.
1,2, 3, 4, 5
Written test
show their understanding of the main problems (and the corresponding solutions) in the field of control-systems design in vehicles;
use the knowledge acquired in the course to solve problems that they have not be exposed to in class.
select and describe the most appropriate tools for a given application;
be able to communicate in a mixed text, graphical and mathematical way the solution to a specific application problem (modelling or analysis or control problem).
1, 2, 3, 4, 5

Matteo Corno, Lecture notes

Obiettivo della prima parte del corso è introdurre gli aspetti teorici delle basi di dati ed i linguaggi per la gestione e l’interrogazione dei dati in esse contenuti. La seconda parte del corso verte invece sui modelli concettuali, sulle metodologie e sulle tecniche per la progettazione di una base di dati a partire dai suoi requisiti. Superato il corso, lo studente sarà in grado di progettare e realizzare basi di dati di media complessità, e sviluppare applicazione che le utilizzino.

Descrittori di Dublino
Expected learning outcomes
Conoscenza e comprensione
Conoscere gli elementi basilari di un sistema per la gestione dei dati
Comprendere gli elementi sintattici e la semantica di un linguaggio di interrogazione
Conoscere i principali costrutti usati nel linguaggio SQL per definire e interrogare una base di sti relazionale
Comprendere i meccanismi di astrazione tipici della progettazione concettuale e logica dei dati
Capacità di applicare conoscenza e comprensione
Confrontare metodi formali di natura diversa e confrontare il loro potere espressivo
Comporre interrogazioni complesse a partire da interrogazioni semplici
Progettare gli schemi dei dati allo scopo di rappresentare efficacemente uno specifico problema
Autonomia di giudizio
Valutare la correttezza di una interrogazione relativamente ai fabbisogni informativi di un utente
Valutare le qualità di uno schema di dati
Confrontare i benefici e gli svantaggi di diversi progetti concettuali e logici
Capacità di apprendimento
Essere in grado di comprendere e apprendere nuovi paradigmi di accesso alle basi di dati
Essere in grado di confrontare e coniugare le conoscenze e competenze apprese nel contesto della progettazione dei dati con quelle dell’ingegneria del software

INTRODUZIONE AL CORSO
Il sistema informativo nell'azienda
Caratteristiche dei DBMS
TEORIA RELAZIONALE
Il modello relazionale dei dati, sua definizione (informale e formale) e concetti fondamentali
Algebra relazionale: operazioni unarie e binarie
Interrogazioni in algebra relazionale e loro ottimizzazione
Datalog: definizione, interrogazioni ricorsive, specifica di vincoli di integrità,
LINGUAGGI PER BASI DI DATI
Introduzione a SQL: standardizzazione di SQL, domini SQL, vincoli e semplice definizione di tabelle
SQL come DDL: integrità referenziale, definizione di schemi e loro modifica, cataloghi relazionali
Interrogazioni SQL semplici, interrogazioni con ordinamenti e raggruppamenti, i nterrogazioni SQL complesse
Comandi di modifica e viste in SQL
Aspetti evoluti del DDL (cenni): indici, controllo dell'accesso, viste e controllo dell'accesso, trigger, procedure, transazioni
PROGETTAZIONE DI BASI DI DATI
Fasi della progettazione
Le astrazioni nella progettazione dei dati
Il modello Entità-Relazioni: definizione, associazioni, identificatori e gerarchie, proprietà delle gerarchie
Il ciclo di progettazione: fasi di progettazione, strategie, qualità di un progetto concettuale
Progettazione logica: fasi del progetto logico, eliminazione delle gerarchie, gestione degli identificatori, gestione degli attributi, gestione delle associazioni. Normalizzazione (cenni).
Progettazione fisica (cenni)
Uso di un database commerciale (MySQL o PostgreSQL)

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
L’esame prevede una verifica scritta su tutti gli argomenti dell’insegnamento.
Modalità di verifica
Descrizione
Risultato di apprendimento perseguito
Prova scritta ed eventuale prova orale se richiesta dal docente
Esercizi di tipo progettuale:
· Formulazione di interrogazioni usando i linguaggi formali
· Formulazione di interrogazioni e vincoli di integrità usando SQL
· Progettazione concettuale e logica di una base di dati usando il modello relazionale dei dati
· Domande di carattere teorico sugli argomenti del corso
1,2,3,5
Le modalità e gli appelli di esame saranno comunque in accordo con quanto previsto dalla Scuola di Ingegneria Industriale e dell'Informazione, e riportato sul relativo sito Web.

Paolo Atzeni, Stefano Ceri, Piero Fraternali, Stefano Paraboschi, Riccardo Torlone, Basi di Dati (6 ed.), Editore: McGraw-Hill, Anno edizione: 2023, ISBN: 978-8838656545
Note: Vanno bene anche le edizioni precedenti del libro. Altri suggerimenti bibliografici possono essere reperiti sui siti dei singoli docenti
D. Braga, M. Brambila, A. Campi, Eserciziario di Basi di Dati, Editore: Esculapio, Anno edizione: 2006, ISBN: 88-7488-139-8
Note: Vanno bene anche altre edizioni dello stesso libro.

To make students understand the organizational impact of the core components of a company's information system and teach them how to design consistent organizational processes and information technology (IT). To provide a decision framework supporting IT investment management.
Far comprendere allo studente l'impatto organizzativo dei componenti più tradizionali del sistema informativo aziendale e renderlo capace di progettare congiuntamente processi organizzativi e sistemi informatici, misurando operativamente i benefici anche economici dei progetti informatici. Fornire un framework a supporto delle decisioni manageriali sugli investimenti in informatica.

Knowledge and understanding
The student will learn:

• coordination techniques of information management processes
• content of the operational database in service companies
• content of the operational database in manifacturing companies
• organization of information management processes in service and manifacturing companies
• impact of main IT choices on the organization in service and manifacturing companies
• concept of ERP
• decision framework for ERP software selection
  Applying knowledge and understanding:
  With the analysis of numeorus case studies, students learn how to apply the knowlede and techniques explained in the course to real business cases.
  Making judgements:
  With this course, students will learn how to:
• tackle the analysis and design of organizational processes
• understand the impact of a new technology on organizational processes
• jointly design organization processes and IT solutions
  Communication skills:
  The student will learn the language of IT managers.
  Learning skills:
  With numerous case studies and guest managers from various organizations, students will learn to take the managerial perspective on IT choices, by developing an awareness of the tight relationship between technical and organizational choices and overcoming the misconception of the "best" technical solution.
  Conoscenza e comprensione:
  A seguito del superamento dell'esame lo studente conosce:
• le tecniche di coordinamento dei processi di gestione dell'informazione
• il contenuto del database operativo delle aziende di servizi
• il contenuto del database operativo delle aziende manifatturiere
• l'organizzazione dei processi di gestione dell'informazione nelle aziende di servizi e manifatturiere
• l'impatto delle principali scelte informatiche sull'organizzazione dell'informazione nelle aziende di servizi e manifatturiere
• il concetto di ERP
• un framework decisionale per la software selection nel caso dei sistemi di gestione d'impresa
  Capacità di applicare conpscenza e comprensione:
  Attraverso l'analisi di numerosi casi di studio, ed a seguito del superamento dell'esame, lo studente acquisisce la capacità di applicare le conoscenze e le tecniche apprese a casi reali, per quanto di complessità limitata.
  Autonomia di giudizio:
  A seguito del superamento dell'esame, lo studente è in grado di:
• affrontare l'analisi di processi organizzativi
• comprendere l'impatto di una nuova tecnologia su tali processi
• disegnare congiuntamente processi e soluzione tecnica
  Abilità comunicative:
  Lo studente apprende il linguaggio proprio dei sistemi informativi aziendali.
  Capacità di apprendimento:
  Anche grazie ai casi di studio analizzati durante il corso e alle testimonianze aziendali, lo studente è in grado di assumere la prospettiva manageriale sulle scelte tecnologiche, sviluppando la consapevolezza del legame fra scelte tecniche e organizzazione e superando l'idea della soluzione tecnica ottima.

The course provides a methodology to align IT design choices with business objectives. The course defines the concept of IT arhitecture, classifies the fundamental IT design choices and maps these choices onto the IT architecture from both a software and an infrastructural point of view. The IT architecture is then discussed for the manufacturing, utilities and financial services industries, by considering both intra- and inter-organizational processes along the industry value chain (e-business). On this basis, the course provides the methodological tools to support the analysis of organizational requirements. Particularly, in the area of executive information systems, the KPI approach is discussed.
Based on the concept of IT architecture, the course provides a functional map of Enterprise Resource Planning (ERP) systems, by distinguishing core and extended functionalities. The course explains how information systems have evolved over time and how ERPs represent the result of a continuous and still ongoing functional integration process. The course starts by reviewing the literature of the information perspective of organisational theory. This literature provides a framework to understand the organisational change caused by ERP projects. Then, the course discusses the core functional areas of ERP systems: accounting&finance, operations, and management&control. The course will be based on lectures and case-study discussion sessions.
Il corso si propone di fornire gli strumenti metodologici necessari per allineare le scelte progettuali informatiche con gli obiettivi delle imprese. A tal scopo, il corso si propone di classificare e organizzare le scelte progettuali fondamentali definendo l’architettura del sistema informativo di un’organizzazione, dal punto di vista hardware, software e infrastrutturale. Tale architettura viene poi approfondita per i fondamentali processi organizzativi di produzione, marketing e finanza e per i processi inter-organizzativi di gestione di clienti e fornitori (e-business). Su questa base, viene fornita una metodologia di analisi e specifica dei requisiti informativi di un’impresa.

Prova scritta obbligatoria, con prove in itinere
Prova orale condizionata (a scelta del docente)
Written exam (multiple choice with open questions). Learning results according to Dublin descriptor: 1, 2, 3, 5.The exam may be complemented by, or replaced with, an oral examination at the professor's discretion.
Esame scritto (test a scelta multipla con domande aperte). Risultato di apprendimento perseguito secondo il descrittore di Dublino: 1, 2, 3, 5. L'esame può essere integrato da, oppure sostituito con, una prova orale, a discrezione del docente.

Course slides and recordings. https://webeep.polimi.it

The course is about how to reliably transmit (or store) information over a faulty communication channel (or storage device). Leveraging on the knowledge of the error statistics of the faulty medium, the focus is on the information processing that leads to error correction.
The goal of the course is to teach students the techniques for designing state of the art error correcting codes for the classical communication media, and how error correction can be used to face the new challenges posed by quantum communication and computation.
Since the application of these concepts is a fundamental aspect in every modern communication system, another goal is to establish concrete links of these concepts with advanced technologies used to process the information in different systems. To this aim, a project might be assigned to apply the concepts and principles learned during the lessons.
Besides the above objectives, the course implements “Collaborative Classes”, which is a new initiative launched by Politecnico di Milano (PoliMi) to internationalize teaching. Collaborative Classes represent an innovative teaching and learning paradigm that entails online collaboration between a professor at Politecnico di Milano and a professor at a foreign university, accompanied by their respective classes.
For the Classical and Quantum Error Correction course, a collaboration has been established with the Technical University of Munich (TUM) in the form of joint lectures (1 CFU = 10 hours) with the support of digital platforms. After the lectures, professors will assign projects to students to be carried out on a voluntary basis in mixed groups of two students (one from Polimi and one from TUM).

1 - Knowledge and understanding
After the end of the course, students should be able to:

• define quantitative measure of information
• know theoretical and methodological skills related to error correction
• understand properties of linear block codes, especially LDPC and Polar codes
• understand the new challenges in designing codes for quantum computing
  2 - Applying knowledge and understaning
  After the end of the course, students should be able to:
• understand and solve complex problems in different interdisciplinary areas
• integrate and individually find and re-adapt known solutions to growing problems
• apply methodologies to design capacity-achieving error correcting codes
• summarize and present the results achieved during the analysis and specification activities
  3 - Making judgements
  After the end of the course, students should be able to:
• define mathematical models for the purpose of error correction in communication systems
• evaluate concrete schemes for error correction in data communication and storage

1. Introduction
Introduction to the course and to classical error correction. Binary linear block codes: encoding and decoding. Concatenated codes.
2. Modern codes
Codes on Graphs. Low-density parity-check (LDPC) codes. EXIT charts and weight distributions for LDPC ensembles.
3. Polar codes
Code construction. Efficient encoding. Successive cancellation decoder. Code design. List decoders. Polarization effect.
4. From classical to quantum error correction
Introduction to quantum states, channels and measurements.
5. Principles of quantum error correction
Quantum redundancy, stabilizer measurement, and syndrome extraction. The surface code. Practical considerations for quantum error correction.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), individuale, senza revisioni periodiche, con presentazione/discussione finale
The assessment will consist in a written exam followed by oral examination.
The written part consists of numerical exercises (2 or 3) and open questions (2 or 1) on the topics covered in the course. The written exam can assign up to 30 points, which constitute the starting point for the final mark after the oral. The mark 30 cum laude will be assigned when the total score after the oral is equal to 33.
A voluntary project can replace the oral. In this case the written exam will assign up to 23 points, which constitute the starting point for the final mark after the presentation of the project laboratory. The project can assign up to 10 points. The mark 30 cum laude will be assigned when the total score after the presentation of the project laboratory is equal to 33.

Sarah J. Johnson, Iterative Error Correction. Turbo, Low-Density Parity-Check and Repeat-Accumulate Codes., Editore: Cambridge University Press, Anno edizione: 2010
T. Richardson and R. Urbanke, Modern Coding Theory, Editore: Cambridge University Press, Anno edizione: 2008
Quantum error correction: an introductory guide https://arxiv.org/abs/1907.11157v1
Daniel Gottesman, An Introduction to Quantum Error Correction and Fault-Tolerant Quantum Computation https://arxiv.org/pdf/0904.2557

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

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

This multidisciplinary course will introduce students to the fundamental concepts of molecular communication and its role in understanding how information propagates in plants when used as biosensors for environment monitoring. Green Infrastructures are trees, shrubs, herbs, green walls, green roofs, etc. The main goal of the course is to introduce students to the main functional models based on ideas from molecular communication to understand the extent to which plants can be used to sense and collect information from the surrounding environment. Molecular communication is an emerging bio-inspired communication field where molecules are used for encoding, transmitting, and receiving information, in the same way that living cells communicate. The course will give the chance to be initiated into a very exciting cutting-edge research field, which will soon influence many diverse research fields, such as engineering, chemistry, and biology.

1 - Knowledge and understanding
At the end of the course, students should be able to:

• know the different options to realize communication at the nanoscale among nano-precise entities, or nanomachines
• know the basic principles of propagation of information in plants
• develop an interdisciplinary approach that brings together expertise from plant biology, system modelling, and chemistry
  2 - Applying knowledge and understanding
  At the end of the course, students should be able to:
• understand and solve complex problems in different interdisciplinary areas
• integrate and individually find and re-adapt known solutions to existing problems
• summarize and present the results achieved during the analysis and specification activities
  3 - Making judgements
  After the end of the course, students should be able to:
• define mathematical models by considering the informational aspects of a system and the related measures

1. Course Presentation
2. Introduction to Biological and Molecular Communication (cell communication pathways; information storage in biology and cell signaling; molecular communication theory for biochemical systems; classification of propagation models; characterization of the Brownian motion and its simulation; diffusion in 1D and 3D spatial dimensions; biological communication systems: how do they work in nature; analysis of biological communication systems). Propagation of Information in Plants (scales of molecular communication; molecular communication in plants: intracellular, intercellular, and inter-organ; plasmodesmata modeling; Xylem and phloem modeling: action potentials and variational action potentials; communication among complex organisms; response to external stimuli; mechanosensitive signaling in plant modeling). Biosensors and Molecular Communication Applications.
3. Computer-based activities.

Prova scritta facoltativa (a scelta dello studente), senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
The assessment consists of an interdisciplinary project that applies course concepts to green infrastructures. Knowledge and understanding and applied skills are evaluated through the design and preparation of a technical report. Judgement is assessed through the justification of methodological choices. Communication abilities are evaluated through an oral presentation, while learning skills emerge from the integration and autonomous use of course topics in the project. Students may also choose to take an optional written examination at the end of the course covering the course topics.
The final grade is determined according to the following criteria: knowledge and understanding of the course topics (up to 8 points); ability to apply the acquired knowledge in the project (up to 8 points); judgement, including the justification of methodological choices (up to 6 points); communication abilities, assessed through the written report and oral presentation (up to 4 points); and learning skills, assessed through the autonomous integration and application of the course topics (up to 4 points).

Course slides and handouts https://webeep.polimi.it

Il corso intende fornire agli studenti le conoscenze e gli strumenti necessari per progettare e pianificare reti di comunicazione. La prima parte del corso si focalizza sulle reti ottiche: vengono presi in considerazione diversi tipi di reti (access, metro e core). Gli approcci progettuali si basano sulla modellazione matematica (programmazione lineare intera) e gli approcci euristici sono discussi. Nella seconda parte del corso viene trattata la teoria del traffico per la progettazione di reti a commutazione di circuiti, e in particolare per risolvere problemi di dimensionamento (Erlang, Engset) e per studiare metodologie avanzate per la modellazione del traffico. Sono discussi casi di studio specifici per fornire esempi dei diversi approcci progettuali.

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

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

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

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

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

The course explains the basic theory of Computer Graphics, using several programming examples with the OpenGL API. The goal is to show how to design, engineer and develop computer graphics software for various applications (scientific visualisation, simulation, gaming)

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

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

1. Introduction to Information System Security

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

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

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

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

Knowledge and understanding (DD1):

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

Hardware infrastructure of datacenters:

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

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

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

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

See the program of the module in the master course

See the program of the module in the master course

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

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

The course, held substantially in presence in the laboratory-classroom, aims to provide students the basic skills to learn using modern instrumentation for data acquisition by programmable and reconfigurable virtual systems, according to user requirements for different applications. Methods to process and analyze statistically large data sets, and transmit and save them, will also be discussed.
The course includes, in addition to the traditional classroom lectures, tutorials and project workshops (not mandatory) where students will study in deep specific topics, addressing different issues. The verification of the knowledge acquired is based on written tests during the course and on the evidence of recovery. The final evaluation will be based on a final exam where the students need to develop a custom application.

Knowledge and understanding (DdD1)
Students will learn how to:
what is the modern instrumention and the "measurement chain" used to acquire data from environment;
which are the fundamentals rules used to acquire date without losing informations;
the basic structures of a data acquisition programming language;
how to manage data set: elaborate, manipulate and save it in different kind of format.
Applying knowledge and understanding (DdD2)
Given specific project cases, students will be able to:
Detail the corresponding requirements;
To understand the requirements and to design the correct structure to manage the data;
Analize and comment on specific architectural choices;
Develop and test code fulfilling the design specifications.
Lifelong learning skills (DdD3 - DdD5)
Students will learn how to develop a real project collaborating with other students in the development of the project;
Students choosing to work on the research project, will learn how to conduct a research activity on some specific aspect engineering, developing some innovative solution and some empirical experiment.

Introduction to measurement instrumentation. The analog-to-digital conversion; resolution and accuracy limits for numeric values; effective number of bits of the A/D converter and the dithering techniques to increase resolution. Description of the Data AcQuisition systems (DAQ) performances and limits; mode of installation and use of PCI cards for the acquisition channel and the generation of control signals. Introduction to the concepts of data interpolation and fitting. How to count something: the counters.
Virtual instrumentation. The measurement instruments available on the market. The LabVIEW graphical environment. The front panel. The block diagram. Control palette and function palette, constants, indicators, and controls. How to display data. Nodes and lines. Loop structures, case, sequence. Array and cluster. SubVI. Logic functions. Development of example programs. Debugging. Waveform Data. File management. Storing and recalling data into/from a file. Development of an algorithm for the measurement of the signal frequency.
Elements of hardware and software. Data acquisition cards. Specifications. Acquisitions with buffering. Local and global variables. Built-in elements. Practical examples and their implementation.
Design and implementation of Virtual Instruments (VIs). Design and testing of a “digital voltmeter”. The acquisition section. The digital processing section. Indirect measures based on signal processing. Multichannel acquisition. Practical aspects of the multiplexer. Signal Conditioning. Development of some practical applications. Processing of multi-channel data. Testing and calibration of a virtual instrument.
Applications. Output functions. Arbitrary signal generator. AD and DA converters total error evaluation. Use of the counters. Control gate. Acquisitions for long times. Use of virtual instruments through the Internet. Queues and semaphores to administrate data.
Instrumentation programming. Communication interfaces (RS232, GPIB). The driver for stand-alone instrumentation: VISA. Communication with oscilloscopes and function generators via various communication interfaces.
Laboratory activities
Creation of virtual instruments for the acquisition from the field of experimental data: processing, graphical representation, data logging and statistical analysis (average, moving average, variance) of numerical data. Realization of programs for the measurement of analog and digital signals. Generation of analog and digital signals. Development of specific projects for students. Every student of Data Acquisition Systems held 32 hours of laboratory divided into 8 sessions during each session will be carried out individually but always under the guidance of the professor, the implementation of programs progressively more advanced. The details of the evaluation process of the workshop will be presented during the first hours of lesson of the course.

Prova scritta obbligatoria, con prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
Exams assign a score of thirty, from 0 up to the maximum grade of 30 cum laude, extracting it from the results achieved with the work in the classroom and with the project/exam in the presence, and the outcome of a short theory written test. For anyone who wants to further improve the rating will be possible to perform an oral or develop an optional data acquisition project.
In any case, examination procedures will be consistent with the rules adopted by the Politecnico di Milano and the School of Eng. Industrial and Information and the dates of each exam will be communicated well in advance.
Type of assessment
Description
Dublin descriptor
Written test
Theory of acquisition:
· Knowledge of the fundamentals of the theory of acquisition;
· Knowledge of the state of the art of measurement instrumentation;

• Knowledge of fundamentals of interpolation and fitting.
  Solution of acquisition problems
  · Development of the right structure of acquisition;
  · Estimation of the computational costs;
  · Managing data and how to elaborate them;
  · Software testing debugging and analysis exercises.
  Exercises focusing on design aspects
  · Definition of the architecture for a distributed measurement system
  · Organisation of different parallel tasks inside a project.
  1,2
  1, 2, 3, 4, 5
  Assessment of laboratory artifacts (optional)
  Assessment of the design and experimental work of acquisition developed by students individually
  2, 3, 4, 5

C. Svelto, R. Ottoboni, M. Norgia, A. Pesatori, Misure. Dai fondamenti alla strumentazione, Editore: Società Editrice Esculapio - Bologna, Anno edizione: 2022, ISBN: 8893853043
Note: Costo circa 19 Euro. Disponibile presso la Biblioteca di PoliMI.
M. Norgia, A. Pesatori, C. Svelto, "Esercizi di Misure", Editore: Società Editrice Esculapio - Bologna, Anno edizione: 2006, ISBN: 88-7488-150-9
Note: Costo circa 15 Euro. Disponibile presso la Biblioteca di PoliMI.

The course introduces the basic data quality concepts, models, and techniques. It aims to provide the tools to assess and improve the quality of data used in different applications and contexts to avoid errors and inefficiencies. It especially focuses on the role of data quality as a fundamental preprocessing activity for cleaning and preparing data to be analyzed with different analytics applications (e.g., descriptive, predictive analysis).
The goal of the course is to enable students to select and use the methods that are necessary to manage poor data quality in the data analysis pipeline to obtain valuable and reliable results.

Dublin descriptors
Expected learning outcomes
1. Knowledge and understanding
The students will learn:
The main concepts related to data quality and data preparation
To Identify the data quality issues in a data source
To design a data preparation pipeline
2. Applying knowledge and understanding
At the end of the course, the student will be able to:
Profile a dataset by gathering its main characteristics and issues
Evaluate the quality level of the dataset
Design a data preparation pipeline to solve data quality issues
Understand the most suitable data preparation actions on the basis of the analysis to perform

Poor data quality is related to data errors, inconsistencies or delays that often negatively affect the output of the processes (from business processes to pure computational process). Such issue is perceived as important in Data Analytics field since an adequate quality level of the input data increases the reliability and value of the obtained results. The course will introduce the techniques and procedures to assess and improve data quality considering different data sources (e.g., traditional databases, data streams, unstructured data sources).
This course includes the following topics:
Basic elements of Data Quality: description of the main dimensions and related metrics in different data sources
Data profiling techniques: methods and tools for analyzing the data source, such as collecting descriptive statistics, identifying data types and recurring patterns, counting null and distinct values, and detecting functional dependencies.
Data quality assessment techniques: description of objective assessment methods and design of data quality controls.
Data Quality improvement techniques: methods and tools for data cleaning (standardization, detection, and imputing of missing values, error localization and correction, outliers detection, duplicate detection) and process-based improvement.
Data Quality issues in data fusion activities: techniques for duplicate detections (probabilistic and empirical techniques) and conflict resolution
Data Quality assessment in the uncertainty analysis; evaluation of the variability of the analytics results due to the poor quality of the input data,
Data Governance and data quality: description of the way in which organizations should assign people roles and responsibilities, define the processes and the technologies for managing data and especially, data quality aspects.
In the course seminars, some of the described techniques will be implemented by using Python.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
Written exam: Theoretical questions on the course topics
Mandatory project in Python aimed to apply all the concepts presented in the course to a real-world dataset.
Dublin descriptors 1,2,3,5

Carlo Batini, Monica Scannapieco, Data and Information Quality - Dimensions, Principles and Techniques. Data-Centric Systems and Applications, Editore: Springer, Anno edizione: 2016, ISBN: 978-3-319-24104-3
Arkady Maydanchik, Data Quality Assessment, Editore: Technics Publications, Anno edizione: 2012
Ihab F. Ilyas, Xu Chu, Data Cleaning, Editore: Association for Computing Machinery, New York, NY, United States, Anno edizione: 2019, ISBN: 978-1-4503-7152-0
Ziawasch Abedjan, Lukasz Golab, Felix Naumann, Thorsten Papenbrock, Data Profiling, Editore: Morgan & Claypool, Anno edizione: 2018, ISBN: 978-1681734460
Craig Fisher, Eitel Lauria, Shobha Chengalur-Smith, Introduction to Information Quality, Editore: MIT Press, Anno edizione: 2012

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

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

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

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

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

The course introduces Data Mining and overviews the most important algorithms used in this field. It consists of three main parts. The first part introduces the basics of data mining, including the representation of data, their exploration, and preparation. The second part delves into the four main areas, association rules, clustering, regression, and classification. Finally, we focus on advanced topics such as time series, anomaly detection, explainability, and AutoML.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will learn to:

• Structure a data mining pipeline
• The fundamental characteristics of the most important algorithms used in all the major steps of the pipeline
• Identify architectural styles and patterns
  Applying knowledge and understanding
  Given specific data mining process, students will be able to:
• Analize and comment on specific architectural choices
• Highlight possible criticalities
• Identify existing biases
• Apply the theory to assess the reliability of the results produced
  Making judgements
  Given a data mining task, students will be able to:
• Analyze and understand the goals, assumptions and requirements associated with that task
• Select the best environment to implement each step of the data mining process
• Select the best infrastructure
  Communication
  Students will learn to:
• Analyze the design choices that a data analytics solution entails
• Present and critically discuss the results of a data mining process
  Lifelong learning skills
  Students will learn
• how to tackle problems in real-world scenario and
• how to critically analyze a proposed solution and the result it produced.

Introduction to Data Mining
Data representation
Text representation and embeddings
Data exploration and visualization
Association rules
Clustering

• Hierarchical
• Representation-based
• Density-based
  Regression
  Classification
• Logistic regression
• Naive Bayes and Bayesian Belief Network
• k-nearest neighbor
• Decision trees
• Ensemble methods
  Advanced Topics
• Time series
• Anomaly detection
• Explainability
• Blackbox optimization
• AutoML

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The assessment is based on a written, closed-book test at the end of the course, taken in person, in class. It typically consists of questions on the course topics. It can include conceptual questions and exercises requiring a significant modeling effort. The test is worth a maximum of 30 points; three additional bonus points are awarded based on the time taken to submit the exam. 30 cum laude is assigned when the total score is 32 or above. Students have approximately 2 hours to answer the test. Students can take the test at any exam session during the year. The written test can be taken only in person. The discussion of the graded exams is also in person only. Instructors can summon students who passed the written test for an additional (in-person) oral exam covering the entire course program.
Type of assessment
Description
Dublin descriptor
Written test

• Solution of numerical problems
• Computation of score functions used in data mining algorithms- Execution of fundamental algorithms using small datasets
• Interpretation of code fragments to understand what they compute
  Exercises focusing on design aspects
• Evaluation of trade-offs between different proposed solutions
• Design of data mining pipelines for a given scenario
• Critical comparison of existing methods
  2,3,4,5

Mohammed J. Zaki and Wagner Meira, Jr.,, Data Mining and Analysis: Fundamental Concepts and Algorithms (Second Edition), Editore: Cambridge University Press, Anno edizione: 2020, ISBN: 978-1108473989
Note: Freely available at http://www.dataminingbook.info/
Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Anuj Karpatne, Introduction to Data Mining, Global Edition (2nd Edition), Editore: Pearson, Anno edizione: 2016, ISBN: 978-0273769224 https://www.pearson.com/store/p/introduction-to-data-mining-global-edition/P200000005534/9781292456133
Christoph Molnar, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, ISBN: 979-8411463330 https://christophm.github.io/interpretable-ml-book/
Note: Free browsable version available online.

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

DdD1: Knowledge and understanding
Students will learn how to:

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

The course is roughly divided in five parts:

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

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

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

The goal of the course is to make students familiar with concepts of communication and information theory and with their application to the analysis and design of complex telecommunication systems. Moreover, the course provides students with the tools that are needed to understand and apply advanced technologies that are presently used in telecommunication systems. Also, understanding, analyzing and designing optical and radio communication systems is one of the objectives of the course.
The course is finalized to teach students advanced concepts of digital communication as equalization, predistortion, synchronization, MIMO systems. The goal is to make the student familiar with these concepts and with their application to digital transmitters and receivers of radio and optical communication systems. Design of equalization algorithms in the presence of dispersive channels, predistortion algorithms for memoryless non-linear channels, carrier and timing synchronization systems, MIMO receivers are among the goals of the course. Also, learning the mathematical tools needed to understand how these systems work and how their performance can be analyzed is among the objectives of the course. The goal of using commercial software tools as Simulink and Matlab to encode software for the simulation of communication systems will be achieved by laboratory sessions.
Blended learning and flipped classroom will be adopted for about 2/5 of the course (2 CFU) to show the relationship between digital communication and other courses in the field of communication and signal processing and to gain the ability of autonomously develop software programs that put together concepts from different courses applying them to a real-world project.

Dublin Descriptors
Expected learning outcomes
1 - Knowledge and understanding
Understand the general principles of advanced topics of digital communication. Analyze and understand the requirements associated to a communication system in terms of equalization, predistortion, synchronization.
2 - Applying knowledge and understanding
Identify and define at the block-diagram level a proper equalization, predistortion, synchronization scheme for the most common modulation schemes. Encode software by common commercial tools as Matlab and/or Simulink to simulate the chain of transmitter, channel, receiver of data communication systems.
3 - Making judgements
Judge if a given equalization, predistortion, synchronization scheme is appropriate for a practical radio or optical communication system. Recognize the design space and the degrees of freedom that can be exploited to reach given objectives in terms of performance of equalization, predistortion, synchronization, with specific application to practical radio and optical communication systems.
4 - Communication
Communicate to engineers in technical documents and presentations about the analysis and design of equalization, predistortion, synchronization, MIMO systems using the appropriate mathematical tools as Fourier analysis, probability theory, calculus and matrix algebra. Communicate to non-engineers by block diagrams and writings about the needs and constraints that advanced telecommunication systems are subject to in terms of receiver performance.

Equalization. Wiener’s method. Discrete-time AWGN model, FIR and unconstrained linear equalization, adaptive equalization, decision feedback equalization, channel capacity with decision feedback equalization. Maximum likelihood sequence detection by the Viterbi algorithm. Maximum a posteriori probability sequence detection by the BCJR algorithm.
Precoding. Tomlinson-Harashima precoding, partial response systems, examples.
Predistortion. AM-AM and AM-PM characteristic of power amplifiers. Adaptive predistortion of memoryless power amplifiers.
Elements of MIMO transmission. The MIMO principle, the MIMO Gaussian channel, detection for the memoryless MIMO channel, detection for the MIMO channel with memory.
Phase-lock loop. Continuous-time PLL, discrete-time PLL, first-order and second-order loop; common phase detectors, design of the loop filter in the presence of phase noise.
Carrier and timing recovery. Decision-directed phase detector, S-curve, power of M phase detector, differential encoding, feed-forward and feed-back carrier recovery. Square-law timing detector, Gardner detector; digital re-sampling.
Laboratory activities. Use of Simulink and Matlab for simulation of digital communication systems.

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
At the exam, the student will be required to demonstrate the achievement of the learning objectives. The exam can be taken in any one of the July, September, February sessions. The exam consists in a written part based on a question about the theory, in an oral part based on the theory and on the written exam. The oral part can include an optional (up to the student) presentation of the laboratory activities. The student will be asked to answer questions regarding the theory, to draw block diagrams of digital communication systems and to explain them by writings and equations, to compute system performance in terms of error probability and/or mean square error, to explain and comment on the code encoded during the laboratory activity (optional). The written part is finalized to check DD1,DD2,DD3. The oral part is finalized to check DD1,DD2,DD3,DD4.
The written plus oral can assign up to 26/30 points. Optional project (code plus written report) based on the laboratories: 4/30 points + possibility of laude.

Barry Lee Messerschmitt, Digital Communication
Notes of the teacher https://beep.metid.polimi.it/

The goal of the course is to make students familiar with concepts of communication and information theory and with their application to the analysis and design of complex telecommunication systems. Moreover, the course provides students with the tools that are needed to understand and apply advanced technologies that are presently used in telecommunication systems. Also, understanding, analyzing and designing optical and radio communication systems is one of the objectives of the course.
The course is finalized to teach students the basics of digital communication as bit rate and symbol rate, bandwidth, filtering of data signals, basics concepts of information theory, modulation and coding. Design of the transmit filter and of the receive filter in practical cases of interest, calculation of the limits of communication in terms of channel capacity, selection of the proper modulation scheme according to the constraints imposed by the system, performance evaluation of modulation and coding schemes are among the goals of the course.

Dublin Descriptors
Expected learning outcomes
1 - Knowledge and understanding
Understand the general principles of data transmission in digital communication systems, analyze and understand the requirements associated to a communication system in terms of signal bandwidth, filtering, modulation, coding.
2 - Applying knowledge and understanding
Identify and define at the block-diagram level a proper filtering, modulation and coding scheme for the most common channel models, compute the performance of transmission systems in terms of error probability for the most common channel models.
3 - Making judgements
Judge if a given filtering, modulation and coding scheme is appropriate for a practical radio or optical communication system. Recognize the design space and the degrees of freedom that can be exploited to reach given objectives in terms of performance of filtering, modulation and coding, with specific application to practical radio and optical communication systems.
4 - Communication
Communicate to other engineers with presentations and documents about filtering, modulation and coding schemes using the appropriate mathematical tools as Fourier analysis, probability theory, calculus and matrix algebra. Communicate to non-specialists the requirements, the constraints and the features of communication systems using graphical representation (block diagrams) and writings.

Representation of Signals. Geometric representation of signals, Schwarz inequality, projection onto the signal space, Gram-Schmidt orthogonalization, geometric representation of noise, signal-to-noise ratio, matched filter. Maximum a Posteriori (MAP) detection and Maximum Likelihood detection in memoryless systems, minimum squared distance detection, correlation detection. Baseband equivalent of passband signals.
Limits of Communication. Entropy, equivocation, mutual information. Capacity of the discrete-time channel, capacity of the AWGN channel, capacity of the channel with coloured Gaussian noise, water filling.
Modulation. Nyquist filter. QAM and PSK modulation. Error probability, union bound. Law of 6 dB/bit for PSK, law of 3 dB/bit for QAM, back to the AWGN capacity formula. Modulation based on orthogonal signals, examples, performance evaluation. OFDM, cyclic prefix, frequency domain equalization for OFDM.
Binary Channel Codes. Channel capacity with a power constraint, examples of binary block codes, performance of soft and hard decoding. Generator matrix and parity check matrix, generator polynomial and parity check polynomial. Trellis representation of the code and trellis decoding. Convolutional codes, trellis decoding of convolutional codes, performance of convolutional codes. Concatenated coding.
Codes in the signal space. Multidimensional constellations, lattices, lattice partitions.
Exercise activity. Exercises on performance evaluation of communication systems.

Prova scritta obbligatoria, con prove in itinere
Prova orale obbligatoria
At the exam, the student will be required to demonstrate the achievement of the learning objectives. There are two types of exam. Students are strongly encouraged at least to try with Type 1.
Type 1: midterm call. Three written checks of your competence during the classes, about 30 minutes each. Each one of the checks will check DD1, DD2, DD3. First check: chapters 1-3 of the handouts, at the third week. Second check: chapter 4-6 of the handouts, at the fifth week. Third check: chapters 7-8 of the handouts, at the seventh week. Each check can include an exercise and an open question. If you are not sufficient on at least 2 over 3 checks, then you fail with Type 1 and are forced to take Type 2. If you are sufficient on 2 over 3, you gain a bonus of 2 points. If you are sufficient on 3 over 3, you gain a bonus of 5 points. You will have a feedback about the results on the next week after the check. The oral is at the midterm call only. The oral discussion is finalized to the check of DD1,DD2,DD3,DD4. It starts with the discussion of your checks. If after the discussion you are sufficient, I propose a grade and you have two options: take the proposed grade or ask for a question with the aim of improving the proposed grade (of course, improving is not guaranteed).
If in your study plan you have DC1 (5 credits) you can take the laude if 3 over 3 checks are sufficient and if you ask for the oral question, taking a total of at least 33 points.
If in your study plan you have the full DC (10 credits) and the sum of your points on DC1 exceeds 30 points, the exceeding points will be counted for the full DC (see after for the rules).
If in your study plan you have DC1 and DC2 separately, the exceeding points possibly gained on DC1 will not be counted for DC2, but you can take the laude on DC1 as explained before.
Type 2: standard exam, other calls than the midterm one. Written exam (90 minutes) based on exercises and on a question on the theory. The written is finalized to check DD1, DD2, DD3. You will have a feedback about your written exam only at the oral. Oral exam based on the discussion of the written and on questions on the theory. The oral is finalized to check DD1, DD2, DD3, DD4. Maximum 30 points, no possibility of laude.

Barry Lee Messerschmitt, Digital Communication
Notes of the teacher https://beep.metid.polimi.it/

The goal of the course is to make students familiar with concepts of communication and information theory and with their application to the analysis and design of complex telecommunication systems. Moreover, the course provides students with the tools that are needed to understand and apply advanced technologies that are presently used in telecommunication systems. Also, understanding, analyzing and designing optical and radio communication systems is one of the objectives of the course.
The course is finalized to teach students advanced concepts of digital communication as equalization, predistortion, synchronization, MIMO systems. The goal is to make the student familiar with these concepts and with their application to digital transmitters and receivers of radio and optical communication systems. Design of equalization algorithms in the presence of dispersive channels, predistortion algorithms for memoryless non-linear channels, carrier and timing synchronization systems, MIMO receivers are among the goals of the course. Also, learning the mathematical tools needed to understand how these systems work and how their performance can be analyzed is among the objectives of the course. The goal of using commercial software tools as Simulink and Matlab to encode software for the simulation of communication systems will be achieved by laboratory sessions.
Blended learning and flipped classroom will be adopted for about 2/5 of the course (2 CFU) to show the relationship between digital communication and other courses in the field of communication and signal processing and to gain the ability of autonomously develop software programs that put together concepts from different courses applying them to a real-world project.

Dublin Descriptors
Expected learning outcomes
1 - Knowledge and understanding
Understand the general principles of advanced topics of digital communication. Analyze and understand the requirements associated to a communication system in terms of equalization, predistortion, synchronization.
2 - Applying knowledge and understanding
Identify and define at the block-diagram level a proper equalization, predistortion, synchronization scheme for the most common modulation schemes. Encode software by common commercial tools as Matlab and/or Simulink to simulate the chain of transmitter, channel, receiver of data communication systems.
3 - Making judgements
Judge if a given equalization, predistortion, synchronization scheme is appropriate for a practical radio or optical communication system. Recognize the design space and the degrees of freedom that can be exploited to reach given objectives in terms of performance of equalization, predistortion, synchronization, with specific application to practical radio and optical communication systems.
4 - Communication
Communicate to engineers in technical documents and presentations about the analysis and design of equalization, predistortion, synchronization, MIMO systems using the appropriate mathematical tools as Fourier analysis, probability theory, calculus and matrix algebra. Communicate to non-engineers by block diagrams and writings about the needs and constraints that advanced telecommunication systems are subject to in terms of receiver performance.

Equalization. Wiener’s method. Discrete-time AWGN model, FIR and unconstrained linear equalization, adaptive equalization, decision feedback equalization, channel capacity with decision feedback equalization. Maximum likelihood sequence detection by the Viterbi algorithm. Maximum a posteriori probability sequence detection by the BCJR algorithm.
Precoding. Tomlinson-Harashima precoding, partial response systems, examples.
Predistortion. AM-AM and AM-PM characteristic of power amplifiers. Adaptive predistortion of memoryless power amplifiers.
Elements of MIMO transmission. The MIMO principle, the MIMO Gaussian channel, detection for the memoryless MIMO channel, detection for the MIMO channel with memory.
Phase-lock loop. Continuous-time PLL, discrete-time PLL, first-order and second-order loop; common phase detectors, design of the loop filter in the presence of phase noise.
Carrier and timing recovery. Decision-directed phase detector, S-curve, power of M phase detector, differential encoding, feed-forward and feed-back carrier recovery. Square-law timing detector, Gardner detector; digital re-sampling.
Laboratory activities. Use of Simulink and Matlab for simulation of digital communication systems.

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
At the exam, the student will be required to demonstrate the achievement of the learning objectives. The exam can be taken in any one of the July, September, February sessions. The exam consists in a written part based on a question about the theory, in an oral part based on the theory and on the written exam. The oral part can include an optional (up to the student) presentation of the laboratory activities. The student will be asked to answer questions regarding the theory, to draw block diagrams of digital communication systems and to explain them by writings and equations, to compute system performance in terms of error probability and/or mean square error, to explain and comment on the code encoded during the laboratory activity (optional). The written part is finalized to check DD1,DD2,DD3. The oral part is finalized to check DD1,DD2,DD3,DD4.
The written plus oral can assign up to 26/30 points. Optional project (code plus written report) based on the laboratories: 4/30 points + possibility of laude.

Barry Lee Messerschmitt, Digital Communication
Notes of the teacher https://beep.metid.polimi.it/

The primary objective of the course is to prepare students to address the challenges associated with the development of state-of-the-art digital electronic systems.
To this end, the course provides not only theoretical and methodological knowledge, but also advanced practical skills in the implementation of digital processing architectures, with particular emphasis on programmable hardware devices such as Field-Programmable Gate Arrays (FPGAs).
Building upon the knowledge of digital electronics acquired in introductory courses, students will develop advanced competencies and acquire the methodologies and techniques required for the design of high-speed, high-performance digital systems, as well as architectures dedicated to digital signal processing.

Dublin Descriptor 1: Knowledge and understanding
Expected learning outcomes:
· Understand the principles of configurable computing.
· Understand the architecture and implementation issues of FPGAs.
· Explain the complete implementation flow of a digital design on an FPGA.
· Demonstrate knowledge of the fundamental Digital Signal Processing (DSP) algorithms suitable for FPGA implementation.
· Understand the principles of pipelined and parallel processing.
· Explain pipelined and parallel implementation techniques for fundamental DSP structures (e.g., FIR and IIR filters).
· Understand timing analysis and timing constraints in FPGA-based designs.
Dublin Descriptor 2: Applying knowledge and understanding
Expected learning outcomes:
· Analyze system requirements and design a corresponding digital architecture.
· Evaluate alternative architectural solutions according to design specifications.
· Apply digital design methodologies to assess the feasibility of a processing architecture.
· Design, implement, and validate digital architectures on FPGA devices that satisfy a given set of specifications.
Dublin Descriptor 3: Making judgements
Expected learning outcomes:
· Select the processing techniques and architectural solutions best suited to specific application requirements.
· Evaluate the correctness of a digital design by defining appropriate verification and validation strategies.
· Compare alternative implementations in terms of performance, resource utilization, and implementation cost, identifying potential risks and appropriate mitigation strategies.
Dublin Descriptor 4: Communication
Expected learning outcomes:
· Present and effectively communicate the design process, implementation results, and technical outcomes.
· Justify and discuss design choices, architectural decisions, and implementation trade-offs with peers and instructors using appropriate technical terminology.
Dublin Descriptor 5: Lifelong learning skills
Expected learning outcomes:
· Develop and manage a realistic FPGA-based design project.
· Independently acquire and apply new digital signal processing techniques and FPGA design methodologies.
· Transfer the acquired knowledge and skills to the solution of real-world digital signal processing problems.

The course is based on the use of a personal laptop computer and of an Evaluation Board ("Basys 3 Artix-7 FPGA Trainer Board") whose supplying by the student is mandatory. Students enrolled in the course will be given precise indications for supplying the Evaluation Board.
Contents
Review of Digital Electronics Fundamentals
· Logic levels and noise margins.
· Contamination and propagation delays.
· Power dissipation.
· Area utilization.
Programmable Logic Devices
· Principles of operation.
· Spatial vs. Temporal Computing.
· Overview of major programmable architectures: CPLDs, FPGAs, SoCs, and ACAPs.
FPGA Architectures
· Logic blocks.
· I/O blocks.
· Clock distribution networks.
· Power distribution and management.
· FPGA configuration technologies.
Pipeline and Parallel Processing
· Principles of pipelined architectures.
· Parallel processing techniques.
· Design trade-offs for high-performance digital systems.
· Optimization for area, timing, and power.
Timing Issues in Digital Design
· Metastability and timing hazards.
· Setup and hold requirements.
· Static timing analysis for single-clock designs.
· Clock Domain Crossing (CDC).
FPGA Development Using the Xilinx Vivado Design Suite
· Hardware description using VHDL.
· FPGA design flow: simulation, synthesis, implementation, and debugging.
· Design constraints and timing closure.
· Memory resources (RAMs and FIFOs).
· Communication protocols (AXI4-Stream).
Design and Implementation Activities
· Laboratory exercises, in-class design assignments, and individual homework assignments focused on the design, implementation, and optimization of FPGA-based digital systems, including DSP applications and hardware accelerators.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
The final assessment is graded on a 31-point scale and consists of a written examination divided into two parts: a theoretical part (maximum 16 points) and a practical part (maximum 15 points).
Both parts of the examination must be passed in order to obtain the final grade. The final mark is calculated as the sum of the scores achieved in the theoretical and practical parts. Honors ( lode ) are awarded to students achieving a final score of at least 30.5/31.
· The theoretical part consists of a traditional written examination completed on paper (or equivalent methods). It includes exercises, theoretical questions, waveform analysis, and VHDL code analysis. A minimum score of 9 out of 16 is required to pass this part.
· The practical part consists of the design and implementation of a VHDL project and is intended to assess the practical and design skills acquired during the course. A minimum score of 9 out of 15 is required to pass this part.
The practical assessment may be completed in one of two ways: i) during the laboratory activities scheduled within the course or, ii) during one of the official examination sessions held after the end of the lectures.
i) Laboratory option: during the semester, laboratory sessions are organized in which VHDL projects are assigned for development and implementation on the evaluation board. Students may participate by forming their own working groups. Each group is assigned a project to be completed by a specified deadline and assessed before the first official examination session. All members of the same group receive the same score. Students are required to use their own laptop computer and evaluation board. Any additional hardware required (e.g., joysticks or audio modules) will be provided during the laboratory sessions.
ii) Examination-session option: during the official examination sessions, the practical assessment consists of developing and implementing a VHDL project on the evaluation board. Students must use only their own laptop computer, with no Internet access, and their own evaluation board. If required, additional hardware (e.g., joysticks or audio modules) will be provided during the examination. No groups are allowed. Each student works independently and receives an individual evaluation.
Only the practical assessment score may be retained ( "frozen" ), subject to the following conditions:
· Students who obtain a laboratory score (laboratory option) of at least 9/15 and subsequently choose to retake the practical assessment during an official examination session permanently forfeit their laboratory score.
· The practical assessment score, whether obtained through the laboratory activities or during an official examination session, remains valid untill the fifth exam section.
· If the student retakes the practical assessment, the previously retained score is automatically forfeited.
· Neither the theoretical assessment score nor the overall final examination grade may be retained.
The instructor reserves the right to require an additional oral examination in the following cases:
· students who completed the practical assessment as laboratory option, when clarification is needed following the evaluation of the laboratory assignment
· students whose performance shows a significant discrepancy between the theoretical and practical parts of the examination.

Volnei A. Pedroni, Circuit Design and Simulation With VHDL, Editore: The MIT Press, Anno edizione: 2010, ISBN: 0262014335
Note: Second Edition
Peter J. Ashenden, The Designer's Guide to VHDL, Editore: Morgan Kaufmann, Anno edizione: 2006, ISBN: 0120887851
Note: Third Edition
David Harris, Sarah Harris, Digital Design and Computer Architecture, Editore: Morgan Kaufmann, Anno edizione: 2012, ISBN: 9789382291527
Note: Second Edition

The course is an introduction to the fundamental notions of Discrete Mathematics, one of the fastest growing areas of modern mathematics. These notions are nowadays more and more used in the development, analysis and comprehension of mathematical models in every area of engeneering. Among the many topics that go under the name of discrete mathematics, in the course we consider set theory, enumerative combinatorics, graph theory, elementary number theory and modular arithmetics, and abstract algebra.

At the end of the course, students who have mastered contents and methodology, will be able to express the fundamental concepts of Discrete Mathematics through definitions, theorems, examples and counterexamples, to justify the effectiveness of mathematical procedures used to solve the problems presented in the course and to properly use the mathematical language while arguing (Dublin Descriptor 1, or DD1).
Regarding the application of the acquired knowledge and understanding, students will be able to:

• use formal rules for computations on symbols, independently from the meaning of the symbols (DD2);
• solve equations over the integers or over the integers modulo m (DD2);
• compute the number of elements of sets defined by a property (DD2);
• analyze graphs (DD2).

SET THEORY: Basic notions of set theory. Natural numbers and the induction principle.
ENUMERATIVE COMBINATORICS: Cardinality of a set. Basic counting principles. Principle of inclusion and exclusion. Formulas of Sylvester and Da Silva. Binomial numbers, Stiefel's formula and other identities on binomial numbers. Permutations and selections from a set. Derangements. Binomial theorem. Stirling numbers of the second kind. Partitions of a natural number. Ferrers diagrams. The Tower of Hanoi problem. Formal power series. Partial fractions. Generating functions. Linear homogeneous recursions. Fibonacci and Lucas numbers. Closed form of the Fibonacci numbers.
GRAPHS: Basic definitions. Planarity of graphs. Euler's formula. Bipartite graphs. Eulerian graphs. Hamiltonian graphs. Trees. Binary trees. Spanning trees. Vertex colorings. Chromatic number. Edge colorings. Chromatic index. K\"{o}nig's theorem. Matchings in bipartite graphs. Hall's theorem. Adjacency matrix, incidence matrix with respect to an orientation, Laplacian matrix. Theorem of Poincaré. Spectrum of a graph.
ELEMENTARY NUMBER THEORY AND MODULAR ARITHMETIC: Integers. Divisibility and prime numbers. Euler's function. Congruences. Integers modulo m. Euler's theorem. Fermat's little theorem. Chinese remainder theorem. Wilson's primality test.
ABSTRACT ALGEBRA: Elements of group, rings and fields. Finite fields.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), individuale, senza revisioni periodiche, con presentazione/discussione finale
The aim of the exam is to verify if every student has got the following expertises:
1. ability of applying theoretical results to solve exercises (DD2),
2. knowledge of definitions (DD1),
3. results on the topics of the course (DD1 and DD2),
4. proofs of the main mathematical theorems presented in the course (DD1).
The final evaluation consists of an oral exam on the topics presented in the course, part of which is devoted to the solution of exercises.

Notes of Discrete Mathematics WeBeep page of the course

Questo insegnamento offre una panoramica dei principali dispositivi impiegati nella trasmissione dell’informazione, con particolare attenzione agli ambiti di maggiore interesse per le tecnologie dell’informazione e della comunicazione. Il corso introduce i principi fisici e gli aspetti elettromagnetici alla base del funzionamento di sorgenti, modulatori, filtri e amplificatori, illustrando le tecnologie utilizzate per la loro realizzazione alle microonde e alle frequenze ottiche.
Per ciascun componente saranno esaminati i parametri caratteristici, l’architettura funzionale e i requisiti necessari per l’integrazione nei sistemi di comunicazione. Particolare attenzione sarà dedicata alla riduzione dei consumi energetici, all’aumento della capacità trasmissiva e alla scalabilità dei sistemi, mettendo in evidenza i limiti teorici, tecnologici e fisici delle diverse soluzioni.
L’obiettivo è fornire una visione organica delle tecnologie e dei dispositivi utilizzati nelle interconnessioni, dai sistemi di calcolo distribuito ai collegamenti a lunga distanza.
Al termine del corso, lo studente sarà in grado di comprendere il funzionamento dei principali dispositivi, valutarne prestazioni e limiti e individuare le soluzioni più adatte ai diversi contesti applicativi.

Descrittori di Dublino
Risultati di apprendimento attesi
Conoscenza e comprensione
· conoscere i dispositivi più comuni impiegati per la trasmissione dell’informazione e le relative figure di merito, prestazioni e limiti
· comprendere i meccanismi della propagazione guidata delle onde elettromagnetiche e il loro utilizzo nella realizzazione di dispositivi in guida d’onda
· conoscere i principi della modulazione e della conversione di frequenza alle frequenze ottiche e a radiofrequenza
· conoscere i meccanismi di generazione e amplificazione dei segnali ottici
Capacità di applicare conoscenza e comprensione
· dimensionare strutture guidanti per la radiazione elettromagnetica
· progettare semplici dispositivi per il filtraggio di segnali a radiofrequenza e ottici;
· analizzare e progettare sorgenti di radiazione costituite da schiere di antenne
· analizzare e progettare dispositivi per la (de)modulazione e l’amplificazione dei segnali
· analizzare le prestazioni di sorgenti ottiche incoerenti (LED) e coerenti (laser) per la trasmissione dell’informazione
Abilità comunicative
· comunicare i risultati della propria attività in modo chiaro e convincente
Capacità di apprendimento
· applicare le conoscenze acquisite allo studio di sistemi di trasmissione reali che impiegano i dispositivi presentati

Dispositivi per l’informazione: introduzione e concetti generali. Il problema dell’interconnessione e lo scaling della quantità di informazione. Limiti teorici, tecnologici e fisici. Energia per bit.
Richiami di sistemi di comunicazione. Introduzione ai dispositivi per l’interconnessione. Schema funzionale dei front-end RF e ottico. Specifiche, requisiti e parametri caratteristici dei dispositivi.
Onde elettromagnetiche e propagazione guidata. Richiami sulle onde elettromagnetiche. Guide d’onda metalliche. Equazione agli autovalori. Il concetto di modo guidato. Proprietà dei modi: frequenza di cut-off, funzione modale, costante di fase e di attenuazione. Diagramma di dispersione. Velocità di fase e velocità di gruppo. Impedenza d’onda. Potenza. Microstriscia: struttura, parametri caratteristici, criteri di dimensionamento. Cenni alle guide d’onda dielettriche: angolo critico, diagramma di dispersione e forma dei modi.
Filtri. Parametri caratteristici dei filtri: selettività in frequenza, banda passante, reiezione, dispersione. Filtri risonanti a parametri discreti. Frequenza di risonanza. Fattore di qualità intrinseco ed estrinseco. Accoppiamento critico. Filtri risonanti a parametri distribuiti e circuito equivalente a parametri discreti. Filtri risonanti a microonde (microstriscia): filtri ad una porta (arresta-banda) e filtri a due porte (passa-banda). Risposta in frequenza, criteri di progetto e sensibilità alle tolleranze di fabbricazione. Esempi di risonatori ottici passivi (Fabry-Pérot e microring). Applicazioni: filtri, (de)multiplexer, linee di ritardo. Cenni ai filtri a risonatori accoppiati.
Convertitori di frequenza e modulatori. Principi della conversione di frequenza e della modulazione.
Modulatori ottici. Figure di merito. Modulazione diretta e esterna. Modulatori di fase e di intensità. Modulatori risonanti e non risonanti. Effetto termoottico ed elettroottico. Modulatori elettrottici in silicio: plasma dispersion effect, iniezione e svuotamento di carica, prestazioni e limiti. Cenni ai modulatori elettro-assorbitivi. Applicazioni ai tranceivers per datacenters.
Amplificatori. Parametri caratteristici degli amplificatori. Amplificatori ottici. Interazione luce-materia: emissione spontanea e emissione stimolata. Inversione di popolazione. Guadagno e saturazione. Amplificatori in fibra drogata all’erbio (EDFA): schema circuitale, pompaggio ottico, banda di guadagno, rumore (cenni), prestazioni e limiti. Amplificatori a semiconduttore (SOA): livelli energetici nei semiconduttori, struttura di un SOA, pompaggio elettrico, prestazioni e applicazioni. Cenni su amplificatori RF.
Sorgenti di onde irradiate. Richiami sulle antenne. Array di antenne. Fattore di array. Array lineari uniformi. Array a singolo fascio e multifascio. Beam steering e beam width. Dipendenza dalla frequenza. Effetto degli errori di fase. Applicazioni: microwave phased array, optical beam forming networks per sistemi 5G, LIDAR.
Sorgenti ottiche. Light emitting diodes (LED): principio di funzionamento e struttura di un LED a semiconduttore. Spettro di emissione, banda e coerenza temporale. Figure di merito, efficienza quantica interna ed esterna. Applicazioni alle comunicazioni (Visible Light Communications e Li-Fi).
Sorgenti laser: principio di funzionamento e struttura di un laser. Risonatori ottici attivi. Guadagno di soglia. Laser a semiconduttore. Modi di cavità. Laser singolo modo e multimodo. Lunghezza d’onda di emissione. Larghezza di riga e coerenza temporale. Corrente di soglia. Caratteristiche dei laser per comunicazioni.
Laboratorio sperimentale
Il corso prevede delle lezioni sperimentali (4 ore) nel laboratorio di Photonic Devices (https://photonics.deib.polimi.it/laboratories/), durante le quali gli studenti potranno osservare direttamente il funzionamento di alcuni dei dispositivi presentati a lezione e approfondirne gli aspetti applicativi.

Prova scritta obbligatoria, con prove in itinere
Prova orale facoltativa (a scelta dello studente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
L’esame consiste in due prove così strutturate:
prova scritta (open book) sugli argomenti trattati nella prima parte del corso (propagazione guidata, risonatori e filtri) con esercizi numerici e possibilità di consultare liberamente libri e appunti durante la prova.
Su questa parte del corso è prevista anche una prova in itinere.
prova orale sugli argomenti trattati nella seconda parte del corso (modulatori, amplificatori, array di antenne, sorgenti ottiche) o in alternativa un approfondimento tematico (progetto) che potrà essere svolto in piccoli gruppi (massimo 3 studenti).
Modalità di verifica
Descrizione
Risultato di apprendimento perseguito
Prova scritta
· risoluzione di problemi numerici sul dimensionamento di strutture guidanti per le onde elettromagnetiche
· esercizi di tipo progettuale sul dimensionamento di filtri (radio frequenza e/o ottici)
1, 2
1,2,5
Prova orale
· discussione volta a evidenziare la capacità dello studente di collegare e applicare a casi concreti gli argomenti trattati nel corso
· valutazione di un progetto o di un approfondimento tematico, svolto in gruppo o individualmente
1,2,4,5
Il superamento della prova scritta non è prerequisito per la prova orale o per la consegna del progetto.
Gli studenti che non hanno sostenuto o superato la prova scritta (o la prova in itinere) possono comunque sostenere la prova orale e recuperare la prova scritta in un appello successivo, purché della medesima sessione.

F. Morichetti, Appunti delle lezioni ed esercizi, Anno edizione: 2020
Note: Reperibili durante il corso su indicazione del docente
Slides del corso (Ed. 2024) https://webeep.polimi.it/
Note: Reperibili sul sito WeBeep del corso

The course aims at describing the principles and paradigms that guide the design and development of modern distributed systems. The course identifies the issues to be resolved in developing a distributed system and it describes the successfull approaches in addressing these issues, in terms of abstract models, algorithms, and case studies.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will learn the principles and paradigms that guide the design and development of modern distributed systems.
Applying knowledge and understanding
At the end of the course students will be able to identify the critical issues in developing a distributed system and to apply the distributed algorithms they learnt to solve them.
Making judgements
At the end of the course students will be able to evaluate and compare the various alternatives in designing and implementing a distributed system.
Communication
Students will learn how to describe a distributed algorithm and how to present their solution to the typical issues that arise during the design and development of a distributed system.
Learning skills
At the end of the course students will be able to autonomously learn new disributed algorithms and new methodologies and techniques to develop distriuted systems.

Specific topics addressed during the course:
Principles of concurrent programming for distributed systems
Modelling distributed systems
Basic communication facilities
Naming
Synchronization
Fault tolerance
Consistency and Replication
Security
Simulation

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The written exam includes open-ended questions covering the various topics discussed during the course, alongside numerical and design exercises. During the course, a group project will be assigned; students who wish to improve their written exam score may develop this project and present their work upon completion. Additionally, an oral exam may complement or substitute the written test at the professor's discretion.
The following table provides a detailed overview of the elements that will be considered in the various assessment activities.
Type of assessment
Description
Dublin descriptor
Written test
Solution of numerical problems and exercises focusing on design aspects
Theoretical, open questions on the topics addressed during the course
1,2, 3, 4, 5
1, 4, 5
Assessment of laboratorial artefacts
Assessment of the project (optionally) developed by students
2, 3, 4, 5
Oral presentation
Assessment of the presentation of the project (optionally) developed by students and/or oral exam at the teacher's choice.
2, 3, 4, 5

Andrew S. Tanenbaum, Maarten Van Steen, Distributed Systems: Principles and Paradigms (2nd Edition), Editore: Prentice Hall, Anno edizione: 2007
George Coulouris, Jean Dollimore, Tim Kindberg, Distributed Systems: Concepts and Design (4th Edition), Editore: Addison Wesley, Anno edizione: 2005, ISBN: 0321263545

The course focuses on developing students' knowledge of Remote Sensing Optical Data and their usage.
Syllabus
Topics of the course are:
1. Introduction to remote sensing
2. Basic definitions and laws of electromagnetic radiation 3. Satellites, sensors and data formats
4. Geometric correction or georeferencing
5. Atmospheric influence and radiometric correction
6. Histogram manipulation
7. Template filters
8. Band algebra and vegetation indexes
9. Components Analysis
10. The classification problem
11. Clustering and unsupervised classification
12. Supervised classification
13. Environmental applications of Remote Sensing
14. The ESA SNAP Platform

The course builds on basic principles and concepts (in the field of electric circuit analysis, signals and systems, electronics, and electromagnetic fields) to provide an introduction to Electromagnetic Compatibility (EMC). The course is designed to help students develop a deeper understanding of electromagnetic interference and the prevention of it through suitable design strategies. The addressed subjects cover: nonideal behavior of components, transmission line circuit models for EMC, conducted emissions and susceptibility, radiated emissions and susceptibility, antennas (basic principles, test and measurement antennas for EMC), crosstalk, shielding, grounding, measurement instrumentation, setups and facilities for EMC testing, governmental and functional requirements, system design for EMC. Practical applications considered in this course concern EMC aspects in complex electrical and electronic systems and environments, such as those found in aerospace, automotive, electric power generation, transmission/delivery, and railway industry sectors.

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

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

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

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

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

This is a course devoted to mastering techniques for signal recovery from noise in modern data acquisition systems. In a blend of electronics, physics and signal processing we will cover amplifiers, noise, and signal conditioning techniques, keeping an eye on both the physical/theoretical working principles and the real characteristics. Students are expected to gain skills on the design of relatively simple data acquisition systems and on the selection criteria of their various components.

Expected learning outcome
Dublin descriptor
On completion of this course, students will:
· understand the principles and operation of operational amplifiers (OAs) and their limitations;
· have a sound knowledge of linear circuits made with OAs and of the techniques used to analyze the circuits and assess their performance;
· know the problems related to resistive sensors and their connection in a Wheatstone bridge configuration, either local or remote;
· understand noise in circuits and acquisition systems, its sources and properties;
· understand the principles of signal recovery from noise and know the different techniques.
1) knowledge and understanding
Specific examples and case studies will be provided to make students able to:
· design simple linear circuits employing OAs;
· design a proper sensor connection and a suitable amplifier accounting for non-idealities in the signal path;
· choose the correct signal recovery technique based on the signal and noise characteristics.
2) applying knowledge and understanding

Operation amplifiers (OAs): voltage and current amplifiers, input and output impedances. Negative feedback and application to amplifier design. Elementary linear stages and impedances. Real OAs: DC and AC parameters. Instrumentation amplifiers and CMRR. Frequency response of OA circuits, stability of the feedback loop and frequency compensation. Capacitive loading of OA stages.
Wheatstone bridge and connections: signal readout from resistive sensors: single-ended and differential signals, Wheatstone bridge and balancing, 2-, 3- and 4-wire connections. Temperature compensation.
Noise: signals and noise in time and frequency domains. Autocorrelation and power spectral density. Thermal noise in resistors, Nyquist derivation. Shot noise and Poisson random process model. Flicker noise. Noise in linear circuits and OAs, equivalent input noise. Noise factor and figure, signal-to-noise ratio (S/N). Feedback and noise.
Signal recovery: the case of white noise: low-pass and time-variant filters. Weighting functions. Gated integrators and improvement of S/N. Boxcar averagers. Discrete-time filters and their representation in time and frequency domains. Comparison between continuous- and discrete-time filters. Optimum filtering.
The case of low-frequency noise: high-pass filters and effect on pulsed signals. Baseline restorers. Amplitude modulation and synchronous detection. Lock-in amplifiers, analog and digital LIAs.

Prova scritta obbligatoria, senza prove in itinere
Students are required to pass a written, open-book exam, consisting of two problems with four questions each. Such parts refer to the main blocks of the course and are meant for the students to show
For the first part:

• An understanding of the operating principles of realistic circuits built using operational amplifiers;
• The capability to analyze such circuits and evaluate their performance;
• Some design capabilities, when modifications of the proposed scheme is asked in order to reach a specified goal;
• The capability to evaluate the circuits from the viewpoint of noise;
  and for the second one:
• An understanding of the basic principles of signal recovery;
• The capability to evaluate the signal and noise performance in a particular signal acquisition scheme;
• Some design capabilities, when they need to propose what signal recovery scheme is most suitable to solve a specific problems.
  Texts and solutions of previous examinations are available on the instructor’s website.

Lecture notes http://home.deib.polimi.it/spinelli/corsi/ele/ele.shtml
Sergio Franco, Design with operational amplifiers and analog integrated circuits, 4th Ed., Editore: Mc Graw Hill, Anno edizione: 2015, ISBN: 978-0-07-802816-8
http://home.deib.polimi.it/rech/Download/SignalRecoveryBook.pdf
A. S. Sedra, K. C. Smith,, "Microelectronic circuits", Editore: Oxford Univ. Press, Anno edizione: 2009, ISBN: 0195323033
T. H. Wilmshurst, "Signal recovery from noise in electronic instrumentation", Editore: Hilger, Anno edizione: 1990, ISBN: 0750300582

Presentare i concetti fondamentali del comportamento dei campi elettromagnetici alla base del funzionamento delle principali applicazioni ICT. Descrivere le leggi fisiche che regolano i campi elettromagnetici in regime tempo invariante (statica) e mostrare possibili applicazioni. Introdurre e commentare le equazioni di Maxwell che descrivono le interazione di tipo elettromagnetico in regime tempo variante su scala non microscopica. Le leggi teoriche vegono utilizzate per comprendere, in linea generale, il funzionamento di alcuni dispositivi di uso comune (sensori, lettore di impronte digitali, schermi LCD, stampanti a getto di inchiostro, ecc.). Le equazioni di Maxwell vengono applicate, nella seconda parte del corso, allo studio della trasmissione delle onde elettromagnetiche in spazio libero e in forma guidata, con particolare attenzione alle applicazioni più importanti nell'ambito ICT. L’insegnamento, che non adotta modalità di didattica innovativa, introduce ad un buon livello di formalismo matematico che facilita uno studio completo dei campi elettromagnetici e delle loro applicazioni.

Descrittori di Dublino
Risultati di apprendimento attesi
DD1 - Conoscenza e comprensione
Conoscere gli strumenti matematico/fisici di base per lo studio dei campi elettromagnetici
Comprendere le leggi che regolano il comportamento dei campi elettromagnetici in regime statico, tempo variante e sinusoidale stazionario
DD2 - Capacità di applicare conoscenza e comprensione
Applicare le leggi teoriche per comprendere, in modo semplificato, il funzionamento di alcuni dispositivi di uso comune
Applicare le leggi teoriche per lo studio della propagazione delle onde piane in spazio libero e in presenza di mezzi omogenei e disomogenei a tratti
Applicare le leggi teoriche per lo studio della propagazione delle onde guidate con particolare riferimento alle linee di trasmissione
Essere in grado di elaborare collegamenti fra i vari argomenti del corso e di ordinarli in senso logico per risolvere problemi concreti
DD4 - Abilità comunicative
Comunicare in modo chiaro e preciso le conoscenze e la ratio ad esse sottese
DD5 - Capacità di apprendimento
Avere acquisito padronanza delle leggi fondamentali dell’elettromagnetismo e saperli applicare a casi concreti
Essere in grado di utilizzare gli strumenti e i concetti appresi per studiare problemi più complessi

Lezioni
Elettrostatica e magnetostatica: Campi scalari e vettoriali e operatori che li descrivono. Il campo elettrico, le sue sorgenti e le leggi che ne descrivono il comportamento (leggi di Coulomb e di Gauss). Potenziale, energia e capacità elettrostatica. Il campo magnetico, le sue sorgenti, e le leggi che ne descrivono il comportamento. Energia magnetostatica. Induttanza e resistenza elettrica. Effetto Joule. Comportamento elettrico e magnetico dei materiali.
Campi elettromagnetici in regime tempo variante: Il campo elettromagnetico in regime tempo variante e leggi che ne descrivono il comportamento: le equazioni di Maxwell. Comportamento elettromagnetico dei materiali. Anisotropia e dispersione. Teorema di unicità della soluzione del problema elettromagnetico e teorema di Poynting. I campi elettromagnetici in regime sinusoidale stazionario.
Introduzione alle onde piane: L’onda piana: proprietà fondamentali. La propagazione delle onde piane in mezzi con e senza perdite. L’effetto pelle nei conduttori elettrici. Riflessione e trasmissione delle onde nei mezzi stratificati.
Linee di trasmissione ideali: La propagazione delle onde nelle linee di trasmissione TEM e quasi TEM. Onde di tensione e corrente e equazioni delle linee di trasmissione. Velocità di propagazione, impedenza caratteristica e coefficiente di riflessione. Rapporto d’onda stazionaria. Impedenza di ingresso. Richiami del diagramma di Smith. Trasferimento di potenza nelle linee di trasmissione e principali reti di adattamento (trasformatore in quarto d’onda, adattatore stub semplice).
Linee di trasmissione con perdite: Equivalente circuitale di un tratto di linea con perdite e significato fisico dei parametri che la caratterizzano. La propagazione nelle linee con piccole perdite. Bilancio di potenza nelle linee con perdite. Esempi di linee di trasmissioni: cavi di rete.
Esercitazioni
Esempi di calcolo del campo elettrostatico e magnetostatico in presenza di sorgenti elementari. Metodo delle cariche immagine. Esempi di calcolo del campo elettromagnetico in regime tempo variante.
Soluzione di semplici problemi di propagazione delle onde piane in spazio libero e in presenza di più materiali differenti.
Calcolo dei principali parametri che caratterizzano le linea di trasmissione. Calcolo dell’impedenza lungo una linea di trasmissione. Dimensionamento di reti adattanti. Calcolo del bilancio di potenza nelle linee di trasmissione senza e con perdite.

Prova scritta obbligatoria, con prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
L’esame prevede una verifica scritta e una prova orale su tutti gli argomenti dell’insegnamento.
Sono previste le prove in itinere: la prima verte sul programma fino alle equazioni di Maxwell comprese mentre la seconda verte su onde piane e linee di tramissione. Entrambe prevedono anche domande di teoria a risposta multipla. Al termine delle prove in itinere non è normalmente prevista una prova orale (che comunque può essere svolta a discrezione del docente o a scelta dello studente).
Durante il corso sono previsti progetti (non obbligatori) su temi legati agli argomenti del corso (da concordare preventivamente con il docente). Il progetto può essere svolto anche in gruppo (massimo 4 studenti) e si conclude con una breve relazione tecnica e una presentazione in aula. Il progetto prevede un incremento del voto finale da 0 a 3 punti (fino ad un massimo di 1.5 punti per la relazione tecnica e 1.5 per la presentazione).
Modalità di verifica
Descrizione
Risultato di apprendimento perseguito
Prova scritta
risoluzione di problemi numerici di elettrostatica, magnetostatica e sulla propagazione delle onde piane
esercizi di tipo progettuale sul dimensionamento delle linee di trasmissione e delle reti adattanti
DD1, DD2
DD1, DD2, DD5
Prova orale
domande di carattere teorico su tutti i principali argomenti del corso
domande volte a evidenziare la capacità dello studente di collegare e applicare a casi concreti i vari argomenti del corso
DD1, DD4, DD5
DD1, DD2, DD4, DD5

Carlo Riva e Gian Guido Gentili, Appunti di Campi Elettronagnetici (Ottava edizione), Editore: Maggioli Editore, Anno edizione: 2024, ISBN: 8891671448
Note: https://www.maggiolieditore.it/appunti-campi-elettromagnetici.html
Carlo Riva and Gian Guido Gentili, Handbook of electromagnetics, Editore: Maggioli Editore, Anno edizione: 2019, ISBN: 8891636133
Note: https://www.maggiolieditore.it/handbook-of-electromagnetics.html
Fawwaz T. Ulaby, Umberto Ravaioli, Fondamenti di campi elettromagnetici (Ottava edizione), Editore: Pearson, Anno edizione: 2021, ISBN: 9788891906618
Note: ISBN digitale: 9788891906625
https://he.pearson.it/bundle/469?isbn=9788891906618
B. Guru e H. Hiziroglu, Electromagnetic field theory fundamentals, Editore: Cambridge University Press, Anno edizione: 2004, ISBN: 0521830168
M. D'Amico, S. Maddio, C. Riva,, "Elementi di Campi Elettromagnetici - Una selezione di esercizi, complementi ed applicazioni, Editore: Maggioli Editore, Anno edizione: 2022, ISBN: 8891646361
Note: Eserciziario
M. D'Amico e G.G. Gentili, Esercizi di Campi Elettromagnetici, Editore: CUSL, Anno edizione: 1996, ISBN: 8881320312

The goal of the course is to provide the students with the theoretical knowledge and the methodologies necessary for modelling, analyzing, and designing wireless links for space-borne systems, both at radio and optical frequencies, in diversified Earth-space scenarios (e.g. Earth observation missions, satellite communications - SatCom, global navigation satellite systems - GNSS). Moreover, the course provides the students with the instruments for understanding and applying the advanced technologies used in modern wireless links. The course offers a balanced compromise between theoretical aspects and practical ones: first, the physical aspects of the phenomena are duly introduced; second, the theory behind them is addressed; third: a practical mathematical framework is derived useful for system design and analysis. The course also includes brief seminars providing an overview on “real world” systems (e.g. experimental measurements) and offers a practical laboratory session focused on designing a realistic Earth-space link.

Knowledge and understanding (DD1)
Students will understand and learn
The main features of electromagnetic waves
The physical concepts governing the unguided propagation of electromagnetic waves in different materials
The effects induced by the atmosphere (troposphere and ionosphere) on the propagation of electromagnetic waves, as well as the impact of such effects on Space Communication and Navigation systems
The impact of noise on Space Communication and Navigation systems
The mathematical framework for the analysis and design of Earth-space links operating at radio and optical frequencies
The key elements and blocks composing complex communication systems relying on unguided electromagnetic wave propagation
Some elements of GNSSs
Applying knowledge and understanding (DD2)
Students will be able to:
Select the most suitable wireless link and system (e.g. operational frequency, type of satellite orbit) for a given scenario and service to be provided
Dimension the elements of a wireless link to meet target requirements, such as the link availability and quality
Analyse the performance of wireless links in diversified scenarios based on system specifications, as well as on operational and standardization constraints (e.g. ITU-R Radio Regulations)
Identify and evaluate the key tradeoffs that drive the design of wireless links
Identify the fundamental system limitations impacting the design of a wireless link

Theory:

• Introduction: contents of the course and main protagonists in the history of electromagnetic wave propagation and space-borne systems.
• The electromagnetic spectrum: frequency bands and spectrum management.
• Main features of electromagnetic waves (frequency, wavelength, polarization,...) and electromagnetic characterizations of materials (electric permittivity and magnetic permeability, conductivity,...).
• Basic propagation mechanisms at radio frequency and optical wavelengths (Free Space Optics): direct wave, reflected wave, evanescent waves.
• Propagation in the ionosphere. Refraction, attenuation, Faraday rotation, phase advance and group delay. Basic principles of radio-positioning in Global Navigation Satellite Systems (GNSS) and impact of the ionosphere on such systems.
• Propagation in the non-ionized atmosphere (troposphere) and impact on Earth-space (HAPs, LEO, MEO, GEO satellites, Deep Space probes) links. Clear air propagation: refraction and ray bending, attenuation, scintillation, tropospheric scatter. Propagation through clouds. Adverse weather disturbances: attenuation, depolarization, electromagnetic interference due to hydrometeors.
• Atmospheric and extra-atmospheric noise sources: impact on the signal detection and some concepts on ground-based passive remote sensing.
• Statistical characterization of the radio channel and system design: the link budget. Basics on fade mitigation techniques (e.g. site, frequency and time diversity, uplink power control, …).
  Practice:
• Problem solving on all the topics listed above.
• Presentation and use of the following software tools: propagation of plane waves through multi-layered materials.
• Mentions to ITU-R recommendation, propagation series.
  Laboratory activities:
• Practical session focused on designing a realistic Earth-space link, making reference to ITU-R recommendations (e.g. P-series) and transmission standards (e.g. DVB-S2).

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
The exam includes a written test and an oral test:
The written test consists in the solution of 4 problems relative to the course topics (e.g. ionospheric propagation and impact of rain on wireless links): past exams, with solutions, are available on the course webpage. This test aims at assessing the ability of the student to solve problems Earth-space link design and EM wave propagation using the tools and concepts presented during the lectures (DD1, DD2). The test is open book: the student can use any material (e.g. personal notes, books) during the written test, but NOT electronic devices. The duration of the written test is typically between 2 hours and 2 hours and 15 minutes.
The oral test is optional for those obtaining a score equal to or higher than 18 in the written exam (increase in the score of up to 3 points), and mandatory for those who obtain a score of 17 in the written exam. The oral test aims at evaluating the understanding of the physical concepts at the basis of the propagation of electromagnetic waves (DD1, DD2, DD4). The student will be typically asked to explain and discuss, in a conversational way, key concepts, rather than deriving analytical expressions.

C. Riva, G. G. Gentili, Handbook of Electromagnetics, Editore: Maggioli Editore
J.E. Allnutt, Satellite to ground radiowave propagation, Editore: IET
A. Paraboni, M. D'Amico, Radiopropagazione, Editore: McGraw-Hill
Jean-Marie Zogg, GPS - Essentials of Satellite Navigation, Editore: u-blox https://www.zogg-jm.ch/Dateien/GPS_Compendium(GPS-X-02007).pdf
L. Luini, Lecture notes of the course

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

DdD1 - Knowledge and understanding
Students will acquire:

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

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

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

The goal of the course is to provide specific competencies and design methodologies needed to realize embedded applications including the choice of the technologies, the management of the design flow and the selection of the toolchain.
Such applications range from Cyber Physical Systems for consumer electronics to industrial and medical devices. In general, any consumer electronics product is an embedded system. Their design requires the coordination of cross competencies to identify the optimal solution under a number of aspects including flexibility, standardization, cost, size, energy and power, performance, etc.
The course aims at creating a valuable bridge to the best industrial practices, providing the competencies on: architectures for component-off-the-shelf and SoC based embedded systems exploiting sensors; communication and interfacing standards popular in the embedded system market, energy aware design of software and hardware including run-time management of the resources.
Hands-on lab sessions will be suggested to the students (participation is optional) to show how to use development boards to interface external sensors and peripherals. Case studies related to real products will be presented to make students aware on the real trade-offs to be considered.
To enhance the knowledge related to the development of software for embedded applications it is suggested to follow also the course Advanced Operating Systems.

At the end of the course, the students will be capable of analyzing an embedded application and moving from the requirements to the realization.
The goal is to become capable of coordinating the use of different skills and technologies in order to identify the best solution under a large variety of aspects such as flexibility, standardization, cost, size, power dissipation and performance, the methodological aspects related to the flow of the design, and so on.
It is expected that, especially during the development of the project, a significant skill in programming embedded systems will be acquired.
Knowledge and understanding (DdD 1)
The student will be able to analyze and understand the main requirements of an embedded application.
The student has a wide knowledge of the main technologies to design an embedded system.
The student has a clear view of the steps needed to realize an embedded application, from several standpoint including project management, cost analysis, selection of the toolset and of the platform for prototyping.
Apply knowledge and understanding ( DdD 2)
The student will be capable of analyzing a Hw/Sw embedded designs considering several performance metrics.
The student is capable to design the Hw/Sw architecture of an embedded systems interfaced with the real world, moving from the requirements to the realization.
The student has the capability to design energy aware systems submitted to cost and timing constraints.
The student, depending on the choice of a project work, will be capable of designing embedded software and/or hardware using market-available technologies.
The student can read technical documentation regarding microcontrollers and sensors used for embedded applications.
Making Judgements ( DdD 3)
Student will be asked to analyze a project work to improve the ability to analyze the functional and extra-functional characteristics of an embedded application, while autonomously looking for the best tradeoff in terms of selection of the Hw/Sw architecture and use of the development tools.
Communication (DdD 4)
Students will learn how to organize a research report, including the search and critical analysis of the state of the art, description of an innovative research idea with its positioning in the gernal scenerario of the pertaining scientific field, and final presentation through the support of slides of the investigation carried out.
Lifelong learning skills ( DdD 5)
The student will become capable of coordinating the use of different skills and technologies in order to identify the best solution under a large variety of aspects such as flexibility, standardization, cost, size, power dissipation and performance, the methodological aspects related to the flow of the design, and so on.
The student will be capable of navigating autonomously in the state of the art and market ready technologies suitable for the design of embedded applications.

The content of the course starts by systematizing the skills on hardware architectures, communication and basic software used for the realization of embedded systems, with reference to existing standards and to the peculiarities of the system to be designed. It is then shown how to run a design flow leading to an "optimal" solution both in terms of the constraints of traditional project (such as dissipated power or performance), and considering the impact that technological solutions have under the profile of the design flow (e.g., cost or development time).
Introduction and background
General characteristics of an embedded system.
Requirements and constraints for the different application fields.
Trends of the market and of the best design practices.
Development flow for Hw-Sw architectures.
Energy and power optimization.
Hardware platforms for Embedded Systems
Software executors: general purpose, dedicated processors, DSP, Network processors, Microcontrollers, Multi-Many cores and clusters.
Communication architectures: standard buses, Network-on-Chip (NoC), field buses, wireless interfaces, examples.
Hardware executors: characteristics of the application specific IC and related design flow, systems for fast prototyping, reconfigurable and updatable systems, examples of off-the-shelf (PCB-based) designs and wireless sensor networks.
Software architectures for Embedded Systems
Abstraction layers: assembler, library, system software, middleware.
Software development environment and toolchains for the realization of the embedded software
Introduction to the real-time operating systems: general features, scheduler, configuration, dynamic management of the resources, soft real time features, energy optimization and resource management.
Analysis of the embedded software: formalisms and tools for analysis/profiling of the code, energy optimization.
Examples: RT extension of Linux, Android, RTX, operating systems for wireless sensor networks, frameworks for run-time management of resources, Linux Device Drivers model.
Analysis and optimization of Hw/Sw Systems
Analysis of the project constraints and selection of the best implementing architecture with particular emphasis on realization time and cost.
Problems related to the estimation of Power/Thermal/Performance at the different abstraction levels
Optimization of power and performance at the different abstraction levels.
Analysis and modeling of thermal problems with particular attention to the multi-core architectures with NoC-based communication.
Management and planning of embedded systems: models for the development flow, design for resuse and estimation of hw and sw development time and cost, project management.
Case studies
Example of the analysis to be performed during a feasibility study.
Example of real designs at different granularities, from the wireless sensor networks to embedded application exploiting multiprocessor cores, systems with MEMS, software for white goods and automotive, certification.
In each of the use case the set of trade-offs to be considered will be highlighted.
Laboratory
Hands-on lab sessions will be proposed to the students (participation is optional) to show how to use development boards to interface external sensors and peripherals, as well as the development of desk researches on specific topics

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
Altro
The examination follows the official rules of the Politecnico di Milano and more information can be found on the web site of the lecturer.
In general, it is foreseen a mandatory written test with open theoretical questions and possibly some exercises (70% of the total score). In addition to this step, during the course it will be presented a set of possible topics for projects/desk-based researches, whose evaluation will be part of the final score (30%).
The goal is to check the skill of the student to organize the concepts presented during the course and to cooperate with other students to the development of representative case studies, mimicking real-life problems popular in embedded applications.
The written test is linked to the Dublin Descriptors: 1,2,5
The project/desk research is linked to the Dublin Descriptors: 2,3,4,5
Reporting and presentation is linked to the Dublin Descriptors: 1,3,4,5

Slide & Papers
Note: see web site of the lecturer
Fornaciari, William, Soudris, Dimitrios (Eds.), Harnessing Performance Variability in Embedded and High-performance Many/Multi-core Platforms, Editore: Springer, Anno edizione: 2019, ISBN: 978-3-319-91962-1 https://www.springer.com/gp/book/9783319919614
Note: Only some chapter will be considered
Marwedel, Peter, Embedded System DEsign, Editore: Springer, Anno edizione: 2006, ISBN: 978-0-387-30087-0 https://www.springer.com/gp/book/9780387300870
Carlo Brandolese, William Fornaciari, Sistemi embedded -, Editore: Pearson, Anno edizione: 2007, ISBN: 9788871923420
Note: There exists alternative books in english
F.Vahid, T.Givaris, Embedded System Design - A unified Hardware/Software Introduction, Editore: Wiley, Anno edizione: 2002, ISBN: 978-0471386780

Obiettivo dell’insegnamento è fornire agli studenti i fondamenti della Fisica dello Stato Solido, al livello della laurea magistrale.
La prima parte ha come obiettivo lo studio dei solidi che possiedono un ordine a lungo raggio (cristalli), le loro proprietà strutturali (simmetria) e la loro struttura elettronica statica. Dopo un'introduzione alla cristallografia, i principali metodi sperimentali utilizzati per determinare la struttura geometrica dei cristalli vengono discussi, unitamente alla relativa teoria. La meccanica quantistica viene quindi utilizzata per determinare la struttura elettronica a bande, sia introducendo le proprietà generali degli stati elettronici in un potenziale periodico, sia presentando i principali metodi approssimati per il calcolo dei livelli energetici. Successivamente, l'effetto di un campo esterno statico viene studiato mediante alcuni concetti cruciali come: densità degli stati, Hamiltoniano equivalente, massa efficace, dinamica semiclassica e rappresentazione elettrone-buca.
Nella seconda parte si vogliono fornire agli studenti i principi basilari per la soluzione approssimata dell'equazione di Schroedinger a molti corpi per un cristallo a temperatura finita, includendo le vibrazioni reticolari: le approssimazioni di Born-Oppenheimer e di campo medio. La dinamica reticolare classica viene introdotta (costanti di forza interatomica e matrice dinamica, onde reticolari con le loro frequenze e polarizzazioni), e quindi quantizzata presentando i nuovi concetti di fononi acustici e ottici. Su questa base proprietà termodinamiche come il calore specifico e la dilatazione termica sono interpretabili a livello microscopico. Sono introdotte le proprietà ottiche macroscopiche dei solidi e, sempre utilizzando la meccanica quantistica, le proprietà ottiche dei cristalli vengono poi derivate dal trattamento microscopico delle transizioni elettroniche. Infine, le proprietà di trasporto (conducibilità termica ed elettrica e le loro dipendenze dalla temperatura) sono introdotte e spiegate in termini di cinetica fisica.
Tutti gli argomenti vengono illustrati anche mediante esempi.

Dopo aver seguito l’insegnamento e superato l’esame lo studente:

• descrive i principi matematici e fisici necessari per comprendere cosa sia un corpo solido, in particolare un cristallo, e conosce la struttura geometrica dei cristalli più importanti (DdD1);
• descrive le principali quantità che definiscono lo stato cristallino, così come i modelli teorici e i metodi sperimentali adatti a descriverlo e a determinarne la struttura, con particolare riferimento ai reticoli periodici perfetti 'congelati', con una base di atomi (DdD1);
• è in grado di argomentare sulle principali proprietà elettroniche statiche (struttura a bande) di specifici cristalli, comprendendo i fondamenti della descrizione quantistica di un elettrone in un potenziale periodico, e i principali metodi approssimati per il calcolo della struttura a bande (DdD1);
• è in grado di utilizzare i modelli teorici di cui sopra per sviluppare la descrizione del moto accelerato di un elettrone in un cristallo specifico, soggetto a un campo esterno statico, utilizzando la rappresentazione elettrone-buca (DdD2);
• descrive i modelli teorici necessari per risolvere in modo approssimato l'equazione di Schrodinger a molti corpi per un cristallo, includendo le vibrazioni dei nuclei (DdD1);
• descrive i fondamenti della dinamica reticolare, sia classica sia quantistica (onde reticolari e fononi) (DdD1);
• è in grado di applicare la meccanica quantistica per comprendere e prevedere le proprietà macroscopiche, termodinamiche e ottiche, di cristalli specifici (DdD2);
• è in grado di applicare la meccanica quantistica e la cinetica fisica per comprendere e prevedere le proprietà macroscopiche di trasporto (conducibilità elettrica e termica) di cristalli specifici (DdD2);
• possiede una conoscenza dei valori delle grandezze fisiche principali della Fisica dello Stato Solido, anche con riferimento ad esempi di cristalli specifici (DdD1).

• Proprietà fenomenologiche e definizione di materiali solidi
• Corpi solidi: ordine e simmetria; cristalli semplici: reticoli e ordine traslazionale; reticolo reciproco; cristalli complessi: reticolo e base; legame cristallino; osservabili tensoriali
• Teoria dello scattering: ampiezza di scattering di particelle quantistiche: approssimazione di Born; scattering elastico - legge di Bragg, diffrazione di raggi X
• L'equazione di Schrodinger per un cristallo: approssimazioni di Born-Oppenheimer e di campo medio
• Stati elettronici: teorema di Bloch, riduzione alla prima zona di Brillouin e condizioni periodiche al contorno; modello a elettroni liberi (energia di Fermi e densità di stati); struttura a bande: approssimazione di elettroni quasi liberi; struttura a bande: approssimazioni di legame forte e LCAO; metodo generale per il calcolo della densità di stati
• Dinamica dell'elettrone in campi esterni: Hamiltoniano equivalente; teorema della massa efficace; livelli di impurezza; dinamica semiclassica in un cristallo ideale; corrente elettrica e oscillazioni di Bloch; elettroni e buche (lacune); eccitoni
• Teorema adiabatico e moti vibrazionali
• Dinamica reticolare: Calore specifico reticolare: modello di Einstein; cristalli semplici 1-D: onde reticolari e fononi acustici; catene lineari con due atomi per cella: modi acustici e modi ottici; dinamica reticolare 3-D in generale: tensore delle costanti di forza interatomiche e matrice dinamica; fononi in un cristallo complesso 3-D; modello di Debye del calore specifico; dilatazione termica e processi anarmonici; polaritoni
• Proprietà ottiche: Descrizione macroscopica della dispersione e dell'assorbimento dei fotoni; relazioni di Kramers e Kronig per la suscettività dielettrica dipendente dalla frequenza; modello di Drude-Lorentz, proprietà ottiche dei metalli; trattazione quantistica dell'assorbimento di fotoni; proprietà ottiche dei semiconduttori a gap diretto; il caso del gap indiretto; proprietà ottiche dei polaritoni
• Fenomeni di trasporto: Leggi di Ohm e di Fourier locali; andamento sperimentale della conducibilità elettrica e termica in funzione della temperatura; cinetica fisica e equazione di Boltzmann; teoria microscopica della conducibilità elettrica e termica dei metalli; teoria microscopica della conducibilità termica degli isolanti (scattering fonone-fonone)

Prova orale obbligatoria
La valutazione consiste in un esame orale. Suo obiettivo è la verifica della conoscenza e della comprensione degli argomenti del corso, ed anche della capacità di applicare criticamente questa conoscenza generale per sviluppare la descrizione di: principali quantità che descrivono cristalli specifici; modelli teorici della struttura a bande per cristalli specifici; risposta ideale degli elettroni di un cristallo a campi esterni statici; disaccoppiamento approssimato elettroni-fononi; proprietà vibrazionali (fononi); proprietà termodinamiche e ottiche di cristalli specifici; proprietà di trasporto di cristalli specifici.
Lo studente deve essere in grado di descrivere chiaramente e discutere criticamente gli argomenti, con le relative ipotesi, le criticità, il significato fisico e l'approccio matematico (non sono strettamente richieste derivazioni complete, ma ovviamente la loro conoscenza può contribuire al voto finale; tuttavia la comprensione della fisica alla base dell'approccio matematico è considerata l'aspetto fondamentale).
Viene anche valutata la conoscenza di: sistemi di unità di misura, valori delle grandezze fisiche principali della Fisica dello Stato Solido, struttura dei cristalli più importanti e loro principali proprietà.

Carlo E. Bottani, Lecture Notes, Anno edizione: 2020
Note: disponibile sul sito BEEP del corso
C.Kittel, Introduction to Solid State Physics, 8th Edition, Editore: Wiley, Anno edizione: 2004
N.W. Ashcroft and N.D. Mermin, Solid State Physics, Editore: HRW International, Anno edizione: 1981
J.M. Ziman, Principles of the theory of solids (2-nd edition), Editore: Cambridge University Press, Anno edizione: 1995
F. Bassani, U.M. Grassano, Fisica dello Stato Solido, Editore: Bollati Boringhieri, Anno edizione: 2000
H. Ibach. H. Luth, Solid-State Physics, Editore: Springer, Anno edizione: 2009

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

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

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

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

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

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

Scopo dell'insegnamento è fornire allo studente gli elementi di base della robotica, con particolare riferimento ai robot manipolatori. L'inquadramento della robotica industriale e delle sue molteplici applicazioni motiva lo studio dei fondamenti della materia, dalla cinematica del robot, alla pianificazione e generazione delle traiettorie di movimento, al controllo in anello chiuso del moto.
Gli obiettivi dell'insegnamento sono del tutto allineati con gli obiettivi formativi del Corso di Laurea in Ingegneria dell'Automazione, esponendo lo studente a una delle tecnologie, la robotica, di maggiore rilievo nell'automazione.

I risultati di apprendimento attesi possono essere classificati secondo i descrittori di Dublino.
Dd1 (Knowledge and understanding)
Lo studente:

• conosce i principali elementi che caratterizzano un robot industriale
• dispone degli strumenti per caratterizzare posizione e orientamento di un corpo rigido
• comprende il modello cinematico di un robot
• conosce la pianificazione del moto e il controllo dei robot
  Dd2 (Applying knowledge and understanding)
  Lo studente:
• usa un approccio matematico strutturato per rappresentare la cinematica di un manipolatore robotico
• sa calcolare una traiettoria che il robot deve eseguire nello spazio dei giunti o nello spazio Cartesiano
• usa i più comuni dispositivi e algoritmi per misurare e attuare il moto dei robot manipolatori
• comprende l'architettura di un sistema di controllo di un robot industriale ed è in grado di eseguire la taratura dei relativi regolatori
• sa usare strumenti di simulazione e analisi numerica di manipolatori robotici
  Dd3 (Making judgement)
  Gli studenti saranno in grado di decidere quali tecniche di modellazione, pianificazione del moto e controllo adottare e come applicarle per la pianificazione e generazione del movimento dei manipolatori robotici

Introduzione alla robotica: Robot industriali, robot di servizio, mercato della robotica, trend in atto.
Caratterizzazione e selezione del robot: Specifiche tecniche del robot. Scelta del robot in base all'applicazione. Strumentazione.
Posizione e orientamento del corpo rigido: Formalismi per rappresentare la posizione e l'orientamento di un corpo rigido. Matrici di rotazione, angoli di Eulero, quaternioni. Matrici di trasformazione omogenea.
Cinematica del robot: Legami tra le posizioni dei giunti e dell'organo terminale. Cinematica diretta: convenzione di Denavit Hartenberg. Cinematica inversa.
Cinematica differenziale del robot: Legami tra le velocità dei giunti e dell'organo terminale. Lo Jacobiano del robot. Singolarità cinematiche. Cinetostatica.
Pianificazione del moto: Programmazione del moto del robot: linguaggi di programmazione. Pianificazione del percorso e pianificazione della traiettoria. Traiettorie nello spazio dei giunti. Traiettorie nello spazio operativo: traiettorie di posizione e orientamento.
Attuatori e sensori per la robotica: Motori DC e brushless. Sensori di posizione.
Controllo dei manipolatori robotici: Controllo indipendente ai giunti. Controllo del giunto robotico. Effetti della flessibilita' ai giunti.
Argomenti avanzati di robotica (cenni): Interazione con l'ambiente: sensori di forza e di visione. Robot ridondanti. Robot collaborativi

Prova scritta obbligatoria, senza prove in itinere
L'esame consiste in una prova scritta sull'intero programma dell'insegnamento, formata da esercizi e domande aperte sulla teoria. La prova scritta sarà integrata da un colloquio solo nei casi in cui il docente ne ravvisi l’opportunità.
Lo studente dovrà, in sede di esame:

• dimostrare di sapere operare correttamente con i formalismi per la caratterizzazione di posizione e orientamento di un corpo rigido (Dd1, Dd2)
• caratterizzare con gli opportuni strumenti metodologici la cinematica di un manipolatore robotico assegnato (Dd1, Dd2)
• calcolare cinematica diretta e inversa e determinare le singolarità di un manipolatore robotico assegnato (Dd1, Dd2)
• calcolare traiettorie nello spazio dei giunti e nello spazio operativo (Dd1, Dd2)
• discutere le proprietà di attuatori e sensori del moto (Dd2)
• discutere il sistema di controllo di posizione e velocità di un manipolatore robotico (Dd1, Dd2, Dd3)
• progettare i regolatori di posizione e velocità nel dominio delle frequenza (Dd2, Dd3)

Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani e Giuseppe Oriolo, Robotica. Modellistica, Pianificazione e Controllo, Editore: McGraw-Hill, Anno edizione: 2008, ISBN: 9788838663222 https://www.mheducation.it/9788838663222-italy-robotica-3ed
Gianantonio Magnani, Gianni Ferretti, Paolo Rocco, Tecnologie dei sistemi di controllo, Editore: McGraw-Hill, Anno edizione: 2007, ISBN: 978-8-83-867275-0 https://www.ilovebooks.it/tecnologie-sistemi-controllo-p-1450.html
Bruno Siciliano, Luigi Villani, Giuseppe Oriolo, Alessandro De Luca, Foundations of Robotics, Editore: Springer, Anno edizione: 2025, ISBN: 978-3-031-85522-1 https://doi.org/10.1007/978-3-031-85523-8

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The course provides a comprehensive overview of the fundamentals of signals and transmission systems and serves as a prerequisite for advanced coursework in communications. The course provides students with the tools to analyse deterministic signals and random processes, including a thorough treatment of Fourier analysis, practical examples, and real-world communications and signal-processing problems. Each topic is treated both theoretically and practically. The ability to apply the knowledge gained will be continuously assessed through recurring class exercises, homework, and simulation activities. The students are supported by a tutor in their understanding.

Knowledge and understanding (DD1)
Students will gain a solid background on signals, probability, and stochastic processes, as well as the understanding of the main functionality of digital communications.
Applying knowledge and understanding (DD2)
Students can apply the principles and tools of signals and systems acquired during the course to various disciplines. Linear systems, signal operations, and Fourier Transforms are widely used in mathematics, modeling, problem-solving, and advanced courses in data analytics. The students will be able to see connections among theoretical concepts and recognize how to apply them to telecommunication engineering.
Communication (DD4)
Students will learn to present course topics using the specialized vocabulary required for advanced studies in communications. They will develop the ability to communicate concepts through graphical and symbolic representations of signals and systems, as well as through appropriate formats for statistical analysis (e.g., histograms and word clouds). Furthermore, they will learn to express ideas and procedures using formal signal notation and terminology.

Deterministic signals.
Elementary signals & operations, continuous and discrete. Linear time-invariant systems: convolution, correlation and their applciations. Fourier analysis: properties, bandwidth and energy spectrum, sampling theorem, periodic signals, and the Discrete Fourier Transform. Examples and applications: sampling and aliasing, harmonic distortion, IQ modulation and complex baseband representation.
Probability.
Review of the basic elements of probability: definition, experiment & events, distribution, and densities: histograms. Conditioned probability & Bayes rules, some distributions (Normal, uniform, exponential). Moments (mean, variance, and correlation) and elements of statistics.
Stochastic random processes.
Introduction to processes: realisations and ensemble statistics - stationarity & ergodicity, cross-correlation & power spectrum density. Stochastic processes through a linear time-invariant system. Examples and applications: thermal and quantisation noise, delay estimation, time-correlated processes, signal restoration from noisy observations.
Digital communication.
Principle of baseband transmission of digital signals

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
The assessment is based on a written and an oral exam at the end of the course. The written exam consists of numerical exercises covering the course topics. During the course, students will engage in live test sessions, laboratories, and exercises in a flipped-classroom format that can replace the written component. The oral exam focused on theoretical questions and discussions of exercises.
In particular, students will be evaluated for the following aspects:
1) understanding the basic notations, formalisms and operations of signals, probability and processes ( DD1,DD4 )
2) understand the probabilistic structures of stochastic processes, as well as the use and misude of probability (DD1)
3) applying the basics of signals, systems and stochastic processes in real-life problems - though simplified ( DD2 )

The course is aimed at illustrating the fundamentals of the mathematical theory of interactions between agents. It starts with the discussion of the main assumptions underlying the theory, and it continues by considering the possible descriptions of a game: the extensive form and the strategic form. Both the cooperative and non cooperative theory will be considered. The goal is to explain how rationality can explain and/or predict and/or suggest the behavior of interacting agents. This is not limited to human being, it can also be applied to animals, networks of computers and so on.

Knowledge and understanding
1) To know the fundamentals of interactive decision theory.
2) To know some of the proofs of fundamental theorems in non cooperative game theory.
3) To know some of the proofs of fundamental theorems in cooperative game theory.
Ability in applying knowledge and understanding
1) To be able to model simple interactive situations as games.
2) To be able to state and explain the proofs of fundamental theorems in game theory.
3) To solve exercises.
Making judgements
1) To be able to state translate a problem in a game and analyze it.
Communication skills
1) To be able to explain and illustrate (in written form) a definition, the statement of a theorem, its proof.

1) Main assumptions of the theory. Main differences between decision theory and interactive decision theory.
2) Non cooperative games. Games in estensive form. Games with perfect information, backward induction. Combinatorial games.
3) Zero sum games. Conservative values. The case of equilibrium in pure strategies. Extending finite games to mixed strategies. The von Neumann theorem. Finding optimal strategies and the value of a finite game by means of Linear Programming.
4) The Nash non cooperative model, Nash equilibrium and existence of (mixed) equilibria in finite games. Examples. Potential games, how to find a potential. Examples: congestion games, routing games, network games, location games. Price of stability and of anarchy. Correlated equilibria.
5) Cooperative games, definitions, examples. Core, nucleolus, the Shapley value and power indices.

Prova scritta obbligatoria, senza prove in itinere
1) The exam is written. It consists of some exercises, and two open questions on the theory.
2) Each exercise and each open question is assigned a certain number of points. The sum is 35.
3) Students getting at least 32 points are graded with 30 lode
The exam is aimed at checking that the students:
1) are be able to model simple interactive situations as games.
2) are able to explain and illustrate (in written form) a definition, the statement of a theorem, its proof.
3) are able to solve exercises.
Evaluation of learning outcomes will be carried out in accordance to the Dublin Descriptors:
Type of assessment
Description
Dublin descriptor
Written test

• The solution of practical and computatinal problems
• Answer to theoretical questions
  1,2,3
  1, 2, 3, 4, 5

R. Lucchetti, A primer in game theory, Editore: Esculapio, Anno edizione: 2011
Maschler, Solan, Zamir, Game Theory, Editore: cambridge University Press, Anno edizione: 2013

Geophysical sounding is used for the remote detection and characterization of a target in the subsurface, producing images of its relevant properties for engineering, environmental, geological applications.
The course presents the seismic, electrical and electromagnetic investigation methods, from the fundamental theory up to the data collection and processing, with computer lab sessions and case histories discussion. During the course, part of the time is dedicated to experimental laboratory and field lessons, where students can practice with simulations: in particular, a real acoustic time tomography acquisition and processing will be carried out with the "digital twin" scheme.
Course objective is to frame the geohysical methods in the remote sensing techniques and to give the students competences and skills in the field of subsurface imaging.

Dublin Descriptors
Expected learning outcomes
1 - Knowledge and understanding
Students will gain clear understanding about:
the physics behind each geophysical method (seismic, electrical and EM)
the integration of surface and subsurface remote sensing technologies
signal processing methods for the treatment of seismic, electrical and EM geophysical data
2 - Applying knowledge and understanding
Students will be able to:
design a seismic, electrical or EM survey
define a seismic, electrical or EM data processing flow chart.
use and modify specialized SW to simulate a geophysical “scenario” (e.g. the elastic wave propagation in a layered medium) and to understand “experimentally” the theoretical principles.
3 - Making judgements
Students will be able to:
define the application fields and limits of seismic, electrical and EM geophysical method
analyze a geophysical image
read and understand a standard case history report
4 - Communication
Students will learn to:
write a technical document on a specific case study (e.g.: design and implementation of a remote sensing survey, algorithm development, system analysis, etc.)
explain the physical principles and the way of operation of seismic, electrical and EM geophysical methods
design prototypal demonstrations by modifying/tuning specialized SW deveoloped/provided during the course
Lifelong learning skills
Students will learn to:
manage a remote sensing project, from the definition of the objective to the analysis and presentation of the results.

Seismic methods
Elastic properties of rocks, Hooke law, elastic wave equation, Born approximation, diffraction tomography.
Refraction and reflection seismic methods: data acquisition and processing.
Traveltime tomography.
Examples of application and analysis of case histories.
Electrical and electromagnetic methods
Electrical properties of the rocks. Electromagnetic wave propagation in low loss media.
Electrical prospecting: data acquisition and inversion. Resistivity method, self-potential method, induced polarization method.
Electromagnetic prospecting: data acquistion and interpretation. Conductivity meter, metal detector, magnetotelluric method.
Ground Penetrating Radar: data acquisition and processing.
Examples of application and analysis of case histories.
Principles of rock physics
Physical properties of porous rocks and geophysical measurements: constitutive equations, rock properties estimation and observability.
Field campaign
The course provides a field campaign with some geophysical equipment (GPR, metal detector, electrical resistivity meter) were the students are involved in all the phases, from the design of the acquisition, the data collection and processing and the drawing of a final report.

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, individuale, senza revisioni periodiche, con presentazione/discussione finale
Oral exam and discussion of homeworks and of the field campaign. The homeworks (2-3) are assigned during the course, at the end of the presentation of each geophysical method. They consist in the utilization/design of simulation tools for building realistic scenarios and for discussing the imaging procedures. The exam can be given in italian or in english.

Cenni sulle acquisizioni geofisiche: misure ecografiche, misure tomografiche, misure di campo. Elaborazione di dati sismici: inversione dei primi arrivi, metodi reciproci, filtri mono e bidimensionali, recupero di guadagno, deconvoluzione, analisi di velocita', correzioni dinamiche, soppressione delle multiple, stack, migrazione. Elaborazione di dati georadar: filtri, sottrazione del background, recupero di guadagno, analisi di velocita', focalizzazione. Inversione tomografica: tomografie per tempi di volo, tomografie d'ampiezza. Trattamento di misure di campo: pseudosezioni, metodi di inversione iterativa.

Geophysical sounding is used for the remote detection and characterization of a target in the subsurface, producing images of its relevant properties for engineering, environmental, geological applications.
The course presents the seismic, electrical and electromagnetic investigation methods, from the fundamental theory up to the data collection and processing, with computer lab sessions and case histories discussion. During the course, part of the time is dedicated to experimental laboratory and field lessons, where students can practice with simulations: in particular, a real acoustic time tomography acquisition and processing will be carried out with the "digital twin" scheme.
Course objective is to frame the geohysical methods in the remote sensing techniques and to give the students competences and skills in the field of subsurface imaging.

Dublin Descriptors
Expected learning outcomes
1 - Knowledge and understanding
Students will gain clear understanding about:
the physics behind each geophysical method (seismic, electrical and EM)
the integration of surface and subsurface remote sensing technologies
signal processing methods for the treatment of seismic, electrical and EM geophysical data
2 - Applying knowledge and understanding
Students will be able to:
design a seismic, electrical or EM survey
define a seismic, electrical or EM data processing flow chart.
use and modify specialized SW to simulate a geophysical “scenario” (e.g. the elastic wave propagation in a layered medium) and to understand “experimentally” the theoretical principles.
3 - Making judgements
Students will be able to:
define the application fields and limits of seismic, electrical and EM geophysical method
analyze a geophysical image
read and understand a standard case history report
4 - Communication
Students will learn to:
write a technical document on a specific case study (e.g.: design and implementation of a remote sensing survey, algorithm development, system analysis, etc.)
explain the physical principles and the way of operation of seismic, electrical and EM geophysical methods
design prototypal demonstrations by modifying/tuning specialized SW deveoloped/provided during the course
Lifelong learning skills
Students will learn to:
manage a remote sensing project, from the definition of the objective to the analysis and presentation of the results.

Seismic methods
Elastic properties of rocks, Hooke law, elastic wave equation, Born approximation, diffraction tomography.
Refraction and reflection seismic methods: data acquisition and processing.
Traveltime tomography.
Examples of application and analysis of case histories.
Electrical and electromagnetic methods
Electrical properties of the rocks. Electromagnetic wave propagation in low loss media.
Electrical prospecting: data acquisition and inversion. Resistivity method, self-potential method, induced polarization method.
Electromagnetic prospecting: data acquistion and interpretation. Conductivity meter, metal detector, magnetotelluric method.
Ground Penetrating Radar: data acquisition and processing.
Examples of application and analysis of case histories.
Principles of rock physics
Physical properties of porous rocks and geophysical measurements: constitutive equations, rock properties estimation and observability.
Field campaign
The course provides a field campaign with some geophysical equipment (GPR, metal detector, electrical resistivity meter) were the students are involved in all the phases, from the design of the acquisition, the data collection and processing and the drawing of a final report.

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, individuale, senza revisioni periodiche, con presentazione/discussione finale
Oral exam and discussion of homeworks and of the field campaign. The homeworks (2-3) are assigned during the course, at the end of the presentation of each geophysical method. They consist in the utilization/design of simulation tools for building realistic scenarios and for discussing the imaging procedures. The exam can be given in italian or in english.

The course deals with some important network and graph optimization problems arising in several fields of application such as telecommunication, transportation and logistics. The course presents optimization models for different classes of problems and describes the main methods that have been proposed to tackle them.
The aim of the course is to provide students with a classification of optimization problems related to networks and other applications. Further, the course aims at providing the students with the knowledge of a wide range of optimization methods. The students will learn to model optimization problems arising in real life applications, to represent them as particular case of well known optimization problems, and to deal with them applying both exact and heuristic approaches. Beside theoretical knowledge, the course will provide practical experience in developing and applying the optimization techniques. During lab sessions, the students will be asked to apply their knowledge to deal with problems.

Knowledge and understanding (DdD 1)
The student will learn

• some of the main families of optimization problems (network design and flows, location, scheduling)
• the key heuristic methods, their features and properties
• the principal relaxation approaches
• the valid inequalities and some classical examples
  Applying knowledge and understanding (DdD 2)
  The student will be able to
• identify the mathematical model underlaying an application and, if possible, map it to existing families of optimization problems
• design a heuristic algorithm to tackle a problem, implement it and evaluate its performance
• apply relaxation techniques and implement them to obtain a bound for a problem
• derive valid inequalities based on classical examples and implement them to improve the bound
  Learning skills (DdD 3, DdD 5)
  The students will learn how to tackle complex optimization problems stemming from a variety of real-world applications. They will be able to devise advanced solution algorithms to such problems, and possibly map them to a graph structure to utilize computational efficiency. Moreover, students will be able to verify the quality of their algorithms through the development of adequate bounds.

Introduction. Brief review of mathematical programming models for graph and network optimization problems (paths, trees, minimum cost flow, maximum flow, travelling salesman problem, knapsack problem). Brief review of the Branch-and-Bound method and Cutting Plane algorithms (Gomory cuts).
Flow routing and Fixed Charge Network Design problems. Classification: splittable vs unsplittable flow, single vs multiple paths, multicommodity flows, node and arc fixed costs. Arc-based and path-based ILP models.
Column generation method. Dual problem and complementary conditions, the pricing problem.
Heuristic methods. Greedy algorithm. Local Search. Metaheuristics: tabu search, simulated annealing, variable neighborhood search. Very large scale neighborhood and ILP based neighborhood. Examples of application of heuristic methods.
Location problems. Classification: set covering and set partitioning, p -center and p -median problems, uncapacitated and capacitated facility location problems, single assignment and multiple assignment.
Valid inequalities and cutting planes. Cover inequalities for knpasack problem. Valid inequalities for network design and connectivity requirements.
Relaxation. Continuous relaxation, surrogate relaxation. Lagrangian relaxation: general framework and application to facility location problems and network design problems.
Scheduling problems. Introduction to scheduling problems. Classification: single machine scheduling with different objective functions (total weighted completion time, makespan, lateness, tardiness), parallel machines scheduling problems, flow shop, job shop. Models and algorithms for scheduling problems.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The exam includes a written test and a lab exam.
The written test includes:

• exercises: the student is required to apply the knowledge for solving small examples and for applying the optimization methods to a given problem
• theoretical questions on course topics with open answer: the student is required to demonstrate his/her knowledge of the optimization methods
  The lab part includes homeworks: the student is required to apply the methods and implement them to solve problems

R. Ahuja, T. Magnanti, J. Orlin, Network flows: Theory, Algorithms, and Applications, Editore: Prentice Hall, Anno edizione: 1993
D. Bertsekas, Network Optimization: Continuous and Discrete Models, Editore: Athena Scientific, Anno edizione: 1998
F.W.Glover, G.A.Kochenberger, Handbook of Metaheuristics, Editore: Springer-Verlag, Anno edizione: 2003
J. Desrosiers, M. Lübbecke, G. Desaulniers, J. B. Gauthier, Branch-and-Price, Anno edizione: 2026
H.W. Hamacher, Z. Drezner, Facility Location: Applications and Theory, Editore: Springer, Anno edizione: 2001
B. Korte, J. Vygen, Combinatorial Optimization: Theory and Algorithms, Editore: Springer Verlag, Anno edizione: 2001
M.L. Pinedo, Scheduling - Theory, Algorithms, and Systems, Editore: Springer, Anno edizione: 2008
V.J. Rayward-Smith, I.H. Osman, C.R. Reeves, G.D. Smith, Modern heuristic search methods, Editore: Wiley and Sons, Anno edizione: 1996
M. Piòro, D. Medhi, Routing, Flow, and Capacity Design in Communica- tion and Computer Networls, Editore: Elsevier, Anno edizione: 2004
S.Martello, P.Toth, Knapsack problems, Editore: Wiley, Anno edizione: 1990
Laurence A. Wolsey, Integer Programming, Anno edizione: 2020, ISBN: 1119606527

Obiettivo del corso è che lo studente comprenda i principali concetti utilizzati nei moderni microprocessori entro la fine del semestre.
Il corso tratta le diverse forme di parallelismo presenti nelle applicazioni (livello istruzione, livello dati, livello thread e livello porte logiche) e come esse possano essere sfruttate in combinazione con le diverse caratteristiche architetturali. Il corso tratta l'esecuzione pipelining, superscalare, speculativa e fuori ordine delle istruzioni, le macchine vettoriali e VLIW, il multithreading, i processori grafici e i microprocessori paralleli. Obiettivo finale del corso è mostrare come il software interagisce con lo hardware per fornire prestazioni e mostrare come le tendenze tecnologiche, nelle applicative ed economiche hanno guidato e continuano a guidare i cambiamenti nel settore.

The course is conceived as an experiential-based learning process, where students acquire frameworks, models, and tools to develop their business mainly from the experience and advice of practitioners, i.e., entrepreneurs, venture capitalists, business angels, incubator/accelerator managers, with whom the course is designed. The faculty will provide a core of preliminary concepts through frontal lectures on high-tech start-ups’ creation and funding, after that the students will start to work in teams on their own business models, receiving inputs and feedback from practitioners, who participate as guest speakers, and throughout tutoring sessions.
The course fits into the overall program curriculum pursuing some of the defined general learning goals. In particular, the course contributes to the development of the following capabilities:
Understand and design solutions, applying a scientific and engineering approach, in relation to the setting up of a new business, the design of its business model, and the identification of the proper funding channels
Interact in a professional, effective and constructive way in teams and with external practitioners

At the end of the course, the students will be able to:
Comprehend the functioning of the entrepreneurial ecosystem (DdD1 - Capability of knowledge and understanding; DdD2 - Capability of applying knowledge and understanding)
Understand what is a business model and its building blocks (DdD1 - Capability of knowledge and understanding; DdD2 - Capability of applying knowledge and understanding)
Identify the key challenges for the creation of high-tech start-ups and the funding mechanisms available, and understand when it is appropriate to use each of them, according to the specific start-up’s industry, funding requirements and stage of the life cycle (DdD1 - Capability of knowledge and understanding; DdD2 - Capability of applying knowledge and understanding).
Through the project work, the students will also be able to:
Test and validate the business model in the real world with potential customers (DdD3 - Capability of making judgments; DdD4 - Capability of communication)
Write an effective business proposal to ask for funding (i.e., to business angels, venture capitalists, incubator programs..) (DdD3 - Capability of making judgments; DdD4 - Capability of communication)
Effectively work in teams (DdD3 - Capability of making judgments; DdD4 - Capability of communication)
Interact with practitioners of the financial sector (DdD4 - Capability of communication)
Summarize and present the results achieved in the business proposal in front of potential investors clearly and convincingly. (Capability of communication)

The course program includes the following topics:
The creation of high-tech start-ups. Entrepreneurship in high-tech industries and high-tech start-ups in Italy and in the world
The development of of high-tech start-ups, and their performances, including the success factors
The Business Model: what is a business model and how to fund it through equity capital, debt capital and public subsidies
How to design the Business Model: the value proposition and business model testing; the use of the Business Model Canvas framework; organizational structure and management; market strategy and market positioning; competitors’ analysis; how to protect competitive advantages; business model risk assessment
The financing of high-tech start-ups: a series of guest speakers, including entrepreneurs, venture capitalists, business angels, incubator/accelerator managers, will bring their direct experience through success stories regarding the funding (and creation) of high-tech start-ups (entrepreneurs) and explaining their decision-making and investment process (investors)
How to design and perform an effective elevator pitch.

Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
Valutazione continua condizionata (a scelta del docente)
Students must identify a new business idea based on an innovative technology (e.g., the results of current scientific research) or an innovative way of doing business in an incumbent industry (e.g., an innovative business model), form an entrepreneurial team, develop a document describing the business model, and prepare an elevator pitch.
The examination consists of the evaluation, by the faculty and a commission of practitioners (including guest speakers of the course), of both the business model document and its presentation before the same commission.
In the examination, students must demonstrate that:
They have a good understanding of the challenges involved in creating and funding a new start-up and of how financial intermediaries and other actors of the entrepreneurial and financial ecosystem may support the start-up’s development
(DdD1 – Knowledge and Understanding; DdD2 – Applying Knowledge and Understanding).
They understand the structure and components of a business model and are able to design and develop one tailored to their business idea
(DdD1 – Knowledge and Understanding; DdD2 – Applying Knowledge and Understanding).
They have effectively validated the proposed business model in the real world and are able to critically assess and select the most appropriate funding channels for the venture
(DdD3 – Making Judgements; DdD2 – Applying Knowledge and Understanding).
They are able to summarize, communicate, and defend the key elements of their business model clearly and convincingly in a public setting through an effective elevator pitch
(DdD4 – Communication Skills).
During the tutoring sessions, as well as in the final examination, students must also demonstrate that:
They are able to work effectively within entrepreneurial teams, contributing constructively to the development of the project and the achievement of shared objectives
(DdD4 – Communication Skills; DdD5 – Learning Skills).
They are able to critically incorporate and leverage feedback and advice provided by practitioners and guest speakers to improve their business model and entrepreneurial proposal
(DdD3 – Making Judgements; DdD5 – Learning Skills).

clips on startup creation made available on Beep
Note: Topics:
team formation;
characteristics of entrepreneurial ecosystems;
startup funding;
business models.
Alexander Osterwalder, Yves Pigneur, Business model generation, Editore: John Wiley & Sons, Anno edizione: 2010

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

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

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

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

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

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

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

Module 2 aims to inform students about the content of Accessible Graphic Design, providing a basis in the organisation and design of accessible digital interfaces.
Main contents are: Principles of Universal Design; The design of Information; Use of colours, typography and icons; Ideal contrasts; Responsive design.
Moreover, the module aims to support and revise, from a pure Communication Design point of view, the annual projects developed within the Course of Inclusive Digital Technologies.

Dublin Descriptors
Expected learning outcomes
1. Knowledge and understanding
The students will learn how to:
master the different phases in a human-centered approach to the design of innovative, interactive technologies sustaining inclusive digital services
analyze the system’s users and context of use, and recognize specific needs (e.g., related to physical abilities, gender, socio-economic status)
consider the users’ needs in the design of digital technologies that behave respecting users’ diversity
evaluate design artifacts with a focus on inclusivity
prototype new technological solutions for conducting iterative design-and-evaluation cycles.
2. Applying knowledge and understanding
Given specific project cases, students will be able to:
Detail the corresponding requirements
Analyze conceptual and architectural choices
Apply the learned principles to identify technological solutions addressing inclusivity
Build interactive prototypes fulfilling the high-level and design specifications.
3. Making judgments
Students will be able to:
Analyze and model the goals, assumptions and requirements associated with identified problems for inclusivity
Identify the most adequate design principles and methods
Select the most adequate interactive paradigms and technologies
4. Communication
The students will learn how to explain design decisions at the correct level of abstraction, to highlight the strength of their design while motivating possible weaknesses.
5. Lifelong learning skills
The students will learn how to design inclusive interactive systems based on a human-centered approach.

Course lectures will cover the key principles and methods for the design of inclusive technologies and services:
Human-centered design: user research, participatory design and co-design, user-based evaluation
Interaction, UX and user interface design
Usability and Accessibility: principles, standards, evaluation methods and tools
Principles for Inclusive Design
Human-centered, inclusive AI: recognizing bias and exclusion in AI
Emerging technologies for inclusive digital services, e.g.: Conversational AI, IoT and tangible computing, gesture-based interaction, AR/VR.

Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
The teaching and learning approach is project-based: students will learn by developing a project in which they apply the methodological and theoretical concepts introduced during the lectures.
Project activities will begin after the initial lectures presenting the methodological framework of inclusive and human-centred design. Companies, associations, and researchers working in the field of accessibility and inclusion will be invited to give seminars and propose relevant application domains and challenges for the students’ projects. The teachers and teaching assistants will supervise the project work through weekly reviews, supporting students in the progressive refinement of their analysis, design choices, prototypes, and evaluation activities.
The final assessment will be based on:
the quality of the intermediate project deliverables, including the analysis of users, needs, barriers, and contexts of use, as well as the appropriate application of inclusive and human-centred design methods;
the quality of the proposed solution and, where applicable, of the developed prototype, considering its coherence with the identified requirements and its accessibility, usability, inclusiveness, and technical feasibility;
the quality of the technical and design documentation, including the clarity of the adopted process, the justification of the design decisions, and the critical discussion of the strengths, limitations, and possible improvements of the proposed solution;
the final presentation and discussion of the project, which will assess the students’ ability to communicate the work effectively, use appropriate disciplinary terminology, justify their choices, and demonstrate an individual understanding of the project.
In relation to the learning objectives, the assessment activities will evaluate:
knowledge and understanding, through the correct use of concepts, principles, and methods related to accessibility, inclusive design, and the development of digital technologies for diverse users;
applying knowledge and understanding, through the ability to analyse accessibility and inclusion challenges, identify user requirements, and design, prototype, and evaluate appropriate technological solutions;
making judgments, through the ability to select suitable methods, compare alternative design solutions, critically interpret evidence and feedback, and reflect on the ethical and social implications of the proposed technologies;
communication skills, through the quality of the project documentation, the representation of the design process and results, and the final presentation and discussion;
learning skills, through the ability to incorporate feedback from the weekly reviews, autonomously investigate unfamiliar user needs, methods, standards, and technologies, and progressively improve the proposed solution.
The overall evaluation will consider both the quality of the final outcomes and the methodological rigour, critical awareness, and inclusiveness of the process followed to achieve them. The final discussion will also make it possible to assess the individual contribution and learning of each group member.

Course slides (available on WeBeep) https://webeep.polimi.it/login/index.php

Il corso si propone di introdurre le problematiche che sorgono
all'intersezione tra informatica e diritto. Nella prima parte del corso
verranno mostrate tutte le principali problematiche giuridiche connesse
all'informatica. Nella seconda parte del corso, dedicata all'analisi
forense dei dispositivi digitali e dei sistemi informatici, saranno
illustrate le principali tecniche e metodologie di computer e network
forensics, nonché gli aspetti tecnico/legali relativi alla presentazione
dei risultati in tribunale. Il corso unirà aspetti teorici e pratici,
con analisi di casi di studio

This course is about how to measure, represent, and communicate information efficiently. It explains why bits have become the universal currency for information exchange and how information theory underpins the design and operation of modern technologies such as smartphones, the Internet, and cloud computing. Information measures are first introduced and then applied to the analysis of the fundamental performance limits achievable in data compression and communication over noisy channels.
The goal of the course is to teach students how the concepts of source coding and channel capacity are used to model, analyze, and design modern communication systems in order to enable efficient information processing. Since these concepts constitute a fundamental foundation of virtually every digital communication system, another objective is to establish concrete links between information theoretic principles and advanced communication technologies. To this end, students will have the opportunity to work on projects that apply the concepts and methodologies learned during the course.
In addition to the foundations of classical information theory, the course provides an introduction to Quantum Information Theory. Whereas classical information theory is based on the representation, processing, and transmission of information using classical bits, quantum information theory considers information encoded in quantum bits, or qubits, which obey the principles of quantum mechanics. This part of the course introduces the fundamental concepts of quantum information and highlights the similarities and differences between the classical and quantum frameworks.
The course also includes project activities carried out in collaboration with industrial partners. Depending on the assigned topic, students will address either classical information theory problems, including source coding and channel coding, or introductory applications of quantum information theory. These projects are designed to connect the theoretical concepts presented during the course with practical engineering problems arising in industrial applications.

1 - Knowledge and understanding
Lectures and exercise sessions will allow students to:

• know how to measure information quantitatively
• know theoretical and methodological skills related to the concept of classical and quantum information
• understand properties of entropy, mutual information as applied them to information processing
  2 - Applying knowledge and understaning
  After the end of the course, students should be able to:
• understand and solve complex problems in different interdisciplinary areas
• integrate and individually find and re-adapt known solutions to growing problems
• analyse uncertainty in terms of information entropy and other information measures
• summarize and present the results achieved during the analysis and specification activities
• to communicate the results of their activity in a clear and convincing way
  3 - Making judgements
  After the end of the course, students should be able to:
• define mathematical models by considering the informational aspects of a system and the related measures
• evaluate concrete schemes for compression and communication

1 – Introduction to Information Theory
Historical background. Introduction to information theory and its applications.
2 - Entropy and Source Coding
Entropy of a memoryless source. Coding of memoryless sources. Prefix codes. Kraft inequality. Huffman codes and Shannon codes. Source coding theorems for memoryless sources. Joint entropy and conditional entropy. Chain rules. Entropy of sources with memory. Source coding theorem for memory sources. Practical source coding methods. Universal coding. Arithmetic coding.
3 - Channel Capacity
Channel models. Discrete memoryless channels. Mutual information. Relative entropy. Data processing inequality. Channel capacity. Reliable communication over discrete memoryless channels. Entropy, mutual information, and channel capacity for continuous channels. Additive white Gaussian noise (AWGN) channel. Channel coding theorem. Error exponent. Fano's inequality.
4 – From Classical to Quantum Information Theory
Motivation for quantum information theory. Quantum states. Qubits. Bra–ket notation. Unitary evolution. Quantum gates. Quantum measurement and the Born rule. Density operators. Pure and mixed states. Composite quantum systems. Entanglement. Bell states. No-cloning theorem. Quantum entropy. Properties of quantum entropy. Quantum mutual information. Coherent information.
5 – Project on Selected Topics
A voluntary project laboratory will be assigned during the semester. All projects will be related to the course topics covered in Sections 1–4 and will be carried out either in collaboration with industrial partners, who will propose engineering problems, or on topics proposed by the students and approved by the course instructors. Depending on the selected project, students will address classical information theory, including source and channel coding, or introductory topics in quantum information theory.
Projects will be carried out in groups of two or three students during the second half of the semester. Their objective is to apply the concepts learned in the course to realistic engineering problems. The evaluation will be based on the produced artifacts, such as documentation, software, or experimental results, together with a final presentation. Technical discussions with the course instructors and researchers from the participating companies will complement the project activities.

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
The written exam evaluates Knowledge and understanding (DD1) and Applying knowledge and understanding (DD2) through numerical exercises and open questions on the topics addressed in the course. It can assign up to 23 points, which form the basis for the final mark before the oral examination.
The oral examination evaluates Making judgements (DD3), Communication skills (DD4), and Learning skills (DD5). It can assign up to 10 points.
Students may choose to complete a voluntary project in place of the oral examination. The project evaluates Applying knowledge and understanding (DD2), Making judgements (DD3), and Communication skills (DD4) in the context of an applied study. It can assign up to 10 points.
The mark of 30 cum laude is awarded when the total score after the project presentation reaches 33.

Lecture notes https://webeep.polimi.it/course/view.php?id=14728
Luca Barletta, Gianluigi Liva, Maurizio Magarini, Lecture Notes on Channel Coding https://webeep.polimi.it
Note: Supplemental Material for the Courses Channel Codes for of Iterative Decoding (TUM) and Information Theory (PoliMi).
T. M. Cover, J. A. Thomas, Elements of information theory (1st or 2nd edition), Editore: John Wiley & Sons, 1991 (1st ed.), 2006 (2nd ed.). First edition available online for PoliMi students (http://onlinelibrary.wiley.com/book/10.1002/0471200611)
Sandro Bellini, Information Theory https://webeep.polimi.it/mod/folder/view.php?id=337337
D. J. C. MacKay, Information Theory, Inference, and Learning Algorithms, Editore: D. J. Cambridge University Press, 2003
R. G. Gallager, Information Theory and Reliable Communication, Editore: John Wiley & Sons, 1968

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

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

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

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

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

L’obiettivo del corso è offrire una panoramica sulle attuali tecnologie per il trasferimento dell’informazione, focalizzandosi su trasmissioni in scenari attuali di uso comune (Internet in ambiente residenziale ed in mobilità, connessioni tra datacenter, ambiente IoT per comunicazioni tra oggetti intelligenti). Il corso affronta diverse tipologie di connessione, dalla rete terrestre cablata alle comunicazioni wireless e satellitari a lungo e corto raggio, oltre ad un’introduzione delle future comunicazioni quantistiche. Saranno presentate alcune caratteristiche di base della trasmissione digitale, con particolare attenzione ai parametri fondamentali necessari ad un corretto progetto di sistemi di trasmissione dell’informazione. Saranno evidenziati gli aspetti comuni e i tratti distintivi dei diversi sistemi di comunicazione, in modo da apprendere come analizzare in chiave critica i requisiti richiesti, al fine di progettare e dimensionare il sistema più adeguato a seconda degli specifici ambiti applicativi e dei servizi da supportare. Ogni scenario presenta casi d’uso di sistemi di comunicazioni reali, affrontati dal punto di vista del loro dimensionamento e progetto.

Descrittori di Dublino
Risultati di apprendimento attesi
1. Conoscenza e comprensione
Conoscere i concetti fondamentali dei sistemi di comunicazione (bande di trasmissione, capacità, caratteristiche dei mezzi trasmissivi, multiplazione, indicatori di performance)
Comprendere le principali caratteristiche di un collegamento in fibra ottica, satellitare e radiomobile
Conoscere gli elementi basilari e i vincoili specifici di ogni scenario di connessione
Conoscere le principali tecnologie di interconnessione per sistemi a lungo e corto raggio
Comprendere le differenze tra le diverse tipologie di sistemi di comunicazione
2. Capacità di applicare conoscenza e comprensione
Valutare le prestazioni di un sistema di comunicazione nel trasferimento di informazioni
Dimensionare correttamente un sistema di comunicazione a lunga distanza o a corto raggio
Progettare un sistema di comunicazione in funzione dei requisiti dei servizi e della qualità da garantire
5. Capacità di apprendimento
Avere acquisito padronanza dei concetti fondamentali delle diverse tipologie di sistemi di comunicazione e saperli applicare a casi concreti
Essere in grado di utilizzare le conoscenze e metodologie imparate per affrontare problemi nuovi

Cenni sui sistemi di comunicazione.
Richiami sui principi della trasmissione digitale: bande di trasmissione e portanti, capacità di canale e bit rate.
Propagazione e mezzi trasmissivi: attenuazione di canale e consumi energetici nelle trasmissioni.
Rapporto segnale/rumore, probabilità d’errore e modulazione.
Parametri delle antenne.
Cenni sulle tecniche di multiplazione.
Sistemi di trasmissione in fibra ottica.
Fibra ottica monomodale e fibre speciali. Caratteristiche della fibra ottica: attenuazione, dispersione, non linearità.
Reti in fibra ottica per collegamenti a lunga distanza (terrestri e sottomarini), metropolitani o d’accesso. La multiplazione a divisione di lunghezza d’onda.
Componenti per sistemi di trasmissione in fibra ottica: tipologie di trasmettitori e ricevitori, filtri ottici, amplificatori.
Dimensionamento di un collegamento terrestre/sottomarino a lunga distanza.
Dimensionamento di un collegamento inter-datacenter per servizi in cloud.
Sistemi di trasmissione satellitare.
Effetti atmosferici sulla propagazione delle onde elettromagnetiche nei collegamenti satellitari.
Caratteristiche dei sistemi satellitari: applicazioni e servizi, orbite (GEO, MEO, LEO, ecc.), frequenze (bande Ku, Ka, Q/V), sistemi di mitigazione degli effetti atmosferici.
Dai satelliti GEO alle costellazioni LEO.
Dimensionamento e progetto di un collegamento terra-spazio.
Sistemi di accesso a Internet.
Sistemi di accesso tradizionali: propagazione su doppino telefonico e cavo coassiale. Le tecnologie ADSL/VDSL.
Sistemi FTTx. Le reti ottiche passive (PON).
Dimensionamento di un sistema di accesso a Internet residenziale basato su rete ottica PON.
Tecnologie di interconnessione per l’IoT
Consumo energetico e efficienza energetica in sistemi IoT.
Il problema dell’accesso multiplo.
Tecnologie long range e short range wireless per l’IoT.
Dimensionamento e progetto di sistemi long range low power per l’IoT: il caso LoraWAN.
Dimensionamento e progetto di sistemi short range wireless: il caso BLE.
Introduzione alle tecnologie per il Quantum Internet
Motivazioni e sviluppo del Quantum Internet.
Reti classiche vs reti quantistiche.
Modalità di trasmissione dell’informazione quantistica: l’uso dell’entanglement.
Esempi di trasmissioni quantistiche.

Prova scritta condizionata (a scelta del docente), con prove in itinere
Prova orale condizionata (a scelta del docente)
Valutazione continua condizionata (a scelta del docente)
L’esame prevede una prova scritta al termine del corso. In alternativa lo studente può sostenere tre prove intermedie durante il semestre sostitutive della prova scritta.
Descrizione
La prova scritta sarà costituita da esercizi e/o domande di teoria sui sistemi in fibra ottica, sui sistemi satellitari, sui sistemi di accesso a internet e sui sistemi per l’IoT.
Le prove intermedie potranno comprendere esercizi, domande (aperte e a scelta multipla), piccoli progetti monografici sui sistemi in fibra ottica, sui sistemi satellitari, sui sistemi di accesso a internet e sui sistemi per l’IoT.
Modalità di verifica
Descrizione
Risultato di apprendimento perseguito
Prova scritta/orale
risoluzione di problemi numerici (link budget, prestazioni dei sistemi di comunicazione)
esercizi di tipo progettuale sul dimensionamento delle diverse tipologie di sistemi di comunicazone
rispondere a domande di carattere teorico sulle tecnologie alla base dei diversi sistemi di comunicazione
DdD1, DdD2
DdD1, DdD2, DdD5
DdD1

The primary objective of the LAB EXPERIENCE courses is to engage students in the research environment by involving them in hands-on, laboratory-style activities conducted in coordinated teams. These activities will take place within the research laboratories affiliated with the Master program and will be guided directly by faculty members and researchers. The courses offer the valuable opportunity to experience the role of a researcher firsthand within a complex research setting, while also providing practical training in collaborative research work alongside experienced professionals. The distribution between classroom teaching, laboratory and project/design activities will depend on the specific laboratories activated in each semester although in all cases laboratories and project activities will constitute >75% of the total time.
The LAB EXPERIENCE IN COMMUNICATION NETWORKS focuses on applied research in communication networks, encompassing diverse topics such as Internet of Things (IoT), 5G mobile networks, optical transport networks and SDN/NFV-based network infrastructures. Students will work hands-on with advanced testbeds, design and prototype networked devices, explore data-driven network optimization, and collaborate in real research environments alongside experienced researchers and developers.

1. Knowledge and understanding (DD1)
Depending on the specific laboratory activity undertaken, students will be able to:
identify the main instruments for characterization and design of hardware components and system elements in communication systems
understand tools and algorithms for data generation and processing in communication-related experiments
identify the relevant figures of merit to assess communication-related experiments
2. Applying knowledge and understanding (DD2)
Depending on the specific laboratory activity undertaken, students will be able to:
use the main instruments for characterization and design of hardware components and system elements in communication systems
apply tools and algorithms for data generation and processing in communication-related experiments
identify problems and limitations of practical laboratory testing or processing
based on the observed behavior, assess components and/or system elements and/or tools and/or algorithms in their application context or laboratory setup
4. Learning Skills (DD5)
Depending on the specific laboratory activity undertaken, students will be able to:
work independently or in group to plan and execute physical or simulated experiments

The LAB EXPERIENCE IN COMMUNICATION NETWORKS course offers students the opportunity to explore and apply concepts from modern network technologies in real-world scenarios. Activities may include configuring and testing optical networks using commercial SDN controllers, developing IoT devices from sensors to cloud integration, and deploying and validating 5G mobile systems in virtualized environments. Students will gain experience in project-based teamwork, software/hardware integration, data analytics, wireless protocols, and networked systems design. The actual laboratories activated each semester will vary depending on the number of participants and the availability of infrastructure and supervision.

Progetto/i - Elaborato/i condizionato (a scelta del docente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
Valutazione continua condizionata (a scelta del docente)
The course has a PASS/FAIL evaluation procedure based on the student’s commitment to the proposed lab activities and evaluated considering the following aspects:

• Demonstrate a clear understanding of the principles and objectives of laboratory research and of the used hardware/software tools. (DD1, DD2)
• Address and solve complex experimental problems by selecting suitable methods and tools. (DD2)
• Work independently in planning and executing experiments. (DD5)
  The actual evaluation strategy will depend on the individual laboratories and will be clarified at the beginning of the activities.

The primary objective of the LAB EXPERIENCE courses is to engage students in the research environment by involving them in hands-on, laboratory-style activities conducted in coordinated teams. These activities will take place within the research laboratories affiliated with the Master program and will be guided directly by faculty members and researchers. The courses offer the valuable opportunity to experience the role of a researcher firsthand within a complex research setting, while also providing practical training in collaborative research work alongside experienced professionals. The distribution between classroom teaching, laboratory and project/design activities will depend on the specific laboratories activated in each semester although in all cases laboratories and project activities will constitute >75% of the total time.
The LAB EXPERIENCE IN DATA COMMUNICATIONS course focuses on data transmission across a range of applications explored in dedicated laboratories: from fiber optic and quantum communications to fiber sensing, with the aim to analyze the underlying subsystems using advanced laboratory testing equipment; from use and test of real digital and wireless communication devices and systems to the usage of information theory, and statistical learning, to support the practical design, operation, and implementation of modern communication systems.

1. Knowledge and understanding (DD1)
Depending on the specific laboratory activity undertaken, students will be able to:
identify the main instruments for characterization and design of hardware components and system elements in communication systems
understand tools and algorithms for data generation and processing in communication-related experiments
identify the relevant figures of merit to assess communication-related experiments
Applying knowledge and understanding (DD2)
Depending on the specific laboratory activity undertaken, students will be able to:
use the main instruments for characterization and design of hardware components and system elements in communication systems
apply tools and algorithms for data generation and processing in communication-related experiments
identify problems and limitations of practical laboratory testing or processing
based on the observed behavior, assess components and/or system elements and/or tools and/or algorithms in their application context or laboratory setup
Learning Skills (DD5)
Depending on the specific laboratory activity undertaken, students will be able to:
work independently or in group to plan and execute physical or simulated experiments

The LAB EXPERIENCE IN DATA COMMUNICATIONS course offers a unique opportunity to complement theoretical knowledge with cutting-edge laboratory work in the fields of data transmission. Students will engage in hands-on activities focused on various aspects of data communications, gaining practical insight into key technologies and systems:
use of devices for the transmission and the detection of telecom and sensing signals and quantum bits in optical fibers; test of their performance employing laboratory instrumentation, such as optical and electrical signal analyzers;
implementation and testing of software defined radio systems on state-of-the-art hardware evaluation boards for terrestrial and aerial communications and to collect and analyze data in wireless sensor experiments for communication and localization;
design of efficient compression algorithms in the short block length regime and of quantum error correcting codes via machine learning.
These experiences provide a powerful way to deepen understanding, develop technical skills, and connect theory with real-world applications.
The actual laboratories activated in each semester are determined by number of enrolled students and availability of the laboratories and instruments.

Progetto/i - Elaborato/i condizionato (a scelta del docente), individuale, senza revisioni periodiche, con presentazione/discussione finale
Valutazione continua condizionata (a scelta del docente)
The course has a PASS/FAIL evaluation procedure based on the student’s commitment to the proposed lab activities and evaluated considering the following aspects:

• Demonstrate a clear understanding of the principles and objectives of laboratory research and of the used hardware/software tools. (DD1, DD2)
• Address and solve complex experimental problems by selecting suitable methods and tools. (DD2)
• Work independently in planning and executing experiments. (DD5)
  The actual assessment method will depend on the specific laboratory and will be clarified at the beginning of the activities.

The primary objective of the LAB EXPERIENCE courses is to engage students in the research environment by involving them in hands-on, laboratory-style activities conducted in coordinated teams. These activities will take place within the research laboratories affiliated with the Master program and will be guided directly by faculty members and researchers. The courses offer the valuable opportunity to experience the role of a researcher firsthand within a complex research setting, while also providing practical training in collaborative research work alongside experienced professionals. The distribution between classroom teaching, laboratory and project/design activities will depend on the specific laboratories activated in each semester although in all cases laboratories and project activities will constitute >75% of the total time.
The LAB EXPERIENCE IN MICROWAVES AND OPTICS course is dedicated to applied electromagnetism in its various flavors, as studied in the related laboratories: from designing and testing RF/microwave devices to analyzing real-world electromagnetic propagation data, from the characterization of photonic devices using advanced lab tools, to manufacturing micro and nanotechnologies in Polifab's clean room.

The primary objective of the LAB EXPERIENCE courses is to engage students in the research environment by involving them in hands-on, laboratory-style activities conducted in coordinated teams. These activities will take place within the research laboratories affiliated with the Master program and will be guided directly by faculty members and researchers. The courses offer the valuable opportunity to experience the role of a researcher firsthand within a complex research setting, while also providing practical training in collaborative research work alongside experienced professionals. The distribution between classroom teaching, laboratory and project/design activities will depend on the specific laboratories activated in each semester although in all cases laboratories and project activities will constitute >75% of the total time.
1. Knowledge and understanding (DD1)
Depending on the specific laboratory activity undertaken, students will be able to:
identify the main instruments for characterization and design of hardware components and system elements in communication systems
understand tools and algorithms for data generation and processing in communication-related experiments
identify the relevant figures of merit to assess communication-related experiments
2. Applying knowledge and understanding (DD2)
Depending on the specific laboratory activity undertaken, students will be able to:
use the main instruments for characterization and design of hardware components and system elements in communication systems
apply tools and algorithms for data generation and processing in communication-related experiments
identify problems and limitations of practical laboratory testing or processing
based on the observed behavior, assess components and/or system elements and/or tools and/or algorithms in their application context or laboratory setup
4. Learning Skills (DD5)
Depending on the specific laboratory activity undertaken, students will be able to:
work independently or in group to plan and execute physical or simulated experiments
The LAB EXPERIENCE IN MICROWAVES AND OPTICS course is dedicated to applied electromagnetism in its various flavors, as studied in the related laboratories: from designing and testing RF/microwave devices to analyzing real-world electromagnetic propagation data, from the characterization of photonic devices using advanced lab tools, to manufacturing micro and nanotechnologies in Polifab's clean room.

The LAB EXPERIENCE IN MICROWAVES AND OPTICS course offers a unique opportunity to complement theoretical learning with cutting-edge laboratory experiences in the Microwave and Photonics area. Students will participate in hands-on activities tailored toward different topics concerning applied electromagnetics:
analysis of real-world propagation data from terrestrial and satellite experiments at radio and optical frequencies;
characterization of radio-frequency and photonic devices with laboratory instrumentation, such as generators, laser sources and spectrum analyzers;
fabrication of micro- and nano-technologies in the Polifab clean room, via lithography, deposition and etching through high-precision equipment.
These experiences provide a powerful way to deepen understanding, develop technical skills, and connect theory with real-world applications. The actual laboratories activated in each semester are determined by number of enrolled students and availability of the laboratories and instruments.

Progetto/i - Elaborato/i condizionato (a scelta del docente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
Valutazione continua condizionata (a scelta del docente)
The course has a PASS/FAIL evaluation procedure based on the student’s commitment to the proposed lab activities and evaluated considering the following aspects:

• Demonstrate a clear understanding of the principles and objectives of laboratory research and of the used hardware/software tools. (DD1, DD2)
• Address and solve complex experimental problems by selecting suitable methods and tools. (DD2)
• Work independently in planning and executing experiments. (DD5)
  The actual assessment procedure will depend on the individual laboratories and will be clarified at the beginning of the activities.

The primary objective of the LAB EXPERIENCE courses is to engage students in the research environment through hands-on, laboratory-style activities conducted in coordinated teams. These activities will occur within the research laboratories affiliated with the Master's program and will be guided directly by faculty members and researchers. The courses offer a valuable opportunity to experience the role of a researcher firsthand in a complex research setting, while also providing practical training in collaborative research alongside experienced professionals. The distribution among classroom teaching, laboratory, and project/design activities will depend on the specific laboratories activated during the semester, although in all cases laboratories and project activities will constitute >75% of the total time.

DD1 – Knowledge and understanding
Acquire knowledge of advanced methodologies, experimental techniques, and data analysis tools in selected areas of signal processing and telecommunications research
DD2 – Applying knowledge and understanding
Apply theoretical knowledge to the design, implementation, and validation of laboratory experiments and data analysis workflows. Depending on the specific laboratory activity undertaken, students will be able to use instrumentation for the characterization and design of communication system components, apply tools and algorithms for data generation and processing, identify practical limitations of laboratory testing, and assess the performance of components, systems, and algorithms within their application context.
DD5 – Learning skills
Develop the ability to independently or collaboratively plan and conduct experimental activities, critically analyse results, and adapt methodologies and tools to address new research and engineering challenges.

The Lab Experience in Signals and Data Analysis introduces students to experimental methodologies and data analysis techniques across several research areas, including wireless communications and localisation, radar and geophysical sensing and imaging, AI-assisted signal processing, target detection and tracking, connectivity for intelligent transportation systems, and multimedia forensics. Students will engage in hands-on experiments, collaborating with graduate students, researchers and professors, in laboratories such as IoTLab, Image and Sound Processing Lab, Wisynet Lab, and Ride Lab. They will gain direct experience with state-of-the-art instruments, software tools, and experimental datasets.
The course offers the opportunity to practice and learn in the following areas:
Signal processing for indoor/outdoor localisation, vehicle-to-everything (V2X) channel and link measurement and analysis, target detection and tracking
Signal processing and machine learning techniques in multimedia forensics. Forensics detectors for audio-visual analysis, taking hardware and resource management into account, detecting deepfake speech recordings
AI (such as Large Language Models) as tools for signal processing applied to wireless communication applications, high-frequency, and high-mobility systems
Geophysics and radar sensing, and imaging. Ultra-widebandwidth compact MIMO Radar inSAR configuration. SAR imaging and focusing, interferometry, and tomography. Geophysics experiments on propagation in pipes and acoustic tomography.
These experiences offer a valuable opportunity to enhance understanding, build technical skills, and bridge the gap between theory and real-world applications. The specific laboratories offered each semester depend on student enrollment numbers and the availability of facilities and equipment.

Progetto/i - Elaborato/i condizionato (a scelta del docente), di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
Valutazione continua condizionata (a scelta del docente)
The course has a PASS/FAIL evaluation procedure based on the student’s commitment to the proposed lab activities and evaluated considering the following aspects:

• Demonstrate a clear understanding of the principles and objectives of laboratory research and of the used hardware/software tools. (DD1, DD2)
• Address and solve complex experimental problems by selecting suitable methods and tools. (DD2)
• Work independently in planning and executing experiments. (DD5)
  The actual evaluation strategy will depend on the individual laboratories and will be clarified at the beginning of the activities

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

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

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

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

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

The aim of the course is to provide the students with the fundamental knowledge in the field of low-temperature, technologically relevant plasmas. Particular emphasis is placed on their applications in the field of material engineering and nuclear engineering, such the synthesis of coatings and nanostructures, surface micro- and nano-strutturing, laser-plasma radiation sources and plasma-wall interaction for nuclear fusion research. The course is characterized by a modular structure and a learn-by-doing approach. Each module has curricular laboratory sessions in which the students will put in action their knowledge by means of experimental and/or computational activities. Moreover, a laboratory project will be assigned as the conclusive part of the course: the students, arranged in teams, will design and carry out an experimental campaign dealing with one of the topics presented during the course.

At the end of the course the student:

• Knows and understands the principles of plasma physics, with particular reference to low-temperature and technologically-relevant plasmas, and their applications in science and engineering (DdD1)
• Knows and understands the main properties of laboratory plasmas, with particular reference to the cases discussed during the course (DdD1)
• Is able to apply the acquired knowledge to understand, describe and model the behavior of physical systems in which low-temperature and technologically relevant plasmas are involved (DdD2)
• Is able to design, perform and interpret simple plasma physics experiment and is able to communicate the results in the form of a research paper (DdD2)

The course is organized into 5 modules plus a Project Laboratory.
1) Basic concepts
The course will start with a pre-assessment to gauge the students' previous knowledge and identify any knowledge gaps or misconceptions. Given the diverse background of the students, innovative didactic strategies will be employed, such as “flipped classroom” (with learning material provided by the teacher) and “peer teaching” in working groups. Topics that will be covered include: plasma definition, approaches to describe a plasma (kinetic, multi-fluid, single fluid), characteristic plasma parameters, Debye shielding and sheath-effect, transport of particles and electromagnetic waves in plasmas, optical emission and absorption in a plasma. Special attention is given to dimensional analysis as a powerful tool for grasping the physical foundations of the topics presented.
2) Plasma discharges
This module deals with the phenomenon of plasma discharges and their applications. Frontal lectures will be accompanied by experimental laboratory activities with a glow discharge apparatus. Topics include: collisional ionization, Townsend avalanche, Paschen’s law, DC glow discharge, and an overview of other discharge modes (arc discharge, Dielectric Barrier Discharge, AC discharges, wave-sustained discharge). Applications to medicine and space propulsion can also be discussed, according to students' interests.
3) Plasma-material interaction
This module covers the interactions of plasmas with surfaces, as well as the effects on plasma-exposed materials and the resulting applications. Experimental lab activities will be carried out at the Istituto di Scienza e Tecnologia dei Plasmi (ISTP-CNR Bicocca). Topics include: plasma-surface interactions, plasma sheaths, plasma induced physical processes and sputtering, plasma induced chemical processes, surface functionalization, plasma cleaning, plasma-material interaction in nuclear fusion machines.
4) Laser-generated plasmas
This module provides an overview on laser-plasma interaction in different regimes of laser intensity and relative applications. Frontal lectures will be accompanied by experimental and computational laboratory activities. Topics include: laser-plasma coupling, electromagnetic ionization mechanisms, laser-induced breakdown spectroscopy, laser cleaning, laser-based plasma diagnostics, radiation sources by laser-plasma interaction (electrons, hadrons, high-energy photons).
5) Plasma-based deposition
This module focuses on the deposition of thin films, coatings and nanostructures with controlled properties exploiting plasma-based techniques. Frontal lectures will be accompanied by experimental laboratory activities with Pulsed Laser Deposition and Magnetron Sputtering deposition.
Topics include: plasma assisted physical- and chemical-vapour deposition. Physical vapour deposition. Magnetron sputtering, unbalanced and closed-field sputtering, high-power impulse magnetron sputtering. Pulsed Laser Deposition (PLD): nanosecond- and femtosecond-PLD. Application of coating produced by Physical Vapour Depostion in nuclear fusion and particle acceleration.
6) Laboratory project
Students will be organized in working groups according to their interests and background. In the last part of the semester, each group will develop a project associated to the topics covered in one of the modulus from 2 to 5. The project should focus on a research question proposed by the teacher. Each group, under the teacher’s supervision, will develop a plan to address the research question by means of an experimental (or computational) approach, will carry out the required activities and will deliver a final report, that will be part of the evaluation process. Dedicated sessions of mentoring and review of the projects are foreseen.

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
Valutazione continua obbligatoria
Continuous evaluation and self evaluation activities are carried out during the semester. In particular, short group assignments are associated to each curricular laboratory. Although homework assignments are optional, a good overall performance in these tasks positively affects the final grade.
The final evaluation consists in an oral examination to assess student’s overall knowledge of the course topics, their understanding of relevant physical phenomena, and their problem solving skills especially when facing novel/unfamiliar situations. The report about the group laboratory project (module 6) will be a topic of discussion during the oral examination, also to ascertain the individual contribution and the degree of autonomy of each group member. The ability to organize knowledge discursively, the proficiency in the use of specialized vocabulary, and the effectiveness of written and oral presentation are also evaluated.

Alexander Piel, An introduction to laboratory, space, and fusion plasmas, Editore: Springer, Anno edizione: 2010, ISBN: 978-3319634258
R. Hippler, S. Pfau, M. Schmidt, K. H. Schoenbach, Low Temperature Plasma Physics, Editore: Wiley-VCH, Anno edizione: 2001, ISBN: 3-527-28887-2
U.S. Inan, M. Golkowski, Principles of Plasma Physics for Engineers and Scientists, Editore: Cambridge University Press, Anno edizione: 2011, ISBN: 978-0-521-19372-6
Michael A. Lieberman, Allan J. Lichtenberg, Principles of Plasma Discharges and Materials Processing, Editore: John Wiley & Sons, Inc., Anno edizione: 2005, ISBN: 9780471720010 DOI:10.1002/0471724254
M. Ohring, Materials Science of Thin Films, Editore: Academic Press, Anno edizione: 2002, ISBN: 0-12-524975-6
Leon J. Radziemski, Lasers-Induced Plasmas and Applications, Editore: CRC Press, Anno edizione: 2020, ISBN: 9781003066200
ZAVELANI-ROSSI, VISMARRA, High-Intensity Lasers for Nuclear and Physical Applications, Editore: Esculapio, ISBN: 9788893852463
Andrea Macchi, Appunti su Scariche di Plasma per Applicazioni Tecnologiche, Anno edizione: 2011
Note: Lecture notes in italian, provided by the teacher
Paolo Maria Ossi, Plasmi per superfici, Anno edizione: 2006, ISBN: 978-8873980261
Note: Textbook in Italian

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

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

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

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

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

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

Il corso ha lo scopo di avvicinare lo studente ai concetti di base della logica e dell'algebra. Alla fine del corso lo studente dovrebbe essere in grado di scrivere correttamente una formula in logica proposizionale ed in logica del I ordine, di tradurre concetti in formule, di manipolare le formule trasformandole in particolari forme normali, di interpretare le formule, di produrre semplici dimostrazioni formali e dovrebbe avere familiarità con le principali strutture algebriche. Vengono presentati i “linguaggi” dell’algebra e della logica focalizzando l'attenzione sui concetti di relazioni ed applicazioni e sugli aspetti semantico e sintattico delle logiche proposizionale e del I ordine. Inoltre, facendo uso della logica del I ordine, vengono introdotte le principali strutture algebriche e le nozioni di sottostruttura, relazione di congruenza, struttura quoziente, omomorfismi e isomorfismi fra strutture simili. Proprietà caratteristiche delle varie strutture sono messe in evidenza con esempi che intervengono in molte applicazioni.
La parte di logica sarà anche supportata da una breve attività di esercitazioni al computer con l’utilizzo del programma Spass per illustrare l’uso di un semplice theorem prover.

Le lezioni e le esercitazioni consentiranno allo studente di
conoscere e comprendere (DdD1):
i concetti chiave in matematica: nozioni di funzioni, relazioni e loro proprietà.
le principali tecniche dimostrative.
i principali formalismi logici.
la nozione di sistema formale e di dimostrazione.
le principali strutture algebriche e il concetto di omomorfismo e quoziente.
saper applicare le conoscenza acquisite al fine di (DdD2):
sapere applicare il linguaggio logico alla formalizzazione di alcuni problemi.
avere la capacità di tradurre da linguaggio naturale a linguaggi logici opportuni.
avere la capacità di modellare un problema in un opportuno linguaggio logico o algebrico.
avere la capacità di comprendere alcune nozioni algebriche di base (come l’aritmetica modulare) al fine di comprendere la matematica considerata in corsi più avanzati.
avere la capacità di comprendere alcune tecniche dimostrative specifiche in ambito informatico, come la diagonalizzazione.
avere la capacità di comprendere il funzionamento di un theorem prover e aver le basi per saperlo progettare e utilizzare.
ottenere un autonomia di giudizio (DdD3):
nel saper distinguere tra nozione di sintassi e semantica.
nel saper valutare l’adeguatezza della descrizione e delle specifiche di un problema al fine di analizzare il processo di programmazione e progettazione di un sistema formale in modo rigoroso e non ambiguo.
che permetta di sviluppare maggior senso critico nel processo di formalizzazione di un problema.
ed avere una capacità di apprendimento (DdD5):
che permetta di comprendere concetti chiave e nozioni più avanzati di informatica.
che permetta allo studente di sviluppare una forma mentis orientata al rigore di pensiero e migliorare il processo di astrazione di un problema.
Il docente si attende una comprensione non limitata all'enunciazione di definizioni e risultati e alla risoluzione di esercizi standard, ma critica ed in grado di distinguere le diverse situazioni, e di compiere scelte consapevoli, giustificando i procedimenti seguiti. Si attende inoltre un'adeguata correttezza nei calcoli e un'esposizione ben argomentata nella risoluzione dei problemi proposti.

RELAZIONI ED APPLICAZIONI
1.1 Relazioni, con particolare riguardo alle relazioni binarie su un insieme. Prodotto di relazioni. Proprietà delle relazioni binarie: serialità, riflessività, simmetria, transitività, antisimmetria. Chiusure di una relazione rispetto ad un insieme dato di proprietà.
1.2 Relazioni di equivalenza ed applicazioni. Concetti di insieme quoziente, di kernel di una applicazione, teoremi di fattorizzazione di una applicazione. Cardinalità di un insieme e teorema di Cantor.
1.3 Relazioni d’ordine, i reticoli come insiemi ordinati.
1.4 Operazioni interne ed esterne, proprietà delle operazioni. Unità ed inverso di un elemento rispetto ad una operazione binaria.
LOGICA PROPOSIZIONALE
2.1 Il linguaggio della logica delle proposizioni, formule sintatticamente corrette. Valutazione di una formula, tautologie e contraddizioni. Deduzione ed equivalenza semantica. Forme normali disgiuntive e congiuntive.
2.2 Sistemi formali. Teoria L, teoremi di deduzione, correttezza e completezza, decidibilità. Risoluzione e raffinamenti.
LOGICA DEL I ORDINE
3.1 Il linguaggio del calcolo predicativo del I ordine, termini e formule sintatticamente corrette. Variabili libere e vincolate. Formule chiuse. Valutazione di una formula: il concetto di interpretazione. Formule soddisfacibili, vere, false in un'interpretazione. Formule logicamente valide. Forma normale prenessa, forma di Skolem.
3.2 Sistemi formali: cenni sulla teoria K e risoluzione.
3.3 Teorie del primo ordine e teorie del primo ordine con identità.
3.4 Modelli di una teoria.
3.5 Teoremi di deduzione, di correttezza e completezza. Questioni di decidibilità.
STRUTTURE ALGEBRICHE
4.1 Le principali strutture algebriche: semigruppi, monoidi, gruppi, anelli, corpi, campi (definizioni ed esempi).
4.2 Sottostrutture, criteri relativi.
4.3 Relazioni di congruenza, strutture quozienti. La relazione di congruenza modulo n e cenni di aritmetica modulare
4.4 Strutture simili, omomorfismi ed isomorfismi fra strutture simili, il kernel di un omomorfismo ed il teorema di fattorizzazione degli omomorfismi.

Prova scritta obbligatoria, senza prove in itinere
Altro
La valutazione dello studente avviene attraverso un esame scritto. In occasione dei primi due appelli di gennaio e febbraio verrà proposto un esercizio (facoltativo) sulla formalizzazione di un problema nella sintassi del theorem prover usato durante l’esercitazione/laboratorio il cui punteggio sarà aggiunto al voto conseguito nella prova d’esame se quest’ultimo risulta sufficiente o prossimo alla sufficienza.
Il giorno dell'esame eventuali studenti stranieri potranno avere il testo del compito tradotto in inglese, dietro richiesta preventiva di almeno sei giorni. Agli studenti stranieri sarà possibile svolgere il compito in inglese e verrà indicata una adeguata bibliografia inglese. Gli studenti stranieri possono comunque prendere in considerazione il fatto che per la laurea specialistica è offerto nel primo semestre un corso in inglese con gli stessi contenuti.
Modalità di verifica
Descrizione
Risultato di apprendimento perseguito (DdD)
Prova scritta
risoluzione di problemi numerici
esercizi di tipo progettuale
domande di carattere teorico a risposta aperta sugli argomenti del corso
1,2
1,2,3,5
1,2,3,5
Valutazione elaborato di laboratorio
valutazione di un elaborato relativo all’utilizzo di un theorem prover illustrato nel corso attraverso un’attività pratica (solo per i primi due appelli e non obbligatorio).
2, 3, 5

Dispense sui contenuti del corso
Note: Esercizi e temi d'esame svolti sono a disposizione su WeBeep
A. Frigeri, S. Adami, A. Cherubini, L.Mauri, C. Nuccio, E. Rodaro, Logica e algebra, esercizi svolti e richiami di teoria, Editore: Maggioli Editore, Anno edizione: 2020, ISBN: 978-8891645258
E. Mendelson, Introduzione alla logica matematica, Editore: Bollati Boringhieri, Anno edizione: 1972, ISBN: 9788833952840
Note: Per la parte di logica. Capitoli 1 e 2
Luigia Carlucci Aiello, Fiora Pirri, Strutture, logica, linguaggi, Editore: Pearson, ISBN: 9788891907844
A. Facchini, Algebra e matematica discreta, Editore: Zanichelli, Anno edizione: 2000, ISBN: 9788808097392
A.Asperti, A.Ciabattoni, Logica a informatica, Editore: McGraw-Hill, Anno edizione: 1997, ISBN: 9788838607578
Note: Per la parte di logica. Capitolo 6
D. Mandrioli, P. Spoletini, Mathematical logic for computer science - an introduction, Editore: Esculapio. Progetto Leonardo, Anno edizione: 2010, ISBN: 9788874883738
Note: Per la parte di logica
M. Curzio, P. Longobardi, M. Maj, Lezioni di algebra, Editore: Liguori, Anno edizione: 1994, ISBN: 978-88-207-2228-9
Note: Per la parte di algebra. Capitolo 1, fino a paragrafo 12; Capitolo 2, paragrafo 8; Capitolo 3; Capitolo 4, paragrafi1,2,5; Capitolo 5, paragrafi 1,2,3,4,5,6; Capitolo 11, paragrafi 1,2,3,8,9
Open logic text, Editore: Open source https://openlogicproject.org/
Note: (Testo molto ampio per la parte di logica)
Interfaccia web theorem prover SPASS https://webspass.spass-prover.org/
Note: Software utilizzato per il laboratorio di SPASS

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

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

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

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

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

The course is aimed at providing the fundamental concepts of materials and technologies for the production of printed circuit boards, integrated circuits and microelectromechanical (MEMS) devices. For each microelectronic device, the basic principles, the involved materials and the industrial production processes are discussed. In the field of MEMS technology, surface micromachining and surface modification processes at the microscale will be presented.

Upon completion of the course, the student will (DdD1)
know and understand the relationship between materials selection and production process in microelectronics;
know the main components of a printed circuit board and its production processes;
know the main components of an integrated circuit and its production processes;
know the main components of a microelectromechanical system and its production processes.
These learning outcomes are expected to provide the students with adequate knowledge tools and critical approach for their future activity related to microelectronics and integration technology.

The main topics of the course are
Printed Circuit Board manufacture: base materials, copper foils, laminate fabrication, solder resist, multilayer PCB, passive and active components, PTH and SMT assembling methods, soldering and solder alloys, flexible printed circuits
Introduction to microfabrication and integrated circuits: semiconductor materials, thin films materials and processes, epitaxy, pattern generation and optical lithography, etching processes, thermal oxidation, diffusion and ion implantation, multi-level metallization, chemical-mechanical planarization
Integration: process integration, CMOS transistor fabrication, bipolar technology, 3D integration and TSV technology
Introduction to MEMS and silicon micromachining: microelectromechanical systems, surface and bulk micromachining, bonding and layer transfer, IC-MEMS integration

Prova orale obbligatoria
The examination will be an oral discussion about the course topics, chosen by the examiner so that different parts of the course syllabus are involved.
The student must be also able to clearly describe and critically discuss the proposed topics, highlighting the assumptions, critical points and industrial processing.

Sami Franssila, Introduction to microfabrication, Editore: Wiley, ISBN: 978-0-470-74983-8

Obiettivo dell’insegnamento è permettere agli studenti di acquisire competenze di progettazione di sistemi elettronici basati su microcontrollori, sia per quanto riguarda la progettazione hardware di schede che per lo sviluppo del firmware (in Assembly) e del software di più alto livello (in linguaggio C). L’insegnamento presenta le architetture interne dei microcontrollori ad 8 bit di Microchip e di STMicroelectronics.
Oltre alle lezioni teoriche su architetture, memorie e periferiche interne (12 ore in aula), l’insegnamento verterà su esercitazioni pratiche svolte in modalità laboratorio informatico (14 ore) per lo sviluppo di firmware e routine e su laboratori sperimentali pratici di tipo “hands-on” (24 ore), in cui ogni singolo studente utilizzerà una scheda di sviluppo a microcontrollore, ambiente CAD di sviluppo, e linguaggi di programmazione (assembler e C). In particolare verranno impiegate schede di sviluppo di MikroElektronika, basate su microcontrollori PIC della Microchip.
Nei laboratori pratici, gli studenti saranno divisi in sezioni di circa 30 studenti; ogni sezione frequenterà un laboratorio di 2 ore e mezza consecutive a settimana (a scelta tra i giorni previsti nel calendario), per circa 2 mesi, ovvero un totale di 10 laboratori di 2:30 ore ciascuno, per un totale di 24 ore di laboratorio sperimentale pratico, per ciascuno studente.
Dei 5 CFU dell’insegnamento, 3 CFU saranno erogati in modalità Didattica Innovativa di tipo “Blended Learning” e “Flipped Classroom”, in cui lo studente sarà coinvolto attivamente nelle esercitazioni e nelle attività pratiche di laboratorio sperimentale, mentre l’esercitatore ed i responsabili di laboratorio supervisioneranno lo studente durante lo svolgimento degli esercizi di programmazione Assembly ad esercitazione e di linguaggio C in laboratorio.

Conoscenza e Comprensione (Descrittore di Dublino #1)
Al termine del corso e dopo aver superato l’esame, lo studente:
saprà illustrare le architetture ed il funzionamento delle periferiche dei microcontrollori, comprenderà come programmarle e saprà confrontare le prestazioni tra microcontrollori diversi;
saprà scegliere gli strumenti più adeguati (sistemi di sviluppo, IDE, linguaggi di programmazione) per progettare un sistema elettronico basato su microcontrollore;
saprà selezionare il microcontrollore adatto al sistema elettronico da sviluppare, consultando il data-sheet di diverse famiglie di prodotti e case costruttrici;
saprà programmare in Assembly ed in linguaggio C i microcontrollori ad 8 bit di Microchip;
Capacità di Applicare Competenza e Comprensione (Descrittore di Dublino #2)
Al termine del corso e dopo aver superato l’esame, lo studente:
sarà capace di comprendere i data-sheet di microcontrollori commerciali e valutarne l’impiego;
comprenderà quali specifiche del microcontrollore impatteranno sulle prestazioni di sistema;
saprà progettare circuiti a microcontrollore, programmandoli e debuggandone il firmware, per individuarne problemi e risolverli;
saprà applicare soluzioni innovative all’hardware e al firmware di sistemi a microcontrollore.
Autonomia di Giudizio (Descrittore di Dublino #3)
Grazie alla modalità di Didattica Innovativa di tipo “flipped classroom”, lo studente:
acquisirà l'autonomia di giudizio nell’analizzare e nel progettare l’hardware ed il firmware di una scheda a microcontrollore, valutando pro e contro delle varie implementazioni possibili.
Abilità Comunicative (Descrittore di Dublino #4)
Grazie al lavoro di gruppo in piccoli team di 3-4 persone presente nei laboratori, lo studente
acquisirà competenze comunicative efficaci e capacità di autogestione nel lavoro di gruppo che favoriranno la condivisione di idee, il confronto costruttivo e la collaborazione nella risoluzione di problemi tecnici.
Capacità di Apprendimento (Descrittore di Dublino #5)
Grazie alla modalità di Didattica Innovativa di tipo “flipped classroom”, lo studente:
imparerà ad apprendere in autonomia i miglioramenti introdotti da microcontrollori di futura generazione e i vantaggi di nuove implementazioni firmware;
capirà come affrontare studi successivi di prodotti e soluzioni progettuali che diverranno disponibili durante la sua attività professionale.

Microcontrollori (12 ore di lezione)
Microcontrollori a 8bit: architetture, data-sheet di prodotti commerciali, famiglie e prestazioni.
Memorie dati (RAM e flash) e programmi (ROM, OTP, E2PROM, flash) e registri di configurazione.
Periferiche interne: porte di I/O, counter, timer, convertitori Analogico-Digitale (ADC) e Digitale-Analogico (DAC), modulazione impulsiva (PWM), comunicazione seriale.
Circuiti ausiliari: reset, oscillatore, interrupt, watchdog.
Firmware e software (14 ore di laboratorio informatico)
Schemi circuitali del sistema di sviluppo utilizzato a laboratorio e ambiente di simulazione.
Linguaggio Assembly: istruzioni, cicli macchina, sviluppo di codice.
Linguaggio C: esempi di codice, compilazione, portabilità.
Esempi pratici: modalità di indirizzamento; gestione banchi di memoria; gestione periferiche; routine di interrupt.
Progetti pratici “hands-on” (24 ore di laboratorio sperimentale)
Esempi pratici con scheda di sviluppo dedicata a ciascun studente e ambiente di sviluppo.
Gestione di ingressi/uscite digitali e ingressi analogici; pilotaggio di relè e LED; interfacciamento con memorie e DAC/ADC esterni; gestione degli interrupt; gestione tastiere e display:
Temporizzazione mediante timer interni; polling, loop e interrupt; modalità low-power e sleep.
Comunicazioni seriali: SPI, I2C, RS232; gestione memorie e ADC e DAC esterni e altri dispositivi.
Schermi LCD grafici: interfacciamento; temporizzazioni; data-sheet di prodotti commerciali.

Prova scritta obbligatoria, senza prove in itinere
Altro
L’esame consisterà di due parti, svolte consecutivamente durante la stessa giornata:
Parte “teorica”: durata 30 minuti, strutturata con 14 semplici domande a risposta multipla su Microsoft Forms; se si risponderà correttamente ad almeno 9 domande si verrà ammessi alla parte laboratoriale. La correzione sarà istantanea e generata automaticamente da Forms. Questa parte contribuirà a 7/30 del voto finale (ad esempio, rispondendo correttamente a 12 domande si partirà da un voto finale di 12/14·30/7 = 4 trentesimi). In questa prova lo studente verrà valutato sulle architetture di base del microcontrollore, snippet di codice, schemi circuitali utilizzanti microcontrollori e codice/codifiche utilizzate nel codice C. (DD1)
Parte “laboratoriale” (solo per coloro che avranno superato la parte “teorica”): durata 2:30 ore, concernente la progettazione di un firmware (in C). Questa parte contribuirà a 23/30 del voto finale. La prova sarà strutturata in più sotto sezioni, per ciascuna si dovrà consegnare un archivio contenente l’intera cartella del progetto MikroC. Durante l’esame verrà messa a disposizione la scheda EasyPIC a ciascun esaminando, per collaudare il firmware. In questa prova lo studente verrà valutato sulla base delle sue capacità di prendere decisioni in autonomia sul progetto proposto, scegliere gli strumenti più adatti per portare a termine il lavoro, correttezza e funzionalità del firmware sviluppato insieme alla correttezza del codice C utilizzato. (DD1, DD2, DD3) Il materiale liberamente consultabile è il seguente:
File inizializzazione LCD (lcd.c);
File simulatore proteus (EasyPICVx.rar);
HRLV – MaxSonarDatasheet;
HRLV MaxSonar – EasyPIC;
Datasheet PIC18f45K22;
Schematico EasyPIC (SchematicEasyPIC.pdf).
Le consegne avverranno telematicamente tramite Microsoft Forms. La correzione avverrà in differita ed i risultati saranno pubblicati alcuni giorni dopo la prova.
Lo studente potrà decidere di congelare il voto della parte "teorica" anche per gli appelli successivi, ma dello stesso anno accademico, per poi rifare solamente la parte "laboratoriale" allo scopo di migliorare il voto.
L'esame si intenderà superato se il voto finale sarà uguale o superiore a 18/30.

Franco ZAPPA, MICROCONTROLLERS, Editore: Esculapio, Bologna, Anno edizione: 2017, ISBN: 978-88-9385-022-3

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

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

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

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

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

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

The aim of the course is to introduce the techniques suitable for the design of microwave circuits and components, starting from the basic knowledge on ideal lines and electromagnetics. These techniques are required by the distributed nature of the circuits, usually of physical dimensions larger or much larger than the operational wavelength. Modern planar structures will be considered in particular, focusing on the practical design of passive circuits like matching networks, couplers and dividers, filters, etc.

Lectures and exercise sessions will allow students to analyze and design filters and microwave circuits of passive type (DD1, DD2). Students will learn how to use equivalent circuits (DD1, DD2, DD3) and how to implement into physical planar circuits or waveguide circuits (DD1, DD2); A final project, consisting in a filter design based on some specifications will put in practice the concepts learned in the course. The project is then presented orally (DD4).

Introduction. Circuit components from the electromagnetic point of view, distributed- vs lumped-parameter circuits.
Guiding structures. V/I waves in transmission lines. E/H waves in uniform structures with ideal conductors: TEM, TE/TM and hybrid modes; fundamental and higher order modes (recalls). Transmission lines of planar type: stripline, microstrip, slotline, coplanar waveguide, etc.; quasi-TEM approximation, effective dielectric constant, frequency dependence of line parameters (dispersion), losses.
Microwave junctions. Definition of junction and port: equivalent voltages and currents, normalized waves, reference planes. Matrix description of linear n -port junctions: S matrix, general properties, properties for reciprocal and lossless junctions, change of reference planes, relation to Z / Y matrices. ABCD matrix for cascaded 2-ports. Derivation of the equivalent circuit for n -port junctions and discontinuities by lumped elements and ideal lines.
Microwave circuits. The interconnection of lengths of guiding structures by junctions: modeling of discontinuities, higher-order mode interaction, introduction to CAD software.
Matching netwoks. Narrow and broad band matching: single and double stub, single and multiple quarter-wavelength transformers, frequency response, design examples.
Couplers and dividers. Wilkinson coupler. Directional couplers and hybrids: directivity and isolation, even and odd modes in coupled lines, design examples.
Resonators. Free and forced oscillations, external and inter-resonator couplings, the equivalent circuit of loaded resonators, impedance/admittance inverters.
Filters. The design technique by insertion-loss method: lowpass prototype filter; scaling and frequency mapping for lowpass, bandpass and stopband filters. Design examples (lowpass filters, coupled-lines bandpass filters, etc.)

Progetto/i - Elaborato/i obbligatorio, individuale, senza revisioni periodiche, con presentazione/discussione finale
The final evaluation is preferably based on the design of a microwave filter, both theoretical and assisted by CAD software, to be choosen in agreement with the teacher (DD1, DD2, DD3). The result of the design is then presented orally during the exam (DD4).

David. M. Pozar, Microwave Engineering, 4.th Edition, Editore: John WIley & Sons., Anno edizione: 2011, ISBN: 978-0-470-63155-3
Appunti del corso Webeep
Gian Guido Gentili, Matteo Oldoni, Handbook of Microwave Engineering, Editore: Maggioli, Anno edizione: 2024, ISBN: 9788891675385

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

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

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

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

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

The goal of the course is to provide students with the fundamental methodologies for modelling, analysing, and designing mobile radio networks. Students will learn how radio resources are shared and reused, how mobility is managed, and how coverage, capacity and traffic requirements drive network design choices. In particular, the course covers the architectures, radio interfaces, protocols, and management mechanisms of cellular systems, from 2G to 5G.

Dublin Descriptor
Expected learning outcomes
1 - Knowledge and understanding
Students will learn how to:

• Define mathematical models for the analysis of mobile radio networks
• Analyse complex technologies related to mobile radio networks
• Identify system components and their relations
  2 - Applying knowledge and understanding
  Students will be able to:
• Dimension the components of a mobile radio network to meet traffic requirements
• Evaluate the performance of mobile radio networks across different scenarios
• Apply the mathematical models learned
  3 - Making judgements
  Students will be able to:
• Evaluate the trade-offs that govern the design of mobile radio networks and justify design choices accordingly
• Identify how the fundamental system limitations impact the design of the network
• Recognise the design space and its degrees of freedom that can be exploited to define new technologies
  4 - Communication
  Students will be able to:
• Describe complex network technologies at different levels of detail, also comparing them with respect to specific aspects
• Report on the analysis and results of an advanced topic in a technical document (optional)
  Learning skills
  Students will be able to study autonomously new mobile radio technologies by applying and extending the methods learned during the course

1. Mobile Network Basics
Multiple-access techniques in cellular radio networks
Cellular coverage and resource reuse
Network architecture and functional elements
Mobility management: cell selection, location management, and handover
2. Capacity and radio planning
Mathematical models and algorithms for coverage optimisation
Mathematical models and algorithms for static frequency assignment
Dynamic channel allocation techniques
3. Performance analysis
Models for the capacity of CDMA systems
Traffic models for the performance evaluation at session level
Models for the dimensioning of cells and location areas
4. 2G systems (GSM and GPRS/EDGE)
Architectures, interfaces, physical and logical channels
Signalling architecture, signalling protocols, network databases, call management procedures, mobility management procedures.
GPRS/EDGE: radio interface, IP network, interconnection nodes, mobility management, protocol architecture
5. 3G systems (UMTS and HSPA)
Radio access network
Core network
Radio resource management
High-speed packet data services
6. 4G systems (LTE and EPC)
System architecture
Radio Interface
Core Network
7. 5G systems
System architecture
New Radio interface
Network slicing

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
The assessment will be based on a written exam at the end of the course and optional project activities.
The written exam is worth up to 28 points and comprises numerical exercises, closed-ended questions, and open-ended questions covering all course topics. Optional project activities may contribute up to 4 additional points. The final score is the sum of all points obtained.

Slides and recommended readings are provided by the lecturers through Webeep https://webeep.polimi.it/

Computer systems are increasingly intelligent, distributed, and interconnected. This trend naturally leads to the development of systems composed of autonomous intelligent decision-making entities, called autonomous agents, that interact with each other in complex environments. The advent of AI agents based on Large Language Models is just the last step along this path. Agent-based systems are an abstraction of specific computing systems deployed in several applications, including electronic commerce, control of industrial processes, logistics, ambient intelligence, web services, robotics, space systems, and modeling of complex systems.
This course aims to present general techniques for developing multiagent systems, independently of the implementation technology and of applicative domains. In particular, the course, after shortly presenting methods for developing single agents able to make rational decisions in situations affected by uncertainty, will focus on methods for developing systems composed of multiple agents, with special emphasis on the interaction mechanisms between the agents. Moreover, some real-world applications of agent systems will be discussed. At the end of the course, students will acquire the ability to design and develop distributed systems based on the agent paradigm.
This course extends the (mostly) single-agent point of view taken in other courses (including, for example, Foundations of Artificial Intelligence and Machine Learning) to consider the design and the development of collections of intelligent agents, which are currently envisaged by several recent developments, including Agentic AI, and initiatives, including that on Cooperative Artificial Intelligence.

Dublin descriptors
Expected learning outcomes
Knowledge and understanding
Students will learn:
the models that represent distributed systems as collections of computational systems, called autonomous agents, that interact together,
the models that represent individual autonomous agents as rational utility maximizers,
some significant types of interaction mechanisms between multiple autonomous agents (coalition formation, planning, distributed constraint optimization, learning,...),
some of the relevant algorithms available to implement the interaction mechanisms and their properties (range of application, optimality guarantees,...).
Applying knowledge and understanding
Given application cases that involve multiple decision-makers, students will be able to:
develop agent-based models and systems that are based on the identification of individual decision-making entities (autonomous agents) and on the identification of the interaction mechanisms between them,
analyze and understand existing multiagent systems.
Lifelong learning skills
Students will be able to understand and critically evaluate the modeling based on multiagent systems composed of multiple decision-makers.
Students will be able to understand, learn, and develop new interaction mechanisms for autonomous agents.
Students will be able to apply acquired knowledge when designing complex distributed computational systems.

1. Introduction to the concepts of autonomous agents and multiagent systems.
2. Autonomous agents as rational decision-makers: architecture for intelligent agents, Markov decision processes.
3. Interactions between self-interested agents: short introduction to game theory, coalition formation.
4. Interactions between cooperative agents: multiagent planning, distributed constraint optimization.
5. Multiagent learning: multiagent Markov decision processes and stochastic games, multiagent reinforcement learning, evolutionary game theory.
6. Examples of real-world applications of agent-based systems, including Agentic AI applications.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The students' assessment is based on a closed-book written test at the end of the course, consisting of exercises and questions on all the topics of the course. The test will typically last 75 minutes. The test assigns a maximum of 31 points (30 cum laude is assigned when the total score is strictly larger than 30 points). Students can take the written test at any exam session during the year. The instructor can summon students who passed the written test for an additional (in-person) oral exam that will cover the entire course program. Students cannot request to do oral exams.
Type of assessment
Description
Dublin descriptor
Written test
Solution of numerical problems
Application of algorithms to given problem instances.
Exercises focusing on design aspects
Designing models of autonomous agents and of their interaction mechanisms for simple problems
Theoretical questions on all the course topics with open answer
1, 2
1, 2, 5
1, 5

Gerhard Weiss (editor), Multiagent Systems (second edition), Editore: The MIT Press, Anno edizione: 2013, ISBN: 978-0-262-01889-0 https://ebookcentral.proquest.com/lib/polimi/detail.action?docID=3339590
Note: Web site of the book: http://www.the-mas-book.info
Further teaching material could be possibly made available along the course.

The course of Multimedia Internet (10 credits) is divided into two parts (part 1: 5 credits, part 2: 5 credits). Part 2 is the course of Multimedia Internet Applications (5 credits). The course is organized in such a way that the student can take the 5 credits course of Multimedia Internet Applications (that is, part 2 of the Multimedia Internet course) can do it without having to study any material related to part 1 (i.e., the two parts of the course are independent).
The main objective of the Multimedia Internet course is to teach the students the transport of multimedia contents through the Internet and, more in general, through IP networks (public or proprietary). This topic is analyzed from different points of vie in part 1 and part 2.
Part 1 (Multimedia Internet course only).
Part 1 addresses the transport of multimedia contents through the Internet considering the "user plane", that is, it analyzes the protocols and network architectures used to carry media streams through the network (or across a series of networks) with the necessary Quality of Service level. Therefore, the following topic will be addresses: definition of Quality of Service for different types of media, techniques to guarantee the requested Quality of Service, among which Resouce Allocation, Admission Control, Traffic Conditioning, Scheduling, Active Queue Management.
Part 1 has also a methodological component in which the student will learn how to analyze and design, quantitatively, a multiservice IP network for the transport of multimedia streams.
Part 2 (Multimedia internet course and Multimedia Internet Applications course).
Part 2 also addresses addresses the transport of multimedia contents through the Internet, from the point of view of the protocols used to setup and maintain connections for the transport of media streams. Part 2 is focused on media streaming, voice, audio, and video codecs. Containers for media contents (MP4, MKV, AVI, MOV, WEBM). Session Description Protocol (SDP), Real Time Protocol (RTP/RTCP), Real Time Streaming Protocol (RTSP), HTTP streaming (Progressive client pull, Adaptive Bit Rate (ABR), HTTP Live Streaming (HLS), Server-Sent Events (SSE), WebSockets, WebRTC). The architecture of IPTV systems will be treated, along with cahning, Content Delivery Networks (CDN), Over The Top providers (OTT), usage of Peer To Peer video streaming subsystems in IPTV services. Session Initiation Protocol, Voice Over IP, and interworking of signaling.

Part 1 (Multimedia Internet course only).
Part 1 - Knowledge and understanding.
The student will learn

• how to understand and define correctly and quantitavely a Quality of Service objective, the concept of Service Level Agreement (SLA) and traffic Conditioning agreement.
• The architecture and operation modes of Traffic Conditioners (policers, shapers, markers).
• How Resource Allocation and Admission Control work.
• The type of schedulers used in network routers and how they work.
• How Active Queue Management works and when it is adviceable to use it.
• The IP Differentiated Services and IP Integrated Services network architectures.
  Part 1 - Apply Knowledge and understanding.
  The student will learn quantitative methods to analyze and design IP multiservice networks that use the techniques listed above to transport multimedia services qith Quality of Service.
  The student will learn how to dimension a multiservice network given a requested set of multimedia connections and corresponding Quality of Service targets.
  Part 1 - Learning skills.
• Understanding precisely how multiservice networks work
• Learning new method for the analysis and design of multiservice networks.
  Part 2 (Multimedia Internet course and Multimedia Internet Applications course).
  Part 2 - Knowledge and understanding.
  The student will learn
• to understand how a signaling system works, with special reference to the transport of multimedia contents.
• to understand the issues and complexity of interworking different signaling systems.
• to understand the differences between "underlay" and "overlay" multimedia networks.
  Part 2 - Apply Knowledge and understanding.
  The student will learn
• How to evaluate a signaling architecture and identify the requirements related to interworking signaling systems.
  Part 2 - Learning skills.
• Understanding how the control of a multiservice network works, what are the requiremetns and contraints, related to the type of services to be delivered.

Part 1 (Multimedia Internet course only):
Aanalysis and design of IP multimedia networks with quality of service.
1. Multimedia communications and Quality of Service (QoS) issues. Audio/video digital media. Service Level Agreements (SLA); definition of QoS requirements; customer-proivider contracts.
2. The traffic Profile. The Traffic Conditioning Agreement (TCA). Controlling TCA. Traffic regulation: policing; marking; shaping.
3. Resource allocation for QoS guarantee. The usage of TCA and SLA. Scheduling policies.
4. Practical architectures for QoS-oriented delivery. The IP Differentiated Services architecture. The IP Integrated Services architecture. The MultiProtocol Label Switching. QoS and routing. COPS (Common Open Policy Service).
5. Design of IP networks with QoS. The Network Calculus; managing regulated traffic; traffic envelopes and service envelopes. Design of FIFO, SP, GPS and EDF schedulers. Design of networks of schedulers with both short-range dependent and long-range dependent traffic.
Part 2 (Multimedia Internet course and Multimedia Internet Applications course):
Multimedia services in the Next Generation Network.
1. The Next Generation Network and ultra-large bandwidth services. NGN architectures, Fiber to the Exchange, Fiber to the cabinet, Fiber to the Building, Fiber to the Home. point-to-point and Passive Optical Network systems. The Next generation Network in Italy, Europe. IP peering models. Net Neutrality. IP interconnection systems.
2. Media streaming, voice, audio and video codecs. Compression of media, lossless and lossy codecs, color spaces.
3. The Session Description Protocol and its usage for media negotiation.
4. The Real Time Protocol (RTP) and the Real Time Control Protocol (RTCP).
5. Setup and confioguration of multimedia session with the Real Time Streaming Protocol (RTSP).
6. HTTP streaming, progressive client pull, Adaptive Bit Rate (ABR), HTTP Live Streaming (HLS), Server-Sent Events (SSE), WebSockets, WebRTC).
7. Transport Protocols in media streaming, usage of TCP, UDP, and SCTP.
8. The IPTV service, architectures and video servers, storage, RAID architectures. Caching and Content Delivery Networks (CDN), Over The Top providers (OTT).
9. Usage of Peer To Peer video streaming in modern IPTV systems.
10. The Session Initiation Protocol and interworking of signaling.

Prova scritta obbligatoria, con prove in itinere
Part 1 (Multimedia Internet course only):
Written exam.
Exercises 1 and 2, numerical. Dublin Descriptors: (1. Knowledge and understanding. 2. Apply Knowledge and understanding. 3. Learning skills.)
Exercise 3. Written open question on Protocols/Network architectures Dublin Descriptors: (1. Knowledge and understanding. 2. Apply Knowledge and understanding. 3. Learning skills.).
Part 2 (Multimedia Internet course and Multimedia Internet Applications course):
Written exam.
Exercises 1, 2, 3: Written open question on Protocols/Network architectures Dublin Descriptors: (1. Knowledge and understanding. 3. Learning skills.)
Exercise 4: A specific case signaling architecture to be examined and commented by the student, with the purpose of highlighting possible weak points, issues and if needed suggestion of improvments Dublin Descriptors: (1. Knowledge and understanding. 2. Apply Knowledge and understanding. 3. Learning skills.)

The material of Part 1 is available as a set of slidesets, available on the WeBeep platform. https://webeep.polimi.it
Note: For Part 1 the reference material is a set of pdf files providing a full coverage of the topics developed in the lessons and exercitations.
The material of part 2 is a single pdf file, avaliable on the WeBeep platform. https://webeep.polimi.it
Note: The material is a pdf text that develops in detail all the topics of Part 2

This course explores innovative web technologies, guiding students in understanding how interaction on the Web can be extended and enhanced through the integration of advanced multimodal interactive technologies.
The course is structured into three specialised modules:
Web-Visual: Focuses on traditional paradigms for organizing and navigating visual content.
Web-Conversational: Explores the integration of conversational AI and Large Language Models into web platforms to enable fluid, natural-language interaction.
Web-eXtended Reality: Deepens immersive interactive technologies and their integration into web platforms.
By combining theoretical insights with hands-on exercises, this course guides students to learn how to address the growing demand for innovative multimodal web solutions. It is ideal for those students eager to create impactful and engaging multimodal web technologies.

By the end of the course, students will be able to:
Manage the design and development of advanced web applications, with a strong focus on user interaction and experience
Design and prototype conversational systems for the Web, based on Conversational AI models and Large Language Models (LLMs).
Integrate eXtended reality (XR) components into web applications to create immersive user experiences.
Dublin Descriptors
Knowledge and Understanding Students will learn how to:
understand the principles and paradigms underlying multimodal interaction and their relevance to the Web;
master the phases of a human-centered design process for advanced multimodal interactive applications;
analyze users, contexts, and tasks to identify opportunities for integrating multimodal interaction;
recognize the affordances and limitations of web-based frameworks supporting visual, conversational, and extended reality (XR) modalities;
evaluate multimodal web applications in terms of usability, user experience, and interaction quality.
Applying Knowledge and Understanding Given specific design and development scenarios, students will be able to:
specify requirements for multimodal interactive web applications;
design and prototype visual, conversational, and XR components to extend web interaction;
integrate Conversational AI and Large Language Models (LLMs) into web platforms to enable natural-language interaction;
combine immersive XR technologies with web standards to create engaging, interactive experiences;
develop functional multimodal web prototypes using contemporary frameworks and tools.
Making Judgments Students will be able to:
analyze and model multimodal interaction workflows and architectures;
critically assess trade-offs among different modalities and technologies according to project goals and constraints;
select and justify appropriate design principles, frameworks, and integration strategies for multimodal web solutions.
Communication Students will learn how to:
communicate design decisions and technical solutions clearly, using appropriate levels of abstraction for technical and non-technical audiences;
document multimodal architectures and design rationales effectively, highlighting usability and user experience aspects.
Lifelong Learning Skills Students will learn how to:
autonomously explore emerging multimodal interaction paradigms and web technologies;
apply a continuous learning approach to adapt to the evolution of interactive technologies and frameworks;
transfer the acquired design and development methodologies to new domains and interaction contexts.

The course is structured across three modules, each combining the introduction of methodological concepts and technological frameworks in an integrated format of lectures, guided exercises, and hands-on development sessions.
Module 1: Web-Visual:
Web design and principles
Web usability and accessibility
Key concepts of Web architectures and architectural patterns
Fundamentals of Web technologies and languages (client-side and server-side)
Module 2: Web-Conversational:
Design of Conversational Agents for the Web
Integration of Conversational AI and LLMs into web platforms; hands-on sessions with major frameworks and libraries (e.g., OpenAI).
Automatic Speech Recognition, Text-to-Speech and Speech-to-Text technologies.
Modulo 3: Web-eXtended Reality:
Overview of the XR interaction paradigm.
Design of AR experiences.
Integration of XR technologies into web platforms; hands-on sessions with major frameworks for XR on the web (e.g., WebXR, Three.js).
Introduction to additional AI-based input channels for Web interaction, such as image processing and object recognition.

Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
Valutazione continua facoltativa (a scelta dello studente)
The exam is mainly project-based. A project consists of the conceptual design, grounded in the design principles introduced during the lectures, and the development of a mid-fidelity prototype demonstrating the application of the learned technologies to the implementation of the proposed design. The design and implementation will be guided by a set of specifications and requirements provided by the instructor at the beginning of the course.
Teamwork is strongly encouraged, although individual projects might also be permitted.
Throughout the course, intermediate assignments will support students in gradually applying the concepts discussed in class to their projects. These deliverables will contribute to the incremental construction of the final project outcome. Regular review sessions will provide formative feedback on the intermediate submissions and support the ongoing development of the project.
On the official exam dates, each team will present the final result of their work. The assessment will focus on both the overall quality of the final project and the technical skills demonstrated during its development, with particular attention to each team member's individual contribution. The final exam will therefore:

• Evaluate the project outcome along with the completeness of the designed prototype in relation to the assigned requirements; the methodological and technical correctness of the solution; the originality of the solution; and the effort invested in the project activity.
• Verify students’ understanding of the course content with attention to the individual comprehension of each team member.
  T ype of assessment
  Description
  Dublin descriptor
  Assessment of project artefacts
  Assessment of the quality of the design and experimental work developed by students in groups, and of the correct understanding of concepts, paradigms, and technologies
  1, 2, 3, 5
  Oral presentation
  Assessment of the presentation of the work developed by students in groups
  2, 3, 4, 5

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

The course aims at enabling students to learn how to apply data analysis to communication networks and systems problems and evaluate the performance of the adopted strategies.
Practical problems will be discussed and analysed during the course, ranging from network resource allocation to network failures management and similar other use cases.
Further AI/ML concepts, such as Federated learning, Explainable Artificial Intelligence, Transfer learning, etc., will be also discussed in the specific context of communication networks.
The approaches learned and the methodologies developed during the course will allow the students to know and understand how to design and manage efficient communication network infrastructures and critically analyze data retreived from communication systems.

1. Knowledge and understanding (DD1)
Students will be able to:

• identify typical application scenarios in which data-analysis techniques can be used to address problems in the communication networks and systems domain
• identify the main functional and architectural components of network data-analysis frameworks and describe their interaction
• identify the important metrics used to evaluate the performance of data-analysis algorithms applied to communication networks data
• understand the key trade-offs between accuracy and complexity for the various data-analysis algorithms
  2. Applying knowledge and understanding (DD2)
  For the specific use-cases at hand, students will learn how to:
• apply and evaluate the performance and complexity of various data-analysis techniques to address network resource allocation and failure management
• face practical issues which may occur in network data-analysis projects, such as, among the others.:
• how to deal with missing or noisy data
• how to combine heterogeneous sources of information (i.e., labelled and unlabelled data)
• how to select important features
• how to provide generalizable results
• develop projects where data-analysis techniques (such as regression, classification, and clustering) are effectively applied to perform network resource allocation and failure management
  3. Making judgement (DD3)
  For the specific network data-analysis use-cases, students will be able to:
• compare and select the most suitable algorithms to be adopted
• evaluate which source/type of information is relevant and decide which one can be ignored
• identify and evaluate proper performance metrics
• assess the performance of the developed data-analysis techniques
  4. Communication (DD4)
  Students are expected to:
• report and critically discuss in oral (and, if necessary, written) form the data-analysis solutions adopted to address an assigned problem in the communication network domain
• work in a team and present the work done to an audience including other students
• propose innovative ideas and use-cases where data analysis is suitable to address problems in the communication network domain

1 – Introduction.
Overview of applications and methodologies. Typical pipeline of network data-analysis projects. Challenges and relevant metrics.
2 - Application traffic classification. Motivation and objectives.
3 – Quality of Transmission (QoT) estimation. Motivation and applications in optical networks.
4 – Network resources allocation. Traffic prediction for network design and reconfiguration.
5 – Network failure management in microwave networks.
6 - Innovative concepts (Federated learning, Explainable Artificial Intelligence, Transfer learning, Online learning) and their application to networking data.

Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The verification of knowledge for the course consists of a group project and an oral discussion on the developed project and course subjects.
Students will be evaluated considering the following aspects:
1) understanding and explaining how to apply data analysis to networking use-cases (DD1, DD2, DD4)
2) understanding and explaining how to comprehensively evaluate and measure the performance of data-analysis algorithms (DD2, DD3, DD4)
3) communication ability in explaining the solution proposed in the project (DD4)
4) ability to identify and propose innovative ideas and use-cases in the communication network domain (DD3, DD4)
Projects will be carried out in general in small groups (2/3 students per group) and the evaluation will be individually provided after the oral discussion.
In case of negative evaluation, the student is allowed to participate to the following tests of the academic year.

Slides and suggested readings provided by the lecturer through Wbeep platform https://webeep.polimi.it/
Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning, Editore: Springer, ISBN: 978-0-387-84858-7

The course addresses the study of Network Automation (NA) systems and solutions in the era of Software Defined Networking (SDN), Machine Learning (ML), Digital Twins (DT) and Network Function Virtualization (NFV).
The first part of the course is dedicated to NA, presenting a paradigmatic NA system architecture and describing its components. Also, the concepts of control loop, digital twin, agentic AI and intent are reviewed. Moreover, the general concepts of data structuring and data modeling will be introduced.
In the second part of the course, fundamental concepts about the approaches and methodologies used in SDN and NFV are provided. In particular, the course presents the basic elements to understand the SDN control plane, focusing on the controllers and their interfaces, and finally, the Network Application (NetApp) plane is introduced. NetApps are presented in a practical and operative way, as the components providing intelligence for the automation of modern networks. Then, the basic elements related to Network Function Virtualization (NFV) are introduced, explaining service orchestration and the MANO model. Finally, the new switching and orchestration systems for SDN and NFV are introduced and compared to the traditional implementations. Special attention will be given to modern standard SDN interface protocols such as OpenFlow, NETCONF and RestCONF.
The third part of the course concerns the study of use-cases related to advanced networking technologies, in which network automation can be exploited to increase performance and decrease costs. Such use-cases will potentially include [1]: technologies for the wide area network/network core (MPLS, Carrier Grade Ethernet, Segment Routing, Optical IP-over-WDM networks), technologies for enterprise networking (VPN, VPLS, SD-WAN), technologies for data-centric networks (virtual-machine communication, intra-datacenter switching architectures (fat-tree, leaf-spine, folded-Clos), inter-datacenter communication protocols (TRILL, SPB, VXLAN).
For each use-case we will present the current solutions based on traditional protocols, and then provide an insight on how network automation can be effectively applied to improve the performance. This analysis will be the foundation for the development of projects by the students.
The course indeed also comprises a large part devoted to hands-on experimental activity: the basic knowledge and tools required for this activity are presented and explained during the laboratory hours and exploited by students to complete their project assignments. The tools include: Mininet, Ryu, OpenStack. Students will also have access (in turns) to the BONSAI laboratory at DEIB. The experimental activity related to NFV and SDN for optical networks will also be supported by laboratory testbeds provided by leading manufacturing companies of network equipment and orchestration software.
[1] The set of use-cases that will be actually selected for a specific academic year will be precisely defined at the beginning of the course and may vary from year to year also w.r.t. to the changes of interest in the scientific and commercial community

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

• Familiarize with Software Defined Networking (SDN)
• Learn the fundamental concepts of NFV
• Describe the main functions needed in a router with relation to the IP network layer
• Identify the main subsystems of packet-switching equipment, focusing in particular on their traditional and SDN-oriented architectures
• Familiarize with the basic features of the SDN paradigm and of the south-bound interface protocols (e.g. OpenFlow, NETCONF)
  Applying knowledge and understanding (DD2)
  Students will be able to:
• Apply effective methods to perform the automation of network functions via SDN
• Apply effective methods to perform the softwarization of network functions in an NFV environment
• Design and compare the main intra-datacenter switching-fabric architectures
• Build and run a virtual testbed to test SDN OpenFlow switches
• Solve problems related to simple automation use-cases related to WAN/core network
• Solve problems related to simple automation use-cases related to enterprise networking
• Solve problems related to simple automation use-cases related to data-centric networking
  Making judgement (DD3)
  Students will be able to:
• Understand the trade-off between different complexity metrics (namely, execution and reconfiguration time, memory occupation) in algorithms for the network automation
• Understand the trade-off between blocking properties and cost in switching fabrics
• Appreciate the main differences between the classical and the SDN paradigm when applied to various network context
• Understand the use and importance of virtualization in modern networking
  Communication (DD4)
  Students will learn to:
• Orally report and critically discuss all the topics covered by the course
• Give evidence of what has been learned by demonstrating algorithms in simple examples during the oral exam
• Work in a team and present the work done to other colleague

Theory and Lab Lectures
1 – Basic concepts
Introduction to Network Automation.
The general architecture of modern networks based on Software Defined Networking (SDN): data plane, control plane, network-application plane
South-bound interface protocols: OpenFlow, Netconf, YANG modeling, Restconf.
Introduction to Network Function Virtualization (NFV): NFV Reference Architecture, VNF Interfaces, VNF Scaling
Virtual infrastructure managers. Service orchestrators (MANO model)
Overview about the architecture of the Internet.
2 – Network automation use-cases [1]
Network technologies for the wide area network/network core (MPLS, Carrier Grade Ethernet, Segment Routing, Optical IP-over-WDM networks): current solution, automation strategies
Network technologies for enterprise networking (VPN, VPLS, SD-WAN): current solution, automation strategies
Network technologies for data-centric networks (virtual-machine communication, intra-datacenter and inter-datacenter communications, VXLAN): current solution, automation strategies
[1] The set of use-cases that will be actually selected for a specific academic year will be precisely defined at the beginning of the course and may vary from year to year also w.r.t. to the changes of interest in the scientific and commercial community
3 – Laboratory introduction
Virtualization: basic concepts about virtual machines and containers, VirtualBox, description of the VM provided for the course
SDN – OpenFlow laboratory: introduction to the Ryu controller, MiniNet, OpenFlow mini-lab, template network application
SDN – NETCONF/YANG laboratory: introduction to YANG modeling, NETCONF emulation, SM-Optics controller and tesbed, template network application
NFV: universal CPE, zero-touch deplyment procedures, Ensamble Orchestrator and Connector, simple VNF chains
Exercises
The exercise sessions will be related to the basic concepts (e.g. simple availability model, simple traffic-engineering or switching problems, etc.) and to the network protocols currently in use for the use-cases presented in the course.
Software Laboratory
1 – Virtualization Lab tools: Virtual Box, MiniNet
2 – SDN controllers: The Ryu framework; ONOS
3 – Skeleton controller to develop the projects
4 – ADVA NFV Ensemble orchestration suite
Project Laboratory
1 - Project assignment and development (teamwork), under the assistance of a project tutor (from either PoliMi or an external company according to the specific project topic)
2 - Software checks and project testing (teamwork), under the assistance of a project tutor (from either PoliMi or an external company according to the specific project topic)
Innovative Learning
The innovative learning action in the course will be provided by the following active-classroom forms:
Selected topics covered in a flipped-classroom form
Active student participation to the software-laboratory sessions, bringing and working on their own device
Periodical on-line assessment of knowledge (via on-line tests) during the course
Tutorial sessions both for all the students and for individual project teams

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
Valutazione continua facoltativa (a scelta dello studente)
The verification of knowledge for the course consists of a final oral exam and a project activity.
The oral exam assesses the student’s ability to:
1- understand and explain the principles of SDN and NFV, network architectures, and automation mechanisms (DD1);
2- apply such concepts to analyze and design network automation and virtualization solutions (DD2);
3- evaluate trade-offs in complexity, cost, and performance among alternative designs (DD3);
4- communicate technical concepts clearly and effectively through discussion and demonstrations (DD4).
For the project activity, students work in teams to implement network automation techniques based on SDN/NFV use-cases presented during the course. Each team must develop SDN applications and/or virtual network functions, test them in a lab environment, and defend the project in a final presentation before peers and the professor (DD2, DD3, DD4).
The final grade is the average of the two scores obtained in the oral exam and the project.
Participation in innovative-learning classroom activities will be scored and contribute as an elective bonus to the final evaluation.
The participation of each student to innovative-learning classroom activities will be scored and will contribute to the final evaluation as an elective bonus.

Jason Edelman, Scott S. Lowe & Matt Oswalt, Network Programmability and Automation, Editore: O'Reilly Media, Inc., Anno edizione: 2018
Note: Note: Also available as e-book
William Stallings, Foundations of Modern Networking: SDN, NFV, QoE, IoT, and Cloud, Editore: Addison Wesley, Anno edizione: 2015
Note: Note: Also available as e-book
Guido Maier, Sebastian Troia et al., Material distributed by the lecturer: slides and exercise notes and video recordings available
Note: Note: Material available on WeBeeP

The goal of the course is to teach students the methodologies for understanding, analyzing and designing datacenter networks. Moreover, the course provides the students the instruments for reviewing latest innovations (from both industry and the research community) in terms of advanced traffic processing at datacenter-scale.

1 - Knowledge and understanding
Students will learn how to:

• Define the basic networking components of datacenter networks
• Define the techniques used to scale datacenter networks
• Analyze complex technologies related to high-speed packet processing
• Identify system components and their relations
  2 - Applying knowledge and understanding
  Students will be able to:
• Dimension specific datacenter network functionalities based on initial requirements
• Evaluate the performance of datacenter networks in different scenarios
• Apply the programming languages learned for advaced packet-processing operations
  3 - Making judgements
  Students will be able to:
• Understand the fundamental trade-off that govern the design of datacenter networks
• Identify how the fundamental systems (and algorithms) limitations impact the design of datacenter networks
• Recognize the design space and its degrees of freedom that can be exploited to define new technologies
  4 - Communication
  Students will learn to:
• Write and describe complex network technologies at different levels of detail, also comparing them with respect to specific aspects
• Apply critical thinking to advanced topics related to datacenter networking and fast packet-processing
  5 - Learning skills:
  Students will be able to learn autonomously the most advanced techniques related to state-of-the-art systems for datacenter networking

1. Basics

• Introduction to datacenter networks and their challenges
• Topologies
• Traffic characterization
  2. Network programmability
• Software-Defined Networking
• OpenFlow
• Switch programmability
  3. Data structures for high-speed networking
• Ternary Content Addressable Memories
• Hash tables
• Bloom Filters
• Counting Bloom Filters
• Invertible Bloom Filters
• Count-Min Sketches
  4. Datacenter Monitoring
• Packet mirroring
• Monitoring at scale
• Inband Network Telemetry
• Advanced Netflow
  5. Datacenter control
• Layer 3 load balancing
• Layer 4 load balancing
• Congestion control
  6. End-Host Networking
• Programmable NICs
• Kernel networking
• New protocols for fast end-host networking
• Virtual networking

Altro
The assessment will be based on an exam at the end of the course and on an additional project activity.
The exam is based on questions covering the entire course (dublin descriptors 1, 2, 3, 4, 5)
The project activity will provide additional points and it is based on testing/developing packet-processing functions for datacenter-scale networking (dubling descriptors 2, 4, 5)

Meta's Hyperscale Infrastructure: Overview and Insights https://dl.acm.org/doi/full/10.1145/3701296
A Scalable, Commodity Data Center Network Architecture https://dl.acm.org/doi/10.1145/1402946.1402967
OpenFlow: Enabling Innovation in Campus Networks https://dl.acm.org/doi/10.1145/1355734.1355746
Forwarding Metamorphosis: Fast Programmable Match-Action Processing in Hardware for SDN https://dl.acm.org/doi/10.1145/2486001.2486011
Data Sketching https://cacm.acm.org/practice/data-sketching/
Packet-Level Telemetry in Large Datacenter Networks https://conferences.sigcomm.org/sigcomm/2015/pdf/papers/p479.pdf
Understanding Host Network Stack Overheads https://dl.acm.org/doi/abs/10.1145/3452296.3472888

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

Knowledge and Understanding (DD1)

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

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

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

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

Present the different security models (information-theoretic and computational). Present the archetypal problems of cryptography (confidentiality, integrity, authentication, non repudiation) and teach the most common solutions in the symmetric and asymmetric key settings. Present some more advanced protocols.

The student will be able to:

• Understand the security guarantees of popular cryptographic schemes and the security requirements of network applications (DD 1)
• Know how to combine simple cryptographic primitives preserving their security guarantees (DD 1, 2)
• Evaluate the success probability of a given attack against a given configuration (DD 2)
• Evaluate the performance of various algorithms in different attack scenarios and make design choices (DD 2)
• Undestand and evaluate the specification of a cryptographic scheme (DD 1, 2)

Digital communications model with insecure channels. Archetypal problems (confidentiality, integrity, authentication)
Information-theoretic security model. Classic ciphers.
Secure hash functions. Construction and applications of secure hash functions.
Computational security model. Pseudorandom generators. Stream ciphers. Block ciphers. Modes of operation.
Message Integrity Control.
Asymmetric cryptography. Digital signatures.
Factoring problem. Asymmetric schemes based on the RSA assumtion.
Discrete logarithm problem. Asymmetric schemes based on the Diffie-Hellman assumptions.
Elliptic Curves.
Secret sharing schemes and committments.
Advanced Algorithms (Multiparty Computation, Blockchain, Quantum Cryptography)

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Written test (closed books). Additional oral exam upon instructor's request.
The test consists in multiple short exercises in which the student will be asked to:

• execute simple cryptographic algorithms;
• compute the complexity and the cost estimation of various cryptosystems;
• understand the cryptographic primitives used during lab activities;
• discuss the impact of parameter and design choices on the security properties.
  The oral examination consists in open-ended questions on the security definitions, properties, and theorems about the cryptosystems discussed in class.

Jean-Philippe Aumasson, Serious Cryptography, 2nd ed., Editore: no starch press, Anno edizione: 2024, ISBN: 978-1718503847
Nigel Smart, Cryptography, An Introduction: Third Edition, Anno edizione: 2016 https://nigelsmart.github.io/Crypto_Book/
Wade Trappe and Lawrence Washington, Introduction to Cryptography with Coding Theory (2nd edition), Editore: Prentice Hall, Anno edizione: 2005, ISBN: 978-0131862395
Note: disponibile anche in Italiano

In the past decades a growing interest has been dedicated to networked systems and large-scale plants where parts of the system, e.g., sensors, actuators, are not collocated and are possibly distributed over a large geographic area. These classes of systems include, e.g., large chemical plants, power distribution networks, industrial factories, water distribution networks, and multiple agents cooperating with each other through suitable coordination schemes (e.g., unmanned aerial and ground vehicles and teams of robots).
The control of these systems may require communication through a shared transmission network and/or to resort to complex (e.g. decentralized, distributed, or hierarchical) schemes.
However, one the one hand, communication through a shared transmission network involves a number of issues, e.g., bandwidth limitation, delays, packet dropouts, which can compromise the stability properties of the network. On the other hand, distributed and decentralized control structures entail structural constraints and limitations, and call for non-standard analysis and control design tools.
The course covers the necessary mathematical and technical tools for the analysis and the design of possibly complex control systems subject to communication and structural issues.

Lectures and exercise sessions will allow students to:
Understand the challenges, opportunities, and issues related to networked control systems.
Understand the possible sources of communication problems, including bandwidth limitations, delays, and packet dropouts.
Learn how to devise mathematical models where communication issues are formally described, and perform stability analysis of networked control systems, i.e., where communication takes place through a serial communication bus.
Understand the theoretical limitations (e.g., decentralized fixed modes) related to decentralized and distributed control structures, and learn how to design decentralized and distributed regulators with linear matrix inequalities (LMIs).
The hands-on activities allow students to l earn how to use Matlab and Yalmip to
analyze the properties of dynamical systems, e.g., the stability of linear parameter-varying systems and Networked Control Systems using LMIs
design state-feedback centralized, decentralized, and distributed controllers using LMIs based on different design criteria, e.g., H2 and Hinfinity.
With respect to the study programme, the course will allow students to reach the following generic learning outcomes:
[DdD1] Knowledge and understanding
[DdD2] Capability to apply knowledge and understanding
[DdD3] Making judgements
[DdD4] Communication skills
[DdD5] Learning skills

The course will cover the following topics:
INTRODUCTION: Introduction on networked control systems, control architectures for large-scale and networked systems, and application areas. Issues and challenges of networked control systems.
PRELIMINARIES: Basic and more advanced concepts in control theory (with focus on linear systems) will be recalled and covered, in order to provide consistent background to the students, e.g.
Stability of continuous-time and discrete-time systems;
Controllability and observability;
Basics on linear matrix inequalities in control systems analysis and design.
CONTROL WITH COMMUNICATION ISSUES.
Communication issues in networked control systems. Control networks, packet transmission, and transmission protocols.
Control with bandwidth limitations: minimum channel capacity requirements.
Network-induced delays and packet dropouts. Stability analysis in case of network-induced delays and packet dropouts.
DECENTRALIZED AND DISTRIBUTED CONTROL STRUCTURES.
Definition of decentralized fixed modes and stabilization with decentralized regulators.
Design of decentralized and distributed controllers using linear matrix inequalities for both continuous and discrete-time systems.
The course consists of:
Theoretical lectures (about 30 hours).
Numerical exercise lectures (about 6 hours).
Computer laboratory lectures (about 8 hours), where the described analysis and design methods will be implemented using dedicated computer tools and programs (e.g., MATLAB and YALMIP).
Project laboratory lectures (about 4 hours): the teacher will assign a project to each group of 1-4 students regarding the design of decentralized and distributed controllers for a selected benchmark case study. The project discussion will be matter of evaluation during the oral exam.

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The Exam consists of two parts aiming to evaluate, not only the students’ knowledge and understanding of the course topics, but also their ability to apply the acquired concepts and theoretical/practical tools.
Regarding “Control with communication issues”, a written test must be taken, consisting of numerical exercises and theoretical questions. The numerical exercises (DdD2 - capability to apply knowledge and undestanding) will assess the capability of students to: (a) devise mathematical models where communication issues are formally described; (b) perform stability analyses of networked control systems, possibly in simplified cases.
Open or multiple-choice questions will be used to assess the knowledge of students (DdD1 - Knowledge and understanding) of the following topics.
Communication issues in networked control systems.
Mathematical modelling and stability analysis of networked control systems under communication issues.
Regarding “Decentralized and distributed control structures”, a project work must be completed (DdD2 - capability to apply knowledge and undestanding; DdD3 - Making judgements; DdD5 Learning skills) and presented (DdD4 - Communication skills) in a short oral discussion, where the comprehension of the main theoretical concepts will also be examined (DdD1 - knowledge and understanding). The students’ ability to apply the following tools and concepts will be evaluated.
Centralized, decentralized and distributed fixed mode analysis.
Design of decentralized and distributed controllers for large-scale systems.

Slides, lecture notes, and computer programs will be made available to students on the BeeP Portal. https://beep.metid.polimi.it/
R. J. Baillieul, P. J. Antsaklis, Control and communication challenges in networked real-time systems, Editore: Proceedings of the IEEE, 95 (1), pp. 9-28.
J. R. Moyne and D. M. Tilbury, The emergence of industrial control networks for manufacturing control, diagnostics, and safety data, Editore: Proceedings of the IEEE, 95 (1), pp. 29-47.
R. J. Baillieul, Feedback coding for information-based control: operating near the data-rate limit, Editore: Proceedings of the IEEE Conference on Decision and Control, 2002, pp. 3229-3236.
W. Zhang, M. S. Branicky, S. M. Phillips, Stability of networked control systems., Editore: IEEE Control Systems Magazine, 2001, pp. 84-99.
J. P. Hespanha, P. Naghshtabrizi, Y. Xu, A survey of recent results in networked control systems., Editore: Proceedings of the IEEE, 95 (1), pp. 138-162.
J. Lunze, Feedback Control of Large-Scale Systems, Editore: Prentice Hall International Series in Systems and Control Engineering, Anno edizione: 1992
Zecevic, D. D. Siljak, Control of complex systems. Structural constraints and uncertainty, Editore: Springer, Anno edizione: 2010

The primary objective of the course is for students to learn the fundamental abstractions and features of available networked software platforms while gaining concrete practical experience on their use in different application scenarios. Specific domains covered in the course are the Internet of Things, Edge Computing, Big Data Analytics, Concurrent Systems, Event-driven Applications, and High-performance Computing. The course promotes a practical approach, as the explanation of system abstractions and features is tightly linked to their practical application. The lecturers adopt a novel teaching methodology that enables students to invest most of their time in in-class lab sessions and hands-on work, with support from the lecturers.
For each software platform covered in the course, the learning activities use a combination of online videos and in-class lab sessions. The online videos introduce abstractions and fundamental features of a given platform. The lab sessions allow students to clarify doubts with the lecturers and experiment with the technology at hand.

By the end of the course, students are expected to be familiar with the primary abstractions and features in the software platforms covered in the course, be able to design and implement complete applications of medium complexity, and be acquainted with the practical issues arising when deploying and running these systems in realistic settings. The Dublin Descriptors applicable to the course follow:
Dublin Descriptor 1 (knowledge and understanding): the lab sessions of the course provide the opportunity to test the students’ understanding while facing the practical issues in deploying and running the systems.
Dublin Descriptor 2 (applying knowledge and understanding): the students apply the learned abstractions and features of each software platform in selected settings, drawing connections between theoretical concepts and experimentation.
Dublin Descriptor 5 (lifelong learning skills): the students are expected to acquire the ability to solve medium-complexity experimental problems on their own, using either the software platforms covered in the course or others that are based on similar abstractions or offer similar features.

For each of the software platforms we consider, we provide first an introduction to the specific domain, followed by a description of key abstractions and features, intertwined with concrete examples. A different software platform is considered for each of the aforementioned domains:
Internet of Things: ContikiNG
Computing Continuum: Node.js and Node-RED
Big Data Analytics: Apache Spark
Concurrent Systems: Akka
Event-driven Applications: Apache Kafka
High-performance Computing: MPI

Prova orale facoltativa (a scelta dello studente) o condizionata (a scelta del docente)
Valutazione continua facoltativa (a scelta dello studente)
Altro
The course adopts two evaluation modes: continuous evaluation or oral examination.
The continuous evaluation (DD2, DD5) is accomplished in student groups. Groups are randomly formed by the lecturers and different for each of the software platforms considered in the course. The goal is to combine the students’ learning with in-class experimental activities that allow the teachers to assess their preparation as learning proceeds throughout the semester. In addition to online videos, in-class Q&A sessions, and lab sessions, dedicated evaluation labs are scheduled throughout the semester. The evaluation labs build on what the students learned and experimented with online videos and regular lab sessions, and are instrumental to check their understanding of a given platform. During an evaluation lab, the student groups are to complete an exercise the lecturer hands out at the beginning of the lab. The work accomplished at the end of an evaluation lab is graded by the lecturers and contributes to determining the final grade, possibly integrated by selected oral discussions. This mode allows students to complete the coursework by the end of the lecture period.
The oral examination (DD2, DD5), differently, seeks to test the individual students’ preparation by verifying the students' understanding of the software platforms discussed in the course in an oral discussion, and may also include questions on selected programming assignments. The oral examinations are individual and scheduled during the exam period after the end of the lecture period.
Groups may start with the continuous evaluation but individual students may possibly switch, at any time during the semester, to the oral examination mode.

Teaching material provided by the lecturer

The growing interest towards new improved maintenance strategies for both existing and new infrastructures requires the civil engineer to deal with the basics of Structural Health Monitoring (SHM). This is considered a fundamental step towards a sustainable, resilient and smart infrastructure management. The course aims at providing the basic knowledge about SHM, following three main steps: measurement systems, data management and analysis and in the end the final evaluation, with care about the quality of all the phases.
The course aims at providing a practical approach to these problems mainly through laboratory activity and field testing, by which the student will become familiar with the basic aspects of sensing, data acquisition and processing. Lessons will just provide the needed info for hands-on sessions.
Specific attention will be devoted to dynamic measurements, as a tool and a new emerging technology aimed at helping SHM.
A reference will be made to the use of measurements to validate models or to start model updating, enriching the continuous information exchange between experimental and numerical approaches.
At the end of the course the student will be able to make the proper choices to develop a Structural Health Monitoring application and to perform a complete test on own, to be discussed during the exam.

About knowledge and understanding, students are expected to acquire the basics about those aspects closely related to Structural Health Monitoring,, in a close relationship to their general knowledge of civil engineering, to be aware about how to properly design and use a Structural Health Monitoring System.
They will acquire knowledge about the main sensors adopted for Structural Health Monitoring, about data acquisition principles and the basics of the frequency domain analysis
Concerning Application, the students will practice on some proposed cases first, then they will be asked to develop small projects, to demonstrate they reached the goals planned for the Structural Health Monitoring expert, starting from the sensor choice, up to the best data management strategy.
Communication skills are also included, as the they will be asked to discuss their projects, with an autonomous evaluation capability for the defense of their job.
A final goal is the capability to interact with different specialists, also in areas not commonly part of the civil engineering cultural knowledge base, with simplified approach, training then to properly face new situations.

The main topics are:
The meaning of Structural Health Monitoring
Typical approaches of Structural Health Monitoring
Metrology basics
Sensors for Structural Health Monitoring
Static and dynamic aspects in measurements
Data acquisition
Signal analysis, with specific emphasis on the analyses in the frequency domain
The course will be completed with many practical examples or specific workshops, dealing with real examples from existing systems, mainly developed for both structural health and biomedical quantities monitoring.

Progetto/i - Elaborato/i obbligatorio, individuale, senza revisioni periodiche, con presentazione/discussione finale
There are some check points, consisting in small projects which are going to be carried out with the teacher's and or instructor’s help, constituting the base for the exam discussion.
The projects will include both questions about the theoretical knowledge and concerning the experimentation that students will develop on their own.
Every student is asked to bring the reports to the final exam, where a final defense and discussion will be carried out to individually justify the adopted choices. The student needs to show good capability to present the project and discuss it, showing to have gained good critical skills and technical language.

Il corso si propone di presentare i principali metodi di ottimizzazione non-lineare, continua e discreta. Contenuti: Condizioni di ottimalita' per problemi non vincolati e vincolati, funzioni Lagrangiane e dualita'; metodi di tipo gradiente, di tipo Newton e a passo ristretto; programmazione quadratica ricorsiva e metodi con funzioni di penalita'. Complementi di ottimizzazione discreta: rilassamenti, metodi di branch-and-bound e poliedrali, euristiche.

This course introduces basic concepts and methods of Numerical Analysis with the aim of providing reliable and accurate approximations to problems recurrent in the engineering practice, described via linear and nonlinear mathematical models. The different numerical methods are theoretically derived with particular attention to key concepts like convergence, consistency, and stability.
The course is organized in lectures and computer labs. Laboratory sessions are meant to provide practical feedback to the theoretical knowledge with the implementation of the algorithms and methods presented during the lectures. All computations are carried out using the Matlab scientific software environment.

Knowledge and understanding:
‐ the basic concepts and the possible errors due to the floating point arithmetics;

• the main numerical methods to approximate data and functions, solve linear and nonlinear equations, derivatives and integrals, and ordinary differential equations;
• the main properties of the considered approximation schemes;
• essential use of the MATLAB language.
  Applying knowledge and understanding:
• apply the learned numerical schemes implementing computer codes in the MATLAB language;
  ‐ using appropriate numerical methods to solve some problems arising from engineering applications.
  Making Judgement:
• be able to select the most appropriate tool for the problem at hand;
• be able to critically assess the quality of the solution and the properties of the adopted numerical method.
  Communication skills:
• be able to interact with other students and teachers during exercise sessions and using the WeBeep forum.

• Floating-point arithmetics: different sources of the computational error; absolute vs relative errors; the floating point representation of real numbers; the roundoff unit; the machine epsilon; floating-point operations; over- and under-flow; numerical cancellation;
• Numerical approximation of nonlinear equations: the bisection and the Newton methods; the fixed-point iteration; convergence analysis (global and local results); order of convergence; stopping criteria and corresponding reliability; generalization to the system of nonlinear equations (hints);
• Numerical methods for continuuous optimization: Free and constrained optimization; Line-search; Newton and quasi-Newton descent directions; Gradient and Conjugate Gradient; Trust region Methods; Constrained optization;
• Numerical approximation of systems of linear equations: direct methods (Gaussian elimination method; LU and Cholesky factorizations; pivoting; sparse systems: Thomas algorithm for tridiagonal systems); iterative methods (the stationary and the dynamic Richardson scheme; Jacobi, Gauss-Seidel, gradient, conjugate gradient methods (hints); choice of the preconditioner; stopping criteria and corresponding reliability); accuracy and stability of the approximation; the condition number of a matrix; over- and under-determined systems: the singular value decomposition (hints);
• Numerical approximation of functions and data: Polynomial interpolation (Lagrange form); piecewise interpolation; cubic interpolating splines; least-squares approximation of clouds of data;
• Numerical approximation of derivatives: finite difference schemes of the first and second order; the undetermined coefficient method;
• Numerical approximation of definite integrals: simple and composite formulas; midpoint, trapezoidal, Cavalieri-Simpson quadrature rules; Gaussian formulas; degree of exactness and order of accuracy of a quadrature rule;
• Numerical approximation of ODEs: the Cauchy problem; one-step methods (forward and backward Euler and Crank-Nicolson schemes); consistency, stability, and convergence (hints).

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Five exam sessions are scheduled on the dates established by the Faculty calendar.
The exam consists of a written test during which it will be required:
To answer theoretical questions on the topics covered during the course, which may deal with the statement and/or the proof of relevant theoretical results; the precise definition of concepts such as convergence, consistency, and stability of a numerical scheme; the deduction of a numerical method;
To program small computer codes based on MATLAB to solve numerical problems of the type seen at the lectures and during exercise sessions.
An oral assessment may follow the written exam if the teacher deems it necessary.
We assess:
1) The capability of the student to apply the knowledge and technique acquired during the course (Dublin Descriptors 1 and 2);
2) The student's ability to evaluate the result's appropriateness and correctness (Dublin Descriptors 2 and 3);
3) The ability to develop simple numerical computer codes autonomously and evaluate their effectiveness for the problem at hand (Descriptors 3 and 5).

Quarteroni, Alfio, Saleri, Fausto, Gervasio, Paola, Scientific Computing with MATLAB and Octave, Editore: Springer, Anno edizione: 2010, ISBN: 978-3-642-12429-7
Quarteroni, Alfio, Sacco, Riccardo, Saleri, Fausto, Numerical Mathematics, Editore: Springer, Anno edizione: 2007, ISBN: 3-540-34658-9

Il corso parte dalle basi introdotte nel corso "Computer Security" per
introdurre argomenti avanzati nei campi della cybersecurity e della
sicurezza offensiva. Il corso mescola una parte fissa di didattica
frontale (focalizzata su argomenti quali exploitation e reverse
engineering) con una parte "flipped classroom" che si rinnova di anno in
anno, dedicata all'analisi e alla discussione di argomenti di ricerca di
frontiera. Le lezioni frontali sono completate da esercizi pratici,
risolti in parte in classe e in parte come compito a casa

The goal of the course is to provide advanced skills in machine learning and optimization for challenging real-world data-driven applications. More precisely, the course explores mathematical models, optimization algorithms, and machine learning tools. The course focuses on four real-world applications in which computational data science is crucial: pricing in e-commerce, digital advertising, matching, and non-stationary environments. For every real-world application, the goals of the course are: providing a description of the real-world scenario, providing mathematical models of the scenario, providing optimization algorithms for the scenario, providing machine learning tools to deal with uncertainty. The treatment of the topics will be both from a theoretical point of view, describing models/algorithms and their properties, such as approximation guarantees and computational efficiency, and from a practical point of view, applying the algorithm during some laboratory sessions.

DdD1. Knowledge and Understanding

• Analyze real-world applications through mathematical models derived from microeconomics.
• Examine and interpret the properties and assumptions of such models.
• Evaluate the performance of algorithms and compare them with theoretical lower bounds.
  DdD2. Applying Knowledge and Understanding
• Model real-world applications as learning problems.
• Identify the most suitable class of algorithms to solve a given problem.
• Apply existing computational tools to implement and test practical solutions.
  DdD3. Making Judgments
• Assess the performance and robustness of proposed solutions.
• Make informed decisions among alternative methods offering different theoretical or empirical guarantees.
  DdD4. Communication
• Present and discuss problem formulations, modeling choices, and obtained results using appropriate technical language.
  DdD5. Lifelong Learning Skills
• Design algorithms to address problems for which no existing methods are available.
• Analyze and critically discuss the theoretical properties of newly developed algorithms.

0. Introduction to online learning
0.1. Online learning setting
0.2. Learning with full feedback
0.2.1 Online gradient descent
0.2.2. Adversarial expert problem
0.3. Learning with bandit feedback
0.3.1. Stochastic multi-armed bandits
0.3.2. Adversarial multi-armed bandits
1. Pricing in e-commerce
1.1. Introduction to pricing
1.2. Learning in pricing
1.2.1. Learning with discrete prices
1.2.2. Learning the demand curve
1.3. Implementing learning algorithms for pricing
2. Matching and combinatorial bandits
2.1. Introduction to matching
2.2. Matching algorithms
2.3. Combinatorial bandits
2.3.1. Combinatorial bandits with UCB
2.3.2. Combinatorial bandits with TS
2.4. Implementing learning algorithms for combinatorial bandits
3. Advertizing and learning with budget
3.1. Introduction to advertizing
3.2. Online learning with budget constraints
3.2.1. UCB for online learning with budget constraints
3.2.2. Primal-dual methods for online learning with budget constraints
3.3 Implementing learning algorithms for online problems with budget
4. Contextual Bandits
4.1. Introduction to contextual bandits
4.2 Online learning in contextual bandits
4.2.1 Learning with a small number of contexts
4.2.2 Learning in linear contextual bandits
4.3 Implementing learning algorithms for contextual bandits
5. Non-stationary enviroments
5.1. Introduction to non-stationary environments
5.2. Sliding window
5.3. Change detection
5.4. Implementing learning algorithms for non-stationary environments

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The exam is structured as follows:

• Written exam (2 hours --- max 16 points). The exam is composed of 4 questions relative to all the topics. Here, the students are required to answer some theoretical questions about topics discussed during the course. The capabilities the students must show are:
• Understanding the theoretical properties of the algorithms (DdD1).
• Modeling real-world applications by means of mathematical models (DdD2).
• Identifying the most suitable class of algorithms to solve a problem, using the already available tools to solve a problem in practice (DdD2, DdD3).
• Project work (max 16 points). The project work can be carried out by groups of students, where each group is composed of at most 4 students. The students can exploit the lectures delivered by the teaching assistant to organize the project work. However, attending the lectures delivered by the teaching assistant is not mandatory. Here, the students are required to solve problems that are beyond the topics discussed during the course. The capabilities the students must show are:
• Design an algorithm for a problem not discussed during the course (DdD3, DdD5).
• Present orally the proposed algorithms and discuss their properties (DdD3, DdD4, DdD5).

Aleksandrs Slivkins, Introduction to Multi-Armed Bandits, Editore: Now Publishers, Anno edizione: 2019
Elad Hazan, Introduction to Online Convex Optimization, Editore: MIT Press, Anno edizione: 2022

Communications networks see an exponential growth of traffic demand, driven by multimedia services and mobile applications requiring higher and higher bandwidth. Traditionally, network traffic used to be based on circuit-switched, time-division multiplexed (TDM) connections between pairs of endpoints. Today, most network traffic is packet switched, based on Internet Protocol (IP), generated by a multitude of services in bursty and time-varying patterns, thus imposing more complex requirements on data transport performance. Only optical technologies enable flexible and transparent transport of various data formats at the highest transmission capacity.
This course provides an ample overview of fundamental topics about transport networks based on optical systems. In the first part, a primer of traditional TDM multiplexing systems (PDH and SDH) is provided. IP packet data transport over optical systems is treated. Architectures of optical Next-Generation Access Networks and Passive Optical Networks are illustrated with deployment considerations.
In the second part, the wide area of network synchronization is explored, providing a wide overview on fundamental topics including jitter, clock models, network synchronization architectures and requirements for time and frequency distribution in a variety of environments, from traditional TDM to most advanced 4G and 5G networks.
Finally, basic elements of traffic models and engineering are surveyed, for both circuit and packet traffic, up to covering advanced topics as self-similar fractal models to describe the behavior of packet network traffic.
All course topics are presented by providing the necessary theoretical background, yet always highlighting most relevant practical and application aspects.

1. Knowledge and understanding (DD1)
After having attended the course and having fruitfully passed the exam, the student should know at least the main elements of:
TDM digital multiplexing systems;
SDH systems, from frame structures to equipment and systems;
architectures of optical access networks;
how packet traffic (namely, IP) is transported over transmission systems;
principles of network synchronization, as a fundamental requirement for fixed and mobile networks from 2G to 5G;
models of synchronization signals and clocks;
models of circuit and packet traffic in transport networks.
2. Applying knowledge and understanding (DD2)
Owing to the various numerical exercises discussed in class, on which at least half of the exam tests is based, the students should not only acquire the capacity to apply the concepts learned from lessons to practical cases, but also improve their ability of numerical resolution and design in practical problems, which is an essential ability for an Engineer.
Moreover, the lessons always highlight the practical and application aspects that are most relevant to the subject, thus encouraging the students to learn to apply autonomously the concepts understood to new and different problems.
5. Lifelong learning skills (DD5)
The learning skills of the students are developed by an interactive approach in teaching, which always strives to involve participation and interaction and to stimulate curiosity for new topics.
When available, complete texts, such as textbooks, chapters, white papers are recommended as study material rather than succinct slides, in order to stimulate the ability of students to learn concepts on a complete text and technical documentation, rather than limiting the scope to few essential sentences condensed to few words.
Likewise, exams avoid multiple-choice questions, to develop the skills of students in active problem solving.

1. BASICS OF TDM OPTICAL MULTIPLEXING SYSTEMS.
Time Division Multiplexing (TDM). Synchronous and asynchronous digital multiplexing. Plesiochronous Digital Hierarchy (PDH). Synchronous Digital Hierarchy (SDH): hierarchical levels, frame structures, examples of mapping of PDH signals over SDH, overheads, SDH equipment and systems.
2. TRANSMISSION OF DIGITAL DATA OVER OPTICAL LINES.
Physical interfaces for TDM systems. Bit error rate estimation (BIP-n,m coding). Scrambling. Frame alignment.
3. TRANSPORT OF PACKET DATA.
IP over SDH. Virtual Concatenation and Link Capacity Adjustment Scheme (LCAS).
4. OPTICAL ACCESS NETWORKS.
Legacy copper access networks. Architectures of optical Next-Generation Access Networks (NGAN). Optical access systems for FTTx. Passive Optical Networks (PON).
5. NETWORK SYNCHRONIZATION.
Principles of network synchronization. Timing signals. Jitter and wander. Frame synchronization. Evolution of network synchronization from circuit to packet networks. Synchronization in packet-switched networks. Synchronization of mobile wireless networks from 2G to 5G. Principles of Network Time Protocol (NTP) and IEEE1588 Precision Time Protocol (PTP). Synchronization networks. Models of clocks in telecommunications and characterization of their stability.
6. TRAFFIC MODELS AND ENGINEERING.
Basics concepts of Markov Chains and queuing theory. Circuit traffic models and engineering. Packet traffic models in transport networks: self-similarity and long-range dependence. Estimation and measurement of traffic long-range dependence.
It is recommended to attend all classroom lessons for optimal understanding of all course topics.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The learning assessment consists first in a written exam, including various numerical exercises and questions on the subject matter. Both are structured in such a way, to verify the "expected learning outcomes" DD1, DD2, and DD5 as described above, in particular not only the knowledge and understanding of course topics, but also the ability of students to solve numerical problems and to design solutions.
An oral examination is usually required after the written test, based on the first assessment result achieved with it. No intermediate evaluations are planned. All exams are all-inclusive over the whole course program.
The student, who is not satisfied of the assessment score received, is admitted to next exams to improve his/her evaluation until the end of the Academic Year. Handing over a written test cancels any valid mark previously achieved.

S. Bregni, Sistemi di trasmissione PDH e SDH - Multiplazione, Editore: McGraw-Hill Education Italy, Anno edizione: 2004, ISBN: 978-8-83-867340-5
S. Bregni, Synchronization of Digital Telecommunications Networks, Editore: J. Wiley and Sons, Anno edizione: 2002, ISBN: 0-471-61550-1
Stefano Bregni, Lecture notes (slides and selected book chapters) on all course topics, Anno edizione: 2020

The Course is aimed to offer the comprehension of the present Optical Communications, the transmission technology supporting the worldwide ubiquitous telecommunication network, connecting continents, countries, cities, plants and users’ homes through the pervasive fiber infrastructure. The Course is self-contained and provides the knowledge for designing optical communications systems in different scenarios, from the long-haul transport to the metro-access network and the data-center interconnections. During the Course, experts of the industrial sectors of the optical communications (manufacturers and operators) will be invited for suitable tutorials. The course is complemented by numerical exercises and simulation activities.

Regarding the Dublin Descriptor DD1 (knowledge and understanding), the lectures and exercise sessions of the course will provide a solid background, allowing the students to understand the basis of optical fiber transmission and to face all the linear and non-linear impairments related to the propagation of the light in fiber, with and without optical amplification. In this way, the student will understand the reasons why optical fiber systems are today the backbone of fixed telecommunication networks.
Regarding the Dublin Descriptor DD2 (applying knowledge and understanding), the students will be able to apply the principles and modelling tools acquired during the course in the design and optimization of optical communications systems, also in case of coherent detection, in different scenarios and applications. The students will be able to clearly see connections among theoretical concepts and recognize how to apply them autonomously to all the telecommunication networks.
Regarding the Dublin Descriptor DD3 (making judgements), the students will be able to undestand the fundamental limitations due to the dispersion, non-linearities, optical noise; to understand the fundamental trade-off between transmission rate, throughput and reach; to recognize the critical parameters in the system design and operation, evaluating the performance and analyzing the capabilities of each system technological solution.
Regarding the Dublin Descriptor DD4 (communication) the students will learn how to present the course topics with a specialized vocabulary, necessary for the following advanced coursework in the study of communications and for the future work career.

Introduction. The role of the optical communications in the telecommunications stacks. Introduction to the WDM layer.
Optical Fibers. The fiber-optic technology and structure. Attenuation issues. Optical transmission windows and WDM grids. Propagation in optical fibers. The concept of mode. Propagation in case of parabolic-index and step-index profile. The Gaussian approximation. Multi-mode and single-mode propagation.
Fiber transmission impairments. Chromatic dispersion and polarization mode dispersion. Dispersion management and dispersion compensation. Non-linear effects in fiber transmission. Kerr-effect: SFM, XFM and FWM. Interaction of linear and non-linear effects. Raman and Brillouin effect.
Key components for optical communications: semiconductor lasers, modulators, optoelectronic receivers. Optical amplifiers. EDFA and Raman amplification. ASE noise.
Optical communications digital systems. Power-budget, SNR, BER, Q-factor. Direct-detection systems without optical amplification: quantum-limited operation; design and modeling; short and medium-haul applications, metro and access networks. Coherent transmission systems in multi-span amplified links: receiver block diagrams, Gaussian noise model, long-haul applications.
Novel multiplexing techniques: spatial division multiplexing. Multi-mode, few-mode and multi-core fibers.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The assessment is based only on a written exam. The written exam is based on numerical exercises and open theoretical questions, covering all the topics of the course. Students are evaluated considering the following aspects: understanding and explaining the operating principles of optical fiber propagation (DD1, DD2, DD4); understanding and explaining how to comprehensively evaluate and measure the performance of an optical communications system (DD2, DD3, DD4); communication ability in answering questions (DD4).
During the written exam the student can use only a pocket calculator (NO laptop, tablets etc. Any type of cellphone should be switched OFF). No other technical material is allowed (thus no books, handouts, old exercises, etc). Possible formulary is provided by the teacher. If the final grade of the written exam is 32, the student will get directly “30 cum laude”. An optional oral exam is only at the discretion of the teacher in particular conditions (i.e. assignment of 30 cum laude when the final grade of the written exam is 31). The optional oral exam is always organized a few days after the written exams.

Govind P. Agrawal, Fiber-Optic Communication Systems, Editore: WILEY, Anno edizione: 2021, ISBN: 9781119737360
Gerd Keiser, Fiber Optic COmmunications, Editore: Springer, Anno edizione: 2021, ISBN: 978-9813346642
Appropriate notes and slides provided by the teacher by the WEBEEP site https://webeep.polimi.it

Questo corso introduce il concetto di interconnessioni ottiche in sostituzione del cablaggio in rame nei sistemi di comunicazione e elettronici. Il corso discute il perché ed il modo in cui le interconnessioni ottiche stanno diventando un'alternativa essenziale per i futuri sistemi di comunicazione e il loro utilizzo all'interno dei sistemi elettronici. Lo scopo è anche quello di comprendere le opzioni tecnologiche per le interconnessioni ottiche ad alta velocità, i vantaggi di ciascuna opzione tecnologica e l'evoluzione prevista in queste tecnologie. Il corso copre gli aspetti principali delle interconnessioni ottiche, dalla propagazione in fibre ottiche ai principali approcci tecnologici ai dispositivi e all'integrazione con l'elettronica.

The course aims to provide a deep knowledge of modern optical and optoelectronic measurements. It starts with a review of LASER principles and measurements of main optical parameters of LASER light (spectrum, power, coherence, beam profile), to proceed with applications of optical instrumentation and measurements in scientific and technological fields. The class will study measurement methods and electro-optical systems for electronic material and device characterization (e.g., optical spectroscopy and laser interferometry), optical methods and instrumentation for optical communications, and electro-optical instrumentation for automotive and industrial applications, including optical telemeters, interferometers, laser vibrometers, and velocitmeters.

We expect that the student will be able to understand and solve problems related to optical instrumentation and measurements.
In particular, the student will develop the ability to design and use practical optical telemeters, interferometers, and velocimeters, and understand the capabilities of optical laboratory instrumentation (optical power meter, optical spectrum analyzer, wavelength meter). These abilities will be tested with written exercises and tests and eventually an oral interrogation.
At the end of the course and after successfully passing the exam, the student should reach the following learning results, with reference to the Doublin Descriptors:
Knowledge and understanding (Dublin Descriptor 1, DD1)
knows how to analyze block diagrams of optical measurement setups and to understand the choice of different laser sources on the system performance;
knows how to design coherent optical detection systems, evaluating signal and noise performance;
understands how to use an optical telemeter, interferometer, velocimeter, and optical measurement instrumentation (optical spectrum analyzer, optical power-meter, wavelength-meter).
Applying knowledge and understanding (Dublin Descriptor 2, DD2)
can apply the acquired understanding to assess the operation and performance of available optical measurement instrumentation;
can apply the acquired knowledge and skills to design an optoelectronic sensor or an optical interferometer;
can discuss pros and cons, as well as performance trade-offs, among different optical measurement instruments.
Making judgements (Dublin Descriptor 3, DD3)
given a specific problem and project cases in the field of optical measurements, should be able to analyze the problem, highlight the peculiar requirements and characteristics, and compare autonomously different choices in terms of hardware and system performance;
can communicate effectively the results of his activity regarding the study and design of optical instrumentation, and can autonomously take design decisions.
Communication (Dublin Descriptor 4, DD4)
will be able to write reports about the analysis and design of optical instrumentation;
will be able to write comparisons between different lasers and techniques for the optical measurements.
Learning skills (Dublin Descriptor 5, DD5)
will be able to study and understand autonomously, even different instrumentation
from the one studied in the course;
will be able to understand and calculate the limiting performance of different and new optical measurements and measurement systems;
will be able to deepen his knowledge in the studied topics, and to read and understand scientific and technical publications dealing with laser instrumentation and optical measurements.

Review of incoherent/coherent optical sources and lasers, photodetectors (direct and coherent detection), amplitude and frequency noise in optical oscillators.
Laser telemetry: triangulators, Time-Of-Flight (pulsed/sine-mod.), LIDAR, and DIAL.
Measurements on optical sources: emission spectrum, spatial profile and beam divergence of lasers and LEDs.
Optical power and energy measurements (optical power/energy meters). Polarization measurements. Optical Spectrum Analyzers (OSA). Monochromator and wavemeter for the measurement of optical wavelengths. Measurements for optical communications: Polarization Mode Dispersion (PMD); Optical Time Domain Reflectometry (OTDR); eye diagram and BER measurements on the optical signal.
Laser Doppler Velocimetry (LDV) and Particle Image Velocimetry (PIV), optical autovelox (pulsed tele-laser and laser-barrier).
Laser interferometry: Michelson, Mach-Zehnder, Sagnac, Twyman-Green, Double-Beam, Double-Frequency interferometers. Limits due to phase noise and quantum detection in laser interferometry. Electronic speckle‑pattern interferometry. Self-mixing interferometry. Interferometric techniques for industrial measurements: displacement, planarity, angle, and vibration measurements. Laser interferometry for Gravitational-Wave detection.
Measurements of temperature, vibrations, and intrusion, using optical fibers.
ALL THE SLIDES OF THE COURSE AND PREVIOUSLY REGISTERED LECTURES ARE AVAILABLE IN ADVANCE on the OneDrive space of the Course.
Essay optional topics
Stability and active stabilization of laser oscillators: natural and technical fluctuations of amplitude and frequency of the optical field and their suppression using optoelectronic control loops.
Ultra-high-resolution optical measurements: hi-res and Doppler-free laser spectroscopy; atom manipulation (laser cooling and trapping of atoms); optical frequency clocks.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The exam is based on a written test about the topics of the course and on an optional oral interrogation, which is mandatory when requested by the professor.
No partial examinations are given during the semester. A pre-exam in December is possible and usually made available to the students.
The written test, approximately 1.5-to-2 hours long, is typically made of 3-to-4 exercises, mostly based on theory applications and the corresponding analysis and numerical calculations. Some theoretical questions can be included in the written test. Logical order and clarity in answering the questions and solving the problems will be considered in the evaluation. The given classwork is appropriate for testing the expected learning results.
In agreement with the class, up to 2 auto-evaluative class works (about 1 hour each) will be done voluntarily.
A LARGE SELECTION OF PREVIOUS CLASS WORKS WITH SOLUTIONS IS AVAILABLE ON THE COURSE WEBSITE.
The dates of the exams will be notified well in advance by the Professor to the students and published on the web pages of the course.
The official and final exam dates are those published on the School and POLIMI websites.
In the unfortunate event of a student delivering for correction a significantly incomplete classwork, or a severely insufficient one, highlighting a deep lack of knowledge in different and important topics of the course, this may imply the need to skip the next and "near" (at less than one month distance) exam date.
In such an unfortunate case, the professor will very likely use the right to question the student the next time he or she takes and passes a written test.
Prof. Cesare Svelto phone: 02-2399.3610, mobile: 3409761929, e-mail: [email protected]
The written exam, with the assigned exercises & design problems & theoretical questions in it, will be aimed at verifying the student's capabilities also with reference to the following Doublin Descriptors. In particular, the student will/can be asked to provide:
Knowledge and understanding (Dublin Descriptor 1, DD1)
description and analysis of block diagrams of optical measurement setups will be asked;
comparison of different laser sources and their impact on the optical system performance;
analysis of coherent optical detection systems, including signal and noise figures;
explanation about the application of an optical telemeter, interferometer, velocimeter, and optical measurement instrumentation (optical spectrum analyzer, optical power-meter, wavelength-meter).
Applying knowledge and understanding (Dublin Descriptor 2, DD2)
assessment of the operation and performance of available optical measurement instrumentation;
design of an optoelectronic sensor or an optical interferometer;
discussion about pros and cons, as well as performance trade-offs, among different optical measurement instruments.
Making judgements (Dublin Descriptor 3, DD3)
complete analysis of the problem (specific problem and/or project cases in the field of optical measurements), highlighting the peculiar requirements and characteristics, and comparing autonomously different choices in terms of hardware and system performance;
effective written communication, in scientific/engineering language, and reporting individual results and proposals regarding the study and design of optical instrumentation.
Communication (Dublin Descriptor 4, DD4)
reports about the analysis and design of optical instrumentation;
analysis and comparison of different lasers and techniques for optical measurements.
Learning skills (Dublin Descriptor 5, DD5)
design and analysis of optical instrumentation to solve optical measurement tasks;
quantitative analysis of the limiting performance of different and new optical measurements and measurement systems;

Silvano Donati, Photonic Instrumentation: Sensing and Measuring with Lasers, Editore: CRC Press, Anno edizione: 2023, ISBN: 9781032489063
Note: Some 50% of the textbook can be used for a deeper insight of the topics described in the Course. The slides and didactical material, and REGISTRATIONS of lectures and exercise classes, (available on the Course OneDrive website) are more than adequate for a full preparation of the exam.

L’optoelettronica studia i dispositivi per la generazione, rivelazione e controllo della luce, allo scopo di effettuare misure per raccogliere ed elaborare informazioni, trasmettere dati o convertire energia. Considerata la varietà dei fenomeni e dei dispositivi, lo studio dell’optoelettronica richiede uno sforzo interdisciplinare che coinvolge la fisica dei materiali (in particolare i semiconduttori), la meccanica quantistica, l'ottica e l’analisi dei relativi circuiti elettrici ed elettronici.
Nell'insegnamento OPTOELETTRONICA si illustrano i principali aspetti dei dispositivi per la generazione (LASER, LED, ecc.), trasmissione (guide d'onda, fibre ottiche, ecc.) e rivelazione (fotodiodi, celle fotovoltaiche, ecc.) della luce, nell'intervallo di lunghezze d'onda dall'ultravioletto all'infrarosso. Lo scopo è quello di fornire i fondamenti della fisica e della progettazione dei dispositivi e dei circuiti optoelettronici.

Sulla base dei descrittori di Dublino (DdD), i risultati attesi sono i seguenti:
DdD 1: (conoscenza e comprensione)
comprensione dei fenomeni di propagazione della luce nei mezzi e di interazione con i mezzi,
comprensione dei concetti di fibra e guida per la trasmissione dell’informazione,
comprensione del funzionamento delle sorgenti di luce (LASER e LED) per la conversione di segnali elettrici in segnali ottici,
comprensione del funzionamento dei fotorivelatori per la conversione di segnali ottici in segnali elettrici,
comprensione del funzionamento delle celle fotovoltaiche per conversione di energia solare in energia elettrica.
DdD 2: (capacità di applicare conoscenza e comprensione)
soluzione di semplici problemi sulla trasmissione luminosa e conversione di segnali elettrici/ottici,
dimensionamento di semplici dispositivi optoelettronici.
DdD 3: (autonomia di giudizio)
autonomia nel giudicare quale sia il dispositivo, il circuito, o il sistema più appropriato per una specifica applicazione dell'optoelettronica, come ad esempio la generazione di impulsi luminosi, la loro propagazione e infine la loro rivelazione e conversione in impulso elettrico
autonomia nel giudicare il migliore sistema fotovoltaico per la conversione di energia solare in energia elettrica

Introduzione

• Introduzione all'optoelettronica
• Richiami di onde elettromagnetiche: Equazioni di Maxwell, pacchetti d'onda, velocità di gruppo.
• Ottica geometrica, riflessione interna totale, onda evanescente ed effetto di Goos-Hanchen. Tunneling ottico e beam splitter.
• Strati antiriflesso, distributed Bragg reflector (DBR).
• Coerenza spaziale e coerenza temporale.
• Effetto di interferenza, cavità di Fabry-Perot.
  Guide d'onda ottiche planari
• Struttura a core/cladding, modi di propagazione, V-number, condizione di propagazione a singolo modo.
• Dispersione in guida: dispersione intermodale, dispersione intramodale, dispersione di guida e di materiale.
  Fibre ottiche
• Fibra step-index: modi di propagazione, V-number, effetti di dispersione, attenuazione da scattering, assorbimento e piegamento.
• Fibra graded-index (GRIN): apertura numerica, profilo di indice.
  Laser
• Emissione stimolata, inversione di popolazione, modi di cavità. Laser a 3 livelli e laser a 4 livelli.
• Calcolo dei coefficienti di emissione e assorbimento. Sezione d'urto.
• Guadagno ottico, soglia di pompaggio per l'innesco di oscillazioni laser. Potenza di output. Vita media di cavità, modi di cavità.
• Laser He-Ne: spettro di uscita, allargamento Doppler.
• Effetti di allargamento: allargamento omogeneo (naturale, collisionale, pressione) e allargamento inomogeneo (Doppler, struttura amorfa).
• Erbium-doped fiber amplifier (EDFA): concetto fisico e sistema di amplificazione.
  Light-emitting diode
• Richiami della fisica dei semiconduttori: struttura a bande, band gap, densità di stati, doping, correnti di deriva e diffusione. Gap diretto e gap indiretto. Semiconduttori III-V. Giunzione p-n.
• Elettroluminescenza da iniezione, riassorbimento e ricombinazione superficiale.
• LED a eterogiunzione: struttura, allargamento di riga, dipendenza dalla temperatura e dal drogaggio.
• LED a quantum well (QW): singola QW, multiple QW.
• Materiali, lunghezza d'onda, e strutture tipiche.
• Efficienza quantica interna ed esterna, efficienza di estrazione, efficienza di conversione di potenza, brillanza visiva ed efficacia luminosa.
• Caratteristiche di LED, LED bianchi, circuiti per LED, risposta in frequenza.
  Diodi laser
• Inversione di popolazione, cavità ottica.
• Laser a eterostruttura. Buried double heterostructure.
• Laser a QW.
• Caratteristiche potenza/corrente di alimentazione. Efficienze di conversione.
• Equazione del diodo laser: stato stazionario, corrente di soglia, guadagno ottico di soglia, potenza di uscita.
• Vertical cavity surface emitting laser (VCSEL).
  Fotorivelatori
• Principio di funzionamento. Teorema di Shockley-Ramo. Coefficiente di assorbimento. Efficienza quantica e responsivity.
• Fotodiodo pin: capacità di svuotamento, tempo di risposta.
• Fotodiodo a valanga (APD): ionizzazione ad impatto, coefficiente di moltiplicazione, tempo di risposta.
• Circuiti per fotodiodi.
  Celle fotovoltaiche
• Struttura e principio di funzionamento. Caratteristica di buio e sotto illuminazione. Funzionamento in corto circuito, circuito aperto, con carico esterno. Fill factor. Circuito equivalente.
• Materiali ed efficienza di conversione.

Prova scritta obbligatoria, senza prove in itinere
L'esame consiste in una prova scritta su tutti gli argomenti svolti nell'insegnamento, comprendente le lezioni teoriche e le esercitazioni pratiche.
L'esame scritto comprende da 3 a 6 esercizi (organizzati in problemi numerici su dispositivi e circuiti optoelettronici) e quesiti teorici atti a verificare la conoscenza degli argomenti affrontati.
Gli esercizi serviranno a valutare il raggiungimento dei risultati di apprendimento DdD 1, 2 e 3, mentre i quesiti teorici serviranno a valutare il raggiungimento del risultato di apprendimento DdD 1.

S. O. Kasap, Optoelectronics and Photonics: Principles and Practices. Second edition., Editore: Pearson education limited, Anno edizione: 2013

Il corso ha l'obiettivo di fornire le basi dell'ottica contemporanea e di presentare i principi e le tecnologie relativi alle immagini ottiche. Partendo dalla teoria della diffrazione e dell'ottica di Fourier, saranno analizzati i sistemi ottici per la formazione ed il filtraggio delle immagini. Verranno anche introdotti esempi di sistemi di acquisizione e riproduzione delle immagini.

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

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

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

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

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

The course addresses the problem of capacity planning of computer infrastructures. The topics considered are: performance modelling concepts and techniques, workload and traffic characterization, simulation of users behavior, measurement techniques and tools for end-to-end response time of web applications.

The aim of this course is to improve the capacity of computer engineering students to critically analyze some key concepts in computer science and engineering. The first part of the course will deal with the presentation of the conceptual tools to be used in the analysis. The second part will host the discussion of key concepts and issues of computer science and engineering, with a specific focus on the philosophy of AI. The third part will consist in the supervision of the final project.
The course includes lectures by the instructor and invited lecturers; class participation will be expected, and students should apply what they learn through readings and lectures by looking at some issues in computer science and engineering in a critical way.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will:
Be acquainted with conceptual tools coming from the philosophical tradition;
Learn how to look at usual problems from a different perspective and how this different perspective can be useful in order to help provide solutions to these problems;
Be able to deal rigorously with qualitative forms of reasoning, such as in philosophy.
Applying knowledge and understanding
Students will be able:
To use critical skills in clarifying and analyzing foundational problems in computer science and engineering;
To apply conceptual tools coming from philosophy to solve some problems in computer science and engineering;
To analyze the presuppositions and the consequences of some of the most debated issues in the philosophy of computer science.
Making judgements
Students will be able:
To autonomously analyze the philosophical and foundational issues of computer science and engineering, and in particular of those issues encountered during the course of their studies;
To evaluate and select the appropriate knowledge in the effort of elaborating and justifying a philosophical argument on a topic autonomously selected.
Communication
Students will learn to:
Exercise and improve their skills in critical writing;
Present in an effective way the results of their independent research, being able to justify their choices.
Lifelong learning skills
Students will:
Be better prepared in their critical skills;
Be able to deal with argumentative forms of reasoning;
Be capable of looking at technical problems from a different conceptual perspective.

The course will cover different topics from both a theoretical and a more practical point of view. In particular, the course will be organized in three parts:
Historical-conceptual introduction to philosophical problems of computer science and engineering: the philosophy of computer science and its topics; the disciplinary status of computer science and computer engineering; the reasons of a philosophical analysis.
Critical analysis of key concepts of computer science and engineering: computation and its philosophical aspects; the notion of algorithm; the Turing test and the issues of machine intelligence; the relationships between minds, brains, and machines; the physical symbol system hypothesis; computational models of consciousness; simulations and experiments.
Supervision of the final project: it is required to present an original research (in group) on one of the topics presented during the course or on related topics. This project will be supervised by the instructor in order to meet the standards required by scientific publications.
Additional information can be found onWeBeep.
Required bibliography
Scientific papers available on WeBeep.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, con revisioni periodiche, con presentazione/discussione finale
Grading will be on the following basis: either 50% final project (group presentations in class) + 50% written test on the course topics or 100% written test on the course topics.
Type of assessment
Description
Dublin descriptor
Final project
(class presentation in group)
Students are required to:
Develop a philosophical argument in which the presented claims are justified by the use of argumentative skills and the relevant literature;
To present this argument in a class presentation.
1, 3
4, 5
Written test
Questions on the course topics that require to have carefully studied the suggested bibliography and the ability to apply it to case-studies;
Questions on the philosophical and foundational issues associated to computer science and engineering that require to have studied the suggested bibliography and to have developed critical skills in analyzing these problems with the tools provided by philosophy.
1, 2
3, 4, 5

Alan Turing, Computing Machinery and Intelligence, Editore: Mind, Anno edizione: 1950, Fascicolo: 59 (236), 433-460

The course “Photonic Computing” explores the potential of photonic technologies as an alternative and complement to conventional digital electronic solutions. Students are introduced to photonic integrated circuits and free-space optical systems, progressing from fundamental building blocks to complex and programmable architectures.
The course examines a broad range of computing applications, including photonic implementations of matrix operations and linear transformations, analog photonic processors, neuromorphic photonic circuits, and architectures for artificial intelligence and machine learning. It also addresses the role of photonics in enabling high-speed, energy-efficient interconnects for distributed computing systems, with examples spanning intra- and inter-data-center communications.
A comprehensive overview of state-of-the-art technologies is provided, along with selected case studies and a forward-looking discussion on the role of photonics in the future of high-performance computing (HPC) as outlined in current technology roadmaps.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will
know the main advantages/current limitations of photonic analog computing versus digital electronic computing;
comprehend the physical mechanisms that can be used to perform mathematical operations in the optical domain;
know the state-of-the-art of photonic technologies and devices that are used in computing applications and in distributed systems;
know the basic criteria and design rules of simple photonic processors
Applying knowledge and understanding
Students will be able to
map linear algebra operations into a photonic hardware
design simple photonic circuits that perform computational operations on optical signals
design of a fiber optic link for distributed computing systems
Making judgments
Students will be able to
· compare the performance of different photonic circuits implementing specific mathematical operations
· identify the most suitable photonic architecture to solve computational problems
Lifelong learning skills
Students will be able to apply the acquired knowledge to different and more complex computational problems, and autonomously conceptualize and design new photonic computing architectures and interconnections

Motivation & perspectives. Energy, bandwidth & latency bottlenecks of current digital electronics technologies for high performances computing (energy/bit metrics, I/Os density, memory wall, beyond-Moore scalability and sustainability). Photonics in datacentes and AI infrastructures. Analog vs digital processing. Advantages of photonic technologies for high performance computing.
Fundamentals of optics: Representation of optical signals in time and frequency domain; basics on electromagnetic waves in free space and waveguiding structures; optical fibers and integrated waveguides: technology and characteristic parameters (attenuation, group delay, chromatic dispersion, polarization). Temporal coherence and orthogonality of waves; principles of optical interference.
Implementation of matrix algebra in photonics. Sum and multiplication in the optical domain. Photonic building blocks for computing (waveguides, power splitters/combiners, phase shifters). Transmission matrix representation. Mach-Zehnder interferometer (MZI) as a 2x2 coherent adder. MZI architectures for coherent vector matrix multiplication. Mapping unitary and non-unitary operators on MZI meshes. MZI architectures (diagonal, triangular, rectangular). Singular value decomposition (SVD). Fidelity. Photonic Discrete Fourier transform (DFT).Microring resonators (MRR). MRR architectures for non coherent vector matrix multiplication. Broadcast and weight. Control strategies for MRR and MZI based architectures, technologies and programmability. Free space solutions: metasurfaces, spatial light modulators (SLM) and multiplane light converters (MPLC).
Linear operators and photonic analog processors: Analog implementation of differential/integral operators. Photonic temporal differentiator and integrator: frequency and time domain response, performance and limits. Photonic solvers of first-order and higher order ordinary differential equation (ODE). Hilber transform. Recursive programmable photonic circuits. Programmable optical solvers of systems of differential/integral equaltions. Analog matrix inversion.
Optical Neural Networks (ONN): implementation of photonic neurons, architectures of optical neural networks (ONN). Integrated ONN (coherent/non coherent) and free space ONN. Nonlinear activation functions in photonics. Electronic vs photonic neuronal functions.
Photonics for distributed computing. Optical interconnections for intra- and inter-datacenter communications. Design of a fiber optic link, power budget and time rise budget. Switches in datacenters: requirements and characteristics. Optical switches: 2D and 3D topologies, techologies (MZI, MRR, MEMs), performance, port-count scalability. Optical vs electrical switches
Case studies:
Coherent Photonic Adder for Communication and Sensing
All-optical calculation of orthongonal channels of arbitrary transmission system (optical SVD)
Mathematical coprocessors (analog matrix inversion)
Photonics circuits and architectures for AI and machine learning.
Students will have the opportunity to visit to the Photonic Devices Lab (https://photonics.deib.polimi.it/) and see live experiments of photonic chips that perform computational operations in real time (2 hours)
During the course two seminars will be held (remotely) by professors from international research groups carrying out pioneering research in the field of photonic computing (2 hours).

Prova scritta obbligatoria, con prove in itinere
Prova orale facoltativa (a scelta dello studente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The exam consists of two tests structured as follows:

• written test (open book) on the topics covered in the first part of the course (Fundamentals of optics, Implementation of matrix algebra in photonics). On this part of the course there is also a mid-term written test.
• oral test on the topics covered in the second part of the course (Linear operators and photonic analog processors, Optical Neural Networks, Photonics for distributed computing ) or alternatively a thematic study (project) which can be carried out in pairs (2 students) on selected topics agreed with the teacher.
  Type of assessment
  Description
  Dublin descriptor
  Written test
  · numerical exercises focusing on photonic devices and circuits for computing
  1, 2, 3
  Oral test
  · oral exam on the second part of the course;
  · assessment of the thematic study (project) through an oral presentation
  1, 2, 3, 4, 5
  Passing the written test is not a prerequisite for the oral test. Students who have not passed the mid-term written exam can still take the oral test and recover the written test in a subsequent date of the same session.

Course slides (Ed. 2025) https://webeep.polimi.it/
Note: Available on the course's WeBeep website.
Mahdi Nikdast, Sudeep Pasricha, Gabriela Nicolescu, Ashkan Seyedi, Di Liang, Silicon Photonics for High-Performance Computing and Beyond, Editore: CRC Press, Anno edizione: 2024, ISBN: 9781032122441
José Capmany, Daniel Pérez, Programmable Integrated Photonics, Editore: Oxford University Press, Anno edizione: 2020, ISBN: 0198844409
Paul R. Prucnal, Bhavin J. Shastri, Neuromorphic Photonics, Editore: CRC Press, Anno edizione: 2017, ISBN: 9781315370590

Photonics is emerging in various areas of communications, electronics and sensors to support the need for transparency, speed, and ability to handle huge data streams and multiplexing, as request from the evolution of communication systems today. Integrated photonics devices are penetrating many fields other than communication and optical interconnects: optical signal processing for 5G, lidar, automotive, exascale computers, biosensing, imaging and much more. The course examines in detail the integrated optics sector, focusing on theoretical, technological and applicative aspects. We discuss various issues related to the perspective of photonics technologies, the actual and future markets and potentialities, the generic foundry scheme and, in detail, down to photonics devices: waveguides, passive devices such as filters and (de)multiplexers, modulators, integrated lasers and photodetectors, amplifiers and technologies and materials. When communications occur on very small scales as inside electronics chip, chip-to-chip or board-to-board then it comes to optical interconnects, an emerging field that will be the lifeblood of the future high speed electronic. An introduction to the emerging field of programmable photonics is given, the course is an excellent complement of the 5cr course "Photonic Computing".

Dublin Descriptors
Expected learning outcomes
1 - Knowledge and understanding
Students will learn how to:
Understand mathematical and physical models for the analysis of photonics devices
Define and use models for the synthesis of photonics devices
Learn technologies related to integrated photonic devices
Identify the required components and their use in practical systems
2 - Applying knowledge and understanding
Students will be able to:
Analyze and design most of the integrated photonic building blocks
Understand the physical processes behind a mathematical description
Determine the photonics devices to build a photonic system application
Acquire a professional language to interact with various levels (technology, components, systems)
3 - Making judgements
Students will be able to:
Identify the most suitable technology for a given photonic application
Identify the technological limits and the physical limits in photonics
Recognize the design space and its degrees of freedom that can be exploited to define new applications
Have a perception on the roadmap of integrated photonics in the future
Lifelong learning skills: be aware of a Key Enabling Technology

In particular, the course covers:
The perspective of photonics integration. Integrated optic for optical communications, sensors and optical interconnects (electronics). The generic foundry scheme in photonics. Technological platforms: glass on silicon, Lithium Niobate, Indium Phosphide, Silicon Photonics. The ubiquitous market of photonics devices. Potentialities and limits.
Photonic devices: Integrated-optic components. The propagation in planar guided-wave structures. Guided, radiative and leaky modes. The coupled mode theory. Bent waveguides, directional couplers and Y. Star couplers. Filters, (de)multiplexer and AWG. Switch. Integrated-optic modulators: phase and amplitude, novel modulation formats. Travelling wave electrodes. Grating: uniform, apodized and chirped. Fiber Bragg gratings. Isolator and circulator. Integrated Lasers and Phototodetectors.
Materials and technologies: Materials for integrated optic circuits. Electro-optical and magneto optical effects. Technologies for integrated optic circuits: silica and silicon nitride, Lithium Niobate, Indium Phosphide and Silicon Photonics. The photorefractive effect and fiber Bragg gratings. Technological processes for the realization of passive components. Layer deposition, photolitography and etching. The packaging issue. The CleanRoom. The generic foundry approach.
Photonic circuits: The circuits for the photonic networks and switching. Components for Wavelength Division Networks. Filters. The wavelength routers. Switching fabric for optical signals. Add-drop and cross connects. Principles of optical signal processing. Components and circuits for EDFA. Gain and noise figure. Principles of photonic computing.
Applications: Application in communication and electronics. Examples of microwave photonics. Optical beam forming networks.The optical interconnect problem. Analysis of the electrical interconnect and the optical alternative. Limits, advantages, constraints. From short range rack to rack to intra-chip links. Integration technologies for combining optoelectronics, optics, and electronic integrated circuits: Silicon Photonics and Indium phosphide. Monolithic vs hybrid solutions. CMOS compatible solutions.
Examples of data-intensive on-chip communication are considered and discussed. The control and calibration layer: how to manage a Photonic Integrated Circuit. Programmable photonics. Applications scenario. Circuits for Free Space Optical communication.

Prova orale obbligatoria
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
Oral discussion on the entire program.
Optional tasks assigned during the course. Selected tasks are presented by the students to the class in short presentations.
Periodic optional Socrative tests.

On line resources: www.polimi.it
Note: Updated material in Beep
Lecture notes, slides, additional material and software available on line.
Lecture notes in italian and english
Beep website Photonic Devices www.polimi.it
Note: Log in your Beep account

il corso intende presentare le basi teoriche e pratiche necessarie per trattare i dati acquisiti da un arbitrario sistema di sensori al fine di estrarne le informazioni di interesse. Il programma proposto vuole essere propedeutico rispetto a tutte le tematiche legate all’analisi numerica dei dati affrontate nei corsi della Laurea Magistrale. Al tempo stesso, verrà dato grande rilievo ad esempi specifici presi dal mondo del telerilevamento elettromagnetico, intesi per far comprendere l’utilizzo pratico dei metodi nei contesti applicativi.
Tutti gli argomenti del corso verranno presentati sia con tradizionali lezioni frontali che con un approccio di tipo “hands-on”, attraverso il quale gli studenti apprenderanno a risolvere problemi complessi tramite calcolo numerico e a interpretare i risultati.

Il corso intende fornire agli studenti la basi teoriche per la soluzione dei problemi lineari e sviluppare il loro senso critico nella scelta del metodo per la soluzione e nell’interpretazione dei risultati.
DdD 1 - Conoscenza e comprensione: conoscere le differenti tecniche per la soluzione di problemi lineari; comprenderne le basi matematiche.
DdD 2 - Capacità di applicare conoscenza e comprensione: essere in grado di comprendere gli aspetti salienti di un problema (parametrizzazione, informazione a priori, struttura delle osservazioni) e selezionare di conseguenza la tecnica di soluzione più adatta.
DdD 3 - Autonomia di giudizio: saper prendere decisioni sul modo di risolvere un problema complesso.
DdD 4 - Abilità comunicative: saper esporre i risultati del proprio lavoro in modo esaustivo e convincente

Aspetti Preliminari (10 ore):
Il paradigma del telerilevamento: il problema diretto e il problema inverso. Dati, modello, parametri, disturbi, informazione a priori. La linearizzazione, la matrice Jacobiana, problemi lineari, soluzione, la matrice di risoluzione
Introduzione all’uso di Matlab per il calcolo scientifico
Applicazioni all’elaborazione numerica dei segnali: la matrice di Fourier, filtraggio, risoluzione, ambiguità, trasformata con campionamento sparso
L’effetto dei disturbi. La covarianza e la covarianza campionaria. La matrice di coerenza. Errori sul modello. Calcolo della covarianza della soluzione attraverso esperimenti ripetuti
Applicazioni: possibili soluzioni di un problema lineare e calcolo dell’errore. Errori di modello: il caso dello Snow Water Equivalent tramite interferometria SAR satellitare
La geometria del problema inverso (10 ore)
Spazi vettoriali, prodotti interni ed esterni. Lo spazio delle righe e lo spazio delle colonne. Inversione ai minimi quadrati nel caso sovracondizionato: derivazione algebrica. Il residuo. La speranza di abbattere il rumore sui dati. Derivazione geometrica della soluzione ai minimi quadrati del problema sovracondizionato. Inversione ai minimi quadrati nel caso sottocondizionato: derivazione algebrica e interpretazione geometrica. Spazio nullo destro e sinistro. Relazione tra i quattro sottospazi fondamentali. Commento su esistenza e unicità della soluzione.
Applicazioni: calcolo dell’errore in funzione del volume dei dati. Il caso della misura di deformazione e topografia nell’interferometria SAR satellitare per orbite casuali, per progressione perfettamente regolare e quasi regolare (l’effetto del condizionamento)
La decomposizione ai valori singolari (12 ore)
Autovalori e autovettori. Autovalori e autovalori per matrici Hermitiane. Rango di una matrice quadrata. Decomposizione ai valori singolari. Proprietà. Interpretazione della soluzione ai minimi quadrati alla luce della decomposizione a valori singolari. Ampiezza dei valori singolari e amplificazione del rumore. Rango e condizionamento di una matrice di dimensioni arbitrarie. Inversione con SVD troncata.
Applicazioni: analisi dell’errore in funzione dell’ordine del troncamento. Misura della ionosfera tramite acquisizioni Radar; Tomografia Radar di aree vegetate; il problema della doppia localizzazione (SLAM) per un Radar aereo. Localizzazione GNSS.
Aspetti probabilistici del problema inverso (12 ore)
Il ruolo del rumore sui dati. Inversione a minima varianza; sbiancamento del rumore; inversione a massima verosimiglianza; introduzione alla stima Bayesiana; Inversione MMSE; inversione MAP; Iterative Recursive Least Squares
Applicazioni: stima di uno spostamento correlato in presenza di rumore atmosferico; interpolazione tramite kriging; Tomografia Radar di aree urbane. Il phase unwrapping e l’Interferometria SAR; Image restoration;
Estrazione e condizionamento del segnale (6 ore):
Rappresentazione di un segnale a radio-frequenza; spettro di potenza; misura della potenza: il prodotto tempo banda.
Applicazioni: Radiometria satellitare; Radiometria satellitare con due o più antenne. Radar passivi.

Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
La valutazione avviene mediante un progetto, individuale o di gruppo, con presentazione e discussione finale. Il progetto verifica la capacità di modellizzare un problema concreto e applicare le conoscenze acquisite (DdD1-2), operando scelte motivate sulla formulazione del problema e sulla tecnica di inversione ( autonomia di giudizio, DdD3 ). La discussione dei risultati, anche attraverso il confronto con previsioni teoriche, simulazioni o dati reali, concorre alla valutazione della capacità critica. La presentazione finale permette inoltre di valutare la capacità di esporre e argomentare in modo chiaro e convincente il lavoro svolto ( abilità comunicative, DdD4 ).
Non sono previste prove intermedie.

Giuseppe Drufuca, Problemi inversi, ISBN: 9788881323623
Gilbert Strang, Linear Algebra and its applications
Note del docente

The course addresses the problem of control, diagnosis and supervision of production systems. In particular, the Supervisory Control Theory is studied, where the focus is on the automatic synthesis of the controller of plants modelled with labelled automata in presence of any admissible logic specification. In addition, the course will study the automatic synthesis of the diagnoser automaton, able to identify faults based on system observable events. A hint will be given also on the implementation of the supervisor and diagnoser on traditional controllers (PLC). The applications are mainly in the discrete manufacturing field, but also for energy production systems.

The diligent and careful student will learn how to synthesize an automatic controller based on logic specification, within the Supervisory Control Theory, as well as a diagnoser. He will receive a hint on possible implementations. (DdD 1 - Knowledge and understanding; DdD 2 - Applying knowledge and understanding)

Alignment on the basics of automation problems and PLC
Introduction to industrial supervision and control, and formal methododologies
Logic models: labelled automata, formal languages, labelled Petri nets.
Synchronous composition, asycnhronous composition, composition with priorities.
Refinement. Sub-automaton and super-automaton.
Static and dynamic specification. Event-based and state-based specification.
Supervisor properties: controllability, non blockingness.
Synthesis of the supreme controllable supervisor
Implementation of the supervisor. Ladder logic.
Example of a discrete production system supervision.
Monitoring and diagnosis of faults: non deterministic automata.
State observer and event-based diagnosis.
Extensions to hybrid automata: basic modeling, properties, evolution
Hints to applications to energy production systems.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Written examination. The exam typically consists of few exercises (3 to 5) with some questions. The duration is approximately less tha 2 hours. The exam will investigate the capabilities of the student to model the behaviour of process and discrete production systems through the formalism of labelled automata (DdD 1 - Knowledge and understanding; DdD 2 - Applying knowledge and understanding), to suitably model the specification on such systems (DdD 1 - Knowledge and understanding; DdD 2 - Applying knowledge and understanding), to synthesize the supremal controllable nonblocking supervisor (DdD 2 - Applying knowledge and understanding), to model a system with basic faults, to identify fault symptoms, to compute the determistic observer for an indeterministic faulty model and finally to synthesize the corresponding diagnoser (DdD 2 - Applying knowledge and understanding). A hint on possible implementations of a basic supervisor can be also asked (DdD 1 - Knowledge and understanding).

Notes on Supervisory Control Theory
Note: Lecture notes, solved exams and exercises available on the course web site

Aim of the course is to give to the students the knowledge of the principles at the basis of Quantum Communications. The applications of these principles to some relevant examples of quantum communication protocols, like quantum key distribution for secure transmission of information, also exploiting the quantum resource of entanglement (as in quantum teleportation and in quantum repeaters), will be presented.

Dublin descriptor DD1 (knowledge and understanding). Students will learn the basic quantum principles for the understanding of the applications of quantum communications.
Dublin descriptor DD2 (applying knowledge and understanding). Students will be able to analyze and describe quantum circuits for manipulating qubits as combination of quantum gates and representing them by means of the Bloch sphere. Students will acquire the capability of implementing the main quantum communication protocols and to calculate the performance of quantum key distribution systems.
Dublin descriptor DD5 (lifelong learning skills). Students will acquire the tools for comprehending the main aspects of present and future applicative developments in the field of quantum communications.

Theoretical bases: Quantum states, observables and quantum measurement. Uncertainty principle. Complementary observables. Quantum bits (qubits). Dirac notation. Single-qubit quantum gates. Pauli operators. Measurement of qubits. Bloch sphere representation. Pure and mixed states. Density operator. Von Neumann entropy. Entanglement of qubits. Partial trace. Bell states. Bell inequality. Multiple-qubit gates. CNOT gate. No-cloning theorem. Accessible information and Holevo bound.
Quantum communications protocols: Quantum key distribution (QKD). BB84 protocol. Quantum bit error rate (QBER), secure key rate and security threshold. Trusted nodes. Examples of physical implementation of QKD. Photon-number splitting attack and QKD with decoy states. Entanglement-based QKD. E91 and BBM92 protocols. Superdense coding. Quantum teleportation. Entanglement swapping. Entanglement distillation. Entanglement distribution. Quantum repeaters.

Prova scritta obbligatoria, senza prove in itinere
The examen is written, with open questions and exercises on the topics explained during the lectures. It is possible to use at the examen only a standard calculator and a printed formulario. In detail the examen comprises:

• exercises to be solved numerically, in order to assess the expected learning outcomes related to Dublin descriptors DD1 and DD2;
• questions of theoretical type with open response, in order to assess the expected learning outcomes related to Dublin descriptors DD1, DD2 and DD5.
  The maximum score of the examen is 33 and the grade of 30 cum laude is obtained when the score is at least 32.

Reference notes for theory/examples/exercises presented on the blackboard during the lessons https://webeep.polimi.it/login/index.php
M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information, Editore: Cambridge University Press, Anno edizione: 2000

The course provides the theoretical foundations and practical skills required to understand electromagnetic (EM) sensing, with a particular focus on radar imaging and its environmental, industrial, and commercial applications.
The course covers the complete radar imaging chain, from sensor technologies and antenna arrays to image formation, signal processing, calibration, and integration with Geographic Information Systems (GIS). The fundamentals of wave propagation, scattering, radiometry, and optical sensing are introduced and compared with those of Radar. Advanced radar concepts, including Synthetic Aperture Radar (SAR), multistatic radar, and Multiple-Input Multiple-Output (MIMO) systems, are presented together with interferometric imaging techniques.
Through lessons, but also dedicated laboratory sessions, students learn - and practice - how radar images are generated, processed, and interpreted, and how to exploit radar-specific information such as moving target detection, millimetric deformation measurements, surface roughness and moisture estimation, and imaging through optically opaque media.
Applications include geohazard monitoring, climate and environmental observation, infrastructure monitoring, automotive sensing, and health monitoring from space. Laboratory activities provide hands-on experience, including processing and analysis of real data. Through project-based activities, students develop practical skills in radar signal processing, image interpretation, and problem solving. Guest seminars from industry professionals may complement the course.

Dublin Descriptors
Expected learning outcomes
1 - Knowledge and understanding
Students will gain clear understanding about:
the physics behind electromagnetic remote sensing technologies
sensors principles, with attention to distributed sensors and sensors arrays
signal processing methods for the treatment of electromagnetic remote sensing data
specific properties of Radar, Lidar, Optical images, and the possible applications
applications based on cohernt images, interferometry and tomography
2 - Applying knowledge and understanding
Students will be able to:
Design a Radar and remote sensing survey
Define a data processing flow chart for processing Radar data.
Simulate electromagnetic acquisitions and process them using Matlab
Get remote sensing data from image providers/space agencies, integrate and process in a GIS
3 - Making judgements
Students will be able to:
Identify qualitatively and quantitatively pros and cons of different remote sensing methods and products serving their professional activities
Understand the principles that govern the design of Radar remote sensing systems
Define a data processing flow chart for processing Radar data.
Recognize the design space and its degrees of freedom that can be exploited to define new technologies
4 - Communication
Students will learn to:
Write a technical document on a specific case study (e.g.: design and implementation of a remote sensing survey, algorithm development, system analysis, etc.)
5-Lifelong learning skills
Students will learn to:
Manage a project, starting from definition of specfics, to solution development, validation and verification

Remote sensing principles, methods and applications: from IR to visible. How electromagnetic sensors observe the Earth, from optical to microwave systems. Fundamentals of image formation, radiometric measurements, calibration, and georeferencing. Overview of current Earth observation missions and applications..
Coherent imaging. Principles of coherent sensing, including wave propagation, polarimetry, speckle, coherence, and radiometric resolution. Lidar, Radar and Sonar coherent imaging. Real and synthetic antenna arrays, beamforming, and image formation, with applications to high-resolution imaging and change detection
RADAR basics. Principles of Doppler and pulsed radar, ranging, waveform design, mono and bistatic configurations, radar cross section, and the radar equation. Radar SAR: image interpretation, and the principles underlying the design of radar imaging solutions for specific applications.
Advanced radar systems and applications: Interferometric, tomographic, polarimetric, and coherent change detection techniques. Applications to digital elevation model generation, millimetric deformation monitoring, infrastructure health assessment, geohazard and environmental monitoring, and volumetric imaging. MIMO radar, 3D imaging, and super-resolution methods for industrial and automotive sensing.

Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The exam is oral and assesses the students' understanding of the fundamentals of radar and electromagnetic imaging.
Part of the course is dedicated to an application-oriented project, carried out individually or in small groups. AI-based tools may be used to support the project.
The oral examination may include a discussion of the project to assess the students' understanding of the underlying principles ( DD1 ), their ability to interpret results and relate the proposed solution to real-world engineering applications ( DD2 ), their capability to design innovative remote sensing systems and solutions for specific applications ( DD3 ), and their critical evaluation of the proposed approach with respect to alternative solutions ( DD4 ). The examination also assesses the students' communication skills in presenting and discussing their project ( DD5 ).
During the course, interactive quiz sessions are used to help students assess their progress, reinforce key concepts, and receive immediate feedback on their learning.

Andrea Monti-Guarnieri, Electromagnetic Imaging, Anno edizione: 2015 https://polimi365-my.sharepoint.com/:b:/g/personal/10111669_polimi_it/EQjmg-9bGbNIlTWAkCjIUWgBq9loKAyqHNuN7xH90AFpQw?e=cpadXT
A. Ferretti, A. Monti Guarnieri, C. Prati, F. Rocca, D. Massonnet, InSAR Principles: Guidelines for SAR Interferometry Processing and Interpretation, Editore: ESA TM-19

Recommender systems are algorithms that approximate aspects of human decision making to predict users' needs and preferences. More formally, they use data mining and machine learning to help users find interesting and useful products. Products can be almost anything: physical items (e.g., smartphones), places (e.g., restaurants), digital content (e.g., news, movies, music), and more. Recommendations are produced from a variety of inputs, ratings and comments on products, individuals' or communities' past preferences and choices, social networks, and context of use.
Recommender systems are now present in most of the online services we use and, as they mediate our interactions with many platforms, they affect our lives in ways we are not often fully aware of. Consider, for example, the potential impact of a service that selects the news seen by a large group of people, or that affects their economic conditions by suggesting job offers, or by recommending to others the content (music, art) they created.
A well-known success story is Netflix, which launched the Netflix Prize in 2006, a competition offering a 1-million-dollar award for improving its video-streaming recommender system. The Netflix Prize was a very popular initiative that drew attention to the recommender systems field and contributed to its substantial progress. The technology behind recommender systems has evolved into a rich collection of tools that enable practitioners and researchers to develop effective recommenders. We study a selection of algorithms from the most important families of approaches, including how they work, how to use them, how to evaluate them, and their strengths and weaknesses from an applied perspective. Topics include content-based filtering, collaborative filtering, dimensionality reduction, deep learning, graph-based methods, and hybrid techniques. The approach is hands-on, with evaluation including a competition similar to the Netflix Prize that involves implementing and testing algorithms.

DD1 Knowledge and understanding
The student will know and understand:
The characteristics and limitations of a selection of models representative of the main families of recommendation algorithms at a conceptual level, and when each is appropriate.
How to design rigorous evaluation from multiple perspectives by selecting appropriate experimental protocols and metrics, and the common risks related to bias, and fairness.
DD2 Applying knowledge and understanding
Given a practical recommendation problem, the student will be able to:
Explore the design space of recommendation methods and select an approach consistent with the scenario requirements, justifying the choice against viable alternatives.
Implement, train, and evaluate models under rigorous protocols, selecting metrics aligned with the dimensions of interest and analyzing trade-offs across accuracy and diversity, while considering scalability.
DD3 Making judgments, DD5 Learning skills
By participating in the Engaged exam or opting for a project, the student will learn to:
Improve model quality through an iterative process of trial, evaluation, and analysis, making motivated decisions.
Learn autonomously from documentation and research literature, adapt methods to the specific scenario, and design ad hoc variants to improve recommendation quality.

1) Introduction to Recommender Systems

• Applications and goals
• Taxonomy
  2) Non-Personalized Recommenders
• Understanding preferences, ratings and interactions
• Inferring preferences
• Top popular, Best rated
• Global effects (biases)
  3) Evaluation
• User and provider utility
• Comparative evaluation: On-line vs Off-line vs. Lab
• User-centered metrics and evaluation: relevance, novelty, diversity
• Relevance: error vs. accuracy metrics vs. ranking metrics
• Cold Start, New-Item and New-User problems
• Experimental protocols: datasets and the fallacy of hidden data
• On-line evaluation: basic concepts
  4) Content-Based Filtering (CBF)
• Introduction to content-based recommenders
• Similarity between items
• Similarity between users and items: building user profiles
• Attributes and their normalization: TF-IDF
  5) Collaborative Filtering (CF)
• Introduction to User-User Collaborative Filtering
• K-Nearest-Neighbor and Shrink Term
• Introduction to Item-Item Collaborative Filtering
• Strengths and weaknesses of Item-Item vs. User-User CF
• Domain knowledge vs. Machine Learning approaches: CF as an optimization problem
  6) Dimensionality Reduction Recommenders
• Introduction to dimensionality-reduction recommenders: Matrix Factorization
• Concepts behind Latent Semantic Analysis and Singular Value Decomposition (SVD)
• Training Matrix Factorization: FunkSVD, ALS and others
• Extending Matrix Factorization: SVD++ and AsySVD
• Bayesian Personalized Ranking (BPR)
  7) Hybrid Recommenders
• Overview of hybridization techniques
• Collaborative filtering with side information
• Factorization Machines
  8) Advanced topics: Graph-Based Recommenders
• Random walks on bipartite and tripartite Graphs (notation, steady states)
• Random walk algorithms (PageRank, P3, P3Alpha, RP3Beta)
  9) Advanced topics: Graph Convolutional Network Recommenders
• Graph Convolutional Networks
• Simplified GCN for recommendation (LightGCN)
• Popularity bias
• Frequency filtering methods (GF-CF)
  10) Advanced topics: Deep Learning Recommenders
• Neural models for Recommender Systems (basics, loss functions)
• Two Tower models
• Autoencoder models (denoising, variational)

Prova scritta facoltativa (a scelta dello studente), senza prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The learning outcomes of this course have two components: theory and application. In order to obtain a full grade the students must show they have reached the required skills and understanding in both. Students can choose between two types of evaluation.
# Engaged. Students with programming experience, willing to understand the theory of recommender systems but with a stronger focus on how to write recommendation algorithms in practice, can chose the innovative evaluation track, which provides a more practical hands-on learning experience. The evaluation is divided into two parts: a mandatory competition focused on applying recommendation algorithms and an optional exam focused on the theory.
Competition (max 27 points - mandatory), the competition will run at the end of the semester and will only be available during that timeframe.
Students can participate in teams of up to 2 people.
At the end of the competition, the students will need to write a two-page report of their activity or will present their solution in front of the class. Evaluation will be based on the quality of the recommendation model developed and the evidence provided in the report or presentation, which reflects the learning outcomes DD2, DD3, and DD5.
The best teams of the competition will have the opportunity to participate to the International RecSys challenge (https://remaplab.deib.polimi.it/recsys-challenge/). We will publish more details on the competition on the course web page at a later date.
Written Exam (max 5 points - optional), on advanced theory topics. Evaluation will assess DD1.
# Traditional. Students who are primarily interested in understanding the theoretical concepts of recommender systems, can choose to focus on the conceptual and mathematical content. The exam is divided into two parts: a mandatory written exam on theory and, for the optional applied part, either a written exam or a project.
Written exam (max 26 points - mandatory), on theory. Evaluation will assess DD1, and DD2 at a conceptual level.
And optionally one between:
Written exam (max 5 points - optional), on applied topics. Evaluation will primarily assess DD2 in particular on the implementation, training and evaluation of models.
Project (max 5 points - optional), during the semester a limited number of projects *may* be available and will be published on the course web site. Projects typically consist in the implementation and testing of a recommendation algorithm, possibly starting from a scientific article. Projects will be assigned on a first-come-first-served basis. Students can group into teams but further details will depend on the specific project. Note that the projects are tipically connected with our current research activities and therefore will have deadlines. Failing to meet the deadlines will result in a failing grade for the project. Evaluation will consider the student's autonomy, the quality and correctness of the implementation, the soundness of the experimental design, and the clarity of the written or oral report. This reflects learning outcomes DD2, DD3, and DD5.

Research Group (ReMAP Lab) website https://remaplab.deib.polimi.it/
Aggarwal, Charu C., Recommender Systems: The Textbook, Editore: Springer, Anno edizione: 2016, ISBN: 978-3-319-29659-3 https://amzn.eu/d/6omO1G2
Jannach, D., Zanker, M., Felfernig, A., & Friedrich, G, Recommender systems: an introduction, Editore: Cambridge University Press., Anno edizione: 2010 https://amzn.eu/d/7OnIQbC
Note: Basic concepts
Kantor, Paul B., Lior Rokach, Francesco Ricci, and Bracha Shapira, Recommender systems handbook, Editore: Springer, Anno edizione: 2022 https://amzn.eu/d/frO7PLW
Note: Advanced book

Lo scopo dell'insegnamento è fornire le basi della progettazione a livello logico delle reti combinatorie e delle reti sequenziali che costituiscono i componenti fondamentali dei sistemi digitali. Si forniscono le metodologie e le tecniche di progetto necessarie alla realizzazione e ottimizzazione di circuiti combinatori e sequenziali. Vengono inoltre presentati i più comuni blocchi funzionali programmabili utilizzati nella sintesi dei circuiti digitali e vengono approfondite le strutture più utilizzate nella realizzazione dei blocchi dedicati alle operazioni di tipo aritmetico.
L'insegnamento introduce anche gli strumenti concettuali e di progetto che sono alla base della sintesi logica automatizzata, utilizzando il VHDL come linguaggio di descrizione del sistema digitale.

Conoscenza e Comprensione (DdD1):

• Conosce e comprende i principi fondamentali dei sistemi digitali
• Conosce e comprende le tecniche per la realizzazione e ottimizzazione di circuiti digitali combinatori e a stati finiti
• Conosce e comprende il funzionamento dei principali blocchi funzionali presenti all’interno di un calcolatore (aritmetici, di memorizzazione, di smistamento,...)
• Conosce e comprende gli elementi concettuali che sono alla base degli strumenti per la progettazione automatica dei circuiti e sistemi logici digitali
  Capacità di applicare conoscenza e comprensione (DdD2):
• Lo studente è in grado di progettare e ottimizzare reti logiche combinatorie e macchina a stati finiti
• Lo studente è in grado di comprendere il funzionamento di una rete logica digitale progettata da qualcun altro
• Lo studente è in grado di trasformare i requisiti di un progetto digitale in un circuito
  Capacità di apprendimento (DdD5):
• Lo studente è in grado di apprendere in maniera autonoma il funzionamento di altri blocchi aritmetici
• Lo studente è in grado di apprendere autonomamente le tecniche avanzate di progettazione e ottimizzazione delle reti logiche

1. Il livello logico digitale: progetto di reti combinatorie
1.1 Sintesi combinatoria: introduzione alla minimizzazione e cenni ai criteri di costo
1.2 Minimizzazione esatta: Quine McCluskey a singola uscita e a più uscite
1.3 Cenni alla sintesi euristica di funzioni a 2 livelli
1.4 Cenni alla sintesi multilivello
1.5 ROM, PAL, PLA: architetture e sintesi
2. Aritmetica del calcolatore
2.1 Notazione e aritmetica in virgola fissa e in virgola mobile
2.2 Architetture di sommatori e di moltiplicatori
3. Il livello logico digitale: progetto di reti sequenziali sincrone
3.1 Introduzione ai circuiti sequenziali: il concetto di stato, macchine sincrone e asincrone
3.2 Elementi di memoria sincroni a livello e sul fronte D, RS, JK e T
3.3 Le macchine a stati finiti: il modello generale di macchina sequenziale sincrona
3.4 Sintesi di macchine a stati finiti
3.5 Minimizzazione del numero degli stati
4. Introduzione al progetto automatizzato di architetture digitali
4.1 Il flusso di progettazione di architetture hardware
4.2 Il linguaggio VHDL per la descrizione di architetture hardware

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Le prove d’esame sono costituite da verifica scritta con eventuale discussione orale (con verifica scritta sufficiente) solo su richiesta del docente. La prova scritta prevede la risoluzione di problemi numerici, esercizi di tipo progettuale e domande di carattere teorico su argomenti trattati nel corso. (DdD1, DdD2, DdD5)
In sede d'esame lo studente deve dimostrare di essere in grado di:

• progettare e ottimizzare reti logiche combinatorie (DdD2)
• conoscere gli algoritmi di progettazione e ottimizzazione di reti combinatorie (DdD1, DdD5)
• progettare macchine a stati finiti (DdD2)
• conoscere gli algoritmi di progettazione e ottimizzazione di macchine a stati finiti (DdD1, DdD2)
• comprendere il funzionamento di una rete logica digitale progettata da qualcun altro (DdD1, DdD5)
• conoscere il funzionamento dei principali blocchi aritmetici (DdD1, DdD5)
• trasformare i requisiti di un progetto digitale in un circuito (DdD1, DdD2, DdD5)
  Gli appelli d'esame verranno svolti durante gli appositi periodi d'esame previsti nel calendario accademico e saranno comunque in accordo con le regole della Scuola. I risultati delle valutazioni e la modalità di visione degli scritti verranno comunicati dal docente così come la modalità di rinuncia ad una votazione positiva dell'esame. Le date degli appelli saranno comunicate tramite il sito Web di Ateneo. Gli allievi hanno l'obbligo di iscriversi alle prove d'esame.

C. Bolchini, C. Brandolese, F. Salice, D. Sciuto, Reti Logiche, Editore: Apogeo, Anno edizione: 2008
R. H. Katz, G. Borriello, Contemporary Logic Design, Editore: Pearson Education, Anno edizione: 2005
F. Fummi, M. Lora, M. G. Sami, C. Silvano, Progettazione Digitale, Editore: McGraw Hill, Anno edizione: 2023

The course presents the main phenomena, system architectures and components in a radio frequency system.
Satellites, radars, mobile phones and in general any communication system operating at radio frequencies (from MHz up to hundreds of GHz) need a dedicated frontend which handles the baseband signals and their conversion from/to the required carrier frequency, involving amplification, monitoring, mixing... up to the antenna. All such operations are carried out by components and subsystems, whose design along the transmission and reception chain are described in the course.
Specific phenomena addressed during the course include: thermal noise, non-linear distortion (compression, intermodulation, harmonic mixing, out-of-band and in-band components...), power transfer, mismatch, efficiency, power consumption, interference...
The course also provides the basic elements of a wireless radio link budget to balance transmit power, antenna type, modulation, data rate, sensitivity to cover a specific distance. Several digital, mixed and analog architectures are presented.
Specific subsystems and components are also discussed with guidelines for their design: practical transmission lines and cables, amplifiers, oscillators, couplers, dividers...
The course brings together and touches several other neghboring topics, not prerequisite, such as modulation, error correction, antennas, radio propagation, electronic circuits, microwave engineering, taught in other courses, and provides a cohesive view of how each aspect plays a role in achieving a real radio communication.
Techniques for modelling, analysis and design are provided at system and component level, leveraging theoretical foundations and engineering rules along with professional computer softwares for system and device simulation.
Interactive teaching will be used to foster attendance and keeping a steady study pace.

At the end of the course, students will:
Knowledge and understanding (DD1):

• know the name, purpose, parameters and equivalent simplified models of the main blocks, subsystems and components in a radio frequency frontend system
• know their interactions and phenomena in terms of performance and energy efficiency at system-level
• know the design flow and tradeoffs in a radio frequency system and the basic design techniques for the main components
  Applying Knowledge and Understanding (DD2)
• apply the principles of thermal noise and non-linear modelling and radio frequency circuits to design a transmitting/receiving frontend at radio frequencies and assess specifications, capabilities, efficiency and fundamental limitations/tradeoffs
• apply the principles of impedance transformations to design matching or transformation networks for amplifiers and other microwave components
• apply the concepts of system-level design to commercial CAD-EDA software packages for microwave design or to autonomosly-developed softwares
  Judgement and Critical Thinking (DD3)
• design specific radiofrequency blocks (e.g. single and multi-stage amplifiers) using the available transistor parameters and designing interconnections with lumped or distributed elements leveraging the available degrees of freedom and taking autonomous reasonable tradeoffs and design decisions
• gain practical judgment capabilities of reasonable requirements and specifications for the blocks in a radio frequency system

Specific topics (1st part):
Introduction to microwave communication systems. Fundamentals of frequency conversion: the super heterodyne radio receiver. Block diagram of the RF front-end in receivers and transmitters. Basic blocks: Antenna, Amplifier (low noise and power), Mixer, Local Oscillator. Recalls on signals in communication systems: carrier and complex (baseband) modulation.
Introduction to antennas: guided and radiated electromagnetic waves. System aspects of antennas. Fundamental parameters: directivity, radiation pattern, gain, effective area, efficiency, radiation impedance.
The radio link: Friis equation (link budget)
Noise in RF front-end. Definition of the noise temperature. Noise in frequency conversion (SSB and DSB). Evaluation of the equivalent noise temperature of the receiver. Comparison with the direct conversion receiver. Noise generated at antenna output.
Linear and non-linear distortion: definitions and characterization. Transfer function: no- distortion condition for amplitude and group delay. Weak non-linearity in 2-port networks: 1dB compression point and 3th order intercept point.
The analog frontend in the whole communication system: bringing all elements together, from networks and bitstream, to radio waves.
Transceivers architectures: frequency-division and time-division duplexing, orthogonal polarizations, MIMO
System-level simulations with industry-level computer-assisted EDA tools
Specific topics (2nd part):
Matrix characterization of n-port networks. Classical Z and Y matrices and limits of their use in high frequency circuits. Definition of power waves. Scattering matrix: definitions and properties.
Introduction to microwave circuits. Main differences with lumped-element circuits. Conventional voltage and currents. Interconnections and their effects. Discontinuities: meaning and representation. Analysis with CAD techniques.
Impedance matching: basic concepts (matching and maximum power transfer). Matching with lumped elements (L network). Single stub and double stub tuners.
Coupled transmission lines: analysis with the conventional lumped model; even and odd mode parameters. Directional couplers and combiners.
Introduction to the design of narrow band microwave amplifiers. Maximization of transducer gain and stability issues. Evaluation of optimum loads with the Smith Chart. CAD techniques for non-linear analysis of amplifiers: the Harmonic Balance method.
Microwave Oscillators: topological configuration and evaluation of optimum load with the Smith Chart. The dielectric oscillators: scheme and dimensioning criteria.
Overview of semiconductor active devices at microwave frequencies. Basics on fabrication technologies of RF and microwave circuits
Component-level design and simulation with industry-level computer-assisted EDA tools
Industry seminars
Interactive exercises and hands-on experience

Prova scritta obbligatoria, con prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
Valutazione continua facoltativa (a scelta dello studente)
The final grade is given from 0 to 30 e Lode and must be at least 18 points to be sufficient. The score is made by:

• up to 26 points: written test. Either as midterm test+endterm test or as full exam (DD1, DD2). An oral exam can be required by the teacher based on the result of the written test.
• up to 3 additional points: [optional] homework (one intermediate homework in October [flipped classroom, participative teaching, DD2, DD3]; one project at home after end of course [project teaching, DD2, DD3], to be turned in before the chosen exam session)
• up to 4 additional points: [optional] interactive classroom activities (2 interactive graded exercise sessions [graded/self-assessment teaching, DD3]; physical attendance to industry seminars (DD1))
  The additional points will be added to the written test only if the written test is >= 15.
  The written test can be taken either as midterm (13 points, usually in November) + endterm (13 points, only at the first exam session in January), and both parts must be >=7.
  Alternatively, the written test can be taken as full exam, covering the full program of the course, giving up to 26 points. The full exam can be taken in any of the 5 sessions (from the first exam session in January to the last session August-September of next year).
  The written tests are based on numerical exercises and problems similar to those discussed during the course. The purpose of the exam is to assess the student's ability to solve problems and design RF systems and microwave circuits. During the tests students can use books, notes and their own computer (without Internet connection). A laptop is indeed necessary for the exercises of the second part, and thus is needed for the endterm and any of the full exams.

Michel Steer, Microwave and RF Design, A System Approach, Editore: Scitech
David M. Pozar, Microwave Engineering, Editore: John Wiley & Sons
Slides by the teacher
Note: Published and updated on WeBeep of the course
Recorded classes
Note: These will be made available on Webeep before the midterm exam and at the end of the lectures.

This course will introduce the basic concepts and techniques used within the field of autonomous mobile robotics with a special focus on sensing and perception, mapping, localization, and autonomous navigation. We analyze the fundamental challenges for autonomous intelligent systems when these move on wheels or legs and present state-of-the-art solutions in simultaneous localization and mapping, planning, and motion control.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will learn:
· What are the main type of mobile robot kinematics and their characteristics in terms of direct and inverse kinematics equations
· How to structure the autonomous navigation system of mobile robots and autonomous vehicles
· What are the classical method for trajectory planning, i.e., graph and sample based, and the most common algorithms for trajectory planning
· What are the most common algorithm for trajectory following and obstacle avoidance
· What are the most used techniques for localization, mapping and simultaneous localization and mapping (SLAM)
· What are the common sensors used in robot perception, their pros and cons, their use for autonomous navigation and mapping
· The Robot Operating System (ROS) framework and how it can be used in the development of autonomous robots
Applying knowledge and understanding
Given a specific domain design for a mobile robot, the student will be able to:
· Design the system architecture for the autonomous navigation of the robot, its modules, and their algorithms
· Implement the software system controlling a modern autonomous robot based on the ROS middleware
· Design and develop SLAM algorithms for simultaneous localization and mapping
· Derive the kinematic equation for a mobile robot, develop the corresponding odometry system and integrate its outcome into a localization or SLAM system
Making judgments
Given an autonomous robot design problem, students will be able to:
· Identify the proper kinematics for the robot to be developed
· Identify the proper sensors the robot should have to fulfill its task
· Select the most appropriate algorithms for trajectory planning and trajectory following
· Select the most appropriate approach for SLAM, localization or mapping
Communication
The student will learn to:
· Discuss in written form the pros and cons of different kinematics in the realm of a specific design problem
· Describe the learned algorithms in written form, discussing their pros and cons.
Lifelong learning skills
Student will learn to:
· Face the design of the software system for an autonomous robot with a sound background and well defined patterns
· Develop the software controlling an autonomous robot/vehicle based on the ROS middleware and simulate it under Gazebo

The course is composed by a set of lectures on autonomous robotics, ranging from the main architectural patterns in mobile robots and autonomous vehicles to the description of sensing and planning algorithms for autonomous navigation. The course outline is:
Mobile robots kinematics,
Sensors and perception,
Robot localization and map building,
Simultaneous Localization and Mapping (SLAM),
Path planning and collision avoidance,
Robot development via ROS
Lectures will provide theoretical background as well as real-world examples; these will be complemented with practical exercises in simulation for all the proposed topics and the students will be guided in developing the algorithms to control an autonomous robot using ROS, the Robot Operating System (http://www.ros.org/).
The slides from all the lectures will be available on the course website (http://chrome.ws.dei.polimi.it/index.php/Robotics) together with a detailed schedule of the classes, links to relevant papers, recordings, and online resources.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
The course comprises theoretical lectures and practical sessions, the grade will reflect both. Part of the grade will be based on a classical written exam accounting for 26/32, while the remaining part of the grade will be based on practical, at home, coding exercises for up to 6/32.
The home exercises score is valid for the entire duration of the academic year.
Type of assessment
Description
Dublin descriptor
Written test
The solution of practical and design problems
· Computation of kinematics equations for a mobile robot and the corresponding odometry
· Compute the shortest trajectory with a graph based planning algorithm
Answer to theoretical questions
· Describe and discuss the pros and cons of different trajectory planning algorithms together with their functioning
· Describe and discuss the pros and cons of different trajectory following and obstacle avoidance algorithms together with their functioning
· Describe and discuss the pros and cons of different SLAM algorithms together with their functioning
· Describe the architecture and the functioning of ROS
1, 2, 3
1, 2, 3, 4, 5
Assessment of practical homework
Execution of a practical project
· Design of some ROS components implementing navigation capabilities for an autonomous mobile robot
2, 3, 4, 5

Sebastian Thrun, Wolfram Burgard, Dieter Fox, Probabilistic Robotics, Editore: MIT Press, Anno edizione: 2005, ISBN: 978-0262201629 http://www.probabilistic-robotics.org/

The course introduces students to the application of materials science and related technologies for the study, monitoring, and conservation of Cultural Heritage. It provides a multidisciplinary framework for understanding the value of Cultural Heritage and the fundamental principles of its preservation, with particular emphasis on the relationship between material properties, manufacturing techniques, deterioration processes, and conservation strategies.
Students will become familiar with the main categories of tangible cultural heritage, including architectural heritage and sculpture, mural and easel paintings, polychrome artworks, metal artifacts, paper-based objects such as drawings and manuscripts, and modern and contemporary artworks made from polymeric materials and design objects. The specific case studies examined each year may vary according to opportunities for site visits, museum activities, and direct observation of artworks.
The course presents the materials, technologies, and execution techniques that characterize different classes of artifacts, highlighting how their knowledge is essential for conservation and monitoring activities. Particular attention is devoted to the mechanisms of deterioration resulting from environmental exposure and atmospheric pollution, as well as to the principles of preventive conservation and the materials and methods used in conservation interventions. Examples and applications will focus especially on the conservation of architectural surfaces and other outdoor cultural heritage assets.

1 Knowledge and understanding (DdD1)
After attending the course and after the final examination, the student:
knows the main concepts of Cultural Heritage, deterioration, safeguard and conservation, monitoring.
knows the main characteristics of different Cultural Heritage items such as historic buildings and sculptures, paintings and mural paintings, modern and contemporary artworks, bronzes; is able to read and classify artifacts according to material science profile;
is able to draft the main durability and conservation issues of the different Cultural Heritage items;
2 Applying knowledge and understanding (DdD2)
After attending the course and after the final examination, the student:
is able to understand and draft a diagnostic and conservation work;
is able to summarize the results of a bibliographic research in the field of Heritage Science
3. Communication skills (DdD4)
The attending student will be able to present and communicate a selected and complex case study in Heritage Science.
These outcomes are expected to provide the student basic knowledge tools necessary for their involvement in the field of research in Heritage science, but at the same time it offers the opportunity to deepen the knowledge of some investigation techniques, in lab and onsite, and materials technologies.

Main topics:
What is Cultural Heritage: concept and legislation, What are the main international Entities for the preservation of Cultural Heritage
What are preservation and conservation, What is Heritage Science
Built Heritage: deterioration and conservation processes of stone materials and ceramics
Innovative materials and nanomaterials for the conservation
Mural Paintings: an introduction to material characteristics from pre-historic rock art to XXth century murals. Main deterioration and conservation problems. Street Art.
Painting on panel and canvas: painting materials and techniques over the ages. Pigments and binders and their deterioration.
Additional topics according to the case studies and visits
Metallic artifacts, bronzes: properties and deterioration problems, corrosion and conservation.
Street Art and contemporary muralism: properties and major deterioration problems.
Early polymeric materials used in objects and works of art. The challenges of conserving plastics in museum environmental conditions.
Paper, books and manuscripts: properties and deterioration phenomena.

Prova scritta facoltativa (a scelta dello studente), senza prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
For attending Students. The exam consists of a 20 min. oral presentation about a topic selected and agreed with the Teacher and based on a bibliographic research done by the Student in the current scientific literature available at https://www.biblio.polimi.it/; the colloquium will be completed by some questions about the general background on the same topic. The exam will be sufficient after a presentation characterized by sufficient clarity, synthesis and respect of assigned time. The Student should also demonstrate to be able to discuss similar arguments connected to those of the presentation and possess a general overview of the argument.
For not-attending Students. The exam consists of a written multiple choice test about the whole Program. The program is available on WeBeep, together with slides and other texts and documents, at the end of the semester. The test is composed by a number of multiple choice questions and 2 open questions. The Student should get a sufficient evaluation or higher (>18/30).

M. Matteini, R. Mazzeo, A. Moles, Chemistry for Restoration, Editore: Nardini Editore, Anno edizione: 2016, ISBN: 978-88-404-4450-5
Bastidas, D. M., & Cano, E. (Eds.), Advanced Characterization Techniques, Diagnostic Tools and Evaluation Methods in Heritage Science, Editore: Berlin: Springer., Anno edizione: 2018
Siegfried Siegesmund and Rolf Snethlage, Stone in Architecture: Properties, Durability, Editore: Springer, Anno edizione: 2014
David A. Scott, Gerhard Eggert, Iron and Steel: Corrosion, Colorants, Conservation, Editore: Archetype Books, Anno edizione: 2009
David A. Scott, Copper and Bronze in Art: Corrosion, Colorants, Conservation, Editore: Getty Trust Publications: Getty Conservation Institute, Anno edizione: 2002
Yvonne Shashoua, Conservation of Plastics: Materials science, degradation and preservation, Editore: Butterworth-Heinemann, Anno edizione: 2008
Baglioni, Piero, Chelazzi, David, Giorgi, Rodorico, Nanotechnologies in the Conservation of Cultural Heritage A compendium of materials and techniques, Editore: Springer Netherlands, Anno edizione: 2015, ISBN: 978-94-017-9302-5

The course SENSOR SYSTEMS will provide the basis for understanding how sensors work (light and image sensors, temperature sensors, magnetic field sensors, strain and force sensors, displacement, distance and proximity sensors, microphones) and for designing analog and digital front-end circuits. Through the course Students will become familiar with sensors and electronic components data-sheets and also with specific tools to program and debug ARM microcontrollers. During the hands-on labs, each student will practice with a microcontroller, using the main peripherals (GPIO, SPI, I2C, USART, PWM, ADC) and a sensor expansion board, for developing both the firmware and the proper hardware configuration, to test the conceived embedded system. The goal of the course is to enable master degree Students to select the best sensor for a specific application, develop an optimized front-end circuit and acquire the output signal with a microcontroller.
The course will include 24 hours of theoretical classes about sensors and front-end circuits and 26 hours of hands-on design laboratories. In particular, the NUCLEO board by STMicroelctronics, including a 32-bit STM32 microcontroller, will be used during the design laboratories.
3 CFUs will be delivered with Innovative Teaching modality as “Flipped Classroom”, in fact the Students will be asked to develop the projects before classes, in order to actively participate during the hands-on classes. Homework will be given through the course and their discussion will be objective of the exam. For the hands-on design laboratories the Students will organize in teams of 5 people that will collaborate to complete the assigned projects.

Knowledge and understanding (Dublin Descriptor #1)
Students will learn:
the working principle of the most used sensors
the parameters of practical interest for many sensors
the architecture and programming of an ARM microcontroller
Applying knowledge and understanding (Dublin Descriptor #2)
Students will apply their knowledge in practical case studies:
design the front-end circuit for practical case studies
acquire sensors output and display results with an STM32 microcontroller
Making judgements (Dublin Descriptor #3)
Given a practical case study and thanks to the Innovative Teaching, Students will:
analyze the critical issues
select the best sensor to be used
compare different solutions for the sensor front-end
think the best way to validate the system
Communication (Dublin Descriptor #4)
Students will learn to:
explain complex theoretical concept regarding the sensors working principle
express and justify their project choices
present the outcomes of their work in an effective way
Lifelong learning skills (Dublin Descriptor #5)
Students will be enabled to
read and understand sensors and electronic components data-sheets
develop a realistic project using the existing sensors
analyze new problems and find the best sensor and circuital solution to solve them

Sensors and front-end circuits (24 hours theoretical classes)
Basics on sensors: definitions, classifications and characteristics.
Working principle and front-end circuits of the most employed sensors:
light and imaging (photodiode, CCD and CMOS sensor);
temperature (RTD, thermistors, thermocouple, thermal diode, IR thermometer);
magnetic field (Hall sensors, magneto-resistances);
strain and forces (strain gauges, piezoelectric sensor);
displacement, distance and proximity (capacitive, inductive, acoustic, optical, magnetic, touch sensors);
audio (dynamic and condenser microphones).
Microcontrollers for embedded sensors systems (4 hours theoretical classes 26 hours design laboratories)
Introduction on ARM-Cortex M cores, STM32F4 microcontroller by STMicroelectronics peripherals and Nucleo development board.
Basics on the main microcontrollers peripherals (GPIO, PWM, ADC, SPI, I2C, USART, etc.) and simple projects (pushbutton, blinking LED, sending strings to remote terminal, using communication interfaces, etc.).
Sensors expansion board: hardware description and details of the sensors included in the board.
Hands on activities to interface sensors (temperature, light, sound, rotation, accelerations) with the microcontroller using its peripherals and providing an output (sending data to pc or displaying on an LCD or generating a sound through a speaker). Low power mode will be introduced for power saving smart sensors.
The main developed projects will include:
readout of a keybord;
communicate with a PC;
readout of a temperature smart sensor with I2C interface;
measure light intensity through a Light Dependent Resistor;
detect sounds with a microphone;
readout of a rotary encoder;
acquisition of the x, y, z accelerations with a tri-axial accelerometer;
communicate between boards with optical communication.

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
Valutazione continua facoltativa (a scelta dello studente)
Assessment will be based on:
10 weekly reports on the projects developed in teams (the reports will be graded on a sampling basis; at least two reports from each team will be assessed).
A written exam covering the entire course content (theory of sensors, microcontroller architecture, and programming for sensor readout). The written exam will include both multiple-choice and open-ended questions.
A short oral examination based on the 10 projects.
The reports and the oral examination together will account for 25% of the final grade, while the written exam will account for the remaining 75%.

Franco Zappa, Microcontrollers, Editore: Società Editrice Esculapio, Bologna, Anno edizione: 2017, ISBN: 9788893850223
Franco Zappa, Electronic Systems, Editore: Società Editrice Esculapio, Bologna, Anno edizione: 2012, ISBN: 9788893850858 www.editrice-esculapio.com/zappa-electronic-systems/

This course will introduce methods and devices for collecting and processing power system's measurements, finalized to monitor and control energy systems and smart grids.
Considering the measurement devices, the course aims to define the main requirements and specifications for properly design, select and maintain the components of the measurement chain.
For what concerns the topic dealing with the measurement data processing, the course aims to enable the students to specify, design and develop measurement data processing algorithms.

Lectures and exercise sessions will allow the student to (DdD1-2)
know the working principle of the several voltage and current sensors employed in power systems; select the proper set of sensors for specific measurement problems and specify their characteristics;
define the characteristics of the system interfacing sensors and instrumentation, in relation with electromagnetic compatibility requirements
specify the characteristics and architecture of a data acquisition system for a specific measurement task
analyze discrete time signals and systems in order to develop digital signal processing algorithms for optimal estimation, with particular reference to the static and dynamic state estimation in power systems
develop discrete time signal processing algorithms for forecasting in power systems.
The experimental laboratory will allow the student to (DdD1-2-4)
test the learned theoretical concepts
write a document reporting the instruments’ characteristics and the measurement results
develop simple measurement processing algorithms using commercial platforms such as Matlab and Labview

I - Fundamental principles of electrical and electronic measurements
Definition of measurements and measurement chain
The International System of Quantities and the International System of Units
Measurement errors: definition, probabilistic representation
II - Power system's transducers
General concepts and terminology
Current trasnducers: shunt, current transformer, Rogowsky coil, Hall effect current transducers
Voltage transducers: inductive voltage transformer, capacitive voltage transformer, voltage divider, Hall effect voltage transducers
III - Instrumentation
Sensor's interfacing: grounding, shielding, balancing, analog front end, mode rejection ratios, safety requirements
Data acquisition systems: sampling, analog to digital conversion, architectures, merging units
Power meters: power measurements in polyphase systems, Blondel's theroem, polyphase system identification
Energy meters: standards requirements (IEC 62052, IEC 62053, EN 50470, MID)
Advanced Metering Infrastructures (AMI), communication protocols (ModBus, IEC 61850, etc.)
IV - Elements of measurement signal processing
Estimation: estimator definition and general attributes, minimum mean square error, maximum a posteriori, maximum likelihood, measuremnt uncertainty;
Estimators: linear least squares, weighted least squares, recursive least squares, non-linear least squares;

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The examination consists of a compulsory written test for all students. The written test covers the entire course programme and is based on the analysis of a practical case study.
Students will be asked to (DdD1-2-3):

• identify the main requirements of the proposed problem;
• define a suitable measurement system architecture;
• specify the main characteristics of the measurement system components;
• define an appropriate data-processing algorithm.
  During the written examination, the following skills will be assessed (DdD1-2-3-4):
• ability to address the assigned problem using a logical, clear, and well-structured approach;
• ability to focus the analysis on the main features of the proposed problem;
• level of knowledge and understanding of the topics covered in the course;
• ability to perform analytical manipulations and develop relevant demonstrations.
  Students who obtain a written test score above the established threshold may choose to take an optional oral examination. The oral examination covers the course topics and may result in either an increase or a decrease in the score obtained in the written test.

Teaching material for this course is available on the Polimi Beep Portal https://beep.metid.polimi.it/

L'obiettivo del corso è introdurre agli aspetti di sicurezza nelle reti di comunicazione, in particolare Internet, fornendo un'ampia panoramica dalla crittografia ai protocolli e sistemi per la comunicazione sicura in rete.
Dopo un'introduzione alle basi teoriche della crittografia e alla teoria dei numeri (matematica dei numeri interi o matematica discreta), sono presentati i più importanti algoritmi di cifratura sia a chiave simmetrica sia a chiave pubblica. In seguito, sono illustrati metodi e protocolli di autenticazione di messaggi e di utenti, basati su funzioni hash, firme elettroniche, protocolli Sfida e Risposta, password, certificati, nonché i protocolli di instaurazione e distribuzione delle chiavi di cifratura. Infine, sono illustrati protocolli per la sicurezza della comunicazione in Internet, a livello di applicazione, trasporto, e rete.
Il corso potrà essere arricchito da seminari monografici complementari su temi specifici di sicurezza delle reti, tenuti da esperti dell'industria o operatori di telecomunicazioni.
Tutti gli argomenti del corso sono affrontati fornendo la necessaria cultura teorica matematica, ma sempre sottolineando gli aspetti pratici e applicativi di maggiore rilevanza.

1. Conoscenza e comprensione (DD1)
A seguito del corso e del superamento fruttuoso dell'esame, lo studente dovrebbe conoscere:
· le basi teoriche della crittografia e alla teoria dei numeri (matematica discreta);
· i principi dei più importanti algoritmi di cifratura a chiave simmetrica, dai cifrari classici a DES e AES;
· i principi degli algoritmi di cifratura a chiave pubblica, in particolare RSA ed El Gamal;
· metodi e protocolli di autenticazione di messaggi basati su funzioni hash e firme elettroniche;
· metodi e protocolli di autenticazione degli utenti basati su password, Sfida e Risposta, OTP, certificati e PKI;
· i principi dei protocolli di instaurazione e distribuzione delle chiavi di cifratura;
· i principi dei protocolli per la sicurezza della comunicazione in Internet a livello di applicazione, trasporto, e rete.
2. Capacità di applicare conoscenza e comprensione (DD2)
Grazie ai numerosi esercizi numerici, che costituiscono parte rilevante della prova d'esame, lo studente dovrebbe non solo acquisire la capacità di applicare in pratica le conoscenze teoriche di crittografia, ma anche migliorare le sue capacità di risoluzione numerica e calcolo in problemi pratici, capacità che sono essenziali per un Ingegnere.
Inoltre, dato che le lezioni sottolineano sempre gli aspetti pratici e applicativi di maggiore rilevanza, lo studente dovrebbe anche diventare in grado di applicare autonomamente le conoscenze apprese a situazioni e problemi nuovi e diversi.
5. Capacità di apprendimento (DD5)
Le capacità di apprendimento dello studente sono stimolate attraverso l'impiego di due libri di testo molto apprezzati in campo internazionale, uno per la crittografia e uno per i protocolli di sicurezza di rete. I lucidi sono utilizzati per facilitare l'esposizione degli argomenti in circa metà del corso, ma non sostituiscono mai i libri di testo, onde favorire l'attività di studio e comprensione della materia su un testo completo.
Inoltre, durante il corso sono periodicamente proposti micro-progetti facoltativi di calcolo numerico in Excel di algoritmi presentati a lezione, per cui si suggerisce di lavorare in piccoli gruppi, onde stimolare negli studenti la capacità di applicare e sviluppare in maniera autonoma i concetti appresi in classe, estendere la metodologia di ragionamento a problemi non illustrati in classe, superare difficoltà pratiche di realizzazione (problem solving).

1 INTRODUZIONE ALLA CRITTOGRAFIA E ALLE COMUNICAZIONI SICURE.
2 BASI DI TEORIA DEI NUMERI (MATEMATICA DISCRETA).
Nozioni di base di aritmetica modulare. Numeri primi. Operazioni modulo n: congruenze, divisioni, elevamento a potenza. Algoritmo di Euclide. Teoremi di Fermat e di Eulero. Elementi primitivi. Radici quadrate mod n.
3 CIFRARI A CHIAVE SIMMETRICA.
Cifrari classici. Cifrari a blocchi. Cifrari a catena. Algoritmo DES. Algoritmo AES.
4 GENERAZIONE DI BIT PSEUDOCASUALI.
Algoritmo Blum-Blum-Shub. Successioni LFSR da registri a scorrimento retroazionati. Applicazioni ai sistemi di comunicazione (scrambling).
5 CIFRARI A CHIAVE PUBBLICA.
Algoritmo di cifratura a chiave pubblica RSA. Test di primalità. Fattorizzazione. Logaritmo discreto. Algoritmo di cifratura a chiave pubblica di El Gamal.
6 AUTENTICAZIONE DEI MESSAGGI: FUNZIONI HASH E FIRMA ELETTRONICA.
Funzioni di hash. SHA. Attacco del compleanno. Firma RSA. Firma di El Gamal.
7 PROTOCOLLI DI INSTAURAZIONE E DISTRIBUZIONE DELLE CHIAVI.
Instaurazione e distribuzione di chiavi simmetriche, senza e con autenticazione. Protocollo di instaurazione della chiave di Diffie-Hellman. Protocollo station-to-station. Protocollo di Shamir-Massey-Omura. Schema di predistribuzione delle chiavi di Blom. Protocollo wide-mouthed-frog. Protocollo di Needham e Schroeder.
8 AUTENTICAZIONE DI UTENTE
Sicurezza delle password. Protocolli di autenticazione basati su Sfida e Risposta. Password monouso (OTP). Autenticazione biometrica. Kerberos.
9 CERTIFICATI E INFRASTRUTTURA A CHIAVE PUBBLICA (PKI).
Autorità di certificazione. Certificati. Infrastruttura a chiave pubblica (PKI).
10 PROTOCOLLI E SISTEMI PER LA SICUREZZA DI RETE.
Sicurezza a livello di trasporto e di applicazione: SSL/TLS, HTTPS, Secure Email (PGP e S/MIME). Sicurezza a livello di rete: IPsec.
Attività complementari:
SEMINARI MONOGRAFICI SU TEMI DI SICUREZZA DELLE RETI TENUTI DA ESPERTI DELL'INDUSTRIA E DI OPERATORI DI TELECOMUNICAZIONI.
Esempi di argomenti: protocolli di autenticazione, strumenti per password guessing, sicurezza nelle reti radiomobili, malware, attacchi e protezione dei sistemi di autenticazione.
Si consiglia di seguire le lezioni e le esercitazioni per un'ottimale comprensione di tutti gli argomenti.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
La verifica dell'apprendimento consiste principalmente in una prova scritta, comprendente esercizi e quesiti su argomenti di teoria strutturati in modo da verificare un ampio insieme dei risultati di apprendimento attesi DD1 (Conoscenza e comprensione) e DD2 (Capacità di applicare conoscenza e comprensione) dettagliati in precedenza. In particolare, la prova scritta e l'esame orale opzionale verificano non solo la comprensione degli argomenti, ma anche la capacità di applicare le conoscenze teoriche e di risoluzione numerica di problemi pratici, e di rielaborare in maniera autonoma le conoscenze apprese a lezione. Eventuali progetti facoltativi realizzati dagli studenti sono discussi nella prova orale opzionale, il cui fine principale è verificare il livello di comprensione dei concetti e la capacità di sviluppare autonomamente le tecniche apprese nel corso (DD5).
Un esame orale integrativo è possibile su richiesta dello studente e a discrezione del docente, in base allo svolgimento della prova scritta. Non sono previste prove in itinere. Le prove d'esame sono onnicomprensive e vertono su tutto il programma del corso.
Lo studente che non si ritenga soddisfatto della votazione conseguita è ammesso agli appelli successivi per tentare di migliorare il proprio voto sino all’ultima sessione dell’A.A. Consegnando la prova, si annulla automaticamente ogni voto precedentemente conseguito. In caso di consegna di un compito fortemente insufficiente, lo studente potrà essere rimandato alla sessione d'esame successiva saltando un appello.

Wade Trappe, Lawrence C. Washington, Crittografia. Con elementi della teoria dei codici. (2a edizione), Editore: Pearson / Prentice Hall, Anno edizione: 2009
Note: Italian translation from the original edition in English (Introduction to Cryptography with Coding Theory).
William B. Stallings, Network Security Essentials: Applications and Standards. 6th Edition, Editore: Pearson, Anno edizione: 2016
Stefano Bregni, Appunti vari
Note: Appunti distribuiti a cura del docente
AA. VV., Materiale degli eventuali seminari monografici
Note: Appunti distribuiti dal docente e da chi tiene il seminario.

Statistical signal processing provides the foundation for the informed development and critical use of many AI systems. Time series analysis is needed in several interdisciplinary areas of engineering such as audio/video, digital communications, finance, mechanical vibration analysis, imaging and remote sensing, GPS navigation systems, machine learning aiged problem solving, just to mention some.
The teaching activity aims to balance the fundamentals of discrete-time signals and the estimation theory with a set of engineering use-cases. Goal is to strength the capability of student to have a pragmatic, but still rigorous, way to solve stochastic signal related problems. More specifically, students will learn to approach an engineering problem that involves stochastic signals, formalize it into a mathematical model where all the unknowns are stochastic and/or deterministic variables, and find the most appropriate solving method always validated by the knowledge of the statistical bounds in idealistic connditions. The theory on statistical signal processing (60hours) are interleaved with numerical exercising (30hours) on simple settings to consolidate the concepts.
Labs and practical problems are first discussed in class providing real data, then after few days students can prepare for solving the assigned tasks by using Matlab in collaborative Labs where students are randomly grouped in 3-4 to compare and improve their problem-solving capability under the supervision of a tutor. The grouping within the same class enables students to experience intra/inter-group dynamics with other unknown and randomly chosen students, this small-scale simulation of their professional life aims to better refine their mutual professional relationships.
Part of the laboratory activity is in cooperation with industry-teams dedicated to one topic, to be defined during the semester (past was Google data analytics team)

Lectures and exercise sessions will allow the student to (DdD1):
Consolidate the theoretical tools to handle with finite-length single/multiple signals
Have solid knowledge of the estimation theory for statistical signal processing
Know what method to choose depending on statistical properties of stochastic part.
Handle with adaptive time-varying systems and design Bayesian parameters tracking
Introduction to spectral analysis
2D signals and properties
Handle multiple signals from arrays, and extract the relevant parameters
Extract a pattern from data in supervised/unsupervised forms
Design the Matlab flowchart for some essential kernels such as estimation of frequency and delays, extraction signals from noise by filtering and by convolution/deconvolution matrixes.
The project laboratory part of innovative-teaching will allow the student to (DdD2):
Analyze the requirements and write the algorithm flow
Write Matlab-code that solves the required problem
Evaluate multiple solutions for the same problem and develop a criticism.
Write a short document structured as a technical report
Work in team with other students on the same working-group and learn to interact each-other
Overall the teaching will give the student at the end of the course and after passing the exam the capability to design estimators and processing methods for limited-length data, to evaluate the reliability and accuracy of the algorithms, and tools for manipulating and classifying multiple-dimensions discrete signals. At the end of the course and after passing the exam, the student will be able to solve problems involving stochastic stationary or time-varying signals, and to evaluate the cost/benefit tradeoff with a deep knowledge of the corresponding statistical bounds. The laboratory activity in the classification and prediction is in cooperation with Google within the framework Google adopts ADSP course
Outcomes of the students:
Knowledge and understanding: students will learn how to manipulate signals using algebraic tools; apply the estimation theory and bounds to signals related problems; manipulate multidimensional signals for estimation and decision.
Applying knowledge and undestanding: students will be able to define a statistical model for signals' related problems and their solution with fundamental estimation theory methods; solve a broad range of statistical signals' related engineering problems.
Making judgements: students will be able to evaluate the fundamental limits on statistical signal processing applications even in presence of uncomplete/inaccurate conditions; tailor and adapt the statistical methods to complex problems.
Communication: student will learn how to describe a method and sinthetize the limits and results; write a technical report.

The course focuses on statistical signal processing and covers the following topics:
Review of basics: matrix and linear algebra; quadratic and constrained optimization problems.
Introduction to the estimation problem and models: definitions, performance, sufficient statistics, linear and non-linear models.
Estimators: best linear unbiased estimation (BLUE), maximum likelihood estimation (MLE), least squares method. Cramer Rao lower bound.
Bayesian estimators: a-posteriori estimation (MAP, MMSE and LMMSE); Wiener filter; linear prediction and Yule-Walker equations.
Adaptive filters: LMS, RLS methods, convergence analysis and step-size selection.
Bayesian tracking: dynamic model and Kalman filter; examples of positioning.
2D signals properties and physical filters
Array processing and beamforming methods.
Pattern and sequence recognition: Bayesian classification of signals in noise, linear discriminant and support vectors, clustering methods, supervised classification, deep learning methods and updates.
Montecarlo simulation and numerical analysis on some use cases.
Labs and practical problems are assigned during the semester and solutions are discussed within the workshops dedicated to these problems.

Prova scritta obbligatoria, con prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
The exam is in written form with at least one small project complete of the reporting activity and, optionally, the oral exam. More specifically, the written exam lasts 2.5h and it covers the entire theory part of the program with exercises to spot the degree of understanding of the concepts. Exercises are more than necessary to pass the exam, the sum of total marks allotted for all the exercises in written exam usually ranges in 42-45, so each student can choose what exercises to do and how much in depth according to her/his skill. Each exercise is in increasing-difficulty, and marks are ranked accordingly. Marks of the entire written exam saturate to 24. During written students can use the course textbook and for computations, students can use computers only with Matlab.
In parallel, the students have to develop at least one of the 3 projects (called Homeworks, or Hw, within the teaching jargon as they can be freely carried out at home): Hw#1, Hw#2, Hw#3. During the teaching semester, the Hws’ text and data are released to the students so that they can start solving Hws at home while studying theory and exercising. Solving the Hw during the teaching semester is recommended as let the students take benefit of the Project Laboratory as Hws have similar structure except should be completed independently on their own. Hws are of incremental complexity, with increasing marks 1,2,3, so that max Hw marks is 1+2+3=6. Hw#1 is mandatory and there is no restrictions on when to solve the Hws, but students can deliver the Hw-reports only after passing the written exam >17.
For students that wish to have to oral exam, the written exam should be passed with unsaturated mark >27, with at least Hw#1. Oral exam is a discussion that aims to evaluate the maturity and the knowledge of the topics when solving some use cases; during oral the student can use the textbook and notes. Notice that 30Lode is only by Oral exam.
There is an intermediate written exam, typically the end of October, covering half of the program. The second half can be completed in any written exam dates till the end of the winter session of exams. The written exam can be repeated anytime without penalty.

U.Spagnolini, Statistical Signal Processing in Engineering, Anno edizione: 2017, ISBN: 978-1-119-29397-2
Note: Note:
Notes/slides on the book can be downloaded from the folder of the course shared with students during the semester

The course is aimed to deepen the concepts of dynamic modelling and simulation, and to explain the architecture of the most popular relevant software tools. To be more specific, the course is aimed to provide students with the skills needed to address problems of modeling and simulation of complex engineering systems. The first part will be dedicated to illustrate the causal approach to modeling. The second part of the course will be devoted to introducing the acausal approach, based on object-oriented programming concepts, languages and tools. In particular, in this second part, the characteristics of the modeling language Modelica will be illustrated, now standard "de facto" for the modular, multi domain modelling. The characteristics of the object-oriented modelling and simulation environments will be illustrated in reference to some application cases in a computer laboratory. These application cases will include examples of multidomain engineering and control systems.

Lectures will allow students to:
Understand the theory of numerical methods for the solution of systems of ordinary differential equations (ODE) (DdD 1).
Model and numerically solve hybrid dynamic models (events) (DdD 2).
Understand the basic theory of differential-algebraic equations systems (DAE) (DdD 1).
Use an object-oriented modelling language (Modelica) to model complex engineering systems (DdD 2).
Understand symbolic manipulation techniques applied to DAE system in order to derive efficient simulation codes (DdD 1).
Exercises will allow students to:
Build simulation models of complex systems in acausal (OpenModelica) object-oriented modelling environments (DdD 2).
Describe and numerically solve hybrid dynamical systems (DdD 2).
Select solver parameters and interpret the results of the simulations in terms of accuracy and computational efficiency (DdD 2).
After passing the exam, requiring the development of a simulation model, students will be able to:
Make design choices when building a simulation model (DdD 3) (DdD 5).
Justify design choices when building a simulation model (DdD 4).
Communicate the results of the work (DdD 4).

Lectures
1 Introduction.
1.1 System, experiment, model, simulation.
2 Causal approach.
2.1 Causal models and ODE.
2.2 From models to simulations.
2.3 IVP (Initial Value Problem)
2.3.1 Theorem of global existence and uniqueness of an IVP: hypothesis.
2.3.2 Theorem of global existence and uniqueness of an IVP: thesis.
2.3.3 Stability of an IVP.
2.3.4 Stability of the problem and numerical integration.
2.3.5 Numerical integration and stability of the solution of an IVP.
2.4 Elementary methods.
2.4.1 Forward Euler method.
2.4.2 Local truncation error and difference operator.
2.4.3 Consistency and convergence.
2.4.4 0-stability.
2.4.5 Fundamental convergence theorem.
2.4.6 0-stability of the forward Euler method.
2.4.7 Absolute stability.
2.4.8 Region of absolute stability.
2.4.9 Stiffness.
2.4.10 Backward Euler method.
2.4.11 Newton's method.
2.4.12 Modified Newton's iteration.
2.4.13 Trapezoidal method.
2.4.14 Midpoint methods.
2.5 Managing discontinuities.
2.6 Local error and tolerances.
2.7 Methods of step size selection.
2.7.1 Local error estimation: step doubling.
2.7.2 Local error estimation: embedded methods.
2.8 Higher order methods.
2.8.1 General formulation of Runge Kutta methods.
2.8.2 Accuracy, 0-stability and absolute stability regions of Runge-Kutta methods.
2.8.3 Embedded methods.
2.8.4 Implicit Runge-Kutta methods.
2.8.5 Linear multistep methods.
2.8.6 Adams-Bashforth methods.
2.8.7 Adams-Moulton methods.
2.8.8 BDF methods.
2.8.9 0-stability of multistep methods.
2.8.10 Absolute stability of multistep methods.
2.8.11 Predictor-corrector methods.
2.8.12 Milne's estimate.
3 Acausal approach.
4 Introduction to the Modelica language.
4.1 Classes, equations, parameters, constants.
4.2 Records, annotations, types.
4.3 Connectors.
4.4 Inheritance and partial models.
4.5 Events.
4.5.1 Synchronous data-flow principle.
4.5.2 Conditional models.
4.5.3 noEvent().
4.5.4 Discrete event models.
4.5.5 Events synchronization.
4.6 Stream variables and connectors.
4.7 Packages.
4.8 Components and (sub)systems.
4.8.1 Architecture driven approach.
4.9 Expandable connector.
5 DAE systems.
5.1 Examples.
5.2 Differentiation index.
5.3 Initial conditions and constraints.
5.4 Linear constant coefficient DAE systems.
5.5 Kronecker canonical form.
5.6 Algebraic index.
5.7 Index and numerical integration.
5.8 Dummy derivatives method.
5.9 Numerical methods for DAE systems.
5.9.1 Direct discretization methods: backward Euler, BDF, Radau.
6 Modelica code translation.
6.1 Hybrid DAE system.
6.2 Structural analysis.
6.2.1 Bipartite graphs.
6.2.2 Duff Algorithm.
6.2.3 Pantelides theorem.
6.2.4 BLT reordering.
6.2.4 The tearing problem.
Exercises
All exercises will be carried out in a computer laboratory
1. Model of a brushless motor in Modelica.
2. Discrete event models: event iteration.
3. Stream connectors. Control of a mixer.
4. Digital control systems in Modelica. Conditional connectors.
5. Discontinuous friction model. Stick-slip and hunting motions.
6. Conditional components: modelling and simulation of a mechanical stop.
7. Multiphysics modelling: model of an hydraulic piston.
9. Multibody package.

Prova orale obbligatoria
The evaluation test will be both practical and oral and it will be divided into two parts:
In a first part the student will be asked to develop "from scratch" and simulate the model of a freely chosen dynamic system, in an acausal object-oriented modelling environment (either OpenModelica or Dymola). The complexity of the model, the mastery of the modeling tool and the ability to interpret the results will constitute elements of judgment (DdD 1 - DdD 2 - DdD 3 - DdD 4 - DdD 5).
In the second part the student will be asked to show his/her knowledge of the theoretical contents of the course, answering theoretical questions or solving simple exercises. Examples of possible questions can be found in the texts of written exams assigned in the past, available on the course's WeBeeP page (DdD 1 - DdD 2).

U. M. Ascher, L. R. Petzold, Computer Methods for Ordinary Differential Equations and Differential-Algebraic Equations, Editore: SIAM, Anno edizione: 1998, ISBN: 978-0-89871-412-8
Modelica by Examples https://mbe.modelica.university/
Modelica Tutorial https://openmodelica.org/images/M_images/250204-ModelicaTutorial-PeterFritzson-AdrianPop-MODPROD-2025.pdf
F. E. Cellier, E. Kofman, Continuous System Simulation, Editore: Springer US, Anno edizione: 2006, ISBN: 978-1-4419-3863-3

The SMART HOSPITAL - Modulo 2 course provides students with instruments and tools to:
understand the needs for designing a SMART HOSPITAL
optimizing the managment of diagnosis/ treatment/ rehabilitation
understand new technolgy development for remote control
develop a small project for SMART management of the healthcare process

Knowledge and understanding: provides the basis and opportunities for applying engineering methods for the design and implementation of the hospital of the future
Applying knowledge and understanding: problem solving abilities, in particular for operating room and rehabilitation treatments management
Making judgements: case-based analyses and group discussions
Communication: projects results presentation (flipped/ blended)
Lifelong learning skills: self-assessment tools and interaction tools.

Requisites for a smart hospital
Healthcare processes
Personalised medicine / clinical decision support system
Properly design an optimization process
Technologies for augmented reality/ remote control
Perform a case-base evaluation for process optimization/ clinical decision support system

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
Small project (i.e. design of a smart hospital application) implementation with tutor supervision and discussion of the project in oral interview during the exam dates.
During the interview, questions on the course topics will be object of discussion.

The course “Smart Wearables Design and Prototyping” provides advanced electronics design techniques for prototyping a smart wearable system by means of hands-on learning. Throughout the course students will become familiar with devices, processors, sensors and communication protocols used in low-power and small size wearables. The course addresses the fundamental concepts and tools for developing sensing electronics and low-power embedded processing, within the new cross-disciplinary Major on “Smart Wearable Technologies”. Case studies of industrial interest will be discussed.
The course includes 12 hours of theoretical basics for the design of smart wearables, including key design guidelines and architecture as well as examples and use cases, and 38 hours of design and hands-on laboratories, during which teams of 5 students will prototype their own smart eyewear, learning the whole process flow, from the PCB design, to the microcontroller firmware and smartphone app development.
Starting from templates of main-board (with STM32U5 microcontroller, BLE, flash, IMU, USB-C), firmware, glasses frame, smartphone app, each team will prototype a smart-eyewear, by designing sensors-probe (e.g. PPG, IMU, barometer, photodetectors, microphone), firmware, and smartphone app to display acquired data.

Knowledge and understanding (Dublin Descriptor #1)
Students will learn:
the architecture of a portable low-power electronic system
the working principle of key solid-state sensors and their interfaces
to design and properly size a sensor acquisition chain
the architecture and programming of an ARM microcontroller
the potential and constraints of embedded machine learning
to integrate the electronics with mechanics and software
Applying knowledge and understanding (Dublin Descriptor #2)
Students will apply their knowledge in practical case studies:
design of a PCB with sensors to be integrated in the frame
develop the firmware and software needed to acquire and process the sensors data
perform measurements and analyze the data
Making judgements (Dublin Descriptor #3)
Given a practical case study and thanks to hands-on experience, students will:
analyze the critical issues
select the best configurations and components to be used
select the best processing solution
think the best way to validate the system
Communication (Dublin Descriptor #4)
Students will learn to:
work in team
express and justify their project choices
Lifelong learning skills (Dublin Descriptor #5)
Students will be enabled to
read and understand sensors and electronic components data-sheets
develop an electronic project, starting from PCB design to realization and characterization

Smart Wearables Architectures
Introduction: course organization, design and hands-on lab activities team work
Smart wearable architecture, design, challenges (size, power consumption, embedded machine learning) and case-studies (eye tracking, biosensing, context awareness...)
Electronics development
Guidelines for PCB design (materials, power and signal routing, decoupling, high frequency path and termination)
Altium Designer layout CAD (schematic and layout, PCB stack, libraries, rigid/flex, gerber output standards)
Wearable Sensors (image sensors, MEMS…)
Batteries and power management fundamentals
Detailed schematics and layouts of main-board and sensing-probes to be used in the Labs
Design of the sensing probes (board layout, revision, generation of files for manufacturing)
Firmware development
Wearable and Low-Power Computing (MCU, embedded processing)
Communications (wired and wireless protocols)
Cube environment for programming STM32U5 (DMA, interrupts, HAL_functions, peripherals) with ultra low-power modalities
Detailed code of main-board and firmware modules (memory, USB and BLE communication, I2C communication with IMU, hands-on trials)
Design of the firmware for the sensors-probe (interfacing, data analysis, tests)
App development and project validation
Flutter tools for developing Apps (from a provided template)
Design of custom smartphone App (final tests)
Customization of the App to display data from the sensing-probe

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
Assessment will be based on:
A report on the project developed in teams (individual contributions will be taken into account).
A written exam covering the entire course content (both the theoretical topics and the developed project). The written exam will include both multiple-choice and open-ended questions.
A short oral examination based on the written exam and the project report.
Technical knowledge, critical analysis of the design choices, and communication skills will all be evaluated.
The project report and the written exam will each account for 50% of the final grade. The oral examination will be used to verify each student's individual contribution to the project and to clarify any ambiguous or uncertain aspects of the written exam.

Franco Zappa, Microcontrollers, Editore: Società Editrice Esculapio, ISBN: 9788893850223

Presentare allo studente le caratteristiche di base e gli obiettivi delle reti software defined (astrazioni, interfacce, casi d’uso). Impartire nozioni basilari sull’automazione di rete attraverso la realizzazione di semplici applicazioni di rete in ambiente emulato e in ambiente di laboratorio. Impartire i rudimenti della programmazione di un sistema operativo di rete e dell’uso delle interfacce northbound e southbound. L’insegnamento non adotta modalità di didattica innovativa.

Al termine del corso lo studente:

• conosce gli elementi funzionali di un’architettura di rete e ne comprende il ruolo (DdD 1)
• comprendere i principali problemi che devono risolti per implementare protocolli e funzioni di rete e conoscere alcuni algoritmi per risolverli (DdD 1, 2)
• sa prevedere il comportamento di un dispositivo di rete in risposta a specifiche configurazione e comandi (DdD 2)
• sa implementare semplici applicazioni SDN (DdD 1, 2)
• sa confrontare il costo e le prestazioni di diversi approcci algoritmici alla soluzione di un problema dato (DdD 1, 2)

Paradigma Software Defined Networking.
Architettura della parte di comunicazione di un host. Algoritmi per implementare protocolli di rete negli host (copia, demultiplazione).
Architettura di un router o switch. Algoritmi per implementare protocolli di rete in router e switch (address lookup, classificazione, commutazione, scheduling, gestione del buffer)
Astrazioni e protocolli per il controllo di dispositivi di rete (interfaccia southbound, openflow)
Sistema operativo di rete e interfaccia northbound. Paradigma basato su intenti.
Algoritmi esatti e probabilistici per la misura del traffico.
Algoritmi e protocolli per l’ingegneria del traffico.
Laboratorio di automazione di una rete emulata. Programmazione del controllore di rete Ryu.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, con revisioni periodiche, con presentazione/discussione finale
L’esame prevede una prova scritta. La prova orale è facoltativa a richiesta del docente.
La verifica scritta consiste in:

• problemi numerici di valutazione delle prestazioni di algoritmi (DdD 1, 2)
• esercizi di configurazione e automazione di una rete con il controllore ryu e il protocollo openflow. (DdD 1, 2)
  La verifica orale consiste in domande a risposta aperta sulle definizioni, le proprietà degli algoritmi e le architetture viste a lezione. (DdD 1)
  E' possibile svolgere un progetto per incrementare il voto finale. Il progetto verte sugli stessi contenuti del laboratorio e ha come obiettivo la progettazione, sviluppo e validazione di una funzione di rete.

George Varghese, Jun Xu, Network Algorithmics,: An Interdisciplinary Approach to Designing Fast Networked Devices. 2nd ed., Editore: Morgan Kauffmann, Anno edizione: 2022, ISBN: 9780128099278
RYU SDN Framework https://osrg.github.io/ryu-book/en/Ryubook.pdf
Peterson, Cascone, O'Connor, Vachuska, and Davie, Software-Defined Networks: A Systems Approach https://sdn.systemsapproach.org/

The goal of the course is to enable students to master the engineering methods and processes that are necessary to produce complex software with industrial strength quality. The course covers the software process and its organization, requirements, design methods and tools, specification, verification and validation. Each topic is treated both theoretically and practically. Through the course, the flipped class teaching method are exploited to allow students to acquire a good level of autonomy in learning new topics.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will learn how to:

• Identify the phases of software development and the specific activities associated with each phase
• Understand an Alloy specification
• Execute static analysis algorithms on code fragments
• Identify architectural styles and patterns
  Applying knowledge and understanding
  Given specific project cases, students will be able to:
• Detail the corresponding requirements
• Identify, analyze and comment on specific architectural choices
• Apply the theory to assess the availability of a software architecture
• Test whether the software is fulfilling the corresponding specification documents
  Making judgements
  Given a relatively complex problem, students will be able to:
• Analyze and understand the goals, assumptions and requirements associated with that problem and model them
• Define the architecture of the corresponding software system using proper styles and patterns
• Identify and define the needed verification and validation activities
• Estimate the size of the system and the resources needed for its development, identify risks and define mitigation actions
  Communication
  Students will learn how to:
• Write a requirement specification document
• Write a design specification document
• Discuss with the other colleagues during project labs
  Lifelong learning skills
• Students choosing to take the project assignment will learn how to develop a realistic project
• Students choosing to focus on the research project will learn how to conduct a research activity on some specific aspect of software engineering, developing some innovative solution or some empirical experiment.

The software process
Software lifecycle
Project management and cost estimation
Notations and specification methods: Alloy
Problem definition: requirements engineering
Importance of requirements
The Jackson-Zave approach (requirements, specification, domain)
Usage of Alloy and UML to support requirement modeling activities
Methods and technologies for product development
Software architectures and architectural styles
Middleware and software components
Verification and validation
The verification and validation process
Analysis techniques
Testing techniques
The flipped class method will be exploited to help students in learning part of the topics related to requirement engineering and how to apply them in practical contexts.
Project laboratory activities can be optionally taken by interested students. These concern the requirement analysis and design of a realistic project, possibly followed by the development and testing of a running implementation of the assigned project, or the development of innovative research (new tools, formal specifications or empirical experiments) concerning some aspects of software engineering that will be agreed with the instructor.

Prova scritta facoltativa (a scelta dello studente), senza prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
Altro
The assessment will be based on a written exam, which can be replaced by project activities, and two optional tests taken during classes.
The written exam is to be taken in the exam sessions defined by the school and covers all aspects of the syllabus. The exam is open book and will assign up to 30 points. It is organized in two parts that can be taken separately, even in different exam sessions within the same academic year. The first part of the exam (called WE1) assigns up to 16 points and is passed with a minimum of 8. The second part (called WE2) assigns up to 14 points and is passed with a minimum of 7. The written exam can be -- partially or totally -- replaced by project activities as will be detailed during the course. The objective of projects is to help students apply in practice the approaches and principles that are presented in lectures. Projects will be assigned at the beginning of the semester.
The optional tests taken during classes are worth up to 2 points; they will include questions on practical aspects concerning requirement analysis and software design and will be scheduled as part of the class activities during the semester. There is no possibility to take the tests in other periods of the academic year. There is no minimum to pass this part.
The final score will be obtained by summing up the sufficient scores obtained in the various parts (including the tests) and must be greater than or equal to 18. Should the total be below 18, it will be possible to retake one or both written exam parts.
30 cum laude will be assigned when the total score is greater than or equal to 31.
Type of assessment
Description
Dublin descriptor
Written test
Solution of numerical problems:

• Computation of availability estimates for a software architecture
• Cost and effort estimation
• Software testing and analysis exercises
  Exercises and questions focusing on requirement and design aspects:
• Definition of the Alloy model for a given problem
• Definition of the high-level architecture for a software system
• Definition of a test plan and/or of a project plan for a specific software
  1,2
  1, 2, 3, 4, 5
  Project documentation and oral presentation
  Assessment of the design and experimental work developed by students, either individually or in groups, as part of the project;
  Assessment of project presentation
  2, 3, 4, 5

Course website webeep.polimi.it
Note: The course website will be the main reference for learning material, classe schedule, homework and project assignments, as well as announcements concerning the various activities related to the class
Camilli, M., Di Nitto, E., Fuggetta, A., Margara, A., Rossi, M., Tamburri, D.A., SOFTWARE ENGINEERING - A Structured Journey from Inception to Delivery, Editore: Bup, Bocconi University Press, Anno edizione: 2025, ISBN: 9791280623508
Ravi Sethi, Software Engineering: Basic Principles and Best Practices (English Edition), Editore: Cambridge University Press, Anno edizione: 2022, ISBN: 1316511944
Hans van Vliet, Software Engineering: Principles and Practice, 3rd Edition, Editore: Wiley, Anno edizione: 2008, ISBN: 978-0-470-03146-9
Note: Available also as e-book with ISBN 978EUDTE00263
Carlo Ghezzi, Mehdi Jarayeri, Dino Mandrioli, Fundamentals of Software Engineering, Editore: Prentice-Hall, Anno edizione: 2002, ISBN: 0133056996
Len Bass, Paul Clements, and Rick Kazman, Software Architecture in Practice, Editore: Pearson Education, Limited, Anno edizione: 2021, ISBN: 9780136886099 https://ebookcentral.proquest.com/lib/polimi/detail.action?docID=7116234
Note: The book has been purchased by our library. It can be accessed by three contemporary users. Access can take place from Polimi network or by following the instructions available here https://www.biblio.polimi.it/en/services/how-to/access-to-an-electronic-resource
Christoph Fehling, Frank Leymann, Ralph Retter, Walter Schupeck, Peter Arbitter, Cloud Computing Patterns: Fundamentals to Design, Build, and Manage Cloud Applications, Editore: Springer, Anno edizione: 2014, ISBN: 9783709115671 http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=705269
Note: The book has been purchased by our library. It can be accessed by multiple contemporary users. Access can take place from Polimi network or by following the instructions available here https://www.biblio.polimi.it/en/services/how-to/access-to-an-electronic-resource
Martin Kleppmann, Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Editore: O'Reilly Media, Incorporated, Anno edizione: 2017, ISBN: 9781449373320 https://ebookcentral.proquest.com/lib/polimi/detail.action?docID=4825244
Note: The book has been purchased by our library. It can be accessed by a single user at a time. Access can take place from Polimi network or by following the instructions available here https://www.biblio.polimi.it/en/services/how-to/access-to-an-electronic-resource

The course is offered in a 10-ECTS version (Solid State Physics), to which two 5-ECTS courses (Solid State Physics A and Solid State Physics B) are associated and offered jointly. The present detailed program defines aims, educational outcomes and syllabus for every joint course.
Solid State Physics (10 ECTS)
The course goals are the union of those of part A and part B as follows:
Solid State Physics A (5 ECTS)
The course aims at giving students the fundamentals of solid state physics at a graduate level. Long range order solids (crystals), their structural properties (symmetry) and static electronic structure are of main concern. After an introduction to crystallography, the main experimental tools used to determine the geometric structure of crystals are discussed together with the related theory. Then quantum mechanics is used to find the electronic band structure both introducing the general properties of electronic states in a periodic potential and presenting the main approximate methods to calculate the energy levels. Finally, the effect of an external static field on electrons is studied by means of crucial concepts like: density of states, equivalent Hamiltonian, effective mass, semiclassical dynamics and electron-hole representation. All arguments are illustrated by examples.
Solid State Physics B (5 ECTS)
The course aims at giving students the basic principles for the approximate solution of the many body Schrodinger equation for a crystal at a finite temperature including lattice vibrations: Born-Oppenheimer and mean-field approximations. Classical lattice dynamics is then introduced (interatomic force constants and dynamic matrix, lattice waves with their frequencies and polarization) and eventually quantized presenting the new concepts of acoustic and optic phonons. On this basis, thermodynamic properties like specific heat and thermal dilation are made microscopically understandable. Always using quantum mechanics, the macroscopic optical properties of crystals are derived from a microscopic treatment of electronic transitions. At the end transport properties (electrical and thermal conductivities and their temperature dependence) are introduced and explained in terms of physical kinetics. All arguments are illustrated by examples.

Solid State Physics (10 ECTS)
The expected learning outcomes are both those of part A and of part B, as detailed in the following.
Solid State Physics A (5 ECTS)
After the exam, the student:

• knows the mathematics and the physical principles required to understand what is a solid body and in particular a crystal, together with the geometric structure of the most important crystals (DdD1)
• knows the main quantities defining the crystalline state, together with the theoretical models and experimental methods suitable to describe it and to ascertain its structure, with particular reference to perfect “frozen” periodic lattices with a basis of atoms (DdD1)
• is able to forecast the main static electronic properties (band structure) of specific crystals knowing the fundamentals of the quantum mechanics of one electron in a periodic potential and the main approximate methods to compute the band structure itself, and to communicate this knowledge in an effective way (DdD2 - DdD4)
• is able to exploit the above theoretical models to develop the description of the accelerated motion of an electron in a specific crystal subjected also to a static external field using the electron-hole representation (DdD2).
  Solid State Physics B (5 ECTS)
  After the exam, the student:
• knows the theoretical models required to approximately solve the many body Schroedinger equation for a crystal including nuclear vibrations (DdD1)
• knows the fundamentals of both classical and quantum lattice dynamics (lattice waves and phonons) (DdD1)
• is able to apply quantum mechanics to understand and forecast the macroscopic thermodynamic and optical properties of specific crystals, and to communicate this knowledge in an effective way (DdD2)
• is able to apply quantum mechanics and physical kinetics to understand and forecast the macroscopic transport properties (electrical and thermal conductivities) of specific crystals, and to communicate this knowledge in an effective way (DdD2 - DdD4).
  In addition, the student knows the values or the order of magnitude of the main physical quantities and properties of solid state materials discussed in the course, also with reference to examples of specific crystals.

Solid State Physics (10 ECTS)
The topics of this course are both those of part A and part B as listed in the following.
Solid State Physics A (5 ECTS)

• Introduction: phenomenological properties and definition of solid materials
• Solid bodies: order and symmetry; simple crystals: lattices and translational order; reciprocal lattice; complex crystals: lattice and basis; crystal binding; tensorial observables
• Scattering theory: quantum particle scattering amplitude: Born approximation; elastic scattering; Bragg law; X-Ray Diffraction
  Schrodinger equation of a crystal: Born-Oppenheimer and mean field approximations
• Electronic states: Bloch theorem, reduction to the first Brillouin zone and periodic boundary conditions; free electron model (Fermi energy and density of states); band structure: nearly free electron approximation; band structure: tight binding and LCAO approximations; general method for the computation of the density of states
• Electron dynamics in external fields: equivalent Hamiltonian; effective mass theorem; impurity levels; semiclassical dynamics in an ideal crystal; electrical current and Bloch oscillations, electrons and holes, excitons.
  Solid State Physics B (5 ECTS)
• Adiabatic theorem and vibrational motions
• Lattice dynamics: lattice specific heat: Einstein model; simple 1D crystal: lattice waves and acoustic phonons, linear chain with two atoms per cell: acoustic modes and optical modes; general 3D lattice dynamics: interatomic force constant tensor and dynamic matrix; phonons in a 3D complex crystal; Debye model of specific heat; thermal dilation and anharmonic processes;polaritons
• Optical properties: macroscopic description of dispersion and absorption of photons; Kramers-Kronig relations for the frequency dependent dielectric susceptibility; Drude-Lorentz model; optical properties of metals; quantum treatment of photon absorption; optical properties of direct band gap semiconductors; the case of indirect band gap; optical properties of polaritons
• Transport phenomena: local Ohm law and Fourier law; experimental temperature dependence of electrical and thermal conductivities; physical kinetics and Boltzmann equation; microscopic theories of electrical conductivity and of thermal conductivity of metals; microscopic theory of thermal conductivity of insulators (phonon-phonon scattering).
  Some reminders to required preliminary knowledge and theoretical tools, necessary to address the teaching topics and related to quantum mechanics, analytical mechanics and electromagnetism, will be provided through recorded video lectures; these topics will then be resumed and discussed in the classroom in specific sessions devoted to verifying the students' understanding.

Prova orale obbligatoria
The evaluation consists in an oral examination. It aims at verifying both the knowledge and the understanding of the course topics (typically one topic from part A and one from part B) and also the ability to apply critically this general knowledge to develop the description of: (Solid State Physics A) main quantities defining specific crystals, electronic band structure models for specific crystals, response of crystal electrons to external static fields; (Solid State Physics B) vibrational properties, thermodynamic and optical properties of specific crystals, transport properties of specific crystals. The student must be able to clearly describe and critically discuss the topics, with the related hypotheses, the physical meaning, the mathematical approach (complete derivations are not strictly required, but of course their knowledge may contribute to the final mark). The knowledge of the systems of units, the value of the fundamentals physical quantities in Solid State Physics, the structure of the most important crystals and their main properties are addressed as well for both part A and B.

C.Kittel, Introduction to Solid State Physics, 8th Edition, Editore: Wiley, Anno edizione: 2004
N.W. Ashcroft, N.D. Mermin, Solid State Physics, Editore: HRW International, Anno edizione: 1981
H. Ibach, H. Luth, Solid-State Physics, Editore: Springer, Anno edizione: 2009
F. Bassani, U.M. Grassano, Fisica dello Stato Solido, Editore: Bollati Boringhieri, Anno edizione: 2000
J.M. Ziman, Principles of the theory of solids (2-nd edition), Editore: Cambridge University Press, Anno edizione: 1995
E. Kaxiras, J.D. Joannopoulos, Quantum Theory of Materials, Editore: Cambridge University Press, Anno edizione: 2019
Carlo E. Bottani, Lecture Notes, Anno edizione: 2020
Note: available on the BeeP website of the course

The course provides the foundational concepts, methods, languages, and systems for ingesting, processing, and analyzing data that flows to enable real-time decisions. The course covers the topics of Streaming Data Engineering and Streaming Data Science.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will learn how to:
Identify problems that can be addressed with big data techniques tailored for velocity
apply the stream data analysis technologies for solving real-world problems
Applying knowledge and understanding
Given specific project cases, students will be able to:
Define and implement a streaming data analysis solution for the problem
Apply it on real data streams from smart IoT sensors
Making judgements
Given specific project cases, students will be able to:
Learn how to decide which streaming data analysis solution to apply and how to evaluate this decision
Communication
Students will learn to:
Write a report on a project describing and motivating the decisions taken and the results obtained
Present their work in front of their colleagues and teachers
Lifelong learning skills
Students will learn how to develop a realistic streaming data analysis project in all its phases

Streaming Data Engineering
This Streaming Data Engineering part of the course covers theoretical and practical aspects of Event-Based Systems (EBS), Data Stream Management Systems (DSMS), and Complex Event Processing (CEP). It uses the Event Processing Language (EPL) to illustrate typical DSMS and CEP operations. This segment equips students with essential skills for real-time data processing and analysis.
Event-Based Systems emphasizes efficient data collection and integration from various sources and redistribution of events to sinks, ensuring high throughput and fault tolerance. In particular, it presents Apache Kafka because it enables handling large-scale data streams, making it essential for robust data pipeline architectures.
Stream ingestion and Event-based Architectures
Apache Kafka as an essential building block of streaming data pipelines
hands-on producer-consumer APIs
Case study: ingesting data for a global retail
The Event Processing Language is a rich language that allows users to specify typical operations present in DSMS and CEP easily. It provides the ability to filter, aggregate, and transform data as it arrives. Moreover it allows identifying meaningful patterns and relationships in real-time data streams, enabling the detection of complex events from simple events.
Filtering, aggregating, and windowing data streams
Joining Data streams
Using the "every" clause and pattern guards to tame the torrent effect *
Hands-on with EPL on case studies in Retail, IoT, and Industry 4.0
Scaling stream processing with Apache Spark Structured Streaming focuses on the real-time processing and analysis of large datasets. Apache Spark's structured streaming capabilities provide scalability, fault tolerance, and high performance, enabling advanced analytics on streaming data.
Efficient DSMS operations
Hard to implement CEP operations
Hands-on practice with Spark Structured Streaming
Streaming Data Science
The Streaming Data Science part of the course covers the theoretical and practical aspects of time Series Analytics, Streaming Machine Learning, and Continuous Learning, equipping students with essential skills for real-time data analysis.
Time Series Analysis (TSA) focuses on developing models to analyze and forecast data points in chronological order. TSA approaches capture temporal dependencies and patterns, such as trends and seasonality, enabling accurate predictions and insights. TSA methods are also adept at handling non-stationarity in data.
⁠Stationarity and ⁠Decomposition
Time Series Forecasting baselines
⁠Temporal Dependence and SARIMAX forecasting models
⁠Advanced approaches to Time Series Forecasting
⁠Hands-on with the Statsmodels e Darts Python libraries
Streaming Machine Learning (SML) proposes models able to learn from data streams over-time. These models are incrementally updated as soon new data becomes available, avoiding retraining them from scratch, and adapting to many forms of non-stationarity (a.k.a. Concept Drift).
⁠Learning one sample at a time
⁠⁠Learning in the presence of Concept Drifts
⁠Streaming algorithms for classification and regression
⁠Hands-on with the River Python library
Continual Learning (CL) proposes strategies to address the problem of catastrophic forgetting when learning from a data stream using Deep Learning models. Forgetting happens when the model forgets what it learned before while learning a new concept after a drift. The goal is to achieve a balance between the ability to acquire new knowledge (plasticity) and the ability to remember past knowledge (stability).
CL Scenarios
Stability-plasticity dilemma
Evaluation metrics
⁠CL strategies
Hands-on with the Avalanche Python library

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i condizionato (a scelta del docente), individuale, senza revisioni periodiche, con presentazione/discussione finale
Valutazione continua facoltativa (a scelta dello studente)
The exam consists of a theoretical (written exam, with a possible oral discussion if necessary by the instructor) and an optional practical part (project work with oral presentation). Further contributions to the mark may come from optional continuous evaluations along the course using in-presence quizzes during the lessons and other interactive modalities in class (max two marks).
The written exam comprises a mix of theoretical questions regarding any course subjects and exercises regarding the technical content and how to apply it in practice. Students can get up to 30 in the written test. The whole exam is a closed-book evaluation. A minimum score on each part is requested.
The optional practical project requires using one or more of the technologies presented in the lectures. It solves a realistic streaming data analysis problem based on real or realistic datasets publicly available or provided by the teachers. Only students who get at least 27/30 in the written exam can opt for it. The maximum increment for the optional project is three marks.
The final grade is computed as follows: written text result + optional continuous evaluation result + optional practical project result. E.g., written text 27 + optional continuous evaluation 1 + optional practical project 3 = 30L
Type of assessment
Description
Dublin descriptor
Written test / continuous evaluation
Theoretical questions
Exercises focusing on streaming data analysis aspects
1,4
1, 2, 3
Assessment of project artefacts
Assessment of the design and implmenetation of the practical peoject work developed by the student
2, 3, 5
Oral presentation
Assessment of the presentation of the practical peoject developed by the student
2, 3, 4, 5

the event processing language (EPL) https://esper.espertech.com/release-9.0.0/reference-esper/html_single/
Kreps, Jay, I Love logs: Event data, stream processing, and data integration., Editore: O'Reilly Media, Inc., Anno edizione: 2014 https://books.google.it/books?id=gdiYBAAAQBAJ&lpg=PR3&ots=3yOdY3Vi7S&dq=I%20love%20logs&lr&pg=PR3#v=onepage&q=I%20love%20logs&f=false
Spark Structured Streaming https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html
George E. P. Box, Gregory C. Reinsel, Gwilym M. Jenkins, Greta M. Ljung, Time Series Analysis: Forecasting and Control, 5th Edition, ISBN: 978-1-118-67502-1 https://www.wiley.com/en-us/Time+Series+Analysis%3A+Forecasting+and+Control%2C+5th+Edition-p-9781118675021
Geoff Holmes, Ricard Gavaldà, Albert Bifet, Bernhard Pfahringer, Machine Learning for Data Streams: With Practical Examples in MOA, Editore: MIT Press, Anno edizione: 2018 https://www.google.it/books/edition/Machine_Learning_for_Data_Streams/0C9ZDwAAQBAJ?hl=en&gbpv=1
Lesort, T., Lomonaco, V., Stoian, A., Maltoni, D., Filliat, D., Rodríguez, N.D., Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges https://www.sciencedirect.com/science/article/am/pii/S1566253519307377

The objective of the course is to address the problems, solutions, methods, and technologies for big data storage and management, with special emphasis on scalability and persistency. The course is structured in three main parts. The first part covers the main principles and approaches of big data management, spanning issues like scalability, transactionality, and distribution of data. The second part covers the different approaches to unstructured data management, describing data models, query languages, and architectural solutions for non-relational data storage, also known as NoSQL solutions, spanning graph, columnar, documental, key-value, and IR-based databases. The third part discusses the design methodologies for the specification of NoSQL data models.

Students will learn the basic concepts of modern database approaches. They will become able to design, program, and use the different database methods, and they will learn how to select and adopt the best option depending on the business and technical requirements.
Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will learn how to:
Identify problems that can be addressed with big data storage and processing techniques
apply the basic nosql technologies for real-world problems
Applying knowledge and understanding
Given specific project cases, students will be able to:
Define and implement a big data solution for the problem
Apply it on real datasets
Making judgements
Given specific project cases, students will be able to:
Learn how to decide which data storage and processing solution to apply and how to evaluate this decision
Communication
Students will learn to:
Write a report on a project describing and motivating the decisions taken and the results obtained
Present their work in front of their colleagues and teachers
Lifelong learning skills
Students will learn how to learn and become proficient in new data management solutions

The course content is organized in three main chapters:
1. Approaches to Big Data management
Big Data problems and dimensions
Data engineering and data science pipeline
Enterprise-scale data management
Scalability and persistency vs. volatility issues
Cross-source data integration problems and architectures
CAP theorem and implications. Non-relational distribution architectures
Evolution of transactional properties: from ACID to BASE. Modern transactional architectures
Data sharding and replication
Cloud-based, scalable data processing and computing
2. Systems and Models for Big and Unstructured Data
Graph databases
Semantic databases
Columnar databases
Document-oriented databases
Key-value databases
IR-based databases
Each category of systems is covered along 5 dimensions: (1) data model (2) (declarative vs. imperative) query languages; (3) data distribution (4) non-functional aspects (5) architectural solutions.
3. Methods for the Design of Applications
Modeling languages and methods for building unstructured data applications
Design methodology within the data engineering pipeline
Schema-less, implicit-schema, and schema-on-read approaches

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
Valutazione continua facoltativa (a scelta dello studente)
The exam consists of a practical project part (project work) and a written part (written exam, with possible oral discussion if deemed necessary by the instructor). Further contributions to the mark may come from continuous evaluations throughout the course, including in-presence quizzes during lessons and other interactive modalities in class.
The project work consists of solving a realistic big data engineering problem, based on real or realistic dataset publicly accessible via Web API, datasets available online, or provided by the instructors. The project work will be developed in groups throughout the semester.
The written exam is composed of a mix of theoretical questions regarding any of the course subjects, and exercises regarding the technical content and how to apply it in practice (including programming aspects). The theoretical part consists of a mix of questions with open and closed answers. The whole exam is a closed-book evaluation. A minimum score on each part is required. Oral exam may be requested by the instructor in specific cases.
Type of assessment
Description
Dublin descriptor
Written test
Theoretical questions
Exercises focusing on big data storage and processing aspects
1,4
1, 2, 3
Assessment of project artefacts
Assessment of the design and experimental work developed by students in groups
2, 3, 5
Oral presentation
Assessment of the presentation of the work developed by students in groups
Possible oral exam on the topics of the courses, upon request of the teachers
2, 3, 4, 5

Sadalage, Fowler, NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence, Editore: Addison-Wesley, Anno edizione: 2012
Marting Kleppmann, Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Editore: O'Reilly, Anno edizione: 2017, ISBN: 978-1449373320
Marco Brambilla, Emanuele Della Valle, Andrea Tocchetti, et al., Course Notes, Anno edizione: 2021

Goals of the course
Computer science has undergone a very rapid evolution from its start until today. This has brought significant benefits to the quality of life but has also created problems with the reliability of computer based systems. In many cases, in fact, the adopted design techniques proved to be inadequate to the complexity of the addressed problems.
Among the causes of the unsatisfactory reliability of computer systems one of the most frequently mentioned is the lack of a solid theoretical background on which effective design techniques must be founded.
The goal of the course of Theoretical Computer Science is to fill this gap by addressing systematically the fundamental problems of computer science and showing that a rigorous approach based on the theoretical foundations of the discipline has a great relevance on practical applications.

Expected learning outcomes
Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will learn:
The principles and the fundamental models of automatic computation.
The basic principles of the design and analysis of algorithms and data structures.
Applying knowledge and understanding
By means of exercise lessons on specific problems, the student will acquire:
A critical attitude towards the use, the creation and the modification of models of computation.
A capacity to apply, adapt, and evaluate algorithms and data structures in practical applications.
A capacity to evaluate the feasibility and convenience of developing and verifying computer-based systems and software tools.
Lifelong learning skills
After successful completion of the course, the students will be able to evaluate autonomously and critically models of computation, algorithms and data structures.

The course covers the following topics:
1. The models of Computer Science
Automata (finite state and stack automata, Turing Machines)
Grammars
Use of mathematical logic for modeling systems and describing their properties: relations between logic, languages and automata.
2. Computation theory
Power of the models of computation
Church Thesis
Undecidable problems
Techniques for proving undecidability
3. The complexity of computing
Fundamental notions of complexity analysis
Models of computation and relations among their computational complexity
Linear speed-up. Complexity hierarchies.
Hints towards intractable problems and NP-completeness.

Prova scritta obbligatoria, senza prove in itinere
Assessment
The exam is based on written questions, possibly integrated, upon request by the teacher, by further oral questions. The written part of the exam is open book (textbook and personal notes allowed).
Type of assessment
Description
Dublin descriptor
Written test
Solution of algebraic and numerical problems for the computational complexity figures
Solution of design exercises concerning algorithms
Open questions of theoretical nature on the topics covered by the course
1, 2
1, 2, 5
1, 2, 5

Dino Mandrioli, Carlo Ghezzi, Theoretical foundations of computer science, Editore: John Wiley & sons, Anno edizione: 1987, ISBN: 0-471-85918-4
Dino Mandrioli, Paola Spoletini, Mathematical logic for computer science: an introduction, Editore: Esculapio, Anno edizione: 2010, ISBN: 978-88-7488-373-8
Dino Mandrioli, Davide Martinenghi, Angelo Morzenti, Matteo Pradella, and Matteo Rossi, Lecture Notes on Monadic First- and Second-Order Logic on Strings, Anno edizione: 2019
Note: Unedited lecture notes; available for download in PDF format from the course WeBeep platform.

The goal of the course is to provide students with the methodologies needed to model, design, and analyze the performance of telecommunication networks and distributed IT systems, which are the main building blocks of the Internet. The course will provide a set of tools to manage stochastic processes involved in data traffic systems (traffic sources, scheduling policies, servers, and resource allocation, etc.), which can be applied to any technology: from traditional to the most advanced ones.
Students will learn and practice how to 1) describe real systems with theoretical tools and estimate/predict main performance figures, 2) design telecommunication systems and networks according to specific performance indicators. The potential and limits of the modeling tools will be analyzed, eventually resorting to system-level simulations. The principles of discrete-event simulation will be studied and applied to a full-fledged network simulator (f.i., OMNet++, ns3,...) used both in academic and industrial research laboratories.

Dublin Descriptors of this course
The student will learn to...
Knowledge and understanding
Identify and describe stochastic processes
Describe Arrival Processes
Describe Markov Chains and Regenerative Processes
Describe traffic in ITC systems
Classify blocking, queueing and sharing serving systems
Describe the main performance indicators of serving systems
Describe networks of queues and distinguish their types
Indicate the main assumptions of Discrete-Event Simulation
Identify the main building blocks of a network simulator
Applying knowledge and understanding
Derive the main properties of Arrival Processes
Derive the main properties of Markov Chains and Regenerative Processes
Compute the main performance indicators of serving systems
Compute the main performance indicators of networks of queues
Run a complex network simulator (i.e., OMNet ++, ns3)
Develop a stochastic model of a real non-deterministic system (device, network, server, etc.)
Making judgements
Distinguish the main reasons behind the performance of a serving system or a network
Break down telecommunication networks and IT systems into blocks of serving systems
Predict the performance of telecommunication networks and IT systems
Recommend the best actions to improve the performance of telecommunication networks and IT systems
Evaluate the suitability of a serving system for the assigned task and expected performance
Judge whether to resort to analytical models or simulation to analyze the performance of a serving system