Communication Engineering

Educational objectives

GENERAL
Knowledge of i) Probability aspects used in analysis and synthesis of communication systems ii) energetic description of stochastic processes and analysis of power transfer in filtering; iii) fundamentals of mathematical statistics, estimation and detection; iv) FIR filter design techniques, linear prediction and spectral estimation.

SPECIFIC
• Knowledge and understanding: to know the fundamentals of statistical signal processing, specifically concerning with the description, transformation and analysis of stochastic processes, estimation and detection applied to FIR filter design and spectral estimation.
• Applying knowledge and understanding: to know how to apply statistical signal processing techniques in a competent and critical fashion.
• Making judgements: to know how to evaluate the performance of statistical signal processing techniques.
• Communication skills: to know how to describe the solutions adopted to solve statistical signal processing problems.
• Learning skills: ability to continue successive studies, e.g. toward PhD, concerning with advanced statistical signal processing techniques.

Educational objectives

GENERAL
Knowledge of techniques and protocols for medium access in wireless communication systems including both classical channeling techniques (TDMA, FDMA and CDMA) and innovative solutions such as those adopted in Ultra Wide Band (UWB) and cognitive radio systems.
Design of algorithms for resource control and management (Medium Access Control, MAC) in both centralized and distributed systems. Knowledge of solutions for medium access and resource management in 4G and 5G wireless networks in both licensed bands (UMTS, LTE) and unlicensed, shared bands (Wi-Fi, Bluetooth, LTE-U, UWB).
SPECIFIC
• Knowledge and understanding: multiple access techniques, algorithms and protocols for wireless access and resource management in wireless networks. 

• Applying knowledge and understanding: analysis and design of wireless networks as a function of incoming traffic and of the wireless access protocol, combining the analytical approach with the use of software tools for link and network simulation.
• Making judgements: ability to design and dimension a wireless network, correctly identifying constraints and objectives to be met for performance indicators, selecting the best combination of tools to complete the task successfully and efficiently.
• Communication skills: learn to present clearly and coherently topics related to wireless access, combining an accurate analytical description with the ability of providing a comprehensive view of such topics.
• Learning skills: not applicable

Educational objectives

Knowledge of fundamentals of information theory, source and channel coding, crypto and main algorithms employed in practical applications. Basics of biometry. In-deep study of some relevant digital communication techniques.

Specific

· Knowledge and understanding: methods for source and channel co-decoding and crypto, methods of biometry and of digital communications.

· Applying knowledge and understanding: to know how to apply co-decoding techniques, and advanced digital communication techniques, in a competent and critical fashion.

· Making judgements: (none)

· Communication skills: to know how to describe the solutions adopted to solve co-decoding and information communication problems.

· Learning skills: ability to continue successive studies concerning digital communication systems.

Educational objectives

The basic principles of operation of a radar system are introduced. The characteristics of the radar signals processing chain and their radar waveforms are deeply investigated. We evaluate the relationship between radar systems, waveforms, processing techniques, operating environment and achievable performance, identifying the approaches to the specific system design. The search, tracking, navigation and image radar systems are introduced and an introduction is provided to their preliminary design.

SPECIFIC

• Knowledge and understanding: The student must demonstrate to have knowledge and understanding capability about the main radar systems and their signal processing techniques. In addition, understanding is required of the applicability of the basic principles of both systems and techniques to new generation systems.

• Applying knowledge and understanding: The student must be able to apply the principles of operation and the radar signal processing techniques in a competent and critical way. The student must have adequate competences to both devise and support arguments, and to solve new detection and estimation problems. The student must set the radar systems in the appropriate position inside the wider systems for surveillance, navigation, monitoring, or Earth observation.

• Making judgements: The student must be able to integrate knowledge and handle the complexity of the systems for surveillance, navigation, monitoring, or Earth observation. The student must be able to tackle a preliminary system design also in the presence of limited or incomplete information; reflect on the social and ethical responsibilities connected to the application of the technologies for surveillance, navigation, monitoring, or Earth observation.

• Communication skills: The student must be able to describe the solutions selected while addressing the preliminary design of a radar system that fulfils assigned design specifications.

• Learning skills: The student must be able to address the preliminary design of the systems in autonomous manner.

Educational objectives

GENERAL
The course of Techniques and Network Models aims to illustrate both the evolution of network techniques and protocols and to propose the analytical models useful for the sizing of telecommunication networks. In particular, the course is focused on the networking functions, that is the functions that regulate the sharing of network resources (resource sharing) among the information flows in order to obtain fixed QoS requirements and an efficient use of the network resources.

SPECIFIC
• Knowledge and understanding:
At the end of the course, the student will have acquired the knowledge of the networking theory that will allow him to make the most appropriate choices for an efficient project of a network reaching the full satisfaction of the cost and performance requirements. The use of analytical models will also make it possible to quantitatively verify the quality of the choices made.
• Applying knowledge and understanding:
The aim of the course is to provide students with the ability to understand the problems related to the evolution of networking techniques and at the same time to face the basic problems of analysis and sizing of a network. To this end, during the course specific group activities are foreseen for the analysis of problems taken from the recent technical literature.
• Making judgements:
The course aims to enable the student to independently deal with specific problems concerning the analysis and sizing of networks and to evaluate the degree of satisfaction of the project requirements. In particular, the objective is to stimulate a critical approach to the results achieved, which will allow, eventually, to propose changes in the techniques used during the project.
• Communication skills:
In order to increase the presentation skills and to support a discussion, the student is asked to present the results of his / her personal activities both during the course and during the exam and to sustain a critical debate on the results achieved.
• Learning skills:
The course, through the frequency of the lessons and the execution of the proposed in-depth activities, aims to stimulate the student in the learning process, enabling him to acquire the individual ability to critically analyze architectural solutions and to contribute to their evolution.

Educational objectives

KNOWLEDGE AND UNDERSTANDING. At the end of the course, the students learns how to use signals to transmit information or to acquire information from unkown environments.
CAPABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING. The students learns the fundamental applications of signal theory to telecommunications and remote sensing
MAKING AUTONOMOUS JUDGEMENTS. During the course, the students are constantly encouraged to develop their own autonomous judgment, by asking questions in each lesson and suggesting alternative textbooks.
COMMUNICATE SKILLS. The communication skills are taught through the examples given during the lessons and they are tested through the written exams.
LEARNING SKILLS. The students are taught how the information and communication technology (ICT) world is evolving to give them the opportunity to form their specific interests. They are encouraged to form their own autonomous judgment e to better understand what is the kind of work they may prefer to do once graduated.

Educational objectives

1.GOAL OF THE COURSE
Goal of this course is to describe the behaviour and evaluate the performance of the main components (e.g., functional blocks) constituting the analogue and digital communication systems and the packet-switched data networks.
2. Expected results
It is expected that the attending students will acquire the basic notions about architectures and related performance of both analogue and digital the TLC systems. A good underground of analogue and digital signal processing is demanded.
3. Required background
A good background on Signal Processing in the continuous-time and discrete-time domains is required.

Educational objectives

Knowledge of basic topics in the area of applied electromagnetics, including fundamentals of electromagnetics, electromagnetic waves and their propagation in free space and in waveguides, transmission-line models and radiation.
SPECIFIC
• Knowledge and understanding: Knowing and understanding fundamental equations and theorems of electromagnetics, plane waves and their reflection and refraction on a planar interface, transmission-line formalism, basics of guided waves and radiation in free space.
• Applying knowledge and understanding: capability of applying the theory to solve simple numerical problems the topics of the course.
• Making judgements: (none)
• Communication skills: being able to illustrate the topics of the course deriving the results from the fundamental equations, describing as well their physical significance and their importance in applications.
• Learning skills: capability of undertaking further studies in the area of applied electromagnetics, in particular on antennas, propagation and design of high-frequency components.

Educational objectives

GENERAL
The course is aimed to give the theoretical methodologies and the practical knowledge related to the components and circuits used for the electromagnetic signal processing in telecommunication and remote sensing systems. The acquired capabilities will be focused on the features of high-frequency devices with attention to the guided-wave propagation and to the generation, processing, and detection of the signals in microwave and optical systems. The course will be completed with the study of computer-aided design procedures, of the instruments and of measurement techniques of high-frequency devices and circuits.

SPECIFIC
• Knowledge and understanding: to know and understand the methodological aspects of the analysis and characterization of the circuits, components, and devices used at high frequencies; to know the instruments for the measurement and the software for numerical simulation of the devices used at high frequencies.
• Applying knowledge and understanding: to apply the techniques for analysis and design of microwave and optical circuits; to apply the procedures to experimentally measure the characteristics of microwave and optical devices.
• Making judgements: to be able to gather additional information to pursue a higher awareness on the circuits and devices used at high frequencies in the context of ICT.
• Communication skills: to be able to deal with the characteristics of high frequency circuits.
• Learning skills: to be able to continue the learning path for a continuous update of the knowledge on high-frequency devices and circuits; to be able to study in depth the properties related to the various applications of the electromagnetic fields.

Educational objectives

GENERAL
The course is aimed to give the fundamental knowledge concerning antennas and propagation of electromagnetic signals, together with the methodologies for their application in information and communication technologies. The acquired capabilities will be focused on the features of electromagnetic radiation, the antenna types and design techniques, with the relevant applications to the various telecommunication and control systems. The study of the electromagnetic propagation in complex environment will be deepened with reference to the wireless, satellite, and radar systems. The course will be completed with the study of the computer-aided design procedures, measurement techniques, and the evaluation of the environmental impact of the electromagnetic fields.
SPECIFIC
• Knowledge and understanding: to know and understand the methodological aspects of the analysis and characterization of the antennas; to know and understand the methodological aspects of the propagation of the electromagnetic fields into the environment; to know the instruments for the measurement of the electromagnetic fields and the software for numerical simulation.
• Applying knowledge and understanding: to apply the techniques for analysis and design of antennas; to apply the procedures to measure the electromagnetic fields.
• Making judgements: to be able to evaluate autonomously the characteristics of antennas and of the electromagnetic field into complex environment; to be able to gather additional information to pursue a higher awareness on the electromagnetic fields into the environment.
• Communication skills: to be able to depict the radiation properties of the antennas; to be able to communicate the electromagnetic field levels
• Learning skills: to be able to continue the learning path for a continuous update of the antennas systems and on the characteristics of electromagnetic field propagation; to be able to study in depth the properties of radiated electromagnetic fields

Educational objectives

GENERAL
Knowledge of digital signal representation and digital signal processing fundamentals. Development of a more complete vision on certain application aspects such as signal sampling and reconstruction and digital filtering.

SPECIFIC
• Knowledge and understanding: to know digital signal representation methods and processing.
• Applying knowledge and understanding: to know how to apply digital signal processing techniques in a competent and critical fashion.
• Making judgements: (none)
• Communication skills: to know how to describe the solutions adopted to solve digital signal processing problems.
• Learning skills: ability to continue successive studies concerning with advanced digital signal processing techniques such as statistical processing.

Educational objectives

The module aims to provide a general background on the remote sensing systems for Earth Observation from airborne, and especially space-borne platforms. It describes, using a system approach, the characteristics of the system to be specified to fulfil the final user requirements in different application domains. It reviews the physical bases of remote sensing and simple wave interaction models useful for data interpretation. It describes or simply recalls the technical principles of the main sensors operating in different ranges of the electromagnetic spectrum. It provides an overview of the most important applications and bio-geophysical parameters (of the atmosphere, the ocean and the land) which can be retrieved in different regions of the electromagnetic spectrum. It reviews the most important techniques for data processing and product generation, also by proposing practical exercises using the computer. Finally, it provides an overview of the main Earth Observation satellite missions and the products they provide to the final user.

Educational objectives

The purpose of the course is twofold:

(i) To provide the conceptual and analytical tools necessary to understand principles and structure of Radiolocation systems, with specific reference to Satellite Navigation System (GPS, Galileo, etc ...) and to Surveillance Radar Systems (air and maritime traffic control) and Imaging Radar Systems for Earth Observation.

(ii) To illustrate the general outline and the individual components of a radio transceiver, with reference to Satellite Navigation, Radar, and Telecommunications systems. This also includes providing basic elements for its preliminary design.

SPECIFIC

• Knowledge and understanding: demonstrate knowledge and understanding about radiolocation systems and radio receiver structure.

• Applying knowledge and understanding: know how to use the positioning principles through radio sensors and reception schemes in a competent and critical way.

• Making judgements: reflect on social and ethical responsibilities related to the privacy of position information.

• Communication skills: knowing how to communicate information, problems and solutions related to the positioning and structure of radio receivers to specialists and non-specialists.

• Learning skills: develop the skills necessary to undertake subsequent studies, which refer to radio receivers for telecommunications, positioning or surveillance with a high degree of autonomy.

Educational objectives

The main objectives of the course are the following: knowledge about the classification of the telecommunication networks and services; skills in dimensioning of the physical resources in a TLC network; skills in identifying a communication architecture and a network service suitable to satisfy the Quality of Service requirements; knowledge about local area network; knowledge about the Internet network.
The exam consists in written, oral and laboratory tests allowing the evaluation of critical, judgment communication, study skills acquired by the student.

Educational objectives

GENERAL
Knowledge of the fundamental theoretical elements of Machine Learning (ML): i) metric vector spaces and measurement theory; ii) methods and algorithms for ML-oriented, unconstrained and constrained optimization; iii) biologically and non-biologically inspired artificial intelligence; iv) methods for dimensionality reduction and parsimonious representation of data and information in general; v) design of robust algorithms for ML; (vi) methods and protocols for performance analysis of ML algorithms.

SPECIFICS
• Knowledge and understanding: to know the ML’s fundamentals, with particular regard to the definition of learning algorithms discriminative and generative; linear, non-linear; with and without supervision; for static and dynamic applications; on-line, batch and mini-batch.
• Applying knowledge and understanding: knowing how to apply ML techniques and procedures in the most common problems described in the course such as: classification, regression, prediction and clustering; in heterogeneous, noisy and complex data environments.
• Autonomy of judgement: regarding the possible optimal solution of the problem with ML methods,
• Communication skills: Can describe the solutions adopted to solve ML problems.
• Learning skills: autonomous learning on specialist texts; ability to continue any subsequent studies, e.g. PhD, on advanced ML issues and/or specialization on specific application domains (e.g. finance, biomedical, industry, advanced tertiary services, etc.)

Educational objectives

Knowledge of basic topics in the area of applied electromagnetics, including fundamentals of electromagnetics, electromagnetic waves and their propagation in free space and in waveguides, transmission-line models and radiation.
SPECIFIC
• Knowledge and understanding: Knowing and understanding fundamental equations and theorems of electromagnetics, plane waves and their reflection and refraction on a planar interface, transmission-line formalism, basics of guided waves and radiation in free space.
• Applying knowledge and understanding: capability of applying the theory to solve simple numerical problems the topics of the course.
• Making judgements: (none)
• Communication skills: being able to illustrate the topics of the course deriving the results from the fundamental equations, describing as well their physical significance and their importance in applications.
• Learning skills: capability of undertaking further studies in the area of applied electromagnetics, in particular on antennas, propagation and design of high-frequency components.

Educational objectives

1.GOAL OF THE COURSE
Goal of this course is to describe the behaviour and evaluate the performance of the main components (e.g., functional blocks) constituting the analogue and digital communication systems and the packet-switched data networks.
2. Expected results
It is expected that the attending students will acquire the basic notions about architectures and related performance of both analogue and digital the TLC systems. A good underground of analogue and digital signal processing is demanded.
3. Required background
A good background on Signal Processing in the continuous-time and discrete-time domains is required.

Educational objectives

GENERAL
The course aims at providing the student with an overall vision of the image processing issues, such
as the use of transformed domain, filtering, encoding, and of its main applications tc.) (such as
restoration, denoising, enhancement, tomography, etc. At the end of the Course the student is
aware of the main representation domains of signals and images both in continuous and discrete
domain and can manage software for image processing purposes. Through developing in depth
theoretical and practical projects the students gains ability of i) autonomously comprehending

cutting edge image processing papers, ii) presenting their contents, iii) realizing and critically
analysing image processing experiments. The above goals are detailed in the followig
SPECIFIC
• Knowledge and understanding of the discrete and continuous, spatial and frequency image
representation domains. Achieve a big picture of image processing theoretical background. Gain
knowledge and understanding of the main image processing tasks (Recovery, Denoising,
Enhancement, Morphological filtering, Segmentation, etc).
• Applying knowledge and understanding: be able to design novel algorithms for advanced
image processing tasks,
• Making judgements: be able to compute performances and develop a critical evaluation of
the collected results, as well as of the algorithm parameters and their impact on the processing
output.
• Communication skills: present and describe innovative solutions
• Learning skills: Be able to read scientific papers and technical standard on the most
advanced solutions for image processing

Educational objectives

GENERAL OBJECTIVES OF THE COURSE
INTRODUCING THE BASIC ELEMENTS OF THE THEORY OF THE FIRM AND THE DEMAND ACCORDING TO THE NEOCLASSICAL APPROACH BASED ON THE MAXIMIZING BEHAVIOR OF THE AGENTS.
• SHOW HOW USING ECONOMETRIC TECHNIQUES IS POSSIBLE TO TEST EMPIRICALLY THE MAXIMIZING BEHAVIOR HYPOTHESIS.
• INTRODUCE ECONOMIC ANALYSIS FOR DECISIONS AND COMMUNICATION OF PERFORMANCE THROUGH THE BUDGET, THE ANALYSIS OF COSTS AND INVESTMENTS.
• OFFER A GLANCE OVERVIEW ON EFFICIENCY AND PRODUCTIVITY ANALYSIS, USEFUL TO ESTIMATE AND COMPARE THE INEFFICIENCY OF OPERATIONAL UNITS (BUSINESS UNITS, ENTERPRISES, SECTORS, COUNTRIES)

SPECIFIC
• KNOWLEDGE AND UNDERSTANDING: DEMONSTRATE KNOWLEDGE OF THE BASIC ELEMENTS OF THE ECONOMICS AND BUSINESS ORGANIZATION;
• ABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING: TO BE ABLE TO APPLY THE ECONOMIC REASONING LEARNED DURING THE COURSE, IN THEIR OWN ENGINEERING ENVIRONMENT;
• AUTONOMY OF JUDGMENT: KNOWING HOW TO ANALYSE THE ECONOMIC ASPECTS WITH A CRITICAL SPIRIT AND BEING ABLE TO APPLY THE ECONOMIC METHODS IN ONE'S OWN EDUCATIONAL CURRICULUM:
• COMMUNICATION SKILLS: KNOWING HOW TO COMMUNICATE THE CONTENTS LEARNED AND RELATED INFORMATION TO DIFFERENT TYPES OF AUDIENCE;
• LEARNING SKILLS: DEVELOP THE NECESSARY SKILLS TO BE ABLE TO DEEPEN THE CONCEPTS AND METHODS ANALYSED DURING THE COURSE INDEPENDENTLY AND IN THEIR OWN ENGINEERING ENVIRONMENT.

Educational objectives

KNOWLEDGE AND UNDERSTANDING. Fundamentals of CMOS digital circuits, combinational and sequential logic synthesis, elementary microprocessor systems
CAPABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING. design of combinational and sequential logic, design of elementary microprocessor systems
MAKING AUTONOMOUS JUDGEMENTS. Evaluation of design alternatives to be used.
COMMUNICATE SKILLS. Understanding of technical specification of digital components and systems.
LEARNING SKILLS. Any subsequent advancement on digital circuits, architectures and programming.

Educational objectives

GENERAL
The course presents the basic concepts, protocols and architectures of the current network infrastructures. Specific attention is given to the broadband access, the optical backbone and the wireless networking in the future generation.
SPECIFIC
• Knowledge and understanding: To know the protocols and the architectures of the network infrastructures, both wired and wireless, both for access and transport. At the end of the course students will have knowledge on the main technologies and infrastructures of communication networks including, PON, LTE, 5G, SDH, OTN, SDN.
• Applying knowledge and understanding: to know how to apply criteria and techniques for designing a network infrastructure. Knowing how to configure and analyze IP networks and related protocols (both basic and advanced) thanks to the knowledge acquired using the Netkit tool.
• Making judgements: to know how to analyze benefits and limitations of network projects.
• Communication skills: to know how to present a networking project, relevant requirements and proposed solutions.
• Learning skills: ability to develop more advanced studies in the field of future generations of network solutions.

Educational objectives

GENERAL
Module aims to introduce student to the knowledge of design techniques and technologies, regarding long-distance radio-link, in particular satellite communications. It examines the specific segments: Space, Control and User. Moreover, the consequences on the design of solid-state electronic devices operating in the space are analysed, in particular the effects of ionizing radiation. Furthermore, the module aims to know high efficiency power amplifiers (HPA).

SPECIFIC
• Knowledge and understanding: to know analytical methods for evaluating electronic components, and for selecting different and specific design methods in order to build equipment for the Space. Furthermore, to know analytical methods for final stages design of high efficiency.
• Applying knowledge and understanding: to know how to apply methods of design different for environment where they operate, and for reducing energy consumption.
• Critical and judgmental skills: critical capabilities of electronic design and targeted selection of electronic devices. Capabilities acquired with laboratory tests involving the use of development tools (MathWorks, ...), software for simulation CAE (Genesys, ...) of HPA RF circuits, and measuring instruments (oscilloscopes, analyzers, ...).
• Communication skills: be able to describe the electronic circuit solutions adopted to solve problems of adverse operating conditions and of containing energy consumption.
• Learning skills: valid learning for insert in working contexts specialized in designing electronic systems operating in the Space, and for designing HPA final stages.

Educational objectives

This course introduces the neural networks (NN) paradigms, in its various aspects and exceptions, and some others soft computing (SC) methods which, unlike hard computing, are tolerant of imprecision, uncertainty and partial truth.
The educational objectives include the acquisition of the following skills: 1) knowledge and understanding of the problems related to the use of NNs; 2) the ability to apply knowledge on NNs in the most common problems described in the course (knowledge and know-how), 3) development of independent judgment regarding the possible optimal solution with NNs of a given problem, 4) the development of communication skills on the topics covered in the course, 5) the ability to autonomous learning on specialized texts.
In particular, the training objectives are the acquisition of the following knowledge and skills relating to: 1) NNs and (also) non bio-inspired learning models: architectures, mathematical and statistical property, learning algorithms; 2) adaptive filtering and modelling of dynamic and memoryless phenomena; 3) parsimonious data representation and non-redundant information extraction; 4) architecture and learning of deep NNs with strong regularization methods; 5) algorithms for SC methods. Application on analysis of non-structured data: information retrieval; smoothing, modelling and prediction; patterns recognition; clustering; multi-sensors data fusion, blind source separation.

Educational objectives

ENG
THE AIM OF THE COURSE IS TO TEACH STUDENTS TO A WIDE RANGE
NUMBER OF METHODS BY WHICH CAN SOLVE MOST PROBLEMS MATHEMATICAL
-ENGINEERING IN THE FIELD OF COMMUNICATIONS AND THE ELECTRONIC.
WILL BE PROVIDED IN ADDITION, THE TOOLS SUITABLE TO BE ABLE TO EVALUATE THE DISCRETIZATION AND SPREAD ERRORS AND TO BE ABLE TO IMPLEMENT
ITS COMPUTER'S ALGORITHMS.

Educational objectives

The fundamentals of airborne and spaceborne Synthetic Aperture Radar (SAR) systems are introduced, together with the principles concerning SAR system design and operating modes. Both focusing and autofocusing techniques are described. The image processing techniques to extract the information out of the SAR images are considered in details.

SPECIFIC
Knowledge and understanding: to know the fundamentals of SAR systems, SAR system design and main operating modes as well as main techniques for the focusing and autofocusing of SAR images and for the extraction of information from focused images.
Applying knowledge and understanding: to know how to competently do proper choices for SAR systems design and to develop and apply techniques for the focusing/autofocusing and for the information extraction.
Making judgements: to know how to integrate and use the acquired knowledge in order to choose the main system parameters and implement SAR signal processing chains comprising the cascade of many stages and to know how to critically analyze the corresponding results. The acquisition of this skill is strengthened by the activity required by the homework.
Communication skills: to know how to illustrate with proper technical language the solutions chosen to solve SAR system design or SAR signal processing issues and to know how to describe and discuss results coming from specific processing techniques. The acquisition of this skill is strengthened by the final exam consisting in a talk during which the student describes the activity carried out for the homework using a PowerPoint presentation.

Learning skills: to acquire the ability to complement the theoretical studies with practical applications of the studied concepts working to this aim autonomously.

Educational objectives

Modern adaptive and non-adaptive processing techniques are introduced for the control of multiple antenna beams for target direction of arrival estimation, target tracking, cancellation of EM interference and 3D processing. At the end of the class, the student has matured the capability to design a radar system with multiple beams, by setting its main parameters. Moreover he/she knows the main techniques used for multi-channel radar adaptive signal processing and is able to evaluate their performance by means of theoretical and simulated analysis.
SPECIFIC
• Knowledge and understanding: to know and understand advanced radar systems that exploit multiple antenna beams based on methods and technological solutions at the state of the art and beyond.
• Applying knowledge and understanding: to be able to apply methodologies and techniques typical of multi-beam radar in order to solve system design problems and/or to effectively process the received signals.
• Making judgements: to be able to make judgements on alternative technological and design solutions and, consequently, to get the capability to formulate proper choices.
• Communication skills: to know how to critically illustrate the adopted solutions and the obtained results by describing the employed methodologies to specialists of the field, based on appropriate technical language and style.
• Learning skills: to be able to study in an autonomous way and to detect errors and, consequently, to identify proper corrections to be applied based on an autonomous iterative procedure.

Educational objectives

GENERAL
The objective of the course is to present the most recent techniques available to ensure the transfer of multimedia information between mobile users. The course will investigate different network architectures, from wireless networks to cellular ones (UMTS, LTE and 5G).
SPECIFIC
• Knowledge and understanding: to have a global knowledge of a mobile network architecture, from transmission issues to control solutions.
• Applying knowledge and understanding: to manage, by engineering methodology, the networking techniques that allow multimedia communication in mobility conditions.
• Making judgements: (none)
• Communication skills: to know how to describe the solutions adopted to solve problems related to interconnection of mobility users.
• Learning skills: ability to continue successive studies concerning with networking issues of a mobile network.

Educational objectives

1. Goal of the course
--The goal of the course is to describe and analyze the basic protocol stacks, functionalities and offered services of the currently emerging wired Cloud-aided broadband communication networks and systems. Packet-switched data networks and Internet are considered as motivating case of study.
2. Expected results
-It is expected that the attending students will acquire the basic notions requested to understand the performance behavior of the emerging Internet-driven networks.
3. Required background
A good background in Communication systems and Networking is required.

Educational objectives

GENERAL: the objective of the module is to provide the student with the knowledge sufficient to understand the applications and scientific objectives of remote sensing radars conceived either for Earth observation and Planetary missions

SPECIFIC
• Knowledge and understanding: Acquire knowledge of the working principles of radar systems, capability of dimensioning the sistem parameters, capability of determining optimum and suboptimum processing algorithms with the aim of processing the radar products with the guarantee of the best possible performance.
• Applying knowledge and understanding: : at the end of the module the student will have the capability to:
• Analyze remote sensing radar systems to understand their operativity and performance
• Design remote sensing radar systems, even innovative, taking into account the user requirements, evaluating the system parameters and the optimum processing algorithms to meet the objectives of the sensor.

• Making judgements: is developed making exercise in the classroom regarding the design of simple remote sensing systems
• Communication skills: in the classroom the students are stimulated to give answers to simple questions regarding the arguments objects of th lecture creating some links too with previous lectures with similar arguments
• Learning skills: the student after the end of the course can aspire to participate to a PhD competitive exam,to a master, etc.

Educational objectives

KNOWLEDGE AND UNDERSTANDING.
The module provides the basics about the techniques and technologies used in modern multimedia devices for data acquisition and advanced editing of audio and video signals, as well as the use of intelligent sensor networks, Internet of Things, electronic development boards (Arduino Intel Galileo), applications for mobile devices (smartphones, tablets) on Android and iOS platforms for the provision of information and communication services, augmented reality etc. The student will acquire skills to analyze the main characteristics of these devices, evaluating the costs and benefits stemming from the adoption of a particular solution within a multimedia processing system, and to design and implement applications in different real environments.

CAPABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING.
The student will acquire skills that will enable him to design and implement applications for mobile devices on IoS and Android platforms, prototypes on electronic development platforms (Arduino, Raspberry, Intel Galileo), applications of Internet of Things.

MAKING AUTONOMOUS JUDGEMENTS.
Through an intense and systematic practical and laboratory activity, the student will acquire autonomy of judgment regarding the specifics of practical problems and the ability to identify suitable solutions to respond to the required performances.

COMMUNICATE SKILLS.
The topics covered in the course are of general interest in science and industry, in particular in the fields of cultural heritage, e-health, domotics, environment, logistics, transport, safety of people and things. After this course, the student will be able to communicate the knowledge acquired to specialists and non-specialists in the world of work and research, where he will develop his subsequent scientific and professional activities.

LEARNING SKILLS.
The didactic methodology implemented in the course requires an autonomous and self-managed activity of study during the development of homeworks for the didactic and/or experimental investigation of some specific topics.

Educational objectives

Knowledge and understanding. The course aims at providing tools and application examples for
modeling, performance evaluation and dimensioning of networked service systems.
Applying knowledge and understanding. The course aims at making the student able to state and
solve a performance evaluation problem, including the realization of simulation experiments and
the data analysis thereof.
Making judgements. Through mini-projects and class lab, students are encouraged to move from a
system description to a mathematical model defined to answer to quantitative issues on the system
working. Specific attention is paid to a critical review of numerical results and to validity checking of
hypotheses and approximations introduced in the models.
Expected learning achievements. Capability of identifying, solving and using service system
models and traffic models, either via analytical means, or simulations. Exploitation of these tools
for networked service system design. Students are expected to be able to identify a model of a
service system, fit data into model parameters, do analysis or run simulations to assess the system
performance.roximations introduced in the models.

Educational objectives

Introduction to Machine Learning and data driven modelling. Soft Computing, Computational Intelligence. Basic data driven modelling problems: clustering, classification, unsupervised modelling, function approximation, prediction. Generalization capability. Deduction and induction.
Induction inference principle over normed spaces. Models and training algorithms. Distance measures and basic preprocessing procedures.
Optimization problems. Optimality conditions. Linear regression. LSE and RLSE algorithms. Numerical optimization
algorithms: steepest descent and Newton’s method.
Fuzzy logic principles. Fuzzy induction inference principle. Fuzzy Rules.
Classification systems: performance and sensitivity measures. K-NN Classification rule.
The biological neuron and the central nervous system.
Perceptron. Feedforward networks: Multi-layer perceptron. Error Back Propagation algorithm. Support Vector
Machines. Automatic modeling systems. Structural parameter sensitivity. Constructive and pruning algorithms.
Generalization capability optimization: cross-validation and Ockham's razor criterion based techniques.
Min-Max neurofuzzy classifiers; standard and regularized training algorithm. ARC, PARC; Principal Component Analysis; Generalized Min-Max neurofuzzy networks. GPARC.
Swarm Intelligence. Evolutionary Computation. Genetic algorithms. Particle Swarm Optimization, Ant Colony
Optimization. Automatic feature selection.
Fuzzy reasoning. Generalized modus ponens; FIS; fuzzyfication and e defuzzyfication. ANFIS. Basic and advanced
training algorithms: clustering in the joint input-output space, hyperplane clustering.
Outline of prediction and cross-prediction problems: embedding based on genetic algorithms.
Applications and case studies: micro-grids energy flows modelling and control, Smart Grids optimization and control,
classification of TCP/IP traffic flows.
Mining of frequent patterns and rule extraction in large data bases (Big Data Analytics).

Educational objectives

ITALIANO
GENERALI
Scopo del corso è lo studio della tecnica di comunicazione wireless Ultra Wide Band (UWB), e della sua applicazione alla progettazione di reti avanzate quali le reti ad-hoc e le reti di sensori, e in generale di reti wireless distribuite. Il corso analizza le tematiche chiave dei sistemi UWB, allo scopo di evidenziare le potenzialità di una tecnologia che appare come uno dei migliori candidati nella definizione di standard per reti di futura generazione. Il corso affronterà i fondamenti teorici delle comunicazioni UWB, completando la trattazione con esempi pratici e principi di applicazione per ogni argomento trattato.
SPECIFICI
• Conoscenza e capacità di comprensione: tecniche di generazione di segnali UWB, analisi temporale e spettrale dei segnali UWB, progettazione di ricevitori UWB in canali AWGN e multipath, analisi delle prestazioni singolo link e di rete, tecniche di posizionamento e localizzazione basati su tecnologia UWB.
• Capacità di applicare conoscenza e comprensione: analisi e dimensionamento di reti wireless UWB in funzione della tipologia di segnale trasmesso, del canale, e del ricevitore utilizzato, sia attraverso l’approccio analitico che con l’utilizzo di strumenti software per la simulazione di singoli link o di reti.
• Autonomia di giudizio: capacità di affrontare un progetto di dimensionamento di una rete wireless UWB, identificando vincoli e obiettivi imposti sugli indici prestazionali e sulla standardizzazione, selezionando lo strumento o gli strumenti più opportuni per completare in modo corretto ed efficiente il progetto stesso.
• Abilità comunicative: saper esporre coerentemente e chiaramente tematiche relative alle comunicazioni UWB, combinando la padronanza della trattazione analitica, la capacità di sintetizzare le caratteristiche delle tecniche studiate, e la conoscenza e l’utilizzo di strumenti software di simulazione.
• Capacità di apprendimento: (assente)

Educational objectives

GENERAL
Knowledge of some advanced topics in the area of antenna engineering, including both analytical and numerical techniques as well as in-depth analyses of specific classes of radiators.
SPECIFIC
• Knowledge and understanding: knowing electromagnetic principles and techniques for the study of modern antenna systems, advanced array theory, periodic electromagnetic structures, MIMO systems for wireless applications, resonant antennas (patch antennas and dielectric-resonator antennas), leaky-wave antennas (mono- and bi-dimensional), numerical methods (moment method), and selected electromagnetic CAD software.
• Applying knowledge and understanding: being able to apply equivalent-network techniques for the analysis of open radiating structures, both uniform and periodic; being able to design printed patch antennas with canonical shape and mono- and bi-dimensional leaky-wave antennas.
• Making judgements: (none)
• Communication skills: being able to describe the analytical and numerical techniques as well as the design principles of the antennas and antenna arrays described in the course.
• Learning skills: being able to pursue further in-depth studies, both aimed at the Master’s thesis and during post-graduation work (either academic or in a company), on topics relevant to analysis and design of antennas.

Educational objectives

THE COURSE AIMS TO GIVE STUDENTS AN INTRODUCTION TO DISCRETE MATHEMATICS, WHICH IS ONE OF THE MOST INNOVATIVE AREAS OF MATHEMATICS, DEVELOPED SINCE THE SECOND HALF OF THE TWENTIETH CENTURY, FULL OF CHALLENGING PROBLEMS AND EXTREMELY USEFUL FOR APPLICATIONS. DURING THE COURSE, STUDENTS WILL MEET WITH A NUMBER OF ISSUES AND PROBLEMS OF A TYPE COMPLETELY DIFFERENT FROM THOSE ENCOUNTERED IN OTHER TRADITIONAL MATHEMATICS COURSES, AND DEVELOP, THROUGH A SYSTEMATIC EFFORT AIMED AT “PROBLEM SOLVING”, A PRACTICAL APPROACH TO THE STUDY OF PROBLEMS OF GREAT EDUCATIONAL VALUE, ESPECIALLY FOR FUTURE CAREERS.
AT THE END OF THE COURSE , THE SUCCESSFUL STUDENT
• WILL HAVE LEARNED THE METHODS, THE PROBLEMS, AND THE POSSIBLE APPLICATIONS OF DISCRETE MATHEMATICS.
• WILL BE ABLE TO UNDERSTAND, TACKLE AND SOLVE SIMPLE PROBLEMS RELATED TO DISCRETTE MATHEMATICS.
• THROUGH WRITTEN ESSAYS AND POSSIBLE ORAL PRESENTATIONS HE/SHE WILL DEVELOP APPROPRIATE CAPACITY OF JUDGEMENT AND CRITICISM.
• AT THE SAME TIME HE/SHE WILL EXERCISE HIS/HER ABILITY TO PRESENT AND TRANSMIT WHAT HE/SHE HAS LEARNED.
• PERSONAL, INDIVIDUAL STUDY WILL TRAIN HIS/HER CAPACITY OF INDEPENDENT AND AUTONOMOUS LEARNING ACTIVITY.

Educational objectives

Learning of advanced knowledge of Mathematical Analysis
towards applications. Differential Calculus in several variables,
minima and maxima with constraints. Analysis of mathematical models.

A) Knowledge and understanding: to know basic concepts and their use in
exercises of mathematical analysis with the support of
texts and lecture notes in Mathematical Methods for Information Engineering.

B) Applying knowledge and understanding: to be able to use the acquired
knowledge and understanding in solving problems and to communicate the arguments.

C) Making judgements: to be able to collect and understand exercises
results to solve similar problems in in an autonomous context.
To single out common features in different problems

D) Communication skills: to relate about assumptions, problems and
solutions to wide audiences.

E) Learning skills: to acquire the competence that is necessary for advanced study.

Educational objectives

KNOWLEDGE AND UNDERSTANDING.
Through the introduction of the basic concepts concerning the theoretical problems, techniques and practices of design and implementation of circuits and algorithms in machin elearnign and articial intelligence systems based on based on statistical and data-driven learning, in parallel, distributed and quantum computing systems (GPU, TPU, multicore, cloud, etc.), the student will reinforce the knowledge acquired in the first cycle of studies The applications in ICT and Information Engineering will be investigated for the solution of supervised and unsupervised problems on real case studies, in particular regarding optimization, approximation, regression, interpolation, prediction, filtering, pattern recognition and classification, in order to elaborate and apply orginal ideas, will be further investigated also in a research context.

CAPABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING.
Solution of problems related to the design, implementation and testing of computational architectures and computational models, with particular reference to the development in Matlab/Python/Julia/VHDL languages, for the realization of machine learning and artificial intelligence systems in parallel, distributed and quantum environments, in a wider context compared to the field of study of circuits theory and electronic engineering.

MAKING AUTONOMOUS JUDGEMENTS.
Through a systematic laboratory activity, during which the methodologies related to the design and implementation of parallel computing architectures and distributed agent systems will be considered fro machine learning and artificial intelligence, the student will integrate the acquired knowledge to manage the complexity of an inductive learning mechanism where new knowledge, oriented to the solution of applicative problems, is extracted by starting from the limited information due to the pratical organization of the course.

COMMUNICATE SKILLS.
The scenario of ICT technologies is rapidly evolving towards systems in which technological devices implementing machne learning and artificl intelligence algorithms are part of the environment where they are immersed, in particular regarding complex sensor and actuator networks such as smart grids, IoT, energy and freight distribution, biological and social networks, etc. After this teaching, the student will be able to communicate the knowledge acquired to specialists and non-specialists in the fields of research and work in which he will carry on her/his subsequent scientific and/or professional activities.

LEARNING SKILLS.
The teaching methodology implemented in the course requires an autonomous and self-managed activity of study during the development of monothematic homeworks for the didactic and/or experimental investigation, in a vertical way, of some specific topics.

Educational objectives

GENERAL
• Knowledge and understanding of the most advanced multimedia systems and services, like
streaming, broadcasting, video e voice over IP, extended reality services.
• Applying knowledge and understanding: identifying the main architectural and technological issues
involved in communication oriented multimedia systems.

SPECIFIC
• Achieve a big picture of multimedia systems design, including signal processing as well as
networking issues,
• Making judgements: and be able to analyse and design solutions for emerging multimedia services,
such as extended reality, adaptive live streaming.
• Communication skills: present and describe innovative solutions
• Learning skills: Be able to read scientific papers and technical standard on the most advanced
solutions for multimedia systems

Educational objectives

Goal of this course is to provide an overview of the large world of wireless and wired technologies that are will be used for the Smart Environments. These technologies will be able to provide infrastructures of networks and digital information used in the urban spaces and smart environments to build advanced applications.
Recent advances in areas like pervasive computing, machine learning, wireless and sensor networking enable various smart environment applications in everyday life. The main goal of this course is to present and discuss recent advances in the area of the Internet of Things, in particular on technologies, architectures, algorithms and protocols for smart environments with emphasis on real smart environment applications. The course will present the communication and networking aspects as well as the processing of data to be used for the application design. The course will propose two cases studies in the field of smart environments: Vehicular Traffic monitoring for ITS applications and Network cartography. In both cases instruments, models and methodologies for the design of smart environments applications will be provided.

Educational objectives

KNOWLEDGE AND UNDERSTANDING. The module deals with the basic principles of pattern recognition, classification and clustering on both metric and non-metric domains. Successful students will be able to read and understand texts and papers on advanced topics of Pattern Recognition.

CAPABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING. Successful students who pass the final exam will be able to apply the methodological principles and algorithms studied during the course to design innovative Pattern Recognition systems, in multidisciplinary contexts.

MAKING AUTONOMOUS JUDGEMENTS. Successful students will be able to analyze the design requirements and to choose the classification system that best suits the case study.

COMMUNICATE SKILLS. Successful students will be able to compile a technical report and to realize an appropriate presentation concerning any design, development and performance measurement activity related to a Pattern Recognition system.

LEARNING SKILLS. Successful students will be able to further study by their own the topics dealt with in class, realizing the necessary continuous learning process that characterizes any ICT job.

Educational objectives

KNOWLEDGE AND UNDERSTANDING. Being able to use knowledge derived from previously studied courses and to understand new concepts that will enrich the cultural baggage of the student.
CAPABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING. Being able to put into practice a methodology studied during the course in a new problem, albeit related to the examples developed during classroom exercises.
MAKING AUTONOMOUS JUDGEMENTS. Being able to recognize an applicative problem and to justify the choice of a specific methodology to solve it.
COMMUNICATE SKILLS. Being able to understand of the motivations for choosing a specific methodology, its methodological derivation and its implementation in a practical problem.
LEARNING SKILLS. Being able to independently study and implement machine learning solutions through the software tools learned during the course and being able to apply such solutions in problems that are new for the student.

Educational objectives

KNOWLEDGE AND UNDERSTANDING
The student will acquire knowledge of the basic notions regarding the design and implementation of
algorithms and computing architectures for machine learning and artificial intelligence. In this sense, the
applications in the engineering field for the solution of both supervised and unsupervised learning
problems, such as optimization, prediction, clustering and classification, in real applications concerning
electrical and electronic systems for energy management and information processing will be investigated.
The main objective is to provide the student with the ability to understand real problems, with particular
reference to the sustainable development goals, in order to develop original solutions in the scientific and
professional field of reference.
CAPABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING
Solution of problems related to the design, implementation and testing of computing architectures and
computational models based on neural networks and pattern recognition algorithms in complex
environments (i.e., parallel, distributed, and federated), with particular regard to intelligent signal and data
processing, quantum computing, smart sensor and actuator networks, IoT, smart grids, mechatronics,
control and management systems for the storage and conversion of electrical energy, electric vehicles,
biosensors, biometric systems, etc.
MAKING AUTONOMOUS JUDGEMENTS
Through a systematic laboratory activity, during which the methodologies related to the design and
implementation of computing architectures and distributed agent systems for machine learning and
artificial intelligence will be considered, the student will integrate the acquired knowledge to manage the
complexity of inductive learning mechanisms, by starting from the limited information due to the practical
organization of the course.
COMMUNICATE SKILLS
The scenario of ICT technologies is rapidly evolving towards systems in which technological devices that
implement machine learning and artificial intelligence algorithms are an integral part of the environment in
which they are embedded, in particular in complex networks of sensors and actuators such as smart grids,
IoT, electrical energy and freight distribution systems, biological and social networks, etc. Following this
course, the student will be able to communicate the knowledge acquired to specialist and non-specialist
interlocutors in the fields of research and work in which she/he will carry on the subsequent scientific
and/or professional activities, also considering the environmental sustainability issues.
LEARNING SKILLS
The teaching methodology implemented in teaching requires an autonomous and self-managed study
activity during the development of monothematic homeworks for didactic and/or experimental
investigation, i.e., in a vertical way on some specific theoretical and applicative topics.

Educational objectives

The fundamentals of airborne and spaceborne Synthetic Aperture Radar (SAR) systems are introduced, together with the principles concerning SAR system design and operating modes. Both focusing and autofocusing techniques are described. The image processing techniques to extract the information out of the SAR images are considered in details.

SPECIFIC
Knowledge and understanding: to know the fundamentals of SAR systems, SAR system design and main operating modes as well as main techniques for the focusing and autofocusing of SAR images and for the extraction of information from focused images.
Applying knowledge and understanding: to know how to competently do proper choices for SAR systems design and to develop and apply techniques for the focusing/autofocusing and for the information extraction.
Making judgements: to know how to integrate and use the acquired knowledge in order to choose the main system parameters and implement SAR signal processing chains comprising the cascade of many stages and to know how to critically analyze the corresponding results. The acquisition of this skill is strengthened by the activity required by the homework.
Communication skills: to know how to illustrate with proper technical language the solutions chosen to solve SAR system design or SAR signal processing issues and to know how to describe and discuss results coming from specific processing techniques. The acquisition of this skill is strengthened by the final exam consisting in a talk during which the student describes the activity carried out for the homework using a PowerPoint presentation.

Learning skills: to acquire the ability to complement the theoretical studies with practical applications of the studied concepts working to this aim autonomously.

Educational objectives

Modern adaptive and non-adaptive processing techniques are introduced for the control of multiple antenna beams for target direction of arrival estimation, target tracking, cancellation of EM interference and 3D processing. At the end of the class, the student has matured the capability to design a radar system with multiple beams, by setting its main parameters. Moreover he/she knows the main techniques used for multi-channel radar adaptive signal processing and is able to evaluate their performance by means of theoretical and simulated analysis.
SPECIFIC
• Knowledge and understanding: to know and understand advanced radar systems that exploit multiple antenna beams based on methods and technological solutions at the state of the art and beyond.
• Applying knowledge and understanding: to be able to apply methodologies and techniques typical of multi-beam radar in order to solve system design problems and/or to effectively process the received signals.
• Making judgements: to be able to make judgements on alternative technological and design solutions and, consequently, to get the capability to formulate proper choices.
• Communication skills: to know how to critically illustrate the adopted solutions and the obtained results by describing the employed methodologies to specialists of the field, based on appropriate technical language and style.
• Learning skills: to be able to study in an autonomous way and to detect errors and, consequently, to identify proper corrections to be applied based on an autonomous iterative procedure.

Educational objectives

GENERAL
The objective of the course is to present the most recent techniques available to ensure the transfer of multimedia information between mobile users. The course will investigate different network architectures, from wireless networks to cellular ones (UMTS, LTE and 5G).
SPECIFIC
• Knowledge and understanding: to have a global knowledge of a mobile network architecture, from transmission issues to control solutions.
• Applying knowledge and understanding: to manage, by engineering methodology, the networking techniques that allow multimedia communication in mobility conditions.
• Making judgements: (none)
• Communication skills: to know how to describe the solutions adopted to solve problems related to interconnection of mobility users.
• Learning skills: ability to continue successive studies concerning with networking issues of a mobile network.

Educational objectives

1. Goal of the course
--The goal of the course is to describe and analyze the basic protocol stacks, functionalities and offered services of the currently emerging wired Cloud-aided broadband communication networks and systems. Packet-switched data networks and Internet are considered as motivating case of study.
2. Expected results
-It is expected that the attending students will acquire the basic notions requested to understand the performance behavior of the emerging Internet-driven networks.
3. Required background
A good background in Communication systems and Networking is required.

Educational objectives

GENERAL: the objective of the module is to provide the student with the knowledge sufficient to understand the applications and scientific objectives of remote sensing radars conceived either for Earth observation and Planetary missions

SPECIFIC
• Knowledge and understanding: Acquire knowledge of the working principles of radar systems, capability of dimensioning the sistem parameters, capability of determining optimum and suboptimum processing algorithms with the aim of processing the radar products with the guarantee of the best possible performance.
• Applying knowledge and understanding: : at the end of the module the student will have the capability to:
• Analyze remote sensing radar systems to understand their operativity and performance
• Design remote sensing radar systems, even innovative, taking into account the user requirements, evaluating the system parameters and the optimum processing algorithms to meet the objectives of the sensor.

• Making judgements: is developed making exercise in the classroom regarding the design of simple remote sensing systems
• Communication skills: in the classroom the students are stimulated to give answers to simple questions regarding the arguments objects of th lecture creating some links too with previous lectures with similar arguments
• Learning skills: the student after the end of the course can aspire to participate to a PhD competitive exam,to a master, etc.

Educational objectives

KNOWLEDGE AND UNDERSTANDING.
The module provides the basics about the techniques and technologies used in modern multimedia devices for data acquisition and advanced editing of audio and video signals, as well as the use of intelligent sensor networks, Internet of Things, electronic development boards (Arduino Intel Galileo), applications for mobile devices (smartphones, tablets) on Android and iOS platforms for the provision of information and communication services, augmented reality etc. The student will acquire skills to analyze the main characteristics of these devices, evaluating the costs and benefits stemming from the adoption of a particular solution within a multimedia processing system, and to design and implement applications in different real environments.

CAPABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING.
The student will acquire skills that will enable him to design and implement applications for mobile devices on IoS and Android platforms, prototypes on electronic development platforms (Arduino, Raspberry, Intel Galileo), applications of Internet of Things.

MAKING AUTONOMOUS JUDGEMENTS.
Through an intense and systematic practical and laboratory activity, the student will acquire autonomy of judgment regarding the specifics of practical problems and the ability to identify suitable solutions to respond to the required performances.

COMMUNICATE SKILLS.
The topics covered in the course are of general interest in science and industry, in particular in the fields of cultural heritage, e-health, domotics, environment, logistics, transport, safety of people and things. After this course, the student will be able to communicate the knowledge acquired to specialists and non-specialists in the world of work and research, where he will develop his subsequent scientific and professional activities.

LEARNING SKILLS.
The didactic methodology implemented in the course requires an autonomous and self-managed activity of study during the development of homeworks for the didactic and/or experimental investigation of some specific topics.

Educational objectives

Knowledge and understanding. The course aims at providing tools and application examples for
modeling, performance evaluation and dimensioning of networked service systems.
Applying knowledge and understanding. The course aims at making the student able to state and
solve a performance evaluation problem, including the realization of simulation experiments and
the data analysis thereof.
Making judgements. Through mini-projects and class lab, students are encouraged to move from a
system description to a mathematical model defined to answer to quantitative issues on the system
working. Specific attention is paid to a critical review of numerical results and to validity checking of
hypotheses and approximations introduced in the models.
Expected learning achievements. Capability of identifying, solving and using service system
models and traffic models, either via analytical means, or simulations. Exploitation of these tools
for networked service system design. Students are expected to be able to identify a model of a
service system, fit data into model parameters, do analysis or run simulations to assess the system
performance.roximations introduced in the models.

Educational objectives

Introduction to Machine Learning and data driven modelling. Soft Computing, Computational Intelligence. Basic data driven modelling problems: clustering, classification, unsupervised modelling, function approximation, prediction. Generalization capability. Deduction and induction.
Induction inference principle over normed spaces. Models and training algorithms. Distance measures and basic preprocessing procedures.
Optimization problems. Optimality conditions. Linear regression. LSE and RLSE algorithms. Numerical optimization
algorithms: steepest descent and Newton’s method.
Fuzzy logic principles. Fuzzy induction inference principle. Fuzzy Rules.
Classification systems: performance and sensitivity measures. K-NN Classification rule.
The biological neuron and the central nervous system.
Perceptron. Feedforward networks: Multi-layer perceptron. Error Back Propagation algorithm. Support Vector
Machines. Automatic modeling systems. Structural parameter sensitivity. Constructive and pruning algorithms.
Generalization capability optimization: cross-validation and Ockham's razor criterion based techniques.
Min-Max neurofuzzy classifiers; standard and regularized training algorithm. ARC, PARC; Principal Component Analysis; Generalized Min-Max neurofuzzy networks. GPARC.
Swarm Intelligence. Evolutionary Computation. Genetic algorithms. Particle Swarm Optimization, Ant Colony
Optimization. Automatic feature selection.
Fuzzy reasoning. Generalized modus ponens; FIS; fuzzyfication and e defuzzyfication. ANFIS. Basic and advanced
training algorithms: clustering in the joint input-output space, hyperplane clustering.
Outline of prediction and cross-prediction problems: embedding based on genetic algorithms.
Applications and case studies: micro-grids energy flows modelling and control, Smart Grids optimization and control,
classification of TCP/IP traffic flows.
Mining of frequent patterns and rule extraction in large data bases (Big Data Analytics).

Educational objectives

ITALIANO
GENERALI
Scopo del corso è lo studio della tecnica di comunicazione wireless Ultra Wide Band (UWB), e della sua applicazione alla progettazione di reti avanzate quali le reti ad-hoc e le reti di sensori, e in generale di reti wireless distribuite. Il corso analizza le tematiche chiave dei sistemi UWB, allo scopo di evidenziare le potenzialità di una tecnologia che appare come uno dei migliori candidati nella definizione di standard per reti di futura generazione. Il corso affronterà i fondamenti teorici delle comunicazioni UWB, completando la trattazione con esempi pratici e principi di applicazione per ogni argomento trattato.
SPECIFICI
• Conoscenza e capacità di comprensione: tecniche di generazione di segnali UWB, analisi temporale e spettrale dei segnali UWB, progettazione di ricevitori UWB in canali AWGN e multipath, analisi delle prestazioni singolo link e di rete, tecniche di posizionamento e localizzazione basati su tecnologia UWB.
• Capacità di applicare conoscenza e comprensione: analisi e dimensionamento di reti wireless UWB in funzione della tipologia di segnale trasmesso, del canale, e del ricevitore utilizzato, sia attraverso l’approccio analitico che con l’utilizzo di strumenti software per la simulazione di singoli link o di reti.
• Autonomia di giudizio: capacità di affrontare un progetto di dimensionamento di una rete wireless UWB, identificando vincoli e obiettivi imposti sugli indici prestazionali e sulla standardizzazione, selezionando lo strumento o gli strumenti più opportuni per completare in modo corretto ed efficiente il progetto stesso.
• Abilità comunicative: saper esporre coerentemente e chiaramente tematiche relative alle comunicazioni UWB, combinando la padronanza della trattazione analitica, la capacità di sintetizzare le caratteristiche delle tecniche studiate, e la conoscenza e l’utilizzo di strumenti software di simulazione.
• Capacità di apprendimento: (assente)

Educational objectives

GENERAL
Knowledge: i) of the physical principles of the components and devices of optical telecommunication systems; ii) advanced concepts of the architecture of optical telecommunication systems; iii) signal modulation techniques and system performance evaluation; iv) the hierarchy of the layers of optical telecommunication networks, and their interconnections.

SPECIFIC
• Knowledge and understanding: to know the physical mechanisms that determine the operation of optical devices, and the architectures that allow to integrate these components in a point-to-point optical telecommunication system, and subsequently in a complex network at different levels of transparency of the signal. Knowledge of methods for analyzing the performance of optical telecommunication systems.
• Ability to apply knowledge and understanding: knowing how to apply numerical simulation techniques and characterization methods of devices and systems through virtual experiments, in a competent and critical way.
• Autonomy of judgment: knowing how to evaluate the properties and performance of a device and an optical telecommunication system.
• Communication skills: being able to describe the solutions adopted to solve problems of transmission of optical signals through written documents and oral interview.
• Learning skills: ability to learn from multiple sources of information, and to continue any subsequent studies, e.g. PhD, concerning advanced topics of synthesis, analysis and transmission of the optical signal.

Educational objectives

GENERAL
Knowledge of the fundamental theoretical elements of Digital Audio Signal Processing (DASP): i) fundamentals of acoustics; ii) fundamentals of lumped and distributed circuit theory for acoustic modeling; iii) fundamentals of psychoacoustics; iv) confined environment acoustics; v) fundamental concepts design audio signal processing batch and real-time algorithms; vi) Artificial intelligence methods oriented to the audio signal.

SPECIFICS

• Knowledge and understanding skills: to know the fundamentals of DASP, with particular regard to the definition of algorithms for the analysis and synthesis of audio signals.
• Applying knowledge and understanding: knowing how to apply DASP techniques and procedures in the most common problems described in the course such as: filtering of audio signals, computational analysis of complex acoustic scenarios, methods of analysis and synthesis of audio signals.
• Autonomy of judgement: regarding the possible optimal solution of DASP problems.
• Communication skills: knowing how to describe the solutions adopted to solve DASP problems.
• Learning skills: autonomous learning on specialized texts; ability to continue possible subsequent studies, e.g. PhD, on advanced DASP issues.

Educational objectives

GENERAL
• Knowledge and understanding of the most advanced multimedia systems and services, like
streaming, broadcasting, video e voice over IP, extended reality services.
• Applying knowledge and understanding: identifying the main architectural and technological issues
involved in communication oriented multimedia systems.

SPECIFIC
• Achieve a big picture of multimedia systems design, including signal processing as well as
networking issues,
• Making judgements: and be able to analyse and design solutions for emerging multimedia services,
such as extended reality, adaptive live streaming.
• Communication skills: present and describe innovative solutions
• Learning skills: Be able to read scientific papers and technical standard on the most advanced
solutions for multimedia systems

Educational objectives

acquisition of soft skills such as:
i) ability to transfer knowledge
ii) work in coordinated teams
iii) ability to develop their business on demand and on the fly

Educational objectives

acquisition of soft skills such as:
i) ability to transfer knowledge
ii) work in coordinated teams
iii) ability to develop their business on demand and on the fly

Educational objectives

GENERAL
Improvement of English knowledgment
SPECIFIC
• Knowledge and understanding: comprehension of written English
• Applying knowledge and understanding: none
• Making judgements: none
• Communication skills: to know how to describe technical solutions and to present own compositions in English
• Learning skills: to continue successive studies in Communications Engineering

Educational objectives

The course objective is to provide to students the basic elements of modern techniques used for multimedia signal processing. In particular, this course teaches the generation, the processing and storing of multimedia signal, through the use of high-level simulation software and real-time hardware systems. A particular emphasis is focused on the real-time audio signal processing.

SPECIFIC
• Knowledge and understanding: to know the problems, methodologies and applications of multimedia signal processing.
• Applying knowledge and understanding: to develop independently multimedia processing applications.
• Making judgements: to develop adequate critical skills through practical activities in multimedia algorithms implementation.
• Communication skills: to improve ability to critically expose the matters learned during the course.
• Learning skills: to improve autonomous and independent study capacity.

Educational objectives

The basic principles are presented for: (i) computer simulation of typical operational scenarios for remote sensing systems operation, (ii) computer and/or real-time hardware implementation of the main radar signal processing techniques.

SPECIFIC
Knowledge and understanding: to show the ability to understand that enables the application of innovative methodologies/techniques at the state-of the-art with specific reference to those radar systems described during the lectures.
Applying knowledge and understanding: to show the ability to practically apply the concepts and tools previously acquired at a theoretical level, even in contexts requiring the joint use of many different tools.
Making judgements: to know how to integrate and use the previously acquired knowledge in order to implement complex processing chains comprising the cascade of many stages and to know how to critically analyze the corresponding results, with specific reference to those radars systems described during the lectures.
Communication skills: to know how to describe and motivate the solutions chosen to solve specific problems and to know how to discuss the corresponding results, with specific reference those radar systems described in the lectures.

Learning skills: to acquire the ability enabling the autonomous development of practical solutions also in contexts not strictly related to those described in the lectures.

Educational objectives

GENERAL
The aim of the course is to provide a practical approach about the management of IP networks. The course will allow students to critically evaluate the main network protocols studied in previous courses (IP addressing, routing protocols, Ethernet, etc…) and it will describe advanced network solutions (NAT, Virtual LAN, Access Control List, etc…). A network emulator will be used to configure an IP network like in a real scenario, so that to implement the protocols studied; moreover, specific troubleshooting procedures will be described and tested.
SPECIFIC
• Knowledge and understanding: to know the main network protocols used in an IP network.
• Applying knowledge and understanding: to configure an IP network by means of a network emulator providing a configuration interface for IP routers and Ethernet switches.
• Making judgements: to carry out network design solutions as a function of specific network requirements.
• Communication skills: (none).
• Learning skills: ability to continue successive studies concerning with advanced networking.