Master Degree on ICT and Multimedia Technology – Study Plan

Four different curricula, each of them with its own unique flavor. Telecommunications is the general plan that explores the core ICT subjects. Cybersystems, Photonics and Life&Health are cross-disciplinary curricula focused on applications towards complementary topics. 

 

Telecommunications

This study plan brings together classical subjects and novel trends in the fields of digital transmission, networking, signal processing, and electromagnetism.

Mandatory subjects

The course gives the fundamental principles of radio communication in terms of channel behavior and technological exploitation, detailing the contexts of physical layer (PHY), channel access (LINK), and networking procedures (NET). The student will become knowledgeable about modern centralized and distributed wireless systems such as IEEE 802.11 (a/g/n/h) and Wireless Sensor Networks (IEEE 802.15.4). Also, (s)he will get to know related packet-based application-layer / network coding.

The program will be available soon…

Advanced subjects

The course offers a guided tour of 3D computer vision, 3D graphics and machine learning tools to develop virtual and augmented reality applications. After a description of imaging systems, the course reviews how to build a 3D model starting from 2D pictures and/or depth sensors, also by means of machine learning techniques, and finally the process of rendering real or virtual 3D models to standard images and 3D/AR devices. Students will experience computer vision, deep learning and augmented reality techniques during lab sessions. A beginners’ tutorial on Unity will be provided as well.

The course discusses forthcoming 5G communication standards, focusing on two key technologies: orthogonal frequency division multiplexing (OFDM), and multiple-input multiple-output (MIMO) systems. The evolution of mobile networks is presented at different layers (physical: filterbanks, massive MIMO; medium access: mm-wave, OFDMA; network: small cells/cloud), also discussing resource allocation, channel estimation, and decoding techniques for MIMO/OFDM, as well as standard developments.

The course will discuss design, implementation, and experimental characterization of antennas and wireless links. Topics include, among others, antenna parameters, dipole, wire and patch antennas, antenna arrays, antennas for satellite, GPS and mobile communications. During the course students will attend a laboratory of antenna design with professional software.
 

The program will be available soon…

The computer vision courses presents the principles and techniques for image processing, understanding and analysis. The course will show how to extract relevant information from visual data that can be used in challenging real world applications like autonomus driving or smart manifacturing. It presents the mathematical, programming, and technical issues of these tasks and will include a relevant hands-on laboratory part where students will also develop C++ applications based on the OpenCV library.

The objective of this course is to illustrate problems, principles and techniques in modern digital communications. The aim is to give tools for evaluating performance, simulate and design modern digital transmission systems. In particular, we cover both single carrier modulation using decision feedback equalization or sequence detection and multicarrier modulation with OFDM using a cyclic prefix.

The course exploits basic signal analysis knowledge that the student is assumed to have acquired from previous studies to explore advanced concepts in the field of digital signal processing. The course will review Z-transform, linear time-invariant systems, FIR/IIR filters, to investigate the design and usage of digital filters, interpolation/decimation of digital signals, frequency analysis of digital signals. Practical application examples, useful in many areas of information engineering, will be provided.

The course focuses on the physical layer of wired telecommunication systems, with special emphasis on fiber optics. The student will also perform 5 laboratory experiments. The main topics of the course are: principles and fundamental equations of electromagnetism, Poynting vector, polarization; transmission lines and characteristic impedance; metallic waveguides (modes, dispersion, losses); and finally optical fibers (ray model, modes, attenuation, modal and chromatic dispersion).

Game theory is the science of analyzing multi-objective multi-agent problems (i.e., “games”). This involves the games we usually play for fun in our everyday life, but in a more serious context is applied to resource competition, distributed management, efficient allocation over multi-user systems and/or communication networks. This course teaches all the basic concepts, as well as some advanced ones, of game theory. Also, it applies them to scenarios of interest for ICT.

In modern communication systems, securing against malicious behavior is a primary issue, and must be part of the design since the beginning, rather than a patch added as a belated measure. The class introduces fundamental notions and tools in information security, with a focus on the solutions, attacks, and countermeasures that can be deployed at the different layers in modern communication networks. The students will be asked to apply their acquired knowledge to practical use cases, industrial standards, and experimental scenarios.

The subject aims at providing basic knowledge of modern telecommunication architectures, as well as fundamental mathematical tools for the modelling, design and analysis of telecommunications networks and services. The course will also give you some practical experience with network protocols and devices, thanks to a series of lab experiences that will introduce you to the art of router and socket programming. Ancillary to all this knowledge, the course will help you develop some basic management skills that shall belong to the baggage of each engineer. Some of the topics that will be considered by the course are data traffic sources, multimedia streams and content, packet switched networks: basics of data networks, ISO/OSI and TCP/IP protocol stacks, congestion control and scheduling algorithms and the application layer

This course provides knowledge of the concepts of the “IoT” and “Smart cities,” describing their scientific and market trends, as well as the application of these paradigms in practical ICT context. The students will learn about some key platforms and standards (ZigBee, 6LoWPAN, WiFi, Bluetooth Low Energy, SigFox, Lo-Ra), and will review their applications for home automation, industrial applications, autonomous driving, urban monitoring, privacy and security.

Intelligent systems capable of automated reasoning are emerging as the most promising application of ICT. The aim of this course is to provide fundamentals and basic principles of the machine learning problem as well as to introduce the most common techniques for regression and classification. Both supervised and unsupervised learning will be covered, with a brief outlook into more advanced topics such as Support Vector Machines, neural networks and deep learning. The course will be complemented by hands-on experience with Python programming.

The goal of the course is to provide the principles and tools needed to analyze and develop techniques for compression of multimedia data. Both lossless and lossy coding techniques will be considered. Methodologies for the evaluation of coding gain and rate distortion will also be discussed. Finally, applications to present coding standards will be presented, such as data (ZIP), audio (MP3), pictures (JPEG), and video (MPEG) compression, as well as their implication to multimedia communications.

The course gives instruments for theoretical analysis and computer simulation of distributed systems. During the classes, simulation software and mathematical tools will be reviewed, and applied to scientific examples taken from the literature. The expected outcome for the student is to master different characterizations of network systems and their performance metrics, result interpretation, and design of optimization criteria.

The course deals with model coding theory, presenting code on sparse graphs such as Low Density Parity Check Codes (LDPC) and the related decoding algorithms, as well as Fountain Codes, a class of linear random codes that operate on data packet objects. Further, advanced topics such as linear random coding on networks will be treated, as a means to store or distribute data packets in a network, in a way that is robust to link errors and node failures.

The course consists of two parts. The former analyzes design and performance of a transmission link over optical fibers; to this end, standard characterization from digital communications will be revisited, including fibers as channels, light impulse propagation and amplification, and shot noise analysis. The latter discusses quantum theory applied to telecommunications: quantum operators and projectors, spectral decomposition, quantum decision theory, and the design of a quantum communication system.

The course discusses the physical layer of optical communication systems. Fiber optics will be reviewed for coupled mode theory and nonlinear propagation. Then, instruments such as the optical time domain reflectometer and the optical spectrum analyzer will be described. Passive (couplers, isolators, filters) and active (amplifiers, modulators, diode lasers, photodiodes) devices and transmission equipments will be characterized. Students will also have the opportunity to perform 8 laboratory experiments.

This is a theoretical course intended to provide knowledge of the main mathematical tools and modeling techniques for the study of telecommunication networks and networking protocols. The students will get to know the theoretical basics of Markov chains, renewal processes, queueing theory and traffic models. These instruments will be further applied to the analysis of datalink and networking protocols.

This is a theoretical course intended to provide knowledge of the main mathematical tools and modeling techniques for the study of telecommunication networks and networking protocols. The students will get to know the theoretical basics of Markov chains, renewal processes, queueing theory and traffic models. These instruments will be further applied to the analysis of datalink and networking protocols.

The course teaches how to design and develop a distributed application for the management of structured data over time. Programming proficiency (Java or C) is required. At the end of the course, the student will gain competences concerning databases, their data models and properties, and formal languages for querying a database, and will be able to carry out an actual project for the design and development of a database application using a relational database management system (RDBMS).

This course expands programming skills already acquired by the students to give a special emphasis on scientific programming. The students will be guided through the object-oriented programming paradigm to design and develop software in the Python language. It will be given the competence to analyze formal correctness, computability, and complexity of a program, with a clear problem-solving purpose.

The course aims at developing attitudes and skills for innovation from both entrepreneurial (new venture creation) and management points of view. Students with different backgrounds are encouraged to participate to increase the variety of competencies needed for innovation. The course discusses idea selection, feasibility assessment, market analysis, customer profiling, intellectual property rights and the patent filing process, and the creation of a business plan.

This course adopts a mathematical approach to theory and algorithms for optimization in many fields of network and data analysis. Linear optimization will be explored first, with a review of theory and algorithm (Simplex, interior point). This is followed by convex optimization, both unconstrained (gradient-type) and constrained (polyhedral approximation, gradient projection). Finally, the course will discuss large-scale network optimization and clustering methods.

The course reviews properties of semiconductor materials (silicon and compounds), and mechanisms for light absorption and generation in them, as well as spontaneous or stimulated light emission. Optoelectronic devices will also be presented, such as the LED (light emitting diode), lasers, photodetectors. Also, solar cells will be discussed (homojunction and heterojunction) and HEMT (high electron mobility transistors) and their applications.

The program will be available soon…

The lab aims to help students improve their oral communication through the study and practice of the elements contributing to successful communication. The focus is on raising the students’ awareness on the importance of verbal and non verbal language in interactions to make communication more effective. The students will learn the meanings of body language and paralanguage (voice intonation, volume, etc), how they are used in different types of interactions (one-to-one, one-to-many, computer-mediated, etc.), and will have to apply them in a number of assigned tasks. The lab requires the students’ active participation in all class activities, aimed at applying the communication strategies learned.

This course will provide the foundations of the project management. Traditional (such as the Project Management Institute approach) as well as more advanced techniques – such as the Agile Methodology – will be reviewed. Special focus will be put on the methodologies more suited for the ICT environment.

The course aims to introduce students to the non-verbal elements that contribute to communciation. The course lectures will focus on body language, facial expressions and on the non-verbal elements of the voice. The analysis of communication will be based on examples of spoken language produced in authentic video texts. The course integrates lessons in the lab and analysis of videos with the software Elan.

Other
Students must certify that they have a proficiency level “English B2” according to the CEFR scheme. To this end, students can: 
– Submit a certificate issued by a recognized external certification agency;
– Take an internal test at the University of Padova Language Centre (CLA) to verify that they can interact with a degree of fluency and spontaneity that makes regular interaction with native speakers quite possible without strain for either party.

All students are required to take an Internship of at least 2 months either in a Lab of the University of Padova, in an external Research Center, or in an Industry. Your internship might be anywhere – local, national, or international! You can find your internship yourself, with help from the Internship staff, or by faculty or advisor referral. We suggest you to match the Internship with your Final Year Project, in such a way to get the best out of it.

Students are asked to carry out a substantial individual project in their final year. Each project is supervised by a member of faculty, and is assessed via a written report (thesis) and a final presentation in front of an Academic Commission.

Cybersystems

The “Cybersystems” track revolves around network scenarios where computing applications meet the real world, and everything is connected over the Internet. Its fields include the cyberspace (online social networks, web applications, big data), cyberservices (the Internet of things, smart cities, the cloud), and cybersecurity (cryptography, digital forensics)
Mandatory subjects

The subject aims at providing basic knowledge of modern telecommunication architectures, as well as fundamental mathematical tools for the modelling, design and analysis of telecommunications networks and services. The course will also give you some practical experience with network protocols and devices, thanks to a series of lab experiences that will introduce you to the art of router and socket programming. Ancillary to all this knowledge, the course will help you develop some basic management skills that shall belong to the baggage of each engineer. Some of the topics that will be considered by the course are data traffic sources, multimedia streams and content, packet switched networks: basics of data networks, ISO/OSI and TCP/IP protocol stacks, congestion control and scheduling algorithms and the application layer.

The course describes networking phenomena over many scenarios. Although communication networks and the Internet are primary references, similar representations can be used for social networks, ecological systems, and epidemic diseases. The course describes network generation models, and then community structures are reviewed, also outlining the applications to online social communities, brain networks, and biological systems.

The course teaches how to design and develop a distributed application for the management of structured data over time. Programming proficiency (Java or C) is required. At the end of the course, the student will gain competences concerning databases, their data models and properties, and formal languages for querying a database, and will be able to carry out an actual project for the design and development of a database application using a relational database management system (RDBMS).

Advanced subjects 

The course offers a guided tour of 3D computer vision, 3D graphics and machine learning tools to develop virtual and augmented reality applications. After a description of imaging systems, the course reviews how to build a 3D model starting from 2D pictures and/or depth sensors, also by means of machine learning techniques, and finally the process of rendering real or virtual 3D models to standard images and 3D/AR devices. Students will experience computer vision, deep learning and augmented reality techniques during lab sessions. A beginners’ tutorial on Unity will be provided as well.

The program will be available soon…

The computer vision courses presents the principles and techniques for image processing, understanding and analysis. The course will show how to extract relevant information from visual data that can be used in challenging real world applications like autonomus driving or smart manifacturing. It presents the mathematical, programming, and technical issues of these tasks and will include a relevant hands-on laboratory part where students will also develop C++ applications based on the OpenCV library.

This course presents machine learning and signal processing technologies that can be used handling digital evidences in investigations. The aim is to provide students with a set of analysis strategies for hard disk, network streams, image and video data (including authentication and fake detection), data from social networks. These techniques are reviewed and discussed both theoretically and via some case studies. The course entails the intervention of legal specialists that describe the procedural implications of the technical analysis and the juridical consequences on expert decisions.

The course exploits basic signal analysis knowledge that the student is assumed to have acquired from previous studies to explore advanced concepts in the field of digital signal processing. The course will review Z-transform, linear time-invariant systems, FIR/IIR filters, to investigate the design and usage of digital filters, interpolation/decimation of digital signals, frequency analysis of digital signals. Practical application examples, useful in many areas of information engineering, will be provided.

Game theory is the science of analyzing multi-objective multi-agent problems (i.e., “games”). This involves the games we usually play for fun in our everyday life, but in a more serious context is applied to resource competition, distributed management, efficient allocation over multi-user systems and/or communication networks. This course teaches all the basic concepts, as well as some advanced ones, of game theory. Also, it applies them to scenarios of interest for ICT.

In modern communication systems, securing against malicious behavior is a primary issue, and must be part of the design since the beginning, rather than a patch added as a belated measure. The class introduces fundamental notions and tools in information security, with a focus on the solutions, attacks, and countermeasures that can be deployed at the different layers in modern communication networks. The students will be asked to apply their acquired knowledge to practical use cases, industrial standards, and experimental scenarios.

The course deals with the analysis of biosignals, i.e., signals generated by human activity. It will study extraction of information from quasi-periodic signals (denoising, segmentation); application of clustering algorithms over biosignals to classify users and construct dictionaries for compact biometric datasets; use of unsupervised learning to perform quantization; statistical structures such as Bayesian networks and Hidden Markov Models, as well as supervised learning, for pattern and classification problems.

This course provides knowledge of the concepts of the “IoT” and “Smart cities,” describing their scientific and market trends, as well as the application of these paradigms in practical ICT context. The students will learn about some key platforms and standards (ZigBee, 6LoWPAN, WiFi, Bluetooth Low Energy, SigFox, Lo-Ra), and will review their applications for home automation, industrial applications, autonomous driving, urban monitoring, privacy and security.

Intelligent systems capable of automated reasoning are emerging as the most promising application of ICT. The aim of this course is to provide fundamentals and basic principles of the machine learning problem as well as to introduce the most common techniques for regression and classification. Both supervised and unsupervised learning will be covered, with a brief outlook into more advanced topics such as Support Vector Machines, neural networks and deep learning. The course will be complemented by hands-on experience with Python programming.

The course gives instruments for theoretical analysis and computer simulation of distributed systems. During the classes, simulation software and mathematical tools will be reviewed, and applied to scientific examples taken from the literature. The expected outcome for the student is to master different characterizations of network systems and their performance metrics, result interpretation, and design of optimization criteria.

The course deals with model coding theory, presenting code on sparse graphs such as Low Density Parity Check Codes (LDPC) and the related decoding algorithms, as well as Fountain Codes, a class of linear random codes that operate on data packet objects. Further, advanced topics such as linear random coding on networks will be treated, as a means to store or distribute data packets in a network, in a way that is robust to link errors and node failures.

This is a theoretical course intended to provide knowledge of the main mathematical tools and modeling techniques for the study of telecommunication networks and networking protocols. The students will get to know the theoretical basics of Markov chains, renewal processes, queueing theory and traffic models. These instruments will be further applied to the analysis of datalink and networking protocols.

This course reviews the theoretical basis and allows a critical study of the cryptographic protocols used in many applications (authentication, digital commerce). In the first part, we will give the mathematical basic tools (essentially from elementary and analytic number theory) that are required to understand modern public-key methods. In the second part, we will see how to apply this know-how to study and criticize some protocols currently used.

The course provides knowledge of the main tools and methodologies used in the analysis of large datasets, covering the following topics: programming frameworks such as MapReduce, Hadoop, Spark; Association Analysis; Clustering techniques; Graph Analytics (centrality, scale-free/power-law graphs, small world, uncertain graphs); Similarity and diversity search.

The course provides knowledge of the main tools and methodologies used in the analysis of large datasets, covering the following topics: programming frameworks such as MapReduce, Hadoop, Spark; Association Analysis; Clustering techniques; Graph Analytics (centrality, scale-free/power-law graphs, small world, uncertain graphs); Similarity and diversity search.

The course aims at developing attitudes and skills for innovation from both entrepreneurial (new venture creation) and management points of view. Students with different backgrounds are encouraged to participate to increase the variety of competencies needed for innovation. The course discusses idea selection, feasibility assessment, market analysis, customer profiling, intellectual property rights and the patent filing process, and the creation of a business plan.

This course expands programming skills already acquired by the students to give a special emphasis on scientific programming. The students will be guided through the object-oriented programming paradigm to design and develop software in the Python language. It will be given the competence to analyze formal correctness, computability, and complexity of a program, with a clear problem-solving purpose.

The objective of the course is to learn methodologies for web design and development, practicing them through the implementation of an actual application. This implies to provide the students with a strong computer science competence on Web engineering, design methodologies and architectural alternatives. To this end, the students will learn the characteristics of Web 1.0 applications and Web 2.0 application (rich internet application) and develop the usage of Java servlets, Javascript, CSS3 and HTML5.

This course adopts a mathematical approach to theory and algorithms for optimization in many fields of network and data analysis. Linear optimization will be explored first, with a review of theory and algorithm (Simplex, interior point). This is followed by convex optimization, both unconstrained (gradient-type) and constrained (polyhedral approximation, gradient projection). Finally, the course will discuss large-scale network optimization and clustering methods.

The lab aims to help students improve their oral communication through the study and practice of the elements contributing to successful communication. The focus is on raising the students’ awareness on the importance of verbal and non verbal language in interactions to make communication more effective. The students will learn the meanings of body language and paralanguage (voice intonation, volume, etc), how they are used in different types of interactions (one-to-one, one-to-many, computer-mediated, etc.), and will have to apply them in a number of assigned tasks. The lab requires the students’ active participation in all class activities, aimed at applying the communication strategies learned.

This course will provide the foundations of the project management. Traditional (such as the Project Management Institute approach) as well as more advanced techniques – such as the Agile Methodology – will be reviewed. Special focus will be put on the methodologies more suited for the ICT environment.

The course aims to introduce students to the non-verbal elements that contribute to communciation. The course lectures will focus on body language, facial expressions and on the non-verbal elements of the voice. The analysis of communication will be based on examples of spoken language produced in authentic video texts. The course integrates lessons in the lab and analysis of videos with the software Elan.

Other
Students must certify that they have a proficiency level “English B2” according to the CEFR scheme. To this end, students can: 
– Submit a certificate issued by a recognized external certification agency;
– Take an internal test at the University of Padova Language Centre (CLA) to verify that they can interact with a degree of fluency and spontaneity that makes regular interaction with native speakers quite possible without strain for either party.

All students are required to take an Internship of at least 2 months either in a Lab of the University of Padova, in an external Research Center, or in an Industry. Your internship might be anywhere – local, national, or international! You can find your internship yourself, with help from the Internship staff, or by faculty or advisor referral. We suggest you to match the Internship with your Final Year Project, in such a way to get the best out of it.

Students are asked to carry out a substantial individual project in their final year. Each project is supervised by a member of faculty, and is assessed via a written report (thesis) and a final presentation in front of an Academic Commission.

Photonics

Optical, quantum, and nanoscale technologies are investigated within the “Photonics” track. They are expected to revolutionize not only communication systems, which need to exchange immense loads of data every millisecond, but also other digital application scenarios such as quantum computing, medical sensing, renewable energy production
Mandatory subjects

The course focuses on the physical layer of wired telecommunication systems, with special emphasis on fiber optics. The student will also perform 5 laboratory experiments. The main topics of the course are: principles and fundamental equations of electromagnetism, Poynting vector, polarization; transmission lines and characteristic impedance; metallic waveguides (modes, dispersion, losses); and finally optical fibers (ray model, modes, attenuation, modal and chromatic dispersion).

The course will present the operational principle of the most relevant photonics devices (light modulators, optical amplifiers, photonic sensors etc.) and the technologies (materials, waveguides, fibers etc.) used to realize them. Some applications of the photonic devices in will be also reviewed.

The course discusses optical properties of matter at molecular scale. Spectroscopy and radioscopy are introduced to study spectral and reflectance of materials, hyperspectral optical configurations and sensors. The course also covers surface plasmon, Kretschman configuration, nanostructured plasmonic sensors, lithography, metamaterials for lenses. Selected applications are presented to geology and agriculture (e.g. remote water detection), food industry, gas sensing, and medical diagnostics.

Advanced subjects

The course discusses forthcoming 5G communication standards, focusing on two key technologies: orthogonal frequency division multiplexing (OFDM), and multiple-input multiple-output (MIMO) systems. The evolution of mobile networks is presented at different layers (physical: filterbanks, massive MIMO; medium access: mm-wave, OFDMA; network: small cells/cloud), also discussing resource allocation, channel estimation, and decoding techniques for MIMO/OFDM, as well as standard developments.

The course will discuss design, implementation, and experimental characterization of antennas and wireless links. Topics include, among others, antenna parameters, dipole, wire and patch antennas, antenna arrays, antennas for satellite, GPS and mobile communications. During the course students will attend a laboratory of antenna design with professional software.

This course is devoted to the interactions of light with living tissues and their technological applications to non-invasive biomedical imaging and treatments techniques. The topics covered by this course include fundamentals of light and matter, light-tissue interactions (light scattering and absorption in tissues), principles of lasers and non-linear optics as preliminaries to later discuss applications such as optical microscopy, biomedical imaging, spectroscopic techniques, plasmonics and photonic biosensing.

The objective of this course is to illustrate problems, principles and techniques in modern digital communications. The aim is to give tools for evaluating performance, simulate and design modern digital transmission systems. In particular, we cover both single carrier modulation using decision feedback equalization or sequence detection and multicarrier modulation with OFDM using a cyclic prefix.

The course exploits basic signal analysis knowledge that the student is assumed to have acquired from previous studies to explore advanced concepts in the field of digital signal processing. The course will review Z-transform, linear time-invariant systems, FIR/IIR filters, to investigate the design and usage of digital filters, interpolation/decimation of digital signals, frequency analysis of digital signals. Practical application examples, useful in many areas of information engineering, will be provided.

The subject aims at providing basic knowledge of modern telecommunication architectures, as well as fundamental mathematical tools for the modelling, design and analysis of telecommunications networks and services. The course will also give you some practical experience with network protocols and devices, thanks to a series of lab experiences that will introduce you to the art of router and socket programming. Ancillary to all this knowledge, the course will help you develop some basic management skills that shall belong to the baggage of each engineer. Some of the topics that will be considered by the course are data traffic sources, multimedia streams and content, packet switched networks: basics of data networks, ISO/OSI and TCP/IP protocol stacks, congestion control and scheduling algorithms and the application layer

This course provides knowledge of the concepts of the “IoT” and “Smart cities,” describing their scientific and market trends, as well as the application of these paradigms in practical ICT context. The students will learn about some key platforms and standards (ZigBee, 6LoWPAN, WiFi, Bluetooth Low Energy, SigFox, Lo-Ra), and will review their applications for home automation, industrial applications, autonomous driving, urban monitoring, privacy and security.

Nanophotonics is an emerging field of study that deals with emission, propagation, manipulation, and detection of photons in structures of nanometers in size. Within the course, nanostructures will be considered for light generation (quantum wells, nanocrystals, nanowires), light propagation (dielectric and plasmonic nanowaveguides) and light manipulation (photonic crystals, metamaterials, resonant gratings). The course will also review practical methods to build, characterize, and simulate nanophotonic structures and devices.

The course consists of two parts. The former analyzes design and performance of a transmission link over optical fibers; to this end, standard characterization from digital communications will be revisited, including fibers as channels, light impulse propagation and amplification, and shot noise analysis. The latter discusses quantum theory applied to telecommunications: quantum operators and projectors, spectral decomposition, quantum decision theory, and the design of a quantum communication system.

Intelligent systems capable of automated reasoning are emerging as the most promising application of ICT. The aim of this course is to provide fundamentals and basic principles of the machine learning problem as well as to introduce the most common techniques for regression and classification. Both supervised and unsupervised learning will be covered, with a brief outlook into more advanced topics such as Support Vector Machines, neural networks and deep learning. The course will be complemented by hands-on experience with Python programming.

The course gives the fundamental principles of radio communication in terms of channel behavior and technological exploitation, detailing the contexts of physical layer (PHY), channel access (LINK), and networking procedures (NET). The student will become knowledgeable about modern centralized and distributed wireless systems such as IEEE 802.11 (a/g/n/h) and Wireless Sensor Networks (IEEE 802.15.4). Also, (s)he will get to know related packet-based application-layer / network coding.

The course discusses the physical layer of optical communication systems. Fiber optics will be reviewed for coupled mode theory and nonlinear propagation. Then, instruments such as the optical time domain reflectometer and the optical spectrum analyzer will be described. Passive (couplers, isolators, filters) and active (amplifiers, modulators, diode lasers, photodiodes) devices and transmission equipments will be characterized. Students will also have the opportunity to perform 8 laboratory experiments.

The course discusses physical chemistry properties of the solid surface, including surface energy, electrostatic and steric stabilization. It also describes the chemical synthesis of nanoparticles (metals, semiconductors, oxides), nanorods, nanowires, nanotubes, and thin films depositions. The students will synthesize and characterize different nanomaterials in the lab and will also visit nanotechnology research centers to get direct experience of current technological processes.

The course reviews properties of semiconductor materials (silicon and compounds), and mechanisms for light absorption and generation in them, as well as spontaneous or stimulated light emission. Optoelectronic devices will also be presented, such as the LED (light emitting diode), lasers, photodetectors. Also, solar cells will be discussed (homojunction and heterojunction) and HEMT (high electron mobility transistors) and their applications.

The course reviews the main types of photovoltaic cells and modules. Crystalline silicon (c-SI) cells will be investigated in depth, with their whole production chain from polysilicon to the cell. Also thin-film modules will be discussed. Finally, the installation of a photovoltaic cell will be studied, also detailing legal authorizations and standardization requirements for the connection to the main grid.

The program will be available soon…

Besides its tremendous advancements in physics, quantum theory is also expected to revolutionize classical information theory based on 0/1 bits and also how we compute and handle information. In this spirit, this course will discuss quantum principles such as quantum logic and the qubit, no-cloning theorem, quantum copying, quantum entanglement, quantum teleportation, quantum key distribution, quantum information and entropy measures, quantum computing algorithms.

Light is exploited in telecommunications, photovoltaic cells, or laser processes in medicine and industry. The course studies thermal radiation, including that of the sun or conventional light sources, and how optical beams can be transformed for practical applications. The course presents a classification of light types, the radiation-matter interaction, and how the laser can be realized. It also approaches quantum optics to explain irradiative phenomena, outlining the immense potential of quantum technologies.

This course expands programming skills already acquired by the students to give a special emphasis on scientific programming. The students will be guided through the object-oriented programming paradigm to design and develop software in the Python language. It will be given the competence to analyze formal correctness, computability, and complexity of a program, with a clear problem-solving purpose.

The objective of the course is to learn methodologies for web design and development, practicing them through the implementation of an actual application. This implies to provide the students with a strong computer science competence on Web engineering, design methodologies and architectural alternatives. To this end, the students will learn the characteristics of Web 1.0 applications and Web 2.0 application (rich internet application) and develop the usage of Java servlets, Javascript, CSS3 and HTML5.

This course adopts a mathematical approach to theory and algorithms for optimization in many fields of network and data analysis. Linear optimization will be explored first, with a review of theory and algorithm (Simplex, interior point). This is followed by convex optimization, both unconstrained (gradient-type) and constrained (polyhedral approximation, gradient projection). Finally, the course will discuss large-scale network optimization and clustering methods.

The lab aims to help students improve their oral communication through the study and practice of the elements contributing to successful communication. The focus is on raising the students’ awareness on the importance of verbal and non verbal language in interactions to make communication more effective. The students will learn the meanings of body language and paralanguage (voice intonation, volume, etc), how they are used in different types of interactions (one-to-one, one-to-many, computer-mediated, etc.), and will have to apply them in a number of assigned tasks. The lab requires the students’ active participation in all class activities, aimed at applying the communication strategies learned.

This course will provide the foundations of the project management. Traditional (such as the Project Management Institute approach) as well as more advanced techniques – such as the Agile Methodology – will be reviewed. Special focus will be put on the methodologies more suited for the ICT environment.

The course aims to introduce students to the non-verbal elements that contribute to communciation. The course lectures will focus on body language, facial expressions and on the non-verbal elements of the voice. The analysis of communication will be based on examples of spoken language produced in authentic video texts. The course integrates lessons in the lab and analysis of videos with the software Elan.

Other
Students must certify that they have a proficiency level “English B2” according to the CEFR scheme. To this end, students can: 
– Submit a certificate issued by a recognized external certification agency;
– Take an internal test at the University of Padova Language Centre (CLA) to verify that they can interact with a degree of fluency and spontaneity that makes regular interaction with native speakers quite possible without strain for either party.

All students are required to take an Internship of at least 2 months either in a Lab of the University of Padova, in an external Research Center, or in an Industry. Your internship might be anywhere – local, national, or international! You can find your internship yourself, with help from the Internship staff, or by faculty or advisor referral. We suggest you to match the Internship with your Final Year Project, in such a way to get the best out of it.

Students are asked to carry out a substantial individual project in their final year. Each project is supervised by a member of faculty, and is assessed via a written report (thesis) and a final presentation in front of an Academic Commission.

Life & Health

The curriculum of ICT for “Life & Health” provides competences to apply information technology to medical care scenarios. Scenarios of special interest are medical networking applications, signal processing for bioimaging, neurosciences, eHealth / mHealth, physical rehabilitation, and every other field where ICT applications can lead to improved quality of life

Mandatory subjects

The course exploits basic signal analysis knowledge that the student is assumed to have acquired from previous studies to explore advanced concepts in the field of digital signal processing. The course will review Z-transform, linear time-invariant systems, FIR/IIR filters, to investigate the design and usage of digital filters, interpolation/decimation of digital signals, frequency analysis of digital signals. Practical application examples, useful in many areas of information engineering, will be provided.

e-Health deals with applications of ICT for healthcare and the course covers technologies for acquisition, transmission, and processing of data related to healthcare. Particularly, it is divided into three units: (1) data acqusition and analysis, e.g., for biosignals such as EEG and ECG, (2) communication networks for e-health, e.g., body area sensor networks, and (3) e-health applications, such as closed-loop interventions and pervasive continuous monitoring. Hands-on laboratories will be proposed to experience signal acquisition, processing and body area network modelling, as well as guest lectures to meet technical experts and interact also with other professionals involved in the e-health field.

Intelligent systems capable of automated reasoning are emerging as the most promising application of ICT. The aim of this course is to provide fundamentals and basic principles of the machine learning problem as well as to introduce the most common techniques for regression and classification. Both supervised and unsupervised learning will be covered, with a brief outlook into more advanced topics such as Support Vector Machines, neural networks and deep learning. The course will be complemented by hands-on experience with Python programming.

Advanced subjects
The course offers a guided tour of 3D computer vision, 3D graphics and machine learning tools to develop virtual and augmented reality applications. After a description of imaging systems, the course reviews how to build a 3D model starting from 2D pictures and/or depth sensors, also by means of machine learning techniques, and finally the process of rendering real or virtual 3D models to standard images and 3D/AR devices. Students will experience computer vision, deep learning and augmented reality techniques during lab sessions. A beginners’ tutorial on Unity will be provided as well.
 

This course is devoted to the interactions of light with living tissues and their technological applications to non-invasive biomedical imaging and treatments techniques. The topics covered by this course include fundamentals of light and matter, light-tissue interactions (light scattering and absorption in tissues), principles of lasers and non-linear optics as preliminaries to later discuss applications such as optical microscopy, biomedical imaging, spectroscopic techniques, plasmonics and photonic biosensing.

This course is devoted to the interactions of light with living tissues and their technological applications to non-invasive biomedical imaging and treatments techniques. The topics covered by this course include fundamentals of light and matter, light-tissue interactions (light scattering and absorption in tissues), principles of lasers and non-linear optics as preliminaries to later discuss applications such as optical microscopy, biomedical imaging, spectroscopic techniques, plasmonics and photonic biosensing.

This course presents machine learning and signal processing technologies that can be used handling digital evidences in investigations. The aim is to provide students with a set of analysis strategies for hard disk, network streams, image and video data (including authentication and fake detection), data from social networks. These techniques are reviewed and discussed both theoretically and via some case studies. The course entails the intervention of legal specialists that describe the procedural implications of the technical analysis and the juridical consequences on expert decisions.

Game theory is the science of analyzing multi-objective multi-agent problems (i.e., “games”). This involves the games we usually play for fun in our everyday life, but in a more serious context is applied to resource competition, distributed management, efficient allocation over multi-user systems and/or communication networks. This course teaches all the basic concepts, as well as some advanced ones, of game theory. Also, it applies them to scenarios of interest for ICT.

The subject aims at providing basic knowledge of modern telecommunication architectures, as well as fundamental mathematical tools for the modelling, design and analysis of telecommunications networks and services. The course will also give you some practical experience with network protocols and devices, thanks to a series of lab experiences that will introduce you to the art of router and socket programming. Ancillary to all this knowledge, the course will help you develop some basic management skills that shall belong to the baggage of each engineer. Some of the topics that will be considered by the course are data traffic sources, multimedia streams and content, packet switched networks: basics of data networks, ISO/OSI and TCP/IP protocol stacks, congestion control and scheduling algorithms and the application layer

The course deals with the analysis of biosignals, i.e., signals generated by human activity. It will study extraction of information from quasi-periodic signals (denoising, segmentation); application of clustering algorithms over biosignals to classify users and construct dictionaries for compact biometric datasets; use of unsupervised learning to perform quantization; statistical structures such as Bayesian networks and Hidden Markov Models, as well as supervised learning, for pattern and classification problems.

This course provides knowledge of the concepts of the “IoT” and “Smart cities,” describing their scientific and market trends, as well as the application of these paradigms in practical ICT context. The students will learn about some key platforms and standards (ZigBee, 6LoWPAN, WiFi, Bluetooth Low Energy, SigFox, Lo-Ra), and will review their applications for home automation, industrial applications, autonomous driving, urban monitoring, privacy and security.

The goal of the course is to provide the principles and tools needed to analyze and develop techniques for compression of multimedia data. Both lossless and lossy coding techniques will be considered. Methodologies for the evaluation of coding gain and rate distortion will also be discussed. Finally, applications to present coding standards will be presented, such as data (ZIP), audio (MP3), pictures (JPEG), and video (MPEG) compression, as well as their implication to multimedia communications.

The course describes networking phenomena over many scenarios. Although communication networks and the Internet are primary references, similar representations can be used for social networks, ecological systems, and epidemic diseases. The course describes network generation models, and then community structures are reviewed, also outlining the applications to online social communities, brain networks, and biological systems.

The course gives the fundamental principles of radio communication in terms of channel behavior and technological exploitation, detailing the contexts of physical layer (PHY), channel access (LINK), and networking procedures (NET). The student will become knowledgeable about modern centralized and distributed wireless systems such as IEEE 802.11 (a/g/n/h) and Wireless Sensor Networks (IEEE 802.15.4). Also, (s)he will get to know related packet-based application-layer / network coding.

The course covers the theory and practice of modern artificial neural networks, highlighting their relevance both for machine learning applications and for modeling human cognition and brain function. Topics include single-neuron modeling and principles of neural encoding; supervised, unsupervised and reinforcement learning; feed-forward and recurrent networks; energy-based models; large-scale brain organization. Theoretical discussion of various types of network architectures and learning algorithms is complemented by hands-on practices in the computer lab (PyTorch framework).

This is a theoretical course intended to provide knowledge of the main mathematical tools and modeling techniques for the study of telecommunication networks and networking protocols. The students will get to know the theoretical basics of Markov chains, renewal processes, queueing theory and traffic models. These instruments will be further applied to the analysis of datalink and networking protocols.

In this course, the student will learn how to manage devices and medical software in healthcare, so as to devise professional and clinical services involving biomedical equipment. The role of the clinical engineer in the hospital will be also discussed from a more general perspective, including how to model processes in healthcare, the role of clinical project management, and how to transform laboratory research into a product (technology transfer and patenting).

The course tackles molecular-level problems with mathematical and computational thinking techniques. Statistical modeling is used to interpret high-throughput data in genomics and transcriptonomics (signal processing of microarrays and RNA-sequencing). Classes will discuss regulation models for molecular systems, the analysis of genetic polymorphism, functional annotation of data and personalized medicine.

The course targets the principles of user-centered design, cognitive ergonomics, user experience, and usability to investigate how the human experience of interacting with automated computing machines can be made simple, pleasant, and overall satisfactory. Case studies from websites, apps, smart city applications will be presented and paradigm and design criteria will be reviewed and discussed. Also, the program will touch accessibility and universal design of interfaces as well as social computing and ergonomics.

The course provides knowledge about analytical and synthetic engineering methodologies for the study of the central nervous system. Covered topics include cerebral hemodynamics (MRI, arterial spin labeling), brain activation maps and connectivity, map generation from PET images, clustering, PCA and ICA, diffusion tensor MRI. Students will understand potential and limitations of neuroimaging techniques in the study of pathophysiological brain processes.

The course reviews statistical methods applied to large clinical datasets. The main application is the evaluation of public hygiene data with statistical criteria to assess aspects such as the presence of epidemics, the effectiveness of a therapy, and possible directions and evolutions for treatments. During the course, this kind of analysis will be also performed on real data taken from statistical datasets in different societal contexts, through laboratory sessions and group assignments.

This course expands programming skills already acquired by the students to give a special emphasis on scientific programming. The students will be guided through the object-oriented programming paradigm to design and develop software in the Python language. It will be given the competence to analyze formal correctness, computability, and complexity of a program, with a clear problem-solving purpose.

The course discusses optical properties of matter at molecular scale. Spectroscopy and radioscopy are introduced to study spectral and reflectance of materials, hyperspectral optical configurations and sensors. The course also covers surface plasmon, Kretschman configuration, nanostructured plasmonic sensors, lithography, metamaterials for lenses. Selected applications are presented to geology and agriculture (e.g. remote water detection), food industry, gas sensing, and medical diagnostics.

This is a monographic course on neurophysiology, functional neuroscience, and brain computer interfaces. First, it reviews the neurophysiology of movement (reflexes, posture, balance, sensorimotor systems), then synaptic physiology, plasticity, functional organization of brain areas, perception (vision, hearing) and cognitive brain functions. Finally, it presents, through a series of laboratory sessions, brain computer interfaces and their application to recovery from movement disorders, stroke, and ALS.

The integration of physics, statistics, information theory, distributed systems, biochemistry, genetics, and medicine leads to a new research domain with the ambitious goal of giving a physical characterization of organs and living beings. From an initial focus on the basic molecules of life (DNA, proteins) the course moves to cells, tissues, organs, organisms, and entire ecosystems. With methodologies borrowed from statistical physics, it explores the complex biochemical processes that are the constituent of life.

This course discusses the basics of anthropometry and physiology of the musculoskeletal system, and reviews evaluation devices and methodologies, both hardware (e.g., sensors, motion capture systems, force platforms, pressure insoles, electromyography) and software (musculoskeletal simulation code), to design applications for comfort, safety, rehabilitation, orthoses, assistive technologies, prostheses and training or rehabilitation machines. First-hand experimental session will also be held in the laboratories.

The lab aims to help students improve their oral communication through the study and practice of the elements contributing to successful communication. The focus is on raising the students’ awareness on the importance of verbal and non verbal language in interactions to make communication more effective. The students will learn the meanings of body language and paralanguage (voice intonation, volume, etc), how they are used in different types of interactions (one-to-one, one-to-many, computer-mediated, etc.), and will have to apply them in a number of assigned tasks. The lab requires the students’ active participation in all class activities, aimed at applying the communication strategies learned.

This course will provide the foundations of the project management. Traditional (such as the Project Management Institute approach) as well as more advanced techniques – such as the Agile Methodology – will be reviewed. Special focus will be put on the methodologies more suited for the ICT environment.

The course aims to introduce students to the non-verbal elements that contribute to communciation. The course lectures will focus on body language, facial expressions and on the non-verbal elements of the voice. The analysis of communication will be based on examples of spoken language produced in authentic video texts. The course integrates lessons in the lab and analysis of videos with the software Elan.

Other
Students must certify that they have a proficiency level “English B2” according to the CEFR scheme. To this end, students can: 
– Submit a certificate issued by a recognized external certification agency;
– Take an internal test at the University of Padova Language Centre (CLA) to verify that they can interact with a degree of fluency and spontaneity that makes regular interaction with native speakers quite possible without strain for either party.

All students are required to take an Internship of at least 2 months either in a Lab of the University of Padova, in an external Research Center, or in an Industry. Your internship might be anywhere – local, national, or international! You can find your internship yourself, with help from the Internship staff, or by faculty or advisor referral. We suggest you to match the Internship with your Final Year Project, in such a way to get the best out of it.

Students are asked to carry out a substantial individual project in their final year. Each project is supervised by a member of faculty, and is assessed via a written report (thesis) and a final presentation in front of an Academic Commission.