Cybersystems – Multimedia and Digital Arts

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 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.

Course details will be available soon

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 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 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 course is about theory and application of video compression for communications. The course covers the theoretical foundations of signal compression, describes the most common and most recent video compression standards (H.264, HEVC, VVC, MPEG), and focuses on the quality of experience of video streaming services. Laboratories are a relevant part of the course. The students will be guided to the implementation of a simplified but functional video encoder.