Telecommunication Engineering

Educational objectives

GENERAL
Why should you attend this course? Well, let’s consider that an emerging challenge of the incoming Artificial Intelligence of Things (AIoT) era is to attempt to split monolithic “heavy” AI applications (like Machine Learning applications) into a number of “light” sub-applications and, then, allow them to be executed in a distributed way by a number of resource-limited (but inter-connected) IoT devices (like tablets, laptops and cell-phones). It is expected the emerging paradigm of the Middleware Communication will play a key role to provide “virtualized” communication services just behind the Application layer. However, in spite of the potentially booming impact of the depicted scenario, current educational curricula in Information and Communication Technology (ICT) do not seem to still offer courses specifically tailored on the performance analysis and integrated design of networked computing systems for distributed AIoT applications. This is, indeed, the “mantra” of this course on the Broadband Middleware Communication Systems. Specifically, its goal is to provide to the attending students the emerging methodologies and techno-scientific skills that are needed to: (i) analyze the Middleware architectures of the emerging networked computing systems for the distributed support of AI-oriented applications; and: (ii) evaluate their performance, so to be capable to proceed to their optimized design. The emerging paradigms of Cloud Computing, Fog Computing, AIoT and Distributed Learning will be utilized as illustrative cases of practical interest. A solid background in communication systems and networking is required. Basic skills in Python programming are welcome.

SPECIFIC
• Knowledge and understanding: It is expected that, after attending the course, the student acquires the technological skills needed for the design of both system and protocol Middleware broadband architectures targeted to the distributed support of emerging AI applications
• Applying knowledge and understanding: It is expected that the student will be capable to leverage the acquired techno-scientific skills to design broadband networked computing systems for the distributed support of emerging AI services
• Making judgements: It is expected that the student will acquire the skills needed to evaluate the technological, economic and environmental impact of the proposed/adopted Middleware system solutions
• Communication skills: By definition, networked computing systems for the support of distributed applications must be designed by integrating different skills that, up to now, are typically possessed by heterogeneous players. Hence, a goal of this course is to equip the attending student with the multi-disciplinary technological language that is required to operate in these heterogeneous contexts
• Learning skills: By definition, the course integrates a number of multi-disciplinary topics falling under the broad umbrella of the so-called Computer Science. As a consequence, it is expected that the attending student will acquire the skills that are needed to autonomously consult and understand the reference techno-scientific literature that spans the broad areas of the Broadband Networking and Distributed Computer Networks.

Educational objectives

GENERAL
The course aims to provide students with an in-depth understanding of the operating principles, architectures, and applications of modern digital radar systems based on antenna arrays. Students will acquire both theoretical and practical skills in multi-channel radar signal processing, adaptive beamforming, and target detection, localization, and tracking techniques, including the use of multiple beams and moving platforms.
Upon completion of the course, students will be able to design, simulate, and analyze digital radar systems, evaluating their performance, limitations, and potential in various application contexts, including surveillance, security, and environmental monitoring. The curriculum includes the use of software tools for simulating and modeling radar signals, as well as practical laboratory activities to develop a concrete understanding of the techniques learned.

SPECIFIC
• Knowledge and understanding: To know the theoretical principles and architectures of advanced radar systems, particularly those utilizing multiple antenna beams and cutting-edge technologies, including innovative solutions compared to the current state of the art.
• Applying knowledge and understanding: To apply multi-channel radar methodologies to design, dimension, and optimize radar systems, as well as to process and analyze received signals in real-world scenarios, addressing technical and design challenges.
• Making judgements: To be able to critically evaluate different technological and design solutions, adopting approaches based on a thorough assessment of available alternatives and making informed and reasoned decisions.
• Communication skills: To effectively present, in a clear and critical manner, the knowledge acquired and the results achieved, describing the methodologies and solutions applied to expert stakeholders, using precise technical language and an appropriate register for the context. To develop interpersonal and teamwork skills.
• Learning skills: To be capable of autonomous learning, identifying and correcting errors during the practical application of learned techniques, with the ability to follow an iterative process of improvement and adaptation of work strategies.

Educational objectives

GENERAL
The aim of the course is to provide the theoretical and operational principles of Synthetic Aperture Radar (SAR) systems operating from airborne and satellite platforms. The course introduces the fundamental principles underlying SAR system design and the main operational modes, SAR signal processing techniques for focusing and autofocusing, as well as radar image processing methods for information extraction, with reference also to advanced methodologies for multi-dimensional processing. Particular emphasis is placed on the role of SAR in modern Earth observation systems for monitoring and surveillance applications in both civil and scientific contexts, taking into account recent technological advancements and the integration with next-generation satellite platforms or with unmanned platforms. The concepts presented are also applicable to other emerging domains, such as the automotive sector, security screening, and industrial imaging, where similar techniques are of interest for control, diagnostics, and monitoring purposes.

SPECIFIC
• Knowledge and understanding: to demonstrate knowledge of the operating principles and design criteria of SAR systems, as well as the related processing techniques, with reference to state-of-the-art airborne and satellite systems, and to be able to understand their innovative developments.
• Applying knowledge and understanding: to be able to competently and critically apply the operating principles, design criteria, and processing techniques of SAR systems for the understanding and development of technical solutions, including innovative ones, while considering the requirements of the relevant scenarios and the performance requirements of the associated applications.
• Making judgements: to be able to integrate and apply the acquired knowledge for SAR system design and for the development of signal processing chains composed of multiple interconnected stages, as well as to critically analyze the corresponding results. The development of independent judgment is further enhanced through the final course project.
Communication skills: to be able to describe, using appropriate technical language, the solutions adopted to address SAR system design and signal processing problems, and to present and discuss the results obtained from the processing activities. Communication skills are further developed through the final examination, which includes a presentation and discussion of the work carried out in the course project.
• Learning skills: to develop the necessary skills to undertake further studies, such as a thesis project or training and research activities related to SAR systems and techniques, while keeping up to date with technical and scientific advancements in the field.

Educational objectives

GENERAL
The aim of the course is to provide the student with the ability to understand: i) architectures and trends in satellite networks including those using using intersatellite links (ISLs) and Low Earth Orbit (LEO) satellites; ii) key technologies and elements for their selection, standards; iii) the design, the analysis and the optimization in the performance of satellite networks; iv) Internet protocol (IP) based satellite networks; v) Enhancements for satellite networks of the transmission control protocol (TCP).

SPECIFIC
• Knowledge and understanding: The student has an in-depth knowledge of the operating paradigms and techniques used in satellite telecommunications networks.
• Applying knowledge and understanding: The student is able to develop and apply methodological approaches for the design of satellite networks.
• Making judgements: the student must have the ability to design and manage satellite networks, evaluating the impact of the identified solutions with regard to technical and organization aspects.
• Communication skills: The course does not include specific objectives on communication skills.
• Learning skills: The student is able to keep up to date and independently acquire new knowledge in the field of satellite networks, using various self-directed learning tools, including the independent study of relevant technical literature.

Educational objectives

GENERAL
The course provides theoretical and practical skills on the main methods of Deep Learning (DL), with particular reference to problems of classification, regression and generation in high-dimensionality and complexity scenarios. Students will acquire knowledge on: deep neural network (DNN) architectures, including convolutional, recurrent and generative models; supervised, self-supervised and reinforced learning techniques; methods for evaluating model performance; software libraries such as PyTorch and TensorFlow; applications in areas such as computer vision and NLP.

SPECIFIC
• Knowledge and understanding: advanced understanding of DL techniques and related application domains (big data, vision, language, etc.).
• Applying knowledge and understanding: ability to design and develop DL solutions in concrete contexts, using appropriate models and libraries.
• Making judgments: development of critical thinking through practical activities on real data and performance evaluation.
• Communication skills: ability to effectively present methods, results and solutions to both specialists and non-specialists.
• Learning skills: promotion of autonomous learning through individual projects and experimental activities.

Educational objectives

GENERAL
Knowledge of the general structure of cellular radio systems with particole reference to GSM, LTE, 6G, wifi, Bluetooth, ad-hoc networks, radio coverage and cell sizes, modulation techniques, frequency bands used, propagation problems, system capacity (users per band unit), attach procedures, handover, phone call, data exchange. Ability to compare different systems.

SPECIFIC
• Knowledge and understanding of the different types of radio mobile systems, considered in relation to the objectives and costs of the involved infrastructures, also through comparative analysis of the systems
• Ability to apply knowledge and understanding: capability to grasp the selection of parameters and design choices associated with the various systems
• Making judgments: ability to identify appropriate functional block diagrams that summarize the different parts of radio mobile systems
• Communication skills: ability to clearly explain the various standardized procedures used in the context of mobile radio systems
• Learning skills: ability to dimension and quantitatively analyze the behavior of the considered radio mobile systems.

Educational objectives

GENERAL
The course provides a comprehensive and up-to-date overview of the most advanced multimedia systems and services, with the aim of equipping students with a solid foundation in digital communications. Key multimedia applications will be explored, including video and audio streaming, broadcasting, voice over IP services, and emerging technologies related to extended reality.
Special focus will be placed on the underlying technologies that enable these services, with in-depth analysis of protocol architectures and the strategies adopted to ensure quality, reliability, and scalability. Topics will include content encoding and transport mechanisms, quality of service (QoS) management, and the delivery of multimedia over next-generation mobile networks, with particular attention to 5G.
The course blends theoretical foundations with practical insights, enabling students to understand how multimedia technologies are integrated into modern communication systems. It aims to provide knowledge and skills that are valuable both in academic research and in the tech industry.

SPECIFIC
• Knowledge and understanding of the most advanced multimedia systems and services, like streaming, broadcasting, video e voice over IP, extended reality services. Achieve a big picture of multimedia systems design, including signal processing as well as networking issues.
• Applying knowledge and understanding: identifying the main architectural and technological issues involved in communication oriented multimedia systems, with particular reference to 5G systems ..
• Making judgements: 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 Be able to read scientific papers and technical standard on the most advanced solutions for multimedia systems.

Educational objectives

GENERAL
The Programmable Networks course aims to provide students with a deep understanding of network programmability, structured around four key pillars: network automation, software-defined networking (SDN), network softwarization, and dataplane programmability. Students will gain both theoretical and practical knowledge on automated network management, network function orchestration, the separation of control and data planes, and advanced techniques for dataplane programmability.
Through a hands-on approach, the course includes practical exercises on virtualized environments, network automation tools (e.g., YANG and NETCONF), SDN controller management, routing strategy implementation, and programmable switch pipeline development using the P4 language. By the end of the course, students will be able to design, implement, and evaluate innovative network solutions, ensuring flexibility, efficiency, and scalability in modern communication environments.

SPECIFIC
• Knowledge and understanding: Students will acquire an in-depth knowledge of network programmability principles and technologies, including automation, SDN, NFV, MANO, and dataplane programmability.
• Applying knowledge and understanding: Students will be able to design and implement programmable network architectures, leveraging tools and protocols for automation, control, and orchestration.
• Making judgments: Students will develop the ability to critically evaluate the adoption of programmable network solutions in complex environments, considering performance, scalability, and service requirements.
• Communication skills: Students will acquire the ability to effectively communicate network programmability challenges and solutions, both in written and oral form, in technical and academic contexts.
• Learning skills: Students will develop a methodological approach that enables them to stay updated on network programmability advancements, acquiring new knowledge and skills autonomously.

Educational objectives

GENERAL
Knowledge of the fundamental theoretical elements of deep learning methods with particular reference to audio signal processing. Students will acquire knowledge on: i) fundamentals of acoustics; ii) fundamentals of signal processing for audio applications; iii) artificial intelligence and machine learning methods oriented to audio signal (music, speech, various acoustic signals) and various application contexts.

SPECIFIC
• Knowledge and understanding: knowledge of the fundamentals of AI-DASP, with particular regard to the definition of algorithms for the analysis, synthesis and generation of audio signals.
• Ability to apply knowledge and understanding: ability to apply AI-DASP techniques and procedures to the most common problems described in the course.
• Making judgements: regarding the possible optimal solution of AI-DASP problems.
• Communication skills: ability to effectively present methods, results and solutions to both specialists and non-specialists.
• Learning skills: autonomous learning from specialized texts; ability to pursue further studies, for example a PhD, on advanced AI-ASP issues.

Educational objectives

GENERAL
The course provides both theoretical and practical foundations for the design of automatic machine learning systems, addressing problems such as classification, clustering, function approximation, and prediction through Computational Intelligence techniques, including neural networks, fuzzy logic, and evolutionary algorithms. Students will develop the ability to understand advanced literature and scientific texts in the fields of Soft Computing and Computational Intelligence. They will also be able to apply the studied methodologies and algorithms to design innovative systems in multidisciplinary contexts, independently analyzing design requirements and selecting the most suitable machine learning solutions. The course also fosters communication skills, enabling students to produce technical reports and effective presentations to document project development and performance results. Finally, it promotes strong autonomous learning skills, allowing students to independently deepen the topics covered and to continuously update their knowledge in the rapidly evolving ICT domain.

SPECIFIC
• Knowledge and understanding: The course provides the fundamental principles for designing automatic machine learning systems (classification, clustering, function approximation, and prediction) based on Computational Intelligence techniques (neural networks, fuzzy logic, evolutionary optimization algorithms). Students who pass the final exam will be able to read and understand texts and scientific articles on advanced topics in Soft Computing and Computational Intelligence.
• Applying knowledge and understanding: Students who pass the final exam will be able to apply the methodological principles and algorithms studied to design innovative machine learning systems in multidisciplinary contexts.
• Making judgements: Students who pass the final exam will be able to analyze design requirements and choose the machine learning system that best fits the specific case study.
• Communication skills: Students who pass the final exam will be able to write technical reports and deliver appropriate presentations aimed at documenting the design, development, and performance evaluation of a machine learning system.
• Learning skills: Students who pass the final exam will be able to autonomously continue exploring the topics covered in class, engaging in the continuous learning process that characterizes professional development in the ICT field.

Educational objectives

GENERAL
The "Network Infrastructures" course provides an in-depth overview of the main architectures, protocols, and technologies of modern network infrastructures, with a particular focus on broadband access networks, optical transport networks, and next-generation wireless solutions. Students will gain a detailed understanding of the fundamental technologies and protocols for configuring and managing telecommunications networks, covering both theoretical and practical aspects. The course includes hands-on exercises on network configurations using advanced simulation tools, developing essential operational skills in the telecommunications sector. Additionally, key network security solutions and Quality of Service (QoS) support mechanisms will be analyzed, preparing students to understand and address emerging challenges in network infrastructures.

SPECIFIC
• Knowledge and understanding: Students will acquire a deep understanding of network architectures, access technologies (xDSL, PON, LTE, 5G), transport protocols (OTN, MPLS), and routing mechanisms (BGP, SCION).
• Applying knowledge and understanding: Students will be able to configure, analyze, and troubleshoot IP networks using simulation tools such as Kathara.
• Making judgments: Students will develop the ability to critically evaluate different network technologies and select optimal solutions based on security, performance, and scalability requirements.
• Communication skills: Students will be able to present network technology concepts clearly in both technical and general contexts.
• Learning skills: The course will provide methodological foundations to keep up with the continuous evolution of network technologies and independently explore new solutions and emerging standards in the telecommunications sector.

Educational objectives

GENERAL
The course provides an insight into the essential programmable data processing architectures that are at the core of signal processing, communications, and Internet-of-Things (IoT) devices and systems. The electronic architecture, as well the software and firmware environments, of microcontrollers and Field-Programmable-Gate Arrays (FPGAs) allow the appropriate and optimized design of solutions for specific applications.

SPECIFIC
• Knowledge and understanding: Understanding the electronic layer of microcontrollers and FPGAs to employ them efficiently in telecommunications applications
• Applying knowledge and understanding: Hands-on experience in implementation of simple applications on microprocessor and prototyping boards, employing programming-language coding and hardware description languages
• Making judgements: Choosing the most appropriate hardware and interfacing solution for specific applications
• Communication skills: Being able to write a technical report for hardware/software co-design of an embedded systems
• Learning skills: Analyzing specific system design tasks and finding the most appropriate electronic solution

Educational objectives

GENERAL
The course aims to provide fundamental knowledge of the main types of spaceborne radar systems used for planetary exploration and Earth observation, with a particular focus on three core categories: synthetic aperture radar (SAR), used for surface observation; radar altimeters, for measuring surface elevation and topography; and radar sounders, typically employed in planetary exploration missions for subsurface penetration. For each of these systems, the course will introduce the underlying physical principles and design considerations, addressing constraints such as power, mass, spatial resolution, and orbital coverage, as well as the corresponding system architectures. For each category, one currently operational system and a representative application example will be analyzed in greater detail.

SPECIFIC
• Knowledge and understanding: demonstrate knowledge of the operating principles and design criteria of SAR systems, radar altimeters, and radar sounders.
• Applying knowledge and understanding: be able to apply the operating principles and design criteria of SAR systems, radar altimeters, and radar sounders.
• Making judgements: it is developed through targeted exercises focusing on system sizing and architectural choices, starting from the definition of appropriate requirements for the different types of systems under consideration.
Communication skills: be able to use the technical and scientific language specific to the field, also taking into account its multidisciplinary nature.
• Learning skills: be able to independently explore specific topics of interest and pursue further studies in the relevant field.

Educational objectives

GENERAL
The goal of the course is the study of the Ultra Wide Band (UWB) communication technique and its application to the design of ad hoc networks, sensor networks, and distributed wireless networks. Key aspects of UWB systems will be analyzed in order to highlight the potential of a technology that seems to be a solid candidate for the definition of standards and specifications for future wireless systems for communications and positioning. The course will deal with the theoretical foundations of UWB communications, including practical exercises and application principles for each topic.

SPECIFIC
• Knowledge and understanding: techniques for UWB signal generation, time and frequency analysis of UWB signals, design of UWB receivers in AWGN and multipath channels, single-link and network performance analysis, positioning and localization techniques based on UWB technology.
• Applying knowledge and understanding: analysis and design of UWB wireless networks as a function of the transmitted signal, channel, and used receiver, combining the analytical approach with the use of software tools for link and network simulation.
• Making judgements: ability to design and dimension a UWB wireless network, correctly identifying constraints and objectives to be met for performance indicators and standardizations, selecting the best combination of tools to complete the task successfully and efficiently.
• Communication skills: learn to present clearly and coherently topics related to UWB communications, combining an accurate analytical description, the ability of providing a comprehensive view of such topics, and the knowledge and use of software simulation tools.
• Learning skills: Development of independent skills for studying advanced topics in the field of ultrawideband communications through the analysis of state of the art scientific publications.

Educational objectives

GENERAL
The course aims to provide students with the skills needed to understand and design advanced communication systems in which Artificial Intelligence (AI) is natively integrated into the operation of the network.
The first part of the course focuses on AI techniques for improving the efficiency, reliability, and autonomy of communication networks, with particular attention to machine learning, deep learning, reinforcement learning, and graph neural networks.
The second part explores the network architectures and technologies required to support the distributed execution of AI services, such as federated learning, distributed control, and edge-cloud deployment of generative models.
The course prepares students to engage with emerging 6G paradigms, including semantic communications, distributed intelligence, and AI-native infrastructures.

SPECIFIC
• Knowledge and understanding: By the end of the course, students will understand the fundamental principles of integrating AI into communication networks, both in the “AI for Networks” and “Networks for AI” perspectives.
• Applying knowledge and understanding: Students will be able to apply machine learning algorithms to intelligent network management and design network infrastructures capable of supporting distributed AI services.
• Making judgements: The course encourages critical thinking on the benefits, limitations, and ethical implications of using AI in network systems, promoting comparative evaluations between classical and AI-based approaches.
• Communication skills: Students will be able to clearly and technically describe AI-native architectures, models, and solutions for communication systems, even in multidisciplinary and international contexts.
• Learning skills: The course provides students with the tools to continue independently studying advanced topics in 6G, edge intelligence, and intelligent communication systems.

Educational objectives

GENERAL
The course aims to provide students with an introduction to discrete mathematics, which represents one of the most innovative fields in mathematics. Developed starting from the second half of the twentieth century, it is rich in stimulating problems and highly useful in applications. Throughout the course, students will encounter a variety of topics and problems that are completely different from those found in traditional mathematics courses. Through systematic engagement in problem-solving, they will develop a practical approach to studying problems of great educational value, particularly in preparation for their future professional careers.

SPECIFIC
• Knowledge and understanding: By the end of the course, students will be familiar with the methods, problems, and potential applications of discrete mathematics;
• Applying knowledge and understanding: They will be able to understand, tackle, and solve basic problems in discrete mathematics;
• Making judgements: Through written exercises and possible oral presentations, they will develop appropriate critical thinking skills;
• Communication skills: Likewise, they will strengthen their ability to explain and convey what they have learned;
• Learning skills: Individual study will effectively train their capacity for autonomous and independent learning.

Educational objectives

GENERAL
The course aims to provide the methodological tools and applicative knowledge related to the techniques and devices for the main applications of electromagnetism in modern terrestrial and space telecommunications systems and in remote sensing. The skills acquired concern the properties of electromagnetic devices with attention to guided propagation, radiation and sensors used in various applications of ICT and civil and industrial engineering. The training path is completed by the study of computer-aided analysis and design procedures, instrumentation and measurement techniques.

SPECIFIC
• Knowledge and understanding: to know and understand the techniques, tools and methodological aspects in the study and characterization of various high-frequency devices for terrestrial and satellite communications, and electromagnetic technologies for radar and remote observation and control systems in complex environments.
• Applying knowledge and understanding: to be able to apply electromagnetic analysis techniques to the design of different types of high-frequency devices and systems.
• Making judgements: to be able to collect additional information to achieve greater awareness of devices used at high frequencies in the ICT and civil and industrial engineering fields.
• Communication skills: to be able to describe the electromagnetic problems associated with the use of various high-frequency devices for information transmission and sensor applications.
• Learning skills: to be able to extend knowledge in a continuous updating of the problems of applied electromagnetism for the treatment of information at a distance.

Educational objectives

GENERAL
The course aims at providing the student with a holistic vision of the state of the art and the modern methodologies on automatic perception, i.e. the ability of a system to interpret data in a way similar to the way in which human beings use their senses to relate to the world around them. In particular, the main focus of the course is oriented towards the description of the methodologies for automatic vision and listening. The student will learn to develop applications for the high-level understanding of images and sounds in order to make appropriate decisions automatically. The course is completed by a detailed discussion of some complex applications, such as: virtual assistants, healthcare, scene analysis, safety systems, and autonomous driving.

SPECIFIC
• Knowledge and understanding: at the end of the course the student will be able to learn about modern methodologies for the design of automatic vision and listening applications.
• Applying knowledge and understanding: the student will be able to independently develop automatic vision and listening applications and to describe the implemented solutions and their limitations.
• Making judgements: the student will be able to integrate the knowledge acquired during the course with that of the information generally transmitted within the Master Degree Course.
• Communication skills: the student will be able to transmit the knowledge acquired and to illustrate the processes that led to it.
• Learning skills: individual study will adequately train the capacity for autonomous and independent study, and the ability to continue future studies regarding advanced topics of multimedia processing.

Educational objectives

GENERAL
Aim of the course is an advanced knowledge of Mathematical Analysis towards applications, the knowledge of differential calculus in several variables, the study of minima and maxima of real functions of several variables, with a discussion on the assumptions. Moreover the study of minima and maxima of real functions with constraints with a discussion on the constraints and the analysis of mathematical models. The aim of the course is the understanding and the use of mathematics for the formulation of simple models and the knowledge of differential calculus in one and several variables.

SPECIFIC
• Knowledge and understanding: Acquire basic concepts and learn how to apply them in exercises using textbooks and lecture materials from the course Mathematical Methods for Information Engineering.
• Applying knowledge and understanding: Be able to competently apply the acquired knowledge; possess the appropriate skills and understanding to solve problems and support logical reasoning.
• Making judgments: Gather and interpret results developed during the course in order to solve similar problems independently; identify common features in different problems.
• Communication skills: Communicate hypotheses, problems, and solutions to non-specialist audiences.
• Learning skills: Develop the necessary skills to pursue advanced studies.

Educational objectives

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

SPECIFIC
• Knowledge and understanding: knowing the physical mechanisms that determine the operation of optical devices, and the architectures that allow these components to be integrated into a point-to-point optical telecommunication system, and subsequently into a complex network at different levels of transparency of the signal. Knowledge of performance analysis methods of optical telecommunication systems.
• Ability to apply knowledge and understanding: being able to apply numerical simulation techniques and methods of characterization of devices and systems through virtual experiments, competently and critically.
• Making judgments: knowing how to evaluate the properties and performance of an optical telecommunication device and system.
• Communication skills: knowing how to describe the solutions adopted to solve optical signal transmission problems through written papers and oral interviews.
• Learning skills: ability to learn from multiple sources of information, and to continue any subsequent studies, e.g. research doctorate, concerning advanced topics of synthesis, analysis and transmission of the optical signal.

Educational objectives

GENERAL
The Project Management and Organization course aims to provide students with knowledge of the economic and organizational principles that characterize the design, management and results of project teams in the business form. This knowledge is particularly relevant for the so-called project-based organizations, in which the proper design of the team structure, the planning and control of project activities play a decisive role in terms of economic results and compliance with the quality, time and cost requirements of the project itself.
The focus on the issues of Business Organization, moreover, will serve to understand the importance of the skills of the project manager and his collaborators, both in terms of the so-called hard skills as well as in terms of soft skills. To this end, the different characterizations of project leadership and the skills necessary for the effective negotiation of resources, for the correct interaction with line managers and the resolution of intra and inter-team project conflicts will be analyzed.

SPECIFIC
• Knowledge and understanding: Understanding of the fundamental principles that allow to structure and manage the economic, financial and organizational dimensions of complex projects.
• Applying knowledge and understanding: Development, through exercises and case studies, of the ability to apply the knowledge acquired relating to the design, planning and control of complex projects.
• Making judgements: development, through exercises and case studies, of the skills aimed at critically evaluating the impact of management decisions and operations on the economic and financial results of projects.
• Communication skills: Acquisition of technical terms related to economic and financial language that allows students to develop expressive communication and effective interaction skills within project groups.
• Learning skills: Ability to independently identify, also through guidance support and stimuli, further in-depth paths relating to the design and economic management of complex projects.

Educational objectives

GENERAL
The fundamental principles and methodologies are presented for (i) the computer-based simulation of operational scenarios in which radar remote sensing systems can operate, (ii) the implementation, using computers and/or dedicated real-time signal processing hardware, of the main radar signal processing techniques, and (iii) performance evaluation, also considering aspects of cost-effectiveness and implementation complexity.

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 critical sense, 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 the conceptual and analytical tools necessary to understand the functioning and structure of radiopositioning and navigation systems, both outdoor and indoor, with specific reference to (i) global navigation satellite system (GNSS, e.g. GPS, Galileo, etc.) together with terrestrial and satellite augmentation systems to increase their performance and (ii) terrestrial positioning systems using local and personal area radio technologies (e.g. WiFi and Bluetooth Low Energy), as well as through fourth and fifth generation mobile networks. The course also aims to explore aspects of these technologies that play a fundamental role in their use for radiopositioning and navigation, such as synchronization, tracking, and the integration of multiple technologies, both terrestrial and satellite-based, in order to improve the accuracy and robustness of the positioning service.

SPECIFIC
• Knowledge and understanding: to demonstrate a solid understanding of the design principles and technological/implementation aspects of state-of-the-art terrestrial and satellite radiolocation systems, as well as the ability to critically comprehend and analyze their innovative developments.
• Applying knowledge and understanding: to be able to apply design principles and technological-implementation elements of terrestrial and satellite radiolocation systems to the understanding and development of technical solutions, including innovative ones, taking into account the requirements of the operational scenarios and the performance demands of the related applications.
• Making judgments: to develop the ability to interpret complex scenarios, as well as critical thinking skills in relation to issues that may also be interdisciplinary in nature (e.g., social and ethical responsibilities related to the privacy of location information).
• Communication skills: to be able to communicate information, issues, and solutions related to terrestrial and satellite radiolocation systems to both specialist and non-specialist audiences.
• Learning skills: to develop the necessary skills to undertake further studies, whether in the context of a thesis, advanced training, or research activities related to terrestrial and satellite radiopositioning, while remaining up to date with the technical and scientific developments in the field.

Educational objectives

GENERAL
The aim of this course is to provide an overview of the vast world of wireless and wired technologies that will be used in smart environments and cyber-physical spaces. These technologies will enable the development of network infrastructures and platforms for processing digital, multimedia, and extended reality information, applied in urban and intelligent environments.
Recent advancements in fields such as edge computing, machine learning, wireless networks, and sensor networks allow for various smart environmental applications in everyday life. The primary objective of this course is to present and discuss the latest developments in the Internet of Things area, particularly focusing on technologies, architectures, algorithms, and protocols for smart environments, with an emphasis on real-world applications. The course will cover communication and networking aspects, as well as multimedia and extended reality data processing for application design. Two case studies in the domain of smart environments will be presented: vehicular traffic monitoring for ITS applications, and low-power wireless networks. For both cases, tools, models, and methodologies for designing smart environment applications will be provided.

SPECIFIC
• Knowledge and understanding: Understand recent developments in the Internet of Things, particularly technologies, architectures, algorithms, and protocols for smart environments, with a focus on applications and processing platforms. Understand recent advancements in the representation of multimedia and extended reality data.
• Applying Knowledge and Understanding: Students will learn to apply the knowledge gained in real-world IoT platform design. This includes everything from data acquisition and networking solutions to the design of practical smart environment applications, such as vehicular traffic monitoring, networked systems (smart grids and smart monitoring) and processing for extended reality integration.
• Making Judgements: Students will be able to analyze the benefits and challenges associated with smart environment technologies and applications, considering factors such as communication constraints, big data processing, and the integration of extended reality in IoT systems. They will also evaluate the trade-offs of different solutions based on practical case studies.
• Communication Skills: Students will be able to present their projects effectively, discussing design constraints, solutions, and the potential applications of smart environment technologies. They will also gain experience in explaining complex topics such as IoT networking, signal sampling, localization, and XR technologies.
• Learning Skills: The course will prepare students for more advanced studies and research in the fields of ambient intelligence and smart spaces and next-generation IoT systems. They will gain the skills necessary for continued development in the ever-evolving landscape of smart environments and IoT networking and processing.