INTERNET AND MULTIMEDIA ENGINEERING

The unit aims to provide knowledge and skills on the operating principles and analysis and design techniques of antennas and antenna systems. These topics are discussed both from a theoretical point of view and with reference to different applicative examples.

The teaching unit discusses the key concepts related to antennas.

The exam consists of a mandatory preliminary written test, made of multiple-choice questions covering all teaching unit topics, with a maximum admissible score equal to 24/30. Students who achieve a sufficient score (i.e., at least 18/30) in the written test may optionally take a supplementary oral exam to improve their mark and potentially reach the maximum score of 30/30 with honors.

The exam cover the entire course program and it will be aimed at assessing the skills acquired by the students on antennas, with reference to both their operating principles and their use in different fields. The evaluation of the exam will be based on the quality of the presentation, the reasoning ability and the level of knowledge acquired by the students.

Lecture notes provided by the teacher.will be made available.
The following reference books are also suggested:
C. A. Balanis, Antenna Theory Analysis and Design. Wiley, 2005.
S. J. Orfanidis, Electromagnetic Waves and Antennas, 2016. Available online at www.ece.rutgers.edu/~orfanidi/ewa/

The module aims at providing theory and techniques for architectural and functional design of interactive cognitive dynamic systems. Topics are related to data fusion, mutilevel bayesian state estimation and their application to cognitive video and radio domains. Project based learning allows students to acquire design capabilities in the field.

This teaching unit aims at provining to the Master student basic and advanced concepts on the design of methods and techniques for data driven self-awareness in autonomous artificial agents . Signal Processing, Data Fusion and Machine learning under a Bayesian pespective will be the key dimensions on which introduced concepts will be described. Laboratory application and agent design will integrate course theoretical activities

Exam is structured in a written plus an oral part
The written part consists of presentation of a report describing a set of activities and results done by the student aiming at demonstrating knowledge and capabilities he acquired along lecures and lab attendance. Assigned datasets on which activities have to be developed are of the same type to the ones analyzed along lab experiences a report has to be presented. The case study is oriented to show the design capabilities of the student of an integrated processing system capable to analyze a generic dataset based on teaching unit techniques.
The case study have to be assigned/agreed at least three weeks before oral exam date on the basisof student request. The report prepared by the student will have to show that he/she has acquired knowledge and capabilities presented in the course. The report will have to be delivered at indicated online repository at least 4 days before oral exam. The outcome of the evaluation of the written exam will be communicated the day before the oral exam.
Oral exam will consist of discussion of the written exam (report), The student will have to prepare max 20 slides to describe results and approaches presented in the written text. Oral discussion will be oriented to demonstrate course acquired knowledge and capabilities necessary to describe choices performed when developing report , as well as to comment results and performances obtained/expected for the chosen problem. Use of AI tools in the report redaction should be expressely indicated and the student should be ready to explain why he accepted such parts of the text based on course knowledge.
The oral exam will be passed in case the student will be admitted to the oral with at least 12 over 20 AND if the outcome of the oral will be at least 6 over 10. Laude can be assigned during the oral part.

Exam aims at assessing the following aspects about acquired student's knowldge and capabilities:

• Level of Knowledge acquired with respect to theories and methods presented in course lectures by means of oral questions
  -Level of practical and integration capabilities of techniques learned in lab with respect to either the assigned data analytics problem (in case of dataset processing choice) or the design and the specifications of the data analysis system (in case of case of study choice) chosen for the written part of the exam.
  -Level of Capability and knowledge assessed by including in a report for the written part and discussing in the oral motivate performed choices and obtained results on the assigned task perfrmed in the practical part.

Slides of all lectures written by the lecturer will be made available
Books and research papers that can help the student to integrate concepts described during frontal activity are here provided and can be integrated during the year.

• A. R. Damasio, Looking for Spinoza: Joy, Sorrow, and the Feeling Brain, 1st ed. Orlando: Harcourt, 2003. [Online]. Available:http://lccn.loc.gov/2002011347 - S. Haykin, Cognitive Dynamic Systems: Perception-action Cycle, Radar and Radio, ser. Cognitive Dynamic Systems: Perception–action Cycle, Radar, and Radio. Cambridge University Press, 2012.
• P. R. Lewis, M. Platzner, B. Rinner, J. Torresen, and X. Yao, Eds., Selfaware Computing Systems: An Engineering Approach. Springer, 2016.
• K. J. Friston, B. Sengupta, and G. Auletta, “Cognitive dynamics: From attractors to active inference,” Proceedings of the IEEE, vol. 102, no. 4, pp. 427–445, 2014. [Online]. Available: https://doi.org/10.1109/JPROC.2014.2306251
• S. Haykin and J. M. Fuster, “On cognitive dynamic systems: Cognitive neuroscience and engineering learning from each other,” Proceedings of the IEEE, vol. 102, no. 3, pp. 608–628, 2014.

The module provides the fundamental knowledge required to understand the principles and mechanisms for securing telecommunication networks and Internet-based communication systems. Students acquire the essential concepts of cryptography, authentication, and network protection, together with the engineering principles needed to analyze security protocols and evaluate their application in modern communication infrastructures.

This teaching unit introduces the fundamental concepts and techniques for securing modern communication networks and Internet services. The teaching unit covers the principles of cryptography, authentication, and secure communications, as well as the security mechanisms implemented at different layers of the TCP/IP protocol stack. Students will gain an understanding of the technologies used to protect information and network infrastructures against common security threats.

The examination consists of a mandatory written test covering all topics included in the syllabus. The written test consists of multiple-choice questions designed to assess the student's knowledge and understanding of the theoretical concepts, security mechanisms, and protocols presented in the course. The maximum score achievable through the written test is 24/30.
Students who pass the written test may optionally take an oral examination to improve their final grade. During the oral examination, students will be asked to discuss theoretical concepts, cryptographic techniques, authentication mechanisms, network security protocols, and protection technologies covered in the course. The oral examination may also include questions that require analysis of security scenarios and discussion of the operation and effectiveness of different security solutions.
The final grade is determined based on the written test and, when taken, the oral examination. The oral examination allows students to demonstrate a deeper understanding of the course topics and achieve grades above 24/30, up to the maximum mark.

The written examination assesses the student's knowledge and understanding of the fundamental concepts, techniques, and protocols covered in the course. Through multiple-choice questions, students are required to demonstrate familiarity with cryptographic principles, authentication mechanisms, key management techniques, security protocols, and network protection technologies.
The optional oral examination is intended to assess the student's achievement of the module's learning outcomes at a deeper level. During the oral examination, students are expected to explain the operation of cryptographic and authentication mechanisms, analyze the security services provided by protocols operating at different layers of the TCP/IP architecture, and evaluate the effectiveness of network protection solutions in different application scenarios.
Particular attention is given to the use of appropriate technical terminology, clarity of exposition, the ability to establish connections among different security mechanisms and protocols, and the capacity for critical reasoning on the strengths, limitations, and applicability of the technologies covered in the course.
Students with Specific Learning Disorders (SLDs), disabilities, or other certified educational needs may be granted appropriate accommodations, to be agreed upon on a case-by-case basis in accordance with University of Genoa regulations and inclusion policies.

​Course material available on AulaWeb (https://www.aulaweb.unige.it), including lecture slides and additional teaching resources, is sufficient for preparing for the examination.
Suggested textbook for further study:
William Stallings, Cryptography and Network Security: Principles and Practice, 8th Edition, Pearson, 2022.

Upon successful completion of this teaching unit, students will be able to analyze, evaluate, and develop deep learning models and algorithms applied to Earth observation from space and to autonomous systems, with particular attention to task such as anomaly detection, planning and decision making, image semantic segmentation, and generative modeling. Students will be provided with methodological basis as well as with practical expertise within common frameworks for deep learning such as TensorFlow or PyTorch.

The teaching unit introduces the key concepts associated with deep learning and exemplifies its applications in the fields of Earth observation from space and of autonomous systems.

Mandatory written examination about the topics in the syllabus of the teaching unit, with maximum admissible mark equal to 24/30. If a student obtains a sufficient mark in this written exam, then he/she can also optionally take an additional oral examination with maximum admissible mark equal to 30/30 with honors.

Within the mandatory written examination, the student's knowledge of the main concepts discussed in the teaching unit shall be evaluated. Within the optional oral examination, the student's capability to address simple problems of deep learning form space and autonomous systems and his/her capacity to critically discuss the related methodological bases shall be assessed.

Class slides will be provided to the students through AulaWeb. Bishop C., Bishop H., Deep learning, Springer, 2024 Goodfellow I., Bengio Y., Courville A., Deep learning, MIT Press, 2016 R. Cresson, Deep learning for remote sensing images with open source software, CRC Press, 2020.

The teaching is aimed at providing the bases of digital communications: the baseband and bandpass transmissions will be presented, discussed, and compared. The goal is to give the students an adequate knowledge needed to understand the key elements for designing and developing modern telecommunication systems.

A digital communication system conveys information in the form of discrete symbols from its source to a destination some distance away. The approach used to address the subject is from a general viewpoint: since all communication systems share the same basic function of transferring information, the teaching presents the principles and problems of conveying information in terms of digital messages in electrical form through a noisy channel.

Written + oral

The written exam is devoted to assess whether the students have actually learnt the spectra estimation of digital modulations, the use of constellations, and the evaluation of link budgets.
Oral examination will focus on the topics covered during the lectures. The aim is to evalute the degree of learning, autonomy, reasoning skills achived by the student.

A. B. Carlson, P. B. Crilly, J. C. Rutledge, “Communication Systems”, 4th edition, Mac Graw Hill, 2002
B. Sklar “Digital Communications: Fundamentals and Applications”, Prentice Hall 2001
Handouts by the teacher.
Generally, the notes taken during lectures, the provided teaching materials, and the recommended textbook are sufficient for exam preparation.

The teaching provides the bases of techniques for protecting data in digital communications. Specifically, linear block codes and convolutional codes will be covered. The aim is to furnish an adequate knowledge to understand the main components to improve the reliability of modern digital telecommunication systems.

The course introduces the fundamental principles of information theory, with particular attention to the source and channel coding techniques used to ensure reliable and efficient transmissions in telecommunication systems.

Written exam + Oral exam.
There are no differences for Erasmus students.

The exam will assess:
the knowledge and understanding of information theory and digital communication principles;
the ability to apply source and channel coding techniques;
the ability to analyze and compare the performance of different coding systems;
the clarity of exposition and the correct use of technical terminology;
the ability to critically analyze error detection and correction techniques.
Each learning outcome will be verified according to the criteria indicated above.

Slides issued by the lecturer.
(Optional) Books to deepen the telecommunication networks knowledge, such as:
A. B. Carlson, P. B. Crilly, J. C. Rutledge, “Communication Systems: An Introduction to Signals and Noise in Electrical Communication”, McGraw-Hill.
W. E. Ryan, S. Lin, S. G. Wilson, “Channel Codes: Classical and Modern”, Cambridge University Press.
Additional bibliographic material may be indicated during the course.

This teaching unit provides students with the tools to understand the algorithms for the numerical, symbolic and semantic processing of digital images, distinguishing linear and non-linear methods, adaptive approaches and focusing on the criteria for evaluating the results. Students will learn to understand the most advanced approaches and methods, distinguishing established approaches from the most innovative and state-of-the-art solutions. One of the main objectives is to provide the student with the ability to orient himself in a critical and constructive way when faced with the offer of Internet sites that fail to grasp the complexity of the problem, propose confused, standardized and often limited approaches if not incorrect solutions in this specialist sector. Soft -skill competency of functional literacy (reading and critical use of sources), and ability to learn (self-learning). Another objective concerns the ability to analyze a problem, break it down into the main sub-parts and choose the most suitable approaches for each phase (soft-skills competence in project creation: orientation, problem solving and project planning). The practical SW laboratories allow the student to understand the theory and put it into practice with specialized tools capable of analyzing in detail all the steps and intermediate results of the most important state-of-the-art image processing algorithms. Sustainable Development Goals 4 (Quality Education) and 9 (Industry, Innovation and Infrastructure) of the 2030 Agenda are addressed, enabling the student to acquire strong skills to face the era of digital transformation related to various application fields of digital information sources.

Digital Image Representation
Color Spaces
Image Filtering (linear and non-linear)
Edge detection
Image Segmentation
Mathematical morphology
Moments and Hough Transform
Texture analysis
Introduction to Deep Learning for Digital Image Processing/Regression/Recognition - Basic concepts - Convolutional Networks - application examples.

Written test
Laboratory test
Students with learning disorders ("disturbi specifici di apprendimento", DSA) will be allowed to use specific modalities and supports that will be determined on a case-by-case basis in agreement with the delegate of the Engineering courses in the Committee for the Inclusion of Students with Disabilities

The written exam will allow to verify the learning of the topics of the program and the orientation and reasoning ability of the student.
The practical computer test will verify the ability to use the software seen during the practical laboratory exercises.

C. OLEARI, Misurare il colore, Hoepli, II edizione, 2008
R.M. HARALICK , L:G: SHAPIRO, Computer and Robot Vision, Vol. 1, Addison-Wesley, 1991.
P. ZAMPERONI, Metodi dell'elaborazione digitale di immagini, Masson, 1990.
D. H. BALLARD, C. M. BROWN, Computer vision, Prentice Hall, 1982.
Petrou, Maria MP, and Costas Petrou. Image processing: the fundamentals. John Wiley & Sons, 2010.
Shapiro, L., and G. Stockman. Computer Vision. Prentice-Hall Inc., New Jersey (2001)
Jain, Anli K. Fundamentals of digital image processing. Prentice-Hall Inc., 1989
Class slides can be downloaded from aulaweb.
Tests and solutions can be downloaded from aulaweb.

The unit aims to provide knowledge and skills on the fundamental principles and analysis techniques of free and guided electromagnetic propagation, as well as of propagation in complex environments. These topics are discussed both from a theoretical point of view and with reference to different applicative examples.

The course provides a comprehensive introduction to the fundamental principles of electromagnetic theory, equipping students with the tools required to understand the propagation of electromagnetic waves. Students will gain an understanding of how these waves behave in different media, including free space and guided structures. They will develop the ability to analyse electromagnetic phenomena, thereby acquiring a solid foundation for further study in this field.

The written examination consists of multiple-choice questions covering the entire course syllabus. The use of notes, textbooks, or programmable calculators is not permitted during the examination. The examination is passed with a minimum grade of 18/30.

The theoretical questions assess the student’s ability to explain the fundamental principles described in the learning outcomes and to apply the analytical methods presented during the unit. The assessment takes into account:
knowledge and understanding of theoretical concepts;
correctness of the analytical procedures and calculations;
appropriateness of the selected solution methods;
ability to interpret the results obtained;
correct use of physical quantities, units, notation, and technical terminology.
The final grade is determined by the number of correct answers, according to the scoring criteria communicated before the examination.

S. Ramo, J. R. Whinnery, and T. Van Duzer. Fields and waves in communication electronics. John Wiley & Sons, 1994.
G. Franceschetti, Electromagnetics. Theory, Techniques, and Engineering Paradigms, Ed: Kluwer Academic/Plenum Publishers, 1997.

The class aims at giving the students a review of the basic knowledge about signal theory, random phenomena, analog modulations, digital signal processing, and telecommunication networks. The specific objective is to enhance the initial preparation of the students on basic topics of fundamental relevance for the master course, increasing their opportunity to effectively exploit the advanced content of the other courses of study.

The class aims at giving the students a review of the basic knowledge about signal theory, random phenomena, analog modulations and digital signal processing. The specific objective is to enhance the initial preparation of the students on basic topics of fundamental relevance for the master course, increasing their opportunity to effectively exploit the advanced content of the other courses.

The exam consists of oral exam. Oral exam consists on showing the acquired knowledge by means of writing/commenting formulas that describe concepts discussed at lesson by answering to questions. It can include knowledge about using Matlab tools introduced in the lab. Oral exam looks at validating the understanding of concepts by interactive discussion with the examiner.
Students with learning disorders ("disturbi specifici di apprendimento", DSA) will be allowed to use specific modalities and supports that will be determined on a case-by-case basis in agreement with the delegate of the Engineering courses in the Committee for the Inclusion of Students with Disabilities.

The oral exam is aimed to evaluate the level at which the syudent has understood concepts and is able to relate them each other in order to evaluate problem solving skills of the students. The oral exam aims at evaluating the understanding of the theory and to assess the capability of the student and will consist of questions on a random couple of topics. The student is required to reach a minimum level of knowledge in all asked subsjects to pass exam. Answers are evaluated based on the level of capability to describe main aspects of related concepts, meaning and interpretation of mathematical formulas, as well as time required to answer to questions. Typically an exam can take up to half an hour of time. The exam is on presence and the student cannot use any support tool during exam.

Course material on Aulaweb: copy of all lecture slides
A. B. Carlson, P. B. Crilly, Communications Systems (5th Edition), McGraw-Hill Education, 2009
J. F. Kurose, K. W. Ross, Computer Networking: A Top-Down Approach, 6th Edition, MacGraw-Hill, 2013.
Alan V. Oppenheim, Ronald W. Schafer, and John R. Buck. Discrete-Time Signal Processing. Prentice Hall, Upper Saddle River, New Jersey, 1999.

This subject introduces architectures of next-generation mobile communication systems (6G) based on software-defined and cognitive radio exploiting interaction and fusion between sensing and communications. The subject provides basic knowledge for co-design of sensing and communication, processing heterogeneous multisensory signals acquired by autonomous systems, and AI based approaches for jammer and anomaly detection.

Integration of sensing and communication modalities is at the basis of future 6G mobile communication systems and represent an added value of Intelligent Agents when operating in wireless connected environment. Integration of sensing and communications can be performed at different levels going from the physical communication layer to upper levls involving data fusion among heterogeneous sensors. Methods from the signal processing, radar, data fusion and communication domain will be discussed.

Exam is structured in a written plus an oral part
The written part consists of presentation of a report describing a set of activities and results done by the student aiming at demonstrating knowledge and capabilities he acquired along lecures and lab attendance. The student is asked to agree with the professor a topic on which to present a report covering a case study and providing a deeper insight with respect to knowledge and capabilities acquired in lessons and lab experiences.
The case of study have to be assigned/agreed at least three weeks before oral exam date on the basis of student request. The report will have to be delivered at indicated online repository at least 3 days before oral exam. The outcome of the evaluation of the written exam will be communicated the day before the oral exam.
Oral exam will consist of discussion of the written exam, The student will have to prepare max 15 slides to describe results and approaches presented in the report. Oral discussion will be oriented to demonstrate knowledge and capabilities to describe choices performed when developing report, as well as to comment rchoices, esults and performances obtained/expected for the chosen problem.
The oral exam will be passed in case the student will be admitted to the oral with at least 12 over 20 AND if the outcome of the oral will be at least 6 over 10. Laude can be assigned during the oral part.

Exam aims at assessing the following aspects about acquired student's knowldge and capabilities: - Level of Knowledge acquired with respect to theories and methods presented in course lectures by means of oral questions -Level of practical and integration capabilities of techniques learned in lab with respect to either the assigned data analytics problem (in case of dataset processingchoice) or the design and the specifications of the data analysis system (in case of case of study choice) chosen for the written part of the exam. -Level of Capability and knowledge assessed by including in a report for the written part and discussing in the oral motivate performed choices and obtained results on the assigned task perfrmed in the practical part.
Students with learning disorders ("disturbi specifici di apprendimento", DSA) will be allowed to use specific modalities and supports that will be determined on a case- by-case basis in agreement with the delegate of the Engineering courses in the Committee for the Inclusion of Students with Disabilities.

• F. Molisch, Wireless Communications, 2nd ed., Wiley-IEEE Press, 2011.
• F. Liu, C. Masouros, and A. Petropulu, Joint Radar-Communication Systems, Cambridge University Press, 2023.
• F. Liu, C. Masouros, A. Petropulu, H. V. Poor, and L. Hanzo, “Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond,” IEEE Journal on Selected Areas in Communications, vol. 40, no. 6, pp. 1728–1767, Jun. 2022.
• H. Wymeersch, F. Liu, T. Chen, E. de Carvalho, C. Masouros, and M. Z. Win, “Integrated Sensing and Communications: Survey and Recent Advances,” IEEE Communications Surveys & Tutorials, vol. 24, no. 3, pp. 1947–1978, 2022.
• Y. Cui, F. Liu, X. Jing, and J. Mu, “Integrating Sensing and Communications for Cooperative Perception in Autonomous Driving,” IEEE Wireless Communications, vol. 29, no. 6, pp. 12–19, Dec. 2022.
• K. B. Letaief, W. Chen, Y. Shi, J. Zhang, and Y.-J. A. Zhang, “The Roadmap to 6G: AI-Empowered Wireless Networks,” IEEE Communications Magazine, vol. 57, no. 8, pp. 84–90, Aug. 2019.
• DeepSense 6G Team, “DeepSense 6G: A Large-Scale Multi-Modal Sensing and Communication Dataset,” 2024. [Online]. Available: https://www.deepsense6g.net/

The objective of the course is to enable the student to understand how some Internet-based services work and how they perform, with particular emphasis on those involving localisation techniques. This objective is pursued by acquiring a certain amount of knowledge and skills, including 1) Understanding the functioning of the Internet Application Layer, both in general and concerning several reference applications/services (email, web, DNS). 2) Critically analyse the role of localisation techniques in Internet applications, recognising how user location enables advanced functionalities in different application domains (e.g. navigation and mobility (Google Maps, Uber), outdoor activities and gamification (Strava, Pokémon GO), ...) 3) Understand the fundamentals of localisation and positioning techniques used in the Internet and mobile environment, distinguishing between infrastructure-based approaches (e.g., GNSS, Wi-Fi, Bluetooth, UWB) and positioning algorithms (e.g., trilateration, fingerprinting, dead reckoning). 4) Analysing localization technologies' technical and operational characteristics (accuracy, coverage, latency, power consumption, robustness) and assessing their compatibility with different types of Internet applications. 5) Evaluate the performance of localisation solutions in real-life scenarios through metrics such as data accuracy, precision, reliability, and availability.

This module introduces the principles and technologies behind modern Internet services and location-based applications. The course covers major Internet application-layer services (e.g., web, email, and DNS) and examines localization technologies and positioning techniques that enable advanced location-aware applications. The module provides the foundations needed to understand the performance, capabilities, and limitations of Internet services and localization systems in digital environments.

The examination consists of a mandatory written test covering the topics included in the syllabus. The written test consists of multiple-choice questions designed to assess the student's knowledge and understanding of the theoretical concepts, Internet services, application-layer protocols, localization technologies, positioning algorithms, and performance evaluation methods presented in the course. The maximum score achievable through the written test is 24/30.
Students who pass the written test may optionally take an oral examination. During the oral examination, students will be asked to discuss theoretical concepts related to Internet services and localization techniques, including the operation of application-layer protocols, the role of localization in modern Internet applications, the characteristics of positioning technologies, and the evaluation of localization performance. The oral examination may also include questions that require analysis of application scenarios and discussion of the suitability of different localization solutions for specific use cases.
The final grade is determined based on the written test and, when taken, the oral examination. The oral examination allows students to demonstrate a deeper understanding of the course topics and may lead to either an increase or a decrease of the final grade, depending on the level of knowledge, understanding, and reasoning skills demonstrated during the discussion. In cases of clearly insufficient performance, the oral examination may also result in the failure of the overall examination.

The written examination assesses the student's knowledge and understanding of the fundamental concepts, technologies, and methodologies covered in the course. Through multiple-choice questions, students are required to demonstrate familiarity with Internet application-layer services, communication protocols, localization technologies, positioning algorithms, and performance evaluation metrics.
The optional oral examination is intended to assess the student's achievement of the module's learning outcomes in greater depth. During the oral examination, students are expected to explain the operation of Internet services and application-layer protocols, analyze the role of localization in modern Internet applications, compare different positioning technologies and algorithms, and evaluate their suitability for specific application scenarios.
Particular attention is given to the use of appropriate technical terminology, clarity of exposition, the ability to establish connections among Internet services, localization technologies, and application requirements, as well as the capacity for critical reasoning on the performance, strengths, limitations, and applicability of the solutions covered in the course.
Students with Specific Learning Disorders (SLDs), disabilities, or other certified educational needs may be granted appropriate accommodations, to be agreed upon on a case-by-case basis in accordance with University of Genoa regulations and inclusion policies.

Course material available on AulaWeb (https://www.aulaweb.unige.it), including lecture slides and additional teaching resources, is sufficient for preparing for the examination.
Suggested textbook for further study:
J. F. Kurose, K. W. Ross, Computer Networking: A Top-Down Approach, 9th Edition, Pearson, 2025.
S. A. Zekavat, R. M. Buehrer, “Handbook of Position Location: Theory, Practice, and Advances,” Wiley – IEEE Press, 2019.

The module aims to provide knowledge and skills on the foundations of modern networking systems based on the TCP/IP protocol suite. Such protocols will be discussed by especially focusing on network performance, design, and operations aspects. The topics will be presented through a mix of theory and applicative examples.

The class aims at deepening the knowledge of the main architectural aspects and protocols at the basis of the Internet and its evolution. It is essentially organized in four parts, addressing IP and TCP and related protocols, WiFi, protocols and techniques for advanced networking (network management, Quality of Service, multimedia services over best effort networks, virtualisation); and fundamentals of telecommunications network security.

Mandatory written examination about the topics in the syllabus of the teaching unit, with maximum admissible mark equal to 24/30.
Mandatory test on the experimental laboratory, related to the activities in the syllabus, with a maximum admissible mark equal to 1/30 that will be added to the mark of the written examination.
If a student obtains a sufficient mark (equal to 21/30) from the two previous examinations, then he/she can also optionally take an additional oral examination with maximum admissible mark equal to 30/30 with honors.
Students with valid certifications for Specific Learning Disabilities (SLD), disabilities, or other educational needs may request compensatory and/or exemption measures for the exam. The procedures will be defined on a case-by-case basis, in accordance with the University Regulations, in consultation with the instructor and the School's Disability and SLD Coordinator.

Within the mandatory written examination, the student's knowledge of the main concepts discussed in the teaching unit shall be evaluated.
Within the experimental laboratory test, the critical thinking and problem solving capability of students will be evaluated in simple but common networking environments.
Within the oral examination, it will be assessed the student’s capability to address simple problems of networking applications and his/her capacity to critically discuss the related methodological bases.

• Course material on Aulaweb (https://www.aulaweb.unige.it): copy of all lecture slides
• J. F. Kurose, K. W. Ross, Computer Networking – A Top-Down Approach, 9th Ed., Pearson Education, Upper Saddle river, NJ, 2025.
  Students with disabilities or learning disorders are allowed to use specific modalities and supports (e.g., audio and video lesson recordings). See the Further Information section for more details.

The module aims to provide knowledge and skills on the foundations of modern networking systems based on the TCP/IP protocol suite. Such protocols will be discussed by especially focusing on network performance, design, and operations aspects. The topics will be presented through a mix of theory and applicative examples.

The class aims at deepening the knowledge of the main architectural aspects and protocols at the basis of the Internet and its evolution. It is essentially organized in four parts, addressing IP and TCP and related protocols, WiFi, protocols and techniques for advanced networking (network management, Quality of Service, multimedia services over best effort networks, virtualisation); and fundamentals of telecommunications network security.

Mandatory written examination about the topics in the syllabus of the teaching unit, with maximum admissible mark equal to 24/30.
Mandatory test on the experimental laboratory, related to the activities in the syllabus, with a maximum admissible mark equal to 1/30 that will be added to the mark of the written examination.
If a student obtains a sufficient mark (equal to 21/30) from the two previous examinations, then he/she can also optionally take an additional oral examination with maximum admissible mark equal to 30/30 with honors.
Students with valid certifications for Specific Learning Disabilities (SLD), disabilities, or other educational needs may request compensatory and/or exemption measures for the exam. The procedures will be defined on a case-by-case basis, in accordance with the University Regulations, in consultation with the instructor and the School's Disability and SLD Coordinator.

Within the mandatory written examination, the student's knowledge of the main concepts discussed in the teaching unit shall be evaluated.
Within the experimental laboratory test, the critical thinking and problem solving capability of students will be evaluated in simple but common networking environments.
Within the oral examination, it will be assessed the student’s capability to address simple problems of networking applications and his/her capacity to critically discuss the related methodological bases.

• Course material on Aulaweb (https://www.aulaweb.unige.it): copy of all lecture slides
• J. F. Kurose, K. W. Ross, Computer Networking – A Top-Down Approach, 9th Ed., Pearson Education, Upper Saddle river, NJ, 2025.
  Students with disabilities or learning disorders are allowed to use specific modalities and supports (e.g., audio and video lesson recordings). See the Further Information section for more details.

The combination of IoT and Artificial Intelligence is already a reality—though still not widely known—and represents the integration of two frontier technologies that are expected to see growing adoption. Students will acquire the skills to understand and design AIoT systems by integrating IoT devices, connectivity, and intelligent algorithms. They will be able to collect, process, and transmit data across edge, fog, and cloud layers; apply machine and deep learning techniques for predictive and decision-making analysis; and develop solutions for real-world applications (industry, mobility, smart home, healthcare), addressing scalability and reliability challenges.

The course provides the theoretical foundations and practical aspects of Internet of Things (IoT) systems and the application of Artificial Intelligence techniques to distributed sensing environments. It covers IoT architectures, communication technologies, networking protocols, edge and cloud computing paradigms, and the main Artificial Intelligence techniques for sensor data analysis. Particular emphasis is placed on Deep Learning, Edge AI, TinyML, and Federated Learning for the development of intelligent IoT systems.

The examination consists of a written test followed by an oral examination. The written test covers both Internet of Things and Artificial Intelligence topics and consists of multiple-choice questions administered through the AulaWeb platform.
Passing the written test is a prerequisite for admission to the oral examination and does not directly contribute to the final grade. The oral examination covers the entire course syllabus and is designed to assess the student's understanding of the course topics, ability to integrate the different course contents, and capability to apply the acquired knowledge.

The assessment is designed to verify the achievement of the course learning outcomes. In particular, it evaluates students' understanding of Internet of Things (IoT) architectures and technologies, the main communication protocols, and Artificial Intelligence techniques for sensor data analysis, as well as their ability to integrate this knowledge in the analysis and design of intelligent IoT systems.
The written test, consisting of multiple-choice questions, is intended to assess whether students possess the fundamental knowledge required for admission to the oral examination. The oral examination evaluates students' ability to present the course topics using appropriate technical terminology, establish connections among the different course contents, and critically discuss the main technological and methodological solutions covered during the course.

Teaching materials prepared by the instructors (lecture slides, lecture notes, scientific papers, and additional documentation) will be made available through the AULAWEB platform and will constitute the primary study material for the course.
Additional reference textbooks and supplementary bibliographic material, if required, will be indicated through the AULAWEB platform during the course.

In this module, several Machine learning methods applied to Pattern recognition are presented and - in particular - the case of classification of images from real domains is discussed. Students will learn how to estimate a probability density function using a set of training samples. They will be able to classify samples on the basis of the decision theory and linear and nonlinear classifiers (MDM, k-nn, SVMs, Random forest). They will learn how to design a MLP neural network. They will learn how to assess the accuracy of a supervised classifier. They will also be able to utilize some clustering algorithms (including a fuzzy algorithm) and validate the obtained results.

Machine learning is establishing as a very interesting scientific area thanks to the availability of more and more powerful computers and of algorithms allowing its application to the most diverse sectors, including key sectors for economic and societal development. This course presents the main machine learning methodologies aimed to “pattern recognition”, in particular for the classification of structurally represented data.

Mandatory written examination about the topics in the syllabus of the teaching unit, with maximum admissible mark equal to 24/30. If a student obtains a sufficient mark in this written exam, then they can also optionally take an additional oral examination with maximum admissible mark equal to 30/30 with honors.

The assessment is based on questions aimed at verifying the understanding of the concepts taught, the ability to choose the most suitable methodology in the presence of specific cases and to justify the choice, the ability to correctly apply the algorithms presented in class and during the exercises to simple problems.

Slides presented at lesson will be available in Aulaweb.
Recommended books:
Duda, R. O., Hart, P. E., Stork, D. G.: 2001, Pattern classification, 2nd ed., Wiley.
Fukunaga, K.: 1990, Introduction to statistical pattern recognition, 2a ed., Academic Press.
Bishop C. M.: 2006, Pattern Recognition and Machine Learning, Springer.
More specific references are included in each of the chapters in which the slides are grouped.

The module aims at introducing advanced knowledge on the anatomy and the operations of cloud/edge computing infrastructures and platforms (with a special focus on networking aspects) as well as their relation to Network Function Virtualization and Software Defined Networking technologies, in order to finally achieve a deep understanding of 5G and beyond radio mobile networks and their programmability capabilities. The topics will be presented through a mix of theory and applicative examples and experimental laboratories.

The teaching unit provides advanced knowledge of cloud and edge computing infrastructures and platforms, with particular emphasis on network programmability architectures, operational mechanisms, and automated management principles. It introduces the paradigms enabling the softwarization and virtualization of communication networks and analyses their role in the evolution of mobile systems towards 5G and beyond. The unit combines theoretical foundations with practical examples and laboratory activities aimed at developing both conceptual understanding and hands-on skills.

Mandatory written examination about the topics in the syllabus of the teaching unit, with maximum admissible mark equal to 24/30.
Mandatory test on the experimental laboratory, related to the activities in the syllabus, with a maximum admissible mark equal to 3/30 that will be added to the mark of the written examination.
If a student obtains a sufficient mark (equal to 21/30) from the two previous examinations, then he/she can also optionally take an additional oral examination with maximum admissible mark equal to 30/30 with honors.
Students with valid certifications for Specific Learning Disabilities (SLD), disabilities, or other educational needs may request compensatory and/or exemption measures for the exam. The procedures will be defined on a case-by-case basis, in accordance with the University Regulations, in consultation with the instructor and the School's Disability and SLD Coordinator.

Within the mandatory written examination, the student's knowledge of the main concepts discussed in the teaching unit shall be evaluated.
Within the experimental laboratory test, the critical thinking and problem solving capability of students will be evaluated in simple but common 5G and cloud environments.
Within the oral examination, it will be assessed the student’s capability to address simple problems of 5G and cloud applications and his/her capacity to critically discuss the related methodological bases.

Teaching material on Aulaweb (https://www.aulaweb.unige.it): copy of all lecture slides.
The teaching materials are also considered appropriate for students with learning disabilities. If different materials or methods are needed, students are encouraged to contact the instructor.

Operations Research (OR) consists in a set of mathematical models and methods for solving decision problems in a very wide number of application sectors. The purpose of this module is to provide the students with competences in using a set of models for problem solving. In particular, the module mainly considers optimization problems faced by mathematical programming techniques and problems on graph and networks.

Operations Research (OR) consists in a set of mathematical models and methods for solving decision problems in a very wide number of application sectors. The purpose of this course is to provide the students with competences in using a set of models for problem solving. In particular, the course mainly considers optimization problems faced by mathematical programming techniques and problems on graph and networks.

Written exam text.

Written examination, potentially including:

• Exercises requiring the application of algorithms to the problem classes presented in the course * Short theoretical questions * Formulation of simple combinatorial decision-making problems
  Students with learning disorders ("disturbi specifici di apprendimento", DSA) will be allowed to use specific modalities and supports that will be determined on a case-by-case basis in agreement with the delegate of the Engineering courses in the Committee for the Inclusion of Students with Disabilities.

Frederick S Hillier, Gerald J Lieberman, Introduction to Operations Research, 9/e, McGraw-Hill Higher Education, 2010, ISBN: 0073376299
Students with learning disorders ("disturbi specifici di apprendimento", DSA) will be allowed to use specific modalities and supports that will be determined on a case-by-case basis in agreement with the delegate of the Engineering courses in the Committee for the Inclusion of Students with Disabilities.

This teaching unit aims to provide students with knowledge of Operations Research models and methods and of Machine Learning methods applied to data, signal and image recognition. In the first case, the aim is to equip students with the tools to address decision-making problems. Specifically, students will learn linear programming techniques (including modeling, the simplex algorithm, sensitivity analysis, and duality theory) as well as integer programming (branch and bound, cutting planes). They will also learn to model decision-making problems in the telecommunications field using graph theory and network flow models. In the Machine Learning area, students will learn to characterize the distribution of a data set and reduce its dimensionality. They will be able to use various classification techniques, with or without training samples, optimizing their structure and parameters and evaluating their performance. They will be able to apply the learned methods to data sets of various types, including signals and images.

This teaching unit is structured into two modules: Operations Research (OR) and Machine Learning. OR module introduces models and methods from Operational Research to provide students with tools to address decision-making problems. Students will learn linear programming techniques (including modeling, the simplex algorithm, sensitivity analysis, and duality theory) and integer programming. The module covers graph theory and network flow models.
Machine learning is establishing as a very interesting discipline thanks to the availability of more and more powerful computers and of algorithms allowing its application to the most diverse sectors, including key sectors for economic and societal development. This teaching unit presents the main machine learning methodologies aimed to “pattern recognition”, in particular for the classification of data from signals and images.

The exam for the Operations Research module consists of a written test.
The exam of Machine Learning consists of a written test and an oral part. The written text allows students to obtain a maximu mark of 24 out of 30. The oral part is not mandatory and can be taken by students to obtain a higher mark, up to 30 out of 30, with honors.

The exam for the Operations Research module is based on a test that requires the students to solve exercises requiring the application of algorithms to the problem classes presented in the course, to answer to short theoretical questions, and to formulate simple combinatorial decision-making problems.
The written test of Machine Learning consists of multiple choice questions and open-ended questions related to the methods presented at lesson; the solution of very simple problems is required, too. A deeper discussion of the methods and the solution of more complex problems is required to pass the oral part of the exam.

Upon successful completion of this teaching unit, students will be able to develop robust, maintainable, and efficient Python code, with particular attention to the analysis and manipulation of datasets, and simple multimedia processing, including image and video analysis.

The teaching unit will cover the following aspects of Python programming:
Introduction to programming: computer architectures; memory and data; CPU and programs; programming languages; problems, algorithms and programs.
How to use the Python interpreter: invoking the interpreter; passing arguments; interactive mode; notebooks; online coding platforms.
Basic concepts of Python programming: variables and assignments; expressions and statements; operations; printing; comments; debugging; data types; numbers and strings; input.
Functions: built-in functions; function calls; importing modules and functions; mathematical functions; function composition; defining new functions; parameters and arguments; mandatory and optional arguments; order of arguments and keyword assignment; scope of a variable.
Making decisions: boolean expressions and logical operators; conditional and alternative execution; if-elif-else statement; chained and nested conditionals.
Iterations: reassigning and updating variables; while statement; break statement; sequences and loops; the in operator; the for construct.
Data structures (strings, lists, tuples, dictionaries): definition, properties, operations, and methods; indexing vs. assignment; mutability vs. immutability; map, flter, and reduce; referencing and aliasing; packing and unpacking; lookup and reverse lookup; variable-length arguments.
Files: persistence; opening and closing and the with construct; reading and writing; format operator; file names and paths; catching exceptions; pickling.
Modules and packages: defining a module; defining a package; importing a package vs. importing a module vs. importing a function; installing packages.
Classes and objects: classes, types, objects, and instances; instances as return values; attributes and methods; object mutability; instantiation and the __init__ method; operator overloading and special methods; static methods and class methods; inheritance.
Pythonic programming: conditional expressions; EAFP (Easier to Ask for Forgiveness than Permission); list comprehension; generator expressions; any and all operators; sets.
Scientific programming: Numpy, arrays and broadcasting; Pandas, dataframes and series; Scikit Learn and introduction to machine learning with Python; Matplotlib and data visualization in Python
Introduction to Multimedia Processing: overview of multimedia data types, introduction to image and video formats, using PIL and OpenCV basic
Image and Video Processing with OpenCV: reading, displaying, and saving images, color spaces and transformations, filters, edge detection, and contours, capturing video from files and webcams, frame-by-frame processing, simple object detection and tracking

The final exam is designed to assess students’ understanding and practical application of the core concepts covered throughout the course. It evaluates both theoretical knowledge and hands-on programming skills in Python, with a focus on data manipulation and multimedia processing.
Format
Duration: 2 hours
Type: Written and practical (computer-based)
Structure:
Section A – Conceptual Questions (30%) Short-answer and multiple-choice questions covering Python syntax, data structures, object-oriented programming, and key libraries (e.g., Pandas, NumPy, OpenCV).
Section B – Code Analysis and Debugging (30%) Students will analyze, interpret, and correct Python code snippets related to data and multimedia tasks.
Section C – Programming Task (40%) A hands-on coding exercise where students write a complete Python script to solve a problem involving dataset manipulation or basic image/video processing.

1. Weekly Assignments (30%)
Short, practical exercises focused on applying concepts from lectures and labs.
Tasks include writing Python scripts, analyzing datasets, and performing basic image/video processing.
Encourages consistent practice and incremental learning.
2. Midterm Quiz (10%)
A written or computer-based quiz assessing foundational knowledge of Python syntax, data structures, and basic data manipulation.
Includes multiple-choice, short-answer, and code interpretation questions.
3. Final Project (30%)
A capstone project where students design and implement a small application or analysis pipeline.
Data analysis and visualization of a real-world dataset.
Image or video processing application using OpenCV.
Assessed on functionality, code quality, documentation, and presentation.
4. Final Exam (20%)
A comprehensive exam combining theoretical questions and practical coding tasks.
Tests students’ ability to write efficient, maintainable code and solve problems independently.
5. Participation and Engagement (10%)
Active involvement in class activities, coding labs, and peer discussions.
May include peer reviews, group exercises, and contributions to online forums or collaborative platforms.

Allen B. Downey, Think Python: How to Think Like a Computer Scientist (2nd Edition), O’Reilly Media, 2015. A beginner-friendly introduction to programming concepts using Python.
Jake VanderPlas, Python Data Science Handbook, O’Reilly Media, 2016. Covers essential tools for data science in Python, including NumPy, Pandas, Matplotlib, and Scikit-learn.
Charles Severance, Python for Everybody: Exploring Data Using Python 3, CreateSpace, 2016. A gentle introduction to Python with a focus on data handling and practical applications.
Mark Lutz, Learning Python (5th Edition), O’Reilly Media, 2013. A comprehensive introduction to Python, covering both fundamental and advanced topics.
Wes McKinney, Python for Data Analysis (3rd Edition), O’Reilly Media, 2022. Focuses on data wrangling with Pandas, NumPy, and Jupyter—ideal for the data analysis portion of the course.
Adrian Rosebrock, Practical Python and OpenCV (3rd Edition), PyImageSearch, 2016. A hands-on guide to image and video processing using OpenCV and Python.
Python Official Documentation
Pandas Documentation
NumPy Documentation
OpenCV-Python Tutorials
Jupyter Project

The students at the end of the lectures will have acquired theoretical and practical knowledge about modern architectures for telecommunications networks, about applications, and about their control. The aim is to understand different technological solutions and algorithms to control and design interworking elements within a network. The students will acquire the necessary know-how to design and manage advanced interworking technology, understand main protocols at application layer, and investigate technological challenges, still object of research both industrial and academic, as well as quality of service provision over heterogeneous networks, satellite, space, and interplanetary networks.

The importance of quality of service is parallel with the recent evolution of telecommunication networks, which are characterized by a great heterogeneity. Even if the Quality of Service is the object of a great number of studies, questions to answer are: does QoS fit within heterogeneous networks? Which is the impact on the performance if information traverses different network portions that use specific QoS paradigms? These questions and corresponding possible answers are the core of the teaching unit.

The exam consists of a written test consisting of 4 questions of identical value followed by a brief comment on the writing test.
Students with learning disorders will be allowed to use specific modalities and supports that will be determined on a case-by-case basis in agreement with the delegate of the Engineering courses in the Committee for the Inclusion of Students with Disabilities, as better specified in the Further Information section.

The written test is aimed at checking the skills of the students about their skills:
to analyze modern architectures for telecommunications networks and their control;
to select different technological solutions and algorithms to control and design interworking elements within a network;
to design and manage advanced interworking technology and protocols;
to investigate technological challenges, still object of research both industrial and academic, about quality of service provision over heterogeneous networks, satellite, space, and interplanetary networks.
concerning:
technical knowledge,
methodology,
clarity of the description.
Students with learning disorders ("disturbi specifici di apprendimento", DSA) will be allowed to use specific modalities and supports that will be determined on a case-by-case basis in agreement with the delegate of the Engineering courses in the Committee for the Inclusion of Students with Disabilities.

• Mario Marchese , QoS Over Heterogeneous Networks , Wiley & Sons , Chichester , UK , 2007. - Notes on specific topics issued by the lecturer. - Extracts of international regulatory and scientific documentation provided by the lecturer. - Recordings of the lectures of previous years still available on Teams.
• The slides used for each single lecture are made available on Aulaweb.
  Students with disabilities or learning disorders can use specific modalities and supports that will be determined on a case-by-case basis in agreement with the Delegate of the Engineering courses in the Committee for the Inclusion of Students with Disabilities, as indicated in Further Information section.
  Students not following lectures may use the recommended reading material listed above and can ask the lecturer additional indications.

Remote Sensing — Based on the concepts ruling the generation and propagation of electromagnetic wave fields, the objective is to provide the students with basic knowledge about the fundamentals and basic definitions of remote sensing; passive remote sensing in the optical, microwaves, and infrared frequency bands; active remote sensing and radar imaging; instrumentation for remote sensing. Satellite Images — In the framework of space missions for Earth observation, the objectives include providing the students with basic knowledge about statistical modeling, dimensionality reduction, detection, and regression methods with radar, multispectral, and hyperspectral images. In this context, machine learning techniques rooted in the areas of ensemble learning, neural networks, kernel machines, and Bayesian decision theory will be discussed as well.

• Basic concepts ruling the generation and propagation of electromagnetic waves and fields - Fundamentals and definitions of remote sensing - Passive remote sensing in the optical, microwaves, and infrared frequency bands - Active remote sensing and radar imaging - Instrumentation for remote sensing - Space missions for Earth observation - Statistical modeling of remote sensing imagery - Filtering and Semantic segmentation of remote sensing images - Bio/geophysical parameter regression from remote sensing data

Mandatory written examination, made of multiple-choice questions about the topics in the syllabus of the teaching unit, with maximum admissible mark equal to 24/30. If a student obtains a sufficient mark in this written exam, then he/she can also optionally take an additional oral examination with maximum admissible mark equal to 30/30 with honors.

Within the mandatory written examination, the student's knowledge of the main concepts discussed in the teaching unit shall be evaluated. Within the optional oral examination, the student's capability to address simple problems of remote sensing and of satellite image analysis and his/her capacity to critically discuss the related methodological bases shall be assessed.

Bishop C., Pattern recognition and machine learning, Springer, 2006
Campbell J. B. and Wynne R. H., Introduction to remote sensing, Guilford Press, 2011
Elachi C., Van Zyl J., Introduction to the physics and techniques of remote sensing, Wiley, 2006
Goodfellow I., Bengio Y., and Courville A., Deep learning, MIT Press, 2016
Hastie T., Tibshirani R., and Friedman J., The elements of statistical learning, Springer, 2008
Ulaby F. T. and Long D., Microwave radar and radiometric remote sensing, Artech House, 2015
Manolakis D. G., Lockwood R. B., and Cooley T. W., Hyperspectral imaging remote sensing, Cambridge University Press, 2016
Moser G., Analisi di immagini telerilevate per osservazione della Terra, ECIG, 2007
Moser G. and Zerubia J. (eds.), Mathematical models for remote sensing image processing, Springer, 2018
Class slides will be provided to the students through AulaWeb.

The teaching unit provides a comprehensive understanding of the principles, technologies, and systems used in satellite-based communication networks. Students will gain knowledge of topics including satellite orbits, frequency allocation, link budget analysis, modulation and coding techniques, and multiple access methods. Students will also explore the architecture and operation of various satellite systems and protocols and their applications in broadband, broadcast, navigation, and global connectivity

The course addresses the fundamental principles of satellite communications and networks, analyzing the architecture of satellite systems, transmission techniques and next-generation broadband applications, including the integration between satellite networks and 5G/6G systems, Mega LEO constellations and global connectivity solutions.

Oral exam.
There are no differences for Erasmus students.

The exam will assess:
the knowledge and understanding of satellite communication and networking principles;
the ability to apply link engineering and networking concepts to practical scenarios;
the ability to compare and evaluate different satellite architectures and channel access technologies;
the clarity of exposition and the correct use of technical terminology;
the ability to critically analyze issues related to satellite-terrestrial integration and next-generation broadband networks.
Each learning outcome will be verified according to the criteria indicated above.

Slides issued by the lecturer.
(Optional) Books to deepen the telecommunication networks knowledge, such as:
G. Maral, M. Bousquet, Z. Sun, “Satellite Communications Systems: Systems, Techniques and Technology”, Wiley
T. Pratt, J. Allnutt, T. Bostian, “Satellite Communications”, Wiley
M. Höyhtyä, “Satellite Communications and Networks”, Springer
Additional bibliographic material may be indicated during the course.

At the end of this immersive emotional-intelligence focused module students will learn what soft skills are and how to never overlook them in to be easily efficient and performative and ultimately have a successful career. Soft skills are social attributes that enable you to interact effectively with others. These skills are critical for success in most careers, as they help you build and maintain relationships, navigate complex situations, and achieve your goals. Main objectives are: 1. to learn what the most important and most sought after by employers are: efficient communication, teamwork, problem-solving and time management and how to use them successfully. 2. To practice how to use them efficiently, through a variety of role-playing, writing assignments, peer discussion exercises. 3. As a consequence, learning how to self-reflect, self-evaluate, and peer evaluate, according to the latest research in Business Management known as the Harvard model. 4. Being an effective part of a team meaning that you can work well with different personalities and can collaborate, delegate, and provide support when needed. 5. To learn different leadership models and how to use accordingly to different workplaces. 6. To learn about the future of the Global Economy and specifically the so called “Kind Economy”.

This SOFT SKILLS module will be experiential, based on continuous interaction between the lecturer and the students as well as among the students themselves. The course will focus on three main topics:
The empathetic communication model, high-quality communication (with oneself and with others) that stands as one of the most valuable competencies in today's workplace;
Three creative thinking techniques that sharpen problem-solving skills and enhance teamwork;
Selected leadership theories to increase self-awareness, help recognize personal limits, and learn how to overcome them.
Throughout the sessions, students will have the opportunity to engage in practical pair-work and group-work activities and interact continuously.

For attendees: a group presentation. For non-attendees: a 1000-word essay to send via email to [email protected]

Ongoing assessment based on a 360-degree feedback system for those who will attend the course. Essays written by non-attendees will need to show critical thinking skills

Rosenberg, M. (2017) Le parole sono finestre (oppure muri). Introduzione alla comunicazione nonviolenta, Esserci edizioni. / Rosenberg, M. (2015) Nonviolent communication, Puddle Dancer Press
Heifetz, R (2009) Practice of Adaptive Leadership: A Fieldbook for Practitioners, Ingram Publisher Services
Kegan, R & Lahey, L (2018) Immunità al cambiamento: come liberare nuove potenzialità (in noi e nella nostra organizzazione), Edizioni Franco Angeli / Kegan, R & Lahey, L (1018) Immunity to change: how to overcome it and unlock the potential in yourself and in your organisation, Harvard Business Review Press
De Bono, E. & Brunelli, F. (2001) Creatività e Pensiero Laterale, Rizzoli / De Bono, E. (2016) Lateral thinking: a textbook of creativity, Penguins Books Ltd
De Bono, E. (2011) Essere creativi. Come fare nascere nuove idee. I concetti, gli strumenti, le applicazioni del pensiero laterale, Il Sole 24 Ore

La società di Internet si basa su soluzioni tecnologiche per la connessione continuativa e pervasiva di persone e oggetti (IoT). Nel modulo gli studenti vengono introdotti ad un confronto critico tra diverse tecnologie radio in grado di garantire tale connessione globale, comprese soluzioni terrestri e satellitari. Prendendo alcuni casi di studio con requisiti, vincoli e funzionalità diversi, gli studenti sono guidati ad esplorare diverse opzioni e scegliere quella più adatta, economica e performante. Verranno esaminate le strategie di copertura radio confrontando diverse metodologie di pianificazione cellulare in modo che gli studenti possano apprendere come diversi ambienti e territori possano essere serviti in modo pervasivo risparmiando sui costi infrastrutturali adottando soluzioni specifiche. Verranno esaminati standard di telefonia mobile terrestre come GSM/GPRS, WCDMA e LTE e confrontati con tecnologie satellitari come Globalstar e Iridium. Grazie a questa ampia indagine tecnologica gli studenti acquisiranno sicurezza e competenza nella progettazione di soluzioni basate su dispositivi con interfaccia multi-radio in grado di commutare in modo continuo e trasparente all'utente la rete di accesso tramite handover verticale. Lo smartphone sarà considerato come esempio di piattaforma multi-standard in grado di connettere persone e cose oltre la rete telefonica, includendo Wifi, Bluetooth, RFID, BLE, NFC. Il valore aggiunto rappresentato dalla stima della posizione di terminal, persone e cose verrà considerato con riferimento ad applicazioni nel campo della logistica, dei trasporti e della sanità. In tale ottica verranno introdotte metodologie basate sul radio fingering terrestre e sistemi globali basati su satellite (GPS/Galileo/Egnos).

Introduzione alle comunicazioni radio Propagazione fisica della radiofrequenza: onde terrestri, di cielo e di spazio Ellissoidi di Fresnel Attenuazione di tratta Assorbimento atmosferico, attenuazione della pioggia Dispersività temporale e interferenza intersimbolica (ISI) Fading lento (eventi atmosferici) Fading veloce (shadowing) e molto veloce (multipath). Selettività del fading veloce nel tempo, nello spazio, nella frequenza e nella direzione Trasmissione/ricezione in diversità Copertura radio pervasiva Celle eccitate al centro e agli angoli Dimensionamento delle celle Modelli di celle sovrapposte e stratificate Pianificazione delle celle basata su esagono Assegnazione dei canali cellulari basata sul traffico (formula B-Erlang, probabilità di blocco, fattore di raggruppamento) Politiche di prestito di cnale Pianificazione dei cluster e assegnazione del colore delle celle Interferenza del sistema: interferenza isocanale e da canali adiacenti Gestione del supporto alla mobilità Aree di chiamata Segnalazione della gestione della congestione del canale Monitoraggio della qualità dei canali di traffico Hand Over intra ed inter-cella Localizzazione del terminale utente Gestione delle risorse radio Tecnologie di accesso multiplo (FDMA, TDMA, CDMA) Tecnologie duplex (FDD, TDD, CDD) Diffusione del ritardo e banda di coerenza Equalizzazione dei canali Controllo dinamico della potenza di trasmissione Standard digitali mobili globali Terrestre: 2G, 3G e introduzione al 4G Satellite: Iridium e Globalstar Sistemi di posizionamento globale Tecnologie terrestri Tecnologie satellitari: GPS, GPS differenziale, EGNOS Tecnologie IoT RFID Bluetooth Comunicazione a luce visibile Applicazioni Context-based Casi di studio nello scenario automobilistico Casi di studio nello scenario E-Health I contenuti dell'insegnamento forniranno agli studenti competenze utili al raggiungimento dei diversi obiettivi dell'Agenda 2030, in particolare le soluzioni di telecomunicazioni che verranno approfondite forniranno competenze nella progettazione di soluzioni smart per la gestione ottimizzata degli impianti produttivi riconducibili all'Obiettivo 9 “Imprese Innovazione infrastrutturale”, nonché l’attuazione di politiche efficaci per le Smart Cities riconducibili all’Obiettivo 11 “Città e comunità sostenibili”.

L'esame consiste in una prova scritta con esercizi, domande con risposta a scelta multipla, domande con risposta a testo aperto. Nel caso in cui la prova venga superata, gli studenti potranno sostenere facoltativamente una prova orale.

Il livello di apprendimento dello studente viene valutato attraverso esercizi orientati alla risoluzione di problemi pratici che richiedono il corretto utilizzo di formule e attraverso domande orientate a verificare la conoscenza complessiva di argomenti specifici. Agli studenti con disturbi dell'apprendimento (“Disturbi Specifici di Apprendimento”, DSA) sarà consentito l'utilizzo di modalità e supporti specifici che verranno determinati caso per caso in accordo con il delegato dei corsi di Ingegneria nel Comitato per l'Inclusione degli studenti con disabilità.

Tutti gli argomenti delle lezioni verranno riportati integralmente su slides e su appunti dettagliati pubblicati su Aulaweb. Verranno inoltre suggeriti i libri di testo di riferimento reperibili presso la Biblioteca dell'Ateneo.