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Insegnamenti a scelta curriculum ELECTRICAL AND ELECTRONIC ENGINEERING

Corso Ingegneria Elettrica ed Elettronica LM-29
Curriculum Electrical and Electronic Engineering
Orientamento Orientamento unico
Anno Accademico 2022/2023

Modulo: Principles of cybersecurity

Corso Ingegneria Elettrica ed Elettronica LM-29
Curriculum Electrical and Electronic Engineering
Orientamento Orientamento unico
Anno Accademico 2022/2023
Crediti 6
Settore Scientifico Disciplinare ING-INF/05
Anno Secondo anno
Unità temporale Secondo semestre
Ore aula 48
Attività formativa Attività formative a scelta dello studente (art.10, comma 5, lettera a)

Canale unico


Ulteriori informazioni

Nessun materiale didattico inserito per questo insegnamento
Nessun avviso pubblicato
Nessuna lezione pubblicata
Codice insegnamento online non pubblicato

Modulo: Neural Engineering

Corso Ingegneria Elettrica ed Elettronica LM-29
Curriculum Electrical and Electronic Engineering
Orientamento Orientamento unico
Anno Accademico 2022/2023
Crediti 6
Settore Scientifico Disciplinare ING-IND/31
Anno Secondo anno
Unità temporale Secondo semestre
Ore aula 48
Attività formativa Attività formative a scelta dello studente (art.10, comma 5, lettera a)

Canale unico

Docente Nadia Mammone
Obiettivi KNOWLEDGE AND UNDERSTANDING
The course aims at completing the master's student training by extending it with knowledge and skills in the field of artificial intelligence (with specific attention to deep learning methods), focusing the attention on the topics more related to neuroscience. Applications to electroencephalographic (EEG) signal processing are proposed both for the discrimination of brain states and for Brain Computer Interface (BCI). The student, independently, will develop a project according to the suggestions provided by the teacher.
ABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING
The course aims at providing the student with skills on the development of algorithms based on Deep Learning methods, especially for applications in the field of neuroscience. This objective is also pursued through laboratory experiments and training sessions during which the student will have the opportunity to design his/her own experiments, acquire the electroencephalographic signals and process them.
INDEPENDENT JUDGMENT
At the end of the course, and in particular upon exam completion, the student will be able to: define paradigms and experiments for acquiring bio-signals for the achievement of the objectives of the study; design algorithms for processing the acquired signals in order to achieve the goal of the study. The student will be able to rely on the methods covered during the course as well as on his/her own ability to independently explore additional methods found in the literature, thanks to an acquired ability to handle the fundamental concepts of deep learning.
COMMUNICATION SKILLS
Thanks to a continuous student-teacher interaction, the course will focus on the acquisition of the language peculiar of artificial intelligence and computational neuroscience, also allowing the student to refine his/her own ability to express themselves through an appropriate technical language.
LEARNING SKILLS
Upon exam completion, the student will have acquired the ability to design algorithms based on artificial neural networks, both shallow and deep. He/she will be able to: define paradigms and experiments for the acquisition of electroencephalographic signals (EEG), according to the objectives of the study; acquire such signals; process them using Deep Learning algorithms developed ad-hoc; integrate such algorithms with platforms for interfacing them in real time with the EEG acquisition system. Conversely, the practical application of theoretical concepts will reinforce their understanding; the student will thus develop a theoretical-practical approach to engineering topics.
Programma FUNDAMENTALS OF NEURAL ENGINEERING (0.5 CFU)
Fundamentals about neural engineering. Overview about Neural Engineering Applications. Need for a novel perspective in model-based approaches.
Introduction to the electric fields of the brain and to EEG. Fundamentals of EEG rhythms generation. EEG recording and acquisition. Normal vs. abnormal EEG patterns. Neurological disorders and their effects on brain waves (Alzheimer’s Disease, epilepsy, stroke, Parkinson’s Disease, etc). Brain Computer Interfaces (BCI) and EEG-based BCI. Application of Artificial Intelligence to EEG signals.

ARTIFICIAL NEURAL NETWORKS (2 CFU)
General properties of neural processing systems. Biological neuron model. McCulloch-Pitts artificial neuron. Gradient Descent. Nonlinearities: sigmoidal, hyperbolic tangent, ReLu activation functions.
Learning process. Error-Correction. Widrow-Hopf Rule. Hebbian Learning. Competitive Learning. Supervised and Unsupervised learning. Reinforcement Learning. Statistical Nature of the Learning Process.
Network architectures: feedforward and feedback models. Competitive and Self-Organizing models. Knowledge representation. Visualization of processes in Neural Networks.

Perceptrons. Multilayer Perceptrons. Radial-Basis Function Networks. Recurrent Networks. Self-Organizing Maps. Information-Theoretic Models. Temporal processing with neural networks.

DEEP LEARNING (1.5 CFU)
Deep vs shallow models. Convolutional Neural Networks. Visualizing and Understanding Convolutional Networks. Basic concepts of Explainable Machine Learning methods, Stacked AutoEncoders and Generative Adversarial Networks (GAN).

EEG SIGNAL PROCESSING (1 CFU)
Fundamentals of EEG signal processing. Analysis of EEG signals in the time domain. Spectral and time-frequency analysis of EEGs. Dynamical analysis and chaos. Entropic analysis. Different types of complexity. Graph theory applied to EEG. Principal Component Analysis and Independent Component Analysis. Common Spatial Patterns.
Neural Networks-based EEG signal processing. Classification of brain states through Neural Networks.

LABORATORY EXPERIMENTS (1 CFU)
Use of Matlab Deep Learning toolbox and/or Python.
Designing paradigms for offline and online EEG processing applications through EEGlab. Set up of an EEG recording system.
EEG recording and processing. Acquisition of bio-signals meant for the final project development.
Testi docente José C. Principe, Neural and Adaptive Systems: Fundamentals Through Simulations, Wiley
Ian Goodfellow, Yoshua Bengio and Aaron Courville, “Deep Learning” (www.deeplearningbook.org), An MIT Press book
Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. (2021). Dive into deep learning. arXiv preprint arXiv:2106.11342 (https://d2l.ai/index.html)
Paul L. Nunez and Ramesh Srinivasan, Electric fields of the brain - the Neurophysics of EEG (second edition), Oxford University Press
Sani-Chambers, EEG Signal Processing, IEEE- Wiley
Simon Haykin, Neural Networks, IEEE Press
Erogazione tradizionale
Erogazione a distanza No
Frequenza obbligatoria No
Valutazione prova scritta No
Valutazione prova orale
Valutazione test attitudinale No
Valutazione progetto
Valutazione tirocinio No
Valutazione in itinere No
Prova pratica No

Ulteriori informazioni

Nessun materiale didattico inserito per questo insegnamento
Nessun avviso pubblicato
Nessuna lezione pubblicata
Codice insegnamento online non pubblicato

Modulo: Artificial Intelligence

Corso Ingegneria Elettrica ed Elettronica LM-29
Curriculum Electrical and Electronic Engineering
Orientamento Orientamento unico
Anno Accademico 2022/2023
Crediti 6
Settore Scientifico Disciplinare ING-INF/05
Anno Secondo anno
Unità temporale Secondo semestre
Ore aula 48
Attività formativa Attività formative a scelta dello studente (art.10, comma 5, lettera a)

Canale unico


Ulteriori informazioni

Nessun materiale didattico inserito per questo insegnamento
Nessun avviso pubblicato
Nessuna lezione pubblicata
Codice insegnamento online non pubblicato

Modulo: Smart road technologies and performance

Corso Ingegneria Elettrica ed Elettronica LM-29
Curriculum Electrical and Electronic Engineering
Orientamento Orientamento unico
Anno Accademico 2022/2023
Crediti 6
Settore Scientifico Disciplinare ICAR/04
Anno Secondo anno
Unità temporale Secondo semestre
Ore aula 48
Attività formativa Attività formative a scelta dello studente (art.10, comma 5, lettera a)

Canale unico

Docente FILIPPO GIAMMARIA PRATICO'
Obiettivi IT
1. Conoscenza e capacità di comprensione (Acquisizione di specifiche competenze teoriche e operative in materia di Dispositivi e sistemi di monitoraggio infrastrutturale per le smart roads).

2. Capacità di applicare conoscenza e comprensione

3. Autonomia di giudizio (Valutazione e interpretazione dei dati sperimentali propri del settore).

4. Abilità comunicative.

5. Capacità di apprendimento con riferimento ai temi tratti diffusamente ivi inclusi quelli secondari.

6. Modalità di accertamento e valutazione:
Voto finale (<=30)=voto progetto (<=15)+voto orale (<=15). Il progetto consta di 2 parti principali: 1) riassunto del corso. 2) relazione a tema. Esso è corredato da approfondita analisi bibliografica. L’esame orale include: la discussione di un argomento trattato a lezione; la discussione di una tecnologia (relazione a tema).
Agli studenti che abbiano acquisito competenze eccellenti sia nel rapporto scritto che all’orale può essere attribuita la lode.



EN
1. Knowledge and understanding capability (Knowledge and understanding capability in the field of information technologies, transport infrastructure, electronic devices, smart roads).
1.1 Learning ability (references, data bases, projects) in the field of information technologies and infrastructures in transportation systems.

2. Capability of applying (Capability of applying in the field of information technologies and infrastructures in transportation systems).

3. Autonomy and capacity for critical reflection (Autonomy and capacity for critical reflection in the field of information technology for transportation users). Student’s independent work, Individual study of the discipline, exercises dealing with information technologies and infrastructures in transportation systems

4. Communication Skills (Ability in communication skills, written, oral, etc.)

5. Ability to learn (main and secondary topics).

6. The exam (score up to 30 cum laude) includes the analysis of the report (up to 15 points) and several oral questions (up to 15 points). These latter include one topic treated during the lessons and the discussion of the topic treated in the report.
When excellence is noted in the report and in the oral examination the laude may be given.
Programma IT
Materiali, sistemi e tecnologie per le infrastrutture di trasporto (1Credito; M25; M115_1, _2; M129)
Gestione e monitoraggio del patrimonio infrastrutturale (1 credito; M190)
Algoritmi e modellistica avanzata per l’analisi (1 credito; M190)
Analisi comparata sistemi di monitoraggio e prospettive della ingegneria elettronica al servizio dei sistemi di trasporto (0.5 crediti)
Dispositivi e Sistemi di monitoraggio (2 crediti; M290_3)
Esempi di smart roads (0.5 crediti; M290_7, 8, 9)
Sono previste attività di laboratorio personalizzate in ciascuno dei moduli sopra.

EN
Materials and systems for transport infrastructures (1Credit; M25; M115_1, _2; M129)
Management and monitoring (1 credit; M190)
Algorithms (1 credit; M190)
Issues and perspectives of electronics in terms of monitoring systems (0.5 credits)
Devices and systems for monitoring (2 credits; M290_3)
Examples of smart roads (0.5 credits; M290_7, 8, 9)
Laboratory activities are foreseen in each of the modules above.
Testi docente Risorse e bibliografia essenziale/References and Textbooks
AA.VV., Pubblicazioni ed altri testi indicati durante il corso (moduli M12, 25, 115_1, _2, 190, 290_3, _7,_ 8, _9).
Praticò, F.G., QA/QC in Transport Infrastructures: Issues and Perspectives, DOI: 10.5772/21719.
Norme funzionali e geometriche per la costruzione strade D. M. 6792 del 5/11/2001.
Praticò F.G. et al., Evaluating the performance of automated pavement cracking measurement equipment, PIARC Reference 2008R14, ISBN 2-84060-214-8, Pages 59, PIARC, 2008.
Reagan, J, Stimpson, W, Lamm, R, Heger, R, Steyer, R, Schoch, M, Influence Of Vehicle Dynamics On Road Geometrics, Transp. Res. Circular, Issue Number: E-C003, Transportation Research Board, 1998.
Tesoriere G., Boscaino G., Tesoriere G.: Strade Ferrovie ed Aeroporti”, UTET – voll. I, II, III.
Strade: Teoria e tecnica delle costruzioni stradali • Vol.1 Progettazione • Vol.2 Costruzione, gestione e manutenzione
Ullidtz, Per. (1987). Pavement Analysis. Elsevier, Amsterdam.
www.its.dot.gov/strat_plan/index.htm
http://www.its.dot.gov/factsheets/overview_factsheet.htm#sthash.p09ceP1H.dpuf
http://www.its.dot.gov/factsheets/overview_factsheet.htm
Policy Framework for Intelligent Transport Systems in Australia, http://www.infrastructure.gov.au/transport/its/files/ITS_Framework.pdf
Lamm, R., Psarianos, B., Mailaender, T. “Highway Design and Traffic Safety Engineering Handbook” McGraw-Hill Book Co, .., 1999.
European standards.
Alexey Finogeev, nton Finogeev, Ludmila Fionova, Artur Lyapin, Kirill A. Lychagin, Intelligent monitoring system for smart road environment, Journal of Industrial Information Integration, Volume 15, 2019,Pages 15-20,ISSN 2452-414X, https://doi.org/10.1016/j.jii.2019.05.003.
Erogazione tradizionale
Erogazione a distanza
Frequenza obbligatoria No
Valutazione prova scritta No
Valutazione prova orale
Valutazione test attitudinale No
Valutazione progetto
Valutazione tirocinio No
Valutazione in itinere No
Prova pratica No

Ulteriori informazioni

Descrizione Descrizione
Presentazione (dispensa) Descrizione
Nessun avviso pubblicato
Nessuna lezione pubblicata
Codice insegnamento online non pubblicato

Modulo: Electromagnetic compatibility

Corso Ingegneria Elettrica ed Elettronica LM-29
Curriculum Electrical and Electronic Engineering
Orientamento Orientamento unico
Anno Accademico 2022/2023
Crediti 6
Settore Scientifico Disciplinare ING-IND/31
Anno Secondo anno
Unità temporale Secondo semestre
Ore aula 48
Attività formativa Attività formative a scelta dello studente (art.10, comma 5, lettera a)

Canale unico

Docente GIOVANNI ANGIULLI
Obiettivi Course objectives: Electromagnetic Compatibility is the discipline that studies electromagnetic interactions between electrical and electronic devices and systems. The fundamental objectives are aimed at identifying and analyzing the sources of disturbance that characterize the electromagnetic environment in which the various devices are immersed, in determining the possible couplings and the interferences produced, in identifying the protective devices, in defining the constraints of a project. The investigation tools are based on the theory of electromagnetic fields and electrical circuits. This knowledge is of fundamental interest to the electronic engineer.

Knowledge and understanding: upon passing the exam - Starting from the principle that an electrical or electronic apparatus is compatible with one's work environment if it can function satisfactorily without generating or being subject to intolerable disturbances that impair its functioning student must have acquired the primary basic knowledge relating to the concepts of disturbance, the relevant sources, the mechanisms that determine it, and finally the techniques and means of protection to be used on the disturbance source, on the coupling path or on the perturbed apparatus to mitigate the flu effects.

From all these knowledge, the main skills acquired by the student, in terms of ability to apply the knowledge acquired and to adopt the appropriate approach with independent judgment, will consist in the ability to identify and classify the type of disturbance, and to select the most appropriate set of techniques to solve a specific Electromagnetic Compatibility problem.

The exam consists of an oral test accompanied by the discussion of an essay.

The essay aims to assess the student's ability to apply the knowledge acquired during the course to problem-solving

The oral exam aims to ascertain the level of knowledge and understanding of the course contents, assessing autonomy of judgment, learning ability and communication skills. The oral test consists of the discussion of the written test, in questions and/or exercises on the course content.

The final grade of the exams is determined, taking into account both the paper and the oral exam. The evaluation grid adopted is defined as follows: If the student demonstrates a basic knowledge of the main topics, a basic knowledge of technical language, a sufficient interpretative ability, a sufficient ability to apply the basic knowledge acquired, the score achieved will be between 18 and 19; If the student demonstrates an adequate knowledge of the topics but limited mastery of them, a satisfactory property of language, a correct interpretative ability, a more than sufficient ability to independently apply the knowledge to solve the proposed problems, the score achieved will be between 20 and 23 ; If the student demonstrates a knowledge of the topics with a good degree of command, a good property of language, a correct and safe interpretative ability, a good ability to apply most of the knowledge correctly to solve the proposed problems, the score obtained will be between 24 and 27; If the student demonstrates a complete and in-depth knowledge of the topics, an excellent language property, a complete and effective interpretative ability and will be able to independently apply the knowledge to solve the proposed problems, the score obtained will be between 28 and 30; The score of 30 and honors will be achieved by the student capable of demonstrating a complete, in-depth and critical knowledge of the topics, an excellent language property, a complete and original interpretative ability and a full ability to independently apply the knowledge to solve the proposed problems;
Programma First Part: References on: Transmission lines: equations of the transmission lines, circuits with distributed parameters, parameters per unit of length, solution in the time domain (transients). Antennas: antenna gain, directivity, effective aperture, antenna factor, broadband antennas for measurements: biconical and log-periodic.

Second Part: The electromagnetic environment: generation and suppression of transients, presence of nonlinear elements; Shielding: Shielding efficiency for near and far fields, multilayer screens, magnetic screens, screens with openings. Electrostatic discharge: Origin of electrostatic discharge, effects of electrostatic discharge, design techniques to mitigate the effects of electrostatic discharge. Design of electromagnetically compatible systems: Ground connections: earth and signal ground, common point and multiple point ground connection, parasitic ground paths - System configuration: system containers, placement of power filters, internal cable layout and location of connectors, decoupling of subsystems; Test and measurement techniques: Low and high frequency emission measurements Conducted disturbances - Radiated disturbances - Immunity tests; Biological effects of electromagnetic fields.
Testi docente Christopoulos, Christos. Principles and techniques of electromagnetic compatibility. CRC press, 2018.
Erogazione tradizionale
Erogazione a distanza No
Frequenza obbligatoria No
Valutazione prova scritta No
Valutazione prova orale
Valutazione test attitudinale No
Valutazione progetto
Valutazione tirocinio No
Valutazione in itinere No
Prova pratica No

Ulteriori informazioni

Nessun materiale didattico inserito per questo insegnamento
Nessun avviso pubblicato
Nessuna lezione pubblicata
Codice insegnamento online non pubblicato

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