CODE 101492 ACADEMIC YEAR 2021/2022 CREDITS 6 cfu anno 3 INGEGNERIA MECCANICA - ENERGIA E PRODUZIONE 10800 (L-9) - SAVONA SCIENTIFIC DISCIPLINARY SECTOR ING-INF/03 TEACHING LOCATION SAVONA SEMESTER 2° Semester TEACHING MATERIALS AULAWEB OVERVIEW The course is aimed at providing the student with basic knowledge on the topics of telecommunication networks and signal processing/analysis in industrial contexts and especially in the framework of Industry 4.0 AIMS AND CONTENT LEARNING OUTCOMES The course aims to provide students with essential knowledge on telecommunications networks and signal processing / analysis in industrial contexts and, in particular, in the context of Industry 4.0. At the end of the course, the student will know the basic principles of telecommunications networks, the main technologies / standards related to wired and wireless networks applicable in industrial environments, the architecture and Internet protocols and the basic aspects related to the theme of cyber security. He will also have learned the essential concepts related to the representation of analogical and digital information and to the analysis of data through machine learning AIMS AND LEARNING OUTCOMES After the course, the student will have learnt the basic principles of telecommunication networks, the main technologies and standards related to wired and wireless networks for industrial environments, the architectures and the protocols of Internet, and the basic aspects of cyber security. The student will also have learnt the basic concepts associated with information representation through analog and digital signals and with signal analysis through machine learning. TEACHING METHODS Class lectures SYLLABUS/CONTENT Basic principles of telecommunication networks Main technologies and standards for wired and wireless networks in industrial environments Internet architecture and protocols Basic aspects of cyber security Basic principles of information representation through analog and digital signals Basic aspects of signal analysis through machine learning Case studies of machine learning applications in industrial contexts and Industry 4.0 RECOMMENDED READING/BIBLIOGRAPHY Slides used in class and made available on AulaWeb Bishop C., Pattern recognition and machine learning, Springer, 2006 Carlson A. B., Crilly P., Communication systems, McGraw-Hill, 2009 Chiani M., Verdone R., Fondamenti di telecomunicazioni per l’ingegneria gestionale, Pitagora, 2004 Hastie T., Tibshirani R., and Friedman J., The elements of statistical learning, Springer, 2008 Goodfellow I., Bengio Y., and Courville A., Deep learning, MIT Press, 2016 Kurose J. F. and Ross K. W., Computer networking: a top-down approach, 7th Edition, McGraw-Hill, 2017 Stallings W., Data and computer communications, 10th Edition, Prentice Hall, 2013 Stallings W., Wireless communication networks and systems, Global Edition, Prentice Hall, 2016 Tanenbaum A. S. and Wetherall D. J., Computer networks, 5th Edition, Prentice Hall, 2010 TEACHERS AND EXAM BOARD GABRIELE MOSER Ricevimento: By appointment RAFFAELE BOLLA Ricevimento: Appointment upon students' requests (direct or by email). Exam Board GABRIELE MOSER (President) ROBERTO BRUSCHI SEBASTIANO SERPICO RAFFAELE BOLLA (President Substitute) LESSONS LESSONS START https://corsi.unige.it/10800/p/studenti-orario Class schedule NETWORK AND SIGNAL TECHNOLOGIES FOR INDUSTRIAL ENVIRONMENT AND INDUSTRY 4.0 EXAMS EXAM DESCRIPTION Oral examination ASSESSMENT METHODS Within the oral examination, the student's knowledge of the course topics and his/her capability to discuss how to apply and use methodologies and technologies of telecommunication networks and signal representation/analysis in industrial contexts and in Industry 4.0 shall be evaluated. Exam schedule Data appello Orario Luogo Degree type Note 11/01/2022 09:30 GENOVA Orale 25/01/2022 09:30 GENOVA Orale 15/02/2022 09:30 GENOVA Orale 08/06/2022 09:30 GENOVA Orale 28/06/2022 09:30 GENOVA Orale 12/07/2022 09:30 GENOVA Orale 07/09/2022 09:30 GENOVA Orale 15/09/2022 10:00 SAVONA Esame su appuntamento