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NETWORK AND SIGNAL TECHNOLOGIES FOR INDUSTRIAL ENVIRONMENT AND INDUSTRY 4.0

CODE 101492
ACADEMIC YEAR 2022/2023
CREDITS
  • 6 cfu during the 3nd year of 10800 INGEGNERIA MECCANICA - ENERGIA E PRODUZIONE (L-9) - SAVONA
  • SCIENTIFIC DISCIPLINARY SECTOR ING-INF/03
    TEACHING LOCATION
  • SAVONA
  • SEMESTER 2° Semester
    TEACHING MATERIALS AULAWEB

    OVERVIEW

    The course is about the basics of telecommunication networks and signal processing/analysis in the framework of industrial applications and 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, with special focus on supervised classification techniques.

    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
    • Introductory concepts of deep learning

    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
    • 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

    Exam Board

    GABRIELE MOSER (President)

    ROBERTO BRUSCHI

    SEBASTIANO SERPICO

    RAFFAELE BOLLA (President Substitute)

    LESSONS

    EXAMS

    EXAM DESCRIPTION

    Oral examination on the topics included in the syllabus of the course.

    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.

    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

    Date Time Location Type Notes
    10/01/2023 09:30 GENOVA Orale
    24/01/2023 09:30 GENOVA Orale
    14/02/2023 09:30 GENOVA Orale
    08/06/2023 09:30 GENOVA Orale
    27/06/2023 09:30 GENOVA Orale
    11/07/2023 09:30 GENOVA Orale
    06/09/2023 09:30 GENOVA Orale
    14/09/2023 10:00 SAVONA Esame su appuntamento