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

This course also contributes to the achievement of the following Sustainable Development Goals of the UN 2030 Agenda: Objectives no. 4, 8, and 9.

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

LESSONS START

https://corsi.unige.it/10800/p/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

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