OVERVIEW

The unit is about the basics of information and communication technologies (ICT) associated with Internet, cybersecurity, and machine learning in the framework of energy applications.

AIMS AND CONTENT

LEARNING OUTCOMES

The course aims to provide the student with essential knowledge on issues of telecommunications networks and signal processing / analysis in contexts related to systems for energy production, with particular attention to the field 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 Protocols of the Internet and the basic aspects related to the theme of cyber security. He will also have learned the essential concepts related to the representation of analog and digital information and data analysis using machine learning

AIMS AND LEARNING OUTCOMES

After the unit, the student will have learnt the basic principles of telecommunication networks, 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 machine learning. The concepts learnt through the unit will be framed in the context of energy applications.

TEACHING METHODS

Class lectures.

Students who have valid certification of physical or learning disabilities on file with the University and who wish to discuss possible accommodations or other circumstances regarding lectures, course work and exams, should speak both with the teacher and with Prof. Federico Scarpa (federico.scarpa@unige.it ), the Department's disability liaison.

SYLLABUS/CONTENT

• Basic principles of telecommunication networks
• 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 the energy field
• 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)

SEBASTIANO SERPICO

RAFFAELE BOLLA (President Substitute)

LESSONS

LESSONS START

https://corsi.unige.it/en/corsi/11438/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 who have valid certification of physical or learning disabilities on file with the University and who wish to discuss possible accommodations or other circumstances regarding lectures, coursework and exams, should speak both with the instructor and with Prof. Federico Scarpa (federico.scarpa@unige.it), the disability liaison for the Engineering study programs.

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 the studied ICT methodologies and technologies in the energy field shall be evaluated.

Exam schedule