OVERVIEW

The course aims to provide a broad overview of the various types of cyber-physical systems: synchronous, asynchronous, continuous-time dynamic, timed, hybrid systems. The coverage of the systems concerns the topics of modeling, simulation and verification (model checking). Each topic is also addressed through the use of appropriate development tools, such as nuXmv, Spin, Uppaal, Matlab-Simulink. The final part of the course introduces the python language, with particular attention to the application to machine learning and data science

AIMS AND CONTENT

LEARNING OUTCOMES

The course aims to provide an introduction to the analysis and design of cyberphysical systems. This includes synchronous and asynchronous processes, safety and liveness requirements, and dynamic and timed systems. The course aims to provide an introduction to the python language, with particular attention to the application to machine learning and data science

AIMS AND LEARNING OUTCOMES

By attending the course, the student should get a broad knowledge of the various types of cyberphysical sytems, such as: synchronous, asynchronous, continuous-time dynamic, timed, hybrid systems. For each type of system, the topics of modeling, simulation and verification (model checking) are covered. The student will get to know the theoretical bases, then to study some application examples. Exercises are proposed, and usually solved in class, for each of the topics, in order to verify the acquisition of knowledge. The student will also learn to use a development tool for each type of cyberphysical system treated (nuXmv, Spin, Uppaal, Matlab-Simulink / Stateflow).

The final part of the course is dedicated to applications, especially in the context of the Internet of Things. The python language will be introduced, especially for data science and machine learning.

The project envisaged at the end of the course is aimed at stimulating and verifying the student's design and implementation skills, in addition to operational verification in the field of part of the acquired knowledge.

The learning outcomes concern the achievement of the above training objectives, also through the implementation of a project.

PREREQUISITES

Digital Systems Electronics

Programming basics

Computer architecture basics

SYLLABUS/CONTENT

Modeling, simulation and verification of cyberphysical systems
- Introduction
- Synchronous models
- Safety requirements
- Asynchronous models
- Liveness requirements
- Dynamic systems
- Timed systems
- Hybrid systems

Introduction to the python language
- Types of data
- Functions
- OOP
- File system
- Numpy library
- Pandas library
- Pyplot library

RECOMMENDED READING/BIBLIOGRAPHY

R. Alur, Principles of Cyberphysical Systems: 
https://mitpress.mit.edu/books/principles-cyber-physical-systems

J. VanderPlas, Python Data Science Handbook, O'Reilly
https://www.oreilly.com/library/view/python-data-science/9781491912126/ 

TEACHERS AND EXAM BOARD

Exam Board

FRANCESCO BELLOTTI (President)

ALESSANDRO DE GLORIA

LESSONS

LESSONS START

https://easyacademy.unige.it

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Oral exam on the first part (cyber-physical systems), including both theoretical questions and exercises, on the topics covered in class.

Project work (on modeling / simulation of a cyberphysical system or on an IoT / machine learning / data science application)

ASSESSMENT METHODS

The assessment will be made through questions / exercises in the oral exam. Concerning the project, the evaluation will take place in the preparatory talks and during the design/implementation and in the final discussion of a descriptive report of the work performed.

Exam schedule