Cyber-Physical Systems

Major: Information and Communication Systems
Code of subject: 6.126.02.E.82
Credits: 5.00
Department: Telecommunication
Lecturer: Dr. Associate Professor Kaidan Mykola Volodymyrovych
Semester: 6 семестр
Mode of study: денна
Learning outcomes: Ability to apply modern information technology techniques and tools for modeling, designing and developing communication systems, Web-oriented systems, smart systems, sensor systems, embedded systems, micro / nano-systems and their analogues; Ability to integrate and integrate sensory, microsystem and communication components, using their interfaces, for the construction of information systems for monitoring and management of various technical objects; Ability to deploy, administer and accompany information systems based on network technologies and the ability to develop technical documentation. Ability to design, integrate, deploy and administer secure information systems based on heterogeneous infocommunication architecture using modern software platforms and cross-platform technologies. Ability to apply the standards in the field of information systems and technologies in the development of functional profiles, construction and integration of systems, products, services and elements of the organization's infrastructure. Ability to design, develop, adjust and improve the system, communication and software and hardware of information systems and technologies, Internet of Things (IT), computer-integrated systems and system network structure, management of them.
Required prior and related subjects: Circuit Information Systems Information and communication network technologies Mobile applications of information and communication systems
Summary of the subject: The discipline is devoted to the study of practical aspects of the development and construction of various cyber-physics systems, their application and technological features.
Assessment methods and criteria: • written reports on laboratory work, oral examination (30%) • exam control (70% control measure, exam), written-oral form (70%)
Recommended books: Davy Silen, Arno Meisman, Mohamed Ali Fundamentals of Data Science and Big Data. Python and data science-. - СПб .: Питер, 2019. - 336 с Shakla Niwant Machine Learning and TepsorFlow. - СПб .: Питер, 2019. - 336 с .: Coelho LP, Richard W. Construction of machine learning systems in Python. 2016. 302 p. Merkov AB Pattern recognition. Introduction to statistical teaching methods. 2011. 256 p. Merkov AB Pattern recognition. Construction and training of probabilistic models. 2014. 238 p. Vorontsov KV Lectures on machine learning. www.MachineLearning.ru. 2004-2016. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning. Springer, 2014. 739 p. Bishop C. M. Pattern Recognition and Machine Learning. - Springer, 2006. 738 p. Roland Siegwart; Illah Reza Nourbakhsh; Davide Scaramuzza Introduction to Autonomous Mobile Robots (Intelligent Robotics and Autonomous Agents series) Klimash MM, Kaidan MV, Andrushchak VS, Klimash Yu.V. Methods and models for building energy-efficient photonic transport networks - Lviv - 2018. - 212 p.