Topics of the Final State Exam for the Cybernetics and Robotics Master Study Program  (accreditation 2021)

Topics of the Final State Exam relate to the content of the three compulsory subjects of the program and the content of the three compulsory elective subjects of the program chosen by the student from the Group 1. Namely:

Compulsory subjects of the program

  • BE3M33ARO Autonomous robotics: Control architectures of autonomous robotics, planning in robotics, localization and mapping in robotics
  • BE3M38DIT Diagnostics and Testing: Fault detection, fault tolerance, reliability, diagnostics and testing of mechanical, electronic, and electromechanical systems
  • BE3M35LSY Linear systems: Mathematical models of linear and nonlinear dynamical systems. Various concepts of stability. State estimation. State and output feedback

Compulsory elective subjects of the program (Group 1)

  • BE4M33MPV Computer Vision Methods: Object detection in images. Image matching and correspondence search.
  • BE3M35OFD Estimation, filtration and detection: Bayesian approach to parameter estimation. Input-output models of dynamic systems (ARX, ARMAX, OE), their properties and parameter estimation methods. Kalman filter and its modifications for coloured noise, extended Kalman filter. Hypothesis testing. Fault detection methods based on likelihood ratio.
  • BE3M35ORR Optimal and robust systems: Direct and indirect approaches to solving discrete- and continuous-time optimal control problems, model predictive control, trajectory optimization (numerical optimal control), LQR and LQG control, dynamic programming, modelling uncertainty, analysis of robustness, robust control design by minimizing the H∞ norm.
  • BE3M38SPD1 Data Acquisition and Transfer: Principles of IoT and sensor networks, communication interfaces, modulation, media access control, low-power and autonomous devices
  • BE4M33SSU Statistical Machine Learning: Minimizing empirical risk. Maximum likelihood estimation, EM algorithm. Deep networks and their training. Classical and deep neural networks and their learning.
  • BE3M38ZDS1 Analog Signal Processing and Digitization: Principles, methods and circuits for processing and digitizing analog signals, methods of digital signal reconstruction, synchronous detection, noise suppression methods, reduction of non-linearities with an additional dithered signal.
Responsible person: prof. Ing. Jiří Jakovenko, Ph.D.