Full-time study

4 years

prof. Ing. Pavel Václavek, Ph.D.

Chairman :
prof. Ing. Pavel Václavek, Ph.D.
Councillor internal :
doc. Ing. Zdeněk Bradáč, Ph.D.
prof. Ing. Pavel Jura, CSc.
doc. Ing. Petr Beneš, Ph.D.
doc. RNDr. Zdeněk Šmarda, CSc.
Councillor external :
prof. Ing. Pavel Ripka, CSc.
Prof. Ing. Roman Prokop, CSc.
doc. Ing. Eduard Janeček, CSc.
prof. Dr. Ing. Alexandr Štefek, Dr.
prof. Ing. Tomáš Vyhlídal, Ph.D.

The doctor study programme "Cybernetics, Control and Measurements" is devoted to the preparation of the high quality scientific and research specialists in various branches of control technology, measurement techniques, automatic systems, robotics, artificial intelligence and computer vision.
The aim is to provide the doctor education in all these particular branches to students educated in university magister study, to make deeper their theoretical knowledge, to give them also requisite special knowledge and practical skills and to teach them methods of scientific work.
Through a systematic and comprehensive view of management and measurement, graduates of the study program successfully apply to key management and managerial positions and functions in which they use system view, knowledge of system analysis and optimal management.

Graduate of doctoral studies is profiled to independent creative work and critical thinking based on the systemic view of both technical and non-technical systems and the world as a whole. The graduate program is equipped with the necessary knowledge of mathematics, physics, electrical engineering, theory and practice of control and regulation, measuring techniques, robotics, artificial intelligence, image processing and other fields of applied electrical engineering and informatics. One of the characteristic features of graduates is the ability to integrate a broad spectrum of knowledge and to create functional technical as well as organizational and economic systems.
All graduates of the doctoral program Cybernetics, Automation and Measurement demonstrate during their studies:
• mathematical, physical and electrotechnical principles relevant to measurement and control;
• electronic measuring systems, embedded systems, communication systems, control theory, automatic control systems and artificial intelligence;
• design and operation of electrotechnical, electronic, measuring, control and communication systems.
The graduates are well versed in modern technologies (Industry 4.0, Artificial Intelligence, Signal Processing, Computer Vision, Advanced Management Methods, Industrial Measurement and Control Systems, Mobile and Stationary Robotics, Communication Systems, Functional and System Security).
The graduates are trained to find the work in technical practice, creative work, research and development, production, management and managerial positions in technical or business firms and companies at the highest qualification levels.

Graduates will apply in particular:
- in research, development and design teams,
- in the field of professional activity in production or business organizations,
- in the academic sphere and in other institutions involved in science, research, development and innovation,
- in all areas of the company where cybernetic systems or cybernetic principles are being applied
Our graduates are particularly experienced in the analysis, design, creation or management of complex measurement or control systems, as well as in the programming, integration, support, maintenance or sale of these systems.

Doctoral studies are carried out according to the individual study plan, which is prepared by the supervisor in the beginning of the study in cooperation with the doctoral student. The individual curriculum specifies all the duties determined in accordance with the BUT Study and Examination Rules, which the doctoral student must fulfill to successfully finish his studies. These responsibilities are time-bound throughout the study period, they are scored and fixed at fixed deadlines.
Students will write and perform tests of obligatory subjects (Selected Chapters of Control Engineering, Selected Chapters of Measurement Techniques and Exam in English before the state doctoral examination), at least two compulsory elective courses in view of the focus of his dissertation, at least two optional subjects (English for PhD students; Quoting in Scientific Practice; Resolving Innovation Assignments; Scientific Publishing from A to Z).
The student may enroll for the state doctoral exam only after all the tests prescribed by his / her individual study plan have been completed. Before the state doctoral exam, the student draws up a dissertation thesis describing in detail the aims of the thesis, a thorough evaluation of the state of knowledge in the area of ​​the dissertation solved, or the characteristics of the methods it intends to apply in the solution.
The defense of the controversy that is opposed is part of the state doctoral exam. In the next part of the exam, the student must demonstrate deep theoretical and practical knowledge in the field of electrical engineering, control technology, cybernetics and measuring techniques. The state doctoral examination is in oral form and, in addition to the discussion on the dissertation thesis, it also consists of thematic areas related to compulsory and compulsory elective subjects.
To defend the dissertation, the student reports after the state doctoral examination and after fulfilling conditions for termination, such as participation in teaching, scientific and professional activity (creative activity) and at least a monthly study or work placement at a foreign institution or participation in an international creative project. The studies are finished by successful defence of the dissertation thesis.

The doctoral studies of a student follow the Individual Study Plan (ISP), which is defined by the supervisor and the student at the beginning of the study period. The ISP is obligatory for the student, and specifies all duties being consistent with the Study and Examination Rules of BUT, which the student must successfully fulfill by the end of the study period. The duties are distributed throughout the whole study period, scored by credits/points and checked in defined dates. The current point evaluation of all activities of the student is summarized in the “Total point rating of doctoral student” document and is part of the ISP. At the beginning of the next study year the supervisor highlights eventual changes in ISP. By October, 15 of each study year the student submits the printed and signed ISP to Science Department of the faculty to check and archive.
Within the first four semesters the student passes the exams of compulsory, optional-specialized and/or optional-general courses to fulfill the score limit in Study area, and concurrently the student significantly deals with the study and analysis of the knowledge specific for the field defined by the dissertation thesis theme and also continuously deals with publishing these observations and own results. In the follow-up semesters the student focuses already more to the research and development that is linked to the dissertation thesis topic and to publishing the reached results and compilation of the dissertation thesis.
By the end of the second year of studies the student passes the Doctor State Exam, where the student proves the wide overview and deep knowledge in the field linked to the dissertation thesis topic. The student must apply for this exam by April, 30 in the second year of studies. Before the Doctor State Exam the student must successfully pass the exam from English language course.
In the third and fourth year of studies the student deals with the required research activities, publishes the reached results and compiles the dissertation thesis. As part of the study duties is also completing a study period at an abroad institution or participation on an international research project with results being published or presented in abroad or another form of direct participation of the student on an international cooperation activity, which must be proved by the date of submitting the dissertation thesis.
By the end of the winter term in the fourth year of study the students submit the elaborated dissertation thesis to the supervisor, who scores this elaborate. The final dissertation thesis is expected to be submitted by the student by the end of the fourth year of studies.
In full-time study form, during the study period the student is obliged to pass a pedagogical practice, i.e. participate in the education process. The participation of the student in the pedagogical activities is part of his/her research preparations. By the pedagogical practice the student gains experience in passing the knowledge and improves the presentation skills. The pedagogical practice load (exercises, laboratories, project supervision etc.) of the student is specified by the head of the department based on the agreement with the student’s supervisor. The duty of pedagogical practice does not apply to students-payers and combined study program students. The involvement of the student in the education process within the pedagogical practice is confirmed by the supervisor in the Information System of the university.

1. Advanced sensor structures and signal processing methods for diagnostics

   The topic focuses on research in the field of advanced sensor structures (MEMS, fiber-optic, multi-element) and related methods for signal processing electrical and mechanical quantities. These methods can be utilized for non-invasive diagnostics of mechatronic devices as well as for measuring acoustic and ultrasonic signals generated by fault processes. A limitation of conventional piezoelectric sensors, which are currently the most commonly used for dynamic measurements of mechanical quantities, is the complexity of implementing broadband-sensitive elements and their dimensions. Therefore, the research will focus on the use of such sensing elements and associated signal processing methods that enable the evaluation of diagnostic information with higher sensitivity over a broader frequency range, while simultaneously optimizing the size, robustness of the provided data, and energy consumption of the entire sensing system. The ability to define the required sensor parameters at the design stage will ensure high application potential in technical diagnostics, as well as in the chemical and pharmaceutical industries. Special attention will be given to methods based on machine learning and artificial intelligence. The research will be conducted in connection with ongoing and planned national and international projects.

   Tutor: Havránek Zdeněk, Ing., Ph.D.

2. Application of Time-Frequency Methods for the Analysis of Non-Stationary Processes

   Non-stationary processes are common in many technical and natural systems, and their analysis presents a significant challenge for current diagnostic methods. Time-frequency analysis provides effective tools for the detailed examination of time-varying signals; however, its practical application is often limited by insufficient accuracy, high computational demands, or the unsuitability of certain methods for specific applications. This research will focus on the application of time-frequency methods for analyzing non-stationary processes, such as their use in predictive diagnostics of mechanical systems with non-rotational motion or for anomaly detection in measured data from vibration tests. The goal of the study is to identify the limitations of currently used data-driven methods and propose modifications or combinations with knowledge-based approaches.

   Tutor: Beneš Petr, doc. Ing., Ph.D.

3. Autonomous Operations of Unmanned Aerial Systems in Reconnaissance Robotics

   Research on the field of autonomous unmanned aerial reconnaissance focused on environmental data collection, creation of 3D maps and cooperation of multiple mobile platforms. Current approaches in trajectory planning, creation of various types of 3D maps of the machine surrounding, obstacle avoidance and other areas necessary for safe autonomous operation of air robotic reconnaissance vehicles in a complex outdoor environment will be studied. Based on the current state of knowledge of the issue, suitable algorithms and methods for solving this problem will be chosen. The proposed solution will then be tested in a simulated environment and compared with current best methods. The goal is also the implementation on real unmanned aircrafts, that are available at the ÚAMT.

   Tutor: Žalud Luděk, prof. Ing., Ph.D.

4. Deep Learning in Mobile Robotics

   Machine learning methods, especially deep neural networks, find application in a wide range of research disciplines, including mobile robotics. The aim of the topic is to explore the current state of knowledge and potential applications of deep learning in areas of robotics such as environmental understanding, estimation of environment traversability, robust control, multimodal data fusion, and others. Attention must be paid to all major paradigms of machine learning, however, special emphasis should be placed on the area of reinforcement learning. This paradigm, based on the trial-and-error principle and to some extent resembling the way humans learn, proves to be very effective especially in solving complex kinematic tasks and brings a small revolution to mobile robotics. The goal is also to focus on methods that can be implemented on mobile robotic systems due to their computational complexity and which will enhance their capabilities especially compared to current, often analytical, approaches. After selecting a narrower research direction, implementation and testing of algorithms are planned both in simulated and real-world environments using robotic systems available at the ÚAMT workplace.

   Tutor: Žalud Luděk, prof. Ing., Ph.D.

5. Digital twin of electrical drive with PMS motor

   The topic of the doctoral study is to create a digital twin of an electric drive with a permanent magnet synchronous motor—a high-quality drive model that accompanies the real drive throughout its lifetime, is bidirectionally connected to the drive's control unit, and aids in its diagnostics and predictive maintenance. In the first phase, the doctoral student will become familiar with existing solutions described in the available literature. The digital twin will continuously update its parameters based on incoming data from the real drive and will be equipped with online identification algorithms. The goal is not only to monitor the behavior of a healthy drive but also to track behavior in cases of parameter degradation and failures. The development of the digital twin will initially take place in the MATLAB Simulink environment. The focus will be on solutions that can be implemented within the inverter itself or on an embedded platform connected to the inverter, equipped with a high-performance microcontroller. The implemented digital twin will be verified on a real motor connected to a dynamometer using rapid prototyping tools.

   Tutor: Blaha Petr, doc. Ing., Ph.D.

6. Engineering optimization using mixed surrogate models and bio-inspired algorithms

   Solving complex engineering problems is currently challenging, mainly due to their numerical demands and analytical complexity. The thesis will focus on employing bio-inspired optimization algorithms combined with surrogate models (artificial neural networks) to solve complex and computationally intensive optimization tasks in the field of topology optimization.

   Tutor: Matoušek Radomil, prof. Ing., Ph.D.

7. Methods for Measuring and Generating Mechanical Shocks

   The topic of this thesis will focus on the measurement and generation of mechanical shocks, specifically on the calibration of shock sensors and artificial sources of mechanical shocks. Accurate measurement of shock phenomena is crucial for various industrial and scientific applications. An example is the Shock Response Spectrum (SRS) test of satellites. These tests are essential for verifying the resilience of space systems to mechanical shocks that occur, for instance, during rocket stage separation or satellite deployment into orbit. The aim of this research will be to analyze parasitic effects influencing measurement uncertainties and propose new methods to mitigate them, thereby improving the accuracy and reliability of mechanical shock measurements. The research will be conducted using the unique equipment of the testing and calibration laboratory CVVOZE.

   Tutor: Beneš Petr, doc. Ing., Ph.D.

8. Neuroevolution for Automated Design of Neural Networks in Robotic Systems

   The thesis will focus on developing neuroevolution methods for the automated design of artificial neural network structures applicable to robotic systems. The goal is to create a procedure enabling autonomous optimization of network topology for specific robotic tasks such as navigation, manipulation, trajectory planning, or machine vision tasks. The results will be compared to conventional empirical or experimental network design approaches. Ensuring generalization capability and practical applicability of the evolved networks to real-world scenarios will represent the key challenge.

   Tutor: Matoušek Radomil, prof. Ing., Ph.D.

9. Objective measurement and assessment in medicine through advanced cybernetic applications

   According to Evidence Based Medicine, the objective evidence is absolutely necessary for right diagnosis and proper selection of therapy. However, suitable methods providing a sufficiently objective index relevant to the symptoms are lacking, for example, in dermatology, diabetology, physiotherapy or oncology. The goal of this project is to find novel objective diagnostic methods using unconventional view of medical problems from the perspective of cybernetics. Research work will be devoted to the development of missing methods for identifying the status of living systems, which will mainly use objective quantification of symptoms (swelling, inflammation, atrophy, blocking, etc.), mostly by accurate multispectral 3D scanning of selected parameters (eg. 3D temperature distribution, accurate 3D volumetric measurement or topological alignment). The research will continue on the results of the H2020 ASTONISH project, which has already achieved the first positive results in this area. In identifying living systems and their failures, the research work will also follow the latest advanced methods of technical cybernetics, including the use of artificial intelligence. The result will be new accurate objective quantification methods that will bring more effective therapy, shorter recovery time, lower costs and higher quality of health care, not only in the above-mentioned medical fields.

   Tutor: Chromý Adam, Ing., Ph.D.

10. Robot Motion Optimization Using Deep Reinforcement Learning

    The thesis will focus on optimizing the motion efficiency of a robot (particularly an industrial 6-axis manipulator) in complex environments, emphasizing speed, precision, and energy consumption. Quaternion representation will be employed to achieve stable and continuous robot orientation control. Modern methods of Deep Reinforcement Learning (DRL) will be applied, and the results will be compared with existing state-of-the-art methods, such as RRT, Genetic Programming, or Particle Swarm Optimization. Ensuring generalization of the learned strategies to real-world scenarios will be the main challenge of this thesis.

    Tutor: Matoušek Radomil, prof. Ing., Ph.D.

11. Visual Telepresence in Mobile Robotics

    Research in the field of modern progressive approaches to immersive visual telepresence for use in reconnaissance and service mobile robotics. The research will focus on achieving the best possible visual perception and will include the following sub-areas - stereovision, low latency of the sensing, transmission and imaging chain, high resolution, wireless transmission, head position sensing and head movement prediction and more. After selecting a suitable research direction, a custom solution with verification of parameters will be designed and technically implemented. Experimental deployment on one of the robotic devices available at Brno University of Technology is expected.

    Tutor: Žalud Luděk, prof. Ing., Ph.D.