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 for measurement in ultrasonic range

   The topic is focused on research in the field of advanced sensor structures (MEMS, fibre-optical, arrays) and related methods for signal processing in the acoustic field usable for measurement of signals generated in solid materials for their non-disassembly diagnostics and also for measurement ultrasonic signals transmitted in free space. A limitation of the classic piezoelectric sensors, which are currently the most frequently used, is the complexity of the implementation of broadband sensitive elements and their dimensions. Research work will therefore focus on the design, simulation, optimization and characterization of such MEMS structures that provide sensing and analysis of ultrasonic signals in a wider frequency range and research methods for signal processing to optimize dimensions and energy consumption of the whole sensor. Possibility to define the necessary parameters of sensors in the design phase will ensure the subsequent high application potential in technical diagnostics and also in chemical and pharmaceutical industries. The research will be carried out in connection with already running and planned national and international projects.

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

2. 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 DCI.

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

3. Deep Learning Methods in Mobile Robotics

   Research of current approaches to the use of machine learning, especially deep neural networks, for the use in reconnaissance mobile robotics. The current state of knowledge in scientific fields such as real-time terrain traversibility estimation (including 3D), creation of maps around the robot, or multimodal data fusion - typically a combination of data from lidars and cameras - will be investigated. The main emphasis will be on the sub-area of explainable artificial intelligence. The aim will be to examine mainly methods that can be implemented on mobile robotic systems due to computational complexity. After selecting a narrower research area, the selected techniques will be implemented and tested in both simulated and real environments on robotic systems available at DCI.

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

4. Hyperspectral data analysis

   Hyperspectral data form a specific category where the images are taken in a large number of very narrow spectral bands, usually at intervals between 0.4 and 2.5 µm and with the width of 10 nm. The data are thus suitable for such tasks as inspecting and recognizing the proportion or actual presence of various substances in foodstuffs. The traditional processing approach involves spectral factorization of the input data, with the factors represented through fractional maps, and also a comparison via spectral signatures. This project is aimed to produce the data and to investigate the applicability of machine learning methods in hyperspectral data processing for quality inspection purposes.

   Tutor: Jirsík Václav, doc. Ing., CSc.

5. Methods for measuring mechanical shocks

   The topic is focused on the measurement and generation of mechanical shocks - calibration of shock sensors and calibration of artificial sources of mechanical shocks. The objectives of the thesis are to analyze the parasitic effects that affect the overall measurement uncertainties and to search for new methods to suppress them. The SPEKTRA CS18 calibration system and the AVEX SM110 MP impact machine will be available for the research.

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

6. Model Order Reduction for Complex Systems Control

   The topic is focused on the problem of model order reduction and related computation complexity reduction of dynamical systems models. The research will deal with methods suitable for linear as well as non-linear systems with respect to preserving system constraints. The goal of the work is to allow application of advanced control methods like MPC for systems, where direct application is not computationally feasible because of high model dimension. The studies will be performed in close relation to international and national research projects in cooperation with industry.

   Tutor: Václavek Pavel, prof. Ing., Ph.D.

7. Modelling of special safety function with respect on industrial Ethernet

   The topic is aimed on research of new special safety functions special safety functions models for machinery and the process safety. The objectives of the thesis consist of the a thorough analysis of the current safety function models, research a thorough analysis of the current models available safety functions, examining the impact of communication, particularly an industrial Ethernet. The student will be designed new models on the base on the analysis and will develop new algorithms for verification of the relevant safety logic functions and security elements for machinery and process safety. The topic will be solved in relation to national and international projects running in cooperation with industrial partners.

   Tutor: Štohl Radek, Ing., Ph.D.

8. Motor as a sensor

   In safety critical motor control applications like the fail-safe or fail operational ones, the redundancy is often required to be used. Some sensors which are not needed for the normal operation are added to diagnose the correct functionality of the overall drive. The utilization of multiphase motors or special inverter topologies are needed. In multiphase motor control applications, there seems to be an option to use the motor or its part as a redundant sensor. The deterioration of operational parameters is in this case acceptable. Only limited functionality for limited amount of time is needed. The project solution requires to become familiar with different motor types and to select suitable motor type with regard to its usability as a sensor. Later on, the design of motor control algorithms using motor feedback in case of fault will follow. The simulation results will be validated on a real motor with the help of rapid prototyping tools.

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

9. Resilience methods with a focus on industrial embedded systems

   Research in the field of resilience-enhancing methods with a focus on embedded systems (especially containing a multiprocessor or heterogeneous structure in conjunction with a real-time operating system) aplicable in industrial control systems complying the Industry 4.0 initiative. In the initial phase, the categorization of current approaches with a focus on formal approaches (eg model-driven architecture - MDA) is assumed. A significant part of the activities will be devoted to error detection and masking methods (hardware and software) in the system, using formal mathematical tools (eg Petri nets or transitional systems for modeling and verification, temporal logic for requirements definition) and machine learning techniques. Practical verification of research results can be validated on a microprocessor using the FPGA technology. The aim of the research is to explore the possibilities of formal machine learning methods and techniques to increase the reliability and security of embedded systems.

   Tutor: Arm Jakub, doc. Ing., Ph.D.

10. Use of modern sensors in autonomous vehicles

    Research in the field of data fusion of modern mapping and environment sensors in order to obtain robust information about the surroundings of an autonomous vehicle or mobile robot. The research will focus mainly on the combination of optoelectronic vector and matrix sensors, i.e. planar and 3D lidars, RGB and DRGB cameras, thermal imagers, etc. After selecting a suitable research direction, the aim will be to supplement the existing sensor system, or to design a new one and develop algorithms for robust data fusion, which will allow obtaining valid information about the vehicle / robot environment in real time in a wide range of climatic and visibility conditions. The aim will also be to compare the new approach with other methods and verify the properties in the form of experiments.

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

1. round (applications submitted from 01.04.2022 to 15.05.2022)

1. Contactless measurement of minor changes

   Extracting and visualizing small signals hidden within a video recording has many practical applications ranging from helping with medical diagnoses to preventing structural failures due to unseen vibrations or invisible cracks. The aims of the research lie in increasing the effectivity of the already existing algorithms for the contactless detection and measurement of heart rate and in proposing novel methods to capture the widest possible range of vital signals. These two general tasks can be accomplished by, for example, combining eulerian magnification and pulse wave velocity to measure heart rate and blood pressure. Additional outcomes may include new techniques for the detection of interesting micro-motions via adaptive spacial filtering; innovative machine learning procedures for emotion recognition; or novel paradigms to complement the prevailing Eulerian and Lagrangian perspectives.

   Tutor: Jirsík Václav, doc. Ing., CSc.

2. Decentralized Multirobotic Reconnaissance

   Research in the field of current progressive approaches to decentralized multirobotic exploration of complex unknown outdoor areas. The research will focus on systems combining multiple ground and aerial reconnaissance robotic devices into a collaborating robotic group. After selecting the appropriate direction of research, the aim will be to explore or suggest algorithmic approaches to solve this problem. The aim will then be to implement selected algorithms, compare them with current best algorithms and verify their actual parameters using simulations. The aim will also be the subsequent execution of experiments with real robotic systems of the DCI.

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

3. Diagnostics of electrical drives based on reference models

   The topic is focused on the research in the field of mathematical modeling of electric drive faults, analysis of the effects of the fault on the drive states and conversion of the created models into a discrete form suitable for fault diagnosis and compensation using algorithms with a reference model. The goal is to find a compromise between the accuracy and complexity of the proposed models in both continuous and discrete form. Another goal is to design diagnostic algorithms for detecting the presence of faults and potentially their severity. Fault detection algorithms will be implemented in real-time on the inverter so that the diagnostic results can be used to compensate their effect for fail operational control of the drive.

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

4. Methods of increasing the resileince of embedded systems

   Research focused on methods of creating resilient (durable, robust, tough) embedded systems with a focus on embedded systems equipped with a real-time operating system. The research activities are based on methods that are used for the design of embedded systems with increased requirements for functional safety and cybersecurity, while it is assumed that part of the research methods will also focus on the usability of blockchain technology in applications for embedded systems (e.g. for the needs of IoT appications or metrology - electronic calibration certificates). The aim of the research is to explore generally valid principles used to achieve resilience and to adapt the selected principle or principles for the areas of embedded systems in the form of new design patterns, for example.

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

5. 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.

6. Predictive diagnostics

   Predictive diagnostics is a promising research area with rapid development of new data-driven methods. Unfortunately, there are still many areas where this approach fails and a classical knowledge-based approach has to be used. The aim of this research is to combine the methods of both approaches and try to apply them in areas that have been neglected so far, e.g. construction or testing.

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

7. 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.