Full-time study

4 years

doc. Ing. Jiří Hošek, Ph.D.

Chairman :
doc. Ing. Jiří Hošek, Ph.D.
Councillor internal :
prof. Ing. Jaroslav Koton, Ph.D.
prof. Ing. Zdeněk Smékal, CSc.
prof. Ing. Jiří Mišurec, CSc.
doc. Ing. Vladislav Škorpil, CSc.
doc. Ing. Radim Burget, Ph.D.
Councillor external :
prof. Ing. Ivan Baroňák, Ph.D.
doc. Ing. Miloš Orgoň, Ph.D.
doc. Ing. Otto Dostál, CSc.

The student is fostered to use the theoretical knowledge and experience gained through own research activities in an innovative manner. He/She is able to efficiently use the gathered knowledge for the design of own and prospective solutions within their further experimental development and applied research. The emphasis is put on gaining both theoretical and practical skill, ability of self-decisions, definition of research and development hypotheses to propose projects spanning from basic to applied research, ability to evaluation of the results and their dissemination as research papers and presentation in front of the research community.

The doctor study program "Electronics and Information Technologies" aims to generate top research and development specialists, who have deep knowledge of principles and techniques used in communication and data wired and wireless networks and also in related areas and also in data/signal acquisition, processing and the back representation of user data on the level of application layer. The main parts of the studies are represented by areas dealing with information theory and communication techniques. The graduate has deep knowledge in communication and information technologies, data transfer and their security. The graduate is skilled in operation systems, computer languages and database systems, their usage and also design of suitable software and user applications. The graduate is able to propose new technology solution of communication tools and information systems for advanced transfer of information.

Graduates of theprogram "Electronics and Information Technologies" 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 communication systems and information transfer through data networks are being applied and used.
Our graduates are particularly experienced in the analysis, design, creation or management of complex systems aimed for data transfer and processing, as well as in the programming, integration, support, maintenance or sale of these systems.

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 mainly 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 full-time students submit the elaborated dissertation thesis to the supervisor, who scores this elaborate. The combined students submit the elaborated dissertation thesis by the end of winter term in the fifth year of study. The final dissertation thesis is expected to be submitted by the student by the end of the fourth or fifth year of the full-time or combined study form, respectively.
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. Machine learning in photonics

   Photonic systems cover a wide range of areas from data transmission, through sensors to quantum networks. Each photonic system has its own requirements for the transmission infrastructure, but also for input and output parameters. Manual optimization of large networks based on different types of signals is almost impossible. With the help of machine learning, the optimization of both the transmitted signals and the entire infrastructure can be achieved in photonic networks. Last but not least, machine learning algorithms can be used to detect and classify non-standard network behavior to minimize security risks.

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

2. Modern fiber optic transmission systems

   Optical transmission systems are evolving very rapidly to meet the ever-increasing demands of users. In addition to data transmissions, there are also new transmissions such as exact time, stable frequency, radio over fiber, quantum signals transmission, etc. Individual types of signals have different requirements for the transmission infrastructure. Wavelength division multiplexing is now widely used to increase the capacity of optical fibers but it is necessary to address the issue of possible interference. In order to meet the requirements of future transmission systems, it is necessary to address several technical challenges, such as new optical modulation formats with high spectral efficiency, mitigation of linear and nonlinear phenomena in optical fibers, new types of optical fibers or signal amplification with minimal noise.

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

3. Optical fiber infrastructure security

   Fiber optic networks have evolved rapidly in recent years to meet the ever-increasing demand for increasing capacity. Today, optical fibers are widely used in all types of networks due to not only transmission speed or maximum achievable distance but also security. Although fiber optic networks are considered completely secure, there are ways to capture or copy part of the data signal. Both imperfections of passive optical components and, for example, monitoring outputs of active devices can be used. With the advent of quantum computers, current encryption could be broken. It is therefore necessary to address the security of fiber-optic networks, analyze security risks and propose appropriate countermeasures.

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

4. Quantum key distribution secured optical networks

   Today's digital world is dependent on data security during communication but also in storage, for example in e-banking, e-commerce, e-health or e-government. With the advent of quantum computers, there is a risk of potential security breaches today. Quantum Key Distribution (QKD) provides a way to distribute and share secret keys that are necessary for cryptographic protocols. The information is coded into individual photons. Integrating QKD systems into existing network infrastructure used for telecommunications is a topical challenge. Some other major challenges include increasing of the key rate, increasing the range of the QKD system, or reducing the complexity and robustness of existing solutions.

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

5. Reconstruction of degraded audio signals using combination of deep neural networks and model-based techniques

   The thesis will deal with modern approaches to audio signal restoration, specifically focusing on the task of filling in a missing gap in an audio signal and the related task of restoration of saturated samples. Problems of this type are commonly encountered in practice (archival recordings, dropouts in VoIP calls, etc.). Current methods provide a very good interpolation of signals that are stationary and harmonic in the vicinity of the corrupted segment. While current developments in the field of deep neural networks (DNN) are promising, DNNs have been shown to improve their performance when complemented with a physical formulation of the problem (model-based networks). The study will focus on approaches that combine algorithms that have been successful in recent years (optimization-based methods) and DNNs. The work will not neglect the psychoacoustic side of the problem. (Cooperation with the Acoustics Research Institute, Vienna)

   Tutor: Rajmic Pavel, prof. Mgr., Ph.D.

1. round (applications submitted from 01.04.2024 to 30.04.2024)

1. Evaluation and Optimization of Directional Communications Technology in On-Demand Aerial Networks

   Recently, unmanned aerial vehicles and systems attracted attention in many contexts, such as on-demand wireless connectivity provisioning. This doctoral research topic addresses the emerging air-to-everything communication, including autonomous drone interworking over air-to-air links and robust aerial networking via air-to-ground channels. It targets to evaluate and optimize this emerging technology by contributing with efficient features to improve its performance, which notably account for specific effects and behavior of directional millimeter-wave channels. The proposed radio connectivity algorithms, system architectures, and performance evaluation frameworks are expected to become of significant value toward the development of future 5G+/6G wireless systems.

   Tutor: Hošek Jiří, doc. Ing., Ph.D.

2. Improving the perceptual quality of compressed audio signals using deep neural networks

   Athough a great attention is paid to audio coding, coders with a low bit budget still produce perceptually unpleasant results. The study would be focused on the design of an generative deep neural network, which would improve the perceptual quality of the compressed files. The network's input would therefore be the compressed signal, and its output would be the perceptually improved version.

   Tutor: Rajmic Pavel, prof. Mgr., Ph.D.