study programme

Electronics and Communication Technologies

Original title in Czech: Elektronika a komunikační technologieFaculty: FEECAbbreviation: DPC-EKTAcad. year: 2023/2024

Type of study programme: Doctoral

Study programme code: P0714D060009

Degree awarded: Ph.D.

Language of instruction: Czech

Accreditation: 28.5.2019 - 27.5.2029

Mode of study

Full-time study

Standard study length

4 years

Programme supervisor

Doctoral Board

Fields of education

Area Topic Share [%]
Electrical Engineering Without thematic area 100

Study aims

Provide doctoral education to graduates of a master's degree in electronics and communication technologies. To deepen students' theoretical knowledge in selected parts of mathematics and physics and to give them the necessary knowledge and practical skills in applied informatics and computer science. To teach them the methods of scientific work.

Graduate profile

The Ph.D. graduate will be able to solve scientific and complex technical problems in the field of electronics and communications. Graduates of the doctoral program "Electronics and Communication Technologies" will be competent to work in the field of electronics and communication technology as scientists and researchers in fundamental or applied research, as high-specialists in development, design, and construction in many R&D institutions, electrical and electronic manufacturing companies and producers and users of communication systems and devices, where they will be able to creatively use modern computer, communication, and measurement technique.

Profession characteristics

The doctors are able to solve independently scientific and complex engineering tasks in the area of electronics and communications. Thanks to the high-quality theoretical education and specialization in the study program, graduates of doctoral studies are sought as specialists in in the area of electronic engineering and communications. Graduates of the doctoral program will be able to work in the field of electronics and communications technology as researchers in fundamental or applied research, as specialists in development, design and construction in various research and development institutions, electrotechnical and electronic manufacturing companies, where they will be able to creative exploit modern computing, communication and measuring technologies.

Fulfilment criteria

Doctoral studies are carried out in agreement with the individual study plan, which will prepare supervisor together with the doctoral student at the beginning of the study. The individual study plan specifies all the duties given by the BUT Study and Examination Rules, which the doctoral student must fulfill to finish his study successfully. These duties are scheduled into entire the study period. They are classified by points and their fulfilment is checked at fixed deadlines. The student enrolls and performs examination from compulsory subjects (Modern digital wireless communication, Modern electronic circuit design), at least from two compulsory-elective subjects aimed at the dissertation area, and at least from two optional courses such as English for PhD students, Solutions for Innovative Entries, Scientific Publishing from A to Z).
The students may enroll for the state exam only if all the examinations specified in his/her individual study plan have been completed. Before the state exam, the student prepares a short version of dissertation thesis describing in detail the aims of the thesis, state of the art in the area of dissertation, eventually the properties of methods which are assumed to be applied in the research topics solution. The defense of the short version of thesis, which is reviewed, is the first part of the state exam. In the next part of the exam the student has to prove deep theoretical and practical knowledges in the field of electrical engineering, electronics, communication techniques, fundamental theory of circuits and electromagnetic field, signal processing, antenna and high-frequency techniques. The state exam is oral and, in addition to the discussion on the dissertation thesis, it also consists of areas related to compulsory and compulsory elective courses.
The student can ask for the dissertation defense after successful passing the state exam and after fulfilling all conditions for termination of studies such as participation in teaching, scientific and professional activities (creative activities), and a study or a work stay at a foreign institution no shorter than one month, or participation in an international project.

Study plan creation

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 the 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.

Issued topics of Doctoral Study Program

  1. Advanced Optimization Algorithms for Data Clustering

    This thesis aims at developing new stochastic optimization techniques to solve the well-known clustering problem. Clustering is an essential tool of machine learning used in many fields of engineering including recommendation engines, customer segmentation, medical imaging analysis, etc., [1]. Modern clustering algorithms are usually divided into three categories [2]: 1) hierarchical (produce a tree which represents the nested grouping of the data points), 2) partitional (data points are grouped based upon certain criteria known as fitness measure), and 3) overlapping (each data point belongs to all the clusters with a fuzzy membership grade). Real-life clustering problems involve unlabeled data which means that the number of searched clusters is not known a priori. But most of the clustering algorithms need the number of clusters to be set as one of their inputs. Therefore, the partitional clustering problem can be formulated as the optimization problem with a variable number of dimensions [3]. The well-known Particle Swarm Optimization (PSO) algorithm has been extended in [4] to solve the single-objective VND problems. The methodology used there should be extended in the thesis so that it can be effectively used to solve the clustering problem. A general approach to extension of existing stochastic optimization methods should be derived within the thesis. The new VND optimization methods will be implemented in MATLAB and then verified on mathematical benchmark functions, that should mimic the properties of clustering problems. Various existing clustering methods will be implemented in MATLAB. These methods will build a toolbox used for validation of a clustering algorithm relying on newly developed VND optimization algorithms. References [1] Aljarah, Ibrahim, Hossam Faris, and Seyedali Mirjalili, eds. Evolutionary data clustering: Algorithms and applications. Springer, 2021. [2] Nanda, Satyasai Jagannath, and Ganapati Panda. "A survey on nature inspired metaheuristic algorithms for partitional clustering." Swarm and Evolutionary computation, 16 (2014): 1-18. [3] Ryerkerk, Matt, Ron Averill, Kalyanmoy Deb, and Erik Goodman. "A survey of evolutionary algorithms using metameric representations." Genetic Programming and Evolvable Machines 20, no. 4 (2019): 441-478. [4] Kadlec, Petr, and Vladimír Šeděnka. "Particle swarm optimization for problems with variable number of dimensions." Engineering Optimization 50, no. 3 (2018): 382-399.

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

  2. Deep learning for wireless localization based on Bluetooth channel sounding

    In [A], authors used deep learning (DL) techniques to suppress received signal strength (RSS) instabilities caused by multipath propagation in wireless localization. The PhD thesis is focused on wireless localization based on Bluetooth Low Energy Channel Sounding (BLE CS). The thesis is expected to answer following questions: • Can DL improve parameters of BLE CS localization? • Can be the DL localization pre-trained in defined conditions (an anechoic chamber, reference temperature, etc.) and finally trained at the beginning of each measurement to provide improved accuracy? • Can be the DL localization implemented such a way to allow real-time operation? The DL localization should be optimized for personal cars. For experimental validation, the whole setup should be designed comprising the initiator a reasonable number of anchors. Attention should be paid to antennas, localization algorithms and hardware based on KW-x microcontrollers. Note: The project is going to be solved in cooperation with NXP Semiconductors, the Czech Republic. Reference [A] X. Yang et al., DeepWiPos: A deep learning-based wireless positioning framework to address fingerprint instability. IEEE Transactions on Vehicular Technology, 2023, early access. DOI: 10.1109/TVT.2023.3243196

    Tutor: Raida Zbyněk, prof. Dr. Ing.

  3. Improving the security of 5G networks on PHY layer

    The topic of the dissertation is focused on increasing the security of fifth-generation (5G) mobile networks. One of the solved problems will be increasing the resilience of the network against falsification of the identity of the base station. The student will have the task to follow the 3GPP standardization process and design and practically verify the selected solution for detecting a fake base station. OpenRAN tools will be used to implement the base station using software-defined radios.

    Tutor: Maršálek Roman, prof. Ing., Ph.D.

  4. Millimeter wave channel characterization using machine learning

    Steadily growing number of communication devices per area and increasing quality of services require allocation of more frequency resources. Millimeter wave (MMW) frequencies between 30 and 300 GHz have been attracting growing attention as a possible candidate for next-generation broadband cellular networks. Specific limitations of MMW signal propagation, extremely large bandwidth and time variable environment caused by mobile users connected to a backhaul networks traveling in rugged municipal environments create unprecedented challenges to the development of broadband communication systems using advanced technologies for eliminating the undesirable time varying channel features. The general objective of the project is measurement and modelling of the broadband time varying MMW channels between mobile users and infrastructure in delay and spatial domain and extension of our previous research focused on the characterization of intra-vehicle and V2X channels for the purposes of stochastic channel modeling [1]. The parametrization of channel models needs an accurate extraction of the channel parameters such as number, position and amplitudes of multipath components (MPC), clusters, LOS, and NLOS components, etc. in the delay and spatial domains from measured data. Real time capturing of MPCs in a very wide spatial angle is provided, for example, by measuring systems with a fast-spinning antenna. However, such a system produces a huge amount of data. Thus, to get all the MPC related parameters, some automated algorithm is needed. Such algorithms are based for example on identifying the changes in the slope of a channel impulse response or generally on parameter threshold-based identification. Due to the limited accuracy and reliability of many of these methods, we are going to use machine learning (ML) techniques such as Gaussian Mixture Model or K-means algorithm for gathering MPCs with similar parameters behavior [2]. Further the project also envisages the use of supervised ML such as Deep Neural Networks or Support Vector Machine to predict and estimate the channel parameters and examine large and small-scale fading including parameters such as path loss, delay path loss exponent, Doppler spread, angle of arrival, and other variables describing the channel [3]. The above algorithms are expected to be implemented using Machine Learning Workflow with Keras, Tensorflow, and Python [4]. An alternative implementation in MATLAB also possible. The student will be a member of the international team of scientists from Brno University of Technology, TU Vienna, Austrian Institute of Technology Vienna, University of Southern California, National Institute of Technology Durgapur India, and Military University of Technology Warsaw. [1] E. Zöchmann, M. Hofer, M. Lerch, S. Pratschner, L. Bernado, J. Blumenstein, S. Caban, S. Sangodoyin, H. Groll, T. Zemen, A. Prokeš, M. Rupp, A. Molisch, C. Mecklenbräuker, Position-Specific Statistics of 60 GHz Vehicular Channels During Overtaking. IEEE Access, 2019, vol. 7, no. 1, p. 14216-14232. [2] S. M. Aldossari, K.C. Chen, Machine Learning for Wireless Communication Channel Modeling: An Overview, Wireless Personal Communications, 2019, 106, p. 41 – 70. [3] R. A. Osman, S. N. Saleh, Y. N. M. Saleh, M. N. Elagamy, Enhancing the Reliability of Communication between Vehicle and Everything (V2X) Based on Deep Learning for Providing Efficient Road Traffic Information. Applied Science, 2021, vol. 11, art. no. 11382. [4] C. A. Mattmann, Machine Learning with TensorFlow, Second Edition, Manning Publications, 2021.

    Tutor: Prokeš Aleš, prof. Ing., Ph.D.

  5. Mining specific information from speech signals

    Speech is the most natural way of communicating between people [1]. In addition to the transmission of a message, the speech (and the recorded speech signal) also contains features identifying speaker. For research are interesting specific features in speech reflecting the current state of speaker such as mood, stress, lack of sleep, alcohol, etc., as well as information on the presence of some diseases, such as Parkinson's disease. While the message and speaker identity is usually well recognizable by simple listening, most of the specific information remains hidden. However, they can be uncovered using computer analysis of speech signals. Research work will follow the state-of-the-art. At the initial phase, it will need to process an overview study on known methods for mining specific information from speech signals to recognize negative emotions [2], [3] and the state of burn-out. The core of the research will be derivation of glottal pulses from speech signal and their processing in the time domain, frequency domain as well as cepstral domain [4]. An optimal algorithm for deriving effective glottal pulses will be developed. The success and accuracy of the overall approach to detect selected psychology phenomena will be tested on real speech data. Prerequisites for the PhD study are knowledge in the field of digital signal processing (preferably audio and speech signals), pattern recognition, statistics and hypothesis testing. Furthermore, programming proficiency in MATLAB and/or Python is required. Potential candidates must speak English and German. In addition to support from BUT Brno, students from Germany can receive a special scholarship from a German institution. [1] Amon, I. (2020) Die Macht der Stimme. Redline Verlag, München. [2] Chourasia, M., Haral, S., Bhatkar, S., & Kulkarni, S. (2021). Emotion recognition from speech signal using deep learning. In Intelligent Data Communication Technologies and Internet of Things, Springer, Singapore, pp. 471-481. [3] Fahad, M. S., Ranjan, A., Yadav, J., & Deepak, A. (2021). A survey of speech emotion recognition in natural environment. In Digital Signal Processing, 110, 102951. [4] Rabiner, L., & Schafer, R. (2011) Theory and Applications of Digital Speech Processing, Prentice Hall, London.

    Tutor: Sigmund Milan, prof. Ing., CSc.

  6. Sensing, extraction and modeling of impedance responses of various substances

    Recent development indicated importance of impedance characteristic for many scientific fields (agriculture, food quality and safety, material sciences, biology, biomedicine, etc.) [1]. Current research focuses on design and development of sensing methods applicable for measurement of impedance responses of various substances (solid, liquid, organic, inorganic, …). The research targets on modeling of these characteristics based on data acquisition using various sensing approaches and determined for character of analyzed substance. This work includes evaluation of impact of real measuring arrangement (electrodes, materials, cables, measuring device, conditions, etc.). The most important part of this work includes analysis of obtained results and fitting of measured responses to models represented by electrical circuit as well as symbolical representation of measured impedance. Fractional-order character of circuit elements allows precise and detailed construction of accurate model [2]. The application part of this topic includes development of measuring device (and methods of evaluation) for analysis of measured sample based on comparison with known impedance responses (or with specific bands/frequencies of these responses). The initial state of work concentrates on review of state of the art in discussed areas and results achieved at the workplace. It allows to find the most suitable specific topic (methodology, verification and measurement, modeling, discrete/integrated analog/mixed low-power or complex systems design) fitting to interests of candidate. These activities expect involvement in experimental work (in frame of projects of basic research – cooperation with research team including foreign experts) on design and implementation of integer-order as well as fractional-order circuits, modules (sensing readouts) [3] and components in discrete or integrated form and writing and dissemination of publications. This specialization offers significant enhancement of skills and competences in work with modern software tools (PSpice, Cadence Virtuoso/Spectre) of analog/mixed design, further experience with laboratory equipment (vector network analyzer, impedance analyzer) and instrumentation (development of measuring device incl. sensing readouts). [1] T. J. Freeborn, “A Survey of Fractional-Order Circuit Models for Biology and Biomedicine.” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 3, no. 3, pp. 416-424, 2013. [2] S. Kapoulea, C. Psychalinos, A. S. Elwakil, “Realization of Cole-Davidson function-based impedance models: Application on Plant Tissues.” Fractal and Fractional Journal, vol. 4, 54, 2020, doi: 10.3390/fractalfract4040054 [3] R. Sotner, L. Polak, J. Jerabek, “Low-cost remote distance and height sensing analog device for laboratory agriculture environments.” Measurement Science and Technology, vol. 33, no. 6, pp. 1-16, 2022, doi: 10.1088/1361-6501/ac543c

    Tutor: Šotner Roman, doc. Ing., Ph.D.

  7. Wireless Communications using Artificial Intelligence

    Nowadays, different wireless communication systems can share common radiofrequency (RF) bands. In the future, use cases in which the same RF band is utilized by several wireless systems will be increased. Such a so called coexistence of wireless communication systems can be critical (a partial or full loss of wireless services, provided by communication systems) or non-critical (communication systems can coexist without significant performance degradation) [1]-[3]. According to contemporary research [4], [5], machine and deep learning (ML and DL) based technologies can be suitable candidates to make the wireless communication, influenced by various transmission conditions, more reliable and efficient. This work focuses on the research in the development of advanced ML and DL-based algorithms for classification of coexistence scenarios, occurring between different wireless communication systems, in terms of RF signals. At the beginning, definition and measurement of emerging transmission scenarios for mobile and wireless communication systems operating in licensed and unlicensed RF bands must be provided. As a part of the measurement, key environmental circumstances will be investigated (e.g., the influence of the time of day on the network load - population mobility or the influence of hydrometeor on the propagation of waves), which can affect the quality of the radio connection in wireless communications. Attention should be also devoted to the study of parameters having the highest influence on the character of the interfering signal (e.g., idle signal, type of modulation). A large number of parameters enable the ML and DL architectures to learn more features from the data [5]. After that the research will focused on the realization, validation and optimization of artificial intelligence models and algorithms (including ML and DL) to increase the efficiency and reliability of wireless communication link provided under different transmission conditions (e.g., coexistence of wireless communication systems). The created ML/DL models will be trained and validated using data that will be obtained from real and long-term measurement campaigns. The ML/DL algorithms must find tradeoff between complexity, accuracy and efficiency. The ML/DL algorithms are expected to be programmed in Python or MATLAB using available libraries (PyTorch, Keras, TensorFlow) and toolboxes (Deep Learning Toolbox), respectively. At the end, the dataset, obtained from long-term measurement campaigns, as well as the ML/DL models and algorithms will be freely available to the wide scientific community, which will not only ensure the reproducibility of the achieved results, but will also be the basis for further research and development in the field of modern wireless communications. References: [1] A. M. Voicu, L. Simić and M. Petrova, "Survey of Spectrum Sharing for Inter-Technology Coexistence," IEEE Communications Surveys & Tutorials, vol. 21, no. 2, pp. 1112-1144, Secondquarter 2019, DOI: 10.1109/COMST.2018.2882308 [2] D. Zorbas, D. Hacket and B. O'Flynn, " On the Coexistence of LoRa and RF Power Transfer," 2023, First Online: [3] L. Polak and J. Milos, “ Performance analysis of LoRa in the 2.4 GHz ISM band: coexistence issues with Wi-Fi,” Telecommunication Systems, vol. 74, no. 3, pp. 299-309, July 2020. DOI: 10.1007/s11235-020-00658-w [4] Y. Shi, K. Davaslioglu, Y. E. Sagduyu, W. C. Headley, M. Fowler and G. Green, "Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments," In Proc of. Int. Symp. DySPAN, Nov. 2019, pp. 1-10, DOI: 10.1109/DySPAN.2019.8935684. [5] K. Pijackova and T. Gotthans, "Radio Modulation Classification Using Deep Learning Architectures," In Proc of. 31st Int. Conf. Radioelektronika, Apr. 2021, pp. 1-5, DOI: 10.1109/RADIOELEKTRONIKA52220.2021.9420195.

    Tutor: Polák Ladislav, doc. Ing., Ph.D.

1. round (applications submitted from 01.04.2023 to 30.04.2023)

  1. Design of radio circuits for signal processing

    The topic of the dissertation is the design of radio circuits for signal processing. The main goal is to create circuits for the linearization of power amplifiers used in space applications. These new circuits should be able to efficiently and accurately linearize power amplifiers, resulting in improved signal quality and overall system performance. Due to use in space applications, circuits will be designed with regards to extreme conditions, such as high temperatures, low pressures, and high radiation. In addition, the work will focus on unconventional approaches such as the use of machine learning to optimize the design of circuits and improve their properties. [1] Kumar, A., Shipra, & Rawat, M. (2023, February). Bandlimited DPD Adapted APD for 5G Communication. IEEE Transactions on Circuits and Systems II: Express Briefs, 70(2), 496–500.

    Tutor: Götthans Tomáš, doc. Ing., Ph.D.

  2. Image Compression using Artificial Intelligence

    Nowadays, a vast amount of visual data to be stored and/or transmitted is rapidly increasing. The compression efficiency of traditional transformation-based lossy image compression techniques is reaching its limits. Therefore, efficient data representation provided by a suitable image compression technique is vital, especially from the viewpoint of emerging image formats, for instance omnidirectional and light field images [1]-[3]. According to contemporary research [4], [5], machine and deep learning (ML and DL) based technologies can be suitable candidates to make the image compression more reliable and efficient. Consequently, smaller file sizes and higher quality streams can be achieved. This work focuses on the research in the development of robust ML/DL-based perceptually optimized lossy image compression for conventional (2D) and advanced image formats (e.g., omnidirectional-360◦ images). At the beginning, investigation and selection of suitable ML/DL-based architectures for perceptually optimized lossy image compression schemes will be conducted. The image distortions are very specific to ML/DL-based codecs previously unseen with conventional transformation-based compression scheme. Hence, in the next steps, performance evaluation of ML/DL-based perceptually optimized compression schemes using suitable Quality of Experience (QoE) objective and subjective techniques must be provided. For instance, in the case of 360◦ images, the QoE can be influenced by many factors (e.g., compression algorithms, viewing conditions). Definition and prediction of these factors for omnidirectional images becomes very important. After that the research will focus on the benchmarking and optimization of computational performance of the ML/DL-based algorithms. Special attention must also be paid to selecting subsets of samples used for training, validation, and testing to achieve unbiased performance evaluation. Among others, it is necessary to verify the functionality of the proposed methods and procedures (e.g., testing of possible future applications). The ML/DL algorithms must find tradeoff between complexity, accuracy and efficiency. The ML/DL algorithms are expected to be programmed in Python or MATLAB using available libraries (PyTorch, Keras, TensorFlow) and toolboxes (Deep Learning Toolbox), respectively. At the end, the own created image dataset as well as the ML/DL models and algorithms will be freely available to the wide scientific community, which will not only ensure the reproducibility of the achieved results, but will also be the basis for further research and development in the fields of multimedia communication and image processing. References: [1] M. Simka, J. Kufa and L. Polak, "Picture Quality of 360° Images Compressed by Emerging Compression Algorithms," 2022 32nd International Conference Radioelektronika, Kosice, Slovakia, 2022, pp. 1-4, doi: 10.1109/RADIOELEKTRONIKA54537.2022.9764941. [2] J. Gutiérrez et al., "Subjective Evaluation of Visual Quality and Simulator Sickness of Short 360∘ Videos: ITU-T Rec. P.919," in IEEE Transactions on Multimedia, vol. 24, pp. 3087-3100, 2022, doi: 10.1109/TMM.2021.3093717. [3] M. Xu, C. Li, S. Zhang and P. L. Callet, "State-of-the-Art in 360° Video/Image Processing: Perception, Assessment and Compression," in IEEE Journal of Selected Topics in Signal Processing, vol. 14, no. 1, pp. 5-26, Jan. 2020, doi: 10.1109/JSTSP.2020.2966864. [4] Ch.-F. Hsu, T.-H. Hung and Ch.-H. Hsu, “Optimizing Immersive Video Coding Configurations Using Deep Learning: A Case Study on TMIV,” ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 18, no. 1, pp. 1-25, Jan. 2022. DOI: 10.1145/3471191 [5] X. Feng, Y. Liu and S. Wei, "LiveDeep: Online Viewport Prediction for Live Virtual Reality Streaming Using Lifelong Deep Learning," in Proc. of Conf. on VR, March 2020, pp. 800-808. DOI: 10.1109/VR46266.2020.00104

    Tutor: Polák Ladislav, doc. Ing., Ph.D.

  3. Novel analog blocks, concepts and methods for sensing and processing of electrical and nonelectrical quantities

    The integrated circuits are very important for processing of signals from sensors and sensor readouts as a part of modern physical layer of communication systems [1], [2]. They offer significant minimization of system area and low power consumption. Therefore, these concepts are highly useful for biomedical applications (blood analysis – presence of various chemicals, bio-impedances measurement and evaluation, etc. [3], [4]), in mechanics (distance influences capacity) [5], etc. This topic includes study of utilization of discrete of-the-shelf as well as integrated active building cells and blocks (amplifiers, converters, generators, flip-flop circuits, etc.) and study of features of currently available types of sensors for various physical quantities. The recommendations, requirements, models, methodologies and specific solutions for various specific active sensor readouts and processing of signals are expected to be formulated for proposals of novel and advanced systems. The initial state of work concentrates on review of state of the art in discussed areas and results achieved at the workplace. It allows to find the most suitable specific topic (methodology, verification and measurement, modeling, discrete/integrated analog/mixed low-power or complex systems design) fitting to interests of candidate. These activities expect involvement in experimental work (in frame of projects of basic research – cooperation with research team including foreign experts) on design and implementation of integer-order as well as fractional-order circuits [4], modules (sensing readouts) [5] and components in discrete or integrated form and writing and dissemination of publications. This specialization offers significant enhancement of skills and competences in work with modern software tools (PSpice, Cadence Virtuoso/Spectre) of analog/mixed design approaches and further experience in detailed principles of advanced circuit solutions including cooperation on design of application specific integrated circuit. References [1] R. Sotner, J. Jerabek, L. Polak, J. Petrzela, W. Jaikla and S. Tuntrakool, “Illuminance Sensing in Agriculture Applications Based on Infra-Red Short-Range Compact Transmitter Using 0.35 um CMOS Active Device.” IEEE Access, vol. 8, pp. 18149-18161, 2020, doi: 10.1109/ACCESS.2020.2966752 [2] R. Sotner, L. Polak, J. Jerabek, “Low-cost remote distance and height sensing analog device for laboratory agriculture environments.” Measurement Science and Technology, online first, 2022, doi: 10.1088/1361-6501/ac543c [3] C. Vastarouchas, C.Psychalinos, A.S. Elwakil, A.A.Al-Ali, “Novel Two-Measurements-Only Cole-Cole Bio-Impedance Parameters Extraction Technique.” Measurement, vol. 131, pp. 394–399, 2019. doi: 10.1016/j.measurement.2018.09.008 [4] S. Kapoulea, C. Psychalinos, A. S. Elwakil, “Realization of Cole-Davidson function-based impedance models: Application on Plant Tissues.” Fractal and Fractional Journal, vol. 4, 54, 2020. doi: 10.3390/fractalfract4040054 [5] L. Polak, R. Sotner, J. Petrzela, J. Jerabek, “CMOS Current Feedback Operational Amplifier-Based Relaxation Generator for Capacity to Voltage Sensor Interface.” Sensors, vol. 18, 4488, 2018. doi: 10.3390/s18124488

    Tutor: Šotner Roman, doc. Ing., Ph.D.

  4. Space compression at microwave frequencies

    Space compression involves a reduction of free space between optical elements by a thin device/material called a spaceplate[1], [2]. It gained importance recently due to novel approaches in the emerging field of non-local metamaterials. The issue of size reduction becomes more important for quasi-optical systems common to the terahertz and microwave frequency region where the physical size of the elements can be limiting factor in the design process. This project is focused on the research of the space compression structures for microwave frequencies. The main attention should be concentrated on the investigation and understanding of the fundamental limits of spaceplates and the development methods for their design. Further attention should be paid to the experimental characterization of these structures. References: [1] RESHEF, O., et al., An optic to replace space and its application towards ultra-thin imaging systems, Naturre Communication, 2021, vol. 12, art. no. 3512. [2] MRNKA, M., et al., Space squeezing optics: Performance limits and implementation at microwave frequencies. APL Photonics, 2022, vol. 7, no. 7, p. 1-7.

    Tutor: Láčík Jaroslav, doc. Ing., Ph.D.

Course structure diagram with ECTS credits

Any year of study, winter semester
AbbreviationTitleL.Cr.Com.Compl.Hr. rangeGr.Op.
DPC-RE1Modern electronic circuit designcs4CompulsoryDrExS - 39yes
DPC-ET1Electrotechnical materials, material systems and production processescs4Compulsory-optionalDrExS - 39yes
DPC-EE1Mathematical Modelling of Electrical Power Systemscs4Compulsory-optionalDrExS - 39yes
DPC-ME1Modern Microelectronic Systemscs4Compulsory-optionalDrExS - 39yes
DPC-TK1Optimization Methods and Queuing Theorycs4Compulsory-optionalDrExS - 39yes
DPC-FY1Junctions and nanostructurescs4Compulsory-optionalDrExS - 39yes
DPC-TE1Special Measurement Methodscs4Compulsory-optionalDrExS - 39yes
DPC-MA1Statistics, Stochastic Processes, Operations Researchcs4Compulsory-optionalDrExS - 39yes
DPC-AM1Selected chaps from automatic controlcs4Compulsory-optionalDrExS - 39yes
DPC-VE1Selected problems from power electronics and electrical drivescs4Compulsory-optionalDrExS - 39yes
DPX-JA6English for post-graduatesen4ElectiveDrExCj - 26yes
DPC-RIZSolving of innovative taskscs2ElectiveDrExS - 39yes
DPC-EIZScientific publishing A to Zcs2ElectiveDrExS - 26yes
Any year of study, summer semester
AbbreviationTitleL.Cr.Com.Compl.Hr. rangeGr.Op.
DPC-RE2Modern digital wireless communicationcs4CompulsoryDrExS - 39yes
DPC-TK2Applied cryptographycs4Compulsory-optionalDrExS - 39yes
DPC-MA2Discrete Processes in Electrical Engineeringcs4Compulsory-optionalDrExS - 39yes
DPC-ME2Microelectronic technologiescs4Compulsory-optionalDrExS - 39yes
DPC-EE2New Trends and Technologies in Power System Generationcs4Compulsory-optionalDrExS - 39yes
DPC-TE2Numerical Computations with Partial Differential Equationscs4Compulsory-optionalDrExS - 39yes
DPC-FY2Spectroscopic methods for non-destructive diagnostics cs4Compulsory-optionalDrExS - 39yes
DPC-ET2Selected diagnostic methods, reliability and qualitycs4Compulsory-optionalDrExS - 39yes
DPC-AM2Selected chaps from measuring techniquescs4Compulsory-optionalDrExS - 39yes
DPC-VE2Topical Issues of Electrical Machines and Apparatuscs4Compulsory-optionalDrExS - 39yes
DPX-JA6English for post-graduatesen4ElectiveDrExCj - 26yes
DPC-CVPQuotations in a research workcs2ElectiveDrExS - 26yes
DPC-RIZSolving of innovative taskscs2ElectiveDrExS - 39yes
Any year of study, both semester
AbbreviationTitleL.Cr.Com.Compl.Hr. rangeGr.Op.
DPX-QJAEnglish for the state doctoral examen4ElectiveDrExK - 3yes