Job Description
The focus will be on research and development in wireless applications, working from low frequencies through millimetre and terahertz waves to optical frequencies. Our activities include signal processing, electronic circuits, television technology, antennas, and microwave structures or electromagnetic compatibility. Currently, our focus is also on space applications and deep learning.
Desired Skills and Experience
Candidates must hold a Master's degree in electrical engineering or computer sciences. Candidates should have experience with scientific programming and data analysis skills. Good proficiency in English is required, as well as good communication skills. Experience in research methodology (literature review, experimentation) is an advantage.
What we do for you
We offer you the opportunity to join one of the Czech’s premier research teams at BUT. Your energy and commitment are appreciated through a competitive scholarship and research support.
Venue:
The studies start in September 2022 at the Department of Radio Electronics, Brno University of Technology (https://www.urel.fekt.vut.cz/en), Czech Republic.
Salary
The doctoral candidate will be paid with legal allowances (1000 Euros). In the future, you can expect to be involved in other research projects with the possibility of a salary increase. Moreover, Ukrainian applicants will be supported by industry partners.
How to apply:
Applications received before May 15, 2022, will be given full consideration. All qualified individuals are encouraged to apply here: https://www.vut.cz/eprihlaska/en.
Organisation
We are the largest technical university in the Czech Republic and the oldest university in Brno. Since 1899, when Emperor Franz Joseph sanctioned our existence, we have had much work, experience, and results. Today we can offer quality higher education, but we are also heavily involved in science and research. When choosing the best place to study, the university’s prestige is not the only thing to consider. It would help if you also thought about your future classmates and the city where you will live. According to the highly regarded Quacquarelli Symonds (QS) rankings, Brno is one of the world's top ten best student cities. In a survey of nearly 90,000 students and recent graduates, Brno ranked sixth, putting it ahead of cities such as Prague and London.
Brno thrives and prospers not only because of the local students but also because of the local companies. The excellent location, city support, and the presence of universities attracted top IT companies such as Red Hat and IBM to Brno, which is also the birthplace of the global online air ticket search and booking service Kiwi.com. The city is also home to technology companies such as Honeywell and electron microscope manufacturers – the Czech Republic is the leading country in the world in electron microscope production.
Contacts
Prof. Zbynek RAIDA- raida@vut.cz
Assoc. Prof. Tomas GOTTHANS - gotthans@vut.cz
Body-centric structure design by deep learning
Multi-objective synthesis of slot-based antennas
Novel analog blocks, concepts and methods for sensing and processing of electrical and nonelectrical quantities
Tunability range extension in electronically adjustable circuits
Deep Learning Based Prediction of Quality of Experience for Virtual Reality Videos
Deep Learning Based Lossy Compression for 180°/360° Images
Deep Learning for Classification of Coexistence of Wireless Communication Systems
Perspective methods for precise positioning of people and wireless devices in an indoor environment
AI methods to enhance RF Visibility in IoT RFID systems
Advanced Optimization Algorithms for Data Clustering
Advanced Algorithms for Neural Network Training
Electromagnetic analysis of time-varying metasurfaces
Approximate symbolic analysis of large systems
Birdcage RF coil for MRI
Electromagnetic cloaks
Spatio-temporal data analysis by neural networks
Millimeter wave channel characterization using machine learning
Neutron and gamma radiation spectroscopy using proportional detectors
Methods for testing of COTS semiconductor components radiation hardness with respect to space missions
Mining specific information from speech signals
Distributed receiving system for satellite communication
Passive localization of experimental satellites in low earth orbits
Human activity monitoring from 5G/6G or new generation WiFi transmissions
Unconventional approaches for transmission lines modelling and simulation
Turbulent model of the underwater medium for optical wireless communications
Perspective optical wireless communication systems for communication standard B5G a 6G
Body-centric structure design by deep learning
In the project, an artificial intelligence is going to be exploited for the genuine design of body-centric components to be used for sensing vital functions, in-body and on-body communication. Substituting a human intelligence by an artificial one in the design of body-centric systems should be the ambition of the research. A deep learning which mimics a human designer of such components has not been described in the open literature yet. Exploitation of deep learning for reverse engineering and the design of components operating in highly stochastic environments should be further contributions of the project.
In preliminary research, we used deep structures for the classification of planar microwave structures. In consecutive steps, we identified edges on a photograph of a planar filter, estimated an equivalent circuit of a corresponding filter and revealed the type and the order of filtering structure [1]. That way we proved that deep reverse models can be created.
Deep learning is expected to be applied to the design of selected body-centric structures as endoscopic capsules [2] or wireless epidermal devices [3]. Numerical models of structures will be developed in commercial solvers, experiments will be implemented in cooperation with Faculty of Biomedical Engineering, CTU Prague. Deep learning algorithms are expected to be programmed in Python using available libraries (PyTorch, Keras, TensorFlow).
References
[1] Jiri Vesely; Jana Olivova; Jakub Gotthans; Tomas Gotthans; Zbynek Raida; Classification of planar filters by deep learning. Radioengineering, 2022, vol. 31, no. 1, p. 38-49.
[2] Md. Suzan Miah; Ahsan Noor Khan et al.; Antenna system design for improved wireless capsule endoscope links at 433 MHz. IEEE Transactions on Antennas and Propagation. 2019, vol. 67, no. 4, p. 2687-2699. DOI: 10.1109/TAP.2019.2900389. ISSN 0018-926X.
[3] Cecilia Occhiuzzi; Sara Parrella; et al.; RFID-based dual-chip epidermal sensing platform for human skin monitoring. IEEE Sensors Journal. 2021, vol. 21, no. 4, p. 5359-5367. DOI: 10.1109/JSEN.2020.3031664
[4] Ian Goodfellow; Yoshua Bengio; Aaron Courville; Deep Learning. Cambridge (Massachusetts): The MIT Press, 2016. ISBN: 978-0-2620-3561-3.
Multi-objective synthesis of slot-based antennas
At millimeter-wave frequencies, antennas are usually fed by substrate-integrated structures to create a compact geometry and minimize a parasitic radiation of feeders [1]. When integrating, feeders in the substrate can be completed by additional components like filters, phase shifters, power dividers, switches etc. [2]. By etching slots to walls of substrate-integrated structures, slot antennas can be created [1], [3].
The designer of millimeter-wave antennas (antenna arrays) considers all objectives of the design comprising technical parameters, price and limitations defined by a user. In order to meet requirements, known partial solutions are combined and optimized to get as good match with objectives as possible.
Within the project, the described general synthesis of slot antenna systems fed by substrate-integrated structures is going to be implemented in an algorithmic way:
1. Starting from the requested parameters of the antenna to be designed, the optimum antenna configuration should be determined. Components of the designed structure are selected from a library minimizing dimensions and complexity. The structural optimization is performed.
2. Comprising conflicting requirements on antenna properties, the Pareto front of optimal solutions is computed by a multi-objective optimization algorithm; evolutionary approaches or swarm-intelligence ones [4] are expected. In order to increase efficiency of the synthesis, tuning space-mapping approaches might be applied [5].
The developed design procedure is going to be tested on practical examples from the area of vehicular applications.
References
[1] LÁČÍK, J.; MIKULÁŠEK, T.; PUSKELY, J.; RAIDA, Z.; WOLANSKÝ, D. Compact arrays fed by substrate integrated waveguides. Proceedings of ICEAA 2014, Palm Beach (Aruba): IEEE, 2014, p. 448-451. DOI: 10.1109/APWC.2014.6905566
[2] CUPAL, M.; RAIDA, Z. Textile-integrated waveguide switch for ISM band 5.8 GHz. Microwave and Optical Technology Letters, 2020, vol. 62, no. 4, p. 1564-1569. DOI: 10.1002/mop.32242
[3] LÁČÍK, J.; MIKULÁŠEK, T.; RAIDA, Z.; URBANEC, T. Substrate integrated waveguide monopolar ring-slot antenna. Microwave and Optical Technology Letters, 2014, vol. 56, no. 8, p. 1865-1869. DOI: 10.1002/mop.28465
[4] ŠEDĚNKA, V.; RAIDA, Z. Critical comparison of multi-objective optimization methods: genetic algorithms versus swarm intelligence. Radioengineering, 2010, vol. 19, no. 3, p. 369-377. ISSN: 1210- 2512.
[5] WOLANSKÝ, D.; TKADLEC, R.; Coaxial filters optimization using tuning space mapping in CST Studio. Radioengineering, 2011, vol. 20, no. 1, p. 289-294.
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
Tunability range extension in electronically adjustable circuits
Decreasing value of power supply voltage creates limited conditions for electronic tuning of circuits (for example active filters and oscillators) in comparison to standard current systems operating with high DC power supply. The main task of this work focuses on research and study of methods of electronic control of applications (for example filters and oscillators) working as components of modern communication systems. Suitable combination of features for control of active elements [1] and change of character of dependence (e.g. oscillation frequency vs. adjustable parameter) [2], as well as popular fractional-order approaches [3] serve for substantial improvement of tunability range and features of circuits for application in communication systems, in various methods of measurement, etc.
The initial stage of work concentrates on review of state of the art of methods and approaches for tunability enhancement achieved at the workplace as well as on principles of advanced active elements suitable for these purposes. Further activities target on formulation of novel approaches and experimentally verified methodologies using discrete/integrated analog circuits (modern software tools as PSpice, Cadence Virtuoso/Spectre) including cooperation on design of application specific integrated circuit (experience in detailed principles of advanced circuit solutions). Cooperation with international research group (in frame of projects of basic research) on writing and dissemination of publications is possible and expected.
References
[1] R. Sotner, J. Jerabek, N. Herencsar and J. Petrzela, "Methods for Extended Tunability in Quadrature Oscillators Based on Enhanced Electronic Control of Time Constants." IEEE Transactions on Instrumentation and Measurement, vol. 67, no. 6, pp. 1495-1505, 2018. doi: 10.1109/TIM.2018.2799058.
[2] R. Sotner, J. Jerabek, J. Petrzela, O. Domansky, W. Jaikla and T. Dostal, "Exponentially tunable voltage controlled quadrature oscilator." 40th International Conference on Telecommunications and Signal Processing (TSP), 2017, pp. 302-306, doi: 10.1109/TSP.2017.8075992.
[3] R. Sotner, J. Jerabek, L. Polak, L. Langhammer, H. Stolarova, J. Petrzela, D. Andriukaitis, A. Valinevicius, “On the performance of electronically tunable fractional-order oscillator using grounded resonator concept.” AEU - International Journal of Electronics and Communications, vol. 129, pp. 1-17, 2021. doi: 10.1016/j.aeue.2020.153540
Deep Learning Based Prediction of Quality of Experience for Virtual Reality Videos
Nowadays, interest about technology of virtual reality (VR) in multimedia systems is continuously increasing. In such systems, ensuring of excellent Quality of Experience (QoE) for VR videos (180 and 360-degree) will become a very important task in the future [1]. QoE can be influenced by many factors (e.g. compression algorithms, viewing conditions). Definition and prediction of these factors for VR videos become one of the key questions to research. Deep learning (DL) based technologies can be a suitable candidate to address this challenge [2].
This work focuses on the development of DL-based algorithm to predict QoE for VR videos. Attention should be devoted to study of factors having the highest influence on the QoE of VR videos. Among others, it is expected that different objective and subjective approaches [1] will be used for this purpose. The DL-based prediction algorithm should find the tradeoff between complexity, accuracy and efficiency (e.g. time for the training) [3]. Accuracy of the proposed DL-based prediction mechanism should be compared with conventional ones and evaluated in detail. The DL algorithm (or algorithms) is expected to be programmed in Python using available libraries (PyTorch, Keras, TensorFlow) and should be publicly available for research community.
References
[1] L. Polak, J. Kufa and T. Kratochvil, “On the Compression Performance of HEVC, VP9 and AV1 Encoders for Virtual Reality Videos,” in Proc. Of Int. Symp. Of BMSB 2020, Oct. 2020, pp. 1-5. DOI: 10.1109/BMSB49480.2020.9379878
[2] 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
[3] 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
Deep Learning Based Lossy Compression for 180°/360° Images
Nowadays, popularity of 180°/360° images enabling to capture a panoramic (or omnidirectional) view of an environment has been rapidly increasing in many fields (e.g. industry and medicine) [1]. Such pictures with many characteristics result in high resolution leading to acquisition, transmission and storing based problems. Hence, coding algorithms with appropriate settings to compress the image content are necessary. Emerging still image compression algorithms, like JPEG XL, HEIF and AVIF, were not originally developed for compression of omnidirectional images. In recent years a comprehensive investigation into the possibility of using deep learning (DL) techniques for efficient image compression can be witnessed [2].
This work focuses on the development of DL-based lossy image compression technique for 180°/360° images. Attention should be devoted to study of factors having the highest influence on the quality of the compressed images in such format. Among others, it is expected that different objective and subjective approaches [1], [2] will be used for this purpose. The DL-based compression algorithm should find the tradeoff between complexity, accuracy and efficiency (e.g. time for the training) [2]. Following the development of DL-based compression technique, its compression performance should be assessed and compared to existing solutions. It is assumed that publicly available [3] and self-created dataset will be used for training the DL-based architectures. The DL algorithm (or algoritms) is expected to be programmed in Python using available libraries (PyTorch, Keras, TensorFlow) and should be publicly available for research community.
References
[1] L. Polak, J. Kufa and T. Kratochvil, “On the Compression Performance of HEVC, VP9 and AV1 Encoders for Virtual Reality Videos,” in Proc. Of Int. Symp. Of BMSB 2020, Oct. 2020, pp. 1-5. DOI: 10.1109/BMSB49480.2020.9379878
[2] J. Ascenso et al. "Learning-based image coding: early solutions reviewing and subjective quality evaluation," Optics, Photonics and Digital Technologies for Imaging Applications VI. Vol. 11353. International Society for Optics and Photonics, Apr. 2020. DOI: 10.1117/12.2555368
[3] J. Gutiérrez et al., “Toolbox and dataset for the development of saliency and scanpath models for omnidirectional/360° still images,” Signal Processing: Image Communication, vol. 69, pp. 35-42, Nov. 2018. DOI: 10.1016/j.image.2018.05.003
Deep Learning for Classification of Coexistence of Wireless Communication Systems
In the future, different wireless communication systems can share common radiofrequency (RF) bands. Such a so called coexistence of these 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]. Hence, predicting and coordinating the coexistence of these systems will be an important task. Deep learning (DL) based technologies can be a suitable candidate to address such challenges [4].
This work focuses on the development of DL-based algorithm for classification of coexistence scenarios between different wireless communication systems in terms of RF signals. Attention should be devoted to the study of parameters having the highest influence on the character of the interfering signal (e.g. idle signal, modulation scheme, type of modulation). Many parameters enable the DL-based architectures to learn more features from the data [5]. Hence, the DL algorithm must find tradeoff between complexity, accuracy and efficiency. It is assumed that publicly available and self-created dataset will be used for training the DL-based architectures. The DL algorithm (or algorithms) is expected to be programmed in Python using available libraries (PyTorch, Keras, TensorFlow) and should be publicly available for research community.
References
[1] Y. Han, E. Ekici, H. Kremo and O. Altintas, “Spectrum sharing methods for the coexistence of multiple RF systems: A survey,” Ad Hoc Networks, vol. 53, pp. 53-78, Dec. 2016. DOI: 10.1016/j.adhoc.2016.09.009
[2] 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
[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
Perspective methods for precise positioning of people and wireless devices in an indoor environment
Nowadays, there are numerous methods to monitor, track and localize people and wireless devices in indoor environments. In the future, due to new emerging wireless communication systems (for instance the field of Internet-of-Things or Low-Power Wide Area Networks – LPWAN), it is assumed that current localization methods and techniques will need improvement or extension. From this point of view, utilization of machine learning and deep learning (ML and DL) techniques are among perspective solutions [1]-[3].
This dissertation thesis focuses on advanced methods and approaches for precise localization of people and wireless devices in an indoor environment. Development and realization of methods and approaches should be based on techniques evaluating parameters like RSSI, ToA and AoA [1]. Utilization of ML and DL-based approaches to improve efficiency and accuracy of localization methods in an indoor environment is assumed [3]. Testing and verification of the proposed methods and approaches by a set of measurements under laboratory and real conditions is an inseparable part of this work. It is assumed that publicly available and self-created dataset will be used for training the DL-based architectures [3]. The DL algorithm (or algorithms) is expected to be programmed in Python using available libraries (PyTorch, Keras, TensorFlow) and should be publicly available for research community. The final DL algorithm must find tradeoff between complexity, accuracy and efficiency.
References
[1] F. Zafari, A. Gkelias and K. K. Leung, "A Survey of Indoor Localization Systems and Technologies," IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2568-2599, Thirdquarter 2019, DOI: 10.1109/COMST.2019.2911558
[2] J. Pelant et al., "BLE device indoor localization based on RSS fingerprinting mapped by propagation modes," 2017 27th International Conference Radioelektronika (RADIOELEKTRONIKA), 2017, pp. 1-5, DOI: 10.1109/RADIOELEK.2017.7937584
[3] L. Polak et al. Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning. Sensors, 2021, vol. 21, no. 13, pp. 1–25. DOI: 10.3390/s21134605
AI methods to enhance RF Visibility in IoT RFID systems
As of 2022, trillions of items that rely on the RAIN RFID technology have already been connected in IoT applications. The volume will grow with IoT becoming part of our everyday life, covering industry, retail, healthcare, agriculture, education, entertainment etc. The cost advantage of passive backscatter RFID devices trades off known performance bottlenecks. The forward link of RFID systems is no longer the dominating performance limitation factor. With the increasing energy efficiency of RFID tag chips, resolving the bottleneck of the reader receiver sensitivity is becoming the key challenge to RFID visibility in application rollouts.
The aim of the thesis is to study the concepts of receiver architectures and signal processing methods and develop innovative solutions for detecting data from noisy channels in IoT applications. This will cover AI methods for classification, perdition and self-correction of received noisy signals in combination with advanced reconfigurable receiver architectures.
References
[1] P. Sharma, G. Xu, X. Hui, D. L. Hysell and E. C. Kan, "Deep-Learning-Based Occupant Counting by Ambient RF Sensing," in IEEE Sensors Journal, vol. 21, no. 6, pp. 8564-8574, 15 March15, 2021, doi: 10.1109/JSEN.2020.3045035.
[2] H. Salah, J. Robert, H. A. Ahmed, K. Mahmoud and A. Heuberger, "Theoretical Performance Evaluation of UHF-RFID Systems With Multi-Antenna Maximum-Likelihood Decoding," in IEEE Journal of Radio Frequency Identification, vol. 3, no. 2, pp. 108-117, June 2019, doi: 10.1109/JRFID.2019.2909504.
(Internal note: Possibility to apply for funding in the Infineon IPCEI initiative.)
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 as in Fig. 1, 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.
(Internal note: Possibility to apply for funding in the Infineon IPCEI initiative.)
Advanced Algorithms for Neural Network Training
Nowadays, Artificial Neural Networks (ANN) are used in more and more areas of human activity: e.g., market development predictions, early detection of serious diseases, determining the presence of underwater mines, weather forecasting, etc. [1]. These neural networks are built from tens or hundreds of layers of different types and complexities (i.e., number of neurons). For example, the Feed Forward Network (FFN) uses several hidden layers whose number and size (number of neurons in the layer) is unknown, see Fig. 1.
ANN training consists in searching the weights of individual connections among the individual neurons based on a large number of input patterns for which the observed output properties are known. Many efficient algorithms like Levenberg–Marquardt, or global optimization techniques [2] have been proved to train the neural network efficiently. However, the training is performed on an a priori defined structure of the ANN. However, the structure of the ANN is crucial for its efficient and correct training. We have used the multi-objective optimization algorithm with a variable number of dimensions called VNDGDE3 [3] to design the structure of the ANN to design the proper antenna structure in [4].
The VND optimization algorithm should be extended in the thesis to be able to design the ANN structure and train it at once. The novel multi-objective optimization algorithms should be derived in MATLAB and then be applied to design and train the ANN. The novel approach to ANN design and training should be compared to other existing methods.
References
[1] Iba, Hitoshi, and Nasimul Noman. Deep Neural Evolution. Berlin: Springer, 2020.
[2] Kawam, Ahmad AL, and Nashat Mansour. "Metaheuristic optimization algorithms for training artificial neural networks." Int. J. Comput. Inf. Technol 1, no. 2 (2012): 156-161.
[3] Marek, Martin, and Petr Kadlec. "Another evolution of generalized differential evolution: variable number of dimensions." Engineering Optimization 54, no. 1 (2022): 61-80.
[4] Kadlec, Petr. " Design of Artificial Neural Network for Antenna Synthesis using the Optimization with Variable Number of Dimensions." In 2022 32nd International Conference Radioelektronika (RADIOELEKTRONIKA), pp. 1-6. IEEE, 2022.
(Internal note: Possibility to apply for funding in the Infineon IPCEI initiative.)
Electromagnetic analysis of time-varying metasurfaces
The space-time electromagnetic behavior of metasurfaces whose constitutive properties are time-invariant has been extensively studied and is well understood (e.g. [1]). The demanding objectives of 6G communications, however, call for the use of unconventional intelligent technologies that would overcome physical bounds of standard time-invariant systems [2, 3]. A promising hardware technology to achieve the goal is the reconfigurable intelligent metasurface whose electromagnetic properties vary in response to time-varying environments [4]. As the state-of-the-art electromagnetic modeling approaches do not allow its efficient analysis, the PhD research will yield fundamentally new computational and analytical approaches to address the issue, thereby providing key enablers for designing truly intelligent time-varying metasurfaces. Owing to their unrivalled computational efficiency, particular attention will be paid to analytical solutions and time-domain integral equation approaches based on the Cagniard-DeHoop technique [5, 6].
References
[1] Kuester, E. F., Mohamed, M. A., Piket-May, M., & Holloway, C. L. (2003). Averaged transition conditions for electromagnetic fields at a metafilm. IEEE Transactions on Antennas and Propagation, 51(10), 2641-2651.
[2] Fano, R. M. (1950). Theoretical limitations on the broadband matching of arbitrary impedances. Journal of the Franklin Institute, 249(1), 57-83.
[3] Shlivinski, A., & Hadad, Y. (2018). Beyond the Bode-Fano bound: Wideband impedance matching for short pulses using temporal switching of transmission-line parameters. Physical Review Letters, 121(20), 204301.
[4] Engheta, N. (2021). Metamaterials with high degrees of freedom: space, time, and more. Nanophotonics, 10(1), 639-642.
[5] Stumpf, M. (2019). Time-Domain Electromagnetic Reciprocity in Antenna Modeling. Hoboken, NJ: IEEE Press-Wiley.
[6] Stumpf, M. (2021). Pulsed electromagnetic scattering by metasurfaces—a numerical solution based on the Cagniard–DeHoop method of moments. IEEE Transactions on Antennas and Propagation, 69(11), 7761-7770.
(Internal note: Possibility to apply for funding in the Infineon IPCEI initiative.)
Approximate symbolic analysis of large systems
Symbolic analysis allows to describe the behavior of an electronic circuit in the form of a symbolic expression. The project is focused on generating sufficiently simple and interpretable expressions that can be used by circuit designers. Due to the exponential growth of the expression complexity, the interpretability of exact expressions is lost even for circuits containing three transistors. An interesting way to get smaller expressions is to loosen accuracy to achieve simplicity, i.e. to use approximate symbolic analysis [1]. The method mimics designer's work, when they simplify the circuit model by omitting negligible phenomena (e.g. by omitting stray-capacitance currents on low frequencies, etc.). Despite the past development, there is still no widely accepted method of approximate symbolic analysis applicable to the circuits of practical size. Our previous results show a great potential of methods based on circuit model simplification (Simplification Before Generation class - SBG) [2].
During the first phase the research effort will be focused on improving the process of identification of negligible terms based on the Corner Analysis for checking the simplified expression validity on a certain interval of component parameters. The process should be modified to allow the simplification control based on inequalities following from typical device parameters or designer’s previous knowledge.
The further modification of SBG will be aimed at suppressing mathematical nature of existing algorithms, which try to eliminate as many insignificant symbolic terms as possible, which results in problematic interpretability of obtained expressions [3]. The solution should be based on a graph-transformation method [2] and techniques of predictive modeling [4].
A potential candidate should have knowledge of electronic circuit theory, numerical mathematics and graph theory. Furthermore, knowledge of programming (C/C++, Python) is required, because it is expected that the developed methods will be implemented in the symbolic simulator SNAP.
References
[1] M. Fakhfakh, E. Tlelo-Cuautle, F. V. Fernández, Design of Analog Circuits through Symbolic Analysis, Bentham Science, 2012.
[2] Z. Kolka, M. Vlk, M. Horák, “Topology Reduction for Approximate Symbolic Analysis,” Radioengineering, 2011, vol. 20, no. 1, pp. 252-256.
[3] G. Shi, “Topological Approach to Symbolic Pole–Zero Extraction Incorporating Design Knowledge,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 36, no. 11, pp. 1765-1778, Nov. 2017.
[4] D. Biolek, Z. Kolka, V. Biolková, Z. Biolek, S. Kvatinsky, “(V)TEAM for SPICE Simulation of Memristive Devices With Improved Numerical Performance,” IEEE Access, 2021, vol. 9, no. 2, pp. 30242-30255.
Birdcage RF coil for MRI
Magnetic resonance imaging (MRI) has gained increased interest as a diagnostic and research imaging technique. It offers very good soft tissue contrast at high spatial and temporal resolution [1]. MRI is able to move beyond anatomical imaging and it also allows visualizing metabolic functions and chemical processes. Basic components for MRI examination are MRI RF coils which are used for excitation or reception of the magnetic resonance signal.
This project is focused on the research of a birdcage RF coil for MRI [2]-[3]. The main attention is concentrated on the improvement of magnetic field homogeneity in the transmission mode and signal to noise ratio in the receiving mode of a birdcage coil [2]-[4]. To manage this goal, extensive full-wave modeling and optimization in commercial software (e.g. CST Studio Suite or ANSYS HFSS) has to be carried considering relevant numerical phantoms. It is expected that a coil will be equipped by a self-adaptive technique allowing its adaptation for desired operating frequency band. As a result, a novel concept of a birdcage RF coil should be proposed.
References
[1] B. Gruber.; M. Froeling; T. Leiner; D. W. J. Klomp. RF coils: A practical guide for nonphysicists. Journal of Magnetic Resonance Imaging, 2018, vol. 48, no. 3, p. 590–604.
[2] S. F. Ahmad; Y.C Kim; I. C. Choi; H. D. Kim. Recent Progress in Birdcage RF Coil Technology for MRI System. Diagnostics. 2020, vol. 10, no.12.
[3] C.E. Hayes; W. A. Edelstein; J.F. Schenck; O. M. Mueller; M. Eash, An efficient, highly homogeneous radiofrequency coil for whole body NMR imaging at 1.5T. Journal of Magnetic Resonance. 1985, vol. 63, p. 622–628.
[4] M. Vít; M. Burian; Z. Berková; J. Láčík; O. Sedláček; R. Hoogenboom; Z. Raida; D. Jirák, A broad tuneable birdcage coil for mouse 1H/19F MR applications. Journal of Magnetic Resonance, 2021, vol. 329, no. 1.
Electromagnetic cloaks
Electromagnetic cloaks are structures that aim to reduce the reflectivity of the objects that surround or directly make them invisible. The theory of transformation electromagnetics and artificial materials are often exploited for their design [1], [2].
This project is focused on the research of electromagnetic cloaks of desired properties. The main attention should be concentrated on the development of methods for the design of artificial materials/surfaces with required electromagnetic properties. The outputs of the project should find application in the fields of antenna technology, security or defence applications [1]-[3].
After studying the current state of the art, the attention should be concentrated on the development of methods for the design of artificial materials/surfaces with the required electromagnetic properties based on machine learning approaches. For this part of the solution, the use of commercial software allowing full-wave modelling (e.g. CST Studio Suite, ANSYS HFSS) and the MATLAB program is assumed. Consequently, the newly created methods will be used for the design of selected reference structures whose properties will be verified experimentally.
References
[1] D. Kwon; D. H. Werner, Transformation Electromagnetics: An Overview of the Theory and Applications. IEEE Antennas and Propagation Magazine, 2010, vol. 52, no. 1, pp. 24-46.
[2] D. H. Werner; D. Kwon, Transformation Electromagnetics and Metamaterials: Fundamental Principles and Applications. Springer, London, 2014.
[3] G. Moreno et al., Wideband Elliptical Metasurface Cloaks in Printed Antenna Technology. IEEE Transactions on Antennas and Propagation, 2018, vol. 66, no. 7, pp. 3512-3525.
Spatio-temporal data analysis by neural networks
The thesis project focuses on spatio-temporal networks and their use in visual pattern recognition. Spatio-temporal data contains temporal and spatial information simultaneously, and therefore such dependencies are often used together in prediction models. The models are used on a variety of forecasting problems from different areas, such as traffic flow prediction, weather and climate environment, social media, video segmentation, people migration, etc.
In the project we will rely on deep learning to deal with spatio-temporal data. Various network structures were extended from the Recurrent Neural Network to train temporal patterns from historical sequence information to predicting spatio-temporal data [1]. The time-varying data are also represented by graphs or by Graph Convolutional Networks which alternate temporal and spatial convolutions [2]. The aim of this work is to study new representations for spatio-temporal graphs and to design new neural network architectures for data represented by this type of graphs. These models will be programmed in Python and compared with the state-of-the-art datasets for different applications.
Example of spatio-temporal data for weather prediction: Temperature of Central Europe by ECMWF (European Centre for Medium-Range Weather Forecasts).
References
[1] Son, H.; Kim, S.; Yeon, H.; Kim, Y.; Jang, Y.; Kim, S.-E. Visual Analysis of Spatiotemporal Data Predictions with Deep Learning Models. Appl. Sci. 2021, 11, 5853. https://doi.org/10.3390/app11135853
[2] Yu, Bing & Yin, Haoteng & Zhu, Zhanxing. (2018). Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. 3634-3640. 10.24963/ijcai.2018/505.
[3] Mathe, Johan, et al. "PVNet: A LRCN architecture for spatio-temporal photovoltaic PowerForecasting from numerical weather prediction." arXiv preprint arXiv:1902.01453 (2019).
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.
V2V channel measurement in 60 GHz band, Power of MPCs propagating between two passing cars in time-dalay domain
References
[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.
Neutron and gamma radiation spectroscopy using proportional detectors
Starting from 1st September 2022; digitizer and preamplifier HW is being currently developed in frame of a diploma thesis.
Currently Bonner spheres are used to measure field of low-energy neutrons (< 100 keV). This method is cumbersome and time-consuming, which limits its application.
Utilization of proportional detectors is an alternative and very promising method suitable for measuring mixed fields of photons (gamma rays) and neutrons in energetic range from about 20 keV to 1 MeV. The main benefit of using the proportional detector is that it is capable of both energy measurement and particle discrimination (gamma / neutron) in wide energy range and in reasonably short time. However, so far there is only limited success in the processing of proportional detector output signals, namely discrimination of gamma/neutron particles and energy estimation of those particles. The reason is that the response of the detector is dependent not only on energy and type of the particle, but also on the geometry of the detector and actual trajectory of the particle travelling through the detector.
The aim of the thesis is to establish methods (algorithms) for particle discrimination and their respective energy measurement. Currently we have an experimental setup based on FPGA-based data acquisition board, which is intended for experimental data gathering. The data shall be processed in a PC to determine optimum method of energy measurement and particle discrimination. Machine learning techniques are one promising method that shall be (at least) considered. Later on, the FPGA should include those methods (algorithms) so that it can provide measurement results (energy / particle distribution) directly, acting as a stand-alone measurement device.
To support the project, we have active cooperation with Masaryk University Brno, VF inc., Nuclear Research Centre Rez, and Department of Nuclear Reactors at CVUT Prague. Those institutions will provide equipment required for experiments with proportional detectors (gamma and neutron radiation sources).
The measurement hardware (FPGA-based digitizer with preamplifier) is currently under development in frame of master thesis of student Ondrej Kolar. Ondrej is going to follow up with this topic on his PhD thesis.
References
[1] KNOLL, Glenn F. Radiation Detection and Measurement. 3rd edition. Michigan: John Wiley & Sons, 2000. ISBN 0-471-07338-5.
[2] LANGFORD, T.J., C.D. BASS, E.J. BEISE, H. BREUER, D.K. ERWIN, C.R. HEIMBACH a J.S. NICO. Event identification in 3He proportional counters using risetime discrimination. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment [online]. 2013, 717, 51-57 [cit. 2021-12-29]. ISSN 01689002. doi:10.1016/j.nima.2013.03.062
[3] HEEGER, K.M., S.R. ELLIOTT, R.G.H. ROBERTSON, M.W.E. SMITH, T.D. STEIGER a J.F. WILKERSON. High-voltage microdischarge in ultra-low background 3He proportional counters. IEEE Transactions on Nuclear Science [online]. 2000, 47(6), 1829-1833 [cit. 2021-12-29]. ISSN 0018-9499. doi:10.1109/23.914454
Methods for testing of COTS semiconductor components radiation hardness with respect to space missions
Starting from 1st September 2022
In recent years more and more commercial companies and research institutions are willing to build new satellites and space probes. In case of their electronic equipment, so called "space grade" parts are traditionally used. Such components are manufactured with respect to harsh environment (space), where they are intended to work. Among the most demanding factors are temperature cycling, vacuum, mechanical stress and ionizing radiation. Regarding the radiation, space grade components are guaranteed to withstand certain intensities and doses of ionizing radiation before they cease functioning. However, such rugged components are very expensive and often quite obsolete in terms of overall performance when compared to commercial off-the-shelf (COTS) components.
Thus many companies are trying to utilize COTS components in their space probe and satellite design to improve electronic system performance (increase computing power, decrease power consumption). FPGAs are especially interesting from this point of view. To ensure sufficient reliability of the whole electronic system, all the COTS components used in the design shall be tested on radiation hardness. Traditionally, this is achieved using a Cobalt-60 gamma ray source, as it is widely available and easy to use. However, the space environment is more complex, energy spectrum of the Cobalt-60 is not a perfect match.
The aim of the research is to search for alternative methods of electronic components testing, for example utilizing widespread proton accelerators and their parasitic radiative field. It is expected that the methodology will be verified on an FPGA platform.
To support the project, we have active cooperation with Masaryk University Brno, VF inc., Nuclear Research Centre Rez, and Department of Nuclear Reactors at CVUT Prague.
References
[1] VELAZCO, R., MCMORROW, D, ESTELA , J. (Editors). Radiation Effects on Integrated Circuits and Systems for Space Applications. Springer Nature Switzerland AG 2019. ISBN 978-3-030-04660-6.
[2] Y. Kimoto, N. Nemoto, H. Matsumoto, et al., Space radiation environment and its effects on satellites: analysis of the first data from TEDA on board ADEOS-II. IEEE Trans. Nucl. Sci.52(5),1574–1578 (2005)
[3] EIA/JESD57, Test Procedures for the Manegement of Single-Event Effects in Semiconductor Devices from Heavy-Ion Irradiation (EIA/JEDEC Standard, Nov. 2017, available at: https://www.jedec.org/standards-documents/docs/jesd-57)
[4] ASTM F 1192-11, Standard Guide for the Measurement of Single Event Phenomena (SEP) Induced by Heavy Ion Irradiation of Semiconductor Devices (ASTM Standard, West Conshohocken, PA, 2006)
[5] MIL-STD-750-1, Environmental Test Methods for Semiconductor Devices (Department of Defense Test Method Standard, USA, 2012)
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. Figure 1 shows the change in distribution of the fundamental voice frequency F0 when speaking under stress.
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 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.
References
[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.
Distributed receiving system for satellite communication
Nowadays numerous experimental satellites are launched. The telemetric and data downlinks are mainly allocated in the UHF band where there are received by individual ground stations. For any meaningful downlink bandwidths, directional antennas with high gain are necessary. On the other hand, lot of stations with omnidirectional antennas can be utilized as can be seen in SATNOGS [1]. But they suffer from low gain and interferences from both local and other satellites’ signals. Therefore, ways of improvement are searched.
The project is aimed at study of distributed SDR receivers’ performance and at their application for satellite communication. The proposed system should allow simultaneous reception and data extraction from multiple signals. The main target of the study is enhancement of error-free data reception probability, exploitation of distributed system gain and stations redundancy. System is intended for data reception from experimental satellites in UHF band.
References
[1] Dan White at al. Overview of the Satellite Networked Open Ground Stations (SatNOGS) Project, Small satellite conference 2018, online, https://digitalcommons.usu.edu/smallsat/2018/all2018/313/
Passive localization of experimental satellites in low earth orbits
With the increasing amount of small satellite deployed during every rocket lauch, there is arising problem of identification of dozens satellites from radar signals [1], where responses are very similar due to the small satellite standardisation (cubesats, pocketcubes etc.) Therefore identification by emitted signals in downlink bands is used but only doppler information or AOS/LOS times are utilized which results in poor resolution and needs long time to allow the satellites to disperse along the orbital plane.
The aim of the project is a study of actual signals from experimental satellites and its influence on localization. Part of the work will be a study of error sources as atmospheric influence and parameters of SDR receivers. Methods of receivers’ synchronization will be studied and selected ones will be implemented [2]. Measurement with multiple of SDR receivers will follow to get experimental data for localization implementation by time difference of arrival method in the UHF band. The research then will be targeted to optimal solutions for high precision, resolution and other parameters of realized system.
References
[1] Choi J., Jo J.H., Choi E.-J., Yu J., Choi B.-K., Kim M.-J., Yim H.-S., Roh D.-G., Kim S., Park J.-H., Cho S. Space surveillance radar observation analysis: One-year tracking and orbit determination results of KITSAT-1. (2020) Journal of Astronomy and Space Sciences, 37 (2), pp. 105 – 115, DOI: 10.5140/JASS.2020.37.2.105
[2] KADERKA, J.; URBANEC, T. Time and sample rate synchronization of RTL-SDR using a GPS receiver. In 2020 30th International Conference Radioelektronika (RADIOELEKTRONIKA). 2020. p. 100-103. ISBN: 978-1-7281-6469-4.
Human activity monitoring from 5G/6G or new generation WiFi transmissions
Prof. Roman Marsalek, Assoc. Prof. Jiri Blumenstein
Monitoring of user activities using cameras or specialized radar-based equipment has found its use e.g. for monitoring of elderly people or to control the electronic entertainment equipment. The use of such approaches for large-scale monitoring in public spaces is problematic due to the privacy violation and need for specialized hardware.
The aim of this PhD project is to investigate the influence of humans and their activities (e.g. pedestrians, bikers) on the statistical properties of millimeter-wave or sub-THz wireless channels in delay-Doppler domain and to search for suitable machine learning method able to classify the nature of user activities in scenarios of several users performing simultaneously a variety of distinct tasks. The investigated methods will be based on the processing of 5G New Radio, eventually 6G signals and should not require any specialized waveforms nor hardware.
This PhD project includes both the experimental work with the mm-wave test-bed as well as data processing using state-of-the-art machine learning methods. There is an ongoing cooperation of the BUT group with several Austrian colleagues, e.g. Johannes Keppler University in Linz. The unique mm-wave test-bed in 60 GHz band available for experiments.
60 GHz setup during calibration of RF front-end, OFDM pilot frequency shift for running person
References
[1] Ashleibta, A.M., Taha, A., Khan, M.A. et al. 5G-enabled contactless multi-user presence and activity detection for independent assisted living. Sci Rep 11, 17590 (2021).
[2] Dubey, Anand & Santra, Avik & Fuchs, Jonas & Lübke, Maximilian & Weigel, Robert & Lurz, Fabian. (2021). A Bayesian Framework for Integrated Deep Metric Learning and Tracking of Vulnerable Road Users Using Automotive Radars. IEEE Access. 9. 10.1109/ACCESS.2021.3077690.
[3] R. Marsalek, R. Zavorka, M. Pospisil, J. Vychodil, J. Gotthans and J. Blumenstein, "Human Activity Classification via Millimeter-Wave Channel Level Crossing Estimation," 2021 IEEE Microwave Theory and Techniques in Wireless Communications (MTTW), 2021, pp. 301-305
Unconventional approaches for transmission lines modelling and simulation
In today’s high-speed mixed electronic systems the transmission lines play important role as interconnects inside and/or among their subsystems due to wave effects occuring at frequencies above hundreds of megahertz [1]. Real interconnects have to moreover be considered as lossy, often nonuniform, and in some special cases also nonlinear. The losses of transmission lines, especially a skin effect and a dielectric loss, are frequency dependent which is becoming more significant for high frequencies. The transmission lines are commonly described by systems of telegraph equations. As is known, however, a number of physical effects cannot be described with sufficient accuracy by using classical integer-order differential equations. In this field, the theory of fractional-order differential equations (FDE) and corresponding models can be utilized with advantage for their more credible description [2]-[4].
Besides, as a result of imperfect manufacturing processes the real interconnects suffer from random disturbances in values of their parameters which should be taken into account in devices designs. There are various approaches to characterize these random variations, e.g. Monte Carlo or polynomial chaos methods [5]. One of the promissing direction is to apply the theory of stochastic differential equations (SDE) [6]. So far most models and techniques were based on the application of ordinary SDEs [7], [8], an open possibility is to utilize partial SDEs as well.
The topic is a part of broader signal integrity issues being solved in high-speed electronic systems [1]. Currently the above approaches and corersponding models are usually considered separatelly. The aim of the thesis is to develop models and methods for computer simulation of transmission lines which would be able to take into account both fractional nature and stochastic variability of their parameters. It is supposed that Matlab and PSpice programs will widely be utilized at the solution, therefore, their knowledge is required at candidates, as well as their interest of mathematical modelling.
References
[1] S. H. Hall, H. L. Heck, Advanced Signal Integrity for High-Speed Digital Designs, New York: John Wiley & Sons, 2011.
[2] Fractional-Order Modeling of Dynamic Systems with Applications in Optimization, Signal Processing, and Control, Edited by Ahmed G. Radwan, Farooq Ahmad Khanday and Lobna A. Said, Academic Press, 2021.
[3] N. Al-Zubaidi R-Smith, A. Kartci, L. Brancik, “Application of Numerical Inverse Laplace Transform Methods for Simulation of Distributed Systems with Fractional-Order Elements,” Journal of Circuits, Systems, and Computers JCSC, vol. 27, no. 11, p 1-25, 2018.
[4] S. M. Cvetianin, D. Zorica, and M. R. Rapaić, “Generalized time-fractional telegraphers equation in transmission line modeling,” Nonlinear Dynamics, vol. 88, no. 2, p. 1453-1472, 2017.
[5] P. Manfredi, D. V. Ginste, I. S. Stievano, D. De Zutter and F. G. Canavero, "Stochastic transmission line analysis via polynomial chaos methods: an overview," IEEE Electromagnetic Compatibility Magazine, vol. 6, no. 3, p. 77-84, 2017.
[6] S. Särkkä, A. F. Solin, Applied Stochastic Differential Equations. Cambridge: Cambridge University Press, 2019.
[7] A. Zjajo, Q. Tang, M. Berkelaar, J. P. de Gyvez, A. Di Bucchianico, N. van der Meijs, ”Stochastic analysis of deep-submicrometer CMOS process for reliable circuits designs,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 58, no. 1, 2011, p. 164-175.
[8] L. Brancik, E. Kolarova, “Simulation of multiconductor transmission lines with random parameters via stochastic differential equations approach,” SIMULATION-Transactions of the Society for Modeling and Simulation International, vol. 92, no. 6, p. 521-533, 2016.
Turbulent model of the underwater medium for optical wireless communications
Underwater optical communication (UWOC) is one of the perspective directions in optical communications, which is now receiving a lot of attention. The main advantage of underwater communication is real-time communication and high transmission bitrates for short distances [1], [2]. The range of underwater optical links is significantly limited by the spectrally dependent attenuation properties of the water transmission medium depending on the concentration of impurities. The effect of water mass flow (underwater turbulence) on optical signals is also crucial. The turbulence strength depends on several complex water medium parameters (e.g., water temperature, salinity, water flow speed, the refractive index of water, underwater depth, seabed relief) [3], [4].
The aim of the project is a detailed analysis of the underwater medium regarding the water mass flow and the determination of turbulence strength of the underwater transmission medium. The main output of the project will be a turbulent model of the underwater medium, which will define changes in the optical beams' propagation in this medium. Analyzing the propagation of optical beams in the underwater turbulent medium, parameters of the optical beams must be taken into the account (e.g., the intensity profile of the beam, beam coherence, the beam half-width, or the beam wavelength). It is also necessary to determine the degree of the optical signal dispersion for different propagation directions in the turbulent underwater medium. The important goal of the project is to determine the limits of the range and transmission speed of optical underwater links.
References
[1] H. Kaushal and G. Kaddoum, "Underwater Optical Wireless Communication," in IEEE Access, vol. 4, pp. 1518-1547, 2016, doi: 10.1109/ACCESS.2016.2552538.
[2] Z. Zeng, S. Fu, H. Zhang, Y. Dong and J. Cheng, "A Survey of Underwater Optical Wireless Communications," in IEEE Communications Surveys & Tutorials, vol. 19, no. 1, pp. 204-238, Firstquarter 2017, doi: 10.1109/COMST.2016.2618841.
[3] C. T. Geldard, J. Thompson and W. O. Popoola, "Empirical Study of the Underwater Turbulence Effect on Non-Coherent Light," in IEEE Photonics Technology Letters, vol. 32, no. 20, pp. 1307-1310, 15 Oct.15, 2020, doi: 10.1109/LPT.2020.3020368.
[4] S. Zhang, L. Zhang, Z. Wang, J. Quan, J. Cheng and Y. Dong, "On Performance of Underwater Wireless Optical Communications Under Turbulence," 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC), 2020, pp. 1-2, doi: 10.1109/CCNC46108.2020.9045458.
Perspective optical wireless communication systems for communication standard B5G a 6G
The constant development of new standards for wireless communications places increasing demands on individual communication components in terms of transmission speed, transmission capacity, security, versatility, scalability and energy efficiency. Meeting these requirements requires advanced hardware and technology operating in the new spectrum bands [1].
Optical wireless communications, thanks to their specific properties and constant development, occupy an important and irreplaceable place in modern communication technologies, whether for short or long distances. The planned implementation of optical wireless links (FSO) in the B5G and 6G standards for mobile communications is an impulse for a denser deployment of these links in cities and built-up areas. Deployment for temporary (ad-hoc) networks between the UAV (Unnamed Aerial Vehicle) and the ground station is also envisaged.
The subject of the scientific project is to investigate methods of optical signal generation and detection in optical wireless communication systems (FSO and VLC), which are implemented in the B5G standard or are planned in 6G. The research will focus on signal processing in optical transceivers. New advanced types of modulations and channel coding will be analyzed. Experimental work will be focused on the comparison of selected types of modulators and detectors. The research aims to suppress the negative influence of the atmosphere [2-4] on the transmission of the optical signal, to optimize the transmission technology and to increase its reliability and availability.
References
[1] Pärssinen, A.; Alouini, M.; Berg, M.; Kuerner, T.; Kyösti, P.; Leinonen, M. E.; Matinmikko-Blue; M., Mccune, E.; Pfeiffer, U., and Wambacq, P. (2020). White Paper on RF Enabling 6G – Opportunities and Challenges from Technology to Spectrum [White paper]. (6G Research Visions, No. 13). University of Oulu.
[2] Barcik, P.; Wilfert, O.; Dobesch, A.; Kolka, Z.; Hudcova, L.; Novak, M.; Leitgeb, E. Experimental measurement of the atmospheric turbulence effects and their influence on performance of fully photonic wireless communication receiver. Physical Communication, 2018, vol. 31, no. 1, p. 212-217. ISSN: 1874-4907.
[3] Novak, M; Barcik, P.; Skryja, P.; Kolka Z. Service Data Transmission System for Free Space Optics. In 20th Conference on Microwave Techniques COMITE 2021. Brno: IEEE, 2021. s. 1-4. ISBN: 978-1-6654-1454-8.
[4] Poliak, J.; Barcik, P.; Wilfert, O. Diffraction Effects and Optical Beam Shaping in FSO Terminals. In Optical Wireless Communications - An Emerging Technology. Springer International Publishing Switzerland: Springer International Publishing, 2016. p. 1-21. ISBN: 978-3-319-30200- 3.
