Academically oriented

Full-time study

4 years

prof. Ing. Lukáš Sekanina, Ph.D.

Chairman :
prof. Ing. Lukáš Sekanina, Ph.D.
Councillor internal :
prof. Dr. Ing. Jan Černocký
prof. Ing. Tomáš Vojnar, Ph.D.
prof. Dr. Ing. Pavel Zemčík, dr. h. c.
prof. RNDr. Milan Češka, CSc.
prof. Ing. Martin Drahanský, Ph.D.
prof. Ing. Adam Herout, Ph.D.
prof. Ing. Jan M. Honzík, CSc.
prof. Ing. Tomáš Hruška, CSc.
doc. Ing. Jan Kořenek, Ph.D.
prof. RNDr. Alexandr Meduna, CSc.
prof. Dr. Ing. Zbyněk Raida
prof. RNDr. Josef Šlapal, CSc.
prof. Ing. Pavel Václavek, Ph.D.
Councillor external :
prof.,RNDr. Jiří Barnat, Ph.D. (FI MUNI)
prof. Ing. Jiří Sochor, CSc. (FI MUNI)

The goal of the doctoral degree programme is to provide outstanding graduates from the master degree programme with a specialised university education of the highest level in certain fields of computer science and information technology, including especially the areas of information systems, computer-based systems and computer networks, computer graphics and multimedia, and intelligent systems. The education obtained within this degree programme also comprises a training and attestation for scientific work.

• Graduates from the doctoral study programme are trained to independently work in research, development, or management.
• They are able to solve and/or to lead teams solving advanced conceptual, research, development, or production problems in the area of contemporary information technology and its applications.
• They can be engaged to work on creative tasks, to lead research and development teams, or to work in management of companies or organizations whenever there are required abilities to work in an independent and creative way, to analyze complex problems, and to propose and realize new and original solutions. Graduates from the doctoral study programme can also teach and/or scientifically work at universities.

FIT graduates in general and FIT doctoral graduates in particular do not have a problem finding employment at scientific, pedagogical or management positions both in Czech Republic and abroad.

• FIT   graduates of the doctoral study are capable of independent scientific, research and management work in the field of Informatics, Computer Technology and Information Technologies. Graduates are ready to solve challenging conceptual, research and development problems. They can independently conduct research, development and production in the field of modern information technology.
• Typically, they work as creative workers at top scientific research workplaces, as leaders of research and development teams and in scientific and pedagogical work at universities. Graduates of this program are also employed in higher functional positions of larger institutions and companies, where the ability to work independently, analyze complex problems and design and implement new, original solutions is required.
• And, last but not least, graduates typically continue as so-called "postdoc" in their academic careers in Czech Republic or abroad.

The requirements that the doctoral students have to fulfil are given by their individual study plans, which specify the courses that they have to complete, their presupposed study visits and active participation at scientific conferences, and their minimum pedagogical activities within the bachelor and master degree programmes of the faculty. A successful completion of the doctoral studies is conditional on the following:

• The student has to pass a doctoral state examination within which he/she has to prove a deep knowledge of methodologies, theories, and their applications in accordance with the state of the art in the areas of science that are given by the courses included in his/her individual study plan and by the theme of his/her future dissertation thesis. The doctoral state examination also encompasses an evaluation of the presumed goals of the future dissertation thesis of the student, of the chosen solution method, and of the so far obtained original results.
• The student has further to prepare and defend his dissertation thesis.

The rules are determined by the directions of the dean for preparing the individual study plan of a doctoral student.  The plan is to be based on the theme of his/her future dissertation thesis and it is to be approved by the board of the branch.

• obligatory doctoral study programme Courses, the total number of courses a student has to complete and their mapping into particular semesters.
• a Research Plan Content (brief descrition of research content - focuse at the intended research area and the doctoral thesis topic
• a Research Plan ( list of research activities focused at the intended research area and the doctoral thesis topic - conferences and seminars to be attended , work to be published)
• teaching duty according to BUT study rules and regulations
• doctoral study schedule

https://www.fit.vut.cz/fit/info/smernice/sm2018-13-en.pdf

Brno university of technology provides studies for persons with health disabilities according to section 21 par. 1 e) of the Act no. 111/1998, about universities and about the change and supplementing other laws (Higher Education Act) as amended, and according to the requirements in this field arising from Government Regulation No. 274/2016 Coll., on standards for accreditation in higher education, provides services for study applicants and students with specific needs within the scope and in form corresponding with the specification stated in Annex III to Rules for allocation of a financial contribution and funding for public universities by the Ministry of Education, Youth and Sports, specifying financing additional costs of studies for students with specific needs.

Services for students with specific needs at BUT are carried out through the activities of specialized workplace - Alfons counselling center, which is a part of BUT Lifelong Learning Institute - Student counselling section.

Counselling center activities and rules for making studies accessible are guaranteed by the university through a valid Rector's directive 11/2017 concerning the status of study applicants and students with specific needs at BUT. This internal standard guarantees minimal stadards of provided services.
Services of the counselling center are offered to all study applicants and students with any and all types of health disabilities stated in the Methodological standard of the Ministry of Education, Youth and Sports.

The study programme builds on both the ongoing follow-up Master's programme in Information Technology and the new follow-up Master's programme in Information Technology and Artificial Intelligence.
Students can also, according to their needs and outside their formalized studies, take courses and trainings related to the methodology of scientific work, publishing and citation skills, ethics, pedagogy and soft skills organized by BUT or other institutions.

1. Advanced algorithms of Video, Image, and Signal processing

   The topic concerns algorithms of image, video, and/or signal processing. Its main goal is to research and in-depth analyze existing algorithms and discover new ones so that they have desirable features and so that they are possible to efficiently implement. Such efficient implementation can be but does not necessarily have to be part of the work but it is important to prepare the algorithms so that they can be efficiently implemented e.g. in CPU, in CPU with acceleration through SSE instructions, in embeded systems, even in combination with FPGA, in Intel Xeon PHI, in extremely low power systems, or in other environments. It is possible to exploit algorithms of artificial intelligence, such as neural networks, especially CNNs The application possibilities of the algorithms are also important and the application can be but does not have to be part of the work. The algorithms/applications of interest include:

   • recognition of scene contents, events, and general semantics of video sequences (such as identification of traffic situations, identification in scenes in moview, action identification, etc.),
   • classification of video sequences using machine learning (AI)through deep convolution networks neural network or similar approaches (e.g. for industrial quality inspection, object of scene characteristics search, etc.), possibly in combination with object tracking in video using modern methods, 
   • parallel analysis of video and signal (e.g. for detection of coincidence of occurrence of object in video and characteristic signal shape in surveillance applications), fusion of video and sognals,
   • modern algorithms of video, image, and/or signal exploiting "client/server" or "cloud" approaches suitable e.g. for mobile technology and/or embedded systems,
   • algorithms of video compression and analysis through frequency or wavelet transforms or similar methods...

   After mutual agreement, individually selected algorithms can be considered as well as soon as they do belong to the general topic.

   Collaboration on grant projects, such as TACR, MPO, H2020, ECSEL (possible employment or scholarship).

   Tutor: Zemčík Pavel, prof. Dr. Ing., dr. h. c.

2. Advanced methods for processing and visualization of geospatial data

   Today's increasing requirements on data processing and visualization emphasize the importance of developing high-quality, quick, and user-friendly tools used for this purpose. Common types of visualization media (charts) are usually not sufficient to visualize complex multidimensional data. An example of such data is the geospatial data representing the relation of data with a geographical location (such as occurrences of some effects on the Earth's surface). The need to visualize such data goes beyond the academic areas of interest. We can meet with the geospatial visualization in the industry (distribution of bureaus, network topologies), public sector (public transport schemes), media (spread of disease, economic and demographic indicators), but also among ordinary users (private routes tracking). Those visualizations are usually arranged in advanced screens (e. g., dashboards).

   Users can choose between two types of tools for geospatial visualization:

   1. Authoring systems: they allow users to create their custom thematic maps. Examples of such a tool are the systems used for general data visualization (e, g., Tableau or Grafana). Their functionality usually limits the usage of such systems. Hence, it is possible to use the solutions specialized in visualization only (e. g., Mapbox). Such systems usually provide advanced possibilities to create custom thematic maps without coding knowledge. However, learning such systems usually exceeds the knowledge of typical users. Moreover, the maps are usually hosted by the system provider. Since the systems are usually commercial, their free usage is usually limited by the number of map accesses.
   2. Programming libraries - e. g., Leaflet, Mapbox GL, OpenLayers, Google Maps Platform. Those libraries allow users to create various types of thematic maps without any radical limitation. They, however, require the users to have decent coding knowledge and understand the principles of geo-visualization (such as knowledge of geospatial formats - e. g., GeoJSON). Those libraries usually do not offer a palette of thematical maps but allow to create them by using essential graphical elements (such as layers, points, polygons).

   Both approaches have advantages and disadvantages. A prototype of a compromise solution-Geovisto library-was developed during the TACR project (19/20) in cooperation with the Brno University of Technology, Masaryk University, and the Flowmon company. The library is based on the Leaflet library. However, it tries to provide a higher level of abstraction by offering a set of thematic maps (e. g., cartogram, connection map). Those thematic maps can be configured either programmatically or by using controls known from popular authoring systems. The library targets programmers who want to use a ready solution, and they do not want to be limited by the need to deeply study the framework and the requirements of proprietary authoring systems.

   • https://doi.org/10.5220/0010260401010111
   • https://drive.google.com/file/d/1iU5R1Atlbxva8s3hLT758FSzeAbc63VN/view?usp=sharing

   The doctoral thesis's goal will be to study existing approaches to geospatial data visualization and look for new ones. Particularly:

   • analyze the process of the design and creation of thematic maps and propose its simplification
   • design new algorithms for a pleasing arrangement of graphs in the map of (nodes and connections topologies) - e. g., based on edge bundling algorithm
   • apply advanced animations to present geospatial data in time (geo-temporal data analysis)
   • design

   Tutor: Hruška Tomáš, prof. Ing., CSc.

3. Advanced Methods of Computational Photography

   The project is concerned with advanced methods of computational photography. The aim is to research new computational photography methods, which comprises software solutions potentially supported by new optics and/or hardware. Our interest is on HDR image and video processing, color-to-grayscale conversions, spectral imaging, and others.
   

   • Further information: http://cadik.posvete.cz/tmo/
   • Contact: http://cadik.posvete.cz/
   • Cooperation and research visits with leading research labs are possible (Adobe Research, USA, MPII Saarbrücken, Germany, Disney Research Zurich, Switzerland, INRIA Bordeaux, France)

   Tutor: Čadík Martin, doc. Ing., Ph.D.

4. Advanced monitoring of IoT networks

   
   Internet of Things is a communication platform that interconnects different types of devices in smart homes, smart buildings, or smart hospitals. IoT end nodes (things) are usually connected via L2 technology like Zigbee, Z-Wave, WiFi, or Bluetooth to the IoT controller (gateway, hub) that transmit data from IoT end devices the cloud for processing and visualization. This proprietary solution does not support integration with standardized systems for network management (SIEM, SOC). 
   
   IoT devices can be integrated into the central monitoring system using SNMP proxy agents that analyze IoT traffic and creates special MIB objects for specific IoT endpoints. These MIB objects can be managed using SNMP network monitoring and management system. A large amount of monitoring data from IoT devices requires advanced processing and control. 
   
   The research aims to propose and implement methods for the integration of IoT devices into the centralized network monitoring and management system. Having the data, it is necessary to apply advanced methods for their analysis and processing to be used for network management as defined by the FCAPS model. 
   The topic is a part of the research project IGA Application of AI methods to cybersecurity and control systems and national center of competence for cybersecurity. 
   
   
   Co-supervisor: Matoušek Petr, Ing., Ph.D., M.A.

   Tutor: Ryšavý Ondřej, doc. Ing., Ph.D.

5. Advanced Rendering Methods

   The project is concerned with advanced rendering and global illumination methods. The aim is to research new photorealistic (physically accurate) as well as non-photorealistic (NPR) simulations of interaction of light with the 3D scene. Cooperation and research visits with leading research labs are possible (Adobe, USA, MPII Saarbrücken, Německo, Disney Curych, Švýcarsko, INRIA Bordeaux, Francie).
   
   

   • More information: http://cadik.posvete.cz/

   Tutor: Čadík Martin, doc. Ing., Ph.D.

6. Analysis of attacks on wireless networks

   Dissertation focuses on the security of wireless local area networks. As part of the solution, student should become familiar with selected wireless networks and their security. The goals of this work: studying the theory of wireless networks, their properties and possibilities of attacks, testing the basic types of attacks, designing a new method of protection, experiments, evaluating the results and proposing the direction of further research.
   Co-supervised by dr. Kamil Malinka.

   Tutor: Hanáček Petr, doc. Dr. Ing.

7. Artificial Intelligence for High Quality Photoacoustic Image Reconstruction

   Photoacoustic Tomography (PAT) is an emerging 3D biomedical imaging modality for both clinical and pre-clinical imaging that has gained increasing attention in the last decade. In principle, PAT can achieve as high spatial and temporal resolution as ultrasound images, but, because it depends on optical absorption for contrast, it also has the potential to provide functional, molecular and genetic imaging capabilities. However, realising these advantages requires high quality image reconstruction algorithms.

   Several approaches to PAT image reconstruction are currently used, including time reversal, series constructions, and filtered backprojection algorithms. However, there are several aspects that cannot be taken into account in these classical approaches and therefore result in image artefacts, including (1) patient movement, (2) incomplete coverage of the object with ultrasound sensors, (3) angle and frequency variability of the sensor's sensitivity, (4) unstable environment, e.g., variations in temperature, (5) sound speed variations with the object eg. breast, and (6) measurement noise. In addition, large-scale 3D images take time to reconstruct, limiting the imaging frame rate; increasing the frame rate would open up many more potential applications.

   Currently, the reconstruction of photoacoustic images takes several hours on high-end servers and subsequently artefacts are detected and removed. The goal of this thesis is to overcome some of these limitations, by improving reconstruction speed and quality of photoacoustic images using artificial intelligence.

   We can identify three essential approaches for this task. First, use AI to correct or complement the measured data to form a complete and clean data set then use a classical reconstruction algorithm. Second, use AI to complement the image reconstruction task, by intertwining learnable components with classical reconstruction approaches. Third, use a classical reconstruction algorithm to form an image and then use AI to remove the artefacts.

   These topic cover several areas, from the experimental acquisition of photoacoustic data, techniques for image reconstruction and high performance computing, to deep learning and artificial intelligence.

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

8. Artificial Intelligence in Computer Security

   The rising trend in artificial intelligence usage brings novel cybersecurity approaches on both sides - attacker and defender. The most prominent examples are deepfake usage to counterfeit biometric systems or security analytics, using deep learning for cyber-attacks detection. The goal of this work is to analyze all existing approaches, their properties, and potential applications. The work should then propose novel applications of AI for the problems that were not resolved before while also implementing the most interesting application.
   
   

   Co-supervised by dr. Kamil Malinka.

   Tutor: Hanáček Petr, doc. Dr. Ing.

9. Augmented Reality on Mobile Devices

   The goal of the work is to research and create algorithms that will allow for running augmented reality on mobile (ultramobile) devices. It mainly concerns algorithms of pose estimation in the space by the means of computer vision and by using sensors embedded in the device. Furthermore, the work will elaborate on algorithms of rendering of virtual elements into the real-world scene and on applications of augmented reality on mobile devices.
   

   Tutor: Herout Adam, prof. Ing., Ph.D.

10. Automatic detection and recognition of pathologies in retinal images

    The aim of the work is the detection and recognition of pathologies in retinal images (retina of the eye). The work will consist of:
    

    • Introduction to retinal scanning and all sorts of pathologies that occur in retinal images.
    • Design and implementation of algorithms for detection and recognition of the type of pathology in the retinal image, ideally including area calculation and diagnosis.
    • Execution of experiments and summary of achieved results.
    Participation in important international conferences and publication in professional or scientific journals is expected. An internship abroad is possible and strongly supported. This work will be solved in cooperation with ophthalmologists from St. Anne's University Hospital in Brno and University Hospital in Brno, or other ophthalmologists.

    Tutor: Drahanský Martin, prof. Ing., Ph.D.

11. Computer Vision in Traffic Monitoring
    • Research and development of computer vision algorithms.
    • Focus on videos from traffic surveillance cameras.
    • Research of algorithms avoiding user input.
    • Collection and synthesis of suitable data sets.
    • Implementation of experimental prototypes.
    • Design and prototyping of applications.

    Tutor: Herout Adam, prof. Ing., Ph.D.

12. Create a 3D model of head from 2D photos of diverse origins

    The aim of the thesis is to create 3D face model from 2D photos of diverse origins, namely:
    

    • Getting acquainted with creating a 3D model from 2D photos.
    • Specify options - photo size and resolution, photo age, head position, B & W / color photography, and other parameters to create a 3D model from this data.
    • Design and implementation of the algorithm for determining the parameters from the previous point to determine the usability of the input data and enable the creation of a 3D model.
    • Design and implementation of a 3D model creation algorithm from usable input data.
    • Implementation of experiments and summary of achieved results.
    Participation on major international conferences and publishing in scientific or scientific journals is expected. Foreign internship is possible and strongly supported. This thesis will be dealt with in cooperation with the Police of the Czech Republic.

    Tutor: Drahanský Martin, prof. Ing., Ph.D.

13. Deep Neural Networks in Image Analysis with Small Training Data Set

    Deep convolution networks have been a clear trend of machine learning for image analysis in recent years. However, in tasks with a very small and specific data set, where it is not enough to use data augmentation or GAN concepts, their usage is still problematic.
    
    The goal of the dissertation thesis is to explore, analyze and design new architectures of deep convolutional networks and approaches to their learning for image analysis tasks in which the size of the annotated data set is extremely small or is gradually growing. For learning neural networks it is possible to use unannotated data or partially annotated data in the form of a limited user input.
    
    Proposed methods will be applied in the projects on which the supervisor participates.

    Tutor: Španěl Michal, doc. Ing., Ph.D.

14. Detection and Re-Identification of Objects in Image and Video
    • Omnidirectional object detection in real time
    • Re-identification of detected objects, e.g. in shots from different cameras or at different points in time
    • Focused on statinary surveillance cameras
    • Focused on automatic traffic surveillance

    Tutor: Herout Adam, prof. Ing., Ph.D.

15. Detection of mobile apps in encrypted communication

    
    Current internet traffic is mostly encrypted. This is also true for mobile apps that communicate over TLS or DTLS. Detection of mobile apps communicating over the local network is important for network monitoring and cybersecurity. There are several approaches to how mobile apps can be detected in the network traffic. One approach is based on mobile apps fingerprinting using the JA3/JA3S method. Another approach includes statistical analysis of encrypted traffic or the application of machine learning. 
    
    This topic is research and evaluation of current methods for detecting applications in encrypted traffic, their extension, and proposal of advanced methods. The research is focused on mobile apps and automated detection. 
    
    The topic is a part of the research project IGA Application of AI methods to cybersecurity and control systems.
    
    Co-supervisor: Matoušek Petr, Ing., Ph.D., M.A.

    Tutor: Ryšavý Ondřej, doc. Ing., Ph.D.

16. Embedded Systems for Video/Signal Processing

    The topic focuses embedded image, video and/or signal processing. Its main goal is to research capabilities of "smart" and "small" units that have such features that allow for their applications requiring smyll, hidden, distributed, low power, mechanically or climatically stressed systems suitable of processing of some signal input. Exploitation of such systems is perspective and wide and also client/server and/or cloud systems. The units themselves can be based on CPU/DSP/GPU, programmable hardware, or their combination. Smart cameras can be considered as well. Applications of interest include:

    • classification of images or objects using machine learning (AI) using traditional methods or through deep convolution networks neural network or similar approaches (e.g. for industrial quality inspection, etc.),
    • parallel analysis of signal(s) and video (e.g. for robust detection of occurrence of object in industrial or surveillance applications),
    • modern algorithms of video, image, and/or signal exploiting "client/server" or "cloud" (with focus on the technlogy) suitable e.g. for mobile technology and/or embedded systems,
    • other similar topics can be individually consulted and considered.

    A possibility exists in collaboration on grant projects, especially the newly submitted TAČR, H2020, ECSEL ones (potentially employment or scholarship possible).

    Tutor: Zemčík Pavel, prof. Dr. Ing., dr. h. c.

17. Generate damage to synthetic fingerprints and analyze their quality

    The aim of this work is to generate various damages into synthetic fingerprints and analysis of their quality. The work will consist of:
    

    • Familiarization with biometric fingerprint recognition, synthetic fingerprint generators, various damages in fingerprints and fingerprint quality estimation methods.
    • Design and implementation of algorithms for inserting (simulating) different types of damage into synthetic fingerprints.
    • Design and implementation of methods for fingerprint quality estimation, ideally combined with existing methods.
    • Realization of experiments and summary of achieved results.
    Participation on major international conferences and publishing in scientific or scientific journals is expected. Foreign internship is possible and strongly supported. This thesis will be dealt with in cooperation with the Police of the Czech Republic.

    Tutor: Drahanský Martin, prof. Ing., Ph.D.

18. Generative Adversarial Networks in the Conext of Cyber-Security

    
    Generative adversarial networks (GANs) introduced by Goodfellow et al. in 2014 found many interesting applications across various domains. GANs enable to improve the performance of neural network-based classifiers as well as to enrich sample set of hard-to-obtain datasets. Moreover, a combination of two GANs (a.k.a., dual-GANs) enables to perform the unsupervised mapping between two different dataset domains. Such an extension can be further applied as a filter of noise in the datasets.
    
    The goal of this thesis is to investigate existing application domains and suitable scenarios of GANs, while focusing on security aspects. For example, the application of GANs for extracting privacy-sensitive data might be analyzed. Next, the thesis should explore new approaches to attacks utilizing GANs and evaluate their success. Finally, the thesis should propose novel defense techniques and discuss their assumptions and limitations.
    
    Co-supervised by  dr. Ivan Homoliak.

    Tutor: Hanáček Petr, doc. Dr. Ing.

19. [Hardware & Cryptocurrencies] Security Analysis of Hardware Cryptocurrency Wallets and their Client Protocols

    The goal of this thesis is to analyze side-channels and software vulnerabilities in hardware wallets, which are currently considered as a most secure way of storing private keys of users. These hardware wallets are connected to the client machine by a USB, Bluetooth, or other connection. Therefore, we assume two attacker models, one has the physical access to the wallet and another one tampers with the client interface and thus can influence the execution of the client protocol.
    
    Examples of wallets that we want to analyze are Trezor One/T, CoolBitX, Ellipal, Ledger Nano S, etc. 
    
    Hardware skills (oscilloscope) are advantage for the PhD student.
    
    

    Co-supervised by  dr. Ivan Homoliak.

    Tutor: Hanáček Petr, doc. Dr. Ing.

20. Human-Robot Interaction in Collaborative Environment

    The trend of the manufacturing industry is the introduction of collaborative robots into production, which allows for closer human-robot cooperation. The aim is to streamline production by using robots for repetitive activities and workers for complex activities, their robotization would be too expensive and not very scalable. This trend brings new problems in how to communicate effectively with robots: to have an idea of the state of the robot and its understanding of the situation, and to control and program the robot easily and naturally.
    
    The aim of this work is to explore new possibilities of human-robot communication using modern technologies and devices. The solution requires:
    

    • Analyze user needs and available technologies and for information visualization,
    • suggest ways of interaction for selected tasks,
    • prepare experimental solutions,
    • perform experiments with users and evaluate these experiments.
    Technologies suitable for interaction that the researcher should analyze and use experimentally include, in particular, augmented reality (projected or in a mobile device), contactless technologies (sensing of human activities and condition by cameras or depth sensors, muscle activity sensors), haptic and audio feedback apod.
    
    The researcher will have at his disposal an experimental workplace for close human-robot cooperation, which is being developed in the laboratories of the Robo@FIT research group.
    
    Participation in relevant international conferences and publication in scientific journals is expected. Furthermore, cooperation with industrial partners in the field. Last but not least, participation in relevant domestic or foreign projects.
    
    More information after a personal meeting.

    Tutor: Beran Vítězslav, doc. Ing., Ph.D.

21. Hybrid Methods of 3D Data Representation for Their Efficient Processing

    There are many ways to represent and store 3D data. The most commonly used is the polygonal surface mesh, due to its simplicity and versatility. Volumetric representation is often used for some specific tasks because it can significantly reduce complexity of their implementation when compared to polygonal meshes. The emerging trend is hybrid representations, which try to combine the advantages of both of these representations and possibly eliminate the need to change from one representation to another.
    
    The goal of the dissertation thesis is to explore methods of storing and representing 3D data and to design new algorithms for 3D data processing with regard to typical problems such as numerical stability, robustness, open and non-manifold 3D models.
    
    Proposed methods will be applied in the projects on which the supervisor participates.

    Tutor: Španěl Michal, doc. Ing., Ph.D.

22. Image and video quality assessment metrics

    The project deals with image and video quality assessment metrics (IQM). The aim is to explore new ways how to incorporate human visual system properties into IQM. In particular, we will consider perception of HDR images, and utilization of additional knowledge (in form of metadata, 3D information, etc.) about the tested scenes using machine learning (e.g. neural networks).
    

    • Further information: http://cadik.posvete.cz/iqm/
    • Contact: http://cadik.posvete.cz/
    • Cooperation and research visits with leading research labs are possible (MPII Saarbrücken, Germany, Disney Research Zurich, Switzerland)

    Tutor: Čadík Martin, doc. Ing., Ph.D.

23. Image Processing using Neural Networks

    The project is concerned with advanced methods of image processing. The aim is to research new methods using machine learning, in particular deep convolutional neural networks.
    

    • Contact: http://cadik.posvete.cz/
    • Cooperation and research visits with leading research labs are possible (Adobe Research, USA, MPII Saarbrücken, Germany, Disney Research Zurich, Switzerland, INRIA Bordeaux, France)

    Tutor: Čadík Martin, doc. Ing., Ph.D.

24. Improvement of cyberthreat detection and representation of threat data

    This topic focuses on the process and techniques used for cyberthreat detection, obtaining and representation of threat data. The goal of this dissertation thesis is to study the current techniques and propose a solution that effectively improves the process and its results usable for malware detection and threat intelligence, e.g. a multicriterial clustering with focus on explainability.
    
    

    Co-supervised by  dr. Kamil Malinka.

    Tutor: Hanáček Petr, doc. Dr. Ing.

25. Information Extraction from the WWW

    The topic of identifying and extracting specific information from documents on the Web has been the subject of intensive research for quite a long time. The basic obstacles that make this problem difficult are the loose structure of HTML documents and absence of meta-information (annotations) useful for recognizing the content semantics. This missing information is therefore compensated by the analysis of various aspects of web documents that include especially the following:

    • Document HTML code (DOM)
    • Document Text (Keyword Search, Statistical Text Analysis, Natural Language Processing Methods)
    • Visual organization (page content layout, visual properties)

    A background knowledge about the target domain and the commonly used presentation patterns is also necessary for successful information extraction. This knowledge allows a more precise recognition of the individual information fields in the document body.

    Current approaches to information extraction from web documents focus mainly modeling and analyzing the documents themselves; modeling the target information for more precise recognition has not yet been examined in detail in this context. The assumed goals of the dissertation are therefore the following:

    • Analysis of existing domain models such as UML class diagrams, E-R diagrams or ontology.
    • Extending these models with the specification of recognizing particular data in documents (e.g. regular expressions, advanced text classification).
    • Design of information extraction methods based on a comparison of the structure of the information presented in the document and the expected structure of the target information.

    Experimental implementation of the proposed methods using existing tools and experimental evaluation on real-world documents available on the WWW is also an integral part of the solution.

    Tutor: Burget Radek, doc. Ing., Ph.D.

26. Internet of Things Security Analysis

    Dissertation focuses on the security of IoT systems. The goals of this work: studying the theory of IoT systems, their properties and possibilities of attacks, testing the basic types of attacks, designing a new method of protection, experiments, evaluation of results and design of further research.
    Co-supervised by dr. Kamil Malinka.

    Tutor: Hanáček Petr, doc. Dr. Ing.

27. Methods for anomaly detection in industrial communication ICS/SCADA

    
    SCADA (Supervisory Control And Data Acquisition) and Industrial Control Systems (ICS) communication is intended to control and monitor industrial processes in smart factories, smart grids, etc. Disruption of communication may interrupt important industrial processes or cause a blackout of critical supplies like water or energy. 
    
    To protect SCADA/ICS communication, we can apply advanced SCADA/ICS monitoring that creates normal behavior profiles and detects deviations (anomalies). Communication profiles can be described using statistical methods. We can model communication using automata or create models using machine learning. 
    
    The research aims to describe the behavior of industrial communication and propose a suitable representation using multidimensional profiles. It is then necessary to verify what types of anomalies are covered by these profiles and how the accuracy of detection can be increased. 
    
    The topic is a part of the research project Security monitoring of ICS communication in the smart grid (Bonnet). 
    
    Co-supervisor: Matoušek Petr, Ing., Ph.D., M.A.

    Tutor: Ryšavý Ondřej, doc. Ing., Ph.D.

28. Model Drive Architecture for Big Data Processing Systems

    The goal of the research is to design a new method for analysis, development and implementation of software systems for Big data processing. The method should support an easy transformation of domain and platform independent models into various platform specific models of the Big data processing systems in accordance with the principle of Model Driven Architecture (MDA), as well as their subsequent implementation using different technologies, and consequently also easier development, deployment and evaluation of such systems.

    Individual objectives are to explore, evaluate and categorize various big data processing technologies; to create their platform specific and general meta-models; to find and design a common platform independent meta-model of such systems; to integrate the support for these models into existing or new modeling tools; to design and implement source code generators from the models including tools to generate the models from the source code (round-trip engineering); to create automatic validation and performance tests; to support agile development of such systems including their deployment (CI/CD); etc.

    Supervisor-specialist: RNDr. Marek Rychlý, Ph.D.

    Tutor: Ryšavý Ondřej, doc. Ing., Ph.D.

29. Modern Algorithms of Computer Graphics

    The topic concerns algorithms of computer graphics and image synthesis. Its main goal is to research new algorithms so that their features and application possibilities are better understood so that they are improved or newly created.  If suitable, it is possible to work on various platforms, includeing parallel CPUs, such as x86/64, ARM, Xeon PHI, GPU, etc. or other cores in CUDA, OpenCl, VHDL, etc. Algorithms of interest include:

    • rendering using selected computer graphics methods (such as ray tracing, photon mapping, direct rendering of "point clouds", etc.),
    • modeling of scenes and redering using artificial intelligence, including image synthesis using neural netowrks (especially CNNs),
    • processing and rendering of "lightfield" images, their acquisition, or possibly compression, reconstruction of 3D scenes from images and/or video, eventually also fusing with other sensors, such as LIDAR or RADAR,
    • modern algorithms of geometry suitable for applications in cpmputer graphics and perhaps also 3D printing,
    • emerging algorithms of 3D synthesis, holography, wavelet transform, frequency transform, etc.

    After mutual agreement, individually selected algorithms can be considered as well as soon as they do belong to the general topic.

    Collaboration on grant projects, such as TACR, H2020, ECSEL possible (employment or scholarship).

    Tutor: Zemčík Pavel, prof. Dr. Ing., dr. h. c.

30. Modern Methods of Computer Vision
    • Use of modern and promising approaches to computer vision, especially methods of machine learning and convolutional neural networks
    • Identification of promising open problems
    • Design and development of non-traditional modifications of existing approaches
    • Experimental evaluation, use of existing data sets and collection of new ones

    Tutor: Herout Adam, prof. Ing., Ph.D.

31. Modern Methods of Computer Vision
    • Use of modern and promising approaches to computer vision, especially methods of machine learning and convolutional neural networks
    • Identification of promising open problems
    • Design and development of non-traditional modifications of existing approaches
    • Experimental evaluation, use of existing data sets and collection of new ones

    Tutor: Herout Adam, prof. Ing., Ph.D.

32. Multi-channel processing for far-field speech data mining

    The topic deals with joint training of signal processing and speech data mining architectures, by end-to-end approaches. The topic requires interest in mathematics, statistics, machine learning and signal processing, skills in Python and its machine learning libraries are a plus.

    BUT Speech@FIT is a top international research group dealing with speech data mining. It advocates equal opportunities, currently includes 10 nationalities and (within IT) significant share of women.

    More information:  https://speech.fit.vutbr.cz

    Tutor: Černocký Jan, prof. Dr. Ing.

33. Reconstruction of damaged surfaces of CD/DVD/BR/HDD for forensic purposes

    The aim of the thesis is reconstruction of damaged CD/DVD/BR/HDD surfaces, consisting of:
    

    • Getting to know all possible ways of recording on CD/DVD/BR/HDD media.
    • Use of optical or electron microscope in the workplace DITS FIT BUT for scanning damaged surfaces (e.g. broken or cracked media) or in other workplaces (Tescan, Thermo Fisher - FEI).
    • Design and implementation of an application for intelligent (automatic) composition of scanned images into one unit.
    • Add intelligent functionality to read the contents of the media after the reconstruction, i.e., reconstruction or rescue of the data on the carrier.
    • Implementation of experiments and summary of achieved results.
    Participation on major international conferences and publishing in scientific or scientific journals is expected. Foreign internship is possible and strongly supported. This thesis will be dealt with in cooperation with the Police of the Czech Republic.

    Tutor: Drahanský Martin, prof. Ing., Ph.D.

34. Reliable matching of 2D face with 3D face projection

    The aim of this work is to create a reliable matching of 2D face with 3D face projection, namely:
    

    • Introduction to work with 3D model and axes of rotation
    • Design and implementation of the algorithm for determining the correct rotation of the 3D head model according to the 2D face layout
    • Design and implementation of 2D face matching algorithm with 3D face projection with the same parameters
    • Conduct experiments and summarize the results
    Participation in important international conferences and publication in research or scientific journals is expected. Foreign internship is possible and strongly supported. This work will be solved in cooperation with the Police of the Czech Republic.

    Tutor: Drahanský Martin, prof. Ing., Ph.D.

35. Scalable Blockchain-Based Elections

    The goal of the thesis is to propose a scalable decentralized e-voting system based on smart contracts, with maximum voter privacy,  fault tolerance, and coercion resistance. Such an approach should be convenient and robust enough for national elections. The first challenge is to optimize costs the for running expensive zero-knowledge proof verification at smart contract by off-chain constructs. The second challenge is the scalability w.r.t. to the number of participants and vote choices, which depends on the convenient type of the blockchain and its consensus mechanism, such as permissioned blockchains (Proof-of/Stake) and permissioned blockchains (Proof-of-Authority).
    
    Co-supervised by dr. Ivan Homoliak.

    Tutor: Hanáček Petr, doc. Dr. Ing.

36. Security analysis of anonymization networks

    Dissertation focuses on the security of various systems that are intended to ensure the anonymity or pseudonymity of Internet users (for example, TOR networks). The goals of this work: studying the theory of anonymization systems, their properties and possibilities of attacks, testing the basic types of attacks, designing a new method of protection. Experiments, evaluation of results and proposal of further research direction.
    Co-supervised by dr. Kamil Malinka.

    Tutor: Hanáček Petr, doc. Dr. Ing.

37. Security and Performance Analysis of Consensus Protocols utilized in Blockchains
    There are several categories of consensus protocols utilized in blockchains: Proof-of-Stake, Proof-of-Resource, and Byzantine Fault Tolerant protocols. Each of them has different use cases in permissioned and permissionless blockchains, which is mainly given by their performance in terms of throughput. Moreover, each category suffer from different security threats.
    
    The goal of this thesis is to theroeticaly and practically analyze selected categories of consensus protocols in terms of throughput and security. The thesis should contain  evaluation of consensus protocols by simulations enabling to test the response of protocols under different network conditions and honest/adversarial consensus power.
    In particular, new scenarios of attacks should be investigated -- e.g., assuming violation of the protocol assumptions or assumptions about incentives. The work should also leverage principles from game theory and statistical analysis. Examples of attacks to investigate are selfish mining attacks, pool specific attacks, double spending attacks, attacks on shards, posterior corruption attacks, denial of service on the leader committee, long-range attacks, nothing-at-stake attacks, grinding attacks, etc. 
    
    This is a general topic and can be further specified by the category of the consensus protocols or aspects to investigate. 

    Co-supervised by dr. Ivan Homoliak.

    Tutor: Hanáček Petr, doc. Dr. Ing.

38. Semantic Web Technology Application for Document Analysis

    Semantic web technology allows the representation of information and knowledge for the purpose of its further sharing, for example, in computer applications. Available knowledge databases, such as DBPedia, contain a great amount of useful information and facts. On the current web, however, most of the new information is published in the form of documents most often in HTML, whose further processing is problematic mainly due to their free structure and the absence of explicit information about the meaning of individual parts of the content. There exist two ways to overcome this gap between the classical and the semantic web:
    

    • Use the structured knowledge available in semantic repositories as a background knowledge to refine web document analytics and to identify new information in their content, and, on the other hand
    • Integrate web documents as an information source into knowledge databases.

    To achieve both these goals, it is necessary to analyze the capabilities of existing ontological models regarding the modelling of the target domains and mapping these descriptions on the content of real-world web pages and documents. Possible applications include, but are not limited to:

    • Recognizing the topic of an entire document or its individual parts by keywords analysis and mapping to concepts in a semantic database.
    • Recognizing structured data in documents based on their alignment with existing domain models - ontologies.
    • Adding and updating information in semantic databases from web sources.

    Experimental implementation of the proposed methods using the existing tools and experimental evaluation on real-world data and documents is also an integral part of the solution.

    Tutor: Burget Radek, doc. Ing., Ph.D.

39. Speech recognition and data processing for ATC-pilot communication in aviation

    The topic of the work is focused on speech recognition and data processing for ATC-pilot communication in aviation. It will cover all components of automatic speech recognition (ASR), i.e. data processing, acoustic model, vocabulary (including special aviation terminology) and language model.
    
    The topic is related to the European project Horizon 2020 ATCO2, and will be elaborated in cooperation with project partners. 
    
    The assignment requires an interest in mathematics, statistics, machine learning and speech processing, the advantage is proficiency in Python and its libraries for machine learning.

    Tutor: Černocký Jan, prof. Dr. Ing.

40. User Experience and Modern User Interfaces
    • Study and design of advanced web and mobile user interfaces.
    • Validation, optimization, and user testing of web and mobile user interfaces.
    • Statistical processing of data collected from human respondents / users.
    • Design, development, and deployment of practically usable web/mobile system / user interface.

    Tutor: Herout Adam, prof. Ing., Ph.D.

41. Visual Geo-Localization and Augmented Reality on Mobile Devices

    The project deals with geo-localization of mobile devices in unknown environments using computer vision and computer graphics methods. The aim is to investigate and develop new image registration techniques (with geo-localized image database or 3D terrain model). The goal is an efficient implementation of proposed methods on mobile devices as well as search for additional applications in the area of image processing, computational photography, and augmented reality.
    

    • Further information: http://cadik.posvete.cz/locate/
    • Contact: http://cadik.posvete.cz/
    • Cooperation and research visits with leading research labs are possible (Adobe Research, USA, MPII Saarbrücken, Germany, Disney Research Zurich, Switzerland, INRIA Bordeaux, France)

    Tutor: Čadík Martin, doc. Ing., Ph.D.