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Doctoral study program

Topics of dissertation theses

Each year, we announce new topics that reflect the latest trends and advancements in the field. The topics are general and every student shall agree on a specific area of interest and research with their supervisor. Dissertation theses must be submitted 7 years at the latest after the day of enrollment in studies.

Dedecius Kamil, Ing., Ph.D.

Distributed modelling in diffusion networks

Level
Topic of dissertation thesis
Topic description

The topic aims at designing a novel framework for distributed modelling of stochastic processes by networks with information diffusion. Each network node - agent - may assimilate information about inferred variables provided by its adjacent neighbors [1]. Due to the increase of computational power of small, usually wireless devices, the application variety is wide, and spans from the sensor network, smart grids, social networks, and microgrid networks up to the Internet of Things [2].

There are generally three distributed information processing strategies: the incremental approach, the consensus, and the diffusion [1]. We focus primarily on the diffusion strategy, where the shared information gradually diffuses through the whole network by local communication among adjacent neighbors. It usually (but not always) consists of two phases. In the adaptation phase, the agents exchange information about their observations of the modelled process, and incorporate it into their statistical knowledge about the inferred variables. This knowledge is subsequently exchanged and combined during the combination step [1]. There are numerous diffusion-based modelling methods and their modifications, e.g., the recursive least squares (RLS) [3], least mean squares (LMS) [4], Kalman filters [5], particle filters [6], etc. A common feature is their single-problem orientation and independent derivation from the traditional methods. For the sake of universality, we proposed a generic Bayesian framework [7], that is applicable to a vast class of models, and yields the discussed ones as the special cases. Additionally, it provides diffusion-based inference of others, e.g., the mixtures [8]. The Ph.D. topic will build on it. The primary focus will be on the case of heterogeneous (agent-specific) models/parameters, where our publication [9] sketches the possible way towards the solutions.

Literature
  • [1] Sayed, A. H. (2014). Diffusion Adaptation over Networks, in Chellapa, R. and Theodoridis, S. (eds) Academic Press Library in Signal Processing. Academic Press, Elsevier, pp. 323–454.
  • [2] Chen, Y. et al. (2018). The Internet of Things: Secure Distributed Inference. IEEE Signal Process Mag., vol. 35, no. 5, pp. 64-75.
  • [3] Cattivelli, F. S., Lopes, C. G. and Sayed, A. H. (2008). Diffusion Recursive Least-Squares for Distributed Estimation Over Adaptive Networks, IEEE Transactions on Signal Processing, vol. 56, no. 5, pp. 1865–1877.
  • [4] Arablouei, R. et al. (2014). Adaptive Distributed Estimation Based on Recursive Least-Squares and Partial Diffusion, IEEE Transactions on Signal Processing, vol. 62, no. 14, pp. 3510–3522.
  • [5] Hu, J., Xie, L., Zhang, C. (2012). Diffusion Kalman Filtering Based on Covariance Intersection, IEEE Transactions on Signal Processing, vol. 60, no. 2, pp. 891–902.
  • [6] Bruno, M. G. S., Dias, S. S. (2014). Collaborative Emitter Tracking Using Rao-Blackwellized Random Exchange Diffusion Particle Filtering’, EURASIP Journal on Advances in Signal Processing. Springer, 2014(1), pp. 1-19.
  • [7] Dedecius, K., Djurić, P. M. (2017). Sequential Estimation and Diffusion of Information Over Networks: A Bayesian Approach With Exponential Family of Distributions, IEEE Transactions on Signal Processing, vol. 65, no. 7, pp. 1795–1809.
  • [8] Dedecius, K., Reichl, J., Djurić, P. M. (2015). Sequential Estimation of Mixtures in Diffusion Networks, IEEE Signal Processing Letters, vol. 22, no. 2, pp. 197–201.
  • [9] Dedecius, K., Sečkárová, V. (2017). Factorized Estimation of Partially Shared Parameters in Diffusion Networks, IEEE Transactions on Signal Processing, vol. 65, no. 19, pp. 5153–5163.

Sequential modelling of discrete random variables

Level
Topic of dissertation thesis
Topic description

The topic is focused on sequential (online) modelling of discrete random variables, typically integer-numbered counts or binary observations. Under static modelling scenarios, the generalized linear models (GLMs) prevail for such variables, e.g., the logistic regression in the classification problems, or the Poisson regression for count variables [1]. The state-of-the-art literature focuses predominantly on the offline inference of these models from a batch of available observations, exploiting the maximum likelihood methods, or the Bayesian MCMC-based inference.

The goal of the thesis is to propose new computationally effective and cheap methods suitable for fully sequential (online) inference of the discrete variables models as well as the for forecasting. These methods will be grounded in the approximate Bayesian paradigm, for instance, in the Laplacean approximation of the posterior distribution [3], a convenient model approximation [4], or any other appropriate method. The class of target models comprises the Poisson model, the overdispersion models, zero-inflated models, negative binomial model, and mixture models [5-7].

Literature
  • [1] McCullagh, P., Nelder, J. (1989). Generalized Linear Models (2nd ed.). Boca Raton, FL: Chapman and Hall/CRC. ISBN 412317605.
  • [2] Myers, R. H. et al. (2010). Generalized Linear Models. Hoboken, NJ, USA: John Wiley & Sons, Inc.
  • [3] Chen, M. and Wang, X. (2011). Approximate predictive densities and their applications in generalized linear models, Computational Statistics & Data Analysis, vol. 55, no. 4, pp. 1570–1580.
  • [4] Dedecius, K., Žemlička, R. (2020). Sequential Poisson Regression in Diffusion Networks. Submitted to IEEE Signal Processing Letters.
  • [5] Hilbe, J. M. (2007). Negative Binomial Regression. Cambridge: Cambridge University Press.
  • [6] Dean, C. B. and Lundy, E. R. (2016). Overdispersion, in Wiley StatsRef: Statistics Reference Online. Chichester, UK: John Wiley & Sons, Ltd, pp. 1-9.
  • [7] Frühwirth-Schnatter, S. (2006) Finite Mixture and Markov Switching Models. New York: Springer.

Fišer Petr, doc. Ing., Ph.D.

Improvements in the Logic Synthesis and Optimization Process Control

Level
Topic of dissertation thesis
Topic description

Contemporary logic synthesis and optimization tools (both commercial and academic) mostly emphasize computational speed, at expense of result quality. Our recent research has shown, that these tools tend to get stuck in deep local optima, and therefore they often produce very inferior results (in terms of area and/or delay). One of the reasons for it is a deterministic nature of the algorithms. Randomization of the algorithms has been found to be a viable, but just partial solution to this problem [1], [2]. The second, and more important reason of failure is the lack of a global algorithm control. Most of present logic synthesis and optimization algorithms are of an iterative nature, whereas their individual parts (elementary operations) are executed essentially ad-hoc and speculatively. Implementation of efficient top-level algorithm control means should significantly improve performance of logic synthesis and optimization.

The aim of the research is to investigate the behavior of individual elementary synthesis steps (e.g., in the ABC synthesis tool [3]), determine their dependence and orthogonality, and devise an improved overall algorithm control.

Literature
  • [1] P. Fišer and J. Schmidt, “Improving the Iterative Power of Resynthesis,” in Proc. of 15th IEEE Symposium on Design and Diagnostics of Electronic Systems (DDECS), Tallinn (Estonia), April 18-20, 2012, pp. 30-33.
  • [2] P. Fišer and J. Schmidt, “On Using Permutation of Variables to Improve the Iterative Power of Resynthesis,” in Proc. of 10th Int. Workshop on Boolean Problems (IWSBP), Freiberg (Germany), September 19-21, 2012, pp. 107-114.
  • [3] Berkeley Logic Synthesis and Verification Group, “ABC: A System for Sequential Synthesis and Verification” [Online]. Available: http://www.eecs.berkeley.edu/alanmi/abc/.

Randomized Iterative Algorithms in Logic Synthesis

Level
Topic of dissertation thesis
Topic description

Contemporary logic synthesis and optimization tools (both commercial and academic) mostly emphasize computational speed, at expense of result quality. Our resent research has shown, that these tools tend to get stuck in deep local optima, and therefore they often produce very inferior results (in terms of area and/or delay). Randomized iterative algorithms seem to efficiently solve this problem [1], [2] – they offer a trade-off between the run-time and result quality.

Moreover, present studies have shown that most of logic synthesis and optimization tools are very sensitive to randomness accidently introduced “from outside”, by the designer itself [3], [4]. Synthesis then produces results significantly differing in quality, when only slight changes in the source circuit description are made. Such a behavior is highly unwanted. Thus, it is required to analyze this behavior, determine its reasons, and to suggest more efficient algorithms.

The aim of the research is to analyze selected logic synthesis and optimization algorithms [5], identify the reasons of the above-mentioned behavior, and identify points, where randomness can be introduced. The influence of randomness will be then analyzed and algorithms exploiting the randomness in a positive way will be devised [3], [4]. Next, new algorithms minimizing the sensitivity on the external randomness will be developed.

Literature
  • [1] P. Fišer and J. Schmidt, “Improving the Iterative Power of Resynthesis,” in Proc. of 15th IEEE Symposium on Design and Diagnostics of Electronic Systems (DDECS), Tallinn (Estonia), April 18-20, 2012, pp. 30-33.
  • [2] P. Fišer and J. Schmidt, “On Using Permutation of Variables to Improve the Iterative Power of Resynthesis,” in Proc. of 10th Int. Workshop on Boolean Problems (IWSBP), Freiberg (Germany), September 19-21, 2012, pp. 107-114.
  • [3] A. Puggelli, T. Welp, A. Kuehlmann, and A. Sangiovanni-Vincentelli, “Are Logic Synthesis Tools Robust?,” in Proc. of the 48th ACM/EDAC/IEEE Design Automation Conference (DAC), 5-9 June 2011, pp. 633-638.
  • [4] P. Fišer, J. Schmidt, and J. Balcárek, “On Robustness of EDA Tools,” in Proc. of 17th Euromicro Conference on Digital Systems Design (DSD), Verona (Italy), August 27-29, 2014, pp. 427-434.
  • [5] Berkeley Logic Synthesis and Verification Group, “ABC: A System for Sequential Synthesis and Verification” [Online]. Available: http://www.eecs.berkeley.edu/alanmi/abc/.

Test Compression for ASIC Circuits

Level
Topic of dissertation thesis
Topic description

With the increasing complexity of presently manufactured chips, increasingly more data must be delivered to individual chip cores to test them. Compression of this data becomes now inevitable, because of the high cost of the ATE memory and test time expenses. The ratio of expenses spent by chip testing and its development is increasing. Therefore, test compression is and will increasingly be a hot topic. It is very challenging to contribute to the research in this area and to try to overcome established industrial tools in performance.

Different test compression mechanisms have been proposed, some of them are used in industry [1], [2]. Most of them rely on a combination of pseudo-random testing (and possibly BIST), which can be implemented on-chip as whole and does not need any data to be transmitted, and deterministic test. The deterministic test is algorithmically compressed, stored in the ATE memory, and decompressed on-chip.

The aim of the research is to design test compression/decompression methods based on advanced design-for-testability (DFT) architectures [3]. This will comprise of a design of possibly novel decompression architectures, algorithms for test compression for these architectures, and design of the overall HW architecture, where test decompression, BIST and various DFT features will be implemented.

This research will (can) follow up a successfully completed Ph.D. thesis [4], [5].

Literature
  • [1] J. Rajski et al. “Embedded Deterministic Test”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 23, no. 5, pp. 776-792, 2004.
  • [2] Y. Huang, S. Milewski, J. Rajski, J. Tyszer and C. Wang, “Low Cost Hypercompression of Test Data,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 39, no. 10, pp. 2964-2975, 2020.
  • [3] R. Dorsch, H. Wunderlich, “Reusing Scan Chains for Test Pattern Decompression”, Journal of Electronic Testing: Theory and Applications, vol. 18, no. 2, pp. 231-240, 2002.
  • [4] J. Balcárek, P. Fišer, and J. Schmidt, “Techniques for SAT-based Constrained Test Pattern Generation,” in Microprocessors and Microsystems, Elsevier, Vol. 37, Issue 2, March 2013, pp. 185-195.
  • [5] J. Balcárek, „Implicit Representations in Testing and Dependability of Digital Circuits“, Ph.D. Thesis, CTU in Prague, 2016.

Haindl Michal, prof. Ing., DrSc.

Analysis of visual materials properties

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Topic of dissertation thesis
Topic description

This research will (can) follow up a successfully completed Ph.D. thesis [4], [5].

Automatic shape estimation from video

Level
Topic of dissertation thesis
Topic description

The thesis objedtive is to study shape recognition and modelling methods from hand held video sequences targeted for virtual reality application.s Propose and implement (in C++) and appropriate method for automatic 3D shape inference from hand held video sequences. Verify this method on selected budildings models.

Light field estimation from visual scene

Level
Topic of dissertation thesis
Topic description

The thesis objective is to study environmental light field modelling methods from measured visual scenes applied to correct BTF illumination in virtual reality application. Propose and implement (in C++) an appropriate method for realistic illumination model estimated from real measured visual scenes. Verify this method on interior and exterior virtual reality models with variable illumination.

Material appearance measuring on real objects

Level
Topic of dissertation thesis
Topic description

The thesis objective is to propose acquisition methods, capable to estimate advanced visual material representations (BTF, SVBRDF) directly from a real 3D object in natural lighting environment. Propose and implement (in C++) an appropriate method for automatic visual surface material inference from hand held video sequences. Verify this method on the selected surface materials.

Modelling of visual surface material properties

Level
Topic of dissertation thesis
Topic description

The aim of the research is to design test compression/decompression methods based on advanced design-for-testability (DFT) architectures [3]. This will comprise of a design of possibly novel decompression architectures, algorithms for test compression for these architectures, and design of the overall HW architecture, where test decompression, BIST and various DFT features will be implemented.

Multispectral textural features

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Topic of dissertation thesis
Topic description

A multispectral textural features proposal for surface materials analysis in variable observation conditions in visual scenes. The features will be derived from the multidimensional statistical models and their invariant modifications, compared with the state-of-the-art textural features, and applied on BTF data.

Skin Cancer Detection and Treatment Progress Monitoring

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Topic of dissertation thesis
Topic description

Malignant melanoma, as the most dangerous form of skin cancer, has been a rapidly growing threat over the past decades. The effective treatment requires its early diagnoses and surgical excision. The thesis objective is to find discriminative image features and a robust classifier which will allow to recognize skin cancer on colour skin images and to evaluate a treatment progress between several mutitemporal skin images.

Unsupervised dynamic image sgmentation

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Topic of dissertation thesis
Topic description

The objective is to survey recent state-of-the-art of unsupervised image segmentation and to develop an unsupervised segmenter for dynamic colour, multispectral, or range data into homogenous regions. The method will be based on the underlying multidimensional Markovian models and verified on the PTSB benchmark.

Visual quality measures

Level
Topic of dissertation thesis
Topic description

Verification of visual data modelling quality is a difficult and still unresolved problem due to the lack of existing mathematical criteria capable of approximating the human eye's perception. The work will aim at suggestion of an appropriate mathematical criterion which could quantify and rank simulation results according to their similarity to the measured visual template.

Holeňa Martin, prof. Ing. RNDr., CSc.

Advanced methods of evolutionary black-box optimization

Level
Topic of dissertation thesis
Topic description

Optimization tasks encountered in real-world applications more and more frequently optimize objectives that are not mathematically calculated functions, but results of computer simulations or experimental measurements. That kind of optimization, called black-box optimization, presents two great challenges: 1. it is possible to get only the values of such a black-box objective, not its gradient or higher derivatives, 2. the evaluation of the objective typically costs much time and/or money. As far as the first challenge is concerned, evolutionary algorithms have proven very successful for optimization using only the values of the objective during the last decades. However, they typically require a large number of evaluations, which conflicts with the second challenge. That conflict incited an intense reseaech into black-box evolutionary optimization during the last decade, bringing a broad spectrum of topics suitable for PhD theses.

Making use of active learning in optimization

Level
Topic of dissertation thesis
Topic description

Evolutionary algorithms are, in the last 20 years, one of the most successful methods for solving non-traditional optimization problems, such as search for the most suitable documents containing required information, discovery of the most interesting knowledge in available data, or other kinds of optimization tasks in which the values of the objective function can be obtained only empirically. Because evolutionary algorithms employ only function values of the objective function, they approach its optimum much more slowly than optimization methods for smooth functions, which make use of information about the objective function gradients as well, possibly also about its second derivatives. This property of evolutionary algorithms is particularly disadvantageous in the context of costly and time-consuming empirical way of obtaining values of the objective function. However, evolutionary algorithms can be substantially speeded up if they employ the empirical objective function only sometimes when evaluating objective function values, whereas they mostly evaluate only a sufficiently accurate regression model of that function. It is the accuracy of the model that determines how successful surrogate of the original empirical function it will be. Therefore, the model is made more accurate after obtaining each new generation of points in which the empirical function has been evaluated, through re-learning including those points. However, it is possible to go even further and to consider, when choosing points for empirical evaluation, besides the value of the empirical function also how they contribute, during model re-learning, to making it more accurate. Such an approach is termed active learning. However, using active learning to accelerate evolutionary algorithms is only at a very beginning, and should be supported also by the proposed thesis.

Online training of deep neural networks for classification

Level
Topic of dissertation thesis
Topic description

Online training refers to the training of machine learning models when their training data is online updated. In such situations, the probability distribution of the training data is typically non-stationary and evolving. Online training has been used with both main kinds of supervised machine learning – classification and regression, as well as in the context of unsupervised learning. Due to online updating and evolution of training data, online training has to tackle several specific problems, such as the possibility of concept shift, which in the case of the most frequent kind of online learning – incremental online learning – entails the need for tuning the right amount of forgetting via means like choice of the time horizon, for instance. Another desired goal is reducing the computational demands for the frequent updates of the model while keeping its predictive accuracy uncompromised. Last but not least, we might require the online retraining scheme to embrace newly emerging classes on the fly.

In this decade, deep neural networks are the probably most popular and most quickly developing kind of machine learning models. For them, however, research into online learning, especially supervised online learning, and into dealing with the above mentioned problems, is only starting. This makes the proposed topic very timely, particularly in areas providing evolving data in large amounts needed for deep learning, such as web content analysis, network intrusion detection, or malware detection. The last mentioned area is the intended application domain of the proposed research. The PhD co-advisor works as an expert on deep learning in malware detection for the Avast company.

Semi-supervised learning of deep neural networks

Level
Topic of dissertation thesis
Topic description

Semi-supervised learning denotes learning of classification and regression models for which on the one hand a set of labelled data is available, on the other hand another, typically much larger, set of unlabelled data. It always consists in establishing some kind of connection of the items of the unlabelled data to one or more items of the labelled data. That connection can be either crisp or fuzzy and always relies on some kind of similarity between the connected data items. The plethora of similarity measures encountered in this context ranges from norm-based distances in Eucliedean spaces to graph-represented semantic similarities. Moreover, semi-supervised learning can start either from the labelled data, which initiate clustering the unlabelled data, or from forming clusters of the unlabelled data, which are subequently adapted to the available labelled data. Finally, research into semi-supervised learning mutually influences with research into active learning, which consists in choosing among the unlabelled data those that are from some point of view most useful to be labelled.

In this decade, deep neural networks are the probably most popular and most quickly developing kind of both classification and regression models. With them, semi-supervised learning has so far used only the most common variants of both the approach starting from the labelled data and the one starting from clustering the unlabelled data, the latter especially if also the clustering is performed by deep networks, in particular autoencoders. Research into more sophisticated methods of semi-supervised learning is in the context of deep neural networks only at the very beginning. Particularly desirable would be to exploit the ability of deep networks to extract new, more relevant features in the course of learning. The importance of research into semi-supervised learning of deep neural networks is a consequence of the fact that deep learning needs large amounts of data. This importance is especially high in areas where obtaining labelles is difficult, einther because they have to be obtained experimentally, or because it requires a time-consuming involvement of a human expert, such as in the areas of sentiment analysis, network intrusion detection, or malware detection. The last mentioned area is the intended application domain of the proposed research. The PhD co-advisor works as an expert on deep learning in malware detection for the Avast company.

Holub Jan, prof. Ing., Ph.D.

Data Indexing for Bioinformatics

Level
Topic of dissertation thesis
Topic description

The goal of the project is to find more efficient methods for storing and indexing data for various tasks in Bioinformatics.

Natural Language Compression

Level
Topic of dissertation thesis
Topic description

The goal of the project is to find more efficient methods for compression of natural language using a partial knowledge of the language.

XML data compression

Level
Topic of dissertation thesis
Topic description

The goal of the project is to find more efficient methods for storing data in XML format. Those methods should have to provide easy and fast access to stored data as well.

Janeček Jan, doc. Ing., CSc.

Addressing algorithms in IoT systems

Level
Topic of dissertation thesis
Topic description

Significantly developing area of IoT (Internet of Things) has to respect standards of addressing in Internet, wireless access channels and unambiguous standard identifications of elements in control and IoT technologies. The research solutions can rely on the P2P-based address database the former solution based on the Chord DHT technology. The aim of the research area is the design and verification of method that ensure communication in IoT systems with mobile elements.

Advanced methods for behavioural analysis and management of computer networks

Level
Topic of dissertation thesis
Topic description

Specialist supervisor: Ing. Jan Fesl, Ph.D.

The current computer networks create an environment for large-scale distributed applications (banking, government) and for a large number of web services and social networks. The demands of individual areas on the quality of computer network behavior are significantly different. The role of computer network management mechanisms is to ensure, with more or less stable transmission capacities, the provision of high-quality and secure services to important users, even with high service dynamics such as social networks and the need to protect the network from attacks on its operability. The aim of the dissertation thesis is to design and analyze the methods allowing to detect behavior of different users and regulate behavior of key elements of the network based on information about data flows through individual lines of computer network and based on their analysis based on artificial intelligence methods.

Applications' development for IoT systems

Level
Topic of dissertation thesis
Topic description

Significantly increasing area of IoT (Internet of Things) places different requirements on programs' development and modification of their mobile and not always directly accessible elements. Technologies satisfactory in static distributed systems, in simple applications of the client/server category, and in the clouds computing, cease to be sufficient. The aim of the research area is the design and verification of implementation tools needed to support IoT applications.

Dection methods of computer networks misusage

Level
Topic of dissertation thesis
Topic description

Specialist supervisor: Ing. Jan Fesl, Ph.D.

Large-scale computer networks create an environment for a large number of new web / cloud / distributed applications that typically start with some constraints on the location and load of the network infrastructure and the slowly increasing demand for the necessary transmission capacity. In contrast, technologies designed to damage the capabilities of computer networks typically have the character of the largest possible distribution of processes and very fast launching of the attack.

The aim of the thesis is to design and test mechanisms based on machine learning algorithms capable of identifying possible offensive features in data flow classes and managing key network elements to minimize the effects of an attack.

Dynamism in distributed applications

Level
Topic of dissertation thesis
Topic description

The main goal of the project is to solve the problem of increasing changes of the elements´ position localities in distributed applications that are including sensors, remotely controled elements and clients´ devices of the users.

Effective support for distributed applications with microcontrollers

Level
Topic of dissertation thesis
Topic description

The goal of the project is to increase effectiveness of the access to servers´ data and of the distribution of commands to controlled elements. The solution of the project may start with the WSDL file we obtain for SOAP applications with WSDL servers, which generally require high power of computation. We developed and published (IEEE and IASTED conferences) based in the group of finite state automata able to work at small microcontrollers. The goal of the current project could be started with development of the solution able to reduce computation power of sensors and controlled elements, and the required communication capacity as well

Networking Named Areas

Level
Topic of dissertation thesis
Topic description

The CCA networks create an Internet technology of the future, which is supporting multiple and mobile servers, or generally distributed application processes. The goal of the project is a design and efficiency evaluation of application limited networks based on CCN (Content-Centric Networking) models.

Janoušek Jan, doc. Ing., Ph.D.

Processing Tree Data Structures

Level
Topic of dissertation thesis
Topic description

Ordered and unordered trees. Pattern matching, indexing and other types of processing. Processing exactly and approximately.

Jiřina Marcel, doc. RNDr. Ing., Ph.D.

Analysis of Accuracy of Depth Maps from Multiple Sensors

Level
Topic of dissertation thesis
Topic description

Depth maps are images where the intensity of the brightness corresponds to the distance. The accuracy of determining the distance is depends on the target distance. Generally speaking, the closer an object, the higher is the accuracy and vice versa. Simultaneously, distant objects are represented by a smaller number of pixels and thus the accuracy of the resolution is lower.

The aim is to explore the possibility of increasing the accuracy of the depth maps using two or more sensors. The research should focus on the analysis of different mutual arrangement of sensors to one another. The results should be well-founded on the scientific analysis and new algorithms that will refine the desired depth maps based on the gained knowledge should be designed.

Klán Petr, doc. Ing. Mgr., CSc.

User interface in virtual reality

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Topic of dissertation thesis
Topic description

Specialist supervisor: Ing. et Ing. Marek Polčák

The main aim of the thesis is to design and implement an intuitive controller for virtual reality (VR). The controller will be designed according to information obtained from research focused on possibilities of VR controller that use hands, voice and eye tracking input.

Hand controller will use 3D hand models that will be based on image recognition, either using already existing algorithms or newly developed ones. For voice control, native Windows 10 support will be used. Eye tracking data will be obtained from XTAL eye-tracking system, which will provide real-time information about the user‘s sight.

Virtual worlds and their interaction with the real environment

Level
Topic of dissertation thesis
Topic description

The goal is to develop virtual environments that allow to understand the real environment. For example, modeling of extreme situations (eg, fires, aircraft crashes, traffic accidents, avalanche accidents, etc.), saving lives (emergency service, resuscitation, operations, etc.), extraterrestrial and space living (work for NASA, cosmonautics), digital immortality, prevention of violent social phenomena, improvement of cognitive abilities, education for computer thinking (formulation, algorithmization and problem solving), realization of adrenaline experiences etc.

Another goal is to develop virtual environments that allow to perform activities that we normally perform in a real environment. Examples include controlling appliances, withdrawing cash at an ATM, shopping, making payments, conducting conferences, seminars, searching information, publishing research results, working meetings, etc.

Kroha Petr, prof. Dr. Ing., CSc.

Analysis and modelling of technological systems with a consideration of further software development

Level
Topic of dissertation thesis
Topic description

In the areas where software is only a part of a project being worked out, the prototype or the first version of given software product is usually diametrically different from its ideal final form and there are often neither financial nor time resources to refactor the product into that form. This fact has negative consequences in the system testability, its sustainability, extendibility and even in end-user experience, usability and functionality and as a result it makes the system or the whole project more costly or prefigures its failure. One of the reasons of the situation is a chronic lack of the insight to the structure of the software solution, nonexistence of sufficient expressional tools to catch the idea behind and inadequate concentration to the software part of the project in its complexity. The situation is not different in the technological sector where there is almost always a great deal with development, testing and certification of a hardware part but the software one frequently is a stumbling block of the whole development process.

This dissertation thesis goal is an analysis and modeling of needs and requirements for software in technological and scientific areas, evaluation of its heterogeneity and complexity and it will make an effort to develop procedures, expressional and programming tools which can contribute to reduce the negative phenomena mentioned above.

Kubátová Hana, doc. Ing., CSc.

Anomaly detection and mitigation in computer and IoT networks

Level
Topic of dissertation thesis
Topic description

Specialist supervisor: Ing. Tomáš Čejka, Ph.D.

The aim of this work will be research and development of algorithms for detection, identification, and mitigation of security threats and anomalies in computer networks, especially Internet of Things (IoT). From the perspective of network security, it is necessary to consider the IoT area as a threat not only for IoT devices but also for other devices and services in the Internet, since the IoT network can easily become a source of trouble (as it was observed in recent history). It is necessary to monitor IoT network traffic, derive meta information about the traffic based on events and device behavior, and use such meta information for identification and mitigation of threats, choosing a suitable mitigation strategy.

The goal of the dissertation thesis will be to find suitable ways to create long-term models of communication, which represent negative and positive events, and to use such models for anomaly detection, identification and mitigation of sources of troubles with low latency. Dissertability of this topic is based on non-trivial challenges such as processing and filtering huge volume of network traffic and creation of models of network traffic, discovering anomalies, identification of sources of trouble and choosing correct strategy of mitigation of malicious traffic. Contrary to classic IP networks, the IoT communication lays mainly in specific physical layers. This brings new potential attack vectors that are not detectable using ordinary IP traffic analysis. Therefore, it is needed to find new approaches to IoT traffic monitoring. The base of the work will be a research in the area of statistical methods, probabilistic models, and usage of algorithms of artificial intelligence.

Due to the modern networks bandwidth and on-line monitoring requirements, it is necessary to design and develop algorithms using a decomposition among hardware and software components and to use suitable technologies for hardware acceleration (e.g., FPGA).

Dependability models and reliability parameters’ computation with respect to realistic properties of modeled systems

Level
Topic of dissertation thesis
Topic description

Specialist supervisor: Ing. Martin Kohlík, Ph.D.

Currently used dependability models are often based on simplified processes leading to unrealistic estimations of dependability and reliability parameters of the modeled systems [1] or rough pessimistic estimations [2]. The aim of the research should be a methodology of the dependability model design allowing fast and accurate calculations of the dependability parameters of the system. The methodology should take over-time changes (e.g. aging, maintenance, repairs) of the system, the structure (blocks and their fail-safes) of the system, and the ways of acquiring dependability parameter data from real applications into account [3].

Literature
  • Electronic Reliability Design Handbook - MIL-HDBK-338B. US Department of Defense, 1998.
  • M. Kohlík, "Hierarchical Dependability Models Based on Markov Chains", Dissertation thesis, Czech Technical University in Prague, 2015.
  • M. Daňhel, "Prediction and Analysis of Mission Critical Systems Dependability", Dissertation thesis, Czech Technical University in Prague, 2018.

Design methodology of dependable fault-tolerant and attack resistant systems

Level
Topic of dissertation thesis
Topic description

The research of methods and processes in the design of systems with pre-defined reliability parameters using programmable hardware sources (FPGA, processors). The reaserch of the impact of a redundancy at different levels (space, time, software, hardware) to the attack resistance of the whole system. The research of automatization methods of the design processes including the dependability models construction and dependability parameters computations. Partial results and proposed methods will be evaluated by real-life application and benchmarks.

Formalization and automatization of digital system design methods

Level
Topic of dissertation thesis
Topic description

The research area will be based on formal methods and models (Petri Nets, Markov chains, UML diagrams) to use them for simplificationa and automatization of digital design process. The connection of verification methods and dependability modeling at all design periods to obtain an optimized structure according different parameters is assumed. The aim should be the combination of different models and detailed study of their relations and possible automatic conversions . Partial results and proposed methods will be evaluated by real-life applications and benchmarks.

New architectures with guaranteed level of dependability parameters for reconfigurable circuits

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Topic of dissertation thesis
Topic description

Specialist supervisor: Ing. Pavel Kubalík, Ph.D.

Main aims of this research are:

- Design of new architectures based on on-line error control codes available for implementation in reconfigurable hardware (FPGAs). The fulfillment of required reliable parameters is essential, together with a low area overhead, appropriate operational frequency, and low power consumption for mission-critical systems (intelligent cars, robots, etc.) where FPGA are more and more used due to their properties (and price).

- Proposal of appropriate methods to automatically select the best type of fault-tolerance and safety with respect to a particular application, its requirements and the necessary constrains, including design speed.

- Utilization of existing models and modifying them to solve this problem at the system level, and linking them to the hierarchical dependability models, created at the department.

The target platform will be FPGA circuits allowing fault recovery of a part design or completely change of an implemented function. The research will be mainly focused on the utilization of new error control codes for an optimized architecture enabled error detection and correction suitable for FPGA. The standard known fault-tolerant structures such as TMR or a duplex will be taken into account, too.

All proposed architectures and the partial results will be tested on standard known benchmarks and on dedicated sets of own circuits. The evaluation criterion will be mainly focused on realistic calculations of dependable parameters, together with low area occupation requirements.

Research of Dependability Improvement at ISA Level

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Topic of dissertation thesis
Topic description

The proposed research has to verify possibilities, how to achieve (and guarantee) predefined system design constraints (size, power-consumption, dependability characteristics, fault-tolerance and attack-resistance levels via trade-off between hardware and software parts and types.

It is supposed to use Codasip system and RISC-V processor (open-source hardware instruction set architecture (ISA) based on established reduced instruction set computer (RISC) principles, designed (not only) for embedded applications, where low-power and performance are emphasized. Codasip can provide their processors with high-level tools that enable to configure the RISC-V cores, to automatically generate toolchains and synthesizable, human-readable Verilog RTL. Codasip system (https://codasip.com/) offers a verification environment, too.

The research will contain proper evaluation of obtained architecture by improved dependability models. The possible methods will be ISA improvement (e.g. adding the cryptographic instructions, specialized block, using of more application-specific processors). Final experiments will be performed by simulations and FPGA implementations.T

Kůrková Věra, RNDr., DrSc.

Robustness of learning of deep and shallow networks

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Topic of dissertation thesis
Topic description

Deep networks became state-of-the-art performance on a variety of pattern-recognition tasks, most notably visual classiffication ones. Several recent studies revealed that some deep networks can be easily fooled by changing an image in a way imperceptible to humans which can cause a deep network to label the image as something else entirely. Thus it is important to investigate robustness of learning of networks with respect to adversarial patterns.

The goal of the dissertation is to design multiobjective algorithms generating imperceptible perturbations of images leading to their misclassifications. Evolutionary algorithms for generation of adversarial patterns will be developed that maximize the error of the model while being as similar to original training patterns as possible. The role of depth of networks and locality properties of computational units in misclassiffication will be analyzed. Methods of making neural networks more robust to such adversarial patterns will be investigated.

Lhotská Lenka, doc. Ing., CSc.

Anomaly detection in long time series

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Topic of dissertation thesis
Topic description

A distant observation (or "outlier") is one that differs significantly from other elements in the sample where it occurs. The term "anomaly" refers to different problems in different areas. It is very important to detect anomalies as soon as possible to avoid major losses or problems, whether it is a machine failure, a tumor in the human body, etc.

In most areas, the collected / measured data has the character of streamed time series. Due to their inherent properties, such as periodicity, trends, seasonality and irregularity, accurate anomaly detection is a big problem. In addition, in most real scenarios, it is virtually impossible to annotate huge amounts of data. Therefore, unsupervised learning methods such as clustering are often used. However, they do not take into account the time parameter, which is an inseparable context in time series. Therefore, they do not allow to find anomalies that occur in cycles.

The aim of the work will be to design, implement and test a method of unsupervised learning, which will take into account the context, seasonality and trends in the detection of anomalies. The method should be adaptable for different scenarios and cases and able to process data from different areas.

Contextual information in machine learning

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Topic of dissertation thesis
Topic description

Big data raises a number of questions, the most important of which are the recognition and proper use of different types of dependencies, as well as the context, that usually carries useful information. A typical example is the spatial arrangement of data sources, such as sensors for collecting environmental data in different environments. This context, together with the measured time series, represents a very important source of information for output prediction.

In machine learning tasks a key role is played by feature selection. In many cases, feature selection is often more complicated than identifying a single subset of input variables that would together explain the output. There may be interactions that depend on contextual information, i.e. variables that reveal to be relevant only in some specific circumstances. The basic problem to be solved is the identification of the input variables whose relevance or irrelevance for predicting the output only holds in specific circumstances, where these circumstances are assumed to be encoded by a specific context variable. This context variable can be for example a standard input variable, in which case, the goal of contextual analyses is to better understand how this variable interacts with the other inputs for predicting the output. The context can also be an external variable that does not belong to the original inputs but that may nevertheless affect their relevance with respect to the output.

The aim of the work will be to design, implement and test a method of supervised learning, which will use contextual information for more efficient output prediction. The method should be adaptable for different scenarios and cases and able to process data from different areas.

Lórencz Róbert, prof. Ing., CSc.

Algebraic cryptanalysis

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Topic of dissertation thesis
Topic description

Cryptanalysis is an important part of cryptology dealing with cipher security. One of the most basic methods of cryptanalysis working with pairs of plaintext and ciphertext is the algebraic cryptanalysis. Methods of algebraic cryptanalysis can provide early detection of weaknesses of existing or newly created ciphers.

The topic of the thesis is the study and design of cryptanalytic methods based on Gröbner bases, which are suitable for parallel implementation. The aim of this work should be an efficient cryptanalytic method usable for security testing of different types of ciphers.

Combined attacks on cryptographic modules

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Topic of dissertation thesis
Topic description

Specialist supervisor: Ing. Jiří Buček, Ph.D.

In the field of hardware cryptographic devices, there is a continual competition between the development of new attacks and, on the contrary, defenses against them. An attack usually aims to reveal secret information, such as a secret symmetric key, a private key, or a message. One of the relatively new attack approaches is a combination of passive and active attacks on cryptographic devices.

The aim of this work is to explore new possibilities of combination of active and passive physical attacks with knowledge of linear, differential or algebraic cryptanalysis.

Cryptocurrency algorithms

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Topic of dissertation thesis
Topic description

Cryptocurrencies are a new phenomenon that is based on decentralization and allows us to make anonymous payments. Today, there are over a thousand cryptocurrencies that are based on different concepts such as proof-of-work or proof-of-stake.

The goal will be to design a new cryptocurrency that would meet both security and market requirements such as scalability, sufficient transaction processing speed, low latency, and would be environmentally friendly.

Dedicated Hardware for Modular Arithmetic

Level
Topic of dissertation thesis
Topic description

The aim is the design and implementation of dedicated hardware architectures for computing modular arithmetic operations. The results are applicable in elliptic curve cryptography, as well as in other systems that utilize modular arithmetic.

Malware detection

Level
Topic of dissertation thesis
Topic description

Malicious code or malware is one of the biggest security threats today. A huge amount of new malicious code is generated every day, and since it is not possible to analyze each sample separately, it is necessary to develop automatic mechanisms to detect it.

Machine learning algorithms turn out to be a useful tool for automatic detection of malware. With them, zero-day malware can also be detected, but in contrast to standard procedures such as signature-based detection, they achieve higher false positive (FP) ratio. The aim of the dissertation will be to develop an automatic malware detection system that achieves a solid classification accuracy and has a minimum FP.

Mixed-radix conversion (MRC) algorithm for converting results from a system of linear congruences into a system of linear equations

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Topic of dissertation thesis
Topic description

The solution of an integer system of linear equations (SLE) without rounding errors can be done by dividing the solving process into systems of linear congruences (SLC), and then converting the results into a set of solutions of the original SLE. The so-called MRC algorithm is used for this conversion, which has the complexity O(nm2), where n is the matrix dimension and m is the number of SLK (modules) used.

The aim of this work is to find a more efficient way of using the MRC algorithm that benefits from the knowledge of mutual data dependency of the SLE solution. It is also possible to design a parallelization of the newly designed algorithm. The result is an MRC-based method with less than O(nm2) complexity for solving the conversion process of SLC results to SLE results.

Modeling behavior of semiconductor components due to ionizing radiation

Level
Topic of dissertation thesis
Topic description

The behavior of various semiconductor circuits is also dependent, among other factors, on the environment in which they operate. Desirable information for users of various HW devices is the reliability of these devices on age, and to some extent the associated resistance of the semiconductor components to ionizing radiation.

The topic of the dissertation is mathematical modeling of the behavior of HW semiconductor components at various technological levels, depending on irradiation with ionizing/particulate radiation. The aim of this work is to create a model of HW device behavior including aging factors and material degradation due to radiation. The results will be useful for determining the reliability/error-free lifetime of circuitry exposed to radiation or long-term use.

Post-quantum cryptography

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Topic of dissertation thesis
Topic description

The study of suitable post-quantum cryptosystems has long been in the interest of cryptologists. The reason for this is the thriving field of quantum computer technology, which could endanger the security of asymmetric cryptosystems by using suitable factorization algorithms.

The topic of the dissertation is the study and analysis of existing and new methods of post-quantum cryptographic algorithms. The goal is to create an asymmetric cryptosystem that is resistant against quantum-based attacks and is simple to implement and secure.

One of the candidates for post-quantum cryptosystems suitable for analysis and eventual improvement is the McEliece asymmetric encryption algorithm based on binary Goppa codes. This algorithm complies with the security requirements for asymmetric cryptosystems of today, but there is a problem with its large spatial complexity. Trying to reduce the size of the keys in this algorithm can be a good initial challenge for further research.

Study of the behavior of physically unclonable functions (PUFs) and true random number generators (TRNGs)

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Topic of dissertation thesis
Topic description

A quality TRNG is essential for current hardware components of cryptographic. Reliable key generators based on PUF are also required. Such key generation is very much in demand, because the key generated in this way remains the "secret" of the cryptosystem hardware itself.

The topic of the dissertation is the study of the proposed PUF and TRNG in terms of their long-term stable response. The aim of this work is to explore existing and propose new PUF and TRNG solutions that are suitable for long-term generation of high-quality output by TRNG and which also guarantee stable key generation based on PUF responses. The work includes the study and understanding of the behavior of these components at the statistical level and also at the physical/technological level.

Novák Ondřej, prof. Ing., CSc.

Effective test pattern compression methods

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Topic of dissertation thesis
Topic description

Test pattern are generated in ATPG systems. The patterns contain don´t care values. The existence of don´t cares together with similarity of the conseqent patterns could be used in test pattern compression algorithms. The work is aimed to use previously introduced compression algorithm COMPAS created in ITE department of the TU in Liberec. The optimalization could be done in MATLAB and C environment.

Linear finite automata for compression of partially defined patterns

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Topic of dissertation thesis
Topic description

The solvers of linear equations systems can be used for further improvement of the designed linear finite automata quality according to specific requirements. This type of automaton is used for test pattern compression and decompression. The important feature of it is that it can change two states that represent partially defined patterns within a given number of clock cycles. LFSRs with appropriate characteristic polynomials are usually used for the decompression of patterns mentioned above. We suppose that new stuctures of finite automata can be found with the help of the solvers. The properties of the automata could be tailored to the given pattern characteristics.

Novotný Martin, Dr.-Ing.

Cryptographic/Cryptanalytical Architectures in Embedded Systems and Reconfigurable Hardware

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Topic of dissertation thesis
Topic description

Research in methods of implementation and acceleration of cryptologic operations and schemes in embedded systems and in reconfigurable hardware, namely in field-programmable gate arrays.

Pergl Robert, doc. Ing., Ph.D.

Evolvable systems research

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Topic of dissertation thesis
Topic description

Specialist supervisor: prof. Jan Verelst

The topic of evolvability and long-term sustainability is today a big challenge for several engineering disciplines. With increasing complexity of artefacts and systems, their management, maintenance and further development are becoming complicated, economically demanding and, sometimes, systems become gradually insustainable with major negative impact on enterprises and society.

The Normalized Systems Theory (NS) developed at the University of Antwerp (UA) formulates principles for construction of evolvable systems, i.e., long-term sustainable ones. This general theory has been already applied in a domain of enterprise software systems, where it achieves convincing results in the form of dozens of large-scale projects with high evolvability. The research at UA also focuses on applying the theory to other domains such as logistics and IT outsourcing.

Under this topic, students have an opportunity to deal with this interesting and perspective topic in cooperation with UA. It may be formally taken as a double-degree PhD (a preferred variant). The research work is focused mainly on applying the NS theory to other domains and elaborations in the current domains. The expected research results are on the theoretical level (a methodological framework), as well as on the technical level, e.g. in the form of software solutions and tools prototypes. An emphasis is also given to practical application of the results.

Research of conceptual modelling in the context of informatics and software engineering

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Topic of dissertation thesis
Topic description

Conceptual modeling is a traditional discipline that offers a variety of methods and notations for modelling systems at different levels of abstraction, from so-called ontological modelling that seeks to accurately capture reality to modelling technical artefacts and systems. These models can then be used to capture structures, processes and rules, perform verification, validation, simulation, reasoning, to transform models and to generate code, documents, and other artefacts from them. Engineering techniques in informatics and software engineering based on conceptual models increase efficiency, mitigate errors and open up other possibilities such as semantic interoperability, improved application of artificial intelligence and FAIR (Findable-Accessible-Interoperable-Reusable) approaches.

The topic of the research is the advancement of knowledge and applications in this field, which we focus on, also in cooperation with top European and world institutions within CIAO! Enterprise Engineering Network. Research and development of approaches based on conceptual modelling is also one of the priorities of the ELIXIR CZ infrastructure in which CTU is involved and FIT plays an active role. We are also working closely with GO FAIR, and there is a high demand for the research results from practice.

Pokorný Jaroslav, prof. RNDr., CSc.

Query Optimization on a Graph Database

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Topic of dissertation thesis
Topic description

Specialist supervisor: Ing. Michal Valenta, Ph.D.

The main aim of the thesis is to research the techniques of query optimization over the graph database in the form of an attribute oriented graph and also with regard to the specifics of a particular graph and typical queries over it. A suitable application domain is data lineage analysis.

Another aim of the thesis is research and design of methods for dynamic data distribution in distributed graph database based on analysis of queries over this database.

Ratschan Stefan, doc. Dipl.-Ing. Dr. techn.

Constraint Solving

Level
Topic of dissertation thesis
Topic description

We are developing software, theory, and algorithms that can solve mathematical constraints consisting of non-linear equalities and inequalities, conjunctions, disjunctions, and logical quantifiers. We apply the constraint solving technology in several areas (e.g., verification of complex systems, computation of Lyapunov functions), and offer a large range of thesis topics in the area. Details can be found here: http://www.cs.cas.cz/~ratschan/theses/en/constraint_solving.html

Executable Specification

Level
Topic of dissertation thesis
Topic description

One of the big drawbacks of classical methods for specifying software requirements is that it is difficult to check the consistency and adequacy of a given specification. The software engineering community has come up with many remedies to this situation (e.g., model based software engineering, agile software development, executable algebraic specification) that usually aim at allowing the user to see the actual behavior of a given specification as early as possible in the development process, but that make compromises in terms of modeling power or precision. In the thesis, the student will design an executable formal specification language that fulfills two seemingly contradicting objectives:

• High modeling capabilities

• Efficient execution

The key to achieving both objectives at the same time will be the design of annotations for the specification language that provide information necessary of efficient execution. Increasing user efforts in writing annotations will result in increasing efficiency of execution of the given specification. In a first step, we will develop a tool that will serve for educational purposes, allowing students to execute specifications they write as assignments.

Verification of Complex Systems

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Topic of dissertation thesis
Topic description

The Pendolino train from Prague to Ostrava had severe problems in its initial phase: from time to time it stopped in the middle of nowhere due to a software bug. Similar bugs have been the cause of more severe incidents, for example the crash of the Ariane 5 rocket in 1996. We are doing research that will help to avoid such problems in the future, and offer a large range of thesis topics in the area. Details can be found here: http://www.cs.cas.cz/~ratschan/theses/en/verification.html

Schmidt Jan, doc. Ing., Ph.D.

Advanced logic synthesis

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Topic of dissertation thesis
Topic description

Several studies conducted by our group show that recent combinational logic synthesis algorithms can be divided into the following groups:

1. Classic, "textbook" methods based on minimization and decomposition. Thanks to uniform Boolean function representation (by normal forms or decision diagrams), the quality of the result does not depend on the size or style of input representation. These methods, however, are not scalable; they fail on large circuits even if proportionally more computation power is provided.

2. Scalable methods, based of successive transformation of input circuit description into resulting circuit description. These methods work even on large circuits. If the input description is very different from optimum, the algorithm is unable to span that distance with successive transformations and produces poor results. The size of the input description (measured in, say, literals) strongly correlates with the size of the output circuit (measured e.g. in gates). Even industrial systems exhibit this undesirable property.

We study ways to have the best of both: scalability and independent performance. There are multiple directions to explore: randomized algorithms, alternative ways to construct scalable algorithms, more "clever" or "long-distance" transformations, etc.

This is a broad topic, which may be covered by several works. The topic "Extension of AIG Circuit Description to XOR Gate Support",announced by Petr Fišer, is a more focused effort in the same direction.

Implicit methods in compressed test generation

Level
Topic of dissertation thesis
Topic description

Existing test compression methods require precomputed tests as their inputs. If the test generator accepts constraints on input signal values, the entire set of vectors detecting a given fault can be processed at once and the subset conforming the constraints can be selected. Using the overlapping vector methods, we obtain a compressed test.

The explicit representation of a vector set by enumeration leads to a high computational complexity. We can specify the set as all vectors satisfying a Boolean formula. To manipulate such sets, Boolean algebra can be used, and a SAT solver would convert the formula to an explicit representation.

There is more similar applications in the general testing area. It is a question where implicit representation are useful, in what manner they shall be employed and what sort of algorithms utilizes them best. The answer is the topic of the work.

Reconfiguration-Based Fault Tolerant Systems

Level
Topic of dissertation thesis
Topic description

Field Programmable Gate Arrays (FPGAs) offer substantial logistic advantages over traditional application-specific circuits, especially in small production numbers. Thus, they are an attractive way to diminish the cost and design time of embedded systems. For safety-critical applications, however, design methods are lacking.

The gist of this work is to design a (set of) architecture(s) and associated methods for the construction of dependable FPGA-based systems. Several areas have to be covered: reconfiguration algorithms, reliability modeling, state synchronization.

Reliable FPGA architectures

Level
Topic of dissertation thesis
Topic description

Field Programmable Gate Arrays (FPGAs) offer substantial logistic advantages over traditional application-specific circuits, expecially in small production numbers. Thus, they are an attractive way to diminish the cost and design time of embedded systems. To construct a highly reliable circuit for safety-critical applications using an FPGA, however, is quite difficult, as the design environment (the FPGA itself) has been designed for an ordinary/grade dependability (consumer or industrial).

The "right" way to correct this is of course to design an intrinsically reliable FPGA with features targeted for such an application. In academicresearch, we cannot hope to find somewhere the hundreds of person-years necessary to design a production-grade competitive FPGA chip. There is, however, a way to contribute much in this direction.

The VPR system [1][2][3] has been designed for experiments with FPGA architecture, targeting resource utilization, function density etc. There are "virtual FPGA chips" available, i.e. FPGA models where VPR is able to place and route any design. These models are detailed enough even for the transistor-level fault models to be utilized. This way, any devised reliable FPGA architecture can be evaluated with respect to environmental factors. In contrast to a full-fledged industrial chip, manufacuting of a proof-of-concept chip or demonstrator is well possible in the framework of the IMEC Eurochip programme or elsewhere.

Literature
  • Vaughn Betz, Jonathan Rose and Alexander (Sandy) Marquardt: Architecture and CAD for Deep-Submicron FPGAs. ISBN 0-7923-8460-1. Kluwer, 2000
  • Vaughn Betz and Jonathan Rose: VPR: A New Packing, Placement and Routing Tool for FPGA Research. 1997 International Workshop on Field Programmable Logic and Applications, Springer, 1997
  • University of Toronto: VPR and T-VPack 5.0.2: Full CAD Flow for Heterogeneous FPGAs. http://www.eecg.utoronto.ca/vpr/

Šimeček Ivan, doc. Ing., Ph.D.

New methods for powder diffraction dataautoindexing for modern architectures

Level
Topic of dissertation thesis
Topic description

X-ray structure analysis from powder data is an important analytic method for crystal structure determination of samples not offering suitable single crystals. The indexation process is one of the critical bottlenecks for this method usage. Without determination of the crystal lattice parameters the structure solution cannot be done. Existing methods for indexation have problems with low quality data as well as with indexation of phase mixtures. The target of our work is to develop algorithm solving such situation by the help of more computational power expensive algorithms. Efficiency of algorithms should be also improved by parallel implementation.

Numerical linear algebra for GPU clusters

Level
Topic of dissertation thesis
Topic description

Numerical linear algebra for GPU clusters using MPI (Message Passing Interface) library.

Optimization of parallel queues on contemporary systems with distributed shared memory and cache hierarchies

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Topic of dissertation thesis
Topic description

Specialist supervisor: Ing. Daniel Langr, Ph.D.

Contemporary multiprocessor and multicore systems are enhanced with hierarchically structured cache memories. Substantial amount of parallel applications can be designed as a set of sequential code segments synchronized by a parallel queue – a data structure with concurrent access. Performance of parallel access to the queue data structures is limited by an architecture and characteristics of the cache hierarchy. The aim of the research is to investigate the behavior of parallel algorithms, dependency of their parallel performance on the cache hierarchy architecture and related optimizations.

Skopal Tomáš, prof. RNDr., Ph.D.

Similarity Search in Big Data

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Topic of dissertation thesis
Topic description

The complexity of next-generation retrieval systems originates from the requirement to organize massive and ever growing volumes of heterogeneous data and meta-data, together with the need to provide distributed management prevalently based on similarity matching. The problem starts with data acquisition of weakly structured or completely unstructured data, such as images and video, which necessarily need innovative techniques for information extraction and classification to increase their findability. In principle, we consider the object findability and the actual search process as two fundamental and synergic aspects of the retrieval. Both of them pose effectiveness and efficiency challenges which need innovative theories and technologies, and must be studied together to converge to qualitatively new retrieval tools of the future. The dissertation topic is foundational in nature as it addresses the theoretical limits of similarity retrieval in context of the Big Data problem. Fundamental to the thesis is the development of scalable solutions.

Smotlacha Vladimír, RNDr. Ing., Ph.D.

Stable frequency and time transfer in optical network

Level
Topic of dissertation thesis
Topic description

Although optical networks are primarily used for data transmissions, they currently provide a suitable and perspective platform for other applications, including the transmission of a highly stable frequency and accurate time from atomic clocks to distances of up to thousands of kilometers. The advantage of the optical line is that it allows simultaneous bidirectional transmission in the same fiber and thus the interfering effects can be compensated for, as they apply equally in both directions. The used methods of time and frequency transmission work on different network layers, the most accurate of them on the link and physical layer. The student will have to get acquainted in detail with existing methods, measure and evaluate their parameters. The research will be focused on the design of new protocols, methods of encoding transmitted information and carrier signal modulation with the goal to improve accuracy and reliability of the transmitted time and frequency. This is a complex topic that is interdisciplinary and includes computer networks and programming of FPGA circuits with a possible overlap in the design of electronic circuits and possibly opto-electronic circuits.

Starosta Štěpán, doc. Ing., Ph.D.

Effective algorithms for symbolic dynamical systems

Level
Topic of dissertation thesis
Topic description

Symbolic dynamical systems are dynamical systems over sets of infinite sequences (words) with a simple mapping - the shift. A special subset of these systems are systems generated by S-adic systems or by multidimensional continued fractions. Many algorithms dealing with the combinatorial properties of such systems are exponential in the length of the analyzed finite word (factor). The goal of the thesis is to design effective data structures and algorithms analyzing such systems and their properties, such as: enumeration factors of given length, recognizability related properties, modelling of the related numeration system, testing the speed convergence of the multidimensional continued fraction algorithm etc.

Surynek Pavel, doc. RNDr., Ph.D.

Automated Planning for Robotic Agents

Level
Topic of dissertation thesis
Topic description

In automated planning, the task is to find a sequence of actions that after execution generate a specific goal [1,2]. Planning takes place at the abstract level where the planning world in which the task takes place is described using sets of logical atoms. The actions change the planning world using set operations assuming certain preconditions are satisfied. Unlike general planning where actions can in principle change the planning world arbitrarily, planning for robotic agents assumes than actions are performed by one or multiple robotic agents while the topology of the planning world plays a role. The robotic agents are assumed to be localized within the planning world. That is, the limited reach of robots’ effectors, the need to move to the place of action, and occupation of space by other agents [3,4] need to be taken into account. The open question is the design of centralized algorithms for efficient and robust planning considering both a single-robot case and multiple cooperating robotic agents.

Literature
  • [1] Malik Ghallab, Dana S. Nau, Paolo Traverso: Automated Planning and Acting. Cambridge University Press 2016, ISBN 978-1-107-03727-4
  • [2] Malik Ghallab, Dana S. Nau, Paolo Traverso: Automated planning - theory and practice. Elsevier 2004, ISBN 978-1-55860-856-6, pp. I-XXVIII, 1-635
  • [3] Ron Alterovitz, Sven Koenig, Maxim Likhachev: Robot Planning in the Real World: Research Challenges and Opportunities. AI Magazine 37(2): 76-84 (2016
  • [4] Yawen Deng, Yiwen Hua, Nils Napp, Kirstin Petersen: A Compiler for Scalable Construction by the TERMES Robot Collective. Robotics Auton. Syst. 121 (2019)

Multi-Robot Motion Planning and Execution

Level
Topic of dissertation thesis
Topic description

Motion planning in robotics is one of the key steps to execute an abstract plan represented as a sequence of actions through the physical acting of the robot in the environment [1,2]. The motion plan maps to each continuous moment the spatial setting of the individual parts and effectors of the robot, the so-called configuration. Motion planning techniques typically work with a continuous multi-dimensional space of all possible robot configurations, where finding a motion plan corresponds to finding a continuous trajectory in the configuration space [1,3]. The research challenge is represented by motion planning for a multi-robot case with a potential for spatial collision between robots. The size of the joint configuration space increases with the increasing number of robots, and hence finding non-colliding trajectories is more difficult [4]. An interesting question is also the movement of real robots and the response to possible failure of one or more robots.

Literature
  • [1] M. LaValle, S.: Planning Algorithms. Cambridge University Press, 2006
  • [2] Malik Ghallab, Dana S. Nau, Paolo Traverso: Automated Planning and Acting. Cambridge University Press 2016, ISBN 978-1-107-03727-4
  • [3] Choset, H., Lynch, K. M., Hutchinson, S., Kantor, G., Burgard, W., Kavraki, L. E., Thrun, S.: Principles of Robot Motion: Theory, Algorithms, and Implementations. MIT Press, 2005
  • [4] Duong Le, Erion Plaku: Cooperative, Dynamics-based, and Abstraction-Guided Multi-robot Motion Planning. J. Artif. Intell. Res. 63: 361-390 (2018)

Solving Problems in Artificial Intelligence by Reduction to Satisfiability

Level
Topic of dissertation thesis
Topic description

Research in this topic will focus on finding solutions to selected problems in artificial intelligence by reduction to satisfiability [1]. Problems representing suitable candidates for this approach include domain-dependent planning (such path-finding, robot planning) [2], scheduling, circuit design, or combinatorial tasks. The concept of satisfiability is understood here in the broader sense, that is, not only as the basic propositional satisfiability (SAT problem), but also as constraint satisfaction [3] or a combination of satisfiability in various logic theories (SAT-modulo theory - SMT) [4].

Literature
  • Armin Biere, Marijn Heule, Hans van Maaren, Toby Walsh: Handbook of Satisfiability. Frontiers in Artificial Intelligence and Applications 185, IOS Press 2009.
  • Pavel Surynek: Time-expanded graph-based propositional encodings for makespan-optimal solving of cooperative path finding problems. Ann. Math. Artif. Intell. 81(3-4): pp. 329-375, 2017.
  • Francesca Rossi, Peter van Beek, Toby Walsh: Handbook of Constraint Programming. Foundations of Artificial Intelligence 2, Elsevier 2006.
  • Daniel Kroening, Ofer Strichman: Decision Procedures - An Algorithmic Point of View, Second Edition. Texts in Theoretical Computer Science. An EATCS Series, Springer 2016.

Techniques and Algorithms for Multi-agent Path Finding

Level
Topic of dissertation thesis
Topic description

The aim of the research is to design new concepts and related solving algorithms for Multi-Agent Path Finding [1]. The basic variant of the MAPF problem takes place on the undirected graph with agents placed in vertices. We are searching paths for individual agents so that each agent can reache its goal from the specified starting position by following its path and does not collide with any other agent. Contemporary solving techniques for MAPF include a variety of methods ranging from sub-optimal polynomial algorithms [2] through optimal algorithms based on state space search [3] to transformations into different formalisms such as SAT [4]. The open questions offer, in particular, generalized variants of MAPF, where we consider more complex avoidance, maintenance of formations, connectivity maintenance or other global properties (a property that includes all agents), generalized cost functions, or multiple independent (adversarial) teams of agents.

Literature
  • David Silver, “Cooperative pathfinding,” Proceedings of AIIDE, pp. 117–122, 2005.
  • Boris de Wilde, Adriaan ter Mors, Cees Witteveen: Push and Rotate: a Complete Multi-agent Pathfinding Algorithm. J. Artif. Intell. Res. 51: pp. 443-492, 2014.
  • Guni Sharon, Roni Stern, Meir Goldenberg, Ariel Felner: The increasing cost tree search for optimal multi-agent pathfinding. Artif. Intell. 195, pp. 470-495, 2013.
  • Pavel Surynek, Ariel Felner, Roni Stern, Eli Boyarski: Efficient SAT Approach to Multi-Agent Path Finding Under the Sum of Costs Objective. Proceedings of ECAI, pp. 810-818, 2016.

Tvrdík Pavel, prof. Ing., CSc.

Formats and algorithms for elastic distributed matrices

Level
Topic of dissertation thesis
Topic description

Currently, there are several libraries of algorithms that allow to solve large sparse systems of linear equations on massively parallel supercomputers and clusters. Coefficient matrices are distributed and mapped among the memories of processing nodes of these computers where their nonzero elements are stored in sparse matrix storage formats (SMSFs). We have surveyed the state-of-the-art in SMSFs in article Langr, Tvrdik: Evaluation Criteria for Sparse Matrix Storage Formats. IEEE Trans. Parallel Distrib. Syst. 27(2): 428-440 (2016). In most applications the size of matrices during the computation remains fixed. However, there are classes of problems in which the set of equation during the computation changes dynamically. For example, the set of equations is expanded by adding new variables and by adding the corresponding new rows of coefficients into the coefficient matrix or reversely by removing some rows of the coefficient matrix. In case of a symmetric matrix this implies adding or removing of matrix columns. This process of expansion and reduction during a parallel iterative computation can repeat.

The goal of the dissertation thesis is the research of space efficient SMSFs and efficient a scalable methods for handling elastic sparse matrices. The goal is to design and verify by implementation good scalable mapping functions for elastic matrices on multiprocessor systems with distributed memory, inner SMSFs, and algorithms for remapping of expanding or reducing matrices. The work on this project is supported with 5year project OP VVV CRI and students get access and will work on the most powerful supercomputers of the world.

Parallel algorithms for optimization of sparse matrix storage formats

Level
Topic of dissertation thesis
Topic description

Sparse matrices are stored in computer memory in coordinate (COO) or compressed sparse row (CSR) formats. Except for these basic simple formats, a number of further sparse matrix storage formats (SMSF) were developed. We have surveyed the state-of-the-art in SMSFs in article Langr, Tvrdik: Evaluation Criteria for Sparse Matrix Storage Formats. IEEE Trans. Parallel Distrib. Syst. 27(2): 428-440 (2016). Suitability and properties of individual SMSF for various classes of scientific computing depend on the structure of sparse matrices. In order to minimize, e.g., the space complexity of storing a very large sparse matrix on a parallel computer with shared or distributed memory, it is useful to transform the sparse matrices from those basic SMSFs into more space-efficient block-based SMSFs. This typically means a decomposition into as dense as possible square submatrices of the same or close size. The SMSF transformation task is to be solved for big sparse matrices distributed across memories of computing nodes of a parallel system. The computations with sparse matrices are typically iterative and require several communication passes through distributed data representing the structure of a sparse matrix. Their key algorithmic component is sorting. The optimization of the block size requires repeated parallel sorting of data defining the sparse matrix structure by various keys.

The goal of the dissertation thesis is the research of parallel algorithms for efficient and scalable transformations of big sparse matrices in the basic SMSFs into block-based ones. The work on this project is supported with 5year project OP VVV CRI and students get access and will work on the most powerful supercomputers of the world.

Parallel and distributed algorithms for very large sparse matrices

Level
Topic of dissertation thesis
Topic description

Specialist supervisor: Ing. Daniel Langr, Ph.D.

Modern supercomputers are characterized by continuous growth of the computing performance due to increasing number of cores per processor and higher integration, while the memory capacity per node tends to stagnate. For high-performance computing (HPC) on modern supercomuters this technology trends imply that that the memory capacity and latency does not scale with the growth of the computational performance. High-performance simulations and modelling lead to huge sparse matrices that for the reasons above do not squeze into the main memory of supercomuters. The dissertation thesis focuses on the research of new algorithms and representation of such huge sparse matrices suitable for processing on supercomputers and clusters. Possible research topics include paralel optimization heuristics for on-the-fly computation of the matrix elements without the need to store them on the disks and into the memory, heuristics for dynamic storing of computationaly most demanding elements, or heuristics for load balancing of these optimized computations on CPU cores or computational nodes. This research is supported by the OP VVV grant Research Center of Informatics.

Vitvar Tomáš, doc. Ing., Ph.D.

Operations Data Science: Machine learning on massive operational data from systems infrastructures

Level
Topic of dissertation thesis
Topic description

System infrastructures including operating systems, networking, web servers, load balancers, application servers, etc. produce massive amounts of operations data during systems runtime. Operations data science aims to improve various machine learning methods to help understand patterns, correlations or similarities from the past systems’ behavior in order to improve operations practice, for example, systems maintenance, changes in systems design or root cause analysis of incidents. This includes predicting systems behaviors when unplanned or unexpected events occur such as connectivity lost, failures of servers or spikes in systems workloads. This research will improve supervised and unsupervised learning methods tailored for problems from operational data analytics and cover a wide spectrum of underlying machine learning tasks including classification, clustering or regression.

Semantic Annotation for Web Services

Level
Topic of dissertation thesis
Topic description

Semantic Annotation for Web Services

Web Service Discovery using methods of Collective Intelligence

Level
Topic of dissertation thesis
Topic description

Web Service Discovery using methods of Collective Intelligence