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Acceleration of Particles Tracking on CBM Experiment
Roth, Michael ; Kolář, Martin (referee) ; Musil, Petr (advisor)
The focus of this work is to research various methods of particle track reconstruction in the CBM experiment, and the problem of hardware acceleration of these methods. The advantages and disadvantages of the extended methods were discussed and a reconstruction method based on cellular automata and Kalman filters was selected for further study. In particular, the thesis details the development of a simulation model suitable for generating test data to facilitate future implementation of the selected tracking algorithm. Two different particle simulators have been developed and will be used in the following work to calculate the prediction step of the extended Kalman filter and to test the quality of the implemented reconstruction method.
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Using Cellular Automata for Data Encryption
Dvořák, Martin ; Trunda, Otakar (advisor) ; Mráz, František (referee)
Cellular automata are discrete systems with very simple rules but very diverse behaviour. Some cellular automata can generate high-quality pseudorandom bit sequences. This leads us to the question of whether cellular automata could be used in cryptography, as a replacement for stream ciphers for instance. We will create and compare various methods for generating long one-time-pads from short keys, where our methods will utilize cellular automata. Besides direct design of cryptographical algorithms, we will also create an evolutionary algorithm, which will try to connect our building blocks in the best possible way. The outcome of our work will be a Windows desktop application for file encryption. Powered by TCPDF (www.tcpdf.org)
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Spatial modeling of brain tissue
John, Pavel ; Neruda, Roman (advisor) ; Brom, Cyril (referee)
Neural connections in the human brain are known to be modified by experiences. Yet, little is known about the mechanism of the modification and its implications on the brain function. The aim of this thesis is to investigate what impact the spatial properties of brain tissue can have on learning and memory. In particular, we focus on the dendritic plasticity. We present a model where the tissue is represented by a two-dimensional grid and its structure is characterized by various connections between the grid cells. We provide a formal definition of the model and we prove it to be computational as strong as the Turing machine. An adaptation algorithm proposed enables the model to reflect the environmental feedback, while evolutionary algorithms are employed to search for a satisfactory architecture of the model. Implementation is provided and several experiments are driven to demonstrate the key properties of the model. Powered by TCPDF (www.tcpdf.org)
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Mobile robot path planning by means of cellular automata
Holoubek, Tomáš ; Šoustek, Petr (referee) ; Dvořák, Jiří (advisor)
This thesis deals with a path planning using cellular automata algorithms in a rectangular grid environment. Theoretical part starts with an overview of commonly used approaches for path planning and later on focuses on existing cellular automata solutions and capabilities in detail. Implemented cellular automata algorithms and the commonly used path planning algorithms are together with a map generator described in the practical part. Conclusion of this thesis contains results completed in a special application.
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Complexity in Cellular Automata
Hudcová, Barbora ; Mikolov, Tomáš (advisor) ; Kupsa, Michal (referee)
In order to identify complex systems capable of modeling artificial life, we study the notion of complexity within a class of dynamical systems called cellu- lar automata. We present a novel classification of cellular automata dynamics, which helps us identify interesting behavior in large automaton spaces. We give a detailed comparison of our results to previous methods of dynamics classification. In the second part of the thesis, we study the backward dynamics of cellular au- tomata. We present a novel representation of one-dimensional cellular automata, which can be used to charcterize all their garden of eden configurations. We demonstrate the usefulness of this method on examples. 1
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Self-Modifying Cellular Automata
Szabo, Peter ; Drábek, Vladimír (referee) ; Bidlo, Michal (advisor)
This work deals with cellular automata with a concept of self- modification and their comparsion against regular cellular automatons . For this task we constructed a simulator , that lets us define the logic of artificial inteligence, number generator and statistical test, which are used by the automata , on their own . Consequently two experiments are carried out that demonstrate the concept of self- modification .
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Fluid Dynamics Simulation Using Cellular Automata
Režňák, Michal ; Janoušek, Vladimír (referee) ; Peringer, Petr (advisor)
The main objective of this work was to create application for fluid flow simulation using Lattice Gas Cellular Automata. Used simulation models are HPP, FHP-I, FHP-II and FHP-III. The program is implemented in a C++ language in a way that it can run in WebAssembly web standard. Part of the work is comparison between wasm, ams.js formats and native desktop (x86_64). This shows that time for application loading in web browser is much smaller for wasm format than for asm.js and application performance in wasm format is about 24% higher than asm.js but 50% smaller than a native desktop. The application is suitable for educational purpose as a presentation of cellular automata simulation and also as an introduction to the Lattice Boltzmann method for fluid flow simulation.
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Simulation of two-dimensional flow past obstacles using lattice-gas cellular automata
Tomášik, Miroslav ; Scholtz, Martin (advisor) ; Pavelka, Michal (referee)
Cellular automata constitutes a unique approach to the modeling of complex systems. The major phase of their development in continuum mechanics came in the late 80s, but the closer inspection of their macroscopic limit revealed that it does not accurately correspond to hydrodynamic equations. Besides the Lattice-Boltzmann model, various other approaches to improve LGCA have emerged. The main focus of our research is on the Pair-interaction cellular automaton. In this thesis, we propose the non-deterministic variant of this automaton, and we compare it with its predecessor on the simulations of the "exploding cube", Taylor- Green vortex and fully developed turbulence. The results for the non-deterministic automaton seem quiet reasonable, but derivation of the hydrodynamic equations is necessary to conclude in what extent it solves the problem with anisotropic viscosity.
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Spatial modeling of brain tissue
John, Pavel ; Neruda, Roman (advisor) ; Brom, Cyril (referee)
Neural connections in the human brain are known to be modified by experiences. Yet, little is known about the mechanism of the modification and its implications on the brain function. The aim of this thesis is to investigate what impact the spatial properties of brain tissue can have on learning and memory. In particular, we focus on the dendritic plasticity. We present a model where the tissue is represented by a two-dimensional grid and its structure is characterized by various connections between the grid cells. We provide a formal definition of the model and we prove it to be computational as strong as the Turing machine. An adaptation algorithm proposed enables the model to reflect the environmental feedback, while evolutionary algorithms are employed to search for a satisfactory architecture of the model. Implementation is provided and several experiments are driven to demonstrate the key properties of the model. Powered by TCPDF (www.tcpdf.org)
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Simulace dvojrozměrného toku kolem překážek za použití "lattice-gas" celulárních automatů
Tomášik, Miroslav ; Scholtz, Martin (advisor) ; Pavelka, Michal (referee)
Cellular automata constitues original computational methods, that found its application in many disciplines. The special class of cellular automata, so called lattice gas automata were succesfull in dealing with many challenges in hydrodynamic simulations, and they bootstrap one of the most perspective CFD methods, the Lattice Boltzmann models. In the theoretical part, we follow the evolution of the lattice gas automata, explore the theory behind them, and from their microdynamics, we derive the macroscopic equations. In the practical part, we implemented two distincet types of LGCA, the pair-interaction automata and FCHC. We applied them on the flow around obstacles of various shapes. The scientifically most relevant part concerns statistical properties of the turbulent flow simmulated by LGCA, but requires further research to conclude it. Powered by TCPDF (www.tcpdf.org)
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