National Repository of Grey Literature 78 records found  1 - 10nextend  jump to record: Search took 0.00 seconds. 
Detecting elementary particles with Timepix3 detector
Meduna, Lukáš ; Mráz, František (advisor) ; Hric, Jan (referee)
Detecting elementary particles and observing accompanying events in particle colliders is one of the most important field of current research in experimental physics. TimePix and its successor TimePix3 are types of the currently used detectors which are placed beside other in ATLAS experiment conducted by Eu- ropean Organization for Nuclear Research. Such detectors can produce huge amount of data about passing particles at high rate. The goal of the thesis is to develop methods for detecting and classification of elementary particles observed by detector network ATLAS-TPX3. Suitable methods for clustering and/or classification based on semi-labelled data should be identified or new one should be developed. The proposed methods will be implemented and their performance on real data will be evaluated. The results will also include an implementation of framework for preprocessing low level data from detector network ATLAS-TPX3 in real-time and creating outputs that are suitable for subsequent physics investigation (e.g. ROOT framework files) includ- ing the proposed or future methods for particle classification. 5
Echo state networks and their application in time series prediction
Savčinský, Richard ; Mráz, František (advisor) ; Matzner, Filip (referee)
Recurrent neural networks (RNN) enable to model dynamical sys- tems with variable input length. Their disadvantage is in inherently difficult trai- ning which means adjusting weights of connections between neurons connected in the network. Echo state networks (ESN) are a special type of RNN which are by contrast trainable rather simply. They include a reservoir of neurons whose state reflect the history of all signals in the network and that is why this type of network is suitable for simulation and prediction of time series. To maximize the computational power of ESN, very precise adjusting and experimenting are required. Because of that, we have created a tool for building and testing such networks. We have implemented a time series forecasting task for the purpose of examination of our tool. We have focused on stock price prediction, which repre- sents an uncertain and complicated area for achieving precise results in. We have compared our tool to other tools and it was comparably successful. 1
Echo state networks and their application in time series prediction
Savčinský, Richard ; Mráz, František (advisor) ; Matzner, Filip (referee)
Recurrent neural networks (RNN) enable to model dynamical sys- tems with variable input length. Their disadvantage is in inherently difficult trai- ning which means adjusting weights of connections between neurons connected in the network. Echo state networks (ESN) are a special type of RNN which are by contrast trainable rather simply. They include a reservoir of neurons whose state reflect the history of all signals in the network and that is why this type of network is suitable for simulation and prediction of time series. To maximize the computational power of ESN, very precise adjusting and experimenting are required. Because of that, we have created a tool for building and testing such networks. We have implemented a time series forecasting task for the purpose of examination of our tool. We have focused on stock price prediction, which repre- sents an uncertain and complicated area for achieving precise results in. We have compared our tool to other tools and it was comparably successful.
Klasifikace na množinách bodů v 3D
Střelský, Jakub ; Mráz, František (advisor) ; Šikudová, Elena (referee)
Increasing interest for classification of 3D geometrical data has led to discov- ery of PointNet, which is a neural network architecture capable of processing un- ordered point sets. This thesis explores several methods of utilizing conventional point features within PointNet and their impact on classification. Classification performance of the presented methods was experimentally evaluated and com- pared with a baseline PointNet model on four different datasets. The results of the experiments suggest that some of the considered features can improve clas- sification effectiveness of PointNet on difficult datasets with objects that are not aligned into canonical orientation. In particular, the well known spin image rep- resentations can be employed successfully and reliably within PointNet. Further- more, a feature-based alternative to spatial transformer, which is a sub-network of PointNet responsible for aligning misaligned objects into canonical orientation, have been introduced. Additional experiments demonstrate that the alternative might be competitive with spatial transformer on challenging datasets. 1
Comparison of signature-based and semantic similarity models
Kovalčík, Gregor ; Lokoč, Jakub (advisor) ; Mráz, František (referee)
Content-based image retrieval and similarity search has been investigated for several decades with many different approaches proposed. This thesis fo- cuses on a comparison of two orthogonal similarity models on two different im- age retrieval tasks. More specifically, traditional image representation models based on feature signatures are compared with models based on state-of-the-art deep convolutional neural networks. Query-by-example benchmarking and tar- get browsing tasks were selected for the comparison. In a thorough experimental evaluation, we confirm that models based on deep convolutional neural networks outperform the traditional models. However, in the target browsing scenario, we show that the traditional models could still represent an effective option. We have also implemented a feature signature extractor into the OpenCV library in order to make the source codes available for the image retrieval and computer vision community. 1
Using Cellular Automata for Data Compression
Polák, Marek ; Trunda, Otakar (advisor) ; Mráz, František (referee)
In this thesis we research the possibilities of using cellular automata for lossless data compression. We describe the classification of cellular automata and their current usage. We study the properties of various types of elementary cellular automata (i.e. Wolfram rules), describe their equivalence classes, the ways of forward as well as backward simulation, we examine the rules with interesting behavior. The states provided by these rules are evaluated in terms of their orderliness (e.g. the ratio of living cells or approximation of entropy). We implement some standard compression algorithms and compare them in terms of usability for best rated states. By application of acquired knowledge we propose a new compression algorithm, test it on text and image data and compare the results with traditional compression algorithms. Powered by TCPDF (www.tcpdf.org)
Computational Intelligence for Financial Market Prediction
Řeha, Filip ; Pilát, Martin (advisor) ; Mráz, František (referee)
Financial markets are characterized by uncertainty, which is associated with the future progress of world economics and corporations. The ability of an individual to forecast future market behaviour at least to a certain extent would give him an important competitive advantage on the market. The aim of this work is to explore neural networks and genetic programming as possible tools which could be used for financial markets forecasting and apply them on historical financial data. Experiments using neural networks and genetic programming were performed and the results show, that both tools can be employed successfully. On average, neural networks outperformed genetic programming in our experiments. In order to evaluate and visualize the results of our created strategies, the MarketForecaster application was implemented. Powered by TCPDF (www.tcpdf.org)
Genetic programming in Swift for human-competitive evolution
Mánek, Petr ; Mráz, František (advisor) ; Gemrot, Jakub (referee)
Imitating the process of natural selection, evolutionary algorithms have shown to be efficient search techniques for optimization and machine learning in poorly understood and irregular spaces. In this thesis, we implement a library containing essential implementation of such algorithms in recently unveiled programming language Swift. The result is a lightweight framework compatible with Linux- based computing clusters as well as mobile devices. Such wide range of supported platforms allows for successful application even in situations, where signals from various sensors have to be acquired and processed independently of other devices. In addition, thanks to Swift's minimalistic and functional syntax, the implementation of bundled algorithms and their sample usage clearly demonstrates fundamentals of genetic programming, making the work usable in teaching and quick prototyping of evolutionary algorithms. Powered by TCPDF (www.tcpdf.org)
Artificial Player for Hearthstone Card Game
Ohman, Ľubomír ; Gemrot, Jakub (advisor) ; Mráz, František (referee)
The goal of this work was to create an artificial agent that is able to learn how to play Hearthstone with given deck of cards. We decided to use Q-learning algorithm to achieve it. The side effect of this work is the transformation of the simple simulator of Hearthstone into the framework for developing Artificial Intelligence in this game. For the purpose of training and evaluation, commonly played strategies served us as inspiration for the testing agents that we developed. This work contains comparison of the table representation of Q-function and the neural network approximation of it. The original goal was fulfilled partially. We were successful in the creation of the learning agent but he is only able to learn one specific strategy.
Efficient video retrieval using complex sketches and exploration based on semantic descriptors
Blažek, Adam ; Lokoč, Jakub (advisor) ; Mráz, František (referee)
This thesis focuses on novel video retrieval scenarios. More particularly, we aim at the Known-item Search scenario wherein users search for a short video segment known either visually or by a textual description. The scenario assumes that there is no ideal query example available. Our former known- item search tool relying on color feature signatures is extended with major enhancements. Namely, we introduce a multi-modal sketching tool, the exploration of video content with semantic descriptors derived from deep convolutional networks, new browsing/visualization methods and two orthogonal approaches for textual search. The proposed approaches are embodied in our video retrieval tool Enhanced Sketch-based Video Browser (ESBVB). To evaluate ESBVB performance, we participated in international competitions comparing our tool with the state-of-the-art approaches. Repeatedly, our tool outperformed the other methods. Furthermore, we show in our user study that even novice users are able to effectively employ ESBVB capabilities to search and browse known video clips. Powered by TCPDF (www.tcpdf.org)

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