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Dictionary learning for sparse signal reconstruction
Ozdobinski, Roman ; Rajmic, Pavel (referee) ; Mach, Václav (advisor)
This bachelor thesis discusses the dictionary learning for the reconstruction of signal based on sparse representations. There are methods of static and optimized dictionary of matrices described that are used with approximative Orthogonal Matching Pursuit algorithm to reconstruct the missing groups of samples in the audio signal. There is theoretically analyzed algorithm for learning K-SVD dictionary together with its implementation in Matlab. Furthermore, the selected dictionaries are compared to the various types of audio signals.
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Transformation of Trading Strategies in the MetaLang Language on Parallel Codes Accelerated by a Supercomputer
Halfar, Vítězslav ; Šimek, Václav (referee) ; Jaroš, Jiří (advisor)
The aim of this bachelor thesis is to design and implement a software - MetaTester, which deals with testing and optimizing of the automated trading systems made for platform MetaTrader 4. This system handles performance problems of the most widespread business platform in the world, used to trade in the biggest world market - Forex, with the parallelization of processes and IT potential of supercomputers. The thesis describes the architecture of the system, solving problems, the implementation of sectional parts and special techniques to provide the highest computing performance. At the end of the thesis, there are summarized achievements of the platforms MetaTrader and MetaTester.
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Applications of Dictionary Learning Methods for Audio Inpainting
Ozdobinski, Roman ; Rajmic, Pavel (referee) ; Mach, Václav (advisor)
This diploma thesis discusses methods of dictionary learning to inpaint missing sections in the audio signal. There was theoretically analyzed and practically used algorithms K-SVD and INK-SVD for dictionary learning. These dictionaries have been applied to the reconstruction of audio signals using OMP (Orthogonal Matching Pursuit). Furthermore, there was proposed an algorithm for selecting the stationary segments and their subsequent use as training data for K-SVD and INK-SVD. In the practical part of thesis have been observed efficiency with training set selection from whole signal compared with algorithm for stationary segmentation used. The influence of mutual coherence on the quality of reconstruction with incoherent dictionary was also studied. With created scripts for multiple testing in Matlab, there was performed comparison of these methods on genre distinct songs.
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Image extrapolation methods
Ješko, Petr ; Špiřík, Jan (referee) ; Rajmic, Pavel (advisor)
The thesis deals with addition of pixels outside the image. Lists some methods for inpainting using computers and highlights the pitfalls that appear here. Examines methods for interpolation and approximation of functions in order to find the best method for extrapolating the image beyond its borders. Describes the basics of Wavelet transformation and Multiresolution analysis. It is proposed several methods for replenishment of pixels outside the image. PSNR and SSIM are used to compare achieved results. These methods are explained and compared. Briefly discusses the algorithm OMP, falling within the sparse representation of signals, and used in one of the methods. Also discussed is the development environment of MATLAB as a tool for the implementation of algorithms that practically solves the given problem. The practical part describes the implemented methods for adding pixels outside the image.
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Optimal methods for sparse data exchange in sensor networks
Valová, Alena ; Poměnková, Jitka (referee) ; Rajmic, Pavel (advisor)
This thesis is focused on object tracking by a decentralized sensor network using fusion center-based and consensus-based distributed particle filters. The model includes clutter as well as missed detections of the object. The approach uses sparsity of global likelihood function, which, by means of appropriate sparse approximation and the suitable dictionaty selection can significantly reduce communication requirements in the decentralized sensor network. The master's thesis contains a design of exchange methods of sparse data in the sensor network and a comparison of the proposed methods in terms of accuracy and energy requirements.
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Compressive sampling for effective target tracking in a sensor network
Klimeš, Ondřej ; Veselý, Vítězslav (referee) ; Rajmic, Pavel (advisor)
The master's thesis deals with target tracking. For this a decentralized sensor network using distributed particle filter with likelihood consensus is used. This consensus is based on a sparse representation of local likelihood function in a suitable chosen dictionary. In this thesis two dictionaries are compared: the widely used Fourier dictionary and our proposed B-splines. At the same time, thanks to the sparsity of distributed data, it is possible to implement compressed sensing method. The results are compared in terms of tracking error and communication costs. The thesis also contains scripts and functions in MATLAB.
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Optimization of data representation for target tracking using sensor network
Cabalová, Klára ; Veselý, Vítězslav (referee) ; Rajmic, Pavel (advisor)
The aim of this bachelor thesis is to find optimal data representation for target tracking using sensor network. There is described a model of decentralized sensor network and also the application of so called dictionary to represent the measured data. Also, there is theoretically introduced the K-SVD algorithm that is used for dictionary learning and there are learnt dictionaries for data representation based on the model signals. These dictionaries are compared with each other.
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Image extrapolation methods
Ješko, Petr ; Špiřík, Jan (referee) ; Rajmic, Pavel (advisor)
The thesis deals with addition of pixels outside the image. Lists some methods for inpainting using computers and highlights the pitfalls that appear here. Examines methods for interpolation and approximation of functions in order to find the best method for extrapolating the image beyond its borders. Describes the basics of Wavelet transformation and Multiresolution analysis and briefly discusses about spatial filtering, edge detection and the algorithm OMP, falling within the sparse representation of signals. Theoretical knowledge of these areas are used in the design of several methods for adding pixels outside the image. PSNR and SSIM are used to compare achieved results. Also discussed is the development environment of MATLAB as a tool for the implementation of algorithms that practically solves the given problem.
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Determining the optimal patch size for sparse image representation
Šuránek, David ; Zátyik, Ján (referee) ; Špiřík, Jan (advisor)
Introduction of this thesis is dedicated to the description of basic concepts and algorithms for image processing using sparse representation. Furthermore there is mentioned neural network model called Restricted Boltzmann machine, which is in the practical part of the thesis subject of behaving observation in the task of determining the optimal block size for extrapolation using K-SVD algorithm
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Compressive sampling for effective target tracking in a sensor network
Klimeš, Ondřej ; Veselý, Vítězslav (referee) ; Rajmic, Pavel (advisor)
The master's thesis deals with target tracking. For this a decentralized sensor network using distributed particle filter with likelihood consensus is used. This consensus is based on a sparse representation of local likelihood function in a suitable chosen dictionary. In this thesis two dictionaries are compared: the widely used Fourier dictionary and our proposed B-splines. At the same time, thanks to the sparsity of distributed data, it is possible to implement compressed sensing method. The results are compared in terms of tracking error and communication costs. The thesis also contains scripts and functions in MATLAB.
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