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Time-scale analysis of sovereign bonds market co-movement in the EU
Šmolík, Filip ; Vácha, Lukáš (advisor) ; Krištoufek, Ladislav (referee)
The thesis analyses co-movement of 10Y sovereign bond yields of 11 EU mem- bers (Greece, Spain, Portugal, Italy, France, Germany, Netherlands, Great Britain, Belgium, Sweden and Denmark) divided into the three groups (the Core of the Eurozone, the Periphery of the Eurozone, the states outside the Eurozone). In the center of attention are changes of co-movement in the crisis period, especially near the two significant dates - the fall of Lehman Brothers (15.9.2008) and the day, when increase of Greek public deficit was announced (20.10.2009). Main contribution of the thesis is usage of alternative methodol- ogy - wavelet transformation. It allows to research how co-movement changes across scales (frequencies) and through time. Wavelet coherence is used as well as wavelet bivariate and multiple correlation. The thesis brings three main findings: (1) co-movement significantly decreased in the crisis period, but the results differ in the groups, (2) co-movement significantly differs across scales, but its heterogeneity decreased in the crisis period, (3) near to the examined dates sharp and significant decrease of wavelet correlation was observable across lower scales in some states. JEL Classification C32, C49, C58, H63 Keywords Co-movement, Wavelet Transformation, Sovereign Debt Crisis, Sovereign Bond Yields,...
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Time-scale analysis of sovereign bonds market co-movement in the EU
Šmolík, Filip ; Vácha, Lukáš (advisor) ; Krištoufek, Ladislav (referee)
The thesis analyses co-movement of 10Y sovereign bond yields of 11 EU mem- bers (Greece, Spain, Portugal, Italy, France, Germany, Netherlands, Great Britain, Belgium, Sweden and Denmark) divided into the three groups (the Core of the Eurozone, the Periphery of the Eurozone, the states outside the Eurozone). In the center of attention are changes of co-movement in the crisis period, especially near the two significant dates - the fall of Lehman Brothers (15.9.2008) and the day, when increase of Greek public deficit was announced (20.10.2009). Main contribution of the thesis is usage of alternative methodol- ogy - wavelet transformation. It allows to research how co-movement changes across scales (frequencies) and through time. Wavelet coherence is used as well as wavelet bivariate and multiple correlation. The thesis brings three main findings: (1) co-movement significantly decreased in the crisis period, but the results differ in the groups, (2) co-movement significantly differs across scales, but its heterogeneity decreased in the crisis period, (3) near to the examined dates sharp and significant decrease of wavelet correlation was observable across lower scales in some states. JEL Classification C32, C49, C58, H63 Keywords Co-movement, Wavelet Transformation, Sovereign Debt Crisis, Sovereign Bond Yields,...
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Optical Character Recognition
Juřica, Dalibor ; Bartoň, Radek (referee) ; Švub, Miroslav (advisor)
The document is discussing the issue of the computer vision with ability to character recignition in the image. Wavelet transform is used for preprocessing the image. Pixel energy feature is firstly used for searchich candidate text pixels. Density region growing method is then used to collect candidate pixels to the separate regions, which will be candidate text regions. Several of the features are calculated over the regions and the SVM classifier is used to derive, if the region is really a text region or not.
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Algorithms for segmented wavelet image transform
Kořínek, Petr ; Průša, Zdeněk (referee) ; Rajmic, Pavel (advisor)
The thesis deals with wavelet signal transform with the intention of image signal. First part of this work contains the basic methods for signal processing. Fourier transformation and principles of wavelet signal transform is discussed, the fundamentals of segmented discrete-time wavelet transform (SegDTWT) and the segmented discrete-time wavelet 2D signal transform (SegDTWT2D) derivation are described as well. Main purpose of this thesis is in the segmented discrete-time wavelet 2D signal transform (SegDTWT2D) concept and its implementation which can work with two dimension signal.
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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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Real-Time Wavelet Transform in Pure Data
Zemánek, Karel ; Schimmel, Jiří (referee) ; Rajmic, Pavel (advisor)
The bachelors thesis considers of wavelet transform realization in the real-time in Pure Data. The first part of the thesis is devoted to the theoretical minimum of wavelet transform. The second part is devoted to program Pure Data itself. You can see simply patches and also instruction for external programs compact to PD. The third part is devoted to realization of wavelet transtorm in Pure Data program. It contain specific algorithm for wavelet transform calculation and description of exist program.
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Modern Methods of MR Static Image Enhancement
Zbranek, Lukáš ; Přinosil, Jiří (referee) ; Smékal, Zdeněk (advisor)
The aim of this masters thesis is design and implement an appropriate method for highlighting MR images and the identification of rough edges to provide for division of controlled areas. To this purpose is possible to use the Wavelet analysis. For the simulation environment I using MATLAB entviroment, where introduce the comparison for different types of de-noising and too for different mother wavelets. These methods will be implemented on various MR images of termoromandibular joint.
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Analysis of sleep EEG signal
Ježek, Martin ; Kozumplík, Jiří (referee) ; Rozman, Jiří (advisor)
Cílem této práce byl vývoj programu pro automatickou detekci arousalu v signálu spánkového EEG s použitím metod časově-frekvenční analýzy. Předmětem studie bylo 13 celonočních polysomnografických nahrávek (čtyři svody EEG, EMG, EKG a EOG), tj. celkově více než 100 hodin záznamu. Jednalo se o část dat z dřívějších výzkumných prací expertní lékařky v problematice spánku Dr. Emilie Sforzy, Ženeva, Švýcarsko, která rovněž poskytla základní hodnocení těchto dat. V záznamech bylo celkem označeno 1551 arousal událostí. Pro usnadnění výběru konkrétní metody časově-frekvenční analýzy byla následně vytvořena sada nástrojů pro vizualizaci jednotlivých signálů a jejich různých časově-frekvenčních vyjádření. S ohledem na závěry vizuální analýzy, charakter signálu EEG a efektivitu výpočetních metod byla pro analýzu vybrána waveletová transformace s mateřskou vlnkou Daubechies řádu 6. Jednotlivé svody EEG byly dekomponovány do šesti frekvenčních pásem. Z takto odvozených signálů a signálu EMG byly následně stanoveny ukazatele možné přítomnosti události arousalu. Tyto ukazatele byly dále váhovány lineárním klasifikátorem, jehož hodnoty vah byly optimalizovány pomocí genetického algoritmu. Na základě hodnoty lineárního klasifikátoru bylo rozhodnuto o přítomnosti události arousalu v daném svodě EEG – arousal byl detekován, jestliže hodnota klasifikátoru překročila danou mez na dobu více než 3 a méně než 30 vteřin. V celém záznamu pak byl arousal označen, byl-li detekován alespoň v jednom ze svodů EEG. Následně byly odvozeny míry senzitivity a selektivity detekce, jež byly rovněž základem pro stanovení fitness funkce genetického algoritmu. Pro učení genetického algoritmu byly vybrány první čtyři záznamy. Na základě takto optimalizovaných vah vznikl program pro automatickou detekci, který na celém souboru 13 záznamů dosáhl ve srovnání s expertním hodnocením míry senzitivity 76,09%, selektivity 53,26% a specificity 97,66%.
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Speech denoising based on wavelet transform and voice recognition in segments
Chrápek, Tomáš ; Sysel, Petr (referee) ; Rajmic, Pavel (advisor)
The wavelet transform is a modern signal processing tool. The wavelet transform earned itself a great success mainly for its unique properties, such as the capability of recognizing very fast changes in processed signal. The theoretical part of this work is introduction to wavelet theory, more specifically wavelet types, a wavelet transform and its application in systems dealing with signal denoising. A main problem connected to speech signals denoising was introduced. The problem is degradation of the speech signal when denoising unvoiced parts. It is because of the fact that unvoiced parts and noise itself have very similar characteristics. The solution would be to apply different attitude to voiced and unvoiced segments of the speech. The main goal of this diploma thesis was to create an application implementing the speech signal denoising using the wavelet transform. The special attention should have been paid to applying different attitude to voiced and unvoiced segments of the speech. The demanded application is programmed as a grafical user interface (GUI) in MATLAB environment. The algorithm implemented in this form allows users to test introduced procedures with a great comfort. This work presents achieved results and discusses them considering general requirements posed on an application of given type. The most important conlusion of this Diploma Thesis is the fact that some kind of trade-off between sufficient signal denoising and keeping the speech understandable has to be made.
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Wavelet Transform and its Application in the Analysis of Economic and Financial Time Series
Bašta, Milan ; Arlt, Josef (advisor) ; Málek, Jiří (referee) ; Mareš, Milan (referee)
The thesis deals with a brief compilation of the theory of Fourier transform, linear filtration and a triad of wavelet transforms -- the maximal overlap discrete wavelet transform (MODWT), the discrete wavelet transform (DWT) and the continuous wavelet transform (CWT). These transforms are among others applied to the analysis of the time-varying character of variability in the time series, to the detection of events of significant changes of variability, to the removal of noise in the time series (denoising) and to the time-scale analysis of the relationship of two time series. The analyzed time series used in this thesis are the logarithm of the Garman-Klass estimate of the historical volatility, the time series of stock returns and the logarithm of the monthly inflation rate. In some cases artificial time series are analyzed. The procedures and methods introduced in the thesis might be well implemented in the analysis of other economic and financial time series. The contribution of the thesis is a brief and easy-to-use compilation of the wavelet theory and the application of the wavelet transform to such financial and economic time series, where such an analysis tool has never been applied before. New insights into the properties of time series are thus obtained, insights, which might be hardly recovered by traditional means and methods.
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