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Muscle noise filtering in ECG signals
Novotný, Jiří ; Kubičková, Alena (referee) ; Smital, Lukáš (advisor)
This master's thesis deals with the optimization of numerical coefficients of the Wiener filter for muscle noise filtering in ECG signals. The theoretical part deals with ECG signal characteristic and muscle interference. It also contains a summary of the wavelet transform, wavelet Wiener's filtration, methods for calculating of the threshold and thresholding. In the last theoretical part the characteristic optimization techniques, the exhausive search and Nelder-Mead simplex method are mentioned, which were implemented in the practical part of this thesis in MATLAB. The functional verification and Wiener's filter optimization were tested on the standard electrocardiograms database CSE. By using the methods of exhausive search, the initial estimate for the solution method Nelder-Mead was obtained. The optimization method Nelder-Mead gives better results in the orders of hundredths or tenths than the method of exhausive search. The practical part is finished by the comparison of results of implemented algorithm with optimum coefficients, implemented in this thesis, with the results of other methods for filtering muscle interference in ECG signals.
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Boundary effects in signal processing: From classical problems to new opportunities
Popoola, Seyi James ; Druckmüllerová, Hana (referee) ; Cicone, Antonio (advisor)
Tato práce se zaměřuje na hraniční problémy při rozkladu signálů. Problematika hranic klasických metod byla rozsáhle studována, ale v posledních několika desetiletích byla představena nová generace metod. Implementace těchto nových metod má potenciál produkovat efektivním způsobem přesnější, flexibilnější a interpretovatelné výsledky, které mohou pomoci posunout výzkum v reálných aplikacích v různých oblastech. Hraniční problémy pro tyto techniky byly studovány teprve nedávno. Dosud zveřejněné výsledky ukazují, že tyto metody mají svá omezení a předpoklady, které je třeba pečlivě zvážit, aby se předešlo možnému zneužití. V této studii identifikujeme a řešíme hlavní překážky spojené s používáním těchto nových metod a poskytujeme také doporučení, jak je co nejefektivněji využít. Abychom dále ilustrovali možné důsledky nesprávného použití, provádíme komplexní zkoumání jejich aplikace na skutečná data a provádíme numerické simulace. Nakonec navrhujeme soubor osvědčených postupů pro optimalizaci výkonu těchto technik v kontextu rozkladu signálu. Zásadním návrhem je použít před aplikací jakékoli metody rozkladu techniku rozšíření signálu jako prostředek ke zmírnění hraničních efektů.
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Digital Audio Steganography
Kabelka, Petr ; Malaník, Petr (referee) ; Strnadel, Josef (advisor)
The field of steganography deals with concealing information with the goal of hiding their very existence. The goal of this work is to create a summary of existing digital audio steganography methods and implement some of them. This work presents easily extensible and usable multiplatform library for audio steganography written in the Python programming language and a program that uses it. At the end of this work are the implemented methods compared against each other and against the published methods. Because it is hard to find concrete implementations of various published methods, the presented library enables almost anyone to easily use the implemented steganographic methods and to continue working on research in this field without the need to reimplement all of them.
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Selected Aspects of Statistical Significance Testing in Time-Frequency Analysis
Klejmová, Eva ; Kohl,, Zdeněk (referee) ; Fidrmuc, Jarko (referee) ; Poměnková, Jitka (advisor)
Přeložená dizertační práce se zabývá analýzou a posouzením kvality odhadu frekvenční a časově-frekvenční transformace dat a formulaci doporučení pro práci s metodami. Při použití těchto metod vyvstává otázka, jak vyhodnotit, které složky spektrogramu jsou statisticky významné a které nikoli. V této práci analyzujeme vlastnosti standardních testů statistické významnosti. Diskutujeme o jejich výhodách a nevýhodách s ohledem na heteroskedastický charakter dat. Na základě našich experimentů jsou v práci navrženy dva typy testovacích metod, které snižují negativní aspekty standardních testů. Práce jen zakončena vytvořením rámce pro filtrování dat pomocí námi navržených metod.
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Analysis of time-frequency characteristics of signals
Vitouš, Jiří ; Ředina, Richard (referee) ; Poměnková, Jitka (advisor)
This thesis focuses on time-frequency analysis of discrete signals. The aim of this work is to compare the most well known methods for spectro/scalegram estimation. The two main topics discussed are: The compromise between time and frequency resolution and the effect of noise in input data on the quality of estimated spectrograms. To achieve this a database has been created. This database consists of real and artificial signals on which the analysis can be performed and evaluated. This database is used in created demonstration application. This application is also used in a created laboratory task.
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Forecasting electricity prices in the Czech spot market
Černý, Kryštof ; Lebovič, Michal (advisor) ; Rečka, Lukáš (referee)
This master thesis is focused on analysis and forecasting of hourly and daily electricity price on the deregulated Czech daily electricity market. The methods used for estimating and forecasting hourly and daily prices are picked from the ARIMA-GARCH family of models and Neural Networks. For daily price data, the Redundant Haar Wavelet Transform decomposition of the time series is used in combination with ARIMA and Neural Networks models for forecasting. For hourly data, ARIMA and Neural Network models are considered. The forecasting results of daily data indicate that simpler models such as seasonal ARIMA outperform all other methods. Also the wavelet decomposi- tion of the daily series didn't prove useful in enhancing the forecast precision. For hourly data, the Multilayer Perceptron architecture of the neural network outperformed the ARIMA forecast. JEL Classification C20, C22, C45, C53, C65 Keywords Forecasting, Time Series, ARIMA, GARCH, Neural Net- works, Wavelet Transform Author's e-mail krystof.cerny@gmail.com Supervisor's e-mail lebovicm@gmail.com 1
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Wavelet transform
Valter, Boris ; Hlávka, Zdeněk (advisor) ; Dvořák, Jiří (referee)
Wavelet transform is a term from signal analysis. It is mostly used in physics, but also in finance, where we can use it to find a trend in different financial data. In the first chapter we will describe two older methods of signal analysis: Fourier transform and short-time Fourier transform. In the second chapter we show, how wavelet transform works, derive frequently used algorithm for calculating discrete wavelet transform and at the end we show several practical examples. This thesis was considered to deepen the knowledge of time-frequency analysis of signals, for better understanding of the principle, how wavelet transform works, and for potential extending its use. Powered by TCPDF (www.tcpdf.org)
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Automatic detection of microcalcifications in mammogram images
Hývlová, Denisa ; Jakubíček, Roman (referee) ; Harabiš, Vratislav (advisor)
This bachelor thesis is focused on detection of microcalcification in mammography images. The introduction describes connection between their presence and breast cancer, principle of mammography and the DICOM standard used in radiology. In the following part the methods used for microcalcification enhancement and segmentation are explained. Detection algorithm based on wavelet transform, morphological closing and thresholding was designed in MATLAB. For evaluation of the results a graphical user interface was developed and an algorithm for automatic evaluation of the success rate in annotated mammography database was implemented.
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Methods for sleep spindles detection from EEG records
Matoušek, Šimon ; Mézl, Martin (referee) ; Králík, Martin (advisor)
This bachelor work focuses on the detection of sleep spindles in EEG signals. The introductory chapter deals with the EEG signal, describes its components and describes the signal recording process. Explains the term sleep spindle and clarifies polysomnography. In the following chapter, some findings concerning studies that examined and practically used individual methods of sleep spindle detection are summarized in the form of research. The practical part of the work is focused on some sleep spindle detectors. At the end of the work is a comparison of the success of these detectors in comparison with other, previously performed studies. The highest success was achieved with the detector based on signal envelope calculation, where the sensitivity was 56.00 \% and the specificity 55.19 %, and also with the detector using wavelet transforms, where the sensitivity was 81.22 % and the specificity 46.15 %
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Liveness Detection on Touchless Fingerprint Scanner
Fořtová, Kateřina ; Kanich, Ondřej (referee) ; Heidari, Mona (advisor)
This Bachelor's Thesis is focused on liveness detection of fingerprints with using touchless sensor. Work summarizes theoretical introduction to biometrics, fingerprint processing and some of present researches for liveness detection. The new approach is introduced with using Local Binary Pattern algorithm, Sobel and Laplacian operator and Wavelet transform. Artificial Neural Networks, Support Vector Machines and Decision Trees were used for final classification. Several experiments with dataset illuminated by lights with various wavelengths were realized. It was discovered, that fingerprints illuminated by red light reached the best accuracy 90.1% compared to other considered wavelenghts of visible light. The classification with vector based on Local Binary Pattern achieved average accuracy 89.8%, accuracy with vector based on Sobel and Laplacian operator was 91.5%. Several Wavelet families were used for Wavelet transform during experiments. The best accuracy achieved wavelets of Biorthogonal spline wavelet family (85.1%) and wavelets from Reverse biorthogonal spline wavelet family (86.6%).
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