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Study of Biological Material Attributes by using Image Analysis Methods
Jeřábková, Petra ; Mikula, Milan (referee) ; Zmeškal, Oldřich (advisor)
Within the dissertation thesis “Study of Biological Material Attributes by Using Image Analysis Methods”, attention is focused on monitoring of the application of image analysis methods, mostly a fractal analysis, in studying the properties of various yeast species. The thesis includes determining the number of yeast cells and vegetative propagation of yeast using fractal parameters – fractal measure D and fractal dimension K. Attention is also paid not only to the application of the existing image analysis methods, but also to their renovation. The obtained images were evaluated using the box counting method specified by implementation of wavelet transformation. To monitor yeast cells for a longer time, it was first necessary to prepare a suitable microscopic preparation. To distinguish live and dead cells, the following fluorescent dyes were used: acridine orange, fluorescein diacetate, FUN-1, and Calcofluor White M2R. The images of yeast cells were recorded using a still camera or a CCD camera and microscope. Clips of the same size were obtained from the acquired digital photographs and processed by the HarFA program developed at the Faculty of Chemistry, Brno University of Technology. On the results it is possible to see a change in the fractal dimension depending on time, i.e. on the change of a budding cell structure, or to determine the number and radius of yeast cells upon predefined conditions.
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K-complex detection in sleep EEG
Bjelová, Martina ; Mézl, Martin (referee) ; Králík, Martin (advisor)
This paper addresses detecting of K-complexes in sleeping EEG records. Polysomnography is the method, which is used for diagnostic and following therapy of many sleep disorders. For identifnging of sleep stages it is fundamental to know graphoelements, in which they are situate. K-complex is important indicator of second sleep stange and hence is essencial to know to detect this pattern. In this paper we focus on design and implementation of more algorithms for detection of these patterns with various characteristics. Among the proposed methods, the wavelet transform method was best evaluated. Performance of this detection reached values the average senzitivity 63,83 % and average positive predictive value 44,07 %.
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Solution of complex problems using evolutionary algorithms
Belovič, Boris ; Atassi, Hicham (referee) ; Burget, Radim (advisor)
Difficult problems are tasks which number of possible solutions increase exponentially or factorially. Application of common mathematical methods for finding proper solution in polynomial time is ineffective. Signal prediction is an example of diffucult problem. Signal is represented with a time serie and there is no explicit mathematical formula describing the signal. When genetic algorithms are applicated, they try to discover hidden patterns in time serie. These patterns can be used for prediction. Implication rules are used for discovery of these hidden patterns in time serie. Each rule is represented by one chromosome in population. Rules consist of two parts: conditional part and result part. Rules in population are compared with time serie and then the rules are evaluated according to their success in prediction. After the evaluation of rules, simulated evolution is started. Result of this evolution process is a group of rules which represent the most distinct patterns in time serie. These rules are then validated on validation set. Application is implemented in JAVA programming language.
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Static image enhancement using wavelet transform
Candrák, Matúš ; Rajmic, Pavel (referee) ; Smékal, Zdeněk (advisor)
In tomography and ultrasound signal processing, there is the noise build-up into the processing. Bachelor's thesis deals with static images highlighting, with denoising using wavelet transformation and edge detection with basic operators. This work describes some types of wavelts used for denoising of image and basic operators for edge detection in the image. The last part deals with a particular application for image processing, which was created in MATLAB.
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ECG quality estimation
Pospíšil, Jan ; Smíšek, Radovan (referee) ; Smital, Lukáš (advisor)
This bachelor thesis deals with the question of estimation of the quality of the ECG signals, which is a key parameter for determining the diagnosis. The theoretical part deals with the basic knowledge concerning cardiac physiology, electrocardiography and finally the types of interferences that can occur during the measurement. The following practical part will deal with the published methods and the proposal of methods for estimating signal quality and their testing on artificial and real data.
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Image noise reduction method based on discrete wavelet transform
Holiš, Michal ; Přinosil, Jiří (referee) ; Malý, Jan (advisor)
This bachelor's thesis contains theoretical treatise on noises, which occur in digital visual data, their classification and possibilities of their removal. The next part is applied to theory of wavelets, wavelet transforms and their usage in working with one-dimensional, but mostly with two-dimensional signals. It is than mainly applied on visual data with a view to removing of failures contained in these data. The last part of the thesis is about implementation of demonstrational programme. This programme is created for removing of noise from the chosen visual data on the basis of user's chosen variables.
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Processing of images of early spruce needles scanned by MR technology
Raichl, Jaroslav ; Říha, Kamil (referee) ; Gescheidtová, Eva (advisor)
This semester project deals with filtering of the images detected by use of NMR obtained by NMR application measurement of nuclear magnetic resonance (NMR). This thesis includes the theory of nuclear magnetic resonance, digital filters, basic digital filter banks structures, theory of Wavelet transformation and description of Signal to Noise Ratio calculation. Basic procedure of the MR signal denoising is summarized in the theoretical part of the thesis. The denoising of the images detected by nuclear magnetic resonance is described. In experimental part filtering methods for images denoising are described, which are implemented in program Matlab. These methods are based on Wavelet transformation, digital filter banks with proper thresholding. Effectiveness of filtering methods designed was verified on 2D NMR images. All of these 2D images were measure on MR tomography in the Institute of Scientific Instruments Academy of Science of the Czech Republic in Brno.
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Evolutionary Optimization of the EEG Classifier Feature Extractor
Ovesná, Anna ; Hurta, Martin (referee) ; Mrázek, Vojtěch (advisor)
This work focuses on the optimisation of EEG signal classification of alcoholics and control subjects using evolutionary algorithms with a multi-objective approach. The main goal is to maximise the accuracy, sensitivity and specificity of the classification algorithm and minimise the number of features used. Four different classifiers are used, namely Support Vector Machine, k-nearest neighbors, Naive Bayes and AdaBoost. The selection of the best features is optimised using three different evolutionary approaches, two of which convert multi-objective optimisation to single-objective using weighted summation or restricting the maximum number of features. The Pareto optimal solutions are found by the NSGA-II algorithm. Results show that the evolutionary algorithms, combined with appropriate classifiers, reliably distinguish a person with a tendency to alcoholism from one with a healthy relationship towards alcohol.
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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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