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Presented master's thesis deals with detection of brain wakefulness from scalp EEG data with higher order statistics. Part of the thesis is a description of electroencephalography, from the method of signal generation, sensing, electroencephraphy, EEG signal artifacts, frequency bands of EEG signal to its possible processing. Furthermore, the concept of mental fatigue and the possibility of its detection in the EEG signal is described. Subsequently, the principles of higher statistical methods of PCA and ICA and the specific possibilities of decomposition of EEG signal are described using these methods, from which the method of group spatial-frequency ICA was chosen as a suitable method for selection of partial oscillatory sources in EEG signal. In the next part there is described a method of acquisition of data, a the suggestion of solution with selected method and a description of the implemented algorithm, that was applied to real 256-lead scalp EEG data captured during a block task focused on subject allertnes. The absolute and relative power of the EEG signal was decomposed. From the achieved results, we observe that the fluctuations of the spatial frequency patterns of relative power (especially for theta and alpha bands) significantly more closely correspond with the change of reaction time and the error of the subjects performing the task. These observations appear to be relatively consistent with previously published literature, and the current study shows that spatial frequency ICA is able to blindly isolate space-frequency patterns whose fluctuations are statistically significantly correlated with parameters (reaction time, error rate) directly flowing from the given task.

This master's thesis focuses on detection of pathological vertebrae in spinal CT utilized by machine learning. Theoretical part describes anatomy of the spine and occurrence of pathologies in CT image data, contains an overview of existing methods intended for automated detection of pathological vertebrae. Practical part devotes to design a computer aided detection systems to identify pathological vertebrae and to classify a type of pathology. Designed classification system is based on using neural network, which performs classification step and on principal component analysis (PCA), which is used to reducing the original number of observation features. For completing this task were used real data. Conclusion contains evaluation of obtained results.

Rentgenová počítačová tomografie je metoda sloužící ke 3D zobrazování vnitřní struktury objektů. Mikrostruktura objektů ukrývá důležité informace, které mohou být použity k jejich charakterizaci. Tato práce podává spojení mezi datasety získanými pomocí rentgenové počítačové mikrotomografie a oblastí statistického zpracování dat. Výstupem metody, pak bude klasifikace vzorků na základě informací o jejich mikrostruktuře. Z výsledků klasifikace vzorků, pak můžeme vyvodit různé hypotézy týkající se původu vzorků. Tato práce by mimo jiné mohla sloužit jako takový nový vhled do problematiky kombinace dat různého původu, pomocí metod statistické analýzy.

The diploma thesis deals with sensory evaluation of selected types of meat products, specifically sausages. Different types of sausages, which contained added chemicals and spices, were compared. The theoretical part describes the properties of meat and meat products and the basic technological procedures used for their processing and production. Furthermore, added substances and spices, which are often considered as flavorings or preservatives, were characterized. The principles of correct performance of sensory analysis and sensory perception of food were also presented, which include a description of the sensory workplace, sample preparation, its implementation, procedure and various evaluation methods used. The experimental part describes the specific work tools used and the conditions under which the evaluation took place. A total of 12 types of sausages were evaluated, 7 of which came from meat production and 5 were prepared in the laboratory of food chemistry at Brno University of Technology. The differences between the samples of sausages, which differed in content and type of added chemicals and spices, were clearly shown using ray graphs. Then the Grubbs test was used, which excluded outliers from the final evaluation. These values were further excluded from further evaluation. The Kruskall-Wallis test was used to distribute the resulting mean values and to determine whether there was a statistically significant difference in sensory evaluation between the results. An appendix to the Kruskall-Wallis test Dunn's test was used to divide the resulting values into given groups according to statistically significant difference and similarity. The analysis of the main components of the so-called PCA was used to find the differences and similarities of the samples included in the groups.

La classification et le regroupement des données fonctionnelles longitudinales ont fait beaucoup de progrès dans les dernières années. Plusieurs méthodes ont été proposées et ont démontré des résultats prometteurs. Pour ce mémoire, on a comparé le comportement des algorithmes de partitionnement sur un ensemble de données décrivant les trajectoires de voitures dans une intersection de Montréal. La motivation est qu’il est coûteux et long de faire la classification manuellement et on démontre dans cet ouvrage qu’il est possible d’obtenir des prédictions adéquates avec les différents algorithmes. Parmi les méthodes utilisées, la méthode distclust utilise l’approche des K-moyennes avec une notion de distance entre les courbes fonctionnelles. On utilise aussi une classification par mélange de densité gaussienne, mclust. Ces deux approches n’étant pas conçues uniquement pour le problème de classification fonctionnelle, on a donc également appliqué des méthodes fonctionnelles spécifiques au problème : fitfclust, funmbclust, funclust et funHDDC. On démontre que les résultats du partitionnement et de la prédiction obtenus par ces approches sont comparables à ceux obtenus par ceux basés sur la distance. Les méthodes fonctionnelles sont préférables, car elles permettent d’utiliser des critères de sélection objectifs comme le AIC et le BIC. On peut donc éviter d’utiliser une partition préétablie pour valider la qualité des algorithmes, et ainsi laisser les données parler d’elles-mêmes. Finalement, on obtient des estimations détaillées de la structure fonctionnelle des courbes, comme sur l’impact de la réduction de données avec une analyse en composantes principales fonctionnelles multivariées. / The study of the clustering of functional data has made a lot of progress in the last couple of years. Multiple methods have been proposed and the respective analysis has shown their eÿciency with some benchmark studies. The objective of this Master’s thesis is to compare those clustering algorithms with datasets from traÿc at an intersection of Montreal. The idea behind this is that the manual classification of these data sets is time-consuming. We show that it is possible to obtain adequate clustering and prediction results with several algorithms. One of the methods that we discussed is distclust : a distance-based algorithm that uses a K-means approach. We will also use a Gaussian mixture density clustering method known as mclust. Although those two techniques are quite e˙ective, they are multi-purpose clustering methods, therefore not tailored to the functional case. With that in mind, we apply four functional clustering methods : fitfclust, funmbclust, funclust, and funHDDC. Our results show that there is no loss in the quality of the clustering between the afore-mentioned functional methods and the multi-purpose ones. We prefer to use the functional ones because they provide a detailed estimation of the functional structure of the trajectory curves. One notable detail is the impact of a dimension reduction done with multivari-ate functional principal components analysis. Furthermore, we can use objective selection criteria such as the AIC and the BIC, and avoid using cluster quality indices that use a pre-existing classification of the data.

The project consists of two distinct levels i.e. separation level and diagnostic level. At the separation level, statistical models of gaussians and color are separately used to classify each pixel as belonging to backgroung or foreground. Adopted method is mixture of gaussians.A mixture of gaussians model is suitable here because the results of the picture tests will not depend on the lens opening, but rather on the colors in the backgroung. A mixture of gaussians model for return data seems reasonable. The achieved results the used method on the real sequences are presented in the thesis. Diagnostic level is identified human body on the scene. Adopted method is ASM(Active Shape Models) with PCA(Principal Component Analysis). ASM are statistical models of the shape of human bodies which iteratively deform to fit to an example of the object in a new image.

The resume of this master´s thesis is to introduce reader into principal component analysis (PCA), namely, the use of PCA for analysis of ECG. This method allows to reduce quantity of the data without loss of useful information. That is why PCA is widespread for preprocessing of the data for further classification, which this thesis also deals. Data available at the Department of Biomedical Engineering at the University of Technology in Brno were used in this work. All the methods were realized using Matlab.

he thesis deals with proposal of an algorithm and implementation of a program that tracks a motion of the heart muscle in the captured ultrasound video of the heart. The point position estimation is calculated by optical flow method. The Active Shape Model method is used to confirm the accuracy of point's position tracking. The user annotates desired structure of the heart arch first and the application displays new points which represent a new deformed heart shape.

This document introduces the reader to area of face recognition. Miscellaneous methods are mentioned and categorized to be able to understand the process of face recognition. Main focus of this document is on issues of current face recognition and possibilities do solve these inconveniences in order to be able to massively spread face recognition. The second part of this work is focused on implementation of selected methods, which are Linear Discriminant Analysis and Principal Component Analysis. Those methods are compared to each other and results are given at the end of work.