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.

The thesis discusses the design and experimental verification of a new method for detecting partial discharges in oil-cooled high-power transformers, emphasizing the origin of the measured signal from the outer and inner regions of the transformer tank. The detection and spatial localization of partial discharges utilizes UHF (Ultra High Frequency) electromagnetic signal measurement. The UHF partial discharge detection method, also referred as UHF PD, is one of the most advanced techniques for the accurate spatial localization of the signal source position. The focus of the thesis lies in the development of technical and software solutions for the separation of partial discharge signals originating from the internal space of the transformer and the spurious signals from the connection lines. The proposed technical and program solutions constitute a signal discriminatory method. The precise and repeatable diagnostics of the transformer state are guaranteed by observing the measurement methodology and special setting of the diagnostic system. The functionality of the signal discriminatory method was verified during real measurement of the oil-cooled power transformers at the Dukovany nuclear power plant in operation. The output of the designed methodology is to set up a calibration procedure and follow-up steps to ensure objective, repeatable, and comparable measurements for the purposes of regular predictive maintenance in transformers.

Determination sex is one of the most important tasks in the identification of skeletal remains. Previous studies have shown that population differences in size and shape of femora can affect correct sex allocation. This thesis tested the discriminat functions generated from different populations and confirmed population specificity of the femur discriminant functions. Two samples of the identified adult femora for this project was used. First sample originates from the Czech population living in the 1st 20th century (78 males and 81 females) and second sample originates from the Czech population living in the 2nd 20th century (67 males and 35 females). Both samples also help us estimate the influence of secular trend of the femur'variables to the correct sex classification by discriminat functions calculated in the Czech population. The results showed the existence of the secular trend in femora size dimensions. Only the dimensions of the femoral head (vertical head diameter-F18, transverse head diameter-F19 and maximum diameter of the femoral head-MPH) and sagittal subtrochanteric diameter (F10) not chandged significantly during the 20th century. The secular trend has negatively influenced the correct sex classification, always for one subpopulation. Only the dimensions in which the secular trend...

Differences in height among human populations or changes in height over time may be associated with different degrees of sexual dimorphism in this feature. This thesis compared the height and sexual size dimorphism (SSD) between two groups of the population of Medieval Bohemia - rural residents and inhabitants of Prague. A sample of 179 adult skeletons from five Prague sites and 175 individuals from four rural sites, dating from the 11th to 14th century, was used. Sex was determined following the principle of primary and secondary sex diagnosis, which enabled the derivation of population-specific discriminant functions using the dimensions of the humerus, femur, tibia and talus. A subsequent analysis of body height did not show statistically significant differences either in height or in SSD between the Prague and the rural part of the population of Medieval Bohemia, despite the major changes which Czech Medieval society went through in the 13th century. Comparing our results with previously published data on the height of the Czech population during the early modern period indicates a statistically significant reduction in the magnitude of sexual size dimorphism in the population of the17th century, particularly associated with a decrease in the height of men. A significantly higher stature of...

Cluster and discriminant analysis belong to basic classification methods. Using cluster analysis can be a disordered group of objects organized into several internally homogeneous classes or clusters. Discriminant analysis creates knowledge based on the jurisdiction of existing classes classification rule, which can be then used for classifying units with an unknown group membership. The aim of this thesis is a comparison of discriminant analysis and different methods of cluster analysis. To reflect the distances between objects within each cluster, squeared Euclidean and Mahalanobis distances are used. In total, there are 28 datasets analyzed in this thesis. In case of leaving correlated variables in the set and applying squared Euclidean distance, Ward´s method classified objects into clusters the most successfully (42,0 %). After changing metrics on the Mahalanobis distance, the most successful method has become the furthest neighbor method (37,5 %). After removing highly correlated variables and applying methods with Euclidean metric, Ward´s method was again the most successful in classification of objects (42,0%). From the result implies that cluster analysis is more precise when excluding correlated variables than when leaving them in a dataset. The average result of discriminant analysis for data with correlated variables and also without correlated variables is 88,7 %.

Atrial fibrillation is diagnosed in 1-2% of the population, in next decades, it expects a significant increase in the number of patients with this arrhythmia in connection with the aging of the population and the higher incidence of some diseases that are considered as risk factors of atrial fibrillation. The aim of this work is to describe the problem of atrial fibrillation and the methods that allow its detection in the ECG record. In the first part of work there is a theory dealing with cardiac physiology and atrial fibrillation. There is also basic descreption of the detection of atrial fibrillation. In the practical part of work, there is described software for detection of atrial fibrillation, which is provided by BTL company. Furthermore, an atrial fibrillation detector is designed. Several parameters were selected to detect the variation of RR intervals. These are the parameters of the standard deviation, coefficient of skewness and kurtosis, coefficient of variation, root mean square of the successive differences, normalized absolute deviation, normalized absolute difference, median absolute deviation and entropy. Three different classification models were used: support vector machine (SVM), k-nearest neighbor (KNN) and discriminant analysis classification. The SVM classification model achieves the best results. Results of success indicators (sensitivity: 67.1%; specificity: 97.0%; F-measure: 66.8%; accuracy: 92.9%).

This diploma thesis deals with the analysis of experimental electrograms (EG) recorded from isolated rabbit hearts. The theoretical part is focused on the basic principles of electrocardiography, pathological events in ECGs, automatic classification of ECG and experimental cardiological research. The practical part deals with manual classification of individual pathological events – these results will be presented in the database of EG records, which is under developing at the Department of Biomedical Engineering at BUT nowadays. Manual scoring of data was discussed with experts. After that, the presence of pathological events within particular experimental periods was described and influence of ischemia on heart electrical activity was reviewed. In the last part, morphological parameters calculated from EG beats were statistically analised with Kruskal-Wallis and Tukey-Kramer tests and also principal component analysis (PCA) and used as classification features to classify automatically four types of the beats. Classification was realized with four approaches such as discriminant function analysis, k-Nearest Neighbours, support vector machines, and naive Bayes classifier.

The aim of my thesis Use of value engineering elements for the realization of buildings in the municipality is to compare the evaluation methods of procurement in municipality and evaluation methods using elements of value engineering. A tender for reconstruction Šubertovo square was realized in the city Dobruška. In the thesis is shown way of evaluation of competing offers of the city Dobruška itself in sublimit simplified procedure and evaluation of competing offers by selecting more evaluation criteria and selecting the best offer with the highest degree of effectiveness.

The thesis is focused on analysis of polysomnographic signals based on extraction of chosen parameters in time, frequency and time-frequency domain. The parameters are acquired from 30 seconds long segments of EEG, EMG and EOG signals recorded during different sleep stages. The parameters used for automatic classification of sleep stages are selected according to statistical analysis. The classification is realized by artificial neural networks, k-NN classifier and linear discriminant analysis. The program with a graphical user interface was created using Matlab.

Tato práce se zabývá pravděpodobnostními modely pro automatické rozpoznávání řečníka. Podrobně analyzuje zejména pravděpodobnostní lineární diskriminační analýzu (PLDA), která modeluje nízkodimenzionální reprezentace promluv ve formě \acronym{i--vektorů}.  Práce navrhuje dvě rozšíření v současnosti požívaného PLDA modelu. Nově navržený PLDA model s plným posteriorním rozložením  modeluje neurčitost při generování i--vektorů. Práce také navrhuje nový diskriminativní přístup k trénování systému pro verifikaci řečníka, který je založený na PLDA. Pokud srovnáváme původní PLDA s modelem rozšířeným o modelování  neurčitosti i--vektorů, výsledky dosažené s rozšířeným modelem dosahují až 20% relativního zlepšení při testech s krátkými nahrávkami. Pro delší  testovací segmenty  (více než jedna minuta) je zisk v přesnosti  menší, nicméně přesnost nového modelu není nikdy menší než přesnost výchozího systému.  Trénovací data jsou ale obvykle dostupná ve formě dostatečně dlouhých segmentů, proto v těchto případech použití nového modelu neposkytuje žádné výhody při trénování. Při trénování může být použit původní PLDA model a jeho rozšířená verze může být využita pro získání skóre v  případě, kdy se bude provádět testování na krátkých segmentech řeči. Diskriminativní model je založen na klasifikaci dvojic i--vektorů do dvou tříd představujících oprávněný a neoprávněný soud (target a non-target trial). Funkcionální forma pro získání skóre pro každý pár je odvozena z PLDA a trénování je založeno na logistické regresi, která minimalizuje vzájemnou entropii mezi správným označením všech soudů a pravděpodobnostním označením soudů, které navrhuje systém. Výsledky dosažené s diskriminativně trénovaným klasifikátorem jsou podobné výsledkům generativního PLDA, ale diskriminativní systém prokazuje schopnost produkovat lépe kalibrované skóre. Tato schopnost vede k lepší skutečné přesnosti na neviděné evaluační sadě, což je důležitá vlastnost pro reálné použití.