The aim of the thesis was to find out whether NIR spectroscopy is a suitable method for adulteration control of cow and goat milk. Also to which concentration level the method is still effective was sought. Theoretical part summarizes information about the requirements for goat milk, its composition and processing possibilities. It also contains information about food adulteration and the relevant legislation. Infrared spectroscopy is theoretically described, particularly NIR spectroscopy. The experimental part describes the procedure of qualitative analysis using NIR spectroscopy, the method of evaluation and its results. Different samples of goat milk adulterated with various proportions of cow milk were compared. Results were evaluated using discriminant analysis. NIR spectroscopy has been demonstrated as a method suitable for detecting goat milk adulteration.

This mater thesis is focused on building predictive models. Their fundamental task is to provide an early-warning system, giving information about potential enterprise bankruptcy. The main essence and aim of the thesis is to create multivariate classification models by using discriminant analysis and logistic regression. Emphasis is put on their predictive accuracy, which is assessed for period of three years before bankruptcy declaration. Attempts to optimize classification thresholds in order to increase the initial accuracy are also made. Evaluating classification reliability of several existing models and performing profile analysis assessing predictive ability of univariate ratios were accomplished as well.

The theoretical part of this master thesis deals with creation and solution of financial distress and analysing classification models. In the practical part I defined own methods for financial distress prediction of company using discriminant analysis and logistic regression.

The thesis deals with selected classification methods. The thesis describes the basis of cluster analysis, discriminant analysis and theory of classification trees. The usage is demonstrated by classification of simulated data, the calculation is made in the program STATISTICA. In practical part of the thesis there is the comparison of the methods for classification of real data files of various extent. Classification methods are used for solving of the real task – prediction of air pollution based of the weather forecast.

This research is focused on "swimmer's shoulder" and on affection of chosen aspects by kinesiotape. Theoretical part describes a summary of knowledge about origin, diagnostic and treatment of swimmer's shoulder. Kinesiotaping as a nowadays treatment method and its effects are also specified. The thesis provides a brief description of chosen aspects (pain, tactile acuity and proprioception) and their measuring by objectification methods. Practical part deals with measuring, evaluation and comparing of mentioned aspects before and after treatment by kinesiotape. Methods: 20 competitive swimmers (average age 18,3 years, SD ± 3,10) with shoulder pain were chosen for investigating and measuring. Pain was observed by visual analog scale (VAS) and pressure algometry, tactile acuity by esthesiomether and proprioception by a special method, all before and after treatment. A control group without kinesiotape was included to the research. Results: The experiment showed that tactile acuity was altered in patients with kinesiotape by 27,5 mm in average. This result was evaluated as statistically significant (p = 0,0023). Although values of pain measured by VAS and pressure algometry were enhanced, they were not very significant on the importace level 0,05 (p = 0,1540, resp. 0,1575). Proprioception was also...

This thesis deals with the increasingly developing field of biometric systems which is the identification of faces. The thesis deals with the possibilities of face localization in pictures and their normalization, which is necessary due to external influences and the influence of different scanning techniques. It describes various techniques of localization of dominant features of the face such as eyes, mouth or nose. Not least, it describes different approaches to the identification of faces. Furthermore a it deals with an implementation of the Dominant Face Features Recognition application, which demonstrates chosen methods for localization of the dominant features (Hough Transform for Circles, localization of mouth using the location of the eyes) and for identification of a face (Linear Discriminant Analysis, Kernel Discriminant Analysis). The last part of the thesis contains a summary of achieved results and a discussion.

Absolute numerousness judgement (ANJ) is numerical competence in which the concrete number is discriminated. ANJ hasn't been tested in pigeons (Columba livia) yet. Therefore six individuals were tested in discrimination of number "three" from ratio 2 vs. 3: two pigeons on "touch screen" and all six by opening small bowls that were closed by discriminated stimulus. Pigeons didn't reach required level of 70 % (they answered with success approx. 30 - 65 %) in any testing environment. Mixed presentation was created such as control of size, possition and shape of discrimination stimulus. It was also studied wheather pigeons used alternative strategies. Success in ANJ was compared with previous discrimination tasks (Kocourková, 2016) in both environment (discrimination of reduced black and white stimulus on the cap and discrimination of round-shaped black and white areas in the Skinner box). If was found out that pigeons weren't able to learn in the same condition discrimination 2 vs. 3 stimulus during the same number of trials which they needed in the previous discrimination task. During answering to the stimulus they didn't use any alternative strategies with one exception. The effect of social hierarchy in the group on the performance of pigeons wasn't found out. Keywords: Columba livia, numerical...

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Classification is a very common task in information processing and important problem in many sectors of science and industry. In the case of data measured as a function of a dependent variable such as time, the most used algorithms may not pattern each of the individual shapes properly, because they are interested only in the choiced measurements. For the reason, the presented paper focuses on the specific techniques that directly address the curve clustering problem and classifying new individuals. The main goal of this work is to develop alternative methodologies through the extension to various statistical approaches, consolidate already established algorithms, expose their modified forms fitted to demands of clustering issue and compare some efficient curve clustering methods thanks to reported extensive simulated data experiments. Last but not least is made, for the sake of executed experiments, comprehensive confrontation of effectual utility. Proposed clustering algorithms are based on two principles. Firstly, it is presumed that the set of trajectories may be probabilistic modelled as sequences of points generated from a finite mixture model consisting of regression components and hence the density-based clustering methods using the Maximum Likehood Estimation are investigated to recognize the most homogenous partitioning. Attention is paid to both the Maximum Likehood Approach, which assumes the cluster memberships to be some of the model parameters, and the probabilistic model with the iterative Expectation-Maximization algorithm, that assumes them to be random variables. To deal with the hidden data problem both Gaussian and less conventional gamma mixtures are comprehended with arranging for use in two dimensions. To cope with data with high variability within each subpopulation it is introduced two-level random effects regression mixture with the ability to let an individual vary from the template for its group. Secondly, it is taken advantage of well known K-Means algorithm applied to the estimated regression coefficients, though. The task of the optimal data fitting is devoted, because K-Means is not invariant to linear transformations. In order to overcome this problem it is suggested integrating clustering issue with the Markov Chain Monte Carlo approaches. What is more, this paper is concerned in functional discriminant analysis including linear and quadratic scores and their modified probabilistic forms by using random mixtures. Alike in K-Means it is shown how to apply Fisher's method of canonical scores to the regression coefficients. Experiments of simulated datasets are made that demonstrate the performance of all mentioned methods and enable to choose those with the most result and time efficiency. Considerable boon is the facture of new advisable application advances. Implementation is processed in Mathematica 4.0. Finally, the possibilities offered by the development of curve clustering algorithms in vast research areas of modern science are examined, like neurology, genome studies, speech and image recognition systems, and future investigation with incorporation with ubiquitous computing is not forbidden. Utility in economy is illustrated with executed application in claims analysis of some life insurance products. The goals of the thesis have been achieved.

The influence of spectral resolution on land cover classification in Krkonoše Mts. tundra Abstract The aim of this diploma thesis was to specify the spectral resolution requirements for classification and to identify the most important spectral bands to discriminate classes of the predefined legend. Aerial hyperspectral data acquired by AisaDUAL sensor were used. The method applied for the selection of the important bands was discriminant analysis performed in IBM SPSS Statistics. The most discriminative bands were found in intervals 1500-1750 nm (beginning of SWIR), 1100- 1300 nm (longer wavelengths of NIR), 670-760 (red-edge) and 500-600 nm (green light). The classification of the selected bands was realized in ENVI 5.4 using the Support Vector Machine classifier, achieving overall accuracy of 80,54 %, Kappa coefficient 0,7755. The suitability of available satellite data for the classification of tundra vegetation in Krkonoše mountains based on spectral resolution was evaluated as well. Keywords: tundra, Krkonoše, classification, spectral resolution, class separability, discriminant analysis, hyperspectral data