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Land-cover changes and climatic settings of the Úpské rašeliniště mire
Hejda, Tomáš ; Treml, Václav (advisor) ; Šefrna, Luděk (referee)
Mires, or wetlands, are very important habitats, ecosystems, as well as relict and recent reserves. The object of the study of mires is focused on morphological, hydrological and chemical characteristics as they are more discussed globally due to climate change. The present thesis deals with the characteristics of mires on a global scale, which are used in literature and make up their classification. The classification of mires is also addressed on the Czech scale with a focus on the Western High Sudetes. Mires of the Giant Mts. were analyzed according to climate conditions with a focus on the dynamics of land cover areas of Úpské rašeliniště mire and subsequently compared with Scandinavian mires. A particularly strong dependence on the Aapa type can be stated from the analysis. The vegetation above the alpine timberline primarily consisting of dwarf pines (Pinus mugo) and spruce families showed a substantial increase with respect to the impacts of climate fluctuations in the recorded period in Eastern High Giant Mts. Key words: mires, classification, Giant Mts., climate, vegetation change
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Efficient implementation of dimension reduction methods for high-dimensional statistics
Pekař, Vojtěch ; Duintjer Tebbens, Erik Jurjen (advisor) ; Hnětynková, Iveta (referee)
The main goal of our thesis is to make the implementation of a classification method called linear discriminant analysis more efficient. It is a model of multivariate statistics which, given samples and their membership to given groups, attempts to determine the group of a new sample. We focus especially on the high-dimensional case, meaning that the number of variables is higher than number of samples and the problem leads to a singular covariance matrix. If the number of variables is too high, it can be practically impossible to use the common methods because of the high computational cost. Therefore, we look at the topic from the perspective of numerical linear algebra and we rearrange the obtained tasks to their equivalent formulation with much lower dimension. We offer new ways of solution, provide examples of particular algorithms and discuss their efficiency. Powered by TCPDF (www.tcpdf.org)
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Methodology for the Classification and Evaluation of Industrial Heritage: Water Management
Caletka, Martin ; Dráb, Aleš ; Dzuráková, Miriam ; Forejtníková, Milena ; Hronek, David ; Hudcová, Hana ; Julínek, Tomáš ; Račoch, Radka ; Rozkošný, Miloš ; Kuča, Karel ; Borovcová, Alena ; Drnek, Kryštof ; Kolka, Miroslav ; Matěj, Miloš ; Ryšková, Michaela ; Urbánek, Radim ; Frajer, Jindřich ; Létal, Aleš ; Pavelková, Renata ; Kotari, Sixtus Bolom ; Sviták Zbyněk ; Vyskočil, Aleš
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Classification of UAV hyperspectral images using deep learning methods
Řádová, Martina ; Potůčková, Markéta (advisor) ; Kupková, Lucie (referee)
Diploma thesis "Classification of UAV hyperspectral images using deep learning methods" focuses on the classification methods, namely convolutional neural networks (CNN), of hyperspectral (HS) images. Based on a thorough literature review, a comprehensive overview on CNN utilisation in remote sensing is assembled as a basis for identifying suitable methods for the specific task of this thesis. Two methods with an open solution in programming language Python were selected - Capsule Network and U-Net. The main aim of this work is to verify the suitability of chosen methods for the classification of hyperspestral images. The images were acquired by sensors with high spatial resolution carried by a UAV over Krkonoše Mts. tundra. Important step was to prepare input HS data (54 bands, 9cm) to have suitable form for entering the network. Not all the required results were achieved due to the complexity of the Capsule Network architecture. The U-Net method was used in purpose of comparing and verifying the results. Accuracies retrieved from the U-Net overcome results achieved by traditionally used machine learning methods (SVM, ML, RF, etc). Overall accuracy for U-Net was higher than 90% where other mentioned methods did not get over 88%. Especially classes block fields and dwarf pine achieved higher...
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Tennis for people with activity limitations
Slavíková, Magdalena ; Čichoň, Rostislav (advisor) ; Kočíb, Tomáš (referee)
Title: Tennis for people with activity limitations Objectives: The aim of the bachelor's thesis is to summarize the existing knowledge about tennis for people with activity limitations in the context of historical development. Methods: The bachelor thesis is carried out as a review. Scientific literature, popular science papers and other sources, especially Internet sources dealing with the topic of wheelchair tennis were the source of information for this bachelor's thesis. Results: The thesis provides an up-to-date overview of scientific literature related to tennis for people with activity limitations. Part of the work is devoted to the history of this relatively young sports industry. There are described the current rules, the specifics of armaments and equipment, and mentioned the issue of classification in competitions at the international and national levels. Part of the work is devoted to the technique of strokes, which are highly specific and different from standard tennis. Conclusions: In conclusions it should be mentioned the necessary to popularize the sport of people with activity limitations, as one of the means of maintaining their independence and the path to a full life. Key words: tennis, wheelchair tennis, handicap, history, equipment, tennis cart, tennis racket, rules, forehand,...
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Classification of land cover change in Ethiopia using Landsat and Sentinel-2 data
Zadražil, Filip ; Laštovička, Josef (advisor) ; Svoboda, Jan (referee)
This bachelor thesis is focused on the comparison of Random Forest (RF) and CART classifiers on the example of the Ethiopian region of Sidama. An analysis of land cover change between 2014 and 2020 was performed for this region. The cloud-based platform Google Earth Engine (GEE) was used for classifications. Supervised classifications were performed on images from Landsat 8 and Sentinel-2 missions, which were retrieved from the Earth Engine data catalogue. Data from in-situ measurement was used for training polygons, variability of input data over time was verified with Google Earth Pro. In the research part, the work deals with the methods and results of research that were conducted in a topic close to this work. In the empirical part, the work deals with the analysis of Landsat 8 and Sentinel-2 data. The temporal, spatial and spectral resolution were compared. In terms of temporal resolution, it has been shown that Sentinel-2 data allows up to three times more images for the same area thanks to the two satellites scanning in parallel. Spectral and spatial resolution of Sentinel-2 allows better observation of smaller and less distinguishable elements. The data was then used for land cover classifications using RF and CART classifiers in the cloud-based GEE environment. The RF classifier made it...
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Papillary Renal Cell Carcinoma
Procházková, Kristýna ; Hora, Milan (advisor) ; Macek, Petr (referee) ; Král, Milan (referee)
The Pilsen region suffers the highest incidence of kidney tumours worldwide. Approximately 240 new cases diagnosed as C64 (malignant renal tumours outside the pelvis) were recorded in this region of about 580,000 inhabitants in 2015. Clear renal cell carcinoma has long held first place as the most common tumour, with papillary renal cell carcinoma (pRCC) being the second most frequently operated kidney tumour at the Urology Department of the University Hospital in Pilsen. The 2016 WHO classification of kidney tumours recognizes officially only the stratification of pRCC to type 1 (pRCC1) and type 2 (pRCC2). Unfortunately, the current division does not correspond with knowledge derived from everyday practice. Most clinical trials involving pRCC do not differentiate between the subtypes, adhering only to the official type 1 and 2 divisions and the atypical papillary forms being excluded from their studies. We therefore have to face the question of whether the histological pRCC subtype affects the risk of recurrence, or death, in surgically treated patients. The aim of this dissertation work is to take into consideration also all other papillary types which differ from characterization of pRCC1 and pRCC2. The analyses of a group of patients with surgically treated and histologically verified pRCC at...
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Unary Classification of Image Data
Beneš, Jiří ; Petyovský, Petr (referee) ; Horák, Karel (advisor)
The work deals with an introduction to classification algorithms. It then divides classifiers into unary, binary and multi-class and describes the different types of classifiers. The work compares individual classifiers and their areas of use. For unary classifiers, practical examples and a list of used architectures are given in the work. The work contains a chapter focused on the comparison of the effects of hyperparameters on the quality of unary classification for individual architectures. Part of the submission is a practical example of implementation of the unary classifier.
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Data Analysis of a Company Producing Medical Supplies
Kulhánková, Monika ; Bartík, Vladimír (referee) ; Burgetová, Ivana (advisor)
This bachelor's thesis deals with the analysis of the company's sales data, specifically the classification of the customer's type according to his sales data. It provides a theoretical introduction to data mining. It describes the classification process and methods for creating classifiers and presents the CRISP-DM model. This thesis describes the provided data sets, from which the relevant attributes are selected. The data are preprocessed and used in the creation and testing of classification models. The result of this thesis is a comparison of the achieved results.
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