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Classification of retinal blood vessels
Mitrengová, Jana ; Kolář, Radim (referee) ; Odstrčilík, Jan (advisor)
The thesis deals with the classification of the retinal blood vessels in retinal image data. The first part of the thesis deals with the anatomy of the human eye and focuses on the description of the retina and its blood circulation. It further describes the principle of fundus camera and experimental video ophthalmoscope. The second part of the thesis is devoted to a literature search of academic publications that deal with the classification of the retinal vessels into arteries and veins. Subsequently, the principle of selected machine learning methods is presented. Based on the literature research, two methods for the classification of the blood vessels were proposed, the first one using the SVM classifier and the second one using the convolutional neural network U-Net. At the end, the analysis of vascular pulsations was performed. The practical part of the thesis was carried out in Matlab programming interface and images from the RITE, IOSTAR and AFIO database were used for classification and the retinal video sequences taken with an experimental video ophthalmoscope were processed in the analysis of pulsations.
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Detection of atrial fibrilation in long-term ECG
Polcer, Simon ; Kozumplík, Jiří (referee) ; Maršánová, Lucie (advisor)
The bachelor’s thesis deals with the automatic detection of atrial fibrillations in the long-term ECG signals. First, it provides a description of the electrophysiology of the heart, the atrial fibrillation and the automatic methods of their detection. The first method, implemented in this work, is based upon the parameters that were calculated from the irregularities of the RR intervals. The second method uses the stationary wavelet transform and other parameters are computed after the signal transformation. The calculated parameters are subsequently statistically evaluated in the STATISTICA software. Parameters are assessed by the non-parametric Mann-Whitney test, which selects parameters that exhibit statistically significant differences between signals containing atrial fibrillation and sinus rhythm. At the end, the classification is performed by two approaches such as Support vector machine and k-Nearest Neighbours.
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Toolbox for automatic EEG data quality assessment
Meloun, Jan ; Gajdoš, Martin (referee) ; Lamoš, Martin (advisor)
This thesis deals with designing a tool for automatically evaluating the quality of electroencephalographic data. In the theoretical part of the thesis, there is a theoretical basis in the anatomy of the central nervous system and the brain, followed by a description of the origin and propagation of the action potential through the nervous system. Furthermore, the theoretical part of the work is devoted to electroencephalography (EEG) and the description of the EEG recording, including typical artefacts in it. The following describes the methods used to detect and remove artefacts. These are primary methods for extracting data quality features. The practical part of the thesis contains a description of the design of a tool for automatic EEG quality assessment and its testing on artificial and real data. The last part of the work is devoted to the discussion of the results of the success of the detection of channels or sections with artefacts and the possible further extension of the tool.
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Design of a system for detecting devices connected to the electrical network
Homola, Michal ; Kováč, Daniel (referee) ; Musil, Petr (advisor)
This master's thesis deals with the design of a system for detecting devices connected to power line network using the measurement of high-frequency noise through BPL (Broadband over Power Line) modems. The theoretical part involved familiarization with Power Line Communication (PLC), electromagnetic compatibility (EMC), impedance issues in PLC, and characteristics of noise in PLC. In the practical part, the suitability of the chosen PLC modems for the actual measurement was verified, followed by the measurement of temporal and spatial variability of network noise characteristics using these modems.For temporal variability, an experiment involving long-term measurement of refrigerator activity was conducted. For spatial variability, measurements were taken at multiple locations, with some locations serving as a training set and the remaining ones as a testing set. After selecting an appropriate machine learning model, the input data were feature engineered accordingly, followed by their evaluation.
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Support vector machines: theory, applications and software implementations
Podtesov, Daniil ; Matoušek, Radomil (referee) ; Kůdela, Jakub (advisor)
This bachelor thesis deals with the problem of machine learning algorithm called support vector machine learning method. The thesis focuses on the theoretical basis required to understand the mechanism of operation of this method, the application of this method in real life and the implementation of this method in various software. Concepts such as optimal separation superplane, linear decision boundary and support vectors are explained. Furthermore, different variants of this method using different kernel functions are described. In the third part of the paper, the method was not only used to show how it works, but also several techniques were used to improve the results of the application of the method.
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Laptop Touchpad Palm Detection with AI/ML
Menzyński, Mark Alexander ; Kavetskyi, Andrii (referee) ; Drahanský, Martin (advisor)
Situace ohledně detekci a odmítnutí dlaně na laptopech je méně než ideální. Většina výzkumů se zabývá odmítnutím dotyků na dotykových obrazovkách, a na laptopy probíhá téměř žádný. Patrně nějaký uzavřený výzkům probíhá uvnitř výrobců laptopů, ale i přes to je technologie pozadu. Tato práce prozkoumává několik metod plytkého a hlubokého strojového učení, a výsledná přesnost byla zjištěna jako více než dostačující. Také implementuje aplikaci v reálném čase na demonstraci modelu.
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Utilization of artificial intelligence in technical diagnostics
Konečný, Antonín ; Huzlík, Rostislav (referee) ; Zuth, Daniel (advisor)
The diploma thesis is focused on the use of artificial intelligence methods for evaluating the fault condition of machinery. The evaluated data are from a vibrodiagnostic model for simulation of static and dynamic unbalances. The machine learning methods are applied, specifically supervised learning. The thesis describes the Spyder software environment, its alternatives, and the Python programming language, in which the scripts are written. It contains an overview with a description of the libraries (Scikit-learn, SciPy, Pandas ...) and methods — K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees (DT) and Random Forests Classifiers (RF). The results of the classification are visualized in the confusion matrix for each method. The appendix includes written scripts for feature engineering, hyperparameter tuning, evaluation of learning success and classification with visualization of the result.
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Classification of retinal blood vessels
Mitrengová, Jana ; Kolář, Radim (referee) ; Odstrčilík, Jan (advisor)
The thesis deals with the classification of the retinal blood vessels in retinal image data. The first part of the thesis deals with the anatomy of the human eye and focuses on the description of the retina and its blood circulation. It further describes the principle of fundus camera and experimental video ophthalmoscope. The second part of the thesis is devoted to a literature search of academic publications that deal with the classification of the retinal vessels into arteries and veins. Subsequently, the principle of selected machine learning methods is presented. Based on the literature research, two methods for the classification of the blood vessels were proposed, the first one using the SVM classifier and the second one using the convolutional neural network U-Net. At the end, the analysis of vascular pulsations was performed. The practical part of the thesis was carried out in Matlab programming interface and images from the RITE, IOSTAR and AFIO database were used for classification and the retinal video sequences taken with an experimental video ophthalmoscope were processed in the analysis of pulsations.
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Prediction of the company insolvency using machine learning methods in the EU passenger transport industry
Čarnogurská, Anna
The diploma thesis focuses on the application of Support vector machines (SVM) in the area of bankruptcy prediction. Theoretical research deals with the overview of the passenger transport industry in the EU for each mode of transport individually. Potential causes of bankruptcy in the researched industry are presented based on real examples. Empirical analysis examines the accuracy of SVM classifier with different types of kernels and compares its prediction force with the logistic regression model. In the end, obtained results are summarized, commented on in economic terms and discussed with selected studies.
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Precise segmentation of image data
Svoboda, Jan ; Marcoň, Petr (referee) ; Mikulka, Jan (advisor)
The concern of this thesis is a development of an extension module for 3D Slicer platform. The core of the module is an implementation of a Support Vector Machines classifier, which is used for segmentation of the vertebral column image data provided by the University Hospital Brno. One of the goals of the thesis was resampling and registration of these image sequences. CT volumes provided solid contrast and were used as a reference for gaining properly segmented groups of vertebrae. Due to the low quality of the MRI volumes image data, segmentation of MRI images was not completely succesful. The extension module scripted in Python language can be seen as a tool and can be used in the future for different datasets.
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