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Virtual Robot Control Using EEG
Drla, Michal ; Goldmann, Tomáš (referee) ; Tinka, Jan (advisor)
This bachelor thesis aimed to create an application where is user able to control the virtual robot with an EEG signal. The thesis contains a brief introduction that explains how BCI systems which are using EEG work. This introduction not only explains the basics of EEG analysis but also explains brain biology and shows different signals which are extractable from the brain. This thesis also explains the theory of neural networks which are used to implement the analysis. In implementation are shown scripts that were used to collect data and there is also shown the design of the neural network. Results of testing are good, the neural network was making correct decisions and the user was able to control the virtual robot.
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Deep Neural Networks
Habrnál, Matěj ; Zbořil, František (referee) ; Zbořil, František (advisor)
The thesis addresses the topic of Deep Neural Networks, in particular the methods regar- ding the field of Deep Learning, which is used to initialize the weight and learning process s itself within Deep Neural Networks. The focus is also put to the basic theory of the classical Neural Networks, which is important to comprehensive understanding of the issue. The aim of this work is to determine the optimal set of optional parameters of the algori- thms on various complexity levels of image recognition tasks through experimenting with created application applying Deep Neural Networks. Furthermore, evaluation and analysis of the results and lessons learned from the experimentation with classical and Deep Neural Networks are integrated in the thesis.
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Automatic detection of ischemia in ECG using artificial neural network
Noremberczyk, Adam ; Smital, Lukáš (referee) ; Ronzhina, Marina (advisor)
This thesis discusses the utilization of the artificial neural networks (ANN) as electrocardiography (ECG) classifiers of coronary artery disease (CAD) and myocardial infarction (MI) in ECG signal. The first part of this thesis is orientated towards the theoretical knowledge and describes the issue of ECG pathological changes, methods for automatic detection of CAD and MI and the issue of the perceptron and ANN. The second deals with use of Neural Network Toolbox MATLAB® version R2010a. In graphical user interface development environment (Guide) is created application that is used to compare the success of automatic detection of ischemia in ECG using ANN. It allows the user to set various parameter settings UNS and display ECG waveforms.
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Recognition of digits
Gorgol, Martin ; Hynčica, Tomáš (referee) ; Jirsík, Václav (advisor)
This work describes the basic concepts and principles in the field of neural networks. Closer then this work deals with the identification numbers using these networks, in particular, using the back-propagation method. There is a broken process of choosing a set of signs, types of symptoms and of choosing a neural network topology. The aim is to obtain specific results by using the program for working with neural networks.
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Adaptive data compression by neural networks
Kučera, Michal ; Přinosil, Jiří (referee) ; Koula, Ivan (advisor)
Point of the work is using of neural networks for the datecompression. This brings new possibilities as by lossless as lossy compression. Draft of a few compress algorithm show the behaviour, advantages and weak points of these systems. As the solution we use knowledge of the layered perceptron Network and we try by the change of the structure and subparameters to teach such network to compress the data, according to our entry requirement. These networks have also advantages, which are meanwhile impediment to the using practically. The goal of this is to try some algorithms, look into their characteristics and posibility of the using. Then propose next posibility solutions and upgrading of these algorithms.
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Head Pose Estimation in an Image by a Neural Network
Rybnikár, Lukáš ; Goldmann, Tomáš (referee) ; Orság, Filip (advisor)
Artificial neural networks are not a novelty, but their recent rise in popularity is noticeable as well as their gain of attention from the masses. This bachelor thesis focuses on the head pose estimation in an image using the convolution neural networks. The fields of use of neural networks are vast and during last years strong enough hardware has been developed to allow us to train these networks under commonly accessible conditions. In theoretical part there are neural networks introduced with an explanation of what they are, how they work, how they are divided followed by a detailed description of convolutional neural networks. In the practical part the necessary tools used for development needed to perform experiments, such as determining appropriate configuration for neural network and optimization to get the best results possible, are described.
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Face Image Frontalization Application
Tichý, Filip ; Malinka, Kamil (referee) ; Goldmann, Tomáš (advisor)
This work focuses on implementing an application for face frontalization using the CFR-GAN project and rotating the 3D face model followed by rendering. The aim of this work is to evaluate the impact of the application on face recognition accuracy based on the Fidentis dataset. The results are presented in the form of box plots, which depict the Euclidean distances between the generated frontalized images and the real images. It was found that when frontalizing using the rotation of a 3D model from high angles of rotation, the success of facial recognition process increases. Conversely, when frontalizing using the Complete Face Recovery GAN projekt, the recognition success signiĄcantly decreases. The VGG Face algorithm was used for comparing the images. The entire application is implemented in Python using commonly available libraries.
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Cell segmentation using convolutional neural networks
Hrdličková, Alžběta ; Chmelík, Jiří (referee) ; Vičar, Tomáš (advisor)
This work examines the use of convolutional neural networks with a focus on semantic and instance segmentation of cells from microscopic images. The theoretical part contains a description of deep neural networks and a summary of widely used convolutional architectures for image segmentation. The practical part of the work is devoted to the creation of a convolutional neural network model based on the U-Net architecture. It also contains cell segmentation of predicted images using three methods, namely thresholding, the watershed and the random walker.
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