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Detection and measurement of electron beam in TEM images
Polcer, Simon ; Vičar, Tomáš (referee) ; Chmelík, Jiří (advisor)
This diploma thesis deals with automatic detection and measurement of the electron beam in the images from a transmission electron microscope (TEM). The introduction provides a description of the construction and the main parts of the electron microscope. In the theoretical part, there are summarized modes of illumination from the fluorescent screen. Machine learning, specifically convolution neural network U-Net is used for automatic detection of the electron beam in the image. The measurement of the beam is based on ellipse approximation, which defines the size and dimension of the beam. Neural network learning requires an extensive database of images. For this purpose, the own augmentation approach is proposed, which applies a specific combination of geometric transformations for each mode of illumination. In the conclusion of this thesis, the results are evaluated and summarized. This proposed algorithm achieves 0.815 of the DICE coefficient, which describes an overlap between two sets. The thesis was designed in Python programming language.
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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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Detection and measurement of electron beam in TEM images
Polcer, Simon ; Vičar, Tomáš (referee) ; Chmelík, Jiří (advisor)
This diploma thesis deals with automatic detection and measurement of the electron beam in the images from a transmission electron microscope (TEM). The introduction provides a description of the construction and the main parts of the electron microscope. In the theoretical part, there are summarized modes of illumination from the fluorescent screen. Machine learning, specifically convolution neural network U-Net is used for automatic detection of the electron beam in the image. The measurement of the beam is based on ellipse approximation, which defines the size and dimension of the beam. Neural network learning requires an extensive database of images. For this purpose, the own augmentation approach is proposed, which applies a specific combination of geometric transformations for each mode of illumination. In the conclusion of this thesis, the results are evaluated and summarized. This proposed algorithm achieves 0.815 of the DICE coefficient, which describes an overlap between two sets. The thesis was designed in Python programming language.
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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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