The diploma thesis deals with the determination of the self-excited and forced pulsation of the real fluid in the cylindrical area and in the annulus. It also focuses on a case of resonance that has an unfavorable effect on the hydraulic circuit. The result of the thesis is the analytical relations for determination of self-excited and forced shapes of flow and pressure in the closed cylindrical area, for two selected variants of kinematic excitation. In the first variant, excitation is performed in the form of a sinus function. The second variant is expressed as a sawtooth function. A pulsation of the fluid caused by the movement of the body is described for the annulus. Further, in this area, the shapes of fluid pulsation are solved without external excitation.

Popis magnetodynamických vlastností nanomagnetů a nanostrukturovaných magnetických materiálů vyžaduje metody vhodné pro zkoumání typické časové odezvy těchto systémů, tj. v řádu nanosekund a méně. Nedostatek technik, vhodných právě pro charakterizaci v časové doméně, je spojen s možnostmi současné elektroniky. Další možný přístup, jak popsat vlastnosti nanomagnetů, je charakterizace ve frekvenční doméně v pásmu GHz. Nejrozšířenější technikou charakterizace ve frekvenční doméně je měření feromagnetické rezonance (FMR). Ze spekter FMR lze získat cenné informace o systému: parametr tlumení, saturační magnetizace atd. Metoda, kterou využíváme k detekci excitací spinových vln, má za cíl zjednodušení charakterizace. Využíváme termoelektrickou detekci spinových vln v magnetických drátech prostřednictvím anomálního Nernstova jevu. Metoda je založena na disipaci tepla uvnitř magnetické vrstvy v důsledku útlumu spinových vln, a proto dochází k vytvoření teplotního gradientu směrem k substrátu (kolmo k povrchu). To vede k vytvoření elektrického pole kolmého jak na teplotní gradient, tak na směr magnetizace. Napětí je obvykle v řádu V, proto může být měřeno obvyklým laboratorním vybavením. Navzdory své jednoduchosti poskytuje tato metoda velmi zajímavé výsledky a může být použita pro charakterizaci magnonických vlnovodů, magnonických metamateriálů, emitorů spinových vln a dalších zařízení, pracujících se spinovými vlnami.

Bismuth selenide belongs to a class of topological insulators---materials characterized by a intriguing electronic band structure, with a characteristic Dirac conical band on the surface. In this master thesis, the optical response of this material is explored in the infrared spectral range and in a broad range of magnetic fields. We mainly focus on the absorption of light due to free charge carriers having, when the magnetic field is applied, a form of cyclotron resonance. We find that the experimentally observed response is consistent with expectations for massive electrons in bulk rather than massless particles on the surface.

The theoretical part of this work describes perfusion analysis of dynamic contrast enhanced magnetic resonance imaging from data acquisition to estimation of perfusion parameters. The main application fields are oncology, cardiology and neurology. The thesis is focused on quantitative perfusion analysis, specifically it contributes to solving of the the main challenge of this method – correct estimation of the contrast-agent concentration sequence in the arterial input of the region of interest (arterial input function). The goals of the thesis are stated based on literature review and on the expertise of our group. Blind deconvolution is selected as the method of choice. In the practical part of this thesis, a new method for arterial input function identification based on blind deconvolution is proposed. The method is designed for both preclinical and clinical applications. It was validated on synthetic, preclinical and clinical data. Furthermore, possibilities of the longer temporal sampling provided by blind deconvolution were analyzed. This can be used for improved spatial resolution and possibly for higher SNR. For easier deployment of the proposed methods into clinical and preclinical use, a software tool for perfusion data processing was designed.

The aim of this work is to realize and test a prototype portable source for induction heating of small parts. The project contains a theoretical basis of resonant circuits and induction heating. The device design is based on simulation results from the FEMM program, with which a focussing coil for induction heating has been designed. The parts of the thesis are the realization of the designed device, its putting into operation and the programming of the control.

The aim of this diploma thesis is real-time fMRI neurofeedback. In this case, the activity of amygdala is monitored and controled by an emotional regulatory visual task. A procedure to process measured data online and to incorporate it into the stimulus protocol has been proposed. A pilot study was carried out. Offline analysis of measured data was performed, including evaluation of the results of the analysis. The data is processed in MATLAB using the functions of the SPM library.

Tomographic MR (Magnetic Resonance) and sonographic biosignal processing are important non-invasive diagnostic methods used in a medicine. A noise added into processed data by an amplifier of tomograph receiving part and by circuits of sonograph is resulting in a body organ diagnosis degradation. Image data are stored in a standardized DICOM medical file format. Methods using wavelet analysis for noise suppression in image data have been designed and their comparation with classical methods has been made in this work. The MATLAB was utilized for data processing and data rewriting back to the DICOM format.

The thesis deals with the edge detection in the magnetic resonance images and their segmentation. There are adduced the gradient based methods, methods based on zero-crossing in the Laplacian images and also methods combined both of the methods adduced above. These methods are compared to find the best one for the temporo-mandibular joint detection. Consequently sufficient segmentation method for particular parts of the temporo-mandibular joint (the condyle, the acetabulum and the articular disk) separation is chosen.

The aim of this masters thesis is design and implement an appropriate method for highlighting MR images and the identification of rough edges to provide for division of controlled areas. To this purpose is possible to use the Wavelet analysis. For the simulation environment I using MATLAB entviroment, where introduce the comparison for different types of de-noising and too for different mother wavelets. These methods will be implemented on various MR images of termoromandibular joint.

The thesis is focused on methods of machine learning used for recognising the first stage of schizophrenia in images from nuclear-magnetic resonance. The introduction of this paper is focused primarily on physical principles. Further in this work, the attention is given to registration methods, reduction of data set and machine learning. In the classification part, simmilarity rates, support vectors´ method, K-nearest neighbour classification and K-means are described. The last stage of theoretical part is focused on evaluation of the clasification. In practical part the results of reduction data set by methods PCA, CRLS-PCA and subjects PCA are described. Furthermore, the practical part is focused on pattern recognition by methods K-NN, K-means and test K-NN method on real data. Abnormalities which are recognised by some classification methods can distinguish patients with schizophrenia from healthy controls.