In this thesis I present and discuss the results of optical spectroscopy performed on InGaN/GaN single and multiple quantum well (QW) structures. I report on the optical properties of InGaN/GaN single and multiple QW structures, measured at high excitation power densities. I show a correlation exists between the reduction in PL efficiency at high excitation power densities, the phenomenon so-called ``efficiency-droop'', and a broadening of the PL spectra. I also show a distinct change in recombination dynamics, measured by time-resolved photoluminescence (PL), which occurs at the excitation power densities for which efficiency droop is measured. The broadening of the PL spectra at high excitation power densities is shown to occur due to a rapidly redshifting, short-lived high energy emission band. The high energy emission band is proposed to be due to the recombination of weakly localised/delocalised carriers occurring as a consequence of the progressive saturation of the local potential fluctuations responsible for carrier localisation, at high excitation power densities. I report on the effects of varying threading dislocation (TD) density on the optical properties of InGaN/GaN multiple QW structures. No systematic relationship exists between the room temperature internal quantum efficiency (IQE) and the TD density, in a series of nominally identical InGaN/GaN multiple QWs deposited on GaN templates of varying TD density. I also show the excitation power density dependence of the PL efficiency, at room temperatures, is unaffected for variation in the TD density between 2 x107 and 5 x109 cm-2. The independence of the optical properties to TD density is proposed to be a consequence of the strong carrier localisation, and hence short carrier diffusion lengths. I report on the effects of including an InGaN underlayer on the optical and microstructural properties of InGaN/GaN multiple QW structures. I show an increase in the room temperature IQE occurs for the structure containing the InGaN underlayer, compared to the reference. I show using PL excitation spectroscopy that an additional carrier transfer and recombination process occurs on the high energy side of the PL spectrum associated with the InGaN underlayer. Using PL decay time measurements I show the additional recombination process for carriers excited in the underlayer occurs on a faster timescale than the recombination at the peak of the PL spectrum. The additional contribution to the spectrum from the faster recombination process is proposed as responsible for the increase in room temperature IQE.

Fast detection and identification of unknown substances is an area of interest for many parties. Raman spectroscopy is a laser-based method allowing for long range no contact investigation of substances. A Coded Aperture Snapshot Spectral Imaging (CASSI) system allows for fast and efficient measurements of hyperspectral images of a scene, containing a mixture of the spatial and spectral data. To analyze the scene and the unknown substances within it, it is required that the spectra in each spatial position are known. Utilizing the theory of compressed sensing allows for reconstruction of hyperspectral images of a scene given their CASSI measurements by assuming a sparsity prior. These reconstructions can then be utilized by a human operator to deduce and classify the unknown substances and their spatial locations in the scene. Such classifications are then applicable as decision support in various areas, for example in the judicial system. Reconstruction of hyperspectral images given CASSI-measurements is an ill-posed inverse problem typically solved by utilizing regularization techniques such as total variation (TV). These TV-based reconstruction methods are time consuming relative to the time needed to acquire the CASSI measurements, which is in the order of seconds. This leads to a reduced number of areas where the technology is applicable. In this thesis, a Generative Adversarial Network (GAN) based reconstruction method is proposed. A GAN is trained using simulated training data consisting of hyperspectral images and their respective CASSI measurements. The GAN provides a learned prior, and is used in an iterative optimization algorithm seeking to find an optimal set of latent variables such that the reconstruction error is minimized. The results of the developed GAN based reconstruction method are compared with a traditional TV method and a different machine learning based reconstruction method.  The results show that the reconstruction method developed in this thesis performs better than the compared methods in terms of reconstruction quality in short time spans.

K pořízení snímků sítnice, která představuje nejdůležitější část lidského oka, je potřeba speciálního vybavení, kterým je fundus kamera. Z tohoto důvodu je cílem této práce navrhnout a implementovat systém, který bude schopný generovat takovéto snímky bez použítí této kamery. Navržený systém využívá mapování vstupního černobílého snímku krevního řečiště sítnice na barevný výstupní snímek celé sítnice. Systém se skládá ze dvou neuronových sítí: generátoru, který generuje snímky sítnic, a diskriminátoru, který klasifikuje dané snímky jako reálné či syntetické. Tento systém byl natrénován na 141 snímcích z veřejně dostupných databází. Následně byla vytvořena nová databáze obsahující více než 2,800 snímků zdravých sítnic v rozlišení 1024x1024. Tato databáze může být použita jako učební pomůcka pro oční lékaře nebo může poskytovat základ pro vývoj různých aplikací pracujících se sítnicemi.

Cieľom tejto diplomovej práce je vygenerovanie datasetu syntetických snímkov odtlačkov prstov, ktoré vykazujú známky kožných ochorení. Práca sa zaoberá poškodením spôsobeným kožnými ochoreniami v odtlačkoch prstov a generovaním syntetických odtlačkov prstov. Odtlačky prstov s prejavom kožných ochorení boli generované s využitím modelu založeného na Wasserstein GAN s penalizáciou gradientu. Na trénovanie GAN modelu bola použitá unikátna databáza odtlačkov prstov s prejavom kožných ochorení vytvorená na FIT VUT. Daný model bol trénovaný na troch typoch kožných ochorení: atopický ekzém, psoriáza a dyshidrotický ekzém. Sieť generátoru z natrénovaného WGAN-GP modelu bola použitá na vygenerovanie datasetov syntetických odtlačkov prstov. Tieto syntetické odtlačky boli porovnané s reálnymi odtlačkami s využitím NFIQ a FiQiVi nástrojov na určenie kvality spoločne s porovnaním rozložení lokácií a orientácii markantov v snímkoch odtlačkov prstov.

The objective of this master thesis was to provide the optimization of an ion-atom beam source for the improvement of its properties. The improvement of the parameters increases the efficiency of the source during the deposition of gallium nitride ultrathin films (GaN) being important in microeletronics and optoelectronics. After optimization, the depositions of GaN ultrathin films on Si(111) 7x7 at lower temperatures (

This thesis deals with deposition of GaN thin films and GaN selective growth utilizing pyrolyzed resist masks. Carbon masks were prepared on silicon substrates by electron-beam litography and resist pyrolysis. As a further step, Ga and GaN were deposited on the masked substrates by Moleculer Beam Epitaxy (MBE) method. A selective growth of Ga droplets was achieved. These results were used for preparation of GaN crystallites by pulse deposition. It is also shown that direct MBE deposition of GaN on the masked substrates leads to a selective growth of GaN thin films with GaN film growing only on the areas which are not covered by the carbon mask. The results are explained by enhanced surface diffusion of gallium atoms on the surface of the carbon mask.

The thesis describes the procedure during the proposal of the driver circuits for the GaN MOSFET transistors, which are known for their fast switching especially. In the first instance of this thesis the issue of GaN MOSFET transistors is described and also the thesis describes the different types of MOSFET transistors in the way of their electrical and mechanical attributes. The specific type driver circuit is stated in the thesis, which was selected in the semestral thesis. For this circuit the boost converter with an output power 600W and high switching frequency 800kHz was proposed as an attempt measurement circuit. This boost converter was measured after its construction was done. The waveforms captured by the oscilloscope are commented also. In the conclusion the assessment is done about this new technology of power switching transistors.

Die Topologieoptimierung ist die Suche einer optimalen Bauteilgeometrie in Abhängigkeit des Einsatzfalls. Für komplexe Probleme kann die Topologieoptimierung aufgrund eines hohen Detailgrades viel Zeit- und Rechenkapazität erfordern. Diese Nachteile der Topologieoptimierung sollen mittels Deep Learning reduziert werden, so dass eine Topologieoptimierung dem Konstrukteur als sekundenschnelle Hilfe dient. Das Deep Learning ist die Erweiterung künstlicher neuronaler Netzwerke, mit denen Muster oder Verhaltensregeln erlernt werden können. So soll die bislang numerisch berechnete Topologieoptimierung mit dem Deep Learning Ansatz gelöst werden. Hierzu werden Ansätze, Berechnungsschema und erste Schlussfolgerungen vorgestellt und diskutiert.

Neutrino astronomy is expanding into the ultra-high energy (>1017eV) frontier with the use of in-ice detection of Askaryan radio emission from neutrino-induced particle showers. There are already pilot arrays for validating the technology and the next few years will see the planning and construction of IceCube-Gen2, an upgrade to the current neutrino telescope IceCube. This thesis aims to facilitate that planning by providing faster simulations using deep learning surrogate models. Faster simulations could enable proper optimisation of the antenna stations providing better sensitivity and reconstruction of neutrino properties. The surrogates are made for two parts of the end-to-end simulations: the signal generation and the signal propagation. These two steps are the most time-consuming parts of the simulations. The signal propagation is modelled with a standard fully connected neural network whereas for the signal generation a conditional Wasserstein generative adversarial network is used. There are multiple reasons for using these types of models. For both problems the neural networks provide the speed necessary as well as being differentiable -both important factors for optimisation. Generative adversarial networks are used in the signal generation because of the inherent stochasticity in the particle shower development that leads to the Askaryan radio signal. A more standard neural network is used for the signal propagation as it is a regression task. Promising results are obtained for both tasks. The signal propagation surrogate model can predict the parameters of interest at the desired accuracy, except for the travel time which needs further optimisation to reduce the uncertainty from 0.5 ns to 0.1 ns. The signal generation surrogate model predicts the Askaryan emission well for the limited parameter space of hadronic showers and within 5° of the Cherenkov cone. The two models provide a first step and a proof of concept. It is believed that the models can reach the required accuracies with more work.

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