The thesis deals with processing of epoch-wise GNSS measurements from local geodynamic network Sněžník. Its aim is to evaluate the geodynamics in the area of Králický Sněžník Massif and to assess the capabilities of epoch-wise GNSS measurements to detect the geodynamic movements. Within the thesis the comprehensive processing of all the GNSS measurements observed between years 1997 and 2011 is realized using the reprocessed products of first IGS reprocessing Repro1. Bernese GPS software version 5.0 is used for all the processing.

[pt] Redes neurais profundas são responsáveis pelo grande progresso em diversas tarefas perceptuais, especialmente nos campos da visão computacional,reconhecimento de fala e processamento de linguagem natural. Estes resultados produziram uma mudança de paradigma nas técnicas de reconhecimentode padrões, deslocando a demanda do design de extratores de característicaspara o design de arquiteturas de redes neurais. No entanto, o design de novas arquiteturas de redes neurais profundas é bastante demandanteem termos de tempo e depende fortemente da intuição e conhecimento de especialistas,além de se basear em um processo de tentativa e erro. Neste contexto, a idea de automatizar o design de arquiteturas de redes neurais profundas tem ganhado popularidade, estabelecendo o campo da busca por arquiteturas neurais(NAS - Neural Architecture Search). Para resolver o problema de NAS, autores propuseram diversas abordagens envolvendo o espaço de buscas, a estratégia de buscas e técnicas para mitigar o consumo de recursos destes algoritmos. O Q-NAS (Quantum-inspired Neural Architecture Search) é uma abordagem proposta para endereçar o problema de NAS utilizando um algoritmo evolucionário com inspiração quântica como estratégia de buscas. Este método foi aplicado de forma bem sucedida em classificação de imagens, superando resultados de arquiteturas de design manual nos conjuntos de dados CIFAR-10 e CIFAR-100 além de uma aplicação de mundo real na área da sísmica. Motivados por este sucesso, propõe-se nesta Dissertação o SegQNAS (Quantum-inspired Neural Architecture Search applied to Semantic Segmentation), uma adaptação do Q-NAS para a tarefa de segmentação semântica. Diversos experimentos foram realizados com objetivo de verificar a aplicabilidade do SegQNAS em dois conjuntos de dados do desafio Medical Segmentation Decathlon. O SegQNAS foi capaz de alcançar um coeficiente de similaridade dice de 0.9583 no conjunto de dados de baço, superando os resultados de arquiteturas tradicionais como U-Net e ResU-Net e atingindo resultados comparáveis a outros trabalhos que aplicaram NAS a este conjunto de dados, mas encontrando arquiteturas com muito menos parãmetros. No conjunto de dados de próstata, o SegQNAS alcançou um coeficiente de similaridade dice de 0.6887 superando a U-Net, ResU-Net e o trabalho na área de NAS que utilizamos como comparação. / [en] Deep neural networks are responsible for great progress in performance for several perceptual tasks, especially in the fields of computer vision, speech recognition, and natural language processing. These results produced a paradigm shift in pattern recognition techniques, shifting the demand from feature extractor design to neural architecture design. However, designing novel deep neural network architectures is very time-consuming and heavily relies on experts intuition, knowledge, and a trial and error process. In that context, the idea of automating the architecture design of deep neural networks has gained popularity, establishing the field of neural architecture search (NAS). To tackle the problem of NAS, authors have proposed several approaches regarding the search space definition, algorithms for the search strategy, and techniques to mitigate the resource consumption of those algorithms. Q-NAS (Quantum-inspired Neural Architecture Search) is one proposed approach to address the NAS problem using a quantum-inspired evolutionary algorithm as the search strategy. That method has been successfully applied to image classification, outperforming handcrafted models on the CIFAR-10 and CIFAR-100 datasets and also on a real-world seismic application. Motivated by this success, we propose SegQNAS (Quantum-inspired Neural Architecture Search applied to Semantic Segmentation), which is an adaptation of Q-NAS applied to semantic segmentation. We carried out several experiments to verify the applicability of SegQNAS on two datasets from the Medical Segmentation Decathlon challenge. SegQNAS was able to achieve a 0.9583 dice similarity coefficient on the spleen dataset, outperforming traditional architectures like U-Net and ResU-Net and comparable results with a similar NAS work from the literature but with fewer parameters network. On the prostate dataset, SegQNAS achieved a 0.6887 dice similarity coefficient, also outperforming U-Net, ResU-Net, and outperforming a similar NAS work from the literature.

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This is a brief history of Decatur Baptist College. The data concerning this subject have been taken from Wise County newspapers, college catalogs, college annuals, deed record books, Texas Baptist Annuals, literary publications, History of Texas Baptist by J. M. Carroll, letters, and personal interviews. This data has been carefully selected and taken from reliable sources. The material has been organized in a chronological manner under the following headings: origin of the college, material development, internal growth, and the conclusion. No attempt has been made to add or detract from the educational significance of the institution. An attempt has been made to tell the story of Decatur Baptist College in such a manner that both the triumphs and the adversities will be shown.

Technology has revamped every aspect of our life, one of those various facets is fashion industry. Plenty of deep learning architectures are taking shape to augment fashion experiences for everyone. There are numerous possibilities of enhancing the fashion technology with deep learning. One of the key ideas is to generate fashion style and recommendation using artificial intelligence. Likewise, another significant feature is to gather reliable information of fashion trends, which includes analysis of existing fashion related images and data. When specifically dealing with images, localisation and segmentation are well known to address in-depth study relating to pixels, objects and labels present in the image. In this master thesis a complete framework is presented to perform localisation and segmentation on fashionista images. This work is a part of an interesting research work related to Fashion Style detection and Recommendation. Developed solution aims to leverage the possibility of localising fashion items in an image by drawing bounding boxes and labelling them. Along with that, it also provides pixel-wise semantic segmentation functionality which extracts fashion item label-pixel data. Collected data can serve as ground truth as well as training data for the aimed deep learning architecture. A study related to localisation and segmentation of videos has also been presented in this work. The developed system has been evaluated in terms of flexibility, output quality and reliability as compared to similar platforms. It has proven to be fully functional solution capable of providing essential localisation and segmentation services while keeping the core architecture simple and extensible. / Tekniken har förnyat alla aspekter av vårt liv, en av de olika fasetterna är modeindustrin. Massor av djupa inlärningsarkitekturer tar form för att öka modeupplevelser för alla. Det finns många möjligheter att förbättra modetekniken med djup inlärning. En av de viktigaste idéerna är att skapa modestil och rekommendation med hjälp av artificiell intelligens. På samma sätt är en annan viktig egenskap att samla pålitlig information om modetrender, vilket inkluderar analys av befintliga moderelaterade bilder och data. När det specifikt handlar om bilder är lokalisering och segmentering väl kända för att ta itu med en djupgående studie om pixlar, objekt och etiketter som finns i bilden. I denna masterprojekt presenteras en komplett ram för att utföra lokalisering och segmentering på fashionista bilder. Detta arbete är en del av ett intressant forskningsarbete relaterat till Fashion Style detektering och rekommendation. Utvecklad lösning syftar till att utnyttja möjligheten att lokalisera modeartiklar i en bild genom att rita avgränsande lådor och märka dem. Tillsammans med det tillhandahåller det även pixel-wise semantisk segmenteringsfunktionalitet som extraherar dataelementetikett-pixeldata. Samlad data kan fungera som grundsannelse samt träningsdata för den riktade djuplärarkitekturen. En studie relaterad till lokalisering och segmentering av videor har också presenterats i detta arbete. Det utvecklade systemet har utvärderats med avseende på flexibilitet, utskriftskvalitet och tillförlitlighet jämfört med liknande plattformar. Det har visat sig vara en fullt fungerande lösning som kan tillhandahålla viktiga lokaliseringsoch segmenteringstjänster samtidigt som kärnarkitekturen är enkel och utvidgbar.

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<p>  </p> <p>Dicamba (3,6-dichloro-2-methoxybenzoic acid) and 2,4-D (2,4-dichlorophenoxyacetic acid) are two widely used herbicides for broadleaf weed control in soybeans. However, off-target application of dicamba and 2,4-D can cause severe damage to sensitive vegetation and crops. Early detection and assessment of off-target damage caused by these herbicides are necessary to help plant diagnostic labs and state regulatory agencies collect more information of the on-site conditions so to develop solutions to resolve the issue in the future. In 2021, the study was conducted to detect damage to soybean leaves caused by dicamba and 2,4-D by using LeafSpec, an accurate handheld hyperspectral leaf scanner. . High resolution single leaf hyperspectral images of 180 soybean plants in the greenhouse exposed to nine different herbicide treatments were taken 1, 7, 14, 21 and 28 days after herbicide spraying. Pairwise PLS-DA models based on spectral features were able to distinguish leaf damage caused by two different modes of action herbicides, specifically dicamba and 2,4-D, as early as 2 hours after herbicide spraying. In the spatial distribution analysis, texture and morphological features were selected for separating the dosages of herbicide treatments. Compared to the mean spectrum method, new models built upon the spectrum, texture, and morphological features, improved the overall accuracy to over 70% for all evaluation dates. The combined features are able to classify the correct dosage of the right herbicide as early as 7 days after herbicide sprays. Overall, this work has demonstrated the potential of using spectral and spatial features of LeafSpec hyperspectral images for early and accurate detection of dicamba and 2,4-D damage in soybean plants.</p> <p>   </p>

The aim of this thesis is to design and evaluate k-space weighting filters for simultaneously improving the spatial and temporal resolution of cardiovascular MRI, with the ultimate goal of improving the accuracy of quantitative flow measurements, which are important for diagnosis and follow-up of heart dysfunction. Two different k-space filters were implemented and evaluated retrospectively to already acquired data. In addition, evaluation was performed with respect to tapering of the filters in the radial k-space direction, as well as accelerated imaging using undersampling. To better utilize the properties of the golden-angle acquisition, a k-space filter was also implemented where the temporal footprint increased in discrete steps, referred to as rings. The temporal footprint of each ring was calculated according to the Fibonacci sequence, and the starting position for each ring was computed to satisfy the Nyquist criterion. The k-space filters were evaluated in comparison to non-filtered reconstructions of cine and phase-contrast images. Motion-mode images were created from the cine images and used to evaluate the edge sharpness of the septal wall indicating the spatial resolution of the image. Phase-contrast images were used to measure peak flow velocity over the mitral valve, and the myocardial velocity in the early and late filling phases. The resolution of the peak is highly dependent on the temporal resolution. Measuring the peak velocity gave an indication of the temporal resolution, which could be compared to non-filter reconstructions. This study showed that k-space filters adapted to the Nyquist criterion improve the temporal resolution of peak velocity measures. Further investigation is justified to conclude if the performance exceeded the best performing method without k-space filters. However, the k-space filter showed substantial agreement with the best performing temporal footprint without k-space filter. / Syftet med arbetet är att designa och utvärdera k-space viktade filter för att förbättra den spatiala och temporala upplösningen av kardiovaskulär MRI, med målet att förbättra noggrannheten i kvantitativa flödesmätningar, som är viktiga för diagnos och uppföljning av hjärtdysfunktion. Två typer av k-space filter skapades och utvärderades retrospektivt på redan inhämtade data. Dessutom utfördes utvärdering med avseende på avsmalning av filtren i den radiella k-rymdsriktningen, såväl som accelererad avbildning med undersampling. För att bättre utnyttja egenskaperna hos den gyllene vinkeln skapades det ena k-rumsfilter så att det temporala fotavtrycket ökade i diskreta steg, så kallade ringar. Det temporala fotavtrycket för varje ring beräknades enligt Fibonacci talen, och startpositionen för varje ring beräknades så att den uppfyllde Nyquistkriteriet. k-Spacefiltren utvärderades i jämförelse med icke-filtrerade rekonstruktioner av tidsupplösta, anatomiska bilder (cine) och tidsupplösta faskontrastbilder. Bilder i motion-mode skapades från cine-bilderna och användes för att utvärdera kantskärpan av hjärtats skiljevägg (septum), vilket användes som en indikator för bildens spatiala upplösning. Faskontrastbilder användes för att mäta den maximala flödeshastigheten över mitralisklaffen och myokardiets hastighet i den tidiga och sena fyllnadsfasen. Maximal flödeshastighet är starkt beroende av den temporala upplösningen och gav därav en indikation på den temporala upplösningen. Denna studie visade att k-rumsfilter anpassade till Nyquist-kriteriet förbättrar den temporala upplösningen av topphastigheten. Ytterligare undersökning behövs dock för att säkerställa att prestandan översteg den bäst presterande metoden utan k-rumsfilter. Bilder rekonstruerade med filtret visade dock god överensstämmelse med det minsta temporala fotavtrycket, utan filter.