A unique, super spatio-temporal resolution Digital Particle Image Velocimetry (DPIV) system with capability of resolving velocities in a multi-phase flow field, using a very sophisticated novel Dynamically Adaptive Hybrid velocity evaluation algorithm has been developed The unique methodology of this powerful system is presented, its specific distinctions are enlightened, confirming its flexibility, and its superior performance is established by comparing it to the most established best DPIV software implementations currently available. Taking advantage of the most recent advances in imaging technology coupled with state of the art image processing tools, high-performing validation schemes including neural networks, as well as a hybrid digital particle tracking velocimeter (DPTV), the foundation for a unique system was developed. The presented software enables one to effectively resolve tremendously demanding flow-fields. The resolution of challenging test cases including high speed cavitating underwater projectiles as well as high pressure spray demonstrate the power of the developed device. / Master of Science

Yes / Detecting multiple ocular diseases in fundus images is crucial in ophthalmic diagnosis. This study introduces the Fundus-DeepNet system, an automated multi-label deep learning classification system designed to identify multiple ocular diseases by integrating feature representations from pairs of fundus images (e.g., left and right eyes). The study initiates with a comprehensive image pre-processing procedure, including circular border cropping, image resizing, contrast enhancement, noise removal, and data augmentation. Subsequently, discriminative deep feature representations are extracted using multiple deep learning blocks, namely the High-Resolution Network (HRNet) and Attention Block, which serve as feature descriptors. The SENet Block is then applied to further enhance the quality and robustness of feature representations from a pair of fundus images, ultimately consolidating them into a single feature representation. Finally, a sophisticated classification model, known as a Discriminative Restricted Boltzmann Machine (DRBM), is employed. By incorporating a Softmax layer, this DRBM is adept at generating a probability distribution that specifically identifies eight different ocular diseases. Extensive experiments were conducted on the challenging Ophthalmic Image Analysis-Ocular Disease Intelligent Recognition (OIA-ODIR) dataset, comprising diverse fundus images depicting eight different ocular diseases. The Fundus-DeepNet system demonstrated F1-scores, Kappa scores, AUC, and final scores of 88.56%, 88.92%, 99.76%, and 92.41% in the off-site test set, and 89.13%, 88.98%, 99.86%, and 92.66% in the on-site test set.In summary, the Fundus-DeepNet system exhibits outstanding proficiency in accurately detecting multiple ocular diseases, offering a promising solution for early diagnosis and treatment in ophthalmology. / European Union under the REFRESH – Research Excellence for Region Sustainability and High-tech Industries project number CZ.10.03.01/00/22_003/0000048 via the Operational Program Just Transition. The Ministry of Education, Youth, and Sports of the Czech Republic - Technical University of Ostrava, Czechia under Grants SP2023/039 and SP2023/042.

A commercial-off-the-shelf Ku-band Frequency Modulated Continuous Wave (FMCW) synthetic aperture radar (SAR) system is coupled with a custom built two-dimensional scanning system. This system is installed in an alpine environment and pointed at a snow-unstable mountain slope for the duration of a Utah winter. The radar scanning system, designed to be capable of mapping a snowpack and its layers, is employed to create a series of three-dimensional images from a remote location. Individual images demonstrate the ability to directly detect snow layers, Furthermore, successive images are compared to track volume magnitude and phase values over the course of winter, including many snow deposition and melt events. The digital signal processing techniques used to create a high-resolution voxel (a three-dimensional pixel) map describing these snow layers is discussed. Results are discussed and further work is suggested for improving upon the results of this work.

Consensus in scientific literature is that hyperglycemia, which is a condition that manifests in individuals with uncontrolled diabetes, causes compromised bone growth, but the exact mechanisms of are unknown. It has been estimated that 5% of dental implant failures that have previously been linked to unknown causes may be associated with undiagnosed diabetes. It is important to study the early stages of bone growth as it is accepted that they are critical in the long-term success rate of endosseous implants. This study aimed to investigate the bone healing seen in the hyperglycemic group compared to the normal (i.e. control) group, at an early time point, using high-resolution microscopy techniques. Ten young (200-250gram) male Wistar rats were used for this study with five rats assigned to the control group and the other five rats intravenously injected with 65 mg/kg of streptozotocin (STZ) to induce diabetes. An osteotomy model was used to make a 1.3mm defect in the diaphysis of the rat femurs. After five days, the femurs were removed, fixed in glutaraldehyde, dehydrated, and embedded in resin. Structural and chemical analyses were conducted on the samples using a variety of microscopy techniques to examine various factors of bone quality including: bone porosity, relative mineralization level, and the arrangement of collagen and mineral. When analyzing the micro-structure, the hyperglycemic group showed increased porosity in the newly formed bone as compared to the control group. However, no significant differences were found in the nano-structure when analyzing the arrangement of collagen and mineral.Therefore, the results in this thesis suggest that alterations in micro-architecture rather than nano-architecture may play a pivotal role in the compromised bone healing in uncontrolled diabetes at this five-day time point. Future work should investigate additional time points in the bone healing process. / Thesis / Master of Applied Science (MASc) / According to the International Diabetes Federation, 387 million people worldwide are living with diabetes of which 46.3% are undiagnosed. Uncontrolled diabetes results in hyperglycemia, which is a condition where there is an increased level of glucose in the blood. When diabetes is not regulated correctly with medication, it leads to problems in the long-term success rate of dental implants. The objective of this thesis was to investigate the early stages of bone formation, which are accepted to be critical in the long-term success rate of dental implants, in hyperglycemic animal models compared to control groups using various microscopy techniques. The different techniques used allowed for the structural and elemental compositions of bone to be studied on the micro-scale and nano-scale. It was shown that at the 5-day healing time point studied, the micro-structure, rather than the nano-structure, was negatively altered in the hyperglycemic group compared to the control group.

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