Visualization and exploration of nematic liquid crystal (NLC) data is a challenging task due to the multidimensional and multivariate nature of the data. Simulation study of an NLC consists of multiple timesteps, where each timestep computes scalar, vector, and tensor parameters on a geometrical mesh. Scientists developing an understanding of liquid crystal interaction and physics require tools and techniques for effective exploration, visualization, and analysis of these data sets. Traditionally, scientists have used a combination of different tools and techniques like 2D plots, histograms, cut views, etc. for data visualization and analysis. However, such an environment does not provide the required insight into NLC datasets. This thesis addresses two areas of the study of NLC data---understanding of the tensor order field (the Q-tensor) and defect detection in this field. Tensor field understanding is enhanced by using a new glyph (NLCGlyph) based on a new design metric which is closely related to the underlying physical properties of an NLC, described using the Q-tensor. A new defect detection algorithm for 3D unstructured grids based on the orientation change of the director is developed. This method has been used successfully in detecting defects for both structured and unstructured models with varying grid complexity.

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AIM & HYPOTHESIS: Resolvins, derived from omega-3 fatty acids, may actively resolve inflammation. Resolvin E1 (RvE1) binds to Chem-R23 as an endogenous anti-inflammatory and pro-resolving lipid mediator. We hypothesized that RvE1 may also activate osteoblasts to restore critical size bone defects in a calvarial model. MATERIALS & METHODS: An in-vitro calvarial culture system was used to evaluate the stimulative effects of RvE1 compared to Amniotic Growth Factor (AGF) (a known stimulant in this system) on critical size defects under static conditions. Calvaria harvested from 10 mice and separated into 20 calvaria halves were cultured under conditions favoring bone formation. The test groups were defect only, defect plus a collagen membrane, defect plus a collagen membrane plus RvE1, and defect plus a collagen membrane plus AGF. The effect of RvE1 and AGF on healing of a critical size bone defect was assessed with both histological evaluation and alkaline phosphatase assays. RESULTS: RvE1 binds in a receptor-ligand interaction with Chem-R23 in the periosteum to stimulate cellular proliferation and migration into a critical size bone defect of neonatal mouse calvaria. CONCLUSION: These results suggest that RvE1 has a direct effect on osteoblast activity at and around the edge of a critical size 2 mm defect without an inflammatory reaction.

<p> The work described in this thesis is devoted to the application of defect engineering in the development of silicon photonic devices. The thesis is divided into simulation and experimental portions, each focusing on a different form of defect engineered silicon: ion implantation induced amorphous silicon and solid-phase epitaxial regrowth suppressed polycrystalline silicon.</p> <p> The simulations are directed at silicon rib waveguide Raman laser applications. It is shown that a uniform, divacancy defect concentration will not enhance Raman gain. The excess optical loss and free carrier lifetime of rib waveguides with remote amorphous silicon volumes were simulated. Net gain was demonstrated depending on the geometry of the structure. For a waveguide structure with rib width, rib height and slab height of W = 1.5, H = 1.5 and h = 0.8 μm respectively, the optimal separation between the edge of the rib and the amorphous region is ~2.0 μm. Surface recombination velocity modification was shown to be an effective means to reduce free carrier lifetime.</p> <p> Experimental work was devoted to the characterization of a novel form of polycrystalline silicon created by amorphizing the entire silicon overlayer of a silicon-on-insulator wafer. Solid-phase epitaxial regrowth of the amorphous silicon is suppressed upon annealing due to the lack of a crystal seed and results in polycrystalline silicon. This material was characterized with ellipsometry, positron annihilation spectroscopy and x-ray diffraction. The material properties are shown to be heavily dependent on the annealing conditions. Ellipsometry showed that the refractive index at 1550 nm is comparable to crystalline silicon. Positron annihilation spectroscopy showed that the polycrystalline material exhibits a high concentration of vacancy-type defects while vertically regrown crystalline silicon does not. X-ray diffraction showed that the polycrystalline silicon is non-textured, strained in tension and is characterized by grain sizes less than 300 nm.</p> <p> Defect etching and optical measurements using a waveguide geometry were performed in order to characterize the lateral regrowth and the optical loss of the polycrystalline material. Lateral regrowth in the [011] direction was 1.53 and 0.96 μm for 10 minute anneals at 750 and 900 °C respectively, and at least 2.5 μm at 650 °C. Waveguide optical loss measurements with adjacent polycrystalline regions separated from the rib by at least 5.5 μm showed no separation dependence. The intrinsic optical loss of the polycrystalline material was estimated to be 1.05 and 1.57 dB/cm for TM and TE polarizations after a 900 °C anneal. Vertically regrown c-Si was shown to exhibit less than 3.0 dB/cm optical loss after annealing at 550 °C .</p> / Thesis / Master of Applied Science (MASc)

This dissertation presents an automated detection algorithm that identifies severe external defects on the surfaces of barked hardwood logs and stems. The defects detected are at least 0.5 inch in height and at least 3 inches in diameter, which are severe, medium to large in size, and have external surface rises. Hundreds of real log defect samples were measured, photographed, and categorized to summarize the main defect features and to build a defect knowledge base. Three-dimensional laser-scanned range data capture the external log shapes and portray bark pattern, defective knobs, and depressions. The log data are extremely noisy, have missing data, and include severe outliers induced by loose bark that dangles from the log trunk. Because the circle model is nonlinear and presents both additive and non-additive errors, a new robust generalized M-estimator has been developed that is different from the ones proposed in the statistical literature for linear regression. Circle fitting is performed by standardizing the residuals via scale estimates calculated by means of projection statistics and incorporated in the Huber objective function to bound the influence of the outliers in the estimates. The projection statistics are based on 2-D radial-vector coordinates instead of the row vectors of the Jacobian matrix as proposed in the statistical literature dealing with linear regression. This approach proves effective in that it makes the GM-estimator to be influence bounded and thereby, robust against outliers. Severe defects are identified through the analysis of 3-D log data using decision rules obtained from analyzing the knowledge base. Contour curves are generated from radial distances, which are determined by robust 2-D circle fitting to the log-data cross sections. The algorithm detected 63 from a total of 68 severe defects. There were 10 non-defective regions falsely identified as defects. When these were calculated as areas, the algorithm locates 97.6% of the defect area, and falsely identifies 1.5% of the total clear area as defective. / Ph. D.

Railroad tracks require consistent and periodic monitoring to ensure safety and reliability. Unmanned Aerial Vehicles (UAVs) have great potential because they are not constrained to the track, allowing trains to continue running while the UAV is inspecting. Also, they can be quickly deployed without human intervention. For these reasons, the first steps towards creating a track-monitoring UAV system have been completed. This thesis focuses on the design of algorithms to be deployed on a UAV for the purpose of monitoring the health of railroad tracks. Before designing the algorithms, the first steps were to design a rough physical structure of the final product. A small multirotor or fixed-wing UAV will be used with a gimbaled camera mounted on the belly. The camera will take images of the tracks while the onboard computer processes the images. The computer will locate the tracks in the image as well as perform defect detection on those tracks. Algorithms were implemented once a rough physical structure of the product was completed. These algorithms detect and follow rails through a video feed and detect defects in the rails. The rail following algorithm is based on a custom-designed masking technique that locates rails in images. A defect detection algorithm was also created. This algorithm detect defects by analyzing gradient data on the rail surface. / Master of Science

This research focuses on developing a model to predict the yield of a printed circuit board manufactured on a given assembly line. Based on an extensive literature review as well as discussion with industrial partners, it was determined that there is no tool available for assisting engineers in determining reliable estimates of their production capabilities as they introduce new board designs onto their current production lines. Motivated by this need, a more in-depth study of manufacturing yield as well as the electronic assembly process was undertaken. The relevant literature research was divided into three main fields: process modeling, board design, and PCB testing. The model presented in this research combines elements from process modeling and board design into a single yield model. An optimization model was formulated to determine the fault probabilities that minimize the difference between actual yield values and predicted yield values. This model determines fault probabilities (per component type) based on past production yields for the different board designs assembled. These probabilities are then used to estimate the yields of future board designs. Two different yield models were tested and their assumptions regarding the nature of the faults were validated. The model that assumes independence between faults provided better yield predictions. A preliminary case study was performed to compare the performance of the presented model with that of previous models using data available from the literature. The proposed yield model predicts yield within 3% of the actual yield value, outperforming previous regression models that predicted yield within 10%, and artificial neural network models that predicted yield within 5%. A second case study was performed using data gathered from actual production lines. The proposed yield model continued to provide very good yield predictions. The average difference with respect to the actual yields from this case study ranged between 1.25% and 2.27% for the lines studied. Through sensitivity analysis, it was determined that certain component types have a considerably higher effect on yield than others. Once the proposed yield model is implemented, design suggestions can be made to account for manufacturability issues during the design process. / Master of Science