Thesis (M. Eng.), Memorial University of Newfoundland, 1998. / Bibliography: leaves 117-125.

Title from first page of PDF document. Includes bibliographical references (p. 40).

Thesis (M.Eng.)--Memorial University of Newfoundland, 2004. / Bibliography: leaves 134-138.

Indoor localization is the process of localizing people or objects inside a building in the same way GPS does in an outside environment. In recent years, researchers have successfully achieved improvement in indoor localization accuracy. Still there are many limitations to overcome in performing and achieving good accuracy in indoor localization. The interest in estimating the location of something inside a building with good accuracy is very strong. In this thesis we first propose an indoor localization technique relative to Wi-Fi access points along with a novel heuristic search based algorithm, named MuSLoc. Through simulation and comparative studies, we have shown that MuSLoc outperforms other indoor localization models without the help of fingerprinting or crowdsourcing about the environment. MuSLoc provides almost the same accuracy in LOS (Line of Sight) and NLOS (Non-Line of Sight) environments with regular infrastructure that has recently been provided by smart phones. This model doesn't require any additional hardware support in order to perform well. Further, we propose another indoor localization based Wi-Fi device tracker model, named MSTracker, which is able to track both moving and non-moving devices inside a building. This model is also free from specialized infrastructure and can perform well without any training data information. Through real time simulation and analysis we have shown that it performs more accurately than other available models. Through extensive simulations in a real time environment and analysis of performance comparatives with other available models, we have shown that both MuSLoc and MSTracker perform more accurately with COTS than any other method of indoor localization and tracking of objects inside a building. The complete package of MuSLoc and MSTracker can perform perfectly with recently available Wi-Fi modules and smartphones.

This thesis investigates the viability of applying a shape-from-shading technique to SAR imagery. A shape-from-shading algorithm is derived and tested on a single site for which both a Seasat SAR image and Digitial Elevation Model (DEM) were available. The shape-from-shading technique used in this thesis follows an approach proposed by Frankot and Chellappa for processing slant range SAR imagery. The algorithm incorporates a one-step technique for projecting non-integrable surface orientation estimates onto an integrable set in the frequency domain along with the iterative convergent shape-from-shading algorithm of Brooks and Horn. The significant issues and choices made in implementing the shape-from-shading algorithm and in preparing the SAR data and DEM are discussed. The shape-from-shading algorithm was applied to both the test site SAR image and images synthesized from the DEM. Reflectance models were derived from the SAR image and DEM. By quantitatively comparing the shape-from-shading results with the initial conditions used for the experiments, it was found that the algorithm produced substantially better results when applied to the synthesized images; however, when applied to the SAR image, there was no significant improvement over the initial conditions. / Science, Faculty of / Computer Science, Department of / Graduate

Noise and degradation reduction is of significant importance in virtually all systems where these phenomena are present, specifically in the fields of signal and image processing.  The effect of image processing on target detection is of significant interest because noise and degradations can greatly reduce the effectiveness of detection algorithms, due to the presence of high intensity noise which is often mistaken as a target.  In signal processing, noise in vibration data, or any time-series data, can reduce the accuracy of measurement and can prevent the passing of useful information. Many filters that have been developed are designed to reduce a single class of noise, such as Wiener and Frost filters.  When these filters are applied to types of noise that they were not designed for, the effect of the noise reduction can be greatly reduced.  The proposed Two-Stage Non-Adaptive Convolution (TSNAC) filter significantly reduces both additive and multiplicative noise in these two unique systems. The performance of these filters is compared through several Image Quality (IQ) metrics. It will be shown that the proposed TSNAC filter reduces noise and degradations more effectively in both SAR images and synthetic vibration data than the competing filters.  It will show higher IQ scores, greater computational efficiency in target detection, and significant improvement in signal restoration of simulated vibration data. / Master of Science

The objectives of this research are to support the development of state-of-the-art methods using remotely sensed data to detect slides or anomalies in an efficient and cost-effective manner based on the use of SAR technology. Slough or slump slides are slope failures along a levee, which leave areas of the levee vulnerable to seepage and failure during high water events. This work investigates the facility of detecting the slough slides on an earthen levee with different types of polarimetric Synthetic Aperture Radar (polSAR) imagery. The source SAR imagery is fully quad-polarimetric L-band data from the NASA Jet Propulsion Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area encompasses a portion of the levees of the lower Mississippi river, located in Mississippi, United States. The obtained classification results reveal that the polSAR data unsupervised classification with features extraction produces more appropriate results than the unsupervised classification with no features extraction. Obviously, supervised classification methods provide better classification results compared to the unsupervised methods. The anomaly identification is good with these results and was improved with the use of a majority filter. The classification accuracy is further improved with a morphology filter. The classification accuracy is significantly improved with the use of GLCM features. The classification results obtained for all three cases (magnitude, phase, and complex data), with classification accuracies for the complex data being higher, indicate that the use of synthetic aperture radar in combination with remote sensing imagery can effectively detect anomalies or slides on an earthen levee. For all the three samples it consistently shows that the accuracies for the complex data are higher when compared to those from the magnitude and phase data alone. The tests comparing complex data features to magnitude and phase data alone, and full complex data, and use of post-processing filter, all had very high accuracy. Hence we included more test samples to validate and distinguish results.

Humans perceive the world in three dimensions, but many sensing capabilities only display two-dimensional information to users by way of images. In this work we develop two novel reconstruction techniques utilizing synthetic aperture radar (SAR) data in three dimensions given sparse amounts of available data. We additionally leverage a hybrid joint-sparsity and sparsity approach to remove a-priori influences on the environment and instead explore general imaging properties in our reconstructions. We evaluate the required sampling rates for our techniques and a thorough analysis of the accuracy of our methods. The results presented in this thesis suggest a solution to sparse three-dimensional object reconstruction that effectively uses a substantially less amount of phase history data (PHD) while still extracting critical features off an object of interest.

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Yes / A metasurface based decoupling structure that is composed of a square-wave slot pattern with exaggerated corners that is implemented on a rectangular microstrip provides high-isolation between adjacent patch antennas for Synthetic Aperture Radar (SAR) and Multi-Input-Multi-Output (MIMO) systems. The proposed 1×2 symmetric array antenna integrated with the proposed decoupling isolation structure is designed to operate at ISM bands of X, Ku, K, and Ka. With the proposed mutual coupling suppression technique (i) the average isolation in the respective ISM bands listed above is 7 dB, 10 dB, 5 dB, and 10 dB; and (ii) edge-to-edge gap between adjacent radiation elements is reduced to 10 mm (0.28λ). The average antenna gain improvement with the metasurface isolator is 2 dBi. / H2020-MSCA-ITN-2016 SECRET-722424 and the financial support from the UK Engineering and Physical Sciences Research Council (EPSRC) under grant EP/E0/22936/1