Studies in this dissertation were conducted to explore the roles of organic cation transporters (OCTs) in the disposition of N-butylpyridinium Chloride (NBuPy-Cl) and structurally related ILs. Following a single i.v. dose to rats, the blood concentration of NBuPy-Cl and 1-butyl-1-methylpyrrolidinium chloride (BmPy-Cl) decreased in a biphasic manner with a clearance of 3.3 and 7 ml/min, respectively. More than 84% of dosed compounds were excreted in the urine. Depending on the vehicle, the dermal absorption of BmPy-Cl and NBuPy-Cl (5 mg/kg, 125 μg/cm²) was 10-35% at 96 h. Following a single oral (50 mg/kg) administration to rats, the maximum blood concentrations of both ILs were reached in less than 90 min in rats. Most of the orally dosed NBuPy-Cl (62-68 %) was excreted in the urine in 72 h. However, more of the dosed BmPy-Cl was eliminated in the feces Its oral bioavailability was only 47%. The elimination differences between BmPy-Cl and NBuPy-Cl were not altered by the size (0.5, 5, or 50 mg/kg) or frequency (1 or 5 administrations) of oral doses. In all urine and blood samples, only parent compounds were detected. Co-administration of NBuPy-Cl and inulin intravenously to rats revealed that the clearance of NBuPy-Cl exceeded the rat glomerular filtration rate, suggesting a renal secretion processing. The in vitro transport studies demonstrated that NBuPy-Cl, BmPy-Cl and 1-butyl-3-methylimidazolium chloride are substrates (Kt, 9~277 μM), as well as inhibitors (IC₅₀: 0.2~7.5 μM), of rOCT1/2 and hOCT2. Their inhibitory effects increased dramatically with increasing the alkyl chain length. The IC₅₀ values were 0.1, 3.8, 14 and 671 μM (hexyl-, butyl-, ethyl-pyridinium and pyridinium chloride) for rOCT2 mediated metformin transport. Similar structurally related inhibitory kinetics were observed for rOCT1 and hOCT2. In vivo co-administration of NBuPy-Cl prolonged the plasma half-life and reduced renal clearance of the diabetic drug, metformin. In summary, BmPy-Cl and NBuPy-Cl are partially absorbed from gastrointestinal tract. The present in blood is eliminated rapidly in the urine as parent, by renal filtration and OCT-mediated secretion. ILs also compete with other substrates of OCTs and have the potential to alter their pharmacokinetic profiles.

This thesis investigates the sensitivity and separability of post-harvest tillage conditions using polarimetric Synthetic Aperture RADAR in southwestern Ontario. Variables examined include: linear polarizations HH, HV, and VV and polarimetric variables: pedestal height, co-polarized complex correlation coefficient magnitude, left and right co-polarized circular polarizations and co-polarized phase difference. Six fine-quad polarimetric, high incidence angle (49°) RADARSAT-2 images acquired over three dates in fall 2010 were used. Over 100 fields were monitored, coincident with satellite overpasses. OMAFRA’s AgRI, a high-resolution polygon network was used to extract average response from fields. Discrimination between tillage practices was best later in the fall season, due to sample size and low soil moisture conditions. Variables most sensitive to tillage activities include HH and VV polarizations and co-polarized complex correlation coefficient magnitude. A supervised support vector machine (SVM) classifier classified no-till and conventional tillage with 91.5% overall accuracy. These results highlight the potential of RADARSAT-2 for monitoring tillage conditions.

This thesis is focused on the utilization of RADARSAT-2 polarimetric SAR data for mapping two surficial aspects of the Umiujalik Lake area, Nunavut, Canada: i) materials, such as bedrock, boulders, organic material, sand and gravel, thick and thin till; and ii) lineaments. To achieve these tasks, RADARSAT-2 polarimetric SAR images with three west-looking, increasing incidence angles (FQ1, FQ12, and FQ20, respectively) were used alone and in combination with LANDSAT-7 ETM+ and Digital Elevation Model (DEM) image data. The surficial materials mapping study tested: i) the effects of incidence angles on mapping accuracy; and ii) non-polarimetric and polarimetric classifiers. For non-polarimetric analysis, a Maximum Likelihood Classification (MLC) algorithm was applied to different combinations of RADARSAT-2, LANDSAT-7 ETM+, and DEM images, achieving a maximum overall classification accuracy of 85%. Polarimetric analyses first included computation of polarimetric signatures to understand the scattering mechanisms of the considered surficial materials, i.e., surface, volume, and multiple scatterings. It also tested three polarimetric classifiers: supervised Wishart (overall accuracy of 48.7% from FQ12 image), and unsupervised Freeman-Wishart, and Wishart-H/ /A. Three main conclusions were reached: i) high incidence angle greatly decreases classification accuracy for the HH polarized image when used alone, but incidence angle has little effect when the HV polarization is added; ii) combining images with three incidence angles (FQ1, FQ12, and FQ20) gives higher accuracy with the maximum likelihood classifier; and iii) the medium incidence angle image (FQ12) produces the best classification accuracy using polarimetric classifiers. In the second part of the study, surface lineaments were mapped using RADARSAT-2 SAR single-polarized images, RGB HH, HV, VV composites, polarimetric total power images, and LANDSAT-7 ETM+ principal component images. Polarization effect analysis showed that regardless of beam mode, more lineaments were identified on the HH image than on the HV image, and the maximum number of lineaments was identified on the multi-polarized RGB composite. Incidence angle effects results showed that regardless of polarization modes, the FQ12 image yielded more lineaments than the FQ1 or FQ20 images. The majority of lineaments are oriented in NW and NNW directions, which correspond to the ice flow direction during the last glaciation.

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Suite aux lancements successifs de satellites pour l'observation de la Terre dotés de capteur SAR (Synthetic Aperture Radar), la masse de données SAR disponible est considérable. Dans ce contexte, la fusion des mesures de déplacement issues de l'imagerie SAR est prometteuse à la fois dans la communauté de la télédétection et dans le domaine géophysique. Dans cette optique, cette thèse propose d'élargir les approches conventionnelles en combinant les techniques de traitement des images SAR, les méthodes de fusion d'informations et la connaissance géophysique. Dans un premier temps, cette thèse a pour objectif d'étudier plusieurs stratégies de fusion, l'inversion jointe, la pré-fusion et la post-fusion, afin de réduire l'incertitude associée d'une part à l'estimation du déplacement en 3 dimensions (3D) à la surface de la Terre, d'autre part à la modélisation physique qui décrit la source en profondeur du déplacement observé en surface. Nous évaluons les avantages et les inconvénients de chacune des stratégies en ce qui concerne la réduction de l'incertitude et la robustesse vis à vis du bruit. Dans un second temps, nous visons à prendre en compte les incertitudes épistémiques, en plus des incertitudes aléatoires, présentes dans les mesures et proposons les approches classiques et floues basées sur la théorie des probabilités et la théorie des possibilités pour modéliser ces incertitudes. Nous analysons et mettons en évidence l'efficacité de chaque approche dans le cadre de chaque stratégie de fusion. La première application consiste à estimer les champs du déplacement 3D à la surface de la Terre dus au séisme du Cachemire en octobre 2005 et à l'éruption du Piton de la Fournaise en janvier 2004 sur l'île de la Réunion. La deuxième application porte sur la modélisation de la rupture de la faille en profondeur liée au séisme du Cachemire. Les principales avancées sont évaluées d'un point de vue méthodologique en traitement de l'information et d'un point de vue géophysique. Au niveau méthodologique, afin de lever les principales difficultées rencontrées pour l'application de l'interférométrie différentielle à la mesure du déplacement induit par le séisme du Cachemire, une stratégie de multi-échelles basée sur l'information a priori en utilisant les fréquences locales de phase interférométrique est adoptée avec succès. En ce qui concerne la gestion de l'incertitude, les incertitudes aléatoires et épistémiques sont analysées et identifiées dans les mesures du déplacement. La théorie des probabilités et la théorie des possibilités sont utilisées afin de modéliser et de gérer les propagations des incertitudes au cours de la fusion. En outre, les comparaisons entre les distributions de possibilité enrichissent les comparaisons faites simplement entre les valeurs et indiquent la pertinence des distributions de possibilité dans le contexte étudié. Par ailleurs, la pré-fusion et la post-fusion, 2 stratégies de fusion différentes de la stratégie d'inversion jointe couramment utilisée, sont proposées afin de réduire autant que possible les incertitudes hétérogènes présentes en pratique dans les mesures et pour contourner les principales limitations de la stratégie d'inversion jointe. Les bons cadres d'application de chaque approche de la gestion de l'incertitude sont mis en évidence dans le contexte de ces stratégies de fusion. Au niveau géophysique, l'application de l'interférométrie différentielle à l'étude du séisme du Cachemire est réalisée pour la première fois et compléte les études antérieures basées sur les mesures issues de la corrélation des images SAR et optiques, les mesures télésismiques et les mesures de terrain. L'interférométrie différentielle apporte une information précise sur le déplacement en champ lointain par rapport à la position de la faille. Ceci permet d'une part de réduire les incertitudes associées aux mesures de déplacement en surface et aux paramètres du modèle, et d'autre part de détecter les déplacements post-sismiques qui existent potentiellement dans les mesures cosismiques qui couvrent la période de mouvement post-sismique. Par ailleurs, la prise en compte de l'incertitude épistémique et la proposition de l'approche floue pour gérer ce type d'incertitude, fournissent une vision différente de l'incertitude de mesure connue par la plupart des géophysiciens et complétent la connaissance de l'incertitude aléatoire et l'application de la théorie des probabilités dans ce domaine. En particulier, la gestion de l'incertitude par la théorie des possibilités permet de contourner le problème de sous-estimation d'incertitude par la théorie des probabilités. Enfin, la comparaison du déplacement mesuré par les images SAR avec le déplacement mesuré par les images optiques et le déplacement issu des mesures sur le terrain révèle toute la difficulté d'interpréter différentes sources de données plus ou moins compatibles entre elles. Les outils développés dans le cadre de cette thèse sont intégrés dans le package MDIFF (Methods of Displacement Information Fuzzy Fusion) dans l'ensemble des "EFIDIR Tools" distribués sous licence GPL.

In this paper I will give a brief and general overview of the characteristics of spatial data, why it is useful to use such data and how to use the information included in spatial data. The first question to be answered is: how to detect spatial dependency and spatial autocorrelation in data? Such effects can for instance be found by calculating Moran's I, which is a measure for spatial autocorrelation. The Moran's I is also the basis for a test for spatial autocorrelation (Moran's test). Once we found some spatial structure we can use special models and estimation techniques. There are two famous spatial processes, the SAR- (spatial autoregressive) and the SMA- (spatial moving average process) process, which are used to model spatial effects. For estimation of spatial regression models there are mainly two different possibilities, the first one is called spatial filtering, where the spatial effect is filtered out and standard techniques are used, the second one is spatial two stage least square estimation. Finally there are some results of a spatial analysis of R&D spillovers data (for a panel dataset with 22 countries and 20 years) shown. (author's abstract) / Series: Research Report Series / Department of Statistics and Mathematics

The synthetic aperture radar (SAR) onboard Earth observing satellites has been acknowledged as an integral tool for many applications in monitoring the marine environment. Some of these applications include regional sea-ice monitoring and detection of illegal or accidental oil discharges from ships. Nonetheless, a practicality of the usage of SAR images is greatly hindered by the presence of speckle noises. Such noise must be eliminated or reduced to be utilized in real-world applications to ensure the safety of the marine environment. Thus this thesis presents a novel two-phase total variation optimization segmentation approach to tackle such a challenging task. In the total variation optimization phase, the Rudin-Osher-Fatemi total variation model was modified and implemented iteratively to estimate the piecewise smooth state by minimizing the total variation constraints. In the finite mixture model classification phase, an expectation-maximization method was performed to estimate the final class likelihoods using a Gaussian mixture model. Then a maximum likelihood classification technique was utilized to obtain the final segmented result. For its evaluation, a synthetic image was used to test its effectiveness. Then it was further applied to two distinct real SAR images, X-band COSMO-SkyMed imagery containing verified oil-spills and C-band RADARSAT-2 imagery mainly containing two different sea-ice types to confirm its robustness. Furthermore, other well-established methods were compared with the proposed method to ensure its performance. With the advantage of a short processing time, the visual inspection and quantitative analysis including kappa coefficients and F1 scores of segmentation results confirm the superiority of the proposed method over other existing methods.

This thesis investigates the problem of interaction of electromagnetic radiation with human tissues. A Finite Difference Time Domain (FDTD) code has been developed to model a cellular phone radiating in the presence of a human head. In order to implement the code, FDTD difference equations have been solved in a computational domain truncated by a Perfectly Matched Layer (PML). Specific Absorption Rate (SAR) calculations have been carried out to study safety issues in mobile communication.

The objective of this research is a fast circular synthetic aperture radar (F-CSAR) algorithm. Slow-time imaging distinguishes synthetic aperture radar (SAR) from its predecessor imaging radars. SAR slow-time imaging is strongly rooted in Huygens-Fresnel principle and Kirchhoff's approximation based scalar diffraction theory. Slant-plane SAR Green's function and resultant Fourier integral, unlike some Fourier integrals, cannot be analyzed using residue theory from complex analysis and Cauchy-Riemann equations yield analyticity. The asymptotic expansion of 1D and 2D Fourier integrals renders a decomposition of the Green's function leading to SAR data focusing. The research unveils Fraunhofer diffraction patterns in 2D aperture synthesis formulation corresponding to various aperture shapes including circular aperture that appears to be an optimum aperture shape from the mathematical condition in the asymptotic expansion. It is shown that these diffraction patterns may be used for refocusing of defocused images. F-CSAR algorithm is demonstrated using Householder transform recently shown to have improved error bounds and stability. Research is also carried out into various interpolation schemes. Backprojection implementation of CSAR is compared to F-CSAR and elevation coverage renders 3D reconstruction. F-CSAR is also demonstrated using GTRI T-72 tank turntable data.

abstract: Spotlight mode synthetic aperture radar (SAR) imaging involves a tomo- graphic reconstruction from projections, necessitating acquisition of large amounts of data in order to form a moderately sized image. Since typical SAR sensors are hosted on mobile platforms, it is common to have limitations on SAR data acquisi- tion, storage and communication that can lead to data corruption and a resulting degradation of image quality. It is convenient to consider corrupted samples as missing, creating a sparsely sampled aperture. A sparse aperture would also result from compressive sensing, which is a very attractive concept for data intensive sen- sors such as SAR. Recent developments in sparse decomposition algorithms can be applied to the problem of SAR image formation from a sparsely sampled aperture. Two modified sparse decomposition algorithms are developed, based on well known existing algorithms, modified to be practical in application on modest computa- tional resources. The two algorithms are demonstrated on real-world SAR images. Algorithm performance with respect to super-resolution, noise, coherent speckle and target/clutter decomposition is explored. These algorithms yield more accu- rate image reconstruction from sparsely sampled apertures than classical spectral estimators. At the current state of development, sparse image reconstruction using these two algorithms require about two orders of magnitude greater processing time than classical SAR image formation. / Dissertation/Thesis / M.S. Electrical Engineering 2011