Pendant ces dix dernières années, les lancements successifs de satellites pour l´observation de la Terre dotés de capteurs SAR (Synthetic Aperture Radar) ont permis de montrer son fort potentiel, car ces systèmes sont capables de couvrir de vastes régions avec une résolution élevée, ce qui représente un avantage pour le suivi terrain. Cette thèse propose d’élargir les applications conventionnelles, en s’occupant d´investiguer le potentiel et les limites de l’interférométrie SAR pour la mesure de la déformation du terrain dans la région amazonienne, qui n’a pas encore été étudiée dans ce cadre spécifique. L´application consiste à estimer le déplacement de la surface de la Terre sur la zone urbaine de Manaus, la plus grande ville de l'État d'Amazonas au Brésil, qui représente un site important pour l'exploration pétrolière et gazière et pour le transport. Ce site est entouré par des écosystèmes fragiles, qui le rendent très sensible à la présence de l'industrie pétrolière. Dans ce contexte, une compréhension de la dynamique temporelle et de la distribution spatiale des phénomènes de néotectonique est fondamentale pour la définition de bonnes pratiques de gestion de l'environnement. Au niveau méthodologique, afin de lever les principales difficultés rencontrées pour l’application de l’interférométrie différentielle sur une pile de données Radarsat-1, une stratégie multi-échelles et “model free”, basée sur l´information de déformation non-linéaire au cours du temps est adoptée avec succès. La caractéristique essentielle de cette procédure est la séparation du signal de phase en différentes échelles spatiales pour simplifier la séparation des trois composantes de phase (topographie, atmosphère et déplacement). Cela conduit à une plus grande robustesse et permet l'inversion de composantes de phase pour les petites piles d’images. Au niveau géophysique, l’application de l’interférométrie à l’étude du déplacement du terrain est réalisée pour la première fois sur le milieu de l´Amazonie, en complétant les études de géologie structurale antérieures basées sur les mesures issues de la corrélation des images optiques et les mesures de terrain. En complément des connaissances antérieures, la présente étude apporte une information précise sur l'hypothèse de mouvements de la croûte récents liés aux activités néotectoniques du bassin de l'Amazonie. Les résultats indiquent une zone de mouvement de la croûte adjacente à une structure de drainage circulaire dans la ville de Manaus. Les images Radarsat-1 et 2 acquises sur cette région apportent une meilleure compréhension des activités géologiques et du mouvement de la croûte dans le bassin de l'Amazonie. / During the past decade, successive satellites launches for Earth observation with SAR (Synthetic Aperture Radar) sensors onboard have shown its potential, once these systems are able to cover large areas with high resolution, which represents an advantage for surface monitoring. The mass of data generated has enabled the development of radar interferometry and its relevance to the study of small deformations in urban areas or in fault zones, showing that the technique was able to focus on different spatial scales of deformation, as well as temporal scales ranging from a few weeks to more than a decade. Usually, these types of applications have been limited to non-equatorial regions of the world due to the presence of atmospheric disturbances that affect the radar signal. Given the large size and the remote location of tropical basins such as the Amazon, satellite observations remain as a viable approach to validate existing geophysical models. In this context, this thesis proposes to extend conventional applications, taking care to investigate the potential and limitations of SAR interferometry for the measurement of ground deformation in the Amazon region, not yet studied in this specific context. The research aim is to estimate the displacement at the surface of the Earth on the urban area of Manaus, the largest city in the state of Amazonas in Brazil, which is an important site for oil and gas exploration and the transport. This site is surrounded by fragile ecosystems, which make it very sensitive to the presence of the oil industry. Considering this, an understanding of the temporal dynamics and spatial distribution of neotectonic phenomena is fundamental to the development of best practice environmental management. At the methodological level, to overcome the major challenges of the application of differential interferometry on the data stack Radarsat-1, a multi-scale and "model free" strategy, based on the information of a non-linear deformation over time is passed successfully. The essential feature of this process is the separation of the phase signal into small and large scale spatial contributions to simplify the separation of the three phase components (topography, atmosphere and movement). This leads to a more robust processing and allows the phase component inversion for small piles of images. In the geophysical level, the application of interferometry to investigate the ground movement is performed for the first time in the middle of the Amazon, complementing previous studies of structural geology based on measurements from the correlation of optical images and field measurements. In addition to prior knowledge, this study provides accurate information on the hypothesis of recent crustal movements associated with neotectonic activities of the Amazon basin. The results indicate a range of motion of the adjacent crust structure circular drainage in the city of Manaus. The Radarsat-1 and 2 acquired in this region provide a better understanding of geological activity and crustal movement in the Amazon basin.

Synthetic Aperture Radar (SAR) is a type of radar system that can generate high-resolution images with which one can detect subtle changes on the scale of centimetres from space. It can operate in any weather condition and during both day and night, making it unique compared to optical sensors. SAR is used for applications such as environmental monitoring, surveillance, and earth observation. Its ability to penetrate clouds and, to some extent, vegetation, allows for insights into terrain, vegetation structure, and even subsurface features. The importance of modelling the generated data of a SAR system before initiating the construction and development of it cannot be overstated. This thesis presents the implementation of the Reverse BackProjection Algorithm (RBPA) designed to generate raw SAR data efficiently and accurately. The RBPA stands out with its flexibility, enabling researchers and designers to simulate and gauge the SAR system's effectiveness under diverse scenarios. This provides an easy way of fine-tuning configurations for distinct needs concerning scene geometries, orbits, and radar designs. Two versions of the RBPA were implemented, differing slightly in the theoretical approach of azimuth defocusing. On top of this, a bistatic mode and Terrain Observation by Progressive Scans (TOPS) acquisition mode was also implemented. The inclusion of these two modes were specifically due to their relevance for the upcoming European Space Agency (ESA) SAR mission, Harmony. The addition of the TOPS mode required a comprehensive design of the antenna framework. Moreover, this implementation also paves the way for simpler integration of modes in the future. The two versions of the RBPA were profiled, revealing the optimal system and parameter configurations.

In this research, the characterization of snow cover is made from data collected in September, February and March of 2002 and 2003, during Cold-land Processes Field Experiments project of the NASA. These data include snow and forests characteristic measurements, meteorological conditions, digital elevation model (DEM) and polarimetric multifrequency SAR data (C, L and P bands) acquired from AIRSAR-POLSAR airborne sensor. These data will be used to analyze multifrequency polarimetric techniques to characterize snow cover over forested areas (open area, sparse coniferous forest, and dense coniferous forest). Different techniques have been developed to detect wet snow over different forested areas. The methodology of wet snow detection developed by Rott and Nagler (1995) is first analyzed. The best result is obtained in HH polarization (13% for the sparse coniferous forest site and 25% for the dense coniferous forest site). C-band data in circular polarizations improves these results, but the errors remain high (22% for the sparse coniferous forest site and 13% for the dense coniferous forest site). The use of [sigma][omicronn] ratio in dB [sigma][omicronn][subscript LHH] /[sigma][omicronn][subscript CHH], [sigma][omicronn][subscript LHV]/[sigma][omicronn] [subscript CHH], [sigma][omicronn][subscript LHV] /[sigma][omicronn][subscript CHV] and [sigma][omicronn][subscript LVV] /[sigma][omicronn][subscript CHH] allows to detect wet snow ([less-than or equal to] 13% errors) for both the open area and the dense coniferous forest sites. However, with this technique, higher errors ([greater-than or equal to] 16%) are obtained for the sparse coniferous forest site. The analysis of polarimetric signatures in the three bands shows that their shapes vary according to snow conditions (wet or dry) and forest densities. The pedestal height of polarimetric signatures in P band allows to apply a thresholding approach to discriminate between snow conditions (wet or dry). The error matrix generated from polarimetric signature techniques applied to snow pit measurements shows error higher than 6%. For the characterization of snow condition, target decomposition theorems show promising results. For the three bands, the Freeman-Durden and Cloude-Pottier decompositions allow to understand scattering mechanisms of snow-covered-forested areas. Also, a thresholding approach applied to volume scattering power of the Freeman-Durden decomposition in C band as well as to entropy parameter together with angle [alpha] value of Cloude-Pottier decomposition shows abilities to detect wet snow over forested areas. The technique using the volume scattered power shows detection errors higher than 16%. No classification error is obtained in the error matrix generated from entropy values over the snow pits. The analysis of backscattering coefficients as a function of forest density (open area, sparse coniferous forest and dense coniferous forest) shows variations in the signal as a function of frequency, polarization, density and forest structures as well as with ground conditions (snow-free, dry snow, wet snow). Three radar vegetation indexes (IVR, IVRD[subscript HH] and IVRD[subscript VV]) are analyzed. The IVR index in C and L bands, as well as the IVRD[subscript VV] index in L band are sensitive to forest density. The volume scattered power of the Freeman-Durden decomposition also allows to characterize forest density in C, L and P bands.In order to partially reduce the effect of forested area on the backscattering of a snow cover, image difference between the C-band backscattering coefficient (HH polarization) and the C-band volume scattered power in wet snow condition is performed. The error matrix generated over the snow pit shows that a threshold of 1.5 dB applied to the image difference leads to errors less than 6%. The obtained results clearly show the utility of multifrequency, multipolarisation and polarimetric SAR data for wet snow detection over different forested areas.

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High Throughput Screening (HTS) is used in drug discovery to screen large numbers of compounds against a biological target. Data on activity against the target are collected for a representative sample of compounds selected from a large library. The goal of drug discovery is to relate the activity of a compound to its chemical structure, which is quantified by various explanatory variables, and hence to identify further active compounds. Often, this application has a very unbalanced class distribution, with a rare active class. <br /><br /> Classification methods are commonly proposed as solutions to this problem. However, regarding drug discovery, researchers are more interested in ranking compounds by predicted activity than in the classification itself. This feature makes my approach distinct from common classification techniques. <br /><br /> In this thesis, two AIDS data sets from the National Cancer Institute (NCI) are mainly used. Local methods, namely K-nearest neighbours (KNN) and classification and regression trees (CART), perform very well on these data in comparison with linear/logistic regression, neural networks, and Multivariate Adaptive Regression Splines (MARS) models, which assume more smoothness. One reason for the superiority of local methods is the local behaviour of the data. Indeed, I argue that conventional classification criteria such as misclassification rate or deviance tend to select too small a tree or too large a value of <em>k</em> (the number of nearest neighbours). A more local model (bigger tree or smaller <em>k</em>) gives a better performance in terms of drug discovery. <br /><br /> Because off-the-shelf KNN works relatively well, this thesis takes this promising method and makes several novel modifications, which further improve its performance. The choice of <em>k</em> is optimized for each test point to be predicted. The empirically observed superiority of allowing <em>k</em> to vary is investigated. The nature of the problem, ranking of objects rather than estimating the probability of activity, enables the <em>k</em>-varying algorithm to stand out. Similarly, KNN combined with a kernel weight function (weighted KNN) is proposed and demonstrated to be superior to the regular KNN method. <br /><br /> High dimensionality of the explanatory variables is known to cause problems for KNN and many other classifiers. I propose a novel method (subset KNN) of averaging across multiple classifiers based on building classifiers on subspaces (subsets of variables). It improves the performance of KNN for HTS data. When applied to CART, it also performs as well as or even better than the popular methods of bagging and boosting. Part of this improvement is due to the discovery that classifiers based on irrelevant subspaces (unimportant explanatory variables) do little damage when averaged with good classifiers based on relevant subspaces (important variables). This result is particular to the ranking of objects rather than estimating the probability of activity. A theoretical justification is proposed. The thesis also suggests diagnostics for identifying important subsets of variables and hence further reducing the impact of the curse of dimensionality. <br /><br /> In order to have a broader evaluation of these methods, subset KNN and weighted KNN are applied to three other data sets: the NCI AIDS data with Constitutional descriptors, Mutagenicity data with BCUT descriptors and Mutagenicity data with Constitutional descriptors. The <em>k</em>-varying algorithm as a method for unbalanced data is also applied to NCI AIDS data with Constitutional descriptors. As a baseline, the performance of KNN on such data sets is reported. Although different methods are best for the different data sets, some of the proposed methods are always amongst the best. <br /><br /> Finally, methods are described for estimating activity rates and error rates in HTS data. By combining auxiliary information about repeat tests of the same compound, likelihood methods can extract interesting information about the magnitudes of the measurement errors made in the assay process. These estimates can be used to assess model performance, which sheds new light on how various models handle the large random or systematic assay errors often present in HTS data.

The overall aim of this thesis is to increase our knowledge of relevant exposure parameters when discussing possible health implication from exposure to radiofrequency electromagnetic fields (RF), especially effects that might occur at non-thermal levels. In this thesis an effort is made to broaden the exposure assessment and to take the exposure time into account and combine it with the Specific Absorption Rate (SAR) and the field parameters (electric and magnetic field strength) to approach a dose concept. In the first part of the thesis self-reported subjective symptoms among mobile phone users were studied. As a basis for this an epidemiological study among mobile phone users was completed with the main hypothesis that users of the digital transmission system GSM experience more symptoms than users of the older analogue NMT transmission system. The hypothesis was falsified, but an interesting side finding was that people with longer calling time per day experienced more symptoms than people with shorter calling time per day. The time-aspect (long duration phone call etc.) was also found to be relevant for the occurrence of symptoms in association with mobile phone use as well as duration of symptoms. The new suggested dosimetric quantity Specific Absorption per Day (SAD), in which both calling time per day as well as the measured SAR1g are included showed a stronger association to the prevalence of some of the symptoms, such as dizziness, discomfort and warmth behind the ear compared to both CT and SAR1g alone. In the second part whole body exposure conditions were considered. Methods to measure the induced current were examined in an experimental study, where different techniques were compared in different grounding conditions. The results were used in a study of operators of RF plastic sealers (RF operators) where the health status as well as the exposure were studied. The results showed that RF operators are a highly exposed group, which was confirmed by the fact that 16 out of 46 measured work places exceeded the ICNIRP guidelines. Headaches were found to be associated with the mean value of the time integrated E-field during a weld (E-weld) and the warmth sensations in the hands (warm hands) with the time integrated E-field exposure during one day (E-day). The general findings in this thesis indicated that time should be included in the exposure assessment when studying non-thermal effects such as subjective symptoms in connection with RF exposure. The thesis proposes two different methods for doing this, namely timeintegrated exposure [V/m x t and A/m x t] and dose [J/kg].

Doctor of Philosophy / Department of Grain Science and Industry / Ronald L. Madl / Praveen Vadlani / Producers of whole wheat products are interested in marketing the health-promoting benefits of wheat antioxidants. However, they need a steady crop supply with consistent levels of antioxidants. The variable phenolic content in wheat crops is a problem. The objectives of this research were to 1) identify the factor (s) that contribute the most to the variability in phenolic content, 2) understand the mechanism (s) responsible for phenolic synthesis, and 3) artificially trigger that mechanism (s). Phenolics are hypothesized to be part of the defense response of hard red winter wheat (Triticum aestivum L) to stress. The effect of insect feeding, pathogen infection, and heat stress on phenolics in grains from wheat plants cv. Karl 92 was evaluated. Bird-cherry oat aphid (Rhopalosiphum padi) feeding stress significantly explained the variation in phenolic content. Furthermore, the relative allocation of carbon resources to grain yield/phenolic content was influenced by the stage of the plant at which aphid feeding started to occur. Based on these findings, phenolics were hypothesized to be an active defense response acting through a mechanism known as systemic acquired resistance (SAR). In order to prove this hypothesis, several synthetic elicitors of SAR were tested for their effectiveness at inducing de novo phenolic synthesis in wheat foliage and in mature grains. Elicitors that acted through the salicylic- and jasmonic acid signaling pathways were effective at inducing phenolic synthesis by 49% and 177%, respectively, in the leaves 36 hours post spray application. They also elicited a phenolic response in mature grains of up to 21% induction. Enhancement of the levels of naturally occurring phenolic compounds with antioxidant activity in wheat grains through SAR activation is a value addition strategy that can potentially increase the profitability of hard red winter wheat crops. It can also provide manufacturers of whole wheat with natural antioxidants that can potentially be used to substitute their synthetic counterparts in wheat based products.

Les travaux présentés dans cette thèse portent sur l'étude et la réalisation d'un système d'imagerie SAR (synthetic aperture radar) exploitant deux techniques provenant des communications numériques: la configuration MIMO et les signaux OFDM. Dans la première partie de cette étude, différentes méthodes de focalisation des signaux reçus pour la configuration MIMO sont proposées afin de mesurer l'impact de la configuration MIMO sur la robustesse du système d'imagerie SAR par rapport aux bruits. Par ailleurs, on mesure aussi l'impact de la configuration MIMO sur la résolution en azimut. Finalement, un système expérimental est développé au sein du laboratoire afin de confirmer les résultats obtenus par simulation. Dans la deuxième partie de cette étude, une méthode de réduction de l'ambiguïté en distance est proposée et validée par simulation. Cependant, l'utilisation de signaux classiques de type \textit{chirps} montre ses limites pour la réduction de l'ambiguïté en distance. Ainsi, une méthode de conception de signaux OFDM est développée afin de résoudre ce problème. Une dernière étude sur les signaux OFDM est mené dans le cadre de son utilisation dans la configuration MIMO pour l'imagerie SAR. L'impact des signaux OFDM sur la résolution azimutale ainsi que sur les différents paramètres de qualité images est étudié. / The work presented in this thesis focuses on the design and implementation of a SAR system operating with two Digital Communications technology: MIMO configuration and OFDM signals. In the first part of this study, various methods for focusing received signals for MIMO configuration are proposed in order to measure the impact of the MIMO configuration on the robustness. In addition, the impact of the MIMO configuration on the azimuth resolution is measured. Finally, an experimental system is developed in order to validate the results obtained by simulation. In the second part of this study, a range ambiguity suppression method is proposed and validated by simulation. However, the use of conventional chirp signals showed the limits of its use for the range ambiguity suppression. Thus, a design method of OFDM signals is developed in order to solve this problem. The last study on the OFDM signals is carried out in the context of its use with the MIMO configuration. The impact of the OFDM signals on the azimuth resolution and the imaging quality parameters are studied.

Imagens de radar de abertura sintética (SAR) são tipicamente corrompidas pelo ruído \"speckle\" que também degrada imagens geradas por ultra-som, laser, etc. Esta tese propõe algoritmos de filtragem baseados na abordagem \"maximum a posteriori\" (MAP) para redução de \"speckle\" em imagens SAR. Na derivação dos filtros MAP, para imagens obtidas por detecção linear, são utilizadas as distribuições (condicionais) Rayleigh e raiz quadrada de gama na regra de Bayes como modelos para o ruído \"speckle\" em imagens SAR obtidas em amplitudes com 1 e múltiplas visadas, respectivamente, e usadas várias distribuições para o modelo \"a priori\". Toda a formulação dos algoritmos tem por base o modelo multiplicativo que constitui o modelo mais adequado ao \"speckle\". Propõe-se ainda neste trabalho a combinação dos filtros MAP formulados com o algoritmo k-médias e com a técnica de crescimento de regiões, como forma de melhoria da abordagem de filtragem proposta. Os resultados de filtragem foram avaliados segundo critérios (medidas) de melhoria da relação sinal-ruído e perda de resolução. O primeiro critério avalia a redução da intensidade do ruído \"speckle\" sobre regiões homogêneas e para avaliar a perda de resolução decorrente da filtragem é proposta uma nova técnica baseada na transformada de Hough. Os algoritmos foram testados em imagens artificialmente contaminadas por ruído \"speckle\" e em imagens SAR reais apresentando estatísticas Rayleigh e raiz de gama. Os resultados obtidos mostram a melhoria que proporcionam os algoritmos de filtragem MAP, especialmente quando combinados com o classificador k-médias e com a técnica de crescimento de região. O uso da técnica de crescimento de região reforça a conclusão de que o uso de vizinhança estatisticamente mais semelhante ao pixel ruidoso melhora a estimação dos parâmetros de filtragem. As medidas de desempenho e validação dos algoritmos MAP permitiram concluir que os filtros com distribuições \"a priori\" Gaussiana, gama, chi-quadrado e beta apresentaram melhores resultados de filtragem em relação aos demais modelos \"a priori\" quando comparados ao filtro de Kuan e com a técnica de \"wavelets\" para a classe de imagens utilizadas / Synthetic aperture radar (SAR) images are typically corrupted by speckle noise, which also degrade images produced by laser beams, ultrasound, etc. This thesis proposes filtering algorithms based on the \"maximum a posteriori\" (MAP) approach, to reduce speckle in SAR images. To derive the MAP filters for linearly detected images we assumed the multiplicative model for the speckle and used the conditional density functions in the Bayes rule following a Rayleigh and square root of gamma for one-look and N-looks images, respectively, and several different \"a priori\" densities. The MAP filters are combined with the k-means classifier and region growing tools to improve the proposed filtering approach. Measures evaluating both the signal-to-noise improvement and resolution loss due to filtering are computed. To assess the improvement brought by the proposed algorithms we evaluate them with respect to signal to noise ratio and edge preservation. The former is a classical way to evaluate the speckle strenght reduction over homogeneous areas and the latter is a new proposed technique based on the Hough transform that measures distortions at the edges produced by the speckle MAP filtering algorithms. The qualitative analysis of the MAP proposed algorithms includes the methods based on the curvature and wavelets . The algorithms were applied to simulated noisy speckled images and real SAR images with statistics of linearly detected images with one-look and N-looks. The obtained results demonstrated the improvement brought by the speckle MAP filtering algorithms, specially when combined with the k-means clustering algorithm and with the region growing approach. This region growing approach reinforces the conclusion that the use of a neighborhood whose pixels have statistics similar to the noisy pixel provides a better estimation for filtering. The evaluating measures point out that the MAP filters whose \"a priori\" models are the Gaussian, gamma, chi-square and beta presented better results than the other \"a priori\" models proposed in this thesis, the Kuan filter and the wavelets filter, for the class of images that were tested