Patch antennas are fundamental elements in many microwave communications systems. However, patch antennas receive/transmit signals over a very narrow bandwidth (typically a maximum of 3% bandwidth). Design modifications directed toward bandwidth expansion generally yield 10% to 40% bandwidth. The series varactor tuned patch antenna configuration was the bandwidth enhancement method explored in this research; this configuration is implemented by dividing a patch antenna into multiple sections and placing varactors across the resultant gaps. In addition to yielding a large bandwidth, the configuration has a number of ancillary benefits, including straightforward integration and design flexibility. Through the research represented by this work, the properties of the series varactor tuned patch antenna, herein referred to as the Fragmented Patch Antenna (or FPA), were explored and optimized. As a result, an innovative patch antenna was produced that yielded 63.4% frequency tuning bandwidth and covered a frequency range between 2.8 and 5.4 GHz. The wide bandwidth was achieved through a detailed parametric study. The products of this study were the discovery of multiple tuning resonances that were used to expand the tuning bandwidth and the understanding/documentation of the significance of specific antenna dimensions. Measurement results were obtained through the fabrication of a prototype antenna using semiconductor varactors. In the second research phase, the construction of capacitors using the tunable permittivity material Barium Strontium Titanate (BST) was investigated. Using this material in conjunction with nano-fabrication techniques, varactors were developed that had good estimated performance characteristics and were considered appropriate for integration into adaptive microwave circuitry, such as the tunable antenna system. The varactors were constructed by using Focused Ion Beam (FIB) milling to create a nano-scale capacitive gap in a transmission line. A combination of end-point current detection (EPD) and cross-section scanning electron (SEM) and ion beam (FIB) microscope images were used to optimize the milling procedure. The future extensions of this work include the integration of the BST varactors with the antenna design; the configuration of the developed BST varactors lends itself to a straightforward integration with the FPA antenna.

No description available.

Cette thèse présente une méthode pour la détection hors-ligne de multiples ruptures dans des séries temporelles multivariées, et propose d'en exploiter les résultats pour estimer les relations de dépendance entre les variables du système. L'originalité du modèle, dit du Bernoulli Detector, réside dans la combinaison de statistiques locales issues d'un test robuste, comparant les rangs des observations, avec une approche bayésienne. Ce modèle non paramétrique ne requiert pas d'hypothèse forte sur les distributions des données. Il est applicable sans ajustement à la loi gaussienne comme sur des données corrompues par des valeurs aberrantes. Le contrôle de la détection d'une rupture est prouvé y compris pour de petits échantillons. Pour traiter des séries temporelles multivariées, un terme est introduit afin de modéliser les dépendances entre les ruptures, en supposant que si deux entités du système étudié sont connectées, les événements affectant l'une s'observent instantanément sur l'autre avec une forte probabilité. Ainsi, le modèle s'adapte aux données et la segmentation tient compte des événements communs à plusieurs signaux comme des événements isolés. La méthode est comparée avec d'autres solutions de l'état de l'art, notamment sur des données réelles de consommation électrique et génomiques. Ces expériences mettent en valeur l'intérêt du modèle pour la détection de ruptures entre des signaux indépendants, conditionnellement indépendants ou complètement connectés. Enfin, l'idée d'exploiter les synchronisations entre les ruptures pour l'estimation des relations régissant les entités du système est développée, grâce au formalisme des réseaux bayésiens. En adaptant la fonction de score d'une méthode d'apprentissage de la structure, il est vérifié que le modèle d'indépendance du système peut être en partie retrouvé grâce à l'information apportée par les ruptures, estimées par le modèle du Bernoulli Detector. / This thesis presents a method for the multiple change-points detection in multivariate time series, and exploits the results to estimate the relationships between the components of the system. The originality of the model, called the Bernoulli Detector, relies on the combination of a local statistics from a robust test, based on the computation of ranks, with a global Bayesian framework. This non parametric model does not require strong hypothesis on the distribution of the observations. It is applicable without modification on gaussian data as well as data corrupted by outliers. The detection of a single change-point is controlled even for small samples. In a multivariate context, a term is introduced to model the dependencies between the changes, assuming that if two components are connected, the events occurring in the first one tend to affect the second one instantaneously. Thanks to this flexible model, the segmentation is sensitive to common changes shared by several signals but also to isolated changes occurring in a single signal. The method is compared with other solutions of the literature, especially on real datasets of electrical household consumption and genomic measurements. These experiments enhance the interest of the model for the detection of change-points in independent, conditionally independent or fully connected signals. The synchronization of the change-points within the time series is finally exploited in order to estimate the relationships between the variables, with the Bayesian network formalism. By adapting the score function of a structure learning method, it is checked that the independency model that describes the system can be partly retrieved through the information given by the change-points, estimated by the Bernoulli Detector.

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No. of bitstreams: 1 DissCHVP.pdf: 4899707 bytes, checksum: e7de60b5431e48ddbc2b9016dae268c7 (MD5) Previous issue date: 2016-03-10 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / The magnetic resonance (MR) imaging has become an indispensable tool for the diagnosis and study of various diseases and syndromes of the central nervous system, such as Alzheimer’s disease (AD). In order to perform the precise diagnosis of a disease, as well as the evolutionary monitoring of a certain treatment, the neuroradiologist doctor often needs to measure and assess volume and shape changes in certain brain structures along a series of MR images. For that, the previous delineation of the structures of interest is necessary. In general, such task is manually done, with limited help from a computer, and therefore it has several problems. For this reason, many researchers have turned their efforts towards the development of automatic techniques for segmentation of brain structures in MR images. Among the various approaches proposed in the literature, techniques based on deformable models and anatomical atlases are among those which present the best results. However, one of the main difficulties in applying geometric deformable models is the initial positioning of the model. Thus, this research aimed to develop an atlas of 3D salient points (automatically detected from a set of MR images) and to investigate the applicability of such atlas in guiding the initial positioning of geometric deformable models representing brain structures, with the purpose of helping the automatic segmentation of such structures in MR images. The processing pipeline included the use of a 3D salient point detector based on the phase congruency measure, an adaptation of the shape contexts technique to create point descriptors and the estimation of a B-spline transform to map pairs of matching points. The results, evaluated using the Jaccard and Dice metrics before and after the model initializations, showed a significant gain in the tests involving synthetically deformed images of normal patients, but for images of clinical patients with AD the gain was marginal and can still be improved in future researches. Some ways to do such improvements are discussed in this work. / O imageamento por ressonância magnética (RM) tornou-se uma ferramenta indispensável no diagnóstico e estudo de diversas doenças e síndromes do sistema nervoso central, tais como a doença de Alzheimer (DA). Para que se possa realizar o diagnóstico preciso de uma doença, bem como o acompanhamento evolutivo de um determinado tratamento, o médico neurorradiologista frequentemente precisa medir e avaliar alterações de volume e forma em determinadas estruturas do cérebro ao longo de uma série de imagens de RM. Para isso, a delineação prévia das estruturas de interesse nas imagens é necessária. Em geral, essa tarefa é realizada manualmente, com ajuda limitada de um computador, e portanto possui diversos problemas. Por esse motivo, vários pesquisadores têm voltado seus esforços para o desenvolvimento de técnicas automáticas de segmentação de estruturas cerebrais em imagens de RM. Dentre as várias abordagens propostas na literatura, técnicas baseadas em modelos deformáveis e atlas anatômicos estão entre as que apresentam os melhores resultados. No entanto, uma das principais dificuldades na aplicação de modelos geométricos deformáveis é o posicionamento inicial do modelo. Assim, esta pesquisa teve por objetivo desenvolver um atlas de pontos salientes 3D (automaticamente detectados em um conjunto de imagens de RM) e investigar a aplicabilidade de tal atlas em guiar o posicionamento inicial de modelos geométricos deformáveis representando estruturas cerebrais, com o propósito de auxiliar a segmentação automática de tais estruturas em imagens de RM. O arcabouço de processamento incluiu o uso de um detector de pontos salientes 3D baseado na medida de congruência de fase, uma adaptação da técnica shape contexts para a criação de descritores de pontos e a estimação de uma transformação B-spline para mapear pares de pontos correspondentes. Os resultados, avaliados com as métricas Jaccard e Dice antes e após a inicialização dos modelos, mostraram um ganho significativo em testes envolvendo imagens sinteticamente deformadas de pacientes normais, mas em imagens de pacientes clínicos com DA o ganho foi marginal e ainda pode ser melhorado em pesquisas futuras. Algumas maneiras de se realizar tais melhorias são discutidas neste trabalho. / FAPESP: 2015/02232-1 / CAPES: 2014/11988-0

Le problème étudié dans le cadre de cette thèse porte essentiellement sur l'étape de détection de défaut dans un processus de diagnostic industriel. Ces travaux sont motivés par la surveillance de deux sous-systèmes complexes d'un autobus impactant la disponibilité des véhicules et leurs coûts de maintenance : le système de freinage et celui des portes. Cette thèse décrit plusieurs outils dédiés au suivi de fonctionnement de ces deux systèmes. On choisit une approche de diagnostic par reconnaissance des formes qui s'appuie sur l'analyse de données collectées en exploitation à partir d'une nouvelle architecture télématique embarquée dans les autobus. Les méthodes proposées dans ces travaux de thèse permettent de détecter un changement structurel dans un flux de données traité séquentiellement, et intègrent des connaissances disponibles sur les systèmes surveillés. Le détecteur appliqué aux freins s'appuie sur les variables de sortie (liées au freinage) d'un modèle physique dynamique du véhicule qui est validé expérimentalement dans le cadre de nos travaux. L'étape de détection est ensuite réalisée par des cartes de contrôle multivariées à partir de données multidimensionnelles. La stratégie de détection pour l'étude du système porte traite directement les données collectées par des capteurs embarqués pendant des cycles d'ouverture et de fermeture, sans modèle physique a priori. On propose un test séquentiel à base d'hypothèses alimenté par un modèle génératif pour représenter les données fonctionnelles. Ce modèle de régression permet de segmenter des courbes multidimensionnelles en plusieurs régimes. Les paramètres de ce modèle sont estimés par un algorithme de type EM dans un mode semi-supervisé. Les résultats obtenus à partir de données réelles et simulées ont permis de mettre en évidence l'efficacité des méthodes proposées aussi bien pour l'étude des freins que celle des portes / This thesis is mainly dedicated to the fault detection step occurring in a process of industrial diagnosis. This work is motivated by the monitoring of two complex subsystems of a transit bus, which impact the availability of vehicles and their maintenance costs: the brake and the door systems. This thesis describes several tools that monitor operating actions of these systems. We choose a pattern recognition approach based on the analysis of data collected from a new IT architecture on-board the buses. The proposed methods allow to detect sequentially a structural change in a datastream, and take advantage of prior knowledge of the monitored systems. The detector applied to the brakes is based on the output variables (related to the brake system) from a physical dynamic modeling of the vehicle which is experimentally validated in this work. The detection step is then performed by multivariate control charts from multidimensional data. The detection strategy dedicated to doors deals with data collected by embedded sensors during opening and closing cycles, with no need for a physical model. We propose a sequential testing approach using a generative model to describe the functional data. This regression model allows to segment multidimensional curves in several regimes. The model parameters are estimated via a specific EM algorithm in a semi-supervised mode. The results obtained from simulated and real data allow to highlight the effectiveness of the proposed methods on both the study of brakes and doors

This master's thesis is focused on fully automatic calibration of traffic surveillance camera, which is used for speed measurement of passing vehicles. Thesis contains and describes theoretical information and algorithms related to this issue. Based on this information and algorithms, a comprehensive system design for automatic calibration and speed measurement was built. The proposed system has been successfully implemented. The implemented system is optimized to process the smallest portion of the video input for the automatic calibration of the camera. Calibration parameters are obtained after processing only two and half minutes of input video. The accuracy of the implemented system was evaluated on the dataset BrnoCompSpeed. The speed measurement error using the automatic calibration system is 8.15 km/h. The error is mainly caused by inaccurate scale acquisition, and when it is replaced by manually obtained scale, the error is reduced to 2.45 km/h. The speed measuring system itself has an error of only 1.62 km/h (evaluated using manual calibration parameters).

Dans ce mémoire de thèse, nous nous intéressons à la modélisation non paramétrique de données spatiales et/ou fonctionnelles, plus particulièrement basée sur la méthode à noyau. En général, les échantillons que nous avons considérés pour établir les propriétés asymptotiques des estimateurs proposés sont constitués de variables dépendantes. La spécificité des méthodes étudiées réside dans le fait que les estimateurs prennent en compte la structure de dépendance des données considérées.Dans une première partie, nous appréhendons l’étude de variables réelles spatialement dépendantes. Nous proposons une nouvelle approche à noyau pour estimer les fonctions de densité de probabilité et de régression spatiales ainsi que le mode. La particularité de cette approche est qu’elle permet de tenir compte à la fois de la proximité entre les observations et de celle entre les sites. Nous étudions les comportements asymptotiques des estimateurs proposés ainsi que leurs applications à des données simulées et réelles.Dans une seconde partie, nous nous intéressons à la modélisation de données à valeurs dans un espace de dimension infinie ou dites "données fonctionnelles". Dans un premier temps, nous adaptons le modèle de régression non paramétrique introduit en première partie au cadre de données fonctionnelles spatialement dépendantes. Nous donnons des résultats asymptotiques ainsi que numériques. Puis, dans un second temps, nous étudions un modèle de régression de séries temporelles dont les variables explicatives sont fonctionnelles et le processus des innovations est autorégressif. Nous proposons une procédure permettant de tenir compte de l’information contenue dans le processus des erreurs. Après avoir étudié le comportement asymptotique de l’estimateur à noyau proposé, nous analysons ses performances sur des données simulées puis réelles.La troisième partie est consacrée aux applications. Tout d’abord, nous présentons des résultats de classification non supervisée de données spatiales (multivariées), simulées et réelles. La méthode de classification considérée est basée sur l’estimation du mode spatial, obtenu à partir de l’estimateur de la fonction de densité spatiale introduit dans le cadre de la première partie de cette thèse. Puis, nous appliquons cette méthode de classification basée sur le mode ainsi que d’autres méthodes de classification non supervisée de la littérature sur des données hydrologiques de nature fonctionnelle. Enfin, cette classification des données hydrologiques nous a amené à appliquer des outils de détection de rupture sur ces données fonctionnelles. / In this dissertation, we are interested in nonparametric modeling of spatial and/or functional data, more specifically based on kernel method. Generally, the samples we have considered for establishing asymptotic properties of the proposed estimators are constituted of dependent variables. The specificity of the studied methods lies in the fact that the estimators take into account the structure of the dependence of the considered data.In a first part, we study real variables spatially dependent. We propose a new kernel approach to estimating spatial probability density of the mode and regression functions. The distinctive feature of this approach is that it allows taking into account both the proximity between observations and that between sites. We study the asymptotic behaviors of the proposed estimates as well as their applications to simulated and real data. In a second part, we are interested in modeling data valued in a space of infinite dimension or so-called "functional data". As a first step, we adapt the nonparametric regression model, introduced in the first part, to spatially functional dependent data framework. We get convergence results as well as numerical results. Then, later, we study time series regression model in which explanatory variables are functional and the innovation process is autoregressive. We propose a procedure which allows us to take into account information contained in the error process. After showing asymptotic behavior of the proposed kernel estimate, we study its performance on simulated and real data.The third part is devoted to applications. First of all, we present unsupervised classificationresults of simulated and real spatial data (multivariate). The considered classification method is based on the estimation of spatial mode, obtained from the spatial density function introduced in the first part of this thesis. Then, we apply this classification method based on the mode as well as other unsupervised classification methods of the literature on hydrological data of functional nature. Lastly, this classification of hydrological data has led us to apply change point detection tools on these functional data.

No description available.

In this thesis, a modified wear calculation method is developed to calculate the train wheel wear, which can give less precise but faster results compared to the classic wear calculation method. This modified method is developed based on the classic wear calculation method developed by Jendel, which uses Hertz theory and Kalker’s simplified theory to calculate the contact variables and uses Achard theory to calculate the wear volume in an iterative manner. Compared with the classic method, this modified wear calculation method does not execute the multibody simulation (MBS) at each wear step, instead, it executes MBS by different strategies, for example, does MBS only at the first wear step or does it at every several wear steps. This way, a look-up table is utilised to store the contact variables from MBS and when no MBS is executed, the variables stored in the look-up table would be used to calculate the wear.In order to make the implementation of the modified wear calculation method possible, a contact point detection program is developed in this research. Significantly, this contact point detection program considers the material flexibility and can detect multiple contact points, which makes it very precise. It uses the pressure distribution calculated by Winkler theory as a weighting function to consider the material flexibility. In terms of multiple contact points detection, the gap between wheel and rail is regarded as a function, and the derivative relationship of the function is used to detect multiple contact points. Results from the modified wear calculation method are compared with results from the classic wear calculation method. The effects of different strategies are discussed, and the analysis of the error source is carried out in this work.This modified wear calculation method could be used for predicting the wear condition of the wheel when a quick result with only moderate precision is needed. / I den här avhandlingen utvecklas en modifierad beräkningsmetod för slitage av spårfordons hjul, som ger mindre exakta men snabbare resultat jämfört med den klassiska beräkningsmetoden för hjulslitage. Den modifierade beräkningsmetoden är utvecklad baserat på den klassiska beräkningsmetoden för slitage som utvecklats av Tomas Jendel, som använder Hertz-teorin och Kalkers förenklade teori för att beräkna kontaktvariablerna och använder Achard-teorin för att beräkna volymen av materialet som har slitits bort med en iterativ metod. Jämfört med den klassiska metoden utför inte denna modifierade beräkningsmetod flerkroppssimulering (MBS) vid varje steg där normal hjulprofilen uppdateras, utan använder sig av olika strategier. Till exempel genomförs gör MBS bara vid första slitagesteget eller vid några av slitagestegen. Därför används en uppslagstabell för att lagra kontaktvariablerna från MBS och när ingen MBS exekveras, användas variablerna lagrade i uppslagstabellen för att beräkna slitage.För att möjliggöra implementeringen av den modifierade beräkningsmetoden för slitage utvecklas ett kontaktpunktdetekteringsprogram i denna examensarbete.. Det är viktigt att detekteringsprogrammet tar hänsyn till materialflexibiliteten och att det kan detektera flera kontaktpunkter, med hög precision. Programmet använder Winkler-metoden och den tryckfördelning som beräknas enligt Winkler-teorin som en viktning för att beakta materialets flexibilitet. När det gäller detektering av flera kontaktpunkter betraktas gapet mellan hjul och räls som en funktion, och derivatan av den funktionen används används för att detektera flera kontaktpunkter.Resultat från den modifierade beräkningsmetoden för slitage jämförs med resultaten från den klassiska beräkningsmetoden. Effekterna av olika strategier diskuteras och felkällor analyseras.Denna modifierade beräkningsmetod för slitage kan användas för att förutsäga hjulets slitagetillstånd när ett snabbt resultat med endast måttlig precision behövs.

Les caractéristiques locales sont essentielles dans de nombreux domaines de l’analyse d’images comme la détection et la reconnaissance d’objets, la recherche d’images, etc. Ces dernières années, plusieurs détecteurs dits locaux ont été proposés pour extraire de telles caractéristiques. Ces détecteurs locaux fonctionnent généralement bien pour certaines applications, mais pas pour toutes. Prenons, par exemple, une application de recherche dans une large base d’images. Dans ce cas, un détecteur à base de caractéristiques binaires pourrait être préféré à un autre exploitant des valeurs réelles. En effet, la précision des résultats de recherche pourrait être moins bonne tout en restant raisonnable, mais probablement avec un temps de réponse beaucoup plus court. En général, les détecteurs locaux sont utilisés en combinaison avec une méthode d’indexation. En effet, une méthode d’indexation devient nécessaire dans le cas où les ensembles de points traités sont composés de milliards de points, où chaque point est représenté par un vecteur de caractéristiques de grande dimension. / Local features are of central importance to deal with many different problems in image analysis and understanding including image registration, object detection and recognition, image retrieval, etc. Over the years, many local detectors have been presented to detect such features. Such a local detector usually works well for some particular applications but not all. Taking an application of image retrieval in large database as an example, an efficient method for detecting binary features should be preferred to other real-valued feature detection methods. The reason is easily seen: it is expected to have a reasonable precision of retrieval results but the time response must be as fast as possible. Generally, local features are used in combination with an indexing scheme. This is highly needed for the case where the dataset is composed of billions of data points, each of which is in a high-dimensional feature vector space.