Made available in DSpace on 2016-06-02T20:27:40Z (GMT). No. of bitstreams: 1 4629.pdf: 1448309 bytes, checksum: c6f5e451b1247c565791c643df7dc7d5 (MD5) Previous issue date: 2012-10-23 / Financiadora de Estudos e Projetos / This thesis was developed in two parts. Firstly, we consider a pair of fiber-preserving maps f1, f2 : M → M in a fiber bundle with base S1 and fiber Klein bottle. Using an algebraic system of equations we found in what situations the minimal coincidence set over S1 of the pair (f1, f2) is empty. In the second part, motivated by this problem, we consider a fiber-preserving map f : M → M in a fiber bundle with base S1 and fiber torus. Using the one-parameter fixed point theory we studied the minimal fixed point set over S1 of the map f. In some fiber bundle we classified completely this sets. / Esta tese foi desenvolvida em duas partes. Inicialmente, consideramos um par de aplicações que preserva fibra, f1, f2 : M → M, em um fibrado com base S1 e fibra garrafa de Klein. Utilizando-se de um sistema algébrico de equações, descobrimos em que situações o conjunto minimal de coincidências sobre S1 do par (f1, f2) é vazio. Na segunda parte, motivado por esse problema, consideramos uma aplicação que preserva fibra, f : M → M, em um fibrado com base S1 e fibra toro. Usando a teoria algébrica de ponto fixo a 1-parâmetro estudamos o conjunto minimal dos pontos fixos sobre S1 da aplicação f. Em alguns fibrados foi possível obter uma classificação completa desses conjuntos.

In-situ 3-D shape measurements with submicron shape uncertainty of fast rotating objects in a cutting lathe are expected, which can be achieved by simultaneous distance and velocity measurements. Conventional tactile methods, coordinate measurement machines, only support ex-situ measurements. Optical measurement techniques such as triangulation and conoscopic holography offer only the distance, so that the absolute diameter cannot be retrieved directly. In comparison, laser Doppler distance sensors (P-LDD sensor) enable simultaneous and in-situ distance and velocity measurements for monitoring the cutting process in a lathe. In order to achieve shape measurement uncertainties below 1 µm, a P-LDD sensor with a dual camera based scattered light detection has been investigated. Coherent fiber bundles (CFB) are employed to forward the scattered light towards cameras. This enables a compact and passive sensor head in the future. Compared with a photo detector based sensor, the dual camera based sensor allows to decrease the measurement uncertainty by the order of one magnitude. As a result, the total shape uncertainty of absolute 3-D shape measurements can be reduced to about 100 nm.

Ce travail de thèse a porté sur le développement de réseaux de micro- et nanocapteurs opto-électrochimiques pour la bioanalyse. Ils répondent à la demande grandissante dans le domaine de la recherche et du diagnostic pour des outils permettant de réaliser de multiples analyses simultanément avec des échantillons de faibles volumes. Ces nouvelles biopuces de haute densité sont fabriquées à partir de faisceaux cohérents de fibres optiques. Une des deux faces est micro- ou nanostructurée par une attaque chimique, puis fonctionnalisée avec une sonde biologique. La première biopuce est un réseau de nanocapteurs fluorescents à ADN où les sondes ont été immobilisées grâce aux propriétés d’électropolymérisation du pyrrole. La lecture est réalisée à distance au travers du faisceau d’imagerie. En combinant la technique d’immobilisation avec des microleviers électrochimiques, plusieurs sondes différentes ont pu être adressées sur le même réseau nanostructuré. La seconde biopuce permet d’effectuer des immunodosages multiplexés en utilisant l’imagerie électrochimiluminescente résolue à l’échelle d’une microsphère. Le développement de cette technique permet de combiner les avantages de l’électrochimiluminescence avec des immunodosages multiplexés. L’élaboration de ces réseaux allie différentes techniques physico-chimiques, notamment électrochimiques, pour obtenir des biopuces avec un fort potentiel, grâce à une densité et un degré de multiplexage importants. / This work presents the development of optoelectrochemical micro- and nanosensor arrays for bioanalytical applications. These platforms respond to the growing need in research and diagnostic for tools allowing multiple and simultaneous analysis in small-volume samples. These new high density biochips are made from coherent optical fiber bundles: one face is micro- or nanostructured by chemical etching and then functionnalized with biological probes. The first biochip is a fluorescent DNA nanosensor array where probes have been immobilized by electrodeposition of a polypyrrole thin film. The detection of the hybridization is remotely performed through the imaging fiber. Different probes were succesfully addressed onto the same nanostructured array thanks to electrochemical cantilevers. The second biochip allows multiplexed sandwich immunoassays using electrochimiluminescent imaging resolved at the single bead level. In particular, the development of this new readout mechanism allows extending electrochemiluminescent detection for multiplexed immunoassays. Design and implementations of both platforms take advantages of different physical and chemical techniques, especially electrochemical, to obtain biochips with a great potential through high density and high multiplexing level.

L’imagerie de résonance magnétique de diffusion (dMRI) est une technique très sensible pour la tractographie des fibres de substance blanche et la caractérisation de l’intégrité et de la connectivité axonale. A travers la mesure des mouvements des molécules d’eau dans les trois dimensions de l’espace, il est possible de reconstruire des cartes paramétriques reflétant l’organisation tissulaire. Parmi ces cartes, la fraction d’anisotropie (FA) et les diffusivités axiale (λa), radiale (λr) et moyenne (MD) ont été largement utilisés pour caractériser les pathologies du système nerveux central. L’emploi de ces cartes paramétriques a permis de mettre en évidence la survenue d’altérations micro structurelles de la substance blanche (SB) et de la substance grise (SG) chez les patients atteints d’une sclérose en plaques (SEP). Cependant, il reste à déterminer l’origine de ces altérations qui peuvent résulter de processus globaux comme la cascade inflammatoire et les mécanismes neurodégénératifs ou de processus plus localisés comme la démyélinisation et l’inflammation. De plus, ces processus pathologiques peuvent survenir le long de faisceaux de SB afférents ou efférents, conduisant à une dégénérescence antero- ou rétrograde. Ainsi, pour une meilleure compréhension des processus pathologiques et de leur progression dans l’espace et dans le temps, une caractérisation fine et précise des faisceaux de SB est nécessaire. En couplant l’information spatiale de la tractographie des fibres aux cartes paramétriques de diffusion, obtenues grâce à un protocole d’acquisitions longitudinal, les profils des faisceaux de SB peuvent être modélisés et analysés. Une telle analyse des faisceaux de SB peut être effectuée grâce à différentes méthodes, partiellement ou totalement non-supervisées. Dans la première partie de ce travail, nous dressons l’état de l’art des études déjà présentes dans la littérature. Cet état de l’art se focalisera sur les études montrant les effets de la SEP sur les faisceaux de SB grâce à l’emploi de l’imagerie de tenseur de diffusion. Dans la seconde partie de ce travail, nous introduisons deux nouvelles méthodes,“string-based”, l’une semi-supervisée et l’autre non-supervisée, pour extraire les faisceaux de SB. Nous montrons comment ces algorithmes permettent d’améliorer l’extraction de faisceaux spécifiques comparé aux approches déjà présentes dans la littérature. De plus, dans un second chapitre, nous montrons une extension de la méthode proposée par le couplage du formalisme “string-based” aux informations spatiales des faisceaux de SB. Dans la troisième et dernière partie de ce travail, nous décrivons trois algorithmes automatiques permettant l’analyse des changements longitudinaux le long des faisceaux de SB chez des patients atteints d’une SEP. Ces méthodes sont basées respectivement sur un modèle de mélange Gaussien, la factorisation de matrices non-négatives et la factorisation de tenseurs non-négatifs. De plus, pour valider nos méthodes, nous introduisons un nouveau modèle pour simuler des changements longitudinaux réels, base sur une fonction de probabilité Gaussienne généralisée. Des hautes performances ont été obtenues avec ces algorithmes dans la détection de changements longitudinaux d’amplitude faible le long des faisceaux de SB chez des patients atteints de SEP. En conclusion, nous avons proposé dans ce travail des nouveaux algorithmes non supervisés pour une analyse précise des faisceaux de SB, permettant une meilleure caractérisation des altérations pathologiques survenant chez les patients atteints de SEP / Diffusion Magnetic Resonance Imaging (dMRI) is a meaningful technique for white matter (WM) fiber-tracking and microstructural characterization of axonal/neuronal integrity and connectivity. By measuring water molecules motion in the three directions of space, numerous parametric maps can be reconstructed. Among these, fractional anisotropy (FA), mean diffusivity (MD), and axial (λa) and radial (λr) diffusivities have extensively been used to investigate brain diseases. Overall, these findings demonstrated that WM and grey matter (GM) tissues are subjected to numerous microstructural alterations in multiple sclerosis (MS). However, it remains unclear whether these tissue alterations result from global processes, such as inflammatory cascades and/or neurodegenerative mechanisms, or local inflammatory and/or demyelinating lesions. Furthermore, these pathological events may occur along afferent or efferent WM fiber pathways, leading to antero- or retrograde degeneration. Thus, for a better understanding of MS pathological processes like its spatial and temporal progression, an accurate and sensitive characterization of WM fibers along their pathways is needed. By merging the spatial information of fiber tracking with the diffusion metrics derived obtained from longitudinal acquisitions, WM fiber-bundles could be modeled and analyzed along their profile. Such signal analysis of WM fibers can be performed by several methods providing either semi- or fully unsupervised solutions. In the first part of this work, we will give an overview of the studies already present in literature and we will focus our analysis on studies showing the interest of dMRI for WM characterization in MS. In the second part, we will introduce two new string-based methods, one semi-supervised and one unsupervised, to extract specific WM fiber-bundles. We will show how these algorithms allow to improve extraction of specific fiber-bundles compared to the approaches already present in literature. Moreover, in the second chapter, we will show an extension of the proposed method by coupling the string-based formalism with the spatial information of the fiber-tracks. In the third, and last part, we will describe, in order of complexity, three different fully automated algorithms to perform analysis of longitudinal changes visible along WM fiber-bundles in MS patients. These methods are based on Gaussian mixture model, nonnegative matrix and tensor factorisation respectively. Moreover, in order to validate our methods, we introduce a new model to simulate real longitudinal changes based on a generalised Gaussian probability density function. For those algorithms high levels of performances were obtained for the detection of small longitudinal changes along the WM fiber-bundles in MS patients. In conclusion, we propose, in this work, a new set of unsupervised algorithms to perform a sensitivity analysis of WM fiber bundle that would be useful for the characterisation of pathological alterations occurring in MS patients

Medical imagery is increasingly evolving towards higher resolution and throughput. The increasing volume of data and the usage of multiple and often novel imaging modalities necessitates the use of mathematical and computational techniques for quicker, more accurate and more robust analysis of medical imagery. The fields of computer vision and machine learning provide a rich set of techniques that are useful in medical image analysis, in tasks ranging from segmentation to classification and population analysis, notably by integrating the qualitative knowledge of experts in anatomy and the pathologies of various disorders and making it applicable to the analysis of medical imagery going forward. The object of the proposed research is exactly to explore various computer vision and machine learning methods with a view to the improved analysis of multiple modalities of brain and cardiac imagery, towards enabling the clinical goals of studying schizophrenia, brain tumors (meningiomas and gliomas in particular) and cardiovascular disorders. In the first project, a framework is proposed for the segmentation of tubular, branched anatomical structures. The framework uses the tubular surface model which yields computational advantages and further incorporates a novel automatic branch detection algorithm. It is successfully applied to the segmentation of neural fiber bundles and blood vessels. In the second project, a novel population analysis framework is built using the shape model proposed as part of the first project. This framework is applied to the analysis of neural fiber bundles towards the detection and understanding of schizophrenia. In the third and final project, the use of mass spectrometry imaging for the analysis of brain tumors is motivated on two fronts, towards the offline classification analysis of the data, as well as the end application of intraoperative detection of tumor boundaries. SVMs are applied for the classification of gliomas into one of four subtypes towards application in building appropriate treatment plans, and multiple statistical measures are studied with a view to feature extraction (or biomarker detection). The problem of intraoperative tumor boundary detection is formulated as a detection of local minima of the spatial map of tumor cell concentration which in turn is modeled as a function of the mass spectra, via regression techniques.

Physiological cross sectional area (PCSA) is used to compare force-producing capabilities of skeletal muscles. PCSA has been defined as the summation of the cross sectional area of the fiber bundles composing the muscle. As PCSA cannot be measured directly from a specimen, a formula requiring averaged muscle architectural parameters has traditionally been used. The purpose of this study was to develop a finite element method (FEM) to calculate PCSA of extensor carpi radialis longus (ECRL) and brevis (ECRB) directly from digitized fiber bundle data obtained throughout the volume of the muscle and to compare the PCSAs calculated using the FEM and formula methods. Differences were found between the FEM and formula method for both muscles. The FEM provides an approach that takes into account architectural variances while minimizing the need for averaged architectural parameters.

Physiological cross sectional area (PCSA) is used to compare force-producing capabilities of skeletal muscles. PCSA has been defined as the summation of the cross sectional area of the fiber bundles composing the muscle. As PCSA cannot be measured directly from a specimen, a formula requiring averaged muscle architectural parameters has traditionally been used. The purpose of this study was to develop a finite element method (FEM) to calculate PCSA of extensor carpi radialis longus (ECRL) and brevis (ECRB) directly from digitized fiber bundle data obtained throughout the volume of the muscle and to compare the PCSAs calculated using the FEM and formula methods. Differences were found between the FEM and formula method for both muscles. The FEM provides an approach that takes into account architectural variances while minimizing the need for averaged architectural parameters.

The idea underlying the present study was to apply twisting in order to introduce different levels of transverse pressure. The modified structure affected both the bonding level and the evolution of the damage in the yarn. In order to isolate this effect in a broader context, additional parameters were included in the experiment design, namely effects of loading rate, specimen length and filament diameter (directly linked to the fineness of the yarn). These factors have been studied in various contexts by several authors. Some related studies on involved factors will be briefly reviewed.

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The paper presents a meso-scopic modeling framework for the simulation of three-phase composite consisting of a brittle cementitious matrix and reinforcing AR-glass yarns impregnated with epoxy resin. The construction of the model is closely related to the experimental program covering both the meso-scale test (yarn tensile test and double sided pull-out test) and the macro-scale test in the form of tensile test on the textile reinforced concrete specimen. The predictions obtained using the model are validated using a-posteriori performed experiments.