Este trabalho apresenta uma solução geral para a detecção de alinhamentos de dados em conjuntos multidimensionais não ordenados e ruidosos. Nesta abordagem, o tipo requerido de alinhamento de dados pode ser uma forma geométrica (e.g., linha reta, plano, círculo, esfera, seção cônica, entre outras) ou qualquer estrutura, com dimensionalidade arbitrária, que possa ser caracterizada por um subespaço linear. A detecção é realizada por meio de um procedimento composto por três etapas. Na etapa de inicialização, um espaço de parâmetros com p (n − p) dimensões é definido de modo que cada ponto neste espaço represente uma instância do alinhamento requerido, descrito por um subespaço p-dimensional em um domínio n-dimensional. Em seguida, uma grade de acumuladores é criada como sendo a representação discreta do espaço de parâmetros. Na segunda etapa do procedimento, cada elemento no conjunto de dados de entrada (também um subespaço no domínio n-dimensional) é mapeado para o espaço de parâmetros como os pontos (no espaço de parâmetros) representando os subespaços requeridos que contém ou que estão contidos no elemento de entrada. À medida que os elementos de entrada são mapeados, as células do acumulador relacionadas com o mapeamento são incrementadas pelo valor de importância do elemento mapeado. A etapa final do procedimento recupera os subespaços p-dimensionais que melhor se ajustam aos dados de entrada como sendo os máximos locais na grade de acumuladores. A parametrização proposta é independente das propriedades geométricas dos alinhamentos a serem detectados. Além disso, o procedimento de mapeamento é independente do tipo de dado de entrada e é capaz de se adaptar a elementos com dimensionalidades arbitrárias. Essas características permitem a utilização da técnica (sem a necessidade de modificações) como uma ferramenta para a detecção de padrões em uma grande quantidade de aplicações. Por conta de sua natureza geral, otimizações desenvolvidas para a abordagem proposta beneficiam, de forma imediata, todos os casos de detecção. Neste trabalho eu demonstro uma implementação em software da técnica proposta e mostro que ela pode ser aplicada tanto em casos simples de detecção, quanto na detecção concorrente de tipos diferentes de alinhamentos, com diferentes interpretações geométricas e em conjuntos de dados compostos por vários tipos de elementos. Esta dissertação também apresenta uma extensão do esquema de detecção para dados de entrada com distribuição Gaussiana de incerteza. A extensão proposta produz distribuições de valores mais suaves na grade de acumuladores e faz com que a técnica fique mais robusta à detecção de subespaços espúrios. / This dissertation presents a generalized closed-form framework for detecting data alignments in large unordered noisy multidimensional datasets. In this approach, the intended type of data alignment may be a geometric shape (e.g., straight line, plane, circle, sphere, conic section, among others) or any other structure, with arbitrary dimensionality that can be characterized by a linear subspace. The detection is performed using a three-step process. In the initialization, a p (n − p)-dimensional parameter space is defined in such a way that each point in this space represents an instance of the intended alignment described by a p-dimensional subspace in some n-dimensional domain. In turn, an accumulator array is created as the discrete representation of the parameter space. In the second step each input entry (also a subspace in the n-dimensional domain) is mapped to the parameter space as the set of points representing the intended p-dimensional subspaces that contain or are contained by the entry. As the input entries are mapped, the bins of the accumulator related to such a mapping are incremented by the importance value of the entry. The subsequent and final step retrieves the p-dimensional subspaces that best fit input data as the local maxima in the accumulator array. The proposed parameterization is independent of the geometric properties of the alignments to be detected. Also, the mapping procedure is independent of the type of input data and automatically adapts to entries of arbitrary dimensionality. This allows application of the proposed approach (without changes) in a broad range of applications as a pattern detection tool. Given its general nature, optimizations developed for the proposed framework immediately benefit all the detection cases. I demonstrate a software implementation of the proposed technique and show that it can be applied in simple detection cases as well as in concurrent detection of multiple kinds of alignments with different geometric interpretations, in datasets containing multiple types of data. This dissertation also presents an extension of the general detection scheme to data with Gaussian-distributed uncertainty. The proposed extension produces smoother distributions of values in the accumulator array and makes the framework more robust to the detection of spurious subspaces.

Singular Riemannian foliations in spheres provide local models for an extensive kind of singular Riemannian foliations, whose theory contributes in the understanding of Riemannian manifolds. Hence the importance of studying and classifying them, a research subject that still remains open. In 2014, Marco Radeschi constructed indecomposable singular Riemannian foliations of arbitrary codimension, most of them inhomogeneous, which generalized all known examples of that type so far. The present dissertation is a detailed study of his work, along with observations about the progress made on this dynamic field since that paper was published. Besides introducing preliminary notions and examples on singular Riemannian foliations, isometric actions and Clifford theory, it is explained a construction of inhomogeneous isoparametric hypersurfaces, due to Ferus, Karcher and Münzner, that was a fundamental framework for the results of Radeschi. After that, it is described exhaustively the construction of Clifford and composed foliations in spheres, which are the examples that Radeschi created using Clifford systems. In the sequel it is established an extraordinary bijective correspondence between Clifford foliations (merely geometric objects) and Clifford systems (purely algebraic objects). This text finishes examining the relations of homogeneity properties among FKM, Clifford and composed foliations. / Folheações Riemannianas singulares em esferas fornecem modelos locais para folheações Riemannianas singulares mais gerais, cuja teoria contribui na compreensão de variedades Riemannianas. Daí a sua importança de estudá-los e classificá-los, uma área de pesquisa que se mantém aberta. Em 2014, Marco Radeschi construiu folheações Riemannianas singulares indecomponíveis de codimensão arbitrária, a maioria delas não homogêneas, que generalizaram todos os exemplos conhecidos desse tipo até então. A presente dissertação é um estudo detalhado desse trabalho, junto com observações sobre avanços que se têm feito neste dinâmico campo desde a publicação do artigo. Após introduzir as noções e exemplos preliminares de folheações Riemannianas singulares, ações isométricas e teoria de Clifford, é explorada uma construção de hipersuperfícies isoparamétricas não homogêneas, devida a Ferus, Karcher e Münzner (FKM), que foi peça fundamental para os resultados de Radeschi. Em seguida, descreve-se minuciosamente a construção de folheações composta e de Clifford em esferas, que são os exemplos que o autor mencionado anteriormente gerou usando sistemas de Clifford. Continuando com a análise dessas novas folheações Riemannianas singulares, estabelece-se uma extraordinária correspondência biunívoca entre folheações de Clifford (objetos meramente geométricos) e sistemas de Clifford (objetos puramente algébricos). Este texto termina examinando as relações das propriedades de homogeneidade entre folheações FKM, compostas e de Clifford.

Ce manuscrit se compose de deux parties indépendantes. La première partie de cette thèse étudie les structures de Clifford paires. Pour une variété riemannienne munie d’une structure de Clifford paire, nous introduisons l’espace de twisteurs en généralisant la construction d’un tel espace dans le cas d’une variété quaternion-hermitienne. Nous construisons une structure presque-complexe sur l’espace de twisteurs et considérons son intégrabilité lorsque la structure de Clifford est parallèle. Dans certains cas, nous pouvons aussi le fournir d’une métriquekählerienne ou, correspondant à une structure presque-complexe alternative, d’une métrique “nearly Kähler”. Dans un second temps, nous introduisons une structure appelée Clifford-Weyl sur une variété conforme. Il s’agit d’une structure de Clifford paireq ui est parallèle par rapport au produit tensoriel d’une connexion métrique sur le fibré de Clifford et une connexion de Weyl. Nous démontrons que la connexion de Weyl est fermée sauf dans certains cas génériques de basse dimension où nous arrivons à décrire des exemples explicites où les structures de Clifford-Weyl sont non-fermées. La seconde partie de cette thèse étudie des résonances quantiques. Au-dessus d’une variété asymptotiquement hyperbolique paire, nous considérons le laplacien de Lichnerowicz agissant sur les sections du fibré des formes multilinéaires symétriques.Lorsqu’il s’agit de formes bilinéaires symétriques, nous obtenonsune extension méromorphe de la résolvante dudit laplacien à l’ensemble du plan complexe si la variété est Einstein. Cela définit les résonances quantiques pour ce laplacien. Pour les formes multilinéaires symétriques en général, une telle extension méromorphe est possible si la variété est convexe-cocompacte. Dans les deux cas, nous devons restreindre le laplacien aux sections qui sont de trace et de divergence nulles. Nous utilisons ce deuxième résultat afin d’établir une correspondance classique-quantique pour les variétés hyperboliques convexescocompactes.La correspondance identifie le spectre du flot géodésique (les résonances de Ruelle) avec les spectres des laplaciens agissant sur les tenseurs symétriques qui sont de trace et de divergence nulles (les résonances quantiques). / We study independently even Clfford structures on Riemannian manifolds and quantum resonances on asymptotically hyperbolic manifolds. In the first part of this thesis, we study even Clifford structures.First, we introduce the twistor space of a Riemannian manifold with an even Clifford structure. This notion generalises the twistor space of quaternion-Hermitian manifolds. We construct almost complex structures on the twistor space and check their integrability when the even Clifford structure is parallel. In some cases we give Kähler and nearly-Kähler metrics to these spaces. Second, we introduce the concept of a Clifford-Weyl structure on a conformal manifold. This consists of an even Clifford structure parallel with respect to the tensor product of a metric connection on the Clifford bundle and a Weyl structure on the manifold. We show that the Weyl structure is necessarily closed except for some “generic” low-dimensional instances,where explicit examples of non-closed Clifford-Weyl structures are constructed. In the second part of this thesis, we study quantum resonances. First, we consider the Lichnerowicz Laplacian acting on symmetric 2-tensors on manifolds with an even Riemannian conformally compact Einstein metric. The resolvent of the Laplacian,upon restriction to trace-free, divergence-free tensors, is shown to have a meromorphic continuation to the complex plane. This defines quantum resonances for this Laplacian. For higher rank symmetric tensors, a similar result is proved for convex cocompact quotients of hyperbolic space. Second, we apply this result to establish a direct classical-quantum correspondence on convex cocompact hyperbolic manifolds. The correspondence identifies the spectrum of the geodesic flow with the spectrum of the Laplacian acting on trace-free, divergence-free symmetric tensors. This extends the correspondence previously obtained for cocompact quotients

Conformal geometric algebra is defined in the thesis. Representations of geometric objects and possibilities of their geometric transformations are presented. Conformal geometric algebra is applied to the calculation of forward kinematics of a robotic manipulator UR10 from Universal Robots. It is also applied to determine the position of the machine based on the location and rotation of two cameras. Then it is used in an inverse task, where based on records from the two cameras, dimensions of the UR10 manipulator and possibilities of its movement, the mutual position of these cameras is determined. And consequently the possibilities of their location in space. Finally, the derived procedures are implemented in a custom program created in the CluCalc environment, using which a sample example verifying the correctness of these procedures is calculated.

The goal of this thesis is the development of a fast and robust algorithm that is able to detect patterns in flow fields independent from their orientation and adequately visualize the results for a human user. This thesis is an interdisciplinary work in the field of vector field visualization and the field of pattern recognition. A vector field can be best imagined as an area or a volume containing a lot of arrows. The direction of the arrow describes the direction of a flow or force at the point where it starts and the length its velocity or strength. This builds a bridge to vector field visualization, because drawing these arrows is one of the fundamental techniques to illustrate a vector field. The main challenge of vector field visualization is to decide which of them should be drawn. If you do not draw enough arrows, you may miss the feature you are interested in. If you draw too many arrows, your image will be black all over. We assume that the user is interested in a certain feature of the vector field: a certain pattern. To prevent clutter and occlusion of the interesting parts, we first look for this pattern and then apply a visualization that emphasizes its occurrences. In general, the user wants to find all instances of the interesting pattern, no matter if they are smaller or bigger, weaker or stronger or oriented in some other direction than his reference input pattern. But looking for all these transformed versions would take far too long. That is why, we look for an algorithm that detects the occurrences of the pattern independent from these transformations. In the second part of this thesis, we work with moment invariants. Moments are the projections of a function to a function space basis. In order to compare the functions, it is sufficient to compare their moments. Normalization is the act of transforming a function into a predefined standard position. Moment invariants are characteristic numbers like fingerprints that are constructed from moments and do not change under certain transformations. They can be produced by normalization, because if all the functions are in one standard position, their prior position has no influence on their normalized moments. With this technique, we were able to solve the pattern detection task for 2D and 3D flow fields by mathematically proving the invariance of the moments with respect to translation, rotation, and scaling. In practical applications, this invariance is disturbed by the discretization. We applied our method to several analytic and real world data sets and showed that it works on discrete fields in a robust way.

[pt] A álgebra de Clifford é uma álgebra associativa que pode ser realizada matricialmente. O grupo Spin é uma superfície contida na álgebra de Clifford e fechada por multiplicação. Estudamos os geradores de tal grupo, assim como do grupo finito gerado pelos elementos agúdos e o grupo Quat, ambos grupos de matrizes e subconjuntos do grupo Spin. Uma permutação no grupo de permutações, pode ser expressa como uma palavra reduzida, por meio de geradores de Coxeter. Os mapas acute e grave nos fornecem elementos no grupo finito, já mencionado, gerado pelos elementos agúdos, a partir das palavras reduzidas de uma permutação. Um elemento da álgebra de Clifford pode ser escrito como uma combinação linear de elementos do grupo Quat, onde o coeficiente independente é conhecido como parte real. Estudamos resultados que relacionam as características de uma permutação no grupo de permutações, com o elemento a ela relacionado na álgebra de Clifford. / [en] The Clifford algebra is an associative algebra that can be constructed as an algebra of matrices. The group Spin is a surface contained in the Clifford algebra and closed by multiplication. We studied the generators of such group, as well as of the finite group contained in Spin and generated by the acute elements and the group Quat, both matrix groups and subsets of Spin. A permutation in the permutation group, can be expressed as a reduced word, using transpositions to define the family of Coxeter generators. The acute and grave maps provide us with elements in the finite group, already mentioned, generated by the acute elements, based on the reduced words of a permutation. An element of Clifford algebra can be written as a linear combination of elements in Quat, where the independent coefficient is known as the real part. We studied results that relate the characteristics of a permutation in the permutation group, with the element related to it in the Clifford algebra.

Dans cette thèse, une nouvelle représentation des images couleur basée sur une généralisation du signal analytique est introduite. En utilisant l'analogie entre les conditions de Cauchy-Riemann, qui définissent le caractère holomorphe d'une fonction, et l'équation de Dirac dans l'algèbre de Clifford R_{5,0}, un système d'équations dont la solution est le signal monogène couleur est obtenu. Ce signal est notamment basé sur des noyaux de Riesz ainsi que de Poisson 2D, et une représentation polaire, basée sur un produit géométrique, peut lui être associée. Les applications envisagées reposent majoritairement sur cette représentation polaire et sur les informations de couleur et de structures locales s'y rattachant. Des problématiques liées au flot optique couleur, à la segmentation couleur multi-échelle, au suivi d'objets couleur et à la détection de points d'intérêt sont abordées. En ce qui concerne le flot optique, nous nous intéressons à l'extraction du mouvement d'objets d'une certaine couleur en remplaçant la contrainte de conservation de l'intensité par une contrainte de conservation d'angles. Pour la segmentation, une méthode de détection de contours basée sur de la géométrie différentielle et plus particulièrement sur la première forme fondamentale d'une surface, est proposée afin de déterminer les contours d'objets d'une couleur choisie. Pour le suivi d'objets, nous définissons un nouveau critère de similarité utilisant le produit géométrique que nous insérons dans un filtrage particulaire. Enfin, nous resituons la définition du détecteur de Harris dans le cadre de la géométrie différentielle en faisant le lien entre ce dernier et une version "relaxée" du discriminant du polynôme caractéristique de la première forme fondamentale. Ensuite nous proposons une nouvelle version multi-échelle de ce détecteur en traitant le paramètre d'échelle comme une variable d'une variété de dimension 3. / In this thesis, a novel framework for color image processing is introduced based on the generalization of the analytic signal. Using the analogy between the Cauchy-Riemann conditions and the Dirac equation in the Clifford algebra R_{5,0}, a system of equations which leads to the color monogenic signal is obtained. This latter is based on the Riesz and 2D Poisson kernels, and a polar representation based on the geometric product can be associated to this signal. Some applications using color and local structure information provided by the polar representation are presented. Namely, color optical flow, color segmentation, color object tracking and points of interest are developed. Extraction of optical flow in a chosen color is obtained by replacing the brightness constancy assumption by an angle constancy. Edge detection is based on the first fundamental form from differential geometry in order to segment object in a predefined color. Object tracking application uses a new similarity criterion defined by geometric product of block of vectors. This latter is viewed as the likelyhood measure of a particle filter. Last part of the thesis is devoted to the definition of the Harris detector in the framework of differential geometry and a link between this definition and a relaxed version of the characteristic polynomial discriminant of the first fundamental form is given. In this context, a new scale-space detector is provided as the result of handling the scale parameter as a variable in a 3-manifold.

The work of this Ph.D. thesis in mathematics concerns the problem of determining existence, uniqueness, and structure of zero-energy states in supersymmetric matrix models, which arise from a quantum mechanical description of the physics of relativistic membranes, reduced Yang-Mills gauge theory, and of nonperturbative features of string theory, respectively M-theory. Several new approaches to this problem are introduced and considered in the course of seven scientific papers, including: construction by recursive methods (Papers A and D), deformations and alternative models (Papers B and C), averaging with respect to symmetries (Paper E), and weighted supersymmetry and index theory (Papers F and G). The mathematical tools used and developed for these approaches include Clifford algebras and associated representation theory, structure of supersymmetric quantum mechanics, as well as spectral theory of (matrix-) Schrödinger operators. / QC20100629

In this thesis we study quantum-like representation and simulation of quantum algorithms by using classical computers.The quantum--like representation algorithm (QLRA) was  introduced by A. Khrennikov (1997) to solve the ``inverse Born's rule problem'', i.e. to construct a representation of probabilistic data-- measured in any context of science-- and represent this data by a complex or more general probability amplitude which matches a generalization of Born's rule.The outcome from QLRA matches the formula of total probability with an additional trigonometric, hyperbolic or hyper-trigonometric interference term and this is in fact a generalization of the familiar formula of interference of probabilities. We study representation of statistical data (of any origin) by a probability amplitude in a complex algebra and a Clifford algebra (algebra of hyperbolic numbers). The statistical data is collected from measurements of two dichotomous and trichotomous observables respectively. We see that only special statistical data (satisfying a number of nonlinear constraints) have a quantum--like representation. We also study simulations of quantum computers on classical computers.Although it can not be denied that great progress have been made in quantum technologies, it is clear that there is still a huge gap between the creation of experimental quantum computers and realization of a quantum computer that can be used in applications. Therefore the simulation of quantum computations on classical computers became an important part in the attempt to cover this gap between the theoretical mathematical formulation of quantum mechanics and the realization of quantum computers. Of course, it can not be expected that quantum algorithms would help to solve NP problems for polynomial time on classical computers. However, this is not at all the aim of classical simulation.  The second part of this thesis is devoted to adaptation of the Mathematica symbolic language to known quantum algorithms and corresponding simulations on classical computers. Concretely we represent Simon's algorithm, Deutsch-Josza algorithm, Shor's algorithm, Grover's algorithm and quantum error-correcting codes in the Mathematica symbolic language. We see that the same framework can be used for all these algorithms. This framework will contain the characteristic property of the symbolic language representation of quantum computing and it will be a straightforward matter to include future algorithms in this framework.

Dans cette thèse, nous nous intéressons à la reconnaissance d’images couleur à l’aide d’une nouvelle approche géométrique du domaine fréquentiel. La plupart des méthodes existantes ne traitent que les images en niveaux de gris au travers de descripteurs issus de la transformée de Fourier usuelle. L’extension de telles méthodes aux images multicanaux, comme par exemple les images couleur, consiste généralement à reproduire un traitement identique sur chacun des canaux. Afin d’éviter ce traitement marginal, nous étudions et mettons en perspective les différentes généralisations de la transformée de Fourier pour les images couleur. Ce travail nous oriente vers la transformée de Fourier Clifford pour les images couleur définie dans le cadre des algèbres géométriques. Une étude approfondie de celle-ci nous conduit à définir un algorithme de calcul rapide et à proposer une méthode de corrélation de phase pour les images couleur. Dans un deuxième temps, nous cherchons à généraliser à travers cette transformée de Fourier les définitions des descripteurs de Fourier de la littérature. Nous étudions ainsi les propriétés, notamment l’invariance à la translation, rotation et échelle, des descripteurs existants. Ce travail nous mène à proposer trois nouveaux descripteurs appelés “descripteurs de Fourier couleur généralisés”(GCFD) invariants en translation et en rotation.Les méthodes proposées sont évaluées sur des bases d’images usuelles afin d’estimer l’apport du contenu fréquentiel couleur par rapport aux méthodes niveaux de gris et marginales. Les résultats obtenus à l’aide d’un classifieur SVM montrent le potentiel des méthodes proposées ; les descripteurs GCFD se révèlent être plus compacts, de complexité algorithmique moindre pour des performances de classification au minimum équivalentes. Nous proposons également des heuristiques pour le choix du paramètre de la transformée de Fourier Clifford.Cette thèse constitue un premier pas vers une généralisation des méthodes fréquentielles aux images multicanaux. / In this thesis, we focus on color image recognition using a new geometric approach in the frequency domain. Most existing methods only process grayscale images through descriptors defined from the usual Fourier transform. The extension of these methods to multichannel images such as color images usually consists in reproducing the same processing for each channel. To avoid this marginal processing,we study and compare the different generalizations of color Fourier transforms. This work leads us to use the Clifford Fourier transform for color images defined in the framework of geometric algebra. A detailed study of it leads us to define a fast algorithm and to propose a phase correlation for colorimages. In a second step, with the aim of generalizing Fourier descriptors of the literature with thisFourier transform, we study their properties, including invariance to translation, rotation and scale.This work leads us to propose three new descriptors called “generalized color Fourier descriptors”(GCFD) invariant in translation and in rotation.The proposed methods are evaluated on usual image databases to estimate the contribution of color frequency content compared with grayscale and marginal methods. The results obtained usingan SVM classifier show the potential of the proposed methods ; the GCFD are more compact, have less computational complexity and give better recognition rates. We also propose heuristics for choosing the parameter of the color Clifford Fourier transform.This thesis is a first step towards a generalization of frequency methods to multichannel images.