Boolean functions are the main building blocks of most cipher systems. Various aspects of their cryptological characteristics are examined and investigated by many researchers from different fields. This thesis has no claim to obtain original results but consists in an attempt at giving a unified survey of the main results of the subject. In this thesis, the theory of boolean functions is presented in details, emphasizing some important cryptological properties such as balance, nonlinearity, strict avalanche criterion and propagation criterion. After presenting many results about these criteria with detailed proofs, two upper bounds and two lower bounds on the nonlinearity of a boolean function due to Zhang and Zheng are proved. Because of their importance in the theory of boolean functions, construction of Sylvester-Hadamard matrices are shown and most of their properties used in cryptography are proved. The Walsh transform is investigated in detail by proving many properties. By using a property of Sylvester-Hadamard matrices, the fast Walsh transform is presented and its application in finding the nonlinearity of a boolean function is demonstrated. One of the most important classes of boolean functions, so called bent functions, are presented with many properties and by giving several examples, from the paper of Rothaus. By using bent functions, relations between balance, nonlinearity and propagation criterion are presented and it is shown that not all these criteria can be simultaneously satisfied completely. For this reason, several constructions of functions optimizing these criteria which are due to Seberry, Zhang and Zheng are presented.

By way of five microhistories focusing on the lives of Cecelia Davies Sylvester, Hannah Director, Leon Koerner, Harry Adaskin, and Nathan Nemetz, this study examines various modes of integration for Jews within particular periods of British Columbian (BC) history. Each microhistory explores the boundaries that were crossed and fostered by Jews whose careers and social contributions led them outside the confines of the established Jewish community. These Jews represent the vanguard of Jewish integration for each era to which they contributed.

Thesis (Ph. D.)--Florida State University, 2003. / Advisor: Dr. Joseph R. McElrath, Florida State University, College of Arts and Sciences, Dept. of English. Title and description from dissertation home page (viewed Oct. 29, 2003). Includes bibliographical references.

Cette thèse porte sur des méthode de résolution d'équations matricielles appliquées à la résolution numérique d'équations aux dérivées partielles ou des problèmes de contrôle linéaire. On s'intéressen en premier lieu à des équations matricielles linéaires. Après avoir donné un aperçu des méthodes classiques employées pour les équations de Sylvester et de Lyapunov, on s'intéresse au cas d'équations linéaires générales de la forme M(X)=C, où M est un opérateur linéaire matriciel. On expose la méthode de GMRES globale qui s'avère particulièrement utile dans le cas où M(X) ne peut s'exprimer comme un polynôme du premier degré en X à coefficients matriciels, ce qui est le cas dans certains problèmes de résolution numérique d'équations aux dérivées partielles. Nous proposons une approche, noté LR-BA-ADI consistant à utiliser un préconditionnement de type ADI qui transforme l'équation de Sylvester en une équation de Stein que nous résolvons par une méthode de Krylox par blocs. Enfin, nous proposons une méthode de type Newton-Krylov par blocs avec préconditionnement ADI pour les équations de Riccati issues de problèmes de contrôle linéaire quadratique. Cette méthode est dérivée de la méthode LR-BA-ADI. Des résultats de convergence et de majoration de l'erreur sont donnés. Dans la seconde partie de ce travail, nous appliquons les méthodes exposées dans la première partie de ce travail à des problèmes d'équations aux dérivées partielles. Nous nous intéressons d'abord à la résolution numérique d'équations couplées de type Burgers évolutives en dimension 2. Ensuite, nous nous intéressons au cas où le domaine borné est choisi quelconque. Nous établissons des résultats théoriques de l'existence de tels interpolants faisant appel à des techniques d'algèbre linéaire.

By way of five microhistories focusing on the lives of Cecelia Davies Sylvester, Hannah Director, Leon Koerner, Harry Adaskin, and Nathan Nemetz, this study examines various modes of integration for Jews within particular periods of British Columbian (BC) history. Each microhistory explores the boundaries that were crossed and fostered by Jews whose careers and social contributions led them outside the confines of the established Jewish community. These Jews represent the vanguard of Jewish integration for each era to which they contributed.

Boolean functions are accepted to be cryptographically strong if they satisfy some common pre-determined criteria. It is expected that any design criteria should remain invariant under a large group of transformations due to the theory of similarity of secrecy systems proposed by Shannon. One of the most important design criteria for cryptographically strong Boolean functions is the nonlinearity criterion. Meier and Staffelbach studied nonlinearity preserving transformations, by considering the invertible transformations acting on the arguments of Boolean functions, namely the pre-transformations. In this thesis, first, the results obtained by Meier and Staffelbach are presented. Then, the invertible transformations acting on the truth tables of Boolean functions, namely the post-transformations, are studied in order to determine whether they keep the nonlinearity criterion invariant. The equivalent counterparts of Meier and Staffelbach&rsquo / s results are obtained in terms of the post-transformations. In addition, the existence of nonlinearity preserving post-transformations, which are not equivalent to pre-transformations, is proved. The necessary and sufficient conditions for an affine post-transformation to preserve nonlinearity are proposed and proved. Moreover, the sufficient conditions for an non-affine post-transformation to keep nonlinearity invariant are proposed. Furthermore, it is proved that the smart hill climbing method, which is introduced to improve nonlinearity of Boolean functions by Millan et. al., is equivalent to applying a post-transformation to a single Boolean function. Finally, the necessary and sufficient condition for an affine pre-transformation to preserve the strict avalanche criterion is proposed and proved.

Submitted by Maike Costa (maiksebas@gmail.com) on 2016-03-31T12:43:01Z No. of bitstreams: 1 arquivo total.pdf: 1366177 bytes, checksum: 1b02d1a5ce5861390070022558e311b0 (MD5) / Made available in DSpace on 2016-03-31T12:43:01Z (GMT). No. of bitstreams: 1 arquivo total.pdf: 1366177 bytes, checksum: 1b02d1a5ce5861390070022558e311b0 (MD5) Previous issue date: 2015-07-24 / This work is about the Rees algebra of a nite colength almost complete intersection ideal generated by forms of the same degree in a polynomial ring over a eld. We deal with two situations which are quite apart from each other: in the rst the forms are monomials in an unrestricted number of variables, while the second is for general binary forms. The essential goal in both cases is to obtain the depth of the Rees algebra. It is known that for such ideals the latter is rarely Cohen{Macaulay (i.e., of maximal depth). Thus, the question remains as to how far one is from the Cohen{Macaulay case. In the case of monomials one proves under certain restriction a conjecture of Vasconcelos to the e ect that the Rees algebra is almost Cohen{ Macaulay. At the other end of the spectrum, one proposes a proof of a conjecture of Simis on general binary forms, based on work of Huckaba{Marley and on a theorem concerning the Ratli {Rush ltration. Still within this frame, one states a couple of stronger conjectures that imply Simis conjecture, along with some solid evidence. / Este trabalho versa sobre a algebra de Rees de um ideal quase intersec cão completa, de cocomprimento nito, gerado por formas de mesmo grau em um anel de polinômios sobre um corpo. Considera-se duas situa c~oes inteiramente diversas: na primeira, as formas s~ao mon^omios em um n umero qualquer de vari aveis, enquanto na segunda, s~ao formas bin arias gerais. O objetivo essencial em ambos os casos e obter a profundidade da algebra de Rees. E conhecido que tal algebra e raramente Cohen{Macaulay (isto e, de profundidade m axima). Assim, a quest~ao que permanece e qua o distante são do caso Cohen{Macaulay. No caso de monômios prova-se, mediante certa restri cão, uma conjectura de Vasconcelos no sentido de que a algébra de Rees e quase Cohen {Macaulay. No outro caso extremo, estabelece-se uma prova de uma conjectura de Simis sobre formas bin arias gerais, baseada no trabalho de Huckaba{Marley e em um teorema sobre a ltera cão de Ratli {Rush. Al em disso, apresenta-se um par de conjecturas mais fortes que implicam a conjectura de Simis, juntamente com uma evidência s olida.

We investigate the numerical solution of stable Sylvester equations via iterative schemes proposed for computing the sign function of a matrix. In particular, we discuss how the rational iterations for the matrix sign function can efficiently be adapted to the special structure implied by the Sylvester equation. For Sylvester equations with factored constant term as those arising in model reduction or image restoration, we derive an algorithm that computes the solution in factored form directly. We also suggest convergence criteria for the resulting iterations and compare the accuracy and performance of the resulting methods with existing Sylvester solvers. The algorithms proposed here are easy to parallelize. We report on the parallelization of those algorithms and demonstrate their high efficiency and scalability using experimental results obtained on a cluster of Intel Pentium Xeon processors.

By way of five microhistories focusing on the lives of Cecelia Davies Sylvester, Hannah Director, Leon Koerner, Harry Adaskin, and Nathan Nemetz, this study examines various modes of integration for Jews within particular periods of British Columbian (BC) history. Each microhistory explores the boundaries that were crossed and fostered by Jews whose careers and social contributions led them outside the confines of the established Jewish community. These Jews represent the vanguard of Jewish integration for each era to which they contributed.

L’imagerie hyperspectrale (HS) consiste à acquérir une même scène dans plusieurs centaines de bandes spectrales contiguës (dimensions d'un cube de données), ce qui a conduit à trois types d'applications pertinentes, telles que la détection de cibles, la classification et le démélange spectral. Cependant, tandis que les capteurs hyperspectraux fournissent une information spectrale abondante, leur résolution spatiale est généralement plus limitée. Ainsi, la fusion d’une image HS avec d'autres images à haute résolution de la même scène, telles que les images multispectrales (MS) ou panchromatiques (PAN) est un problème intéressant. Le problème de fusionner une image HS de haute résolution spectrale mais de résolution spatiale limitée avec une image auxiliaire de haute résolution spatiale mais de résolution spectrale plus limitée (parfois qualifiée de fusion multi-résolution) a été exploré depuis de nombreuses années. D'un point de vue applicatif, ce problème est également important et est motivé par ceratins projets, comme par exemple le project Japonais HISIU, qui vise à fusionner des images MS et HS recalées acquises pour la même scène avec les mêmes conditions. Les techniques de fusion bayésienne permettent une interprétation intuitive du processus de fusion via la définition de la loi a posteriori de l’image à estimer (qui est de hautes résolutions spatiale et spectrale). Puisque le problème de fusion est généralement mal posé, l’inférence bayésienne offre un moyen pratique pour régulariser le problème en définissant une loi a priori adaptée à la scène d'intérêt. Les différents chapitres de cette thèse sont résumés ci-dessous. Le introduction présente le modèle général de fusion et les hypothèses statistiques utilisées pour les images multi-bandes observées, c’est-à-dire les images HS, MS ou PAN. Les images observées sont des versions dégradées de l'image de référence (à hautes résolutions spatiale et spectrale) qui résultent par exemple d’un flou spatial et spectral et/ou d’un sous-échantillonnage liés aux caractéristiques des capteurs. Les propriétés statistiques des mesures sont alors obtenues directement à partir d’un modèle linéaire traduisant ces dégradations et des propriétés statistiques du bruit. Le chapitre 1 s’intéresse à une technique de fusion bayésienne pour les images multi-bandes de télédétection, à savoir pour les images HS, MS et PAN. Tout d'abord, le problème de fusion est formulé dans un cadre d'estimation bayésienne. Une loi a priori Gaussienne exploitant la géométrie du problème est définie et un algorithme d’estimation Bayésienne permettant d’estimer l’image de référence est étudié. Pour obtenir des estimateurs Bayésiens liés à la distribution postérieure résultant, deux algorithmes basés sur échantillonnage de Monte Carlo et l'optimisation stratégie ont été développés. Le chapitre 2 propose une approche variationnelle pour la fusion d’images HS et MS. Le problème de fusion est formulé comme un problème inverse dont la solution est l'image d’intérêt qui est supposée vivre dans un espace de dimension résuite. Un terme de régularisation imposant des contraintes de parcimonie est défini avec soin. Ce terme traduit le fait que les patches de l'image cible sont bien représentés par une combinaison linéaire d’atomes appartenant à un dictionnaire approprié. Les atomes de ce dictionnaire et le support des coefficients des décompositions des patches sur ces atomes sont appris à l’aide de l’image de haute résolution spatiale. Puis, conditionnellement à ces dictionnaires et à ces supports, le problème de fusion est résolu à l’aide d’un algorithme d’optimisation alternée (utilisant l’algorithme ADMM) qui estime de manière itérative l’image d’intérêt et les coefficients de décomposition. / Hyperspectral (HS) imaging, which consists of acquiring a same scene in several hundreds of contiguous spectral bands (a three dimensional data cube), has opened a new range of relevant applications, such as target detection [MS02], classification [C.-03] and spectral unmixing [BDPD+12]. However, while HS sensors provide abundant spectral information, their spatial resolution is generally more limited. Thus, fusing the HS image with other highly resolved images of the same scene, such as multispectral (MS) or panchromatic (PAN) images is an interesting problem. The problem of fusing a high spectral and low spatial resolution image with an auxiliary image of higher spatial but lower spectral resolution, also known as multi-resolution image fusion, has been explored for many years [AMV+11]. From an application point of view, this problem is also important as motivated by recent national programs, e.g., the Japanese next-generation space-borne hyperspectral image suite (HISUI), which fuses co-registered MS and HS images acquired over the same scene under the same conditions [YI13]. Bayesian fusion allows for an intuitive interpretation of the fusion process via the posterior distribution. Since the fusion problem is usually ill-posed, the Bayesian methodology offers a convenient way to regularize the problem by defining appropriate prior distribution for the scene of interest. The aim of this thesis is to study new multi-band image fusion algorithms to enhance the resolution of hyperspectral image. In the first chapter, a hierarchical Bayesian framework is proposed for multi-band image fusion by incorporating forward model, statistical assumptions and Gaussian prior for the target image to be restored. To derive Bayesian estimators associated with the resulting posterior distribution, two algorithms based on Monte Carlo sampling and optimization strategy have been developed. In the second chapter, a sparse regularization using dictionaries learned from the observed images is introduced as an alternative of the naive Gaussian prior proposed in Chapter 1. instead of Gaussian prior is introduced to regularize the ill-posed problem. Identifying the supports jointly with the dictionaries circumvented the difficulty inherent to sparse coding. To minimize the target function, an alternate optimization algorithm has been designed, which accelerates the fusion process magnificently comparing with the simulation-based method. In the third chapter, by exploiting intrinsic properties of the blurring and downsampling matrices, a much more efficient fusion method is proposed thanks to a closed-form solution for the Sylvester matrix equation associated with maximizing the likelihood. The proposed solution can be embedded into an alternating direction method of multipliers or a block coordinate descent method to incorporate different priors or hyper-priors for the fusion problem, allowing for Bayesian estimators. In the last chapter, a joint multi-band image fusion and unmixing scheme is proposed by combining the well admitted linear spectral mixture model and the forward model. The joint fusion and unmixing problem is solved in an alternating optimization framework, mainly consisting of solving a Sylvester equation and projecting onto a simplex resulting from the non-negativity and sum-to-one constraints. The simulation results conducted on synthetic and semi-synthetic images illustrate the advantages of the developed Bayesian estimators, both qualitatively and quantitatively.