A Data Structure for Spatio-Temporal Databases

The advantages and applications of spatial mechanisms are well documented; however, there are very few being designed. The principal hinderance to the design of spatial mechanisms is the great difficulty involved in specifying spatial problems and in interpreting spatial solutions. Similarly, the development of spatial codes to implement these techniques is held back by the lack of means to easily visualize and verify solutions, particularly in the realm of relational databases. If spatial mechanisms are to be successful, the designer must be able to synthesize, analyse and evaluate, as well as load and extract information, using a single code representing a spatial structure. This entails the implementation of spatial relationships involving spatial data structures. It is with this in mind that the Canadian Hydrographic Service database group embarked on the development of a new type of spatial database structure based on the quadtree concept

Biometric Authentication using Nonparametric Methods

The physiological and behavioral trait is employed to develop biometric authentication systems. The proposed work deals with the authentication of iris and signature based on minimum variance criteria. The iris patterns are preprocessed based on area of the connected components. The segmented image used for authentication consists of the region with large variations in the gray level values. The image region is split into quadtree components. The components with minimum variance are determined from the training samples. Hu moments are applied on the components. The summation of moment values corresponding to minimum variance components are provided as input vector to k-means and fuzzy kmeans classifiers. The best performance was obtained for MMU database consisting of 45 subjects. The number of subjects with zero False Rejection Rate [FRR] was 44 and number of subjects with zero False Acceptance Rate [FAR] was 45. This paper addresses the computational load reduction in off-line signature verification based on minimal features using k-means, fuzzy k-means, k-nn, fuzzy k-nn and novel average-max approaches. FRR of 8.13% and FAR of 10% was achieved using k-nn classifier. The signature is a biometric, where variations in a genuine case, is a natural expectation. In the genuine signature, certain parts of signature vary from one instance to another. The system aims to provide simple, fast and robust system using less number of features when compared to state of art works.Comment: 20 page

Texture-Based Segmentation and Finite Element Mesh Generation for Heterogeneous Biological Image Data

The design, analysis, and control of bio-systems remain an engineering challenge. This is mainly due to the material heterogeneity, boundary irregularity, and nonlinear dynamics associated with these systems. The recent developments in imaging techniques and stochastic upscaling methods provides a window of opportunity to more accurately assess these bio-systems than ever before. However, the use of image data directly in upscaled stochastic framework can only be realized by the development of certain intermediate steps. The goal of the research presented in this dissertation is to develop a texture-segmentation method and a unstructured mesh generation for heterogeneous image data. The following two new techniques are described and evaluated in this dissertation: 1. A new texture-based segmentation method, using the stochastic continuum concepts and wavelet multi-resolution analysis, is developed for characterization of heterogeneous materials in image data. The feature descriptors are developed to efficiently capture the micro-scale heterogeneity of macro-scale entities. The materials are then segmented at a representative elementary scale at which the statistics of the feature descriptor stabilize. 2. A new unstructured mesh generation technique for image data is developed using a hierarchical data structure. This representation allows for generating quality guaranteed finite element meshes. The framework for both the methods presented in this dissertation, as such, allows them for extending to higher dimensions. The experimental results using these methods conclude them to be promising tools for unifying data processing concepts within the upscaled stochastic framework across biological systems. These are targeted for inclusion in decision support systems where biological image data, simulation techniques and artificial intelligence will be used conjunctively and uniformly to assess bio-system quality and design effective and appropriate treatments that restore system health

Multi-scale data storage schemes for spatial information systems

This thesis documents a research project that has led to the design and prototype implementation of several data storage schemes suited to the efficient multi-scale representation of integrated spatial data. Spatial information systems will benefit from having data models which allow for data to be viewed and analysed at various levels of detail, while the integration of data from different sources will lead to a more accurate representation of reality. The work has addressed two specific problems. The first concerns the design of an integrated multi-scale data model suited for use within Geographical Information Systems. This has led to the development of two data models, each of which allow for the integration of terrain data and topographic data at multiple levels of detail. The models are based on a combination of adapted versions of three previous data structures, namely, the constrained Delaunay pyramid, the line generalisation tree and the fixed grid. The second specific problem addressed in this thesis has been the development of an integrated multi-scale 3-D geological data model, for use within a Geoscientific Information System. This has resulted in a data storage scheme which enables the integration of terrain data, geological outcrop data and borehole data at various levels of detail. The thesis also presents details of prototype database implementations of each of the new data storage schemes. These implementations have served to demonstrate the feasibility and benefits of an integrated multi-scale approach. The research has also brought to light some areas that will need further research before fully functional systems are produced. The final chapter contains, in addition to conclusions made as a result of the research to date, a summary of some of these areas that require future work

Solveur Parallèle pour l'Equation de Poisson sur Mailles Superposées et Hiérarchiques, dans le Cadre du Langage Python

Adaptive discretizations are important in compressible/incompressible flow problems since it is often necessary to resolve details on multiple levels, allowing large regions of space to be modeled using a reduced number of degrees of freedom (reducing the computational time). There are a wide variety of methods for adaptively discretizing space, but Cartesian grids have often outperformed them even at high resolutions due to their simple and accurate numerical stencils and their superior parallel performances. Such performance and simplicity are in general obtained applying a finite-difference scheme for the resolution of the problems involved, but this discretization approach does not present, by contrast, an easy adapting path. In a finite-volume scheme, instead, we can incorporate different types of grids, more suitable for adaptive refinements, increasing the complexity on the stencils and getting a greater flexibility. The Laplace operator is an essential building block of the Navier-Stokes equations, a model that governs fluid flows, but it occurs also in differential equations that describe many other physical phenomena, such as electric and gravitational potentials, and quantum mechanics. So, it is a very important differential operator, and all the studies carried out on it, prove its relevance. In this work will be presented 2D finite-difference and finite-volume approaches to solve the Laplacian operator, applying patches of overlapping grids where a more fined level is needed, leaving coarsermeshes in the rest of the computational domain. These overlapping grids will have generic quadrilateral shapes. Specifically, the topics covered will be: 1) introduction to the finite difference method, finite volume method, domain partitioning, solution approximation; 2)overview of different types of meshes to represent in a discrete way the geometry involved in a problem, with a focus on the octree data structure, presenting PABLO and PABLitO. The first one is an external library used to manage each single grid’s creation, load balancing andinternal communications, while the second one is the Python API of that library written ad hoc for the current project; 3) presentation of the algorithm used to communicate data between meshes (being all of them unaware of each other’s existence) using MPI inter-communicators and clarification of the monolithic approach applied building the final matrix for the system to solve, taking into account diagonal, restriction and prolongation blocks; 4) presentation of some results; conclusions, references. It is important to underline that everything is done under Python as programming framework, using Cython for the writing of PABLitO, MPI4Py for the communications between grids, PETSc4py for the assembling and resolution parts of the system of unknowns, NumPy for contiguous memory buffer objects. The choice of this programming language has been made because Python, easy to learn and understand, is today a significant contender for the numerical computing and HPC ecosystem, thanks to its clean style, its packages, its compilers and, why not, its specific architecture optimized versions.Les discrétisations adaptatives sont importantes dans les problèmes de flux compressible/incompressible puisqu'il est souvent nécessaire de résoudre des détails sur plusieurs niveaux, en permettant de modéliser de grandes régions d'espace en utilisant un nombre réduit de degrés de liberté (et en réduisant le temps de calcul). Il existe une grande variété de méthodes de discrétisation adaptative, mais les grilles cartésiennes sont les plus efficaces, grâce à leurs stencils numériques simples et précis et à leurs performances parallèles supérieures. Et telles performance et simplicité sont généralement obtenues en appliquant un schéma de différences finies pour la résolution des problèmes, mais cette approche de discrétisation ne présente pas, au contraire, un chemin facile d'adaptation.Dans un schéma de volumes finis, en revanche, nous pouvons incorporer différents types de maillages, plus appropriées aux raffinements adaptatifs, en augmentant la complexité sur les stencils et en obtenant une plus grande flexibilité. L'opérateur de Laplace est un élémentessentiel des équations de Navier-Stokes, un modèle qui gouverne les écoulements de fluides, mais il se produit également dans des équations différentielles qui décrivent de nombreux autres phénomènes physiques, tels que les potentiels électriques et gravitationnels. Il s'agit donc d'un opérateur différentiel très important, et toutes les études qui ont été effectuées sur celui-ci, prouvent sa pertinence. Dans ce travail seront présentés des approches de différences finies et de volumes finis 2D pour résoudre l'opérateur laplacien, en appliquant des patchs de grilles superposées où un niveau plus fin est nécessaire, en laissant des maillages plus grossiers dans le reste du domaine de calcul. Ces grilles superposées auront des formes quadrilatérales génériques. Plus précisément, les sujets abordés seront les suivants: 1) introduction à la méthode des différences finies, méthode des volumes finis, partitionnement des domaines, approximation de la solution; 2)récapitulatif des différents types de maillages pour représenter de façon discrète la géométrie impliquée dans un problème, avec un focus sur la structure de données octree, présentant PABLO et PABLitO. Le premier est une bibliothèque externe utilisée pour gérer la création de chaque grille, l'équilibrage de charge et les communications internes, tandis que la seconde est l'API Python de cette bibliothèque, écrite ad hoc pour le projet en cours; 3) la présentation de l'algorithme utilisé pour communiquer les données entre les maillages (en ignorant chacune l'existence de l'autre) en utilisant les intercommunicateurs MPI et la clarification de l'approche monolithique appliquée à la construction finale de la matrice pour résoudre le système, en tenant compte des blocs diagonaux, de restriction et de prolongement; 4) la présentation de certains résultats; conclusions, références. Il est important de souligner que tout est fait sous Python comme framework de programmation, en utilisant Cython pour l'écriture de PABLitO, MPI4Py pour les communications entre grilles, PETSc4py pour les parties assemblage et résolution du système d'inconnues, NumPy pour les objets à mémoire continue. Le choix de ce langage de programmation a été fait car Python, facile à apprendre et à comprendre, est aujourd'hui un concurrent significatif pour l'informatique numérique et l'écosystème HPC, grâce à son style épuré, ses packages, ses compilateurs et pourquoi pas ses versions optimisées pour des architectures spécifiques