No description available.

Orientador: Claudia Maria Bauzer Medeiros / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-22T06:24:16Z (GMT). No. of bitstreams: 1 Longo_JoaoSavioCeregatti_M.pdf: 3005926 bytes, checksum: 986140c2ac8b7fc686b351fb6ce666fb (MD5) Previous issue date: 2013 / Resumo: Trabalhar em questões relativas a dados geoespaciais presentes em múltiplas escalas apresenta inúmeros desafios que têm sido atacados pelos pesquisadores da área de GIS (Sistemas de Informação Geográfica). De fato, um dado problema do mundo real deve frequentemente ser estudado em escalas distintas para ser resolvido. Outro fator a ser considerado é a possibilidade de manter o histórico de mudanças em cada escala. Além disso, uma das principais metas de ambientes multi-escala _e garantir a manipulação de informações sem qualquer contradição entre suas diferentes representações. A noção de escala extrapola inclusive a questão espacial, pois se aplica também, por exemplo, _a escala temporal. Estes problemas serão analisados nesta dissertação, resultando nas seguintes contribuições: (a) proposta do modelo DBV (Database Version) multi-escala para gerenciar de forma transparente dados de múltiplas escalas sob a perspectiva de bancos de dados; (b) especificação de restrições de integridade multi-escala; (c) implementação de uma plataforma que suporte o modelo e as restrições, testados com dados reais multi-escala / Abstract: Work on multi-scale issues concerning geospatial data presents countless challenges that have been long attacked by GIScience (Geographic Information Science) researchers. Indeed, a given real world problem must often be studied at distinct scales in order to be solved. Another factor to be considered is the possibility of maintaining the history of changes at each scale. Moreover, one of the main goals of multi-scale environments is to guarantee the manipulation of information without any contradiction among the different representations. The concept of scale goes beyond issues of space, since it also applies, for instance, to time. These problems will be analyzed in this thesis, resulting in the following contributions: (a) the proposal of the DBV (Database Version) multi-scale model to handle data at multiple scales from a database perspective; (b) the specification of multi-scale integrity constraints; (c) the implementation of a platform to support model and constraints, tested with real multi-scale data / Mestrado / Ciência da Computação / Mestre em Ciência da Computação

No description available.

Orientadores: Ricardo da Silva Torres, Hélio Pedrini / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-27T11:52:06Z (GMT). No. of bitstreams: 1 RoccaLayza_VladimirJaime_M.pdf: 7769260 bytes, checksum: a60ae46083facfc74cd79a4ab0c83c23 (MD5) Previous issue date: 2015 / Resumo: Vários sistemas de vigilância baseados no uso de múltiplas câmeras têm sido propostos recentemente. No entanto, a identificação de pessoas em sequências de vídeos obtidas por várias câmeras com vistas não sobrepostas, comumente conhecida como reidentificação de pessoas, é um problema em aberto. As razões para que este problema seja considerado desafiador referem-se principalmente às restrições nas quais o problema deve ser resolvido. Estas restrições são definidas a partir das características do cenário e dos objetos de interesse (as pessoas): primeiro, as características biométricas de pessoas não podem ser utilizadas como características discriminantes; segundo, a aparência das pessoas muda drasticamente em virtude de variações na posição, iluminação e parâmetros de câmera. Tais restrições fazem com que uma mesma pessoa possa ser observada por múltiplas câmeras como uma pessoa diferente para cada uma delas. Nesta pesquisa, busca-se investigar alternativas para a criação de sistemas de vigilância visando à reidentificação de pessoas. Foram empregadas técnicas de recuperação de imagens por conteúdo tais como descritores de imagens tradicionais e propostos recentemente, análise multiescala, e técnicas de rank aggregation. Os experimentos realizados consideram a utilização de quatro bases de dados comumente utilizadas na avaliação de sistemas de reidentificação de pessoas. Os resultados obtidos mostraram que as técnicas de recuperação de imagens por conteúdo são promissoras para a reidentificação de pessoas, obtendo resultados comparáveis aos métodos do estado da arte / Abstract: Several surveillance systems based on the use of multiple cameras have been proposed recently. However, the identification of people in video sequences obtained from several cameras with non-overlapping views, commonly known as the person reidentification problem, is still an open problem. Person reidentification is a challenging problem due to the constraints under which the problem should be solved. These constraints come from the characteristics of the scenario and the objects of interest (people): first, biometric features may not be used as discriminant information; second, appearance is dramatically modified by changes in position, lighting conditions, and camera parameters. Therefore, in these conditions a unique person can be ''seen'' as a distinct person by different cameras. This research is focused on the investigation of alternatives for the creation of surveillance systems aiming at person reidentification. We intend to use content-based image retrieval techniques, such as traditional and recently proposed image descriptors, multiscale analysis, and rank aggregation approaches. Conducted experiments considered the use of four different datasets, commonly used in the evaluation of person reidentification systems. Obtained results show that the content-based image retrieval techniques are promising to reidentify people, producing equivalent results to the state-of-the-art methods / Mestrado / Ciência da Computação / Mestre em Ciência da Computação

Ossos são sólidos não homogêneos com estruturas altamente complexas que requerem uma modelagem multiescala para entender seu comportamento eletromecânico e seus mecanismos de remodelamento. O objetivo deste trabalho é encontrar expressões analíticas para as propriedades elástica, piezoelétrica e dielétrica efetivas de osso cortical modelando-o em duas escalas: microscópica e macroscópica. Utiliza-se o Método de Homogeneização Assintótica (MHA) para calcular as constantes eletromecânicas efetivas deste material. O MHA produz um procedimento em duas escalas que permite obter as propriedades efetivas de um material compósito contendo uma distribuição periódica de furos cilíndricos circulares unidirecionais em uma matriz piezoelétrica linear e transversalmente isotrópica. O material da matriz pertence à classe de simetria cristalina 622. Os furos estão centrados em células de uma matriz periódica de secções transversais quadradas e a periodicidade é a mesma em duas direções perpendiculares. O compósito piezoelétrico está sob cisalhamento antiplano acoplado a um campo elétrico plano. Os problemas locais que surgem da análise em duas escalas usando o MHA são resolvidos por meio de um método da teoria de variáveis complexas, o qual permite expandir as soluções correspondentes em séries de potências de funções elípticas de Weierstrass. Os coeficientes das séries são determinados das soluções de sistemas lineares infinitos de equações algébricas. Truncando estes sistemas infinitos até uma ordem finita de aproximação, obtêm-se fórmulas analíticas para as constantes efetivas elástica, piezoelétrica e dielétrica, que dependem da fração de volume dos furos e de um fator de acoplamento eletromecânico da matriz. Os resultados numéricos obtidos a partir destas fórmulas são comparados com resultados obtidos pelas fórmulas calculadas via método de Mori-Tanaka e apresentam boa concordância. A boa concordância entre todas as curvas obtidas via MHA sugere que a expressão correspondente da primeira aproximação fornece uma fórmula muito simples para calcular o fator de acoplamento efetivo do compósito. Os resultados são úteis na mecânica de osso. / Bones are inhomogeneous solids with highly complex structures that require multiscale modeling to understand its electromechanical behavior and its remodeling mechanisms. The objective of this work is to find analytical expressions for the effective elastic, piezoelectric, and dielectric properties of cortical bone by modeling it on two scales: microscopic and macroscopic. We use Asymptotic Homogenization Method (AHM) to calculate the effective electromechanical constants of this material. The AHM yields a two-scale procedure to obtain the effective properties of a composite material containing a periodic distribution of unidirectional circular cylindrical holes in a linear transversely isotropic piezoelectric matrix. The matrix material belongs to the symmetry crystal class 622. The holes are centered in a periodic array of cells of square cross sections and the periodicity is the same in two perpendicular directions. The piezoelectric composite is under antiplane shear deformation together with in-plane electric field. Local problems that arise from the two-scale analysis using the AHM are solved by means of a complex variable method, which allows us to expand the corresponding solutions in power series of Weierstrass elliptic functions. The coefficients of these series are determined from the solutions of infinite systems of linear algebraic equations. Truncating the infinite systems up to a finite, but otherwise arbitrary, order of approximation, we obtain analytical formulas for effective elastic, piezoelectric, and dielectric properties, which depend on both the volume fraction of the holes and an electromechanical coupling factor of the matrix. Numerical results obtained from these formulas are compared with results obtained by the Mori-Tanaka approach and show good agreement. The good agreement between all curves obtained via AHM suggests that the corresponding expression of first approximation provides a very simple formula to calculate the effective coupling factor of the composite. The results are useful in bone mechanics.

La thèse porte sur la calibration d'un modèle biomathématique multi-échelle expliquant le phénomène de sélection des follicules ovariens à partir du niveau cellulaire. Le modèle EDP consiste en un système hyperbolique quasi linéaire de grande taille gouvernant l'évolution des fonctions de densité cellulaire pour une cohorte de follicules (en pratique, une vingtaine).Les équations sont couplées de manière non locale par l'intermédiaire de termes de contrôle faisant intervenir les moments de la solution, intégrée à l'échelle mésoscopique et macroscopique. Trois chapitres de la thèse présentent, sous forme d'articles publiés, la méthode développée pour simuler numériquement ce modèle. Elle est conçue pour être implémentée sur une architecture parallèle. Les EDP sont discrétisées avec un schéma Volumes Finis sur un maillage adaptatif piloté par une analyse multirésolution. Le modèle présente des discontinuités de flux aux interfaces entre les différents états cellulaires, qui nécessitent la mise en ½uvre d'un couplage spécifique, compatible avec le schéma d'ordre élevé et le raffinement de maillage.Un chapitre de la thèse est dévolu à la méthode de calibration, qui consiste à traduire les connaissances biologiques en contraintes sur les paramètres et sur les sorties du modèle. Le caractère multi-échelle est là encore crucial. Les paramètres interviennent au niveau microscopique dans les équations gouvernant l'évolution des densités de cellules au sein de chaque follicule, alors que les données biologiques quantitatives sont disponibles aux niveaux mésoscopique et macroscopique. / The thesis focuses on the numerical simulation of a biomathematical, multiscale model explaining the phenomenon of selection within the population of ovarian follicles, and grounded on a cellular basis. The PDE model consists of a large dimension hyperbolic quasilinear system governing the evolution of cell density functions for a cohort of follicles (around twenty in practice).The equations are coupled in a nonlocal way by control terms involving moments of the solution, defined on either the mesoscopic or macroscopic scale.Three chapters of the thesis, presented in the form of articles, develop the method used to simulate the model numerically. The numerical code is implemented on a parallel architecture. PDEs are discretized with a Finite Volume scheme on an adaptive mesh driven by a multiresolution analysis. Flux discontinuities, at the interfaces between different cellular states, require a specific treatment to be compatible with the high order numerical scheme and mesh refinement.A chapter of the thesis is devoted to the calibration method, which translates the biological knowledge into constraints on the parameters and model outputs. The multiscale character is crucial, since parameters are used at the microscopic level in the equations governing the evolution of the density of cells within each follicle, whereas quantitative biological data are rather available at the mesoscopic and macroscopic levels.The last chapter of the thesis focuses on the analysis of computational performances of the parallel code, based on statistical methods inspired from the field of uncertainty quantification.

<div>Textile composites have been widely used due to the excellent mechanical performance and lower manufacturing costs, but the accurate prediction of the mechanical behaviors of textile composites is still very challenging due to the complexity of the microstructures and boundary conditions. Moreover, there is an unprecedented amount of design options of different textile composites. Therefore, a highly efficient yet accurate approach, which can predict the macroscopic structural performance considering different geometries and materials at subscales, is urgently needed for the structural design using textile composites.</div><div><br></div><div>Mechanics of structure genome (MSG) is used to perform multiscale modeling to predict various performances of textile composite materials and structures. A two-step approach is proposed based on the MSG solid model to compute the elastic properties of different two-dimensional (2D) and three-dimensional (3D) woven composites. The first step computes the effective properties of yarns at the microscale based on the fiber and matric properties. The effective properties of yarns and matrix are then used at the mesoscale to compute the properties of woven composites in the second step. The MSG plate and beam models are applied to thin and slender textile composites, which predict both the structural responses and local stress field. In addition, the MSG theory is extended to consider the pointwise temperature loads by modifying the variational statement of the Helmholtz free energy. Instead of using coefficients of thermal expansions (CTEs), the plate and beam thermal stress resultants derived from the MSG plate and beam models are used to capture the thermal-induced behaviors in thin and slender textile composite structures. Moreover, the MSG theory is developed to consider the viscoelastic behaviors of textile composites based on the quasi-elastic approach. Furthermore, a meso-micro scale coupled model is proposed to study the initial failure of textile composites based on the MSG models which avoids assuming a specific failure criterion for yarns. The MSG plate model uses plate stress resultants to describe the initial failure strength that can capture the stress gradient along the thickness in the thin-ply textile composites. The above developments of MSG theory are validated using high-fidelity 3D finite element analysis (FEA) or experimental data. The results show that MSG achieves the same accuracy of 3D FEA with a significantly improved efficiency.</div><div> </div><div>Taking advantage of the advanced machine learning model, a new yarn failure criterion is constructed based on a deep neural network (DNN) model. A series of microscale failure analysis based on the MSG solid model is performed to provide the training data for the DNN model. The DNN-based failure criterion as well as other traditional failure criteria are used in the mesoscale initial failure analysis of a plain woven composite. The results show that the DNN yarn failure criterion gives a better accuracy than the traditional failure criteria. In addition, the trained model can be used to perform other computational expensive simulations such as predicting the failure envelopes and the progressive failure analysis.</div><div> </div><div>Multiple software packages (i.e., texgen4sc and MSC.Patran/Nastran-SwiftComp GUI) are developed to incorporate the above developments of the MSG models. These software tools can be freely access and download through cdmHUB.org, which provide practical tools to facilitate the design and analysis of textile composite materials and structures.</div>

abstract: It is increasingly common to see machine learning techniques applied in conjunction with computational modeling for data-driven research in neuroscience. Such applications include using machine learning for model development, particularly for optimization of parameters based on electrophysiological constraints. Alternatively, machine learning can be used to validate and enhance techniques for experimental data analysis or to analyze model simulation data in large-scale modeling studies, which is the approach I apply here. I use simulations of biophysically-realistic cortical neuron models to supplement a common feature-based technique for analysis of electrophysiological signals. I leverage these simulated electrophysiological signals to perform feature selection that provides an improved method for neuron-type classification. Additionally, I validate an unsupervised approach that extends this improved feature selection to discover signatures associated with neuron morphologies - performing in vivo histology in effect. The result is a simulation-based discovery of the underlying synaptic conditions responsible for patterns of extracellular signatures that can be applied to understand both simulation and experimental data. I also use unsupervised learning techniques to identify common channel mechanisms underlying electrophysiological behaviors of cortical neuron models. This work relies on an open-source database containing a large number of computational models for cortical neurons. I perform a quantitative data-driven analysis of these previously published ion channel and neuron models that uses information shared across models as opposed to information limited to individual models. The result is simulation-based discovery of model sub-types at two spatial scales which map functional relationships between activation/inactivation properties of channel family model sub-types to electrophysiological properties of cortical neuron model sub-types. Further, the combination of unsupervised learning techniques and parameter visualizations serve to integrate characterizations of model electrophysiological behavior across scales. / Dissertation/Thesis / Doctoral Dissertation Applied Mathematics 2020

abstract: Geographically Weighted Regression (GWR) has been broadly used in various fields to model spatially non-stationary relationships. Classic GWR is considered as a single-scale model that is based on one bandwidth parameter which controls the amount of distance-decay in weighting neighboring data around each location. The single bandwidth in GWR assumes that processes (relationships between the response variable and the predictor variables) all operate at the same scale. However, this posits a limitation in modeling potentially multi-scale processes which are more often seen in the real world. For example, the measured ambient temperature of a location is affected by the built environment, regional weather and global warming, all of which operate at different scales. A recent advancement to GWR termed Multiscale GWR (MGWR) removes the single bandwidth assumption and allows the bandwidths for each covariate to vary. This results in each parameter surface being allowed to have a different degree of spatial variation, reflecting variation across covariate-specific processes. In this way, MGWR has the capability to differentiate local, regional and global processes by using varying bandwidths for covariates. Additionally, bandwidths in MGWR become explicit indicators of the scale at various processes operate. The proposed dissertation covers three perspectives centering on MGWR: Computation; Inference; and Application. The first component focuses on addressing computational issues in MGWR to allow MGWR models to be calibrated more efficiently and to be applied on large datasets. The second component aims to statistically differentiate the spatial scales at which different processes operate by quantifying the uncertainty associated with each bandwidth obtained from MGWR. In the third component, an empirical study will be conducted to model the changing relationships between county-level socio-economic factors and voter preferences in the 2008-2016 United States presidential elections using MGWR. / Dissertation/Thesis / Doctoral Dissertation Geography 2020

Content: Leather is a fairly durable and flexible material created by tanning animal rawhides and can be found in many household and personal products. However, ensuring that the product endures attack from the environmental elements that contribute to its wear and tear is the key concern of the general consumer. Animal rawhides are soft collagenous connective tissues. The most important function of collagen is a mechanical one - to withstand loads acting on the leather material. The purpose of this paper is to show how findings from recent studies on the mechanics of collagen in connective tissues lend to the goal of structural biologists to establish a complete understanding of the functional significance of collagen in connective tissues. In particular, 28 different types of collagen have been identified - about 90% being type 1 collagen - in the human body. Most types of collagen participate in higher-order assemblies such as networks, filaments, microfibrils, fibrils, fibres/fascicles. These assemblies collectively form a hierarchical architecture in the tissue from the molecular level to the macroscopic level. A complete understanding the functional significance of collagen in connective tissues could direct the development of new technology, e.g. leather design and production. In this paper, I shall discuss findings related to the higher-order assemblies. The conventional understanding of the collagenous fibre-like structures - embedded in a hydrated ground substance - in connective tissue finds an analogy to engineering fibres reinforcing composite materials such as carbon fibre reinforced polymer composites. The macroscopic stress- strain response of the connective tissue to external loads acting on it is consistent with fibre composite behaviour. A structure-mechanical framework, underpinning the hierarchical architecture of the connective tissue, is proposed to explain this mechanical response of the tissue. By integrating models specific to the different levels of the tissue to enable better understanding of the macroscopic nature of the tissue, the framework serves as a representation of reality for guiding further research, especially for the purpose of exploring hypotheses and revealing properties for which only sparse (or no observational data) is available. This paper ends with a discussion on the prospect and challenges for future studies on collagen in connective tissues. Take-Away: A fresh look at the degree of collagen fibril alignment in tissue Rethinking the mechanics of cross-linking between fibrils Interfibrillar mechanics is governed by plastic stress transfer Influence of fibril diameter on interfibrillar stress transfer