897 resultados para Simulation-based methods


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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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This paper presents a novel, fast and accurate appearance-based method for infrared face recognition. By introducing the Optimum-Path Forest classifier, our objective is to get good recognition rates and effectively reduce the computational effort. The feature extraction procedure is carried out by PCA, and the results are compared to two other well known supervised learning classifiers; Artificial Neural Networks and Support Vector Machines. The achieved performance asserts the promise of the proposed framework. ©2009 IEEE.

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A method for context-sensitive analysis of binaries that may have obfuscated procedure call and return operations is presented. Such binaries may use operators to directly manipulate stack instead of using native call and ret instructions to achieve equivalent behavior. Since definition of context-sensitivity and algorithms for context-sensitive analysis have thus far been based on the specific semantics associated to procedure call and return operations, classic interprocedural analyses cannot be used reliably for analyzing programs in which these operations cannot be discerned. A new notion of context-sensitivity is introduced that is based on the state of the stack at any instruction. While changes in 'calling'-context are associated with transfer of control, and hence can be reasoned in terms of paths in an interprocedural control flow graph (ICFG), the same is not true of changes in 'stack'-context. An abstract interpretation based framework is developed to reason about stack-contexts and to derive analogues of call-strings based methods for the context-sensitive analysis using stack-context. The method presented is used to create a context-sensitive version of Venable et al.'s algorithm for detecting obfuscated calls. Experimental results show that the context-sensitive version of the algorithm generates more precise results and is also computationally more efficient than its context-insensitive counterpart. Copyright © 2010 ACM.

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Includes bibliography

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Doping tin dioxide (SnO2) with pentavalent Sb5+ ions leads to an enhancement in the electrical conductivity of this material, because Sb5+ substitutes Sn4+ in the matrix, promoting an electronic density increase in the conduction band, due to the donor-like nature of the doping atom. Results of computational simulation, based on the Density Functional Theory (DFT), of SnO2:4%Sb and SnO2:8%Sb show that the bandgap magnitude is strongly affected by the doping concentration, because the energy value found for 4 at%Sb and 8 at%Sb was 3.27 eV and 3.13 eV, respectively, whereas the well known value for undoped SnO2 is about 3.6 eV. Sb-doped SnO2 thin films were obtained by the sol-gel-dip-coating technique. The samples were submitted to excitation with below theoretical bandgap light (450 nm), as well as above bandgap light (266 nm) at low temperature, and a temperature-dependent increase in the conductivity is observed. Besides, an unusual temperature and time dependent decay when the illumination is removed is also observed, where the decay time is slower for higher temperatures. This decay is modeled by considering thermally activated cross section of trapping centers, and the hypothesis of grain boundary scattering as the dominant mechanism for electronic mobility. © 2012 Elsevier B.V. All rights reserved.

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Identification and classification of overlapping nodes in networks are important topics in data mining. In this paper, a network-based (graph-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in a network to uncover overlapping nodes by generating continuous-valued outputs (soft labels), corresponding to the levels of membership from the nodes to each of the communities. Moreover, the proposed method can be applied to detect overlapping data items in a data set of general form, such as a vector-based data set, once it is transformed to a network. Usually, label propagation involves risks of error amplification. In order to avoid this problem, the proposed method offers a mechanism to identify outliers among the labeled data items, and consequently prevents error propagation from such outliers. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method. © 2012 Springer-Verlag.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Matematica Aplicada e Computacional - FCT

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Pós-graduação em Ciência da Computação - IBILCE

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Ciências Biológicas (Genética) - IBB