987 resultados para [INFO] Computer Science [cs]


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

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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s-graduação em Engenharia Elétrica - FEIS

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Research on the micro-structural characterization of metal-matrix composites uses X-ray computed tomography to collect information about the interior features of the samples, in order to elucidate their exhibited properties. The tomographic raw data needs several steps of computational processing in order to eliminate noise and interference. Our experience with a program (Tritom) that handles these questions has shown that in some cases the processing steps take a very long time and that it is not easy for a Materials Science specialist to interact with Tritom in order to define the most adequate parameter values and the proper sequence of the available processing steps. For easing the use of Tritom, a system was built which addresses the aspects described before and that is based on the OpenDX visualization system. OpenDX visualization facilities constitute a great benefit to Tritom. The visual programming environment of OpenDX allows an easy definition of a sequence of processing steps thus fulfilling the requirement of an easy use by non-specialists on Computer Science. Also the possibility of incorporating external modules in a visual OpenDX program allows the researchers to tackle the aspect of reducing the long execution time of some processing steps. The longer processing steps of Tritom have been parallelized in two different types of hardware architectures (message-passing and shared-memory); the corresponding parallel programs can be easily incorporated in a sequence of processing steps defined in an OpenDX program. The benefits of our system are illustrated through an example where the tool is applied in the study of the sensitivity to crushing – and the implications thereofof the reinforcements used in a functionally graded syntactic metallic foam.

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The increase in new electronic devices had generated a considerable increase in obtaining spatial data information; hence these data are becoming more and more widely used. As well as for conventional data, spatial data need to be analyzed so interesting information can be retrieved from them. Therefore, data clustering techniques can be used to extract clusters of a set of spatial data. However, current approaches do not consider the implicit semantics that exist between a region and an object’s attributes. This paper presents an approach that enhances spatial data mining process, so they can use the semantic that exists within a region. A framework was developed, OntoSDM, which enables spatial data mining algorithms to communicate with ontologies in order to enhance the algorithm’s result. The experiments demonstrated a semantically improved result, generating more interesting clusters, therefore reducing manual analysis work of an expert.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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s-graduação em Matematica Aplicada e Computacional - FCT

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Conselho Nacional de Desenvolvimento Cienfico e Tecnológico (CNPq)

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s-graduação em Ciência da Computação - IBILCE

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

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Conselho Nacional de Desenvolvimento Cienfico e Tecnológico (CNPq)

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Conselho Nacional de Desenvolvimento Cienfico e Tecnológico (CNPq)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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s-graduação em Matematica Aplicada e Computacional - FCT

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s-graduação em Ciência da Computação - IBILCE