116 resultados para Modeling Rapport Using Hidden Markov Models


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In this paper is presented a region-based methodology for Digital Elevation Model segmentation obtained from laser scanning data. The methodology is based on two sequential techniques, i.e., a recursive splitting technique using the quad tree structure followed by a region merging technique using the Markov Random Field model. The recursive splitting technique starts splitting the Digital Elevation Model into homogeneous regions. However, due to slight height differences in the Digital Elevation Model, region fragmentation can be relatively high. In order to minimize the fragmentation, a region merging technique based on the Markov Random Field model is applied to the previously segmented data. The resulting regions are firstly structured by using the so-called Region Adjacency Graph. Each node of the Region Adjacency Graph represents a region of the Digital Elevation Model segmented and two nodes have connectivity between them if corresponding regions share a common boundary. Next it is assumed that the random variable related to each node, follows the Markov Random Field model. This hypothesis allows the derivation of the posteriori probability distribution function whose solution is obtained by the Maximum a Posteriori estimation. Regions presenting high probability of similarity are merged. Experiments carried out with laser scanning data showed that the methodology allows to separate the objects in the Digital Elevation Model with a low amount of fragmentation.

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Discriminative training of Gaussian Mixture Models (GMMs) for speech or speaker recognition purposes is usually based on the gradient descent method, in which the iteration step-size, ε, uses to be defined experimentally. In this letter, we derive an equation to adaptively determine ε, by showing that the second-order Newton-Raphson iterative method to find roots of equations is equivalent to the gradient descent algorithm. © 2010 IEEE.

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Carpolobia lutea leaves (CLL) (Polygalaceae) were earlier screened and the antiulcer ethnomedicinal claim established. This article seeks to quantitatively isolate, elucidate the active compounds from most active CLL fraction. Fractionation was by semi-preparative HPLC; the active fraction was subjected to radical scavenging assays (RSA) and quantification of the total phenolic content (TPC) were also executed. Results: Ethyl acetate fraction (EAF) was observed to be the most pharmacologically active antiulcer fraction when screened using acute ulcer models induced in rodents. The EAF demonstrated significant (p < 0.05-0.001) antiulcer activity in various in vivo induced ulceration models by reducing the ulcer index and increasing the preventive ratio. The EAF demonstrated > 70% in TPC and < 20 % in RSA. Cinnamic and coumaric acids derivatives were isolated from EAF. Cinnamic acids have been implicated and patented as antiulcer agent. Isolated compounds could in part mediate the observed pharmacological activities which lend credence to its ethnobotanical uses.

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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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Pós-graduação em Engenharia e Ciência de Alimentos - IBILCE

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Zootecnia - FCAV

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Agronomia (Irrigação e Drenagem) - FCA

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Pós-graduação em Odontologia - FOAR

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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)