983 resultados para public architecture


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Comunicação apresentada na 69th ASPA Anual Conference, em Dalas, Texas de 7 a 11 de março de 2008

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This paper proposes an FPGA-based architecture for onboard hyperspectral unmixing. This method based on the Vertex Component Analysis (VCA) has several advantages, namely it is unsupervised, fully automatic, and it works without dimensionality reduction (DR) pre-processing step. The architecture has been designed for a low cost Xilinx Zynq board with a Zynq-7020 SoC FPGA based on the Artix-7 FPGA programmable logic and tested using real hyperspectral datasets. Experimental results indicate that the proposed implementation can achieve real-time processing, while maintaining the methods accuracy, which indicate the potential of the proposed platform to implement high-performance, low cost embedded systems.

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Hyperspectral imaging has become one of the main topics in remote sensing applications, which comprise hundreds of spectral bands at different (almost contiguous) wavelength channels over the same area generating large data volumes comprising several GBs per flight. This high spectral resolution can be used for object detection and for discriminate between different objects based on their spectral characteristics. One of the main problems involved in hyperspectral analysis is the presence of mixed pixels, which arise when the spacial resolution of the sensor is not able to separate spectrally distinct materials. Spectral unmixing is one of the most important task for hyperspectral data exploitation. However, the unmixing algorithms can be computationally very expensive, and even high power consuming, which compromises the use in applications under on-board constraints. In recent years, graphics processing units (GPUs) have evolved into highly parallel and programmable systems. Specifically, several hyperspectral imaging algorithms have shown to be able to benefit from this hardware taking advantage of the extremely high floating-point processing performance, compact size, huge memory bandwidth, and relatively low cost of these units, which make them appealing for onboard data processing. In this paper, we propose a parallel implementation of an augmented Lagragian based method for unsupervised hyperspectral linear unmixing on GPUs using CUDA. The method called simplex identification via split augmented Lagrangian (SISAL) aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The efficient implementation of SISAL method presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory.

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Comunicação apresentada no 27th International Congress of Administrative Sciences "Global Competitiveness and Public Administration: implications for education and training" em Abu Dhai, United Arb Emirates de 9 a 14 de Julho de 2007.

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Comunicação apresentada na 17ª Conferência Anual da Network of Intitutes and Schools of Public Administration (NISPA) em Birdua, Montenegro de 14 a 16 dem Maio de 2009.

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O projeto, financiado pelo Programa Aprendizagem ao Longo da Vida, decorreu entre 1 de agosto de 2011 e 31 de julho de 2013 e foi coordenado pelo Instituto de Administração Pública de Praga, tendo como parceiros a Escola de Economia e Direito de Berlim, a Escola Nacional de Administração Pública da Polónia e o INA, de Portugal. A coordenação portuguesa do estudo esteve a cargo da Prof. Doutora Helena Rato e da Dra. Matilde Gago, com a colaboração do Prof. Doutor César Madureira e da Dra. Margarida Quintela, ex investigadores do INA, atualmente na DGAEP.

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Comunicação apresentada na Conferência Anual do IASIA, em Paris, a 6 de julho de 2015

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Comunicação apresentada no Congresso do IIAS-IISA no âmbito do IX Grupo de Estudo: Serviço público e política, realizado em Ifrane, Marrocos de 13 a 17 de junho de 2014