15 resultados para Difficulties of processing

em Repositório Científico do Instituto Politécnico de Lisboa - Portugal


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Relatório de investigação apresentado à Escola Superior de Educação de Lisboa para obtenção de grau de mestre no Mestrado em Ensino do 1.º e 2.º Ciclo do Ensino Básico

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Dissertação de mestrado em ciências da educação especialidade educação especial

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Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e Telecomunicações

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Relatório de Estágio apresentado à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Enisno do 1.º e 2.º Ciclo do Ensino Básico

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Relatório de Estágio apresentado à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ensino do 1.º e 2.º ciclo do Ensino Básico

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Projeto de Intervenção apresentado à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Educação Especial

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Relatório de estágio apresentado à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Jornalismo.

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Doutoramento em Motricidade Humana na especialidade de Dança

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Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Civil

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This paper proposes an efficient scalable Residue Number System (RNS) architecture supporting moduli sets with an arbitrary number of channels, allowing to achieve larger dynamic range and a higher level of parallelism. The proposed architecture allows the forward and reverse RNS conversion, by reusing the arithmetic channel units. The arithmetic operations supported at the channel level include addition, subtraction, and multiplication with accumulation capability. For the reverse conversion two algorithms are considered, one based on the Chinese Remainder Theorem and the other one on Mixed-Radix-Conversion, leading to implementations optimized for delay and required circuit area. With the proposed architecture a complete and compact RNS platform is achieved. Experimental results suggest gains of 17 % in the delay in the arithmetic operations, with an area reduction of 23 % regarding the RNS state of the art. When compared with a binary system the proposed architecture allows to perform the same computation 20 times faster alongside with only 10 % of the circuit area resources.

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A unified architecture for fast and efficient computation of the set of two-dimensional (2-D) transforms adopted by the most recent state-of-the-art digital video standards is presented in this paper. Contrasting to other designs with similar functionality, the presented architecture is supported on a scalable, modular and completely configurable processing structure. This flexible structure not only allows to easily reconfigure the architecture to support different transform kernels, but it also permits its resizing to efficiently support transforms of different orders (e. g. order-4, order-8, order-16 and order-32). Consequently, not only is it highly suitable to realize high-performance multi-standard transform cores, but it also offers highly efficient implementations of specialized processing structures addressing only a reduced subset of transforms that are used by a specific video standard. The experimental results that were obtained by prototyping several configurations of this processing structure in a Xilinx Virtex-7 FPGA show the superior performance and hardware efficiency levels provided by the proposed unified architecture for the implementation of transform cores for the Advanced Video Coding (AVC), Audio Video coding Standard (AVS), VC-1 and High Efficiency Video Coding (HEVC) standards. In addition, such results also demonstrate the ability of this processing structure to realize multi-standard transform cores supporting all the standards mentioned above and that are capable of processing the 8k Ultra High Definition Television (UHDTV) video format (7,680 x 4,320 at 30 fps) in real time.

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Data analytic applications are characterized by large data sets that are subject to a series of processing phases. Some of these phases are executed sequentially but others can be executed concurrently or in parallel on clusters, grids or clouds. The MapReduce programming model has been applied to process large data sets in cluster and cloud environments. For developing an application using MapReduce there is a need to install/configure/access specific frameworks such as Apache Hadoop or Elastic MapReduce in Amazon Cloud. It would be desirable to provide more flexibility in adjusting such configurations according to the application characteristics. Furthermore the composition of the multiple phases of a data analytic application requires the specification of all the phases and their orchestration. The original MapReduce model and environment lacks flexible support for such configuration and composition. Recognizing that scientific workflows have been successfully applied to modeling complex applications, this paper describes our experiments on implementing MapReduce as subworkflows in the AWARD framework (Autonomic Workflow Activities Reconfigurable and Dynamic). A text mining data analytic application is modeled as a complex workflow with multiple phases, where individual workflow nodes support MapReduce computations. As in typical MapReduce environments, the end user only needs to define the application algorithms for input data processing and for the map and reduce functions. In the paper we present experimental results when using the AWARD framework to execute MapReduce workflows deployed over multiple Amazon EC2 (Elastic Compute Cloud) instances.

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Relatório Final apresentado à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ensino do 1º e do 2º Ciclo do Ensino Básico

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Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Civil na Área de Edificações

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Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de redes de Comunicação e Multimédia