38 resultados para Acceleration


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We investigate the decay of accelerated protons and neutrons. Calculations are carried out in the inertial and coaccelerated frames. Particle interpretation of these processes are quite different in each frame but the decay rates are verified to agree in both cases. For the sake of simplicity our calculations are performed in a two-dimensional spacetime since our conclusions are not conceptually affected by this. ©1999 The American Physical Society.

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SAFT techniques are based on the sequential activation, in emission and reception, of the array elements and the post-processing of all the received signals to compose the image. Thus, the image generation can be divided into two stages: (1) the excitation and acquisition stage, where the signals received by each element or group of elements are stored; and (2) the beamforming stage, where the signals are combined together to obtain the image pixels. The use of Graphics Processing Units (GPUs), which are programmable devices with a high level of parallelism, can accelerate the computations of the beamforming process, that usually includes different functions such as dynamic focusing, band-pass filtering, spatial filtering or envelope detection. This work shows that using GPU technology can accelerate, in more than one order of magnitude with respect to CPU implementations, the beamforming and post-processing algorithms in SAFT imaging. ©2009 IEEE.

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A gas of non-interacting particles diffuses in a lattice of pulsating scatterers. In the finite-horizon case with bounded distance between collisions and strongly chaotic dynamics, the velocity growth (Fermi acceleration) is well described by a master equation, leading to an asymptotic universal non-Maxwellian velocity distribution scaling as v∼t. The infinite-horizon case has intermittent dynamics which enhances the acceleration, leading to v∼t ln t and a non-universal distribution. © Copyright EPLA, 2013.

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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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Huge image collections are becoming available lately. In this scenario, the use of Content-Based Image Retrieval (CBIR) systems has emerged as a promising approach to support image searches. The objective of CBIR systems is to retrieve the most similar images in a collection, given a query image, by taking into account image visual properties such as texture, color, and shape. In these systems, the effectiveness of the retrieval process depends heavily on the accuracy of ranking approaches. Recently, re-ranking approaches have been proposed to improve the effectiveness of CBIR systems by taking into account the relationships among images. The re-ranking approaches consider the relationships among all images in a given dataset. These approaches typically demands a huge amount of computational power, which hampers its use in practical situations. On the other hand, these methods can be massively parallelized. In this paper, we propose to speedup the computation of the RL-Sim algorithm, a recently proposed image re-ranking approach, by using the computational power of Graphics Processing Units (GPU). GPUs are emerging as relatively inexpensive parallel processors that are becoming available on a wide range of computer systems. We address the image re-ranking performance challenges by proposing a parallel solution designed to fit the computational model of GPUs. We conducted an experimental evaluation considering different implementations and devices. Experimental results demonstrate that significant performance gains can be obtained. Our approach achieves speedups of 7x from serial implementation considering the overall algorithm and up to 36x on its core steps.

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