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Titlebook: Computational Science and Its Applications - ICCSA 2005; International Confer Osvaldo Gervasi,Marina L. Gavrilova,Chih Jeng Kenn Conference

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樓主: obsess
31#
發(fā)表于 2025-3-26 21:44:03 | 只看該作者
32#
發(fā)表于 2025-3-27 02:36:42 | 只看該作者
Surfactants Applications Directoryin 4.34x and 2.6x better performances for DCT and HT, respectively, than a 4x4 integer transform technique in an H.264 reference codec using 64-bits SIMD operations. We can still have 6.77x and 3.98x better performances using 128-bits SIMD operations, respectively.
33#
發(fā)表于 2025-3-27 06:02:38 | 只看該作者
34#
發(fā)表于 2025-3-27 11:54:32 | 只看該作者
35#
發(fā)表于 2025-3-27 14:32:29 | 只看該作者
New Size-Reduced Visual Secret Sharing Schemes with Half Reduction of Shadow Size one pixel, the secret image is divided into several shadow images whose size is . times than the secret image. The value of . is known as the .. In this paper, we propose the new size-reduced VSS schemes and dramatically decrease the . to a half and meantime the contrast is not compromised.
36#
發(fā)表于 2025-3-27 18:27:35 | 只看該作者
37#
發(fā)表于 2025-3-27 22:13:39 | 只看該作者
38#
發(fā)表于 2025-3-28 05:09:20 | 只看該作者
A Systematic Process to Design Product Line Architectureas a key element of core assets. However, current research on product line engineering has room to provide specific and detailed guidelines of designing product line architectures and reflecting variability in the architecture. In this paper, we present a reference model and a process to design the
39#
發(fā)表于 2025-3-28 08:41:29 | 只看該作者
Variability Design and Customization Mechanisms for COTS Componentsrather than intra-organizational reuse [1]. One of the common forms of reusing commercial-off-the-shelf (COTS) components is to acquire and customize them for each application. Therefore, components must be developed with consideration of commonality and variability in a domain in order to increase
40#
發(fā)表于 2025-3-28 13:34:03 | 只看該作者
A Fast Lossless Multi-resolution Motion Estimation Algorithm Using Selective Matching Unitsy reduce the amount of computation in motion estimation. Our proposed algorithm has no any degradation of prediction quality compared to the original MRME algorithm. The computational reduction of our algorithm comes from fast elimination of unlikely candidate vectors. We fast eliminate inappropriat
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