有限元方法FEM(Finite Element Method)是近似求解数理边值问题的一种数值技术,它在计算电磁学中有着非常重要的应用,但当问题规模较大时或计算量较大时,传统单机FEM难以胜任.文章在基于消息传递(MPI)的分布式并行系统上,采用有限元方...有限元方法FEM(Finite Element Method)是近似求解数理边值问题的一种数值技术,它在计算电磁学中有着非常重要的应用,但当问题规模较大时或计算量较大时,传统单机FEM难以胜任.文章在基于消息传递(MPI)的分布式并行系统上,采用有限元方法对静电磁问题进行并行求解.共轭梯度法作为一种实用的迭代法可以充分利用有限元方法形成的系数矩阵的稀疏性,不需预先估计别的参数就可以计算,预处理共轭梯度法通过降低系数矩阵的条件数,可以进一步加快收敛速度.并行计算技术的运用减少了计算时间并扩展了可处理问题的规模.结果表明,将并行技术应用于电磁有限元计算是有效且可行的.展开更多
A technique to construct an affine invariant descriptor for remote-sensing image registration based on the scale invariant features transform （SIFT） in a kernel space is proposed. Affine invariant SIFT descriptor is...A technique to construct an affine invariant descriptor for remote-sensing image registration based on the scale invariant features transform （SIFT） in a kernel space is proposed. Affine invariant SIFT descriptor is first developed in an elliptical region determined by the Hessian matrix of the feature points. Thereafter, the descriptor is mapped to a feature space induced by a kernel, and a new descriptor is constructed by whitening the mapped descriptor in the feature space, with the transform called KW-SIFT. In a final step, the new descriptor is used to register remote-sensing images. Experimental results for remote-sensing image registration indicate that the proposed method improves the registration performance as compared with other related methods.展开更多
文摘有限元方法FEM(Finite Element Method)是近似求解数理边值问题的一种数值技术,它在计算电磁学中有着非常重要的应用,但当问题规模较大时或计算量较大时,传统单机FEM难以胜任.文章在基于消息传递(MPI)的分布式并行系统上,采用有限元方法对静电磁问题进行并行求解.共轭梯度法作为一种实用的迭代法可以充分利用有限元方法形成的系数矩阵的稀疏性,不需预先估计别的参数就可以计算,预处理共轭梯度法通过降低系数矩阵的条件数,可以进一步加快收敛速度.并行计算技术的运用减少了计算时间并扩展了可处理问题的规模.结果表明,将并行技术应用于电磁有限元计算是有效且可行的.
基金supported by the National Natural Science Foundation of China (Nos. 60972150 and 10926197)
文摘A technique to construct an affine invariant descriptor for remote-sensing image registration based on the scale invariant features transform （SIFT） in a kernel space is proposed. Affine invariant SIFT descriptor is first developed in an elliptical region determined by the Hessian matrix of the feature points. Thereafter, the descriptor is mapped to a feature space induced by a kernel, and a new descriptor is constructed by whitening the mapped descriptor in the feature space, with the transform called KW-SIFT. In a final step, the new descriptor is used to register remote-sensing images. Experimental results for remote-sensing image registration indicate that the proposed method improves the registration performance as compared with other related methods.
