提出了一种并行化LSB(Parallel Least Significant Bit)算法,实现了二维码图像水印的嵌入、提取.对待处理图像数据进行分块并利用Open MP的编译制导指令进行任务分担,实现了运算过程的并行化.该方法嵌入水印的二维码图像具有较好的防伪...提出了一种并行化LSB(Parallel Least Significant Bit)算法,实现了二维码图像水印的嵌入、提取.对待处理图像数据进行分块并利用Open MP的编译制导指令进行任务分担,实现了运算过程的并行化.该方法嵌入水印的二维码图像具有较好的防伪验证功能,而且适合以电子形式保存,并行化处理方法较传统串行算法可缩短1/3左右的时间.展开更多
This article investigates channel allocation for cognitive networks, which is difficult to obtain the optimal allocation distribution. We first study interferences between nodes in cognitive networks and establish the...This article investigates channel allocation for cognitive networks, which is difficult to obtain the optimal allocation distribution. We first study interferences between nodes in cognitive networks and establish the channel allocation model with interference constraints. Then we focus on the use of evolutionary algorithms to solve the optimal allocation distribution. We further consider that the search time can be reduced by means of parallel computing, and then a parallel algorithm based APO is proposed. In contrast with the existing algorithms, we decompose the allocation vector into a number of sub-vectors and search for optimal allocation distribution of sub-vector in parallel. In order to speed up converged rate and improve converged value, some typical operations of evolutionary algorithms are modified by two novel operators. Finally, simulation results show that the proposed algorithm drastically outperform other optimal solutions in term of the network utilization.展开更多
文摘提出了一种并行化LSB(Parallel Least Significant Bit)算法,实现了二维码图像水印的嵌入、提取.对待处理图像数据进行分块并利用Open MP的编译制导指令进行任务分担,实现了运算过程的并行化.该方法嵌入水印的二维码图像具有较好的防伪验证功能,而且适合以电子形式保存,并行化处理方法较传统串行算法可缩短1/3左右的时间.
基金supported in part by the National Natural Science Foundation under Grant No.61072069National Science and Technology Major Project of the Ministry of Science and Technology of China under Grant No.2012ZX03003012
文摘This article investigates channel allocation for cognitive networks, which is difficult to obtain the optimal allocation distribution. We first study interferences between nodes in cognitive networks and establish the channel allocation model with interference constraints. Then we focus on the use of evolutionary algorithms to solve the optimal allocation distribution. We further consider that the search time can be reduced by means of parallel computing, and then a parallel algorithm based APO is proposed. In contrast with the existing algorithms, we decompose the allocation vector into a number of sub-vectors and search for optimal allocation distribution of sub-vector in parallel. In order to speed up converged rate and improve converged value, some typical operations of evolutionary algorithms are modified by two novel operators. Finally, simulation results show that the proposed algorithm drastically outperform other optimal solutions in term of the network utilization.
