ID 原文 译文
46176 针对访问控制中的异常权限配置发现问题，提出一种基于谱聚类的异常权限配置挖掘机制。 To hunt the potential abnormal user-permission configure-tions, a novel spectral clustering based abnormal configuration hunting framework was proposed and recommendations were given to correct these configurations.
46177 实验结果证明，所提方案可以实现更准确的权限配置发现。 Experimental results show its performance over existing solutions.
46178 针对信道路径数未知的大规模多输入多输出（MIMO, multi-input multi-output）系统，提出一种稀疏度自适应的压缩感知信道估计方法——块稀疏自适应匹配追踪（BSAMP, block sparsity adaptive matching pursuit）算法。 A sparsity-adaptive channel estimation algorithm based on compressive sensing was proposed for massive MIMO systems when the number of channel multi-paths was unknown.
46179 利用大规模 MIMO 系统子信道的联合稀疏性，通过设置阈值及寻找最大后向差分位置对支撑集原子进行快速初步选择，同时考虑了观测矩阵非正交性造成的能量弥散，提高算法的估计性能； By exploiting the joint sparsity characteristics of the sub-channels, the proposed block sparsity adaptive matching pursuit (BSAMP) algorithm first selected atoms by set-ting a threshold and finding the position of the maximum backward difference, which reduces the energy dispersion caused by the non-orthogonality of the observation matrix and improves the performance of the algorithm.
46180 通过正则化对原子进行二次筛选，以提高算法的稳定性。 Then a regularization method was utilized to improve the stability of the algorithm.
46181 仿真表明，该算法能快速、准确地恢复稀疏度未知的大规模 MIMO 信道信息。 Simulation results demonstrate that the proposed algorithm recovers the channel state information accurately and shows a high computational efficiency.
46182 针对现有的跳频信号参数估计算法没有充分考虑跳频信号频域稀疏特性的问题，提出一种基于压缩感知的跳频信号参数盲估计算法。 A blind parameter estimation algorithm for frequency-hopping signals based on compressed sensing was pro-posed, in order to solve the problem that the existing parameter estimation algorithms did not take into account the sparse structural characteristics of the signals in frequency domain.
46183 该算法首先采用最大余弦法对经过压缩采样的跳频信号进行分段处理，估计出各段信号的跳频频率， Firstly, the maximum cosine method was used to process the segmented compressed sampling signals, and the hopping frequency was estimated.
46184 然后，利用原子匹配的方法对含有跳变点的各段信号进行处理，精确估计出各个频率跳变时刻， Then, the atom matching algorithm was used to process the signal with the hopping point, and the frequency hopping instance time was estimated accurately.
46185 进而估计跳速和起跳时刻。 Then the hopping speed and hopping time were estimated.