ID 原文 译文
25465 算法利用平稳噪声协方差矩阵关于主对角线对称的特点，构造近场源定位模型下的空间差分矩阵。 The algorithm firstly utilizes the feature that the stationary noise covariance matrix is symmetrical about the main diagonal and constructs the spatial difference matrix only containing the target signal location information．
25466 推导并证明了该矩阵的谱分解特性，以此为基础确定噪声子空间，借助谱峰搜索实现定位参量估计。 Then, it proves the distribution characteristics of the matrix eigenvalues and selects the noise sub-space reasonably． Finally, the DOA and range estimations for near-field sources can be obtained through the spectral searching．
25467 算法通过对消噪声分量有效降低了未知平稳噪声对定位精度的影响，同时避免了应用差分技术解决信源定位时出现的伪峰问题。 The algorithm can effectively suppresses the unknown stationary noise and avoid the pseudo peak problems for the application of the spatial differential method when used to solve the source localization．
25468 均方根误差的仿真结果证明了算法的有效性。 Computer simulations confirm the satisfactory performance of the proposed algorithm．
25469 基于单一边缘节点计算、存储资源的有限性及大数据场景对高效计算服务的需求，本文提出了一种基于深度强化学习的云边协同计算迁移机制。 Based on the computation and storage resources limitation of single edge node and the demand for efficient computing services in big data scenarios, this paper proposes a deep reinforcement learning based cloud-edge collaborative computation offloading mechanism．
25470 具体地，基于计算资源、带宽和迁移决策的综合性考量，构建了一个最小化所有用户任务执行延迟与能耗权重和的优化问题。 Specifically, based on a comprehensive consideration of computing resources, bandwidth and offloading policy, an optimization problem is formulated to minimize the weight sum of execution delay and energy consumption of all user tasks．
25471 基于该优化问题提出了一个异步云边协同的深度强化学习算法，该算法充分利用了云边双方的计算能力，可有效满足大数据场景对高效计算服务的需求； An asynchronous cloud-edge collaborative deep reinforcement learning (ACEC-DＲL) algorithmis proposed to solve such optimization problem. This algorithm can effectively satisfy the demand of efficient computing services in big data scenario by jointly leveraging the computation capabilities of cloud and edge nodes．
25472 同时，面向边缘云中边缘节点所处环境的多样及动态变化性，该算法能自适应地调整迁移策略以实现系统总成本的最小化。 Meanwhile, underthe various and dynamic environments of edge nodes in the edge cloud, this algorithm can adaptively adjust offloading policyto achieve the minimization of system cost.
25473 最后，大量的仿真结果表明本文所提出的算法具有收敛速度快、鲁棒性高等特点，并能够以最低的计算成本获得近似贪心算法的最优迁移决策。 Finally, the extensive simulation results show that the proposed ACEC-DＲL algorithm has the characteristics of fast convergence rate and high robustness, and its optimal offloading policy closely approximates to the solution of greedy algorithm with the lowest computation cost．
25474 安全多方计算是密码学界的一个重要研究方向，本文主要研究区间的安全计算问题。 Secure multi-party computation (SMC) is an important research direction of cryptography. In this paper, we study the secure computation of intervals．