ID 原文 译文
20795 虚拟化技术可有效缓解当前无线传感网络(WSN)中资源利用率较低、服务不灵活的问题。 Virtualization is a new technology that can effectively solve the low resource utilization and service inflexibility problem in the current Wireless Sensor Network (WSN).
20796 针对虚拟化WSN中的资源竞争问题，该文提出一种基于Stackelberg博弈的多任务资源分配策略。 For the resource competition problem invirtualized WSN, a multi-task resource allocation strategy based on Stackelberg game is proposed.
20797 依据所承载业务的不同服务质量(QoS)需求，量化多个虚拟传感网络请求(VSNRs)的重要程度， According tothe different Quality of Service (QoS) requirements of the business carried by Virtual Sensor Network Request(VSNR), the importance of multiple VSNRs is quantified.
20798 进而，利用分布式迭代方法，获取WSN的最优价格策略和VSNRs的最优资源需求量， Then, the optimal price of WSN and the optimalresource requirements of VSNRs are obtained by using distributed iteration method.
20799 最后，根据纳什均衡所确定的最优价格、最优资源分配量，对多个VSNRs分配资源。 Finally, the resource corresponding to multiple VSNRs is acquired according to optimal price and optimal resource allocation determined by Nash equilibrium.
20800 仿真结果表明，所提策略不仅能满足用户的多样化需求，而且提升了节点和链路资源利用率。 The simulation results show that the proposed strategy can not only meet thediversified needs of users, but also improve the resource utilization of nodes and links.
20801 针对现有调制宽带转换器亚奈奎斯特采样重构算法性能不高问题，该文提出一种基于采样值核空间的支撑重构算法和随机压缩降秩方法，将两者结合得到一种高性能采样重构算法。 To solve the low performance problem of the existing Modulated Wideband Converter (MWC)-basedsub-Nyquist sampling recovery algorithm, this paper proposes a support recovery algorithm based on the kernelspace of sampling value and a random compression rank-reduction idea. Combining them, a high-performancesampling recovery algorithm is achieved.
20802 首先利用随机压缩变换在不改变未知矩阵稀疏特性的前提下将采样方程转化为多个新的多测量向量问题， Firstly random compression transforms are used to convert thesampling equation into several new multiple-measurement-vector problems, without changing the sparsity of theunknown matrix.
20803 然后利用采样值矩阵核空间与采样矩阵支撑正交的关系获取联合稀疏支撑集，最后通过伪逆完成重构。 Then the orthogonal relationship between the kernel space of sampling value and the supportvectors of sampling matrix is utilized to obtain joint sparse support set of the unknown. The final recovery is performed by the pseudo inversion.
20804 从理论和实验两个方面对所提方法进行了分析和验证。 The proposed method is analyzed and verified by theory and experiment.