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随机性参数分布式量化估计及其最优比特分配 被引量:1
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作者 沈志萍 陈军勇 邬依林 《控制理论与应用》 EI CAS CSCD 北大核心 2016年第8期1074-1080,共7页
本文研究总比特率给定下随机向量参数分布式量化估计及其最优比特分配问题.与现有文献大都假定每个传感器的量化比特率给定而不是最优分配下研究随机性参数的分布式量化估计问题不同的是,本文将综合考虑最优量化器、最优估计器算法以及... 本文研究总比特率给定下随机向量参数分布式量化估计及其最优比特分配问题.与现有文献大都假定每个传感器的量化比特率给定而不是最优分配下研究随机性参数的分布式量化估计问题不同的是,本文将综合考虑最优量化器、最优估计器算法以及给定总比特率下的最优比特分配问题.针对向量状态标量观测模型,首先借助现有文献给出基于量化观测的最优估计器及其误差协方差阵形式表达,其次得到各传感器的渐近最优量化器实际为著名的Lloyd-max量化器,且各传感器的渐近最优量化级数与信噪比成正比,同时引入一种次优的求解非负整数比特率的方法.考虑到当传感器数目比较大时,初始的最优估计器算法运算量很大,设计了一种渐近等价的迭代量化估计器算法,其计算负担大大减轻,且对于存在延迟或丢包的网络环境亦适用,增强了算法的鲁棒性.仿真结果表明,本文提出的最优比特分配方案估计性能明显优于一般的均匀比特分配方案. 展开更多
关键词 最优比特分配 量化信号 最优设计 分布式算子 分布式量化估计 Lloyd-max量化器 最小均方误差
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Multiparty Quantum Secret Sharing Using Quantum Fourier Transform 被引量:3
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作者 HUANG Da-Zu CHEN Zhi-Gang GUO Ying 《Communications in Theoretical Physics》 SCIE CAS CSCD 2009年第2期221-226,共6页
A (n, n)-threshold scheme of multiparty quantum secret sharing of classical or quantum message is proposed based on the discrete quantum Fourier transform. In our proposed scheme, the secret message, which is encode... A (n, n)-threshold scheme of multiparty quantum secret sharing of classical or quantum message is proposed based on the discrete quantum Fourier transform. In our proposed scheme, the secret message, which is encoded by using the forward quantum Fourier transform and decoded by using the reverse, is split and shared in such a way that it can be reconstructed among them only if all the participants work in concert. Fhrthermore, we also discuss how this protocol must be carefully designed for correcting errors and checking eavesdropping or a dishonest participant. Security analysis shows that our scheme is secure. Also, this scheme has an advantage that it is completely compatible with quantum computation and easier to realize in the distributed quantum secure computation. 展开更多
关键词 quantum secret sharing quantum Fourier transform quantum error correction code
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Improved Algorithm for Distributed Localization in Wireless Sensor Networks 被引量:3
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作者 钟幼平 匡兴红 黄佩伟 《Journal of Shanghai Jiaotong university(Science)》 EI 2010年第1期64-69,共6页
Many sensor network applications require location awareness,but it is often too expensive to equip a global positioning system(GPS) receiver for each network node.Hence,localization schemes for sensor networks typical... Many sensor network applications require location awareness,but it is often too expensive to equip a global positioning system(GPS) receiver for each network node.Hence,localization schemes for sensor networks typically use a small number of seed nodes that know their locations and protocols whereby other nodes estimate their locations from the messages they receive.For the inherent shortcomings of general particle filter(the sequential Monte Carlo method) this paper introduces particle swarm optimization and weighted centroid algorithm to optimize it.Based on improvement a distributed localization algorithm named WC-IPF(weighted centroid algorithm improved particle filter) has been proposed for localization.In this localization scheme the initial estimate position can be acquired by weighted centroid algorithm.Then the accurate position can be gotten via improved particle filter recursively.The extend simulation results show that the proposed algorithm is efficient for most condition. 展开更多
关键词 wireless sensor network node localization particle filter particle swarm optimization weighted centroid algorithm
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