基于骨架提取的水下传感器网络刚性定位判别研究

水下传感器网络可用于海洋资源勘测、污染监测和战术监视等领域,已成为无线传感器网络方向的研究热点.在水下三维空间中如何对位置未知的节点进行可定位性判别,并实施有效定位,从而为水下监测应用提供位置信息是一个基础的关键问题.该文引入刚性图理论,提出了一种基于骨架提取的水下传感器网络刚性定位判别方法.该方法首先将水下传感器网络构建为无向图,然后通过“伪节点剔除”和“割边剔除”等策略提取具有全局刚性的子图,即网络的骨架,从而完成网络及节点可定位性的判别,最后利用层次分析法对可定位节点进行“定位可信度”综合排序,为定位计算提供指导和依据.大量仿真实验结果表明该方法在不同网络规模、节点密度和网络连通性情况下均具有较高的判别成功率和有效性.     

基于特征规整和评分规整的说话人确认研究

在说话人确认系统中,训练和测试的声学环境不匹配将造成性能急剧下降。本文提出了从特征规整和评分规整两个方面进行补偿的方法。首先,改进了基于分段的倒谱均值方差规整(SCMVN)方法,将倒谱系数都规整到相同的段内高斯统计分布,以提高不同环境条件下特征匹配程度;其次,针对由于不同说话人和不同测试环境引起的输出评分分布变化,提出了两阶段的评分规整方法,即先零规整再测试规整(TZnorm)和先测试规整再零规整(ZTnorm)两种得分变换方法,使得失配条件下与说话人无关的决策门限更加鲁棒。基于NIST2002说话人识别评测库上的实验表明,采用SCMVN的特征规整和ZTnorm的评分规整方法能够明显地提高系统性能。与采用倒谱均值减和零规整的基线系统相比,等错误率和最小检测代价分别降低了20.3%和18.1%。     

A Monte Carlo Enhanced PSO Algorithm for Optimal QoM in Multi-Channel Wireless Networks

In wireless monitoring networks, wireless sniffers are distributed in a region to monitor the activities of users. It can be used for fault diagnosis, resource management and critical path analysis. Due to hardware limitations, wireless sniffers typically can only collect information on one channel at a time. Therefore, it is a key topic to optimize the channel selection for sniffers to maximize the information collected, so as to maximize the quality of monitoring (QoM) of the network. In this paper, a particle swarm optimization (PSO)-based solution is proposed to achieve the optimal channel selection. A2D mapping particle coding and its moving scheme are devised. Monte Carlo method is incorporated to revise the solution and significantly improve the convergence of the algorithm. The extensive simulations demonstrate that the Monte Carlo enhanced PSO (MC-PSO) algorithm outperforms the related algorithms evidently with higher monitoring quality, lower computation complexity, and faster convergence. The practical experiment also shows the feasibility of this algorithm.