水下无线传感器网络目标跟踪低复杂度节点行径策略

针对水下无线传感器网络节点选择“组合爆炸冶的问题,研究了低计算复杂度节点选择问题。首先,在量化量测的条件下推导了后验克拉美罗下界(PCRLB)与节点位置的关系,为节点选择提供了准则;然后,将GBFOS 算法、贪心算法和随机算法与推导的PCRLB 相结合,设计了低计算复杂度的节点选择策略。实验结果表明,GBFOS 算法和贪心算法可以在保持跟踪性能不退化的情况下,大幅度降低计算复杂度,非常适合解决密集水下网络节点选择问题。此外,还将GBFOS 算法应用到非理想信道条件下节点选择问题,实验结果显示考虑非理想信道的影响可以大幅提高跟踪性能。     

基于变异系数优选算法的精细导星星库构建

精细导星星库是支撑未来大口径空间望远镜观测的必要组成部分,是用来确定空间望远镜视轴绝对指向的关键依据.导星星库的容量和星库的均匀性对于实现精细导星功能和性能指标都极为重要.为了有效提高星图识别性能,提出基于变异系数的优选筛星算法进行导星筛选并构建星库.经过仿真和均匀性评价准则验证,基于变异系数优选算法构建的精细导星星库分布均匀性好,能降低冗余度,有效提高识别概率.     

一种用于雷达信号调制分类的特征评估方法

针对用于调制分类过程中雷达信号脉内特征参数评估问题,提出了一种多指标联合评估方法.该方法使用复杂性、可分离性和稳定性3个指标对特征参数的调制分类性能进行联合评估,克服了传统评估手段仅使用单一指标难以进行全面科学评估的缺陷,并可以根据应用需求获取相应的最合理的综合评分值.理论分析和计算机仿真结果表明,该方法可以有效评估特征的调制分类性能,具有较强的工程应用价值.     

二进制猫群算法在大规模MIMO天线选择中的应用

在多输入多输出(MIMO)系统中,天线选择技术平衡了系统的性能和硬件开销,但大规模MI-MO系统收发端天线选择复杂度问题一直没有得到很好的解决.基于信道容量最大化的准则,采用两个二进制编码字符串分别表示发射端和接收端天线被选择的状态,提出将二进制猫群算法(BCSO)应用于多天线选择中,以MIMO系统信道容量公式作为猫群的适应度函数,将收发端天线选择问题转化为猫群的位置寻优过程.建立了基于BCSO的天线选择模型,给出了算法的实现步骤.仿真结果表明所提算法较之于基于矩阵简化的方法、粒子优化算法具有更好的收敛性和较低的计算复杂度,选择后的系统信道容量接近于最优算法,非常适用于联合收发端天线选择的大规模MIMO系统中.     

基于用户行为分析的智能家居控制软件的设计

设计并实现了一种基于C4.5决策树算法用户行为分析的智能家居控制软件系统,该软件系统利用智能家居用户行为数据并分析,使家居系统能够根据用户行为习惯规律,安排合理的控制家居电器设备,通过web数据可视化使得用户清楚地掌握家中各种家电设备使用情况,为用户提供人性化和智能化的服务.测试结果表明:该软件系统能够帮助提高家居系统的人性化和智能化程度,数据可视化软件系统用户体验性好,具有重要的理论价值和现实意义.     

SINR Driven Joint Network Selection Policy in the Heterogeneous Internet of Things

In the heterogeneous Internet of things (IoT), the Signal to interference plus noise ratio (SINR) and delay constraint are two important factors that influ-ence the throughput of IoT and the performance of users. Until recently, most network selection policy researches were based on either the Shannon theory or the signal strength, while the combined influence of the delay con-straint and the SINR, which has a significant impact on re-source utilization, is hardly considered. We therefore pro-pose an SINR driven joint network selection policy, which incorporates the delay constraint and the signal strength into the SINR. This policy permits IoT users to access the network with the maximum of SINR from all the available networks under the delay and signal strength constraints. Theoretical analysis and the simulation results show that the joint network selection policy can obtain the higher throughput of IoT and average SINR comparing with other polices.     

Fast and Accurate Visual Place Recognition Using Street‐View Images

A fast and accurate building‐level visual place recognition method built on an image‐retrieval scheme using street‐view images is proposed. Reference images generated from street‐view images usually depict multiple buildings and confusing regions, such as roads, sky, and vehicles, which degrades retrieval accuracy and causes matching ambiguity. The proposed practical database refinement method uses informative reference image and keypoint selection. For database refinement, the method uses a spatial layout of the buildings in the reference image, specifically a building‐identification mask image, which is obtained from a prebuilt three‐dimensional model of the site. A global‐positioning‐system‐aware retrieval structure is incorporated in it. To evaluate the method, we constructed a dataset over an area of 0.26 km². It was comprised of 38,700 reference images and corresponding building‐identification mask images. The proposed method removed 25% of the database images using informative reference image selection. It achieved 85.6% recall of the top five candidates in 1.25 s of full processing. The method thus achieved high accuracy at a low computational complexity.     

Considerations for application‐layer multipath transport control

Multipath transport faces a lot of challenges caused by path diversity, network dynamics, and service diversity. An effective end‐to‐end multipath transport control mechanism becomes essential to efficiently utilize multiple paths. On the base of the general framework of multipath transport system based on application‐level relay proposed in our previous work, this paper presents a multipath transport control mechanism supporting various applications with different transmission requirements. We propose a multipath transport protocol suite, which is extensible and suitable for various applications, and a multipath transport control model in which an application‐dependent splitting granularity named flow block is introduced. Two load distribution models are explored: the earliest idle path first load distribution for reliable data transmission to maximize the data throughput and the packet reordering‐controlled load distribution for real‐time data transmission to minimize the packet reordering thereby reducing end‐to‐end delay and packet loss rate of multipath transport. Simulation results show that the proposed models can effectively improve data throughput for applications with reliable transmission requirements and reduce the total packet loss rate of the destination for applications with real‐time transmission requirements.     

Using particle swarms for the development of QSAR models based on K-nearest neighbor and kernel regression

We describe the application of particle swarms for the development of quantitative structure-activity relationship (QSAR) models based on k-nearest neighbor and kernel regression. Particle swarms is a population-based stochastic search method based on the principles of social interaction. Each individual explores the feature space guided by its previous success and that of its neighbors. Success is measured using leave-one-out (LOO) cross validation on the resulting model as determined by k-nearest neighbor kernel regression. The technique is shown to compare favorably to simulated annealing using three classical data sets from the QSAR literature.     

Data analysis for electronic nose systems

Electronic noses (e-noses) employ an array of chemical gas sensors and have been widely used for the analysis of volatile organic compounds. Pattern recognition provides a higher degree of selectivity and reversibility to the systems leading to an extensive range of applications. These range from the food and medical industry to environmental monitoring and process control. Many types of data analysis techniques have been used on the data produced. This review covers aspects of analysis from data normalisation methods to pattern recognition and classification techniques. An overview of data visualisation such as non-linear mapping and multivariate statistical techniques is given. Focus is then on the use of artificial intelligence techniques such as neural networks and fuzzy logic for classification and genetic algorithms for feature (sensor) selection. Application areas are covered with examples of the types of systems and analysis methods currently in use. Future trends in the analysis of sensor array data are discussed.