Energy-efficient adaptive sensor scheduling for target tracking in wireless sensor networks

Sensor scheduling is essential to collaborative target tracking in wireless sensor networks (WSNs). In the existing works for target tracking in WSNs, such as the information-driven sensor query (IDSQ), the tasking sensors are scheduled to maximize the information gain while minimizing the resource cost based on the uniform sampling intervals, ignoring the changing of the target dynamics and the specific desirable tracking goals. This paper proposes a novel energyefficient adaptive sensor scheduling approach that jointly selects tasking sensors and determines their associated sampling intervals according to the predicted tracking accuracy and tracking energy cost. At each time step, the sensors are scheduled in alternative tracking mode, namely, the fast tracking mode with smallest sampling interval or the tracking maintenance mode with larger sampling interval, according to a specified tracking error threshold. The approach employs an extended Kalman filter (EKF)-based estimation technique to predict the tracking accuracy and adopts an energy consumption model to predict the energy cost. Simulation results demonstrate that, compared to a non-adaptive sensor scheduling approach, the proposed approach can save energy cost significantly without degrading the tracking accuracy.     

实时微控制器与20-SIM仿真模型的共同仿真实现

基于单片机实时控制是嵌入式控制系统的主要部分。讨论如何在20-SIM平台上建立用户自己的目标模板。内容包括系统模型的建立、模型的结构和目标模板的调整。重点讨论模型模板结构和在具体装置中对模型模板的调整过程。最后给出一个测试实验。     

Priority-based target coverage in directional sensor networks using a genetic algorithm

Sensor networks have been applied in a wide variety of situations. Recently directional sensor networks consisting of directional sensors have gained attention. As for the traditional target coverage problem, the limited sensing angle of directional sensors makes it even more challenging. Moreover, individual targets may also be associated with differentiated priorities. Considering the distance between the directional sensors and targets influences sensing quality, this paper proposes the priority-based target coverage problem and strives to choose a minimum subset of directional sensors that can monitor all targets, satisfying their prescribed priorities. Due to the NP-Complete complexity, the minimum subset of directional sensors is approximated by using a genetic algorithm. Simulation results reveal the effects of multiple factors on the size of the resulting subset.     

基于移动目标防御的内网防渗透技术研究

内网安全是当前计算机网络安全中的一个重要研究领域。本文提出了一种基于移动目标防御（Moving Target Defense）思想的内网防渗透技术,在通信过程中将主机的真实地址修改为动态变化的虚拟地址,使得攻击者的扫描探测失效,从而难以获取内网主机地址并开展网络渗透,提升内网的安全性。正常用户可通过主机域名查询虚拟地址,开展正常通信。该技术最后实现为网络接入设备形态,已在内网环境中测试。     

基于Adaboost-CSHG的特定类目标跟踪识别

目标跟踪识别是计算机视觉领域的热点研究对象。首先采用基于Adaboost的目标检测算法,训练得到了特定类目标坦克模型的级联分类器,对图像中的坦克目标完成了 “粗检测”;通过构建类属超图(CSHG)模型,采取Adaboost与CSHG相结合的方式,有效滤除了大量虚警,实现了对坦克目标的“精检测”,同时完成了对目标的跟踪;最后利用基于类属超图的目标识别原理对目标进行识别,实验结果表明该方法在简单背景和复杂背景图像条件下均具有可行性。     

基于自适应背景的目标跟踪算法研究

随着现在的社会发展以及经济进步,我国的科学技术方面发展迅速,特别是在技术监控方面更是突飞猛进。为了更好的对目标遮挡影响进行降低,我国在这方面主要依据自适应的技术发展背景下提出目标跟踪计算法,用来完善我国的监督控制技术。这种计算方式第一是根据对观察目标的基本外观形态进行的鉴定与跟踪,将其自身的运动量进行平均计算;其次是根据时空的运行方向与特征进行跟踪目标的计算,建立比较完善整体的运行模型,再根据这个运动模型以及整体的状态对监督目标进行检测与控制,这期间就会形成一种遮挡掩膜。对于掩膜是一种将程序数据等绘制成光刻板,在程序使用期间非常可靠,并且制造成本比较低,使用方便;最后是在不同的使用情况下将不同参数进行收集,自动的适应运动模型的运行。针对这种计算方式的实验主要是利用两种在国际上经常使用的CAVIAR、York数据进行测试,并且根据这两种数据对测试的精准度与多重目标跟踪等进行评定,检测跟踪的整体性能。通过多方面的研究表明这种方式的跟踪的性能非常好,并且还能很好的将跟踪目标的鲁棒性进行遮挡。     

OpenMP多核计算技术在SAR图像处理中的应用

SAR图像数据量大,常规识别算法复杂、处理耗时,难以满足实时性要求。针对这一问题,提出一种基于OpenMP多核计算的SAR图像目标分类算法。在分析基于模板匹配的SAR图像目标分类算法的基础上,给出基于OpenMP多核计算技术的图像处理并行处理框架,实现SAR图像目标分类算法的并行计算。最后,采用所提方法对3类目标进行分类识别实验,SAR图像分类识别的处理速度提高了8倍,表明了该方法是有效的。     

基于NFC技术的标签模式设计

设计上主要利用业界第一块标准的NFC功能芯片(PN544)来实现NFC技术中的标签模式。该标签模式是模拟ISO14443标准定义的Type A卡。与如今大部分的实现模式不一样的是:通过采用NXP公司的主控芯片(LPC1114)来直接配置PN544。该实现方法是根据NFC协议开发相应的驱动程序。使用SCM公司的读卡器(SDI011G)来验证标签模式的配置是否成功。如果是成功的配置,那么读卡器可以探测到PN544和LPC1114构成的设备。     

Supply chain coordination with risk sensitive retailer under target sales rebate

Target sales rebate (TSR) contracts have been shown to be useful in coordinating supply chains with risk-neutral agents. However, there have been few studies on the cases with risk sensitive agents. As a result, based on the classic Markowitz portfolio theory in finance, we carry out in this paper a mean–variance (MV) analysis of supply chains under TSR contracts. We study a supply chain with a single supplier and a single risk averse retailer. We propose TSR contracts for achieving coordination. We demonstrate how TSR contracts can coordinate the supply chain which takes into consideration the degree of risk aversion of the retailer. We find that the supplier can coordinate the channel with flexible TSR contracts. In addition, we extend the supply chain model to include sales effort decision of the retailer. Conditions for TSR contracts to coordinate the supply chain with sales effort of retailer are also derived.     

Kernel-based regularized-angle spectral matching for target detection in hyperspectral imagery

Target detection is one of the most important applications of hyperspectral imagery in the field of both civilian and military. In this letter, we firstly propose a new spectral matching method for target detection in hyperspectral imagery, which utilizes a pre-whitening procedure and defines a regularized spectral angle between the spectra of the test sample and the targets. The regularized spectral angle, which possesses explicit geometric sense in multidimensional spectral vector space, indicates a measure to make the target detection more effective. Furthermore Kernel realization of the Angle-Regularized Spectral Matching (KAR-SM, based on kernel mapping) improves detection even more. To demonstrate the detection performance of the proposed method and its kernel version, experiments are conducted on real hyperspectral images. The experimental tests show that the proposed detector outperforms the conventional spectral matched filter and its kernel version.