Ground target detection, classification and sensor fusion in distributed fiber seismic sensor network - art. no. 683015

This paper describes the ground target detection, classification and sensor fusion problems in distributed fiber seismic sensor network. Compared with conventional piezoelectric seismic sensor used in UGS, fiber optic sensor has advantages of high sensitivity and resistance to electromagnetic disturbance. We have developed a fiber seismic sensor network for target detection and classification. However, ground target recognition based on seismic sensor is a very challenging problem because of the non-stationary characteristic of seismic signal and complicated real life application environment. To solve these difficulties, we study robust feature extraction and classification algorithms adapted to fiber sensor network. An united multi-feature (UMF) method is used. An adaptive threshold detection algorithm is proposed to minimize the false alarm rate. Three kinds of targets comprise personnel, wheeled vehicle and tracked vehicle are concerned in the system. The classification simulation result shows that the SVM classifier outperforms the GMM and BPNN. The sensor fusion method based on D-S evidence theory is discussed to fully utilize information of fiber sensor array and improve overall performance of the system. A field experiment is organized to test the performance of fiber sensor network and gather real signal of targets for classification testing.

VSR:多sink机会移动传感器网络的数据收集

机会移动传感器网络可应用在野生动物监控,或利用手持设备嵌入的传感器收集城市信息等场景,往往需要将数据从源节点传输到多个基站中的任一个.提出了一个基于虚拟空间的路由机制VSR(virtual space-based routing),采用"存储-携带-转发"的传输模式实现数据收集.每个传感器节点根据与多个sink节点的期望传输延迟映射成高维空间中的一个坐标点,消息传输对应于从源节点移动到空间原点的过程.细粒度的转发决策特性,使VSR自适应于网络的动态变化,具有很好的鲁棒性.此外,VSR机制具有很低的计算和存储开销,非常适合资源受限的传感器节点.两种不同随机特性场景下的模拟实验验证了VSR机制比ZebraNet的基于历史的转发机制和随机转发机制的性能更好.

面向网络生存时间的传感器网络融合延迟分配算法

融合延迟分配策略是影响数据融合效率的重要因素之一,而数据融合的目的是减少数据冗余,降低网络能量消耗,延长网络生存时间.提出了一种面向网络生存时间的延迟分配算法,该算法直接以网络生存时间为优化目标,根据对网络生存时间的贡献分配传输延迟;通过理论推导证明了该算法的有效性;引入动态规划方程,给出了该算法的具体实现;最后通过仿真实验给出了各参数对网络生存时间的影响关系.

无线传感器网络中一种启发式最小连通支配集算法

针对最小连通配集问题设计了一种具有较高能量效率的启发式算法。算法首先把网络中所有的节点作为最小连通支配集的一个初始解,然后利用启发式修剪策略剔除冗余节点从而减小最小连通支配集的大小,直到没有冗余节点存在。文中将算法分成集中式和分布式两种情况进行了详细讨论。仿真结果表明,由于实现简便,该算法与其他已有算法相比较,在算法复杂性和算法稳定运行时间上有一定的优势。

用于工业过程监测的无线传感器网络路由协议研究

微电子、无线通信、自动控制和人工智能等领域的技术进步，推动了无线传感器网络的发展。无线传感器网络改变了人与自然的交互方式，是二十一世纪最具影响的IT技术之一，在军事、环境、医疗、家庭、工业和其它领域有广阔的应用前景。 路由协议是目前传感器网络研究的重要内容，协议设计与网络应用密切相关。在工业无线监测应用中，路由协议设计的主要目标是提高可靠性和降低节点能耗；而传统网络路由协议设计的首要任务是提供高质量的数据服务。这些不同导致传统网络的路由协议不能直接应用于工业过程监测网络。论文针对工业无线监测应用的需求和特点展开研究工作，主要包括以下几方面内容。 论述了工业无线传感器网络路由协议设计所面临的挑战性问题，系统地总结了已有研究成果与不足，具体分析了在工业过程监测环境下无线传感器网络的特点和路由需要重点解决的问题。 分析了工业过程监测应用对传感器网络的路由需求，并实地测试了工厂车间环境下的信道质量。 针对这些工业应用的实际需求，提出了在减少协议开销、降低协议能耗的同时提高数据传输可靠性的路由机制，以满足不同现场设备对数据路由的需求。 针对工业过程监测网络中的上行数据量大且具有周期性的特点，提出了一种基于链路质量估计的逐跳多径路由协议。该协议使数据在每一跳的转发过程中都有多条路径可以使用，在提高转发成功率的同时避免了端到端多路径机制带来的大量开销。 针对工业过程监测应用中下行数据量少且具有非周期性的特点，提出了基于分布式编址算法的主动路由协议。该协议中，传感器节点分配到下行地址之后就可以计算出下一跳转发地址，从而避免了采用基于广播转发的方式，大大减少了路由开销。同时，单播转发的下行数据不会引发“广播风暴”，减轻了对网络中周期性上行数据转发的影响。 针对手持设备所具有的移动性、数据量少和通信不频繁的特点，设计了面向移动设备的低开销按需路由协议。该协议只在手持设备有远程连接需求的时候才建立主路由，然后按需延长，避免手持设备每移动一次就要重新进行路由发现。此外，在路由发现环节利用了已经建立好的主动路由，通过单播方式转发路由发现报文，不但大幅减少了协议开销，同时也保证了所选路由的高质量。 为了测试本文提供的路由协议在工厂车间内的实际效果，构建了一个工作在2.4GHz上的验证系统。该验证系统以网络层的可靠性机制为基础，通过在链路层采用TDMA机制、FDMA机制和在传输层重传等机制的配合，达到了较高的报文传输可靠性，证明本文提出的路由协议能够满足工业过程监测应用的需要。

基于DSP的动态传感器网络实验平台的设计和开发

介绍了一种基于DSP2812的动态传感器网络实验平台的设计与开发.该实验平台的设计由配备各种低成本、低功耗的传感器和无线通信模块的可移动的传感器节点组成.在介绍动态传感器网络实验平台的各个组成部分之后,对系统进行了的基本实验测试,并给出了测试结果.*

基于网络化虚拟仪器的机器人传感器实验平台

设计了基于网络化虚拟仪器的多节点机器人传感器实验平台系统,利用COM、软件插件等技术开发了虚拟控件和信号处理软件模块,用户可通过PPL创建自定义的信号处理模块,为进行面向智能机器人控制的传感器测试、标定、组网等奠定了基础。

Application of ICA in on-line verification of the phase difference of the current sensor

The performance of the current sensor in power equipment may become worse affected by the environment. In this paper, based on ICA, we propose a method for on-line verification of the phase difference of the current sensor. However, not all source components are mutually independent in our application. In order to get an exact result, we have proposed a relative likelihood index to choose an optimal result from different runs. The index is based on the maximum likelihood evaluation theory and the independent subspace analysis. The feasibility of our method has been confirmed by experimental results.

Glucose biosensor based on gold nanoparticle-catalyzed luminol electrochemiluminescence on a three-dimensional sol-gel network

An electrochemiluminescent glucose biosensor was proposed based on gold nanoparticle-catalyzed luminol electrochemiluminescence (ECL). Gold nanoparticles were self-assembled onto silica sol-gel network, and then glucose oxidase was adsorbed on the surface of gold nanoparticles. The surface assembly process and the electrochemistry and ECL behaviors of the biosensor were investigated. The assembled gold nanoparticles could efficiently electrocatalyze luminol ECL ECL intensity of the biosensor depended on scan rate, luminol concentration, and size of gold nanoparticles.

A novel hydrogen peroxide sensor based on horseradish peroxidase immobilized in DNA films on a gold electrode

A novel third-generation hydrogen peroxide (H2O2) biosensor was developed by immobilizing horseradish peroxidase (HRP) on a biocompatible gold electrode modified with a well-ordered, self-assembled DNA film. Cysteamine was first self-assembled on a gold electrode to provide an interface for the assembly of DNA molecules. Then DNA was chemisorbed onto the self-assembled monolayers (SAMs) of cysteamine to form a network by controlling DNA concentration. The DNA-network film obtained provided a biocompatible microenvironment for enzyme molecules, greatly amplified the coverage of HRP molecules on the electrode surface, and most importantly could act as a charge carrier which facilitated the electron transfer between HRP and the electrode. Finally, HRP was adsorbed on the DNA-network film. The process of the biosensor construction was followed by atomic force microscopy (AFM). Voltammetric and time-based amperometric techniques were employed to characterize the properties of the biosensor derived. The enzyme electrode achieved 95% of the steady-state current within 2 s and had a 0.5 mu mol l(-1) detection limit of H2O2. Furthermore, the biosensor showed high sensitivity, good reproducibility, and excellent long-term stability.

Neural network retrieval of ocean surface parameters from SSM/I data

A new algorithm based on the multiparameter neural network is proposed to retrieve wind speed (WS), sea surface temperature (SST), sea surface air temperature, and relative humidity ( RH) simultaneously over the global oceans from Special Sensor Microwave Imager (SSM/I) observations. The retrieved geophysical parameters are used to estimate the surface latent heat flux and sensible heat flux using a bulk method over the global oceans. The neural network is trained and validated with the matchups of SSM/I overpasses and National Data Buoy Center buoys under both clear and cloudy weather conditions. In addition, the data acquired by the 85.5-GHz channels of SSM/I are used as the input variables of the neural network to improve its performance. The root-mean-square (rms) errors between the estimated WS, SST, sea surface air temperature, and RH from SSM/I observations and the buoy measurements are 1.48 m s(-1), 1.54 degrees C, 1.47 degrees C, and 7.85, respectively. The rms errors between the estimated latent and sensible heat fluxes from SSM/I observations and the Xisha Island ( in the South China Sea) measurements are 3.21 and 30.54 W m(-2), whereas those between the SSM/ I estimates and the buoy data are 4.9 and 37.85 W m(-2), respectively. Both of these errors ( those for WS, SST, and sea surface air temperature, in particular) are smaller than those by previous retrieval algorithms of SSM/ I observations over the global oceans. Unlike previous methods, the present algorithm is capable of producing near-real-time estimates of surface latent and sensible heat fluxes for the global oceans from SSM/I data.