智能光网络中一种新型的分布式恢复方法

提出了一种基于固定备用路由和目的端触发的智能光网络中的分布式恢复方法,所选择的任一对源端节点和目的的端节点之间的固定备用路由包括该节点对之间的k条最短路径路由和基于部分链路无关的路由.目的端节点一旦探测到业务通道的失效将立即启动恢复进程,根据网络当前的资源使用情况,在这些备用路由中按照一定的次序选择一条恢复路由并为此分配一条可用的波长(对于没有波长变换器的光网络),然后将恢复通道的建立请求消息发送给该恢复通道的相关节点,这些相关节点可以锁定相关的网络资源,以避免来自其他恢复通道的竞争.所提出的算法增加了固定备用路由的数量,从仿真结果可以看出,该方法明显改善了失效连接的阻塞性能.     

基于蜂窝网格锚点的虚拟力导向节点部署算法

为满足覆盖需求,提出了一种基于蜂窝网格锚点的虚拟力导向节点再部署覆盖增强算法;算法基于传感器节点覆盖圆盘与其邻居节点覆盖圆盘的交点构成正六边形蜂窝时,有效覆盖面积最大理论,设置对随机部署的节点虚拟引力锚点作为虚拟力导向移动的目标,建立锚点对节点的虚拟引力,建立节点之间虚拟斥力来避免节点移动中的碰撞问题;完成随机播撒的节点在虚拟力的作用下的再部署,提高覆盖率,保证覆盖质量;Matlab R2012a仿真实验中,随机部署不同数量的节点,网络覆盖率均较快达到95%以上,满足覆盖需求。     

多层网络级联失效的预防和恢复策略概述

现实生活中,与国计民生密切相关的基础设施网络大多不是独立存在的,而是彼此之间相互联系或依赖的,于是用于研究这些系统的多层网络模型随之产生.多层网络中的节点在失效或者遭受攻击后会因"层内"和"层间"的相互作用而产生级联效应,从而使得失效能够在网络层内和层间反复传播并使得失效规模逐步放大.因此,多层网络比单个网络更加脆弱.多层网络级联失效产生的影响和损失往往是非常巨大的,所以对多层网络级联失效的预防和恢复的研究具有重大意义.就多层网络级联失效的预防而言,主要包含故障检测,保护重要节点,改变网络耦合机制和节点备份等策略.就多层网络发生级联失效后的恢复策略而言,主要包含共同边界节点恢复、空闲连边恢复、加边恢复、重要节点优先恢复、更改拓扑结构、局域攻击修复、自适应边修复等策略.     

基于平滑高斯半马尔可夫模型的移动无线传感器网络生成树算法

针对以往移动无线传感器网络研究只是单纯地对移动群体进行分簇而没有充分利用组群移动的内部稳定性的问题,结合组移动模型中节点运动的规律和内聚性原理,采用平滑高斯半马尔可夫移动模型刻画组内单个节点移动特征,构建了一种适合移动网络的稳定生成树算法(GM-base stable spanning tree algorithm,简称GSST)。实验证明,该算法从单个节点运动变化入手,在预测未来节点运动情况,选择稳定的链路构建网络结构方面,提高了移动网络的稳定性;同时,利用树的分层特征,简化移动网络的组网过程,并实现网络重组局部化。该算法有效延长节点存活率,均衡数据传输量。     

一种传感器网络移动栅栏部署算法

栅栏覆盖是传感器网络研究中的热点问题,现有有关栅栏覆盖研究大多针对静态兴趣区域的栅栏覆盖,而对于实际应用中如海洋污染、森林火灾、部队行军等的栅栏覆盖应用不适用;该文主要针对动态对象研究移动传感器网络的有效栅栏覆盖;抽象了问题模型并提出了栅栏覆盖评价指标,然后结合凸优化理论提出了一种分布式的移动栅栏覆盖算法;通过3个不同场景分别测试了算法在400*600 m的区域中随机部署有50个移动传感器节点对于不同动态对象构建一条封闭栅栏带所花费的时间以及栅栏带动态形成时的移动距离;测试结果算法能够快速有效实现动态区域的栅栏覆盖。     

基于Voronoi图盲区的无线传感器网络覆盖控制部署策略

针对无线传感器网络在二维平面应用场景中的覆盖控制问题, 提出了一种基于泰森盲区多边形形心的覆盖控制部署策略(blind-zone centroid-based scheme, BCBS). BCBS先对监测区域做Voronoi图划分以得到被每个传感器节点覆盖的泰森多边形, 而后根据泰森多边形顶点的覆盖情况分析得出泰森多边形内的盲区, 并构造与盲区形状相近的多边形, 最后以该多边形的几何中心作为传感器节点移动的候选目标位置, 从而达到提高网络覆盖率的目的. 仿真实验结果表明, BCBS在覆盖率、节点分布均匀性与节点覆盖效率等方面相比CBS有明显优势. 关键词： 无线传感器网络 节点覆盖 Voronoi图 多边形形心     

基于蚂蚁算法的移动IP路由选择方法

利用“信息素表”来取代网络节点中的路由选择表,把移动IP网络和蚂蚁算法联系起来,研究将蚂蚁算法用于求解移动IP路由选择当中的最短路径。实验表明,基于蚂蚁算法的移动IP路由选择在网络节点数量较多时比基于遗传算法的移动IP路由选择在查找性能上更忧。     

基于联合分步APIT定位算法的改进

本文研究了无线传感网络( Wireless Sensor Network,WSNs)的节点定位问题,并针对APIT由于锚节点在低密度环境下的节点误判和节点失效等问题给出了改进,在APICT定位算法的基础提出了联合分步定位算法UNION-APICT(Union Approximate Point-In-Circumcircle Test),该算法是结合连通性的测距技术,RSSI测距技术以及质心定位和APICT等技术,来联合解决对未知节点定位问题。通过仿真实验结果表明,改进后的UNION-APICT在APICT算法的基础之上平均定位误差减少了10%-25%,定位性能有了明显的提升;随着通信半径R和最大探测距离rmax的增加,定位误差也在逐渐减小,该算法较APIT和APICT定位算法在锚节点密度、节点覆盖率和定位精度上都有所提高。     

基于混沌人工蜂群算法的无线传感器网络覆盖优化

针对无线传感器网络随机播撒的节点严重冗余并且导致网络寿命短、覆盖效率不高等缺陷,提出了一种混沌人工蜂群算法的无线传感器网络覆盖优化算法;将节点的利用率和覆盖率作为优化目标函数,建立与之对应的数学模型,之后用混沌人工蜂群算法改善人工蜂群算法陷入局部最优、收敛慢等问题,提高算法收敛速度和精度,对节点覆盖模型进行求解,得出网络最优覆盖方案;通过实验仿真,提出的算法提高了无线传感器网络的覆盖率,覆盖率可达93.48%以上,减少了网络节点冗余,提高了网络寿命,降低了网络成本。     

一种局部模式的RGB差值序列交叉区域取样节点定位算法

鉴于当前无线传感器网络移动节点定位技术中存在的实时性差和定位不准确的问题,设计了一种交叠区域采样模式的WSN移动节点定位算法OASM_MNL(overlapping area sampling mode of mobile node localization)。该算法通过获取的信号在能够直接和移动节点通信的信标节点的信号交叠区域里面进行局部采样;通过距离比例因子对平均跳距进行权值化,优化了中跳距的计算公式;通过差值序列对样本点滤波,在此基础上把差值序列绝对值作为加权标准,计算出移动节点的坐标。仿真结果表明,与E-CDL、MCL等常见的移动节点定位算法相比,新算法定位误差降低幅度超过,定位效果良好。     

An improved phase retrieval algorithm for optical aspheric surface measurement

In order to overcome the measurement and calculation difficulty for aspheric surface with phase retrieval technology, an improved phase retrieval algorithm was proposed. Due to significant departure from sphere surface, reflected light from different part of the aspheric surface under test will overlap in some areas in the collected images by CCD with general phase retrieval measurement setup, which will lead to the failure to recover the surface phase. The proposed algorithm will only use those areas without light overlapping in each image in the iteration process and employ several defocused images to recover the whole surface. This algorithm can improve the measurement range for aspheric surface with phase retrieval technology. The experimental system was established and a 180 mm diameter, f/1.6 parabolic mirror and a 180 mm effective diameter, f/1.33 hyperboloid mirror were tested by the proposed method. The experimental results show that the retrieved surface errors are in good consistent with that obtained by interferometer, which confirms the validity of the proposed algorithm.     

An ergodic algorithm for long-term coverage of elliptical orbits

This paper deals with the coverage analysis problem of elliptical orbits. An algorithm based on ergodic theory, for long-term coverage of elliptical orbits, is proposed. The differential form of the invariant measure is constructed via the perturbation on mean orbital elements resulted from the $J_{2}$ term of non-spherical shape of the earth. A rigorous proof for this is then given. Different from the case of circular orbits, here the flow and its space of the dynamical system are defined on a physical space, and the real-value function is defined as the characteristic function on station mask. Therefore, the long-term coverage is reduced to a double integral via Birkhoff--Khinchin theorem. The numerical implementation indicates that the ergodic algorithm developed is available for a wide range of eccentricities.     

An ergodic algorithm for long-term coverage of elliptical orbits

This paper deals with the coverage analysis problem of elliptical orbits. An algorithm based on ergodic theory, for long-term coverage of elliptical orbits, is proposed. The differential form of the invariant measure is constructed via the perturbation on mean orbital elements resulted from the J2 term of non-spherical shape of the earth. A rigorous proof for this is then given. Different from the case of circular orbits, here the flow and its space of the dynamical system are defined on a physical space, and the real-value function is defined as the characteristic function on station mask. Therefore, the long-term coverage is reduced to a double integral via Birkhoff-Khinchin theorem. The numerical implementation indicates that the ergodic algorithm developed is available for a wide range of eccentricities.     

A genetic algorithm-based approach for solving the target Q-coverage problem in over and under provisioned directional sensor networks

Target coverage and network lifetime extension have been addressed as two major research topics over the last two decades. This paper focuses on “target $Q$-Coverage” in Directional Sensor Networks (DSNs) where coverage requirement of each target in the environment differs from that of the others. In such network, how to achieve the coverage requirement and simultaneously prolong the network lifetime is a major problem. In this study, two target-oriented genetic-based algorithms were developed to solve the problem. The first algorithm was developed to cover the targets in an over-provisioned environment, and the second algorithm was developed in an under-provisioned environment. The main objective of the first algorithm is satisfying the coverage requirement of targets by activating minimal sensors, while the second algorithm was developed to achieve a maximum balanced coverage for all the targets in the network. To evaluate the performance of the developed algorithms, they were compared with some state-of-the-art algorithms presented in recent studies. In this regard, several parameters, including Distance Index, Q-Balancing Index, Coverage Quality, Power Consumption, and Activate Sensors were taken into account. The comparative results indicated that the developed algorithms performed efficiently in solving the $Q$-coverage problem in both environments.     

Maximum covering planning of survivable Fiber-Wireless access network considering network connectivity

Fiber-Wireless (FiWi) access network is now becoming a promising architecture for access networks. Survivability is one of the key issues in the planning of FiWi access network because many high-rate traffic flows may be interrupted in case of a single fiber failure, which can cause a huge traffic loss. In addition, the users need ubiquitous broadband access that can be provided by FiWi access network. In this paper, we propose the protection approach called Maximum Covering Planning with Survivability (MCPS), including Integer Linear Programming (ILP) solution and heuristic solution, for the planning of survivable FiWi access network against single distribution fiber failure. The proposed approach aims at maximum coverage under the constraints of network connectivity. When a distribution fiber is broken, the interrupted Optical Network Unit (ONU) can transmit traffic to its backup ONU through the wireless paths between them. Simulation results show the network coverage of clients for different number of ONUs in different urban areas, and demonstrate the relationship between network coverage and the cost represented by the number of WiFi routers.     

Compensation of susceptibility-induced signal loss in echo-planar imaging for functional applications

Functional magnetic resonance imaging favors the use of multi-slice gradient-recalled echo-planar imaging due to its short image acquisition times, whole brain coverage and sensitivity to BOLD contrast. However, despite its advantages, gradient-recalled echo-planar imaging also is sensitive to static magnetic field gradients arising primarily from air-tissue interfaces. This can lead to image artifacts such as voxel shifts and complete signal loss. A method to recover signal loss by adjusting the refocusing gradient amplitude in the slice-select direction, preferably axially, is proposed. This method is implemented as an automated computer algorithm that partitions echo-planar images into regions of recoverable signal intensities using a histogram analysis and determines each region's proper refocusing gradient amplitude. As an example, different refocusing gradient amplitudes are interleaved in a fMRI acquisition to maximize the signal to noise ratio and obtain functional activation in normal and dropout regions. The effectiveness of this method is demonstrated by recovering signal voids in the orbitofrontal cortex, parahippocampal/amygdala region, and inferior visual association cortex near the cerebellum.     

基于遗传算法的自动驾驶仪测试性分析

为了提高自动驾驶仪故障测试与诊断的快速性与准确性,提出了一种基于遗传算法的自动驾驶仪测试性分析方法;此方法解决了遗传算法应用于测试项目选择的难点,构建了最优的故障测试策略,能以最短时间完成测试并满足测试覆盖性的要求;利用故障测试获得的知识,简化故障与测试间的相关性矩阵,加快了故障诊断的速度;最终获得了故障测试与诊断一体化的方法;实验结果表明,此方法有效提高了故障测试与诊断的速度,并且易于实现。     

Two-stage blind deconvolution scheme using useful priors

Hand shake blurry image is a common phenomenon in our daily life. In this paper, a novel blind deconvolution scheme is proposed to recover a single hand shake blurry image. The algorithm is subdivided into two main stages, kernel estimation stage and non-blind deconvolution stage. In the kernel estimation stage, we propose a cost function taking a selected map into consideration. In the non-blind decovolution stage, another cost function is designed using image derivatives prior. We also present an adaptive kernel size selection method instead of traditional manual selection. Extensive experiments on real world blurry images are conducted to demonstrate the performance of our algorithm.