Genetics Based Compact Fuzzy System for Visual Sensor Network

As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract key-information out of it.VWSN applications range from health care monitoring to military surveillance.In a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O resources.In this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as well.The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system.However,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account.We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual perspective.Proposed architecture is designed based on Mamdani’s fuzzy model.Following parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH selection.The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN.The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules.Genetic algorithmbased machine learning optimizes the interpretability aspect of fuzzy system.Simulation results are obtained using MATLAB.The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy(LEACH)and LEACHExpected Residual Energy(LEACH-ERE).

Specific-Scene Oriented Pedestrian Detection in Visual Sensor Network

Pedestrian detection is one of the most important problems in the visual sensor network. Considering that the visual sensors have limited cap ability, we propose a pedestrian detection method with low energy consumption. Our method contains two parts: one is an Enhanced Self-Organizing Background Subtraction (ESOBS) based foreground segmentation module to obtain active areas in the observed region from the visual sensors; the other is an appearance model based detection module to detect the pedestrians from the foreground areas. Moreover, we create our own large pedestrian dataset according to the specific scene in the visual sensor network. Numerous experiments are conducted in both indoor and outdoor specific scenes. The experimental results show that our method is effective.

Research on a New-style Visual Sensor for Measurement

First,the constitution of traditional visual sensor is presented.The linear camera model is introduced and the transform matrix between the image coordinate system and the world coordinate system is established.The basic principle of camera calibration is expatiated based on the linear camera model. On the basis of a detailed analysis of camera model,a new-style visual sensor for measurement is advanced.It can realize the real time control of the zoom of camera lens by step motor according to the size of objects.Moreover,re-calibration could be avoided and the transform matrix can be acquired by calculating,which can greatly simplify camera calibration process and save the time. Clearer images are gained,so the measurement system precision could be greatly improved.The basic structure of the visual sensor zoom is introduced,including the constitute mode and the movement rule of the fixed former part,zoom part,compensatory part and the fixed latter port.The realization method of zoom controlled by step motor is introduced. Finally,the constitution of the new-style visual sensor is introduced,including hardware and software.The hardware system is composed by manual zoom,CCD camera,image card,gearing,step motor,step motor driver and computer.The realization of software is introduced,including the composed module of software and the workflow of measurement system in the form of structured block diagram.

Deployment Optimization Strategy for a Two-Tier Wireless Visual Sensor Network

Wireless visual sensor network (VSN) can be said to be a special class of wireless sensor network (WSN) with smart-cameras. Due to its visual sensing capability, it has become an effective tool for applications such as large area surveillance, environmental monitoring and objects tracking. Different from a conventional WSN, VSN typically includes relatively expensive camera sensors, enhanced flash memory and a powerful CPU. While energy consumption is dominated primarily by data transmission and reception, VSN consumes extra power onimage sensing, processing and storing operations. The well-known energy-hole problem of WSNs has a drastic impact on the lifetime of VSN, because of the additional energy consumption of a VSN. Most prior research on VSN energy issues are primarily focusedon a single device or a given specific scenario. In this paper, we propose a novel optimal two-tier deployment strategy for a large scale VSN. Our two-tier VSN architecture includes tier-1 sensing network with visual sensor nodes (VNs) and tier-2 network having only relay nodes (RNs). While sensing network mainly performs image data collection, relay network only for wards image data packets to the central sink node. We use uniform random distribution of VNs to minimize the cost of VSN and RNs are deployed following two dimensional Gaussian distribution so as to avoid energy-hole problem. Algorithms are also introduced that optimizes deployment parameters and are shown to enhance the lifetime of the VSN in a cost effective manner.

Sensor planning method for visual tracking in 3D camera networks

Most sensors or cameras discussed in the sensor network community are usually 3D homogeneous, even though their2 D coverage areas in the ground plane are heterogeneous. Meanwhile, observed objects of camera networks are usually simplified as 2D points in previous literature. However in actual application scenes, not only cameras are always heterogeneous with different height and action radiuses, but also the observed objects are with 3D features（i.e., height）. This paper presents a sensor planning formulation addressing the efficiency enhancement of visual tracking in 3D heterogeneous camera networks that track and detect people traversing a region. The problem of sensor planning consists of three issues：（i） how to model the 3D heterogeneous cameras;（ii） how to rank the visibility, which ensures that the object of interest is visible in a camera＇s field of view;（iii） how to reconfigure the 3D viewing orientations of the cameras. This paper studies the geometric properties of 3D heterogeneous camera networks and addresses an evaluation formulation to rank the visibility of observed objects. Then a sensor planning method is proposed to improve the efficiency of visual tracking. Finally, the numerical results show that the proposed method can improve the tracking performance of the system compared to the conventional strategies.

多传感器融合技术在智能交通系统中的应用

阐述多传感器融合技术在高速公路智能交通中的应用,包括对路面传感器数据、视觉传感器数据、环境传感器数据的融合和分析。探讨多传感器融合技术在高速公路智能交通中的优化方法。

自适应序贯滤波的机器人融合定位方法研究

针对激光雷达在长直环境下鲁棒性低以及视觉相机受光照条件影响大的问题,提出一种利用序贯卡尔曼滤波将两种传感器采集到的信息进行融合的定位方法,将传感器作为独立的节点逐级传递以实现多层次滤波,解决了异步传感器更新延迟导致算法中断的问题,同时为算法添加自适应成分,避免了移动机器人长距离行驶过程中因缺少动态调节而产生发散的现象。仿真实验结果表明,以自适应序贯滤波的形式,融合定位结果显著降低了单一传感器测量带来的误差,有效提升了移动机器人在未知环境下的定位精度。

基于视觉传感器的智能小车设计与实践

为更好地参加全国大学生智能汽车竞赛,本文提出一套以LPC54608为核心的智能小车系统,设计并制作了硬件电路系统。以单目摄像头为主要传感器,联动其他传感器对赛道情况进行系统感知,提取目标路径并进行优化,而后根据车辆结构设计控制算法来跟踪目标路径。实验测试结果表明,小车在2.4m/s的速度之下能正确识别各种赛道路径,并且在赛道上可以稳定行驶。

电致变色型智能可视化湿度系统

近年来,湿度传感器在食品安全、土壤监测等领域的应用引起了广泛关注。传统湿度传感器具有稳定性好、灵敏度高等优点,但大部分湿度传感系统通常采用有线连接和外接庞大设备来将湿度信号转换为可识别的波形,无法对湿度信息的变化进行实时的可视化监测。将湿度信息直接转换为肉眼可观测的颜色信号为上述问题提供了一种理想解决方案。本研究将湿度传感器与电致变色器件集成一体来制备智能可视化湿度指示系统,通过将湿度信号转换为电压信号来驱动电致变色器件(Electrochromic devices,ECDs),从而实现系统稳定可逆的颜色变化。采用三氧化钨(WO3)作为负极、锌箔(Zn)作为正极制备的ECDs会根据湿度传感器的输出电压的变化来转变不同的工作状态,从而产生肉眼可观测的颜色信号。采用紫外-可见分光光度计与电化学工作站对ECDs的电化学性能以及电致变色性能进行研究和表征。随后,通过示波器和湿度发生平台对调理电路性能进行分析。结果表明:智能电致变色型湿度指示器具有良好的稳定性和快速的响应性能,其中,着色时间与褪色时间仅为7.5和4.5 s,并且在300个循环后,光学调制幅度(ΔT)与初始值相比基本保持不变(保持率可达95%以上)。因此,这种设计新颖、结构简单的可视化湿度系统在人工智能、智能农业等领域具有广阔的应用前景。

仿蜂鸟倾斜悬停的四旋翼系统设计

为了提高常规四旋翼的灵活性,模仿蜂鸟在空中倾斜身体悬停的特点,对一种改进后的轴可动四旋翼进行系统设计与验证。首先,在四旋翼的每个机臂上增加由舵机驱动的倾转装置,使得旋翼可以灵活地改变朝向,建立平台的六自由度模型;接着结合视觉传感器测量飞行平台的位姿,基于串级PID控制和线性的控制分配方法,基于嵌入式平台进行实物实验。结果表明,飞行器实现了静止在空中某一位置倾斜姿态,具有更高的灵活性。

一种基于单片机控制的水位报警装置设计

基于单片机控制的水位报警装置,安装于路桥涵洞、城市地下道等合适位置,在道路积水严重的情况下,可实现声光报警,提示行人或各类型车辆谨慎通行。基本设计思路是:首先通过水位传感器采集模拟信号经模数转换器转换后形成数字信号,输入到单片机中进行分析判断,然后根据水位高度的不同输出不同的控制信号,控制信号经过光电耦合器后,驱动对应的报警模块进行声光报警。

多源融合高精度定位技术在铁路行业的应用展望

铁路是国家物资运输和人员流动的交通大动脉,从铁路建设阶段的控制点布设、线路设计,到铁路运维阶段的平顺性检测、变形监测、列车控制和管理,都离不开高精度的位置信息。传统的全站仪测绘方法存在人力成本高、时效性差等缺点;GNSS用于开阔无信号遮挡的环境效果良好,但用于信号遮蔽的复杂环境则无法定位。随着北斗卫星导航系统的趋于完善和传感器技术的发展,利用卫星等多传感器融合定位的技术逐渐受到人们的关注。GNSS接收机、惯性导航设备、视觉传感器、激光传感器具有互补的特性,以这4种导航传感器为例,利用图优化的方法,实现这些传感器数据的融合高精度定位。通过研究多种传感器融合定位在铁路工程领域的应用,将自动、高效和稳定的新兴技术引入铁路工程测绘,以满足铁路工程对于静态、动态高精度位置信息的需求,为铁路测绘的智能化发展做出积极贡献。

基于视触觉感知的番茄尺寸和姿态解析方法

针对传统农业机器人抓取过程中视觉识别番茄果实尺寸和姿态存在枝叶遮挡的问题,提出了一种基于视触觉感知的番茄尺寸和姿态解析方法。在果实抓取过程中通过视触觉传感器得到果实外轮廓接触局部点云信息,然后通过相机参数标定以及各手指关节变换矩阵,将不同传感器坐标系下的点云信息变换到同一基坐标系下,进而通过点云改进PCA算法和ICP算法解析抓取果实的尺寸和姿态信息。为了评估所提出解析方法的性能,在实验室环境下进行了番茄尺寸和姿态检测试验。通过游标卡尺测量和深度相机扫描分别获得番茄果实尺寸和姿态的真实值,并与本文方法解析结果进行对比。检测试验结果表明,本文方法获得的番茄横向尺寸和纵向尺寸平均相对误差分别为8.66%和11.08%,番茄果轴与视场投影面的水平夹角和垂直偏转角平均相对误差分别为10.03%和14.02%。本文方法解析的番茄果实尺寸与姿态信息,可应用于番茄果实抓取过程中的姿态调控,从而提高番茄果实抓取采摘的可靠性。

基于电容传感器的液体管内含气率测量系统的研究

电容传感器具有非侵入式、低成本、结构简单等特点,被广泛应用于液体管内含气率的测量。基于电容传感器,设计了含气率测量系统,对外径25mm、内径21mm液体管内含气率进行测量。为了验证含气率测量系统的准确性,搭建了可视化验证平台。对管内气液两相进行图像采集,对管道截面处的含气率进行计算;分析了视窗左右截面的含气率偏差,研究了采集位置偏差对验证方法有效性的影响。研究表明:通过传感器系统测量的含气率与图像处理得到的含气率吻合;视窗左右截面的平均含气率的偏差不超过3.097%。本研究设计的含气率测量系统的准确性较好,所提出的可视化验证方法能够有效地对含气率测量系统的准确性进行验证。

无接触式猪体体尺测量技术研究进展

随着视觉传感器和计算机视觉等领域的科技进步,无接触式猪体体尺检测相关的硬件设备和技术方法都发生了巨大的变化。视觉感知升级为三维立体视角,相应的设备也从黑白相机更新为深度设备。设备类型通常决定了基本的实施方案和技术方法,然而目前该领域的研究大多聚焦具体的算法细节,难以满足实际的工程需求。本文以视觉传感器类型为核心,概述了无接触式猪体体尺测量方面不同的工程部署方法,总结了设备使用方法、采集数据的环境条件和方式等,同时对猪体体尺测量的5个三维点云技术任务进行分析,并归纳了目前研究的优缺点、提升空间以及与实际工程应用相结合的方案,最后展望了深度学习与三维点云技术相结合的研究难点。通过对比发现,深度设备和三维点云技术是未来工程应用的方向,基于深度学习的点云分割、点云补全、关键点检测等技术具有良好的应用前景,为无接触式猪体体尺测量技术的后续研究提供了参考。

基于双循环Transformer的三维人体姿态估计

针对视觉传感器采集到的图像进行三维人体姿态估计,提出一种双循环Transformer网络模型,有效地从二维关键关节点中提取时空维度高相关性特征,增大感受野,从而提高三维姿态估计的精度。通过在视觉传感器采集得到的公开数据集Human3.6M上的仿真实验,验证了双循环Transformer算法的性能。分析结果表明,最终估计得到的三维人体关节点的平均关节点位置偏差MPJPE(Mean Per Joint Position Error)为41.6 mm,相比于现有方法有一定提升,可以应用到许多下游相关工作中,有着较强的应用价值。

基于Apriori的视觉传感网络关联数据高效识别方法

在视觉传感网络关联数据识别中,由于数据变化的规律难以捕捉,造成识别结果精确度降低,为此,提出了基于Apriori的视觉传感网络关联数据高效识别方法。通过设计视觉传感网络联合监控系统监控视觉传感网络数据变化情况,获取视觉传感网络关联数据。该系统由处理器单元、数据传输单元等四个模块构成。提取图像中的感兴趣区域完成ROI提取,并通过腐蚀处理等方式对视觉传感网络关联数据实施形态学处理。基于Apriori算法实施视觉传感网络关联数据之间关联规则的挖掘,实现视觉传感网络关联数据的识别。布设实验环境后,使用三个数据集测试该方法的识别性能。测试结果表明,所提方法的微平均查全率均高于94.86%,微平均查准率高于93.02%,最高召回率为98.52%,三个项目的测试值均较高,证明了该方法对视觉传感网络关联数据识别比较准确。

基于老年人心理需求的街道空间视觉感知影响因子研究

随着社会老龄化的不断发展,老年人在城市中的存在和需求日益凸显。城市老年人群体的增加引发了城市街道空间对其心理健康的关注。然而,在城市规划和设计中,以往的研究主要关注身体需求和可步行性,老年人的心理需求往往被忽视。该文旨在填补这一研究空白,专注于城市街道空间对老年人心理的影响。采用虚拟现实技术和可穿戴生理传感器,以全面、精细的方式研究城市街道空间与老年人心理的关系。通过整合科学证据,我们旨在识别关键的影响因素,并深入探讨它们对老年人心理健康的具体影响机制,为创造更适宜老年人的城市环境提供科学支持。

基于BaMgSiO_(4)氧空位缺陷发光的可视化荧光温度传感材料

温度是表征物理化学性质的最基本参数之一,精确的温度测量对于现代科学技术发展起着至关重要的作用。传统基于稀土离子热耦合能级对(TCLs)能量传递的荧光温度传感器因TCLs之间能量差的限制存在测温灵敏度低及信号区分困难等问题。为寻求更优的解决方案,本研究探索了氧空位缺陷发光在荧光温度传感器领域的应用前景。本文通过高温固相法合成了BaMgSiO_(4)陶瓷,由于在高温烧结过程中有少量Ba^(2+)和Mg^(2+)蒸发,陶瓷中会产生氧空位以保持材料电中性。这些氧空位所形成的缺陷能级在332 nm紫外光激发下,发射出372,400,527 nm三种波长的发射光。这三种发射光强度对温度有着不同的敏感性,使得其能够良好应用于荧光温度传感领域。其中,I372和I527组成的温度传感系统相对测温灵敏度在298 K时为2.90%·K^(-1),高于传统TCLs荧光温度传感器的测温灵敏度,突破了TCLs温度传感器的灵敏度天花板。另外,由于372 nm和527 nm波长相差较大,使得BaMgSiO_(4)陶瓷有着室温下绿光发射到458 K高温下蓝光发射的显著变化,实现了温度监控可视化。因此,BaMgSiO_(4)陶瓷因其独特的氧空位缺陷发光特性,为开发荧光温度传感器提供了一种高精度和可视化的新选择,为荧光温度探测技术提供了一条新思路。

基于视觉识别的水面垃圾清理装置设计

【目的】为了快速清理水面垃圾,改善水面环境,设计了一种水面垃圾清理装置。【方法】在分析清理装置工作原理的基础上,建立装置三维结构模型,并对主要部件进行结构设计。所设计的视觉识别系统可以实时监测水面垃圾分布情况,从而快速清理水面垃圾。装置上安装的太阳能板,可以为设备提供额外的动能。为了验证所设计清洁装置的传送带滚轮是否满足强度要求,利用SolidWorks软件对滚轮进行有限元分析。【结果】计算结果显示,所设计的滚轮结构设计合理,强度满足使用要求。【结论】所设计的清洁装置在自动化、精准定位、实时监控和节能环保等方面具有显著的创新性和应用潜力,这为今后智能清理装置的研发和设计提供了借鉴。