基于无线传感器网络的路边停车检测系统设计

为了构建基于无线传感器网络的大规模、低成本、低功耗的城市路边停车检测系统,本文提出了一种停车检测传感器节点硬件及软件设计方案。该系统硬件部分利用磁阻传感器实现车辆磁信号检测．软件部分主要实现基于磁信号的车辆检测算法。实地部署了80多个传感器节点的路边停车检测系统,实际应用表明该系统车辆检测精度高,达到了设计要求。     

基于无线传感器网络的交通信息采集系统研究

实现了一种基于无线传感器网络的交通信息采集系统,采用磁阻传感器,通过测量地磁的变化来检测车辆信息,用无线传感器网络传输数据,该系统不需要大规模挖开路面铺设车辆检测器,具有安装简易,维修方便等优点。同时采用B／S结构实现了交通信息的管理,用户可以在远程终端利用浏览器在线监测,实时分析交通信息数据,并进行网络监控和维护。     

磁阻传感器在车流检测中的应用

为了缓解城市交通拥堵,研究了一种基于磁阻传感器的车流量检测系统,磁阻传感器能将车辆引起的地磁扰动转换为清晰的电压信号输出。该系统包含了信号放大模块、无线通信模块等,多个地磁传感器节点通过无线通信模块与计算机系统相连,将检测到的车流信息反馈到上位机,从而实现对整个路口车流的检测。     

磁阻传感器在铁矿浓度检测中应用

为快速有效地对铁矿浆浓度进行检测,保证精矿质量,采用了基于磁阻传感器的新型检测系统.本文阐明磁阻传感器检测矿浆浓度的工作原理,着重介绍了hmc1021芯片置位和复位电路特点,检测、放大及V/F转换电路设计方法.对磁阻传感器在磁选机中的安放位置进行了详细说明,现场测试证明该系统在使用过程中运行状况良好,达到了设计要求,提...     

基于改进的支持向量机的开关磁阻电机的模型建立

开关磁阻电机具有结构简单、工作可靠、运行效率高等优点。但开关磁阻电机的磁路高度饱和、非线性化,很难建立准确的数学模型,难以实现高精度控制。在研究开关磁阻电机的电磁与转矩特性的基础上,结合支持向量机算法在解决小样本、非线性、高维数、局部极小值问题上的优势,建立了径向基函数核函数的支持向量机的开关磁阻电机的数学模型,通过仿真与传统BP神经网络算法对比,证明该算法具有较高的性能,实验平台检测的数据表明,所构建的开关磁阻电机模型是可行的,证明了模型的正确性和有效性。     

基于邻接传感器及神经网络的车辆分类算法

为了提高车辆分类的性能,基于邻接传感器网络和BP神经网络提出一个有效的车辆分类算法MSVCA.在本算法中,使用成本相对低廉、灵敏度高的地磁传感器,采集车辆对地磁场的磁扰动特征信号,并根据邻接传感器网络本身的几何特性估计车辆长度,最后采用BP神经网络对车辆进行分类.神经网络的输入包括车辆长度、速度以及特征向量序列,输出为预定义的车辆类型.仿真及路面实验获得了93.61%的准确率.结果表明该算法提高了车辆分类的准确性,且具有较高的精度和顽健性.     

基于C8051F021和GMR传感器的焊缝检测通道设计

为了解决在焊缝检测过程中,传感器拾取信号的处理问题.本文提出以SOC(system on chip,片上系统)的C8051F021为基础,采用时间触发器分时处理焊缝和GMR(Giant Magneto Resistive,巨磁阻)焊枪悬浮高度传感器等信息,设计出焊缝的自动焊接检测通道.经试验表明,这些电路抗干扰能力强、实时性好,其采样精度完全满足控制系统的要求.具有很好的实用价值.     

基于地磁感应的无线车辆检测系统的设计研究

为提高车辆检测准确度和系统抗干扰能力,降低系统成本,减少对路面的破坏,提出了基于地磁感应的无线车辆检测系统的设计研究方法。与传统车辆检测器相比,该检测器采用各向异性磁阻传感器进行设计,尺寸小,易于集成。检测器对采集的数据进行处理和特征提取,并通过无线网络系统将数据传输给上位机,无需布线,易于施工。试验结果表明,系统工作稳定可靠,不受环境路况天气等外在因素的影响,与传统车辆检测系统相比,在性能、实时性、成本、寿命、维护等方面整体上有很大的优越性。     

一种新型磁阻传感器在磁选机中的应用

采用新型磁阻传感器在磁选机中进行铁矿矿浆浓度检测。实践表明该检测系统抗干扰能力强,工作可靠性高,稳质、降尾效果十分明显。     

基于巨磁阻传感器的交通流量采集系统的设计

通过介绍巨磁阻传感器的原理以及车辆引起地球磁场扰动的特性,并结合物联网技术,设计了一种准确可靠的交通流量采集系统,详细介绍了系统内各部分的组成及功能。     

强噪声背景下车辆震动信号检测算法研究

针对强噪声背景下车辆震动信号检测问题,结合车辆震动信号特点,提出了2种检测算法:基于功率谱分布的检测算法和基于负熵的检测算法,并将小波去噪用于强噪声背景下地震动信号的提取。该算法与一般检测算法相比具有环境适应性强、检测准确率高和运算量小的特点,这些优点使得该算法更适用于能量受限、工作环境复杂的无线传感网络。仿真结果表明该算法具有很高的检测准确率。     

Application of feature extraction methods for landmine detection using the Wichmann/Niitek ground-penetrating radar

Ground-penetrating radar (GPR) has been proposed as an alternative to classical electromagnetic induction techniques for the landmine detection problem. The Wichmann/Niitek system provides a good platform for novel GPR-based antitank mine detection and classification algorithm development due to its extremely high SNR. When the GPR sensor is mounted on a moving vehicle, the target signatures are hyperbolas in a time-domain data record. The goal of this work is to extract useful features that exploit this knowledge in order to improve target detection. The algorithms can be divided into two steps: feature extraction and classification. Preprocessing is also considered to remove both stationary effects and nonstationary drift of the data and to improve the contrast of the desired hyperbolas. The algorithm is evaluated using real data over primarily plastic antitank mines collected with a fielded GPR sensor at a government test site.     

Decision Fusion of Ground-Penetrating Radar and Metal Detector Algorithms—A Robust Approach

Numerous detection algorithms, using various sensor modalities, have been developed for the detection of mines in cluttered and noisy backgrounds. The performance for each detection algorithm is typically reported in terms of the receiver operating characteristic (ROC), which is a plot of the probability of detection versus false alarm as a function of the threshold setting on the output decision variable of each algorithm. In this paper, we present multisensor decision-fusion algorithms that combine the local decisions of existing detection algorithms for different sensors. This offers an expedient, attractive, and much simpler alternative to the design of an algorithm that fuses multiple sensors at the data level, especially in cases of limited training data where it is difficult to make accurate estimates of multidimensional probability density functions. The goal of our multisensor decision-fusion approach is to exploit the complimentary strengths of existing multisensor algorithms so as to achieve performance (ROC) that exceeds the performance of any sensor algorithm operating in isolation. Our approach to multisensor decision fusion is based on the optimal signal detection theory using the likelihood ratio. We consider the optimal fusion of local decisions for two sensors: a ground-penetrating radar and a metal detector. A new robust algorithm for decision fusion that addresses the problem in which the statistics of the training data are not likely to exactly match the statistics of the test data is presented. ROCs are presented and compared for field data     

LIDAR Sensing for Vehicle Lateral Guidance: Algorithm and Experimental Study

This paper describes a new vehicle detection system based on a laser imaging, detection, and ranging (LIDAR) sensor, and conducts an experimental study to investigate the sensor's role in the current California PATH vehicle lateral guidance systems. The LIDAR sensor is installed on a controlled vehicle and it can measure the relative distance of the vehicle from a preceding vehicle, by scanning the horizontal plane with laser beams. Environmental clutter becomes the main challenge in data processing, when LIDAR tries to track the desired target. A probabilistic data association-based algorithm has been developed to solve this problem, which has been verified in real-time experiments using two Buick LaSabre vehicles. The experimental study also reveals the relation between the LIDAR outputs and the magnetic reference system widely used by the current PATH lateral control systems, and the results provide the guidelines on how this new sensor system may be used for vehicle lateral guidance     

基于PIC16F74单片机的高速公路车辆监测系统

为使高速公路车辆参数测量准确、稳定与经济实用,并实现公路交通的智能化管理,本文提出一种利用巨磁阻传感器作为车辆参数检测元件,以PIC16F74单片机为信号处理与控制核心构成一种新型的高速公路车辆监测系统。文中给出了系统设计方案,并详细介绍了车辆参数监测系统的基本原理,信号处理系统的硬件电路结构与软件流程,并给出现场实验测试数据。实验结果表明,本系统对高速公路上车辆流量的测量精度达100%,对车辆行驶速度检测的精度可达95%以上,可以完全取代目前广泛使用的地埋线圈测量系统。     

以太网与无线WIFI传输的机车车辆检测系统

为了解决传统车辆检测装置安装布线复杂,传输速率慢的缺点,设计了一种无线网络化的车辆检测系统。该系统以STM32为核心建立主控制器,MSP430单片机为核心建立无线传感器网络节点,通过传感器完成多种车辆振动信号的采集。采用无线WIFI与以太网两种通信方式实现控制指令与采集数据的高速传输,同时设计了基于C#.NET平台设计的人机交互界面,实现对车辆运行状态的实时监测。通过实验验证,系统能够完成车辆运行过程中速度,加速度等振动信号的高速采集、传输与处理。     

车辆无线传感器网络广播协议的研究

针对停车场自动化管理的智能交通难题,对利用车辆无线传感器网络对车位检测的辅助定位进行了研究。首先讨论无线传感器网络技术在国内外的发展现状并进行分析,然后提出了一个停车场无线传感器网络设计的总体概述,接着对车辆传感器网络辅助定位的理论进行了深入的分析,最后详细地给出了计算车辆传感器网络广播协议的消息交换的复杂度和消息传输总时间复杂度的计算式。     

Time Synchronization Algorithms in Low-Cost Wireless Sensor Network Systems

Time synchronization is an essential building block of sensor network systems. In this paper, we present a comprehensive study of least squares algorithms for time synchronization in sensor networks, with a focus on continuous monitoring and data collection applications. We propose a set of algorithms to address a number of issues in practical implementation on typical low-cost sensor network platforms, including a scaled signal model to achieve numerical stability in an ill-conditioned problem, sequential estimators for the scaled signal model to reduce computational complexity, a fast initialization scheme to improve energy efficiency, and outlier detection algorithms to improve robustness in long-term autonomous operations. The proposed algorithms are implemented and a measurement-based simulation method is employed to study the performance.