针对现阶段实现瞳孔精确定位算法过于繁琐的问题，提出一种由粗略到精确的瞳孔精确定位算法。首先用灰度积分算法粗略找出人眼部位在人脸上的大概位置，然后用Hough变换定位出人眼瞳孔中心的精确位置，接着用Harris角点检测算法对眼角点进行定位，最后通过瞳孔与眼角点的相互坐标定位出人眼瞳孔的精确位置。通过matlab对所提算法进行检验，证明了该算法是一种实现简单、定位精确的算法。

距离差=时间差×电磁波速度 TA-TB=CONSTANT

基于RSSI的节点定位算法仿真，有误差分析结果

通过模拟实际光学成像过程以及CCD采样过程,生成了标准图像,实现对亚像素定位算法的客观评价,并提出了一种新的基于灰度面积插值的亚像素定位方法。经实验验证,该方法对直线边缘的平均定位精度达到0.11 pixel,对圆边缘点的平均定位精度可达0.24 pixel,优于梯度质心法和灰度矩法。

亚像素软件处理技术可以在一定程度上补偿图像测量系统由硬件限制引起的边缘定位误差,针对工业中的小尺寸圆形零件参数检测,提出了一种基于Zernike矩、二次多项式插值和最小二乘法拟合的圆参数亚像素定位算法。首先根据Zernike正交复数矩建立圆物体边缘点与边缘参数之间的映射关系,并提取边缘点;然后利用二次多项式插值在梯度方向上对提取出的边缘点进行进一步定位;最后利用最小二乘法拟合插值后的边缘点,得到亚像素精度的圆参数。通过小尺寸圆形惯性器件图像边缘提取实验,对该算法的有效性和检测精度进行了研究,给出了惯性器件

基于RSSI的无线传感器网络节点定位算法，何青春，沈树群，节点定位是无线传感器网络中的关键技术之一。基于RSSI的定位技术是现阶段研究的热点，本文提出了基于RSSI的三角形质心定位算法和三�

室内定位算法，有助于研究者认清研究方向，给定位算法一个准确的描述，是很好的参考教材。the possibility for the user to be notified by visible and Waveformof Distance audible warnings using buzzer and led ar to Very close to al 1903 Tme「 samples Fig 4 Distance measurement using the RSSi signal The system is used to determine the distance between Fig. 2. Tag4M Data Acquisition System tags and 2, 3, 4 or more APs. The tag scans after the aps ⅣV. EXPERIMENTS and sends the rssi values for each ap to the ap which is associated The system presented above can be used for objects or In order to read the corresponding rSsi values of all people localization in an indoor enviro found access points, a""operation is implemented at Let's suppose a building where several objects are Tag4M level. The time to scan is smaller than few distributed all over milliseconds, because the tag doesnt associate with the all For a better understanding of the system, it is showed an APs. These values are sent to a pC where the localization experiment where the objects are placed only at one floor. algorithm is implemented in LabVIFW20 10 (see Fig. 5) Each of these objects has attached a tag. The devices are Table I presents a scan operation result. In this case used to determine the location of the objects in the four APs(which are placed in the floor building)named building and the distance between tags and APs. To Hawk, Helicopter, Tag4M and WitagServer are detected measure the distance, the RSSI is processed. This method The corresponding rSSi values(in dBm) measured by the is not very accurate and is strong depended to the Tag4M are reported for every AP environment, but is very easy to implement with the TABLE 1. A SEQUENCE OF RESULTS FOR THE SCANOPERATION The Fig 3 presents the experiment environment for one EXFCUTED ON THE TAG4M DEVICE oor where the colored circles represents the APs and the rectangles represents the objects intended to be located This floor is divided in six rooms separated by walls

节点定位是实现传感器网络应用的前提，控制节点定位误差成为保证网络正常运行的关键。采用基于测距的定位算法，可以达到良好的精度，但需要测量装置，不适合能量受限的无线传感器网络。本文分析了常用的非测距定位算法，并在此基础上提出了一种改进的基于序列的非测距定位算法，以提高无线传感器网络定位算法的性能。

无线传感器网络是由大量随机分布的传感器节点组成，是一种分布式的、自组织的网络。其关键技术包括：网络拓扑控制、节点定位、时钟同步、数据融合、路由协议等。而节点定位问题则是无线传感器网络中的一个最为基本和重要的问题。目前，无线传感器网络定位算法可以分为基于测距和基于非测距的定位算法。基于测距定位常用的测量方法有TOA、TDOA、AOA、RSSI，尽管这些技术相对精度高，但是对硬件要求很高。基于非测距定位常用的测量方法有：DV- Hop、质心、APIT、MDSMAP。 　　DV-Hop为典型的基于非测距定位，其对硬件要求低，实现简单。它的不足之处在于计算平均跳距及定位坐标时会产生误差。因此针对DV

无线传感器网络定位算法代码大全.rar