搜索资源列表
Lidar
- 激光主动照明的雷达方程，照明光源的辐射功率(W)，辐射距离(m)(一般值是1000m)。-Active laser radar equation lighting, illumination of the radiated power (W), radiation from the (m) (normal value is 1000m).
knnclassification
- 模式识别中，有一类特征提取方法为基于类内内间距离的提取方法，本程序可实现类内内间算法-Pattern recognition, there is a class of feature extraction method based on within-class distance between the extraction methods in this program can be realized within the inter-category algorithm
ClassifyHomework
- 模式识别,用平均样本法、平均距离法、最近邻法、K近邻法进行分类。-Pattern recognition, with an average of the sample method, the average distance method, nearest neighbor, K-NN classification.
radar-ambiguity-function
- 雷达模糊度方程仿真程序，包括距离模糊函数，模糊坐标图，数字编码波型及一个雷达实例例程-the simulation code for radar ambiguity function, including range ambiguity function, ambiguity diagram contours, digital coded waveforms and a radar design case program
face
- 人脸识别程序，采用的是knn分类器，基于类内类间距离准则，特征提取。-Face recognition process, using the knn classifier, based on within-class inter-class distance criteria, feature extraction.
BGP
- 用Bellman-Ford 算法实现的路由协议，适用距离矢量协议bgp 等， （matlab 源码）-This program uses the Bellman-Ford algorithm to find the set of routing tables which would be generated in the BGP protocol
SAR_chegnxiang
- synthetic aperture radar signal processing with matlab algorithms 英文书的附带源代码，书是全英文的，作者是Mehrdad Soumekh。我没有电子书，只有复印的。 此成像算法包括一维距离像、条带式、聚束式等多种模式的SAR成像。可以作为有一定SAR基础的同行的编程参考。 友情提供-synthetic aperture radar signal processing with matlab algorithms Eng
smith
- 1.本程序拟已知驻波比系数ρ，波节点的距离l_m_i_n，和频率f的情况下，求解归一化阻抗Z~， 所以在本程序运行时，要首先输入正确的驻波比系数ρ，波节点的距离l_m_i_n，和频率f； 2.本程序由于视野有限，课本后面的那个圆图并未全部显示出来，只是显示了本程序有用的部分 3.编制本程序的小组成员为微波技术实验分组，倪庆等； 4.由于时间原因，未能打成exe可执行文件包； 5.由于圆图信息量大，所以运行后要把窗口最大化才能正常显示； 6.红色的圆点即为阻抗
chirp
- 文件说明： 1， mychirpmdl.mdl是用simulink工具实现的chirp信号脉冲串。具体的参数设置可以看一下matlab的帮助。（双击某一个模块，就会出现一个参数设置对话框，点击help即可查看各个参数的意义） 2， mychirp.m是用matlab语言编写的实现chirp信号脉冲串的源文件。文件里有详细注释。并且参数都是可以设置的，但要注意各个参数之间的关系。 3， mychirp.fig是运行mychirp.m的结果。 4， mypluse.m是调用函数实现的
osjl
- 基于最小欧式距离的模式识别分类，对于简单直观明了。-Pattern recognition based on the minimum Euclidean distance classification, for simple and intuitive to understand.
67506282mahalanobis
- 马氏距离的仿射不变性删除误匹配特征点 对，据此可求取2幅源图像间的仿射变换参数-Mahalanobis distance of the affine invariant features remove the false matching points Yes, according to the source to obtain two parameter affine transformation between images
LPIT_TOOLBOX
- LPI和LPID的主要目的是能够在比截获接收机对雷达进行探测与干扰的距离更远的距离上来探测目标,本工具箱是在MATLAB下的LPI工具箱-LPI and LPID' s main purpose is to intercept receiver than the radar detection and jamming on the distance farther up exploration goals, the toolkit is under the LPI in the MATLA
emd
- 分析矩阵的相关性，即给出两个矩阵，计算两个矩阵之间的距离，并画出矩阵的相关分析图。-this matlab code can compute the two matrix length, and can give analysy ficture
image matching
- 实现了三种图像匹配算法 1：归一化互相关匹配算法 2：基于Hausdorff距离的图像匹配算法 3：图像不变矩匹配算法-Three matching algorithms to achieve 1: normalized cross correlation matching algorithm 2: the image matching algorithm based on Hausdorff Distance 3: Image Invariant Moment Matchin
liushuixian
- 经典的流水线加工问题的仿真模型 在matlab命令窗口中输入yige（a,b,r,l） a表示工件的长，b表示工件的宽 r表示弯道半径 l表示两个工件之间的距离-as described above
floyd
- 实现flody算法，功能：计算任意两点之间距离-Achieve flody algorithm functions: calculate the distance between any two points
cx5
- 用Hausdorff距离对两角点集进行配准，得到点集间的仿射变换，从而实现图像的自动配准。此算法以角点作为Hausdorff距离的配准特征，与直接选用边缘来配准的方法相比较，大大减小计算量。-Hausdorff distance on the corners with a point set registration, be affine transformation between sets in order to achieve automatic image registration. T
knnsearch
- KNN classifiers, training is training set, testing is test set, D is the distance, D=1 is mandistance D=2 is 欧氏距离 D=3是 infinite norm K is the number of selected neighbors- KNN classifiers, training is training set, testing is test set,
Counter
- KNN classifiers, training is training set, testing is test set, D is the distance, D=1 is mandistance D=2 is 欧氏距离 D=3是 infinite norm K is the number of selected neighbors- KNN classifiers, training is training set, testing is test set,
tsp_nn
- KNN classifiers, training is training set, testing is test set, D is the distance, D=1 is mandistance D=2 is 欧氏距离 D=3是 infinite norm K is the number of selected neighbors- KNN classifiers, training is training set, testing is test set,