dyzbhg.m

这个matlab程序实现了Edge Linking and Line Segment Fitting，解压后直接运行demo.m就可以了 ( edgelink.m edge linking function that forms lists of connected edge points from a binary edge image. (Needs findendsjunctions and cleanedgelist below). drawedgelist.m plots out a set of edge lists generated by edgelink or lineseg. edgelist2image.m transfers edgelist data back into a 2D image array. lineseg.m forms straight line segments fitted with a specified tolerance to the lists of connected edge points. maxlinedev.m is also used by lineseg.m to calculate deviations of the edge lists from the fitted segments. findendsjunctions.m finds line junctions and endings in a line/edge image. cleanedgelist.m cleans up a set of edge lists generated by edgelink or lineseg so that isolated edges and spurs that are shorter than a minimum length are removed.)

光谱提取,丰度调整 效果好，高光谱目标检测和端元提取(spectral abundance adjustment )

说明：  珍藏版，可实现，新K均值聚类算法，分为如下几个步骤： 一、初始化聚类中心 1、根据具体问题，凭经验从样本集中选出C个比较合适的样本作为初始聚类中心。 2、用前C个样本作为初始聚类中心。 3、将全部样本随机地分成C类，计算每类的样本均值，将样本均值作为初始聚类中心。 二、初始聚类 1、按就近原则将样本归入各聚类中心所代表的类中。 2、取一样本，将其归入与其最近的聚类中心的那一类中，重新计算样本均值，更新聚类中心。然后取下一样本，重复操作，直至所有样本归入相应类中。 三、判断聚类是否合理 采用误差平方和准则函数判断聚类是否合理，不合理则修改分类。循环进行判断、修改直至达到算法终止条件。(NewK-means clustering algorithm ，Divided into the following several steps: A, initialize clustering center 1, according to the specific problems, from samples with experience selected C a more appropriate focus the sample as the initial clustering center. 2, with former C a sample as the initial clustering center. 3, will all samples randomly divided into C, calculate the sample mean, each the sample mean as the initial clustering center. Second, initial clustering 1, according to the sample into the nearest principle clustering center represents the class. 2, as this, take the its recent as clustering center of that category, recount the sample mean, update clustering center. And then taking off, as this, repeated operation until all samples into the corresponding class. Three, judge clustering is reasonable Adopt error squares principles function cluster analysis.after clustering whether reasonable, no reasonable criterion revisio)

卡尔曼滤波，实现跟踪滤波分析，数据随机产生(kalman filter and tracking problem)

两级空间搜索优化的立体麦克风阵列声源定位matlab程序，采用SRP-PHAT技术，有效抗混响及噪音。(Fast Sound Source Localization Using Two-Level Search Space Clustering Simple demonstration using simulated data)

根据静态苹果图像RGB三值检测苹果表面是否有缺陷，比较准确(Apple RGB image from the static three-valued test whether apple surface defects, more accurate)

说明：  离散频谱的全相位校正法 离散频谱的全相位校正法.rar(Discrete spectrum of the full phase correction method. Rar)

说明：  序列x（n）的功率谱估计用matlab来实现(sequence x (n) of the power spectrum is estimated to achieve using Matlab)

the BER for BPSK in OFDM modulation in the presence of Rayeligh fading channel simulation in matlab programme

基于HFSS的不同形状微带贴片天线的仿真设计(HFSS simulation design different shapes based on microstrip patch antenna)