tail_conv_code16

最小二乘法的源程序代码,和大家交流下,谢谢指教(Least Squares source code, and the exchange of everyone under the advice Thanks)

旨在仿真OOK信号，这是国家重大项目的一个子课题，编译程序验证(Designed to simulate OOK signals, which is a sub task of national major projects, compiler verification)

卡尔曼滤波程序，都是基础的，可以直接套用的，比较方便(Kalman filtering process, are based, can be directly applied, more convenient)

基于图像内容结构、相位相似性的图像质量评价算法（FSIM）(FSIM: image structure and congrency similarity based image quality assessment)

自己编写的最近邻KNN算法，采用的距离是欧氏距离，附加了详细的中文注释，还有一个测试集和一个训练集( I have written KNN nearest neighbor algorithm, the distance is the Euclidean distance, additional detailed notes in Chinese, as well as a test set and a training set)

对于非线性系统 如duffing和Lorenz系统，使用龙格库塔法求其系统响应，并画出相图和分岔图( For nonlinear systems such as duffing and Lorenz system, seeking its response system using Runge-Kutta method, and draw the phase diagram and bifurcation diagrams)

逐步判别分析的主函数 用于分类 [sel,c,c0,re,P]=StepclaassV2(data,[50 50 50],data,10) 输入变量 x为训练集.每行为一个样本，每列为一个变量. Class_x,为训练集的分类情况，一行，为各类样本数量,例如[5 6 9] Test为待分类样本. 输出变量：sel为选择的变量序号，c,c0为拟合出的判别函数.re为对Test的判别结果,P为其后验概率. author 王新 2012.4.8 例子Sample: load fisheriris x=meas Class_x=[50,50,50] Test=meas(1:5,:) Fa1=4 [sel,c,c0,re,P]=StepclaassV2(x,Class_x,Test,Fa1)( Stepwise discriminant analysis for the classification of the main function [sel, c, c0, re, P] = StepclaassV2 (data, [50 50 50], data, 10) input variables x as the training set, each conduct a sample of each as a variable. Class_x, the classification for the training set, a row, the number of samples for all types, for example, [5 6 9] Test sample to be classified. output variable: sel for the selected variables serial number, c, c0 is fitted discriminant function. re discrimination results for the Test, P its posterior probability. author Wang 2012.4.8 examples Sample: load fisheriris x = meas Class_x = [50,50,50] Test = meas (1:5, :) Fa1 = 4 [sel, c, c0, re, P] = StepclaassV2 (x, Class_x, Test, Fa1))

Head Pose Estimation on Low Resolution Images using matlab

matlab 版本的PESQ代码，用于语音质量评价(PESQ wrote by MATLAB, is used to assess speech quality)

采用累计贡献率的方法，包括AHP，因子分析，回归分析，聚类分析，直线阵采用切比学夫加权控制主旁瓣比。( The method of cumulative contribution rate Including AHP, factor analysis, regression analysis, cluster analysis, Linear array using cut than learning laid upon the right control of the main sidelobe ratio.)