pid

基于matlab的虹膜边缘识别 包括边缘检测，归一化，用汉明矩进行特征匹配(Matlab on the edge of iris recognition, including edge detection, normalization, with Hamming moments feature matching)

一个完整的EMD分解程序，希望能够帮助那些需要EMD的人(EMD decomposition of a complete program, hoping to help those who need EMD)

说明：  matlab中的傅里叶变换，线性卷积，循环卷积，在基带等效信号加高斯白噪声信道模型下，BPSK,QPSK,QAM等调制解调的误码率计算。(Do not call any function in the case, resulting in [0,1]uniform distribution of interval random variable matlab program.)

天线选择的参考资料，初学者必备！很有用的哦！(Reference antenna selection, beginners must have! Oh, very useful!)

Analysis of single frequency positioning accuracy of BDS_GPS receiver

这是一个介绍Matlab使用的课件，其内容简单，适合初学者接触和学习Matlab。(This is an introduction to Matlab uses the courseware, their content is simple, suitable for beginners contacts and learning Matlab.)

RS码,LDPC码编码译码功能函数，常用程序(RS code, LDPC coding and decoding performance function, frequently used programs)

harrlike算法实现人脸检测和定位方法，MATLAB环境，经测试，效果非常好(harrlike algorithm face detection and localization methods, MATLAB environment, tested, very good results)

最小二乘辨识的五种算法：Bayes辨识程序，递推最小二乘，梯度校正最小二乘，增广最小二乘，最小二乘一次完成算法(Identification of the five least-squares algorithm: Bayes identification procedures, recursive least squares, least-squares gradient Calibration, Augmented least squares, least-squares algorithm for a finish)

AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm that was proposed by Yoav Freund and Robert Schapire. In this project there two main files 1. ADABOOST_tr.m 2. ADABOOST_te.m to traing and test a user-coded learning (classification) algorithm with AdaBoost. A demo file (demo.m) is provided that demonstrates how these two files can be used with a classifier (basic threshold classifier) for two class classification problem.(AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm that was proposed by Yoav Freund and Robert Schapire. In this project there two main files 1. ADABOOST_tr.m 2. ADABOOST_te.m to traing and test a user-coded learning (classification) algorithm with AdaBoost. A demo file (demo.m) is provided that demonstrates how these two files can be used with a classifier (basic threshold classifier) for two class classification problem.)