MUSIC_based_on_UCA_S3_1

上海交通大学最优化练习题1，复习考试必备习题(Shanghai Jiaotong University, 1 optimization exercises, review exercises necessary tests)

小波变换由于其优异的特性受到许多研究者的热捧，本程序用于实现图像的小波变换(Wavelet transform because of its excellent properties are favorable for many researchers, this program used to implement the wavelet transform)

改良的传输矩阵，可以在此基础上进行啁啾、切纸计算(The improved transfer matrix can be used to calculate chirping and paper cutting on this basis.)

本程序先生成一个“Blocks”信号，然后加上高斯白噪声，再用 db3 小波进行分解， ％ 以说明信号和噪声在各个尺度上的表现。(this program as a "Blocks" signal, then add white Gaussian noise, reuse db3 wavelet decomposition,% to illustrate signal and noise in various scales performance.)

Compressive sensing SuOrEM code

常见的buffer设计，包括：Fast ring buffer, Loop buffer, Bip buffer， circular character buffer，可以方便的集成到相关代码中(common buffer example and code, including fast ring buffer, loop buffer, bip buffer, circular character buffer, and they can integrate to relative code easily.)

Relief算法中特征和类别的相关性是基于特征对近距离样本的区分能力。算法从训练集D中选择一个样本R，然后从和R同类的样本中寻找最近邻样本H，称为Near Hit，从和R不同类的样本中寻找最近样本M，称为Near Miss，根据以下规则更新每个特征的权重： 如果R和Near Hit在某个特征上的距离小于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻是有益的，则增加该特征的权重；反之，如果R和Near Hit在某个特征上的距离大于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻起负面作用，则降低该特征的权重。(The correlation between feature and category in Relief algorithm is based on distinguishing ability of feature to close sample. The algorithm selects a sample R from the training set D, and then searches for the nearest neighbor sample H from the samples of the same R, called Near Hit, and searches for the nearest sample M from the sample of the R dissimilar, called the Near Miss, and updates the weight of each feature according to the following rules: If the distance between R and Near Hit on a certain feature is less than the distance between R and Near Miss, it shows that the feature is beneficial to the nearest neighbor of the same kind and dissimilar, and increases the weight of the feature; conversely, if the distance between R and Near Hit is greater than the distance on R and Near Miss, the feature is the same. The negative effect of nearest neighbor between class and different kind reduces the weight of the feature.)

详细介绍了微弱信号的检测方法 ,做信号检测的可以参考下其中的经典方法(Details of the weak signal detection methods, signal detection can be done under one of the classical method of reference)

这是一篇用MATLAB编写的PSO用法的源码 介绍了改进的PSO的用法(This is the one with the usage of MATLAB source code written in PSO introduced the usage of the improved PSO)

SVM的VC程序，有matlab接口，便于调用，还有例子，用于模式识别，分类，预测。(SVM, VC procedures, matlab interface, easy call, as well as an example, for pattern recognition, classification and prediction)