Channel-Coding

说明：  （1）应用9×9的窗口对上述图象进行随机抽样，共抽样200块子图象； （2）将所有子图象按列相接变成一个81维的行向量； （3）对所有200个行向量进行KL变换，求出其对应的协方差矩阵的特征向量和特征值，按降序排列特征值以及所对应的特征向量； （4）选择前40个最大特征值所对应的特征向量作为主元，将原图象块向这40个特征向量上投影，所获得的投影系数就是这个子块的特征向量。 （5）求出所有子块的特征向量。 ((1) the application of 9 × 9 window of these images at random, a total sample of 200 sub-image (2) all sub-images according to out-phase into a 81-dimensional row vector (3) all 200 lines for KL transform vector, derived its corresponding covariance matrix of eigenvectors and eigenvalues, in descending order by eigenvalue and the corresponding eigenvector (4) a choice to 40 corresponding to the largest eigenvalue eigenvector as the PCA, the original image block to the 40 feature vectors on the projection, the projection coefficients obtained by this sub-block eigenvector. (5) calculated for all sub-block eigenvector.)

用水平集方法实现图像分割，一个很好的算法(Level set method of image segmentation, a good algorithm)

具有高分辨信号的空间谱估计-旋转不变技术(ESPRIT)

说明：  目前各类粒子群算法的应用、改进、参数设置方法(At present the application of various types of particle swarm optimization, improvement, parameter setting method)

attached file contain two algorithm for classification.K_means and K Nearest Neiborhood. and related m_filles in matlab

使用MATLAB从你的iPhone或iPad上的内置传感器采集传感器数据(Use MATLAB to acquire sensor data from built-in sensors on your iPhone or iPad. )

distribution system 33 bus data file

数据资料是数学模型与实际问题接轨的重要途径和手段，有时还可以直接从数据资料中发现规 律组建模型。数据分析和处理是统计学研究的主要内容，也是计算数学的工作领域，近年来随 着计算机科学的发展，对“海量”数据的分析形成了一门新的交叉学科“数据挖掘”。这一章仅 介绍用统计学的最小二乘法构造拟合数据模型，以及用计算数学的函数插值方法处理数据解决 实际问题。(Is a mathematical model of data integration an important practical problem with the ways and means, and sometimes directly from the data can also be found in the law of the formation model. Statistical data analysis and processing is the main research, but also work areas of computational mathematics, computer science in recent years, along with the development of the " mass" analysis of the data formed a new cross-disciplinary " data mining." This chapter describes only the structure with a statistical method of least squares fit data models, and computational mathematics and data processing function interpolation methods to solve practical problems.)

This example shows the complete Kill, Hard Disk

人脸识别，使用深度学习中的DBN算法进行人脸识别算法，数据集是ORL人脸数据集。(FACE Recognition, using the depth learning DBN algorithm recognition algorithm, the data set is a ORL face dataset.)