Rosin

关于matlab主元分析的一个简单单易懂的实例，可供初学入门者参考学习。 (On the matlab principal component analysis is a simple single-understand examples, for novice beginners for reference.)

遗传算法（Genetic Algorithm，GA）是通过对自然界中生物的遗传和优胜劣汰的进化过程进行模拟与抽象，进而形成的一种自适应全局随机优化搜索方法。遗传算法只需提供目标函数作为寻优信息，它从某一随机生成的初始群体出发，经过选择、交叉和变异等遗传操作后对个体进行适应度评价，保留适应度较强的个体遗传到子代种群中，经过多次的迭代计算求得最优个体，即问题的最优解。本程序采用遗传算法可求解微网优化运行。(Genetic Algorithm is an adaptive global by nature, genetic and biological survival of the fittest evolutionary process simulation and abstract, and thus the formation of stochastic optimization search method. Just provide genetic algorithm optimization objective function as an information, which is generated from a random initial population of departure, after genetic manipulation, such as selection, crossover and mutation of individuals fitness evaluation, retained a strong individual genetic fitness to child on behalf of the population, after several iterations of computing the optimal solution to get the optimal individual, that is the problem. This program uses a genetic algorithm to solve the optimal operation of the micro-network.)

Principal Component Analysis on the FERET Dataset

为stanford大学donoho教授所编写的redgit变换源代码。是用c编写的，但已转化至matlab环境使用。(For stanford university, prepared by Professor Donoho redgit transform the source code. C is prepared, it has been transformed to use the matlab environment.)

这是考虑光信道色散，对输入信号的影响，结果仿真了功率谱密度(This is to consider chromatic dispersion of the optical channel, the input signal, the results of simulation of the power spectral density)

超声波信号自适应消噪和滤波程序，可用于除噪，用于去除背景环境影响(Adaptive noise cancellation and filtering procedures can be used for noise removal)

BCH 和RS级联编码及其解码 中间涉及到二进制转化(BCH and RS encode and decoder)

创建一个n维的稀疏数组对象，n是任意值。 定义N可能是大于2的一类n维稀疏阵列。然而，它应该被认为是一种起动方式与普通的MATLAB稀疏矩阵和重塑它有N维度。换句话说，稀疏的数据，首先必须能够作为一个普通的2D MATLAB稀疏矩阵被前n维。事实上，如果目标数组的尺寸mxnxp……yxz，然后将它存储在内部类是一个普通的二维稀疏阵列的尺寸（M×N×P×……×Y）XZ。这导致了某些内存株时使用大量的尺寸。我发现有用的类主要用于中等尺寸像三维图像边缘检测，你经常要举行一个稀疏的3D的边缘地图。(Creates an N-dimensional sparse array object, for arbitrary N.This submission defines a class of N-dimensional sparse arrays for N possibly greater than 2. However, it should really be thought of as a way of starting with an ordinary MATLAB sparse matrix and reshaping it to have N dimensions. In other words, the sparse data must first be able to exist as an ordinary 2D MATLAB sparse matrix before being made N-dimensional. In fact, if the intended array has dimensions MxNxP...YxZ, then the class will store it internally as an ordinary 2D sparse array of dimensions (M*N*P*...*Y)xZ. This leads to certain memory strains when using large numbers of dimensions. I find the class useful mainly for moderate dimensional things like edge detection in 3D imaging, where you often want to hold a sparse 3D edge map. )

本程序是基于Matlab的共轭梯度法数值计算。(This procedure is based on Matlab numerical Conjugate Gradient Method.)

Soft Viterbi decoding algorithm for reed solomon.