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时域处理算法，本源码是关于时域预处理放面的内容，包含简易的算法(Time domain processing algorithms, the source is placed on the surface pretreatment time-domain content, including a simple algorithm)

rand, randn, randint.... you can understand my code

Dv-hop算法的原始算法的源程序，可以了解到原始算法的误差值(Dv-hop algorithm, the original algorithm of the source, I can understand the error of the original algorithm)

Matlab提取主分量矩阵。程序设计步骤： 1、去均值 2、计算协方差矩阵及其特征值和特征向量 3、计算协方差矩阵的特征值大于阈值的个数 4、降序排列特征值 5、去掉较小的特征值 6、去掉较大的特征值(一般没有这一步） 7、合并选择的特征值 8、选择相应的特征值和特征向量 9、计算白化矩阵 10、提取主分量()

one of my homework, for morphorlogy transformation......thanks! and i need to download wavelet based image compression(one of my homework. for morphorlogy transformation ...... thanks ! and i need to download wavelet based image comp ression )

现代通信系统（matlab版）（第二版）配套源代码 John G. Proakis等著 刘树棠 译 电子工业出版社出版(modern communication system (Matlab version) (2nd Edition) source code supporting John G. Proak Liushutang is waiting for the translation of the electronics industry published)

Speaker recognition is the task of validating individual s identity using invariant features extracted from their voices print. Speaker recognition technology common applications include authentication, surveillance and forensic applications. This Paper investigates the performance of three automatic model selections based on Gaussian Mixture Model (GMM). These approaches are Bayesian information criterion (BIC), Bayesian Ying–Yang harmony empirical learning criterion (BYY-HEC) and Bayesian Ying–Yang harmony data smoothing learning criterion (BYY-HDS). Experimental evaluation of these methods is presented.

基于模糊自适应pso的电力系统负荷调度算法，采用的测试案例是标准1ee6g系统，约束条件包括机组出力上限限制、网络损耗和功率平衡。(Based on fuzzy adaptive pso scheduling algorithm of power system load, the test case is a standard 1 ee6g system, constraint conditions including limit the output of generating unit, the network loss and power balance.)

加权隐格式求解1维扩散方程，函数形式，附简单例子，(Weighted implicit scheme for solving one-dimensional diffusion equation)

测试文本，选用著名的gettysburg这篇英文文本做为测试文本(Second, it fuses the features of the first singular value component and the second one, and then gets the complex feature vectors which reflect not only the statistic frequency but also the sequential structure of letters. In the end, the cosine similarity of texts is used to measure the similarity between the query and documents.)