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说明：  HDB3码的编码，图形，功率谱密度。用于通信原理教学等(Code HDB3 coding, graphics, power spectral density. Communication Theory for teaching)

一种未知输入/输出的解耦方法，可用于控制器的设计、故障诊断系统设计等(An unknown input/output decoupling method can be used for controller design, fault diagnosis system design)

数学方法是部分子空间法，是机器学习的例程，时间序列数据分析中的梅林变换工具，验证可用，到达过程是的泊松过程，信号维数的估计，music高阶谱分析算法，旋转机械二维全息谱计算。( Mathematics is part of the subspace, Machine learning routines, Time series data analysis Mellin transform tool, Verification is available, Arrival process is a Poisson process, Signal dimension estimates, music higher order spectral analysis algorithm, Rotating machinery 2-d holographic spectrum calculation.)

说明：  拉丁超立方，去抽取样本，是一种从多元参数分布中近似随机抽样的方法，属于分层抽样技术，常用于计算机实验或蒙特卡洛积分等。(Latin hypercube sampling)

ANISODIFF - Anisotropic diffusion. Scale-space and edge detection using anisotropic diffusion. (Matlab SourceCode) (ANISODIFF- Anisotropic diffusion. Scale-space and edge detection using anisotropic diffusion. (Matlab SourceCode) )

A Linear Subspace Learning Approach via Sparse Coding

G代码现场使用实例矩形路线封闭路线测试Gcode(G codeG codeG codeG codeG codeG code)

wind platform based simulation

function [L,C] = kmeans(X,k) KMEANS Cluster multivariate data using the k-means++ algorithm. [L,C] = kmeans(X,k) produces a 1-by-size(X,2) vector L with one class label per column in X and a size(X,1)-by-k matrix C containing the centers corresponding to each class. Version: 07/08/11 Authors: Laurent Sorber (Laurent.Sorber@cs.kuleuven.be) References: [1] J. B. MacQueen, "Some Methods for Classification and Analysis of MultiVariate Observations", in Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability, L. M. L. Cam and J. Neyman, eds., vol. 1, UC Press, 1967, pp. 281-297. [2] D. Arthur and S. Vassilvitskii, "k-means++: The Advantages of Careful Seeding", Technical Report 2006-13, Stanford InfoLab, 2006.

MATLAB的SVM算法实现，Matlab Support Vector Machine Toolbox，This toolbox was designed as a teaching aid, which matlab is particularly good for since source code is relatively legible and simple to modify. However, it is still reasonably fast if used with the supplied optimiser. However, if you really want to speed things up you should consider compiling the matrix composition routine for H into a mex function. Then again if you really want to speed things up you probably shouldn t be using matlab anyway... Get hold of a dedicated C program once you understand the algorithm.(MATLAB-SVM algorithm, Matlab Support Vector Machine Toolbox, This toolbox was designed as a teaching aid, which matlab isparticularly good for since source code is relatively legible andsimple to modify. However, it is still reasonably fast if usedwith the supplied optimiser. However , if you really want to speedthings up you should consider compiling the matrix compositionroutine for H into a mex function. Then again if you really wantto speed things up you probably shouldn t be using matlabanyway ... Get hold of a dedicated C program once you understandthe algorithm.)