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说明：  局部碰磨事故检测代码，通过功率谱检测，例子检测了四种类型的碰磨事故(Partial rubbing incident detection code, through the power spectrum detection, examples of detection of the four types of rub-impact accident)

这是对广义高斯分布中两个参数alpha,beta的估计方法，对自然图像的大量统计特征就符合这一分布。(function [f, g] = dggbeta(beta, absx) DGGBETA Derivative of generalized Gaussian pdf with respective to beta [F, G] = DGGBETA(BETA, ABSX) Return partial derivative and second order partial derivative This is an utility function for GGMLE ABSX is the distance of the data from the mean Function is undefined for negative beta if beta <= 0 f = NaN g = NaN )

This something that people never understand , SAMA PERSIS BUAT AMBIL PROGRAM

HV non compesated line program

RS纠错编码原理及其实现方法，核心期刊文献。(RS error-correcting coding theory and implementation methods, the core journal literature.)

matlab协方差计算 举例说明协方差cov命令的计算(matlab ccovariance claculator)

matlab经典、现代功率谱估计.、MATLAB实现程序及注解(matlab classic, modern power spectrum estimated. MATLAB procedures and comments)

一束平面波经过一个相位畸变元件后通过小孔衍射，畸变光束的衍射(After a bunch of plane-wave phase distortion components through the hole diffraction beam diffraction distortion)

应用MATLAB实现连续信号的采样与重构仿真(Application of MATLAB to achieve continuous signal sampling and reconstruction of simulation)

共轭梯度法（Conjugate Gradient）是介于最速下降法与牛顿法之间的一个方法，它仅需利用一阶导数信息，但克服了最速下降法收敛慢的缺点，又避免了牛顿法需要存储和计算Hesse矩阵并求逆的缺点，共轭梯度法不仅是解决大型线性方程组最有用的方法之一，也是解大型非线性最优化最有效的算法之一。 在各种优化算法中，共轭梯度法是非常重要的一种。其优点是所需存储量小，具有步收敛性，稳定性高，而且不需要任何外来参数(Conjugate Gradient method (Conjugate Gradient) is between the steepest descent method between Newton method and a method, it only USES a derivative information, but overcome the steepest descent method slow convergence of weakness, but also avoid the Newton law needs to storage and computing Hesse inverse matrix and shortcomings, Conjugate Gradient method is not only solve linear equations with most of the large method, and also one of the most effective solution large nonlinear optimization of one of the algorithm. In all kinds of optimization algorithm, the conjugate gradient method is very important. Its advantage is the storage capacity needed, it has small step convergence, high stability, and doesn t require any exotic parameters numerical experiment, this is the modern scientific computing of the answer above problem sets)