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大量的函数型数据分析研究专注于对函数修匀方法、函数对齐方法(registration)、函数型主成分分析( FPCA)、典型相关性分析(FCCA)及函数型线性模型等的研究。(Functional data analysis、Functional Principal Component Analysis)

这是解调中重要的DDS恢复部分，做成子函数形式，具体的输入输出变量已经表明，请按介绍使用。谢谢站长(This is the most important part, DDS, causing Functions form specific input and output variables have shown, click on the use. Thank you, Captain! !)

Data Encryption by Matrix Multiplication We will only use numbers 0 to 26 for the encryptive mathematics operation. Numbers from 1 to 26 are for letters from A to Z and 0 is for all non-letter characters. To minimize usage of numbers, all letters are converted to upper case. The original message is converted to number array according to the ASCII table and minus 64. We partition whole array into blocks of 8 numbers. The last block may be added several zeros. Then multiply each block by the an 8x8 matrix A which is invertible in Z27 to get cipher data. The decryption is the same process except to use its inverse matrix B. For good visual reading, we convert all non-letter characters to under score for the decryption.

用median 滤波除去图像中的pepper and salt 噪声，并且给给出了lena图像和coin图像的示例(use median filtering to remove the noise of pepper and salt， give examples of lena andcoin)

威布尔检测器和对数正态分布杂波背景检测,威布尔wh检测器(weibull-CFAR log-t cfar mlh-cfar，weibullwh-cfar)

Debye级数展开模型计算双层球形粒子散射角——散射光强曲线： 1. RB1.m 计算第一类Ricatti-Bessel函数的函数 2．RB2.m 计算第二类Ricatti-Bessel函数的函数 3. Alegendr.m 计算角函数 和 的函数 4. MieCoeff.m 计算米尔系数的函数 5. Amp.m 计算散射光复振幅的函数 6. DebyeIntensity.m Debye级数展开模型画包覆粒子散射角-光强图 (Debye series expansion model double spherical particle scattering angle- scattering intensity curve: 1. RB1.m calculated first class Ricatti-Bessel function of the function 2. RB2.m calculate the second category Ricatti-Bessel function of the function 3. Alegendr.m computing angle functions and function 4. MieCoeff.m Mill coefficient calculation function 5. Amp.m calculate the scattering amplitude recovery function 6. DebyeIntensity.m Debye series expansion model painted coated particle scattering angle- light intensity diagram)

Bezier Curve, I haven t yet opened. I got it from a friend

exercise of adaptive control from

梯度下降法是最早最简单，也是最为常用的最优化方法。梯度下降法实现简单，当目标函数是凸函数时，梯度下降法的解是全局解。一般情况下，其解不保证是全局最优解，梯度下降法的速度也未必是最快的。梯度下降法的优化思想是用当前位置负梯度方向作为搜索方向，因为该方向为当前位置的最快下降方向，所以也被称为是”最速下降法“。最速下降法越接近目标值，步长越小，前进越慢。(The gradient descent method is the earliest and most simple and most commonly used optimization method. The gradient descent method is simple to realize. When the objective function is a convex function, the solution of the gradient descent method is a global solution. In general, the solution is not guaranteed to be the global optimal solution, and the gradient descent method is not necessarily the fastest. The optimization idea of gradient descent method is to use the current position negative gradient direction as the search direction, because the direction is the fastest descending direction of the current position, so it is also called the steepest descent method. The faster the slowest descent approach is closer to the target, the smaller the step, the slower the progress.)

Solve the system Ax=b.