wiregrid

线性方程直接解法的matlab程序，可作为数值分析等的学习工具。(The direct solution of linear equations matlab program, can be used as numerical analysis , and other learning tools.)

SQP matlab优化源程序SQP方法(SQP method)

说明：  若非奇异矩阵A能够分解为一个正交矩阵Q与非奇异上三角矩阵R的乘积，即： A=QR。则称其为A的QR分解。 实现QR分解的方法有很多种，包括Givens变换法，Householder变换法，Schemit正交化法。具体原理可以参考《矩阵论》（程云鹏，西工大出版）教材，这里仅给出三种实现QR分解的程序。(If nonsingular matrix A can be decomposed into a product of orthogonal matrix Q and nonsingular upper triangular matrix R, that is: A=QR. It is called QR decomposition of A. There are many methods to realize QR decomposition, including givens transformation, householder transformation, and scheme orthogonalization. The specific principle can refer to the teaching materials of matrix theory (Cheng Yunpeng, published by Xigong University). Here, only three kinds of QR decomposition procedures are given.)

watermarking with informed embedding

当前工程上广泛采用了一种高效滤波器，即CIC（cascaded integrator-comb filter）将其作为第一级来实现抽取、低通滤波。第二级再用一个普通的FIR滤波器就实现使后端设备工作在较低的频率下且硬件花销少、功耗也很低。(Widely used in the current project, a highly efficient filter, that is, CIC (cascaded integrator-comb filter) as the first level to achieve the extraction, low-pass filter. An ordinary second-class and then the realization of the FIR filter so that the work of the back-end equipment at a lower frequency and spending less hardware, power consumption is very low.)

说明：  隐马尔科夫模型的实现，可以使用的，很简单(hmm，is very simple)

MATLAB支持向量机，编程实现，已用C语言转，内附Windows x64和x32(MATLAB SVM Programming implementation, has been used in C language, Windows x64 and X32)

本源码是基于多尺度的retinex理论的实现代码，针对灰度和彩色图像均可使用！！ (The source is based on the theory of multi-scale retinex implementation code for gray and color images can be used! !)

使用wolf方法求最大lyapunov指数，核心算法为c语言，接口为matlab(method for the use of wolf largest lyapunov index, the core algorithm c language, Interface to Matlab)

MATLAB语言常用算法程序集（包括插值、函数逼近、矩阵特征值计算、数值积分、数值微分、方程求根、非线性方程组求解、解线性方程组的直接法、解线性方程组的迭代法、随机数生成、特殊函数计算、第15章 常微分方程的初值问题、第16章 偏微分方程的数值解法、数据统计和分析）(MATLAB language commonly used algorithm for assembly (including interpolation, function approximation, eigenvalue calculations, numerical integration, numerical differentiation, finding roots of equations, nonlinear equations, solving linear equations of the direct method for solving linear equations of the iterative method, random number generation, special function calculation, initial value problems for ordinary differential equations, numerical solution of partial differential equations, statistics and analysis))