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Matlab example for beginners(Matlab instance for beginners)

基于MATLAB的系统分析与设计 小波分析 胡昌华编(HU Chang-compiled based on the the MATLAB system analysis and design of wavelet analysis )

说明：  自适应中值滤波器的滤波方式和传统的中值滤波器一样,都使用一个矩形区域的窗口Sxy,不同的是在滤波过程中,自适应滤波器会根据一定的设定条件改变(即增加)滤窗的大小,同时当判断滤窗中心的像素是噪声时,该值用中值代替,否则不改变其当前像素值,这样用滤波器的输出来替代像素(x,y)处(即目前滤窗中心的坐标)的值。(Adaptive Median Filter for filtering methods and the same as the traditional median filter, use a rectangular area of the window Sxy, different in the filtering process, the adaptive filter in on certain Presumed Conditions change (an increase of ) filter window size, while when the judge is the filter window center pixel noise, the median value is used instead of, or does not change its current pixel value, so that the output with a filter to replace the pixels (x, y) Office (that is, coordinates of the current filter window centered) values.)

一个UPF的例程，内含matlab源代码和相关的文档资料（英文），对UPF的基本原理有详细的介绍(a UPF of routines, containing Matlab source code and related documentation information (English), UPF of the basic tenets is described in detail)

维格那变换，针对维格纳处理的一个例子，让你了解其过程(wignerville tranform)

这是写的压缩感知的几种算法，包括BP,MP,OMP,ROMP几种算法，算法的恢复包括使用一维信号和lena图片，希望对你们有帮助(It is written in the compressed sensing of several algorithms, including the restoration of BP, MP, OMP, ROMP several algorithms, algorithms including the use of a one-dimensional signal and lena pictures, I hope to help you)

说明：  利用matlab实现对DFT离散傅里叶算法对数字信号的处理。(Using DFT and FFT to realize discrete processing of digital signal in Matlab environment)

Newton-Raphson算法是求解非线性方程组和有限元的主要程序，是工程人员的必会的算法(Newton-Raphson algorithm for solving nonlinear equations and the finite element of the main program, the engineering staff of the algorithm will)

单纯形法，求解线性规划问题的通用方法。单纯形是美国数学家G.B.丹齐克于1947年首先提出来的。它的理论根据是：线性规划问题的可行域是 n维向量空间Rn中的多面凸集，其最优值如果存在必在该凸集的某顶点处达到。顶点所对应的可行解称为基本可行解。单纯形法的基本思想是：先找出一个基本可行解，对它进行鉴别，看是否是最优解；若不是，则按照一定法则转换到另一改进的基本可行解,再鉴别；若仍不是，则再转换,按此重复进行。因基本可行解的个数有限，故经有限次转换必能得出问题的最优解。如果问题无最优解也可用此法判别。 (Simplex method, for solving linear programming problems common method. Simplex is the American mathematician GB Danzig in 1947 first put forward. Its theory is based on: linear programming problem feasible region is n-dimensional vector space Rn convex set in the multi-faceted, and its optimal value must be if the vertices of a convex set to achieve. Feasible solution corresponding to the vertex is called a basic feasible solution. The basic idea of the simplex method is: first find a basic feasible solution, to identify it and see whether it is the optimal solution If not, switch to another according to certain rules to improve the basic feasible solution, and then identify if still No, the re-conversion, click here to repeat. Due to the limited number of basic feasible solution, it will be converted by the finite optimal solution can be reached. If the problem can also be the optimal solution method has been applied without discrimination. )

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