jisuandiacixue

经典信号处理程序可以为初学者提供一点点帮助吧 (Classical signal processing procedures can be provided for beginners a little bit of help you)

说明：  Riemann integral. Adaptative method, 2D

second test modulation

模糊减法聚类，模糊聚类的matlab实现！(Fuzzy subtractive clustering)

创建一个n维的稀疏数组对象，n是任意值。 定义N可能是大于2的一类n维稀疏阵列。然而，它应该被认为是一种起动方式与普通的MATLAB稀疏矩阵和重塑它有N维度。换句话说，稀疏的数据，首先必须能够作为一个普通的2D MATLAB稀疏矩阵被前n维。事实上，如果目标数组的尺寸mxnxp……yxz，然后将它存储在内部类是一个普通的二维稀疏阵列的尺寸（M×N×P×……×Y）XZ。这导致了某些内存株时使用大量的尺寸。我发现有用的类主要用于中等尺寸像三维图像边缘检测，你经常要举行一个稀疏的3D的边缘地图。(Creates an N-dimensional sparse array object, for arbitrary N.This submission defines a class of N-dimensional sparse arrays for N possibly greater than 2. However, it should really be thought of as a way of starting with an ordinary MATLAB sparse matrix and reshaping it to have N dimensions. In other words, the sparse data must first be able to exist as an ordinary 2D MATLAB sparse matrix before being made N-dimensional. In fact, if the intended array has dimensions MxNxP...YxZ, then the class will store it internally as an ordinary 2D sparse array of dimensions (M*N*P*...*Y)xZ. This leads to certain memory strains when using large numbers of dimensions. I find the class useful mainly for moderate dimensional things like edge detection in 3D imaging, where you often want to hold a sparse 3D edge map. )

这是一篇详细介绍单演信号的始祖性文章，内容详尽，文章出自信号处理领域的顶级起开Signal Processing，对于想认真学习单演信号的人来说很有帮助(a good paper for the monogenic sigal)

使用分水岭算法进行图像分割，包含边缘检测，傅里叶变换滤波等内容，文件包含.m源码以及测试图片 (Using the watershed algorithm for image segmentation, including edge detection, Fourier transform filtering,containing source code and test images)

eliminate the steady-state error to a step reference

用matlab处理的人脸识别，基于pca算法(Matlab deal with face recognition based on pca algorithm)

Monte-Carlo Integral