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说明：  k-means算法实现图像分割 根据输入的类别数量来计算类别平均值(k-means algorithm for image segmentation This program gets an image and the desired number of partitions and finds the means of the different classes and provides a classified image.)

source code Fourth Order Partial Differential Equations for Noise Removal,

Fast Gaussian Convolution with Recursive Filtering.

毕业设计中，用Matlab实现了机器人最优路径规划中的全局路径规划，算法采用的是基于方向权的 YJROsM 二叉树算法，同时结合了哈夫曼编码二叉树的 RYhlAgF综合。(Graduation design, using Matlab to achieve the optimal path planning of robot global path planning algorithm uses a binary tree algorithm based on the direction of the right YJROsM , combined with the binary tree Huffman coding RYhlAgF comprehensive.)

浙江大学的关于提取图像背景的报告（matlab），里面的方法给我很多启示，希望也能给你们一些启示，由于本人是初学者，有些方法还是看不懂！(Zhejiang University, on the extraction of image background report (matlab), inside the method for giving me a lot of inspiration, hope and also give you some inspiration, because I am a beginner, and some way to watch or do not understand!)

MATLAB 简单易懂的教程，保证在短时间内学会。(MATLAB tutorial easy to understand, to ensure that in a short time learn.)

Assume that a direct sequence spread spectrum system uses the Barker code of length 7 (N=7) as its spreading sequence. a. Randomly generate 8 symbols (d0~d7) from the set {+1,-1}. b. If each chip of the Barker code is denoted as cj, j=0~6, spread each symbol di by the Barker code. (Please refer to p. 17 of the slides.) Use “stem” to plot the signals before spreading and after spreading

matlab 遗传算法 包括交叉，变异，最后得到结果等。(matlab yi chuan suan fa bao kuo jiao cha bian yi zui hou de dao jie guo )

2D FDTD of a region with Mur s absorbing boundary

《系统辨识与MATLAB仿真.pdf》 侯媛彬 汪梅 王立琦 本书共分8章，第1、2章为辨识的基本概念、理论基础和古典辨识方法；第3至6章为现代辨识内容，其中第3章是最小二乘参数辨识，第4章是梯度校正参数辨识，第5章是极大似然法的参数辨识方法，第6章是自适应参数辨识；第7、8章为复杂的非线性系统的智能辨识和混沌辨识，其中第7章是非线性系统的神经网络辨识；第8章是Volterra辨识方法、复杂系统的混沌现象及其辨识。从第2至7章，各章均包含开发的相应程序及其程序剖析。 (" System Identification and MATLAB simulation. Pdf" Hou Yuanbin Wang Mei Wang Liqi book is divided into eight chapters, Chapters 1 and 2 for the identification of the basic concepts, theoretical foundation and classical identification methods first 3-6 chapters of modern identification content, Chapter 3 is the least squares parameter identification, Chapter 4 is the gradient correction parameter identification, Chapter 5 is the maximum likelihood parameter identification method, Chapter 6 is an adaptive parameter identification Chapters 7 and 8 for complex Intelligent identification of nonlinear systems and chaos identification, which is in Chapter 7 of the neural network nonlinear system identification Chapter 8 is Volterra identification method, complex systems and chaos identification. From the first 2-7 chapters, each chapter contains procedures for the development of appropriate programs and their analysis.)