Selfcancellation

通过比较自编MATLAB 的k-means 算法程序和SPSS 中自带的k-means聚类工具，对两个数据集聚类，并分析了聚类结果。(By comparing the k-means algorithm program of self-compiled MATLAB with the K-means clustering tool of SPSS, two data sets are clustered and the clustering results are analyzed.)

说明：  RC常用标值电阻电容,常用标值电阻电容.(RC)

RANSAC算法及其消除错配应用,适用于图像方面的RANSAC配准，并带有示例以帮助理解(RANSAC algorithm and its application to eliminate the mismatch, the image area of ​ ​ the RANSAC for registration, and with examples to help understand)

整数规划，分支定界法，实现1000变量（变量个数可自行设定），只需改变原始数据，就能运行！是比较好的分支分支定界法程序设计(Integer programming, branch and bound method to achieve the 1000 variables (number of variables can be set on their own), simply by changing the original data, you can run! Branch is a better programming branch and bound method )

本人收集matlab实现最小二乘法线性、一元多项式拟合、多元二次拟合、自编函数曲线拟合程序，有文档说明(I collect the linear least squares method matlab realize, one yuan polynomial fitting, multivariate quadratic fit, self-function curve fitting procedures, documentation)

一个matlab类获得从雅虎财经股票行情. 一个matlab类获得从雅虎财经股票行情(A Matlab class for obtaining stock quotes from Yahoo Finance)

MATLABxct经典仿真程序设计源码MATLABxct classic simulation programming source code(MATLABxct classic simulation programming source MATLABxct classic simulation programming source code)

说明：  ＭＯＲＬＥＴ小波脊线提取程序，实现信号小波脊线提取(MORLET wavelet ridge extraction procedures to achieve wavelet ridge extraction)

用matlab制作的模拟动画程序，内容为电梯的升降演示。(Animation produced using matlab simulation program, the content of the elevator lift demonstration.)

Wavelet denoising For using this code need to use signal toolbox and general toolbox in your matlab In the first part of this assignment, we asked to obtain a (black-and-white) digital image of size 512 by 512 and then generate noisy image by adding a Gaussian noise but under the condition of having SNR=20dB by select the suitable value of variance for Gaussian noise formula. Second step is performing wavelet denoising using the hard thresholding (Use the db 6 for four levels) in the condition of finding the optimal thresholding value of T in terms of the SNR obtained. It means that, we should find the highest SNR value by finding the suitable value for threshold. Then we asked to do the same process but this time using soft thresholding. Finally for the last part of question one, we should compare the results of the obtained SNR with the recommendations of 3*sigma for the hard thresholding and 3/2*sigma for the soft thresholding.