geometric

easy code taking picture from web cam and showing the countours - use the logarithm filter

what is vector quantization how and where it can be used

基于耦合模理论给出了光纤光栅中的双模耦合方程，采用传输矩阵法数值模拟了均匀光纤光栅和啁啾光纤光栅的反射谱(fiber grating coupled mode theory transfer matrix method optical properties )

带可调渐消因子的无迹卡尔曼滤波程序MATLAB仿真实例(With adjustable fading factor unscented Kalman filtering procedures MATLAB simulation examples)

这是数字信号处理的一本专业书，里面有许多的经典的例子，可以作为大家学习数字信号处理的教材。(This is a digital signal processing of a professional book, there are many classic examples, as we learn from digital signal processing of materials.)

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.

说明：  用LS算法实现共512个数据的预测，并得到误差平方的仿真图(LS algorithm with a total of 512 data, forecasts, and get squared error of the simulation diagram)

一系列Matlab的m文件的打包，包括Hurst等(A series of Matlab m-file package, including Hurst, etc.)

video acquistion in matlab

说明：  学习通信的人都知道，要进行信号处理的话，算法的编程非常繁琐，这里有一些自适应信号处理的算法，LMS,RLS，MMSE等的算法，希望大家喜欢。(learning communications are aware, the need for signal processing, programming algorithm is very complicated, there is some adaptive signal processing algorithms, LMS, RLS, the MMSE algorithm, hope you like them.)