CursDotNetSharp

说明：  小波变换 图象处理 matlab 应用小波变换来实现压缩图像编码(Image processing matlab wavelet transform based on wavelet transform to achieve compression image coding)

单载波频域均衡算法完整Matlab仿真程序(Single-carrier frequency domain equalization algorithm for a complete simulation program Matlab)

模糊控制隶属度函数及部分代码，幻灯片讲义(Fuzzy control membership function and part of the code)

实现扩频里随机信号和伪码序列的产生，以及关于生成扩频序列的MATLAB程序，并用载波信号实现2PSK调制,在接收端用非相干差分积分实现捕获(Spread spectrum signal generated in the realization and pseudo random code sequence, and generate MATLAB program about spreading sequences and achieve 2PSK modulation carrier signal at the receiving end with the capture of non-coherent differential integrator)

pca做特征降维，然后进行特征空间随机分割构造多个svm分类器，并行处理，对样本进行分类，基于特征空间的svm多分类器(using pca reduce feature dimension,split feature space and then randomly divided over svm classifier construction, parallel processing, the samples were classified, based on multi-feature space classification svm )

功能描述：测试Levinson-Durbin算法 文件名：L_D_sim.m 测试用例： (1)x(n)+0.81*x(n-2)=u(n)，u(n)为高斯白噪声 (2)x(n)=u(n)+u(n-1)+u(n-2),u(n)为高斯白噪声 文件输出：两种测试用例情况下的功率谱 调用函数：function [A,E] = Levinson_Durbin_Algo(R,P) 被调用：无 作者：mingcheng 编写时间：2009-11-13 修改时间：2009-11-13 版本：V1.0 ( Function Description: Test Levinson-Durbin algorithm file name: L_D_sim.m test case: (1) x (n)+0.81* x (n-2) = u (n), u (n) is Gaussian white noise (2) x (n) = u (n)+ u (n-1)+ u (n-2), u (n) is Gaussian white noise file output: two tests use case in case of power spectrum call the function: function [A, E] = Levinson_Durbin_Algo (R, P) is called: No of: mingcheng write time :2009-11-13 modified :2009-11-13 version: V1.0 )

Gardner 同步算法仿真。此m文件把Gardner算法的信号生成到发送，接收及同步一并给出。(Gardner Synchronization Algorithm Simulation. This m file to Gardner algorithm to generate a signal to send, receive and be given simultaneously.)

该软件提供了无标度网络的的生成matlab程序，带有附注说明。(Scale-free networks generated matlab program, with explanatory notes, has been verified.)

this is code for nr method

在MATLAB环境下，背景为非高斯噪声下非常适用的滤波新算法。(in MATLAB environment, the background of the non-Gaussian noise filtering is applied to the new algorithm.)