FRLS

功率谱分析，经过修改的，yuler_walke、welch、relax、相关图、周期图等(Power spectrum analysis, modified, yuler_walke, welch, relax, related maps, cycle map)

数值分析基于Matlab环境下的龙贝格算法(Numerical analysis based on Matlab environment Romberg algorithm)

matlab files to perform toonification on images using bilateral filtering

逐步判别分析的主函数 用于分类 [sel,c,c0,re,P]=StepclaassV2(data,[50 50 50],data,10) 输入变量 x为训练集.每行为一个样本，每列为一个变量. Class_x,为训练集的分类情况，一行，为各类样本数量,例如[5 6 9] Test为待分类样本. 输出变量：sel为选择的变量序号，c,c0为拟合出的判别函数.re为对Test的判别结果,P为其后验概率. author 王新 2012.4.8 例子Sample: load fisheriris x=meas Class_x=[50,50,50] Test=meas(1:5,:) Fa1=4 [sel,c,c0,re,P]=StepclaassV2(x,Class_x,Test,Fa1)( Stepwise discriminant analysis for the classification of the main function [sel, c, c0, re, P] = StepclaassV2 (data, [50 50 50], data, 10) input variables x as the training set, each conduct a sample of each as a variable. Class_x, the classification for the training set, a row, the number of samples for all types, for example, [5 6 9] Test sample to be classified. output variable: sel for the selected variables serial number, c, c0 is fitted discriminant function. re discrimination results for the Test, P its posterior probability. author Wang 2012.4.8 examples Sample: load fisheriris x = meas Class_x = [50,50,50] Test = meas (1:5, :) Fa1 = 4 [sel, c, c0, re, P] = StepclaassV2 (x, Class_x, Test, Fa1))

this is a source code for blind multouser detectyion in mc cdma

Kalman Filter based Tracking Matlab Code

基于SEPIC转换器的太阳能电池最大功率跟踪仿真(The PV MPPT based on SEPIC converter by using PO method)

MVDR方法是波束形成的经典方法，可使期望方向的波束尽可能多通过同时抑制干扰，稳健性强。(MVDR beamforming method is the classic way, can look forward to the direction of the beam while suppressing interference by as much as possible, robust and strong.)

These files contain all the code necessary to run the example in the Webinar "Signal Processing for Machine Learning in MATLAB". They also include code to automate the download and preparation of the dataset used. In that webinar we presented an example of a classification system able to identify the physical activity that a human subject is engaged in, solely based on the accelerometer signals generated by his or her smartphone. We used consolidated signal processing methods to extract a fairly small number of highly-descriptive features, and finally trained a small Neural Network to map the feature vectors into the 6 different activity classes of a pre-recorded dataset. The topics discussed include: * Signal manipulation and visualisation * Design and application of digital filters * Frequency-domain analysis * Automatic peak detection * Feature extraction from signals * Train and test of simple Neural Networks

对一幅图像加入高斯噪声和椒盐噪声后，分别进行均值滤波和中值滤波，比较两种滤波方法的优缺点(Of an image by adding Gaussian noise and salt and pepper noise, the mean filter, respectively, and median filtering, comparing the advantages and disadvantages of the two filtering methods)