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this is nicer project idea u can use it for futuer prospect

matlab 门限模型 这是一个比较好的用matlaB软件做门限模型的方法。(matlab threshold model which is a good software to do with matlaB threshold model.)

该文件被用于人工数据集的测试。这些数据都是随机生成的。在百度中也可以搜索到相关内容。数据集名字为ultwotwo(this file can used for Performance Comparison on Artificial Data)

This paper presents a hybrid parallel active filter scheme using small rated square-wave inverters for supply line harmonic current reduction for high power non-linear loads in the range of 1 - 50 MW to meet IEEE 519 harmonic standards.

SPI with program chip on f28335 with simulink

基于有限元基础编写的空腔内不可压稳态流动(Prepared based on the finite element basis cavity incompressible steady-state flow)

matlab实现停车位车辆检测，主要是利用hough变换实现车道线检测.本程序先是二值化，滤波处理后，利用hough变化检测车道线，故确定车位位置，在车位内根据颜色灰度检测是否有车。(matlab parking space vehicle detection using hough transform lane detection. This procedure first binarization, filtering, hough change detection lane, so to determine the parking position, in the parking according to the color gray-scale detectioncar.)

说明：  数字信号处理教程：MATLAB释义与实现/数字信号处理教程：MATLAB释义与实现( Digital Signal Processing Tutorial: MATLAB Interpretation and Implementation/digital signal processing tutorial: MATLAB Interpretation and Implementation)

Image Enhancement. Takes lenna image as input, applies low pass filter, median filter, weighted and center weighted median filter. Plots the output for each image

采样频率 fs=10000 轴承外环故障信号 fid=fopen( bearingout.dat , r ) 故障 N=1024 xdata=fread(fid,N, int16 ) fclose(fid) xdata=(xdata-mean(xdata))/std(xdata,1) 时域波形 figure(1) plot(1:N,xdata) xlabel( 时间 t/n ) ylabel( 电压 V/v ) db10小波进行4层分解 一维小波分解 [c,l] = wavedec(xdata,4, db10 ) 重构第1～4层细节信号 d4 = wrcoef( d ,c,l, db10 ,4) d3 = wrcoef( d ,c,l, db10 ,3) d2 = wrcoef( d ,c,l, db10 ,2) d1 = wrcoef( d ,c,l, db10 ,1) ( Sampling frequency fs = 10000 bearing outer ring fault signal fid = fopen (' bearingout.dat' , ' r' ) failure N = 1024 xdata = fread (fid, N, ' int16' ) fclose (fid ) xdata = (xdata-mean (xdata))/std (xdata, 1) time-domain waveform figure (1) plot (1: N, xdata) xlabel (' Time t/n' ) ylabel ( ' voltage V/v' ) db10 wavelet decomposition 4 layer one-dimensional wavelet decomposition [c, l] = wavedec (xdata, 4, ' db10' ) 1 ~ 4 reconstructed detail signal d4 = wrcoef (' d' , c, l, ' db10' , 4) d3 = wrcoef (' d' , c, l, ' db10' , 3) d2 = wrcoef (' d' , c, l, ' db10' , 2) d1 = wrcoef (' d' , c, l, ' db10' , 1) )