edege_detection

CDMA的matlab仿真CDMA的matlab仿真CDMA的matlab仿真(simulation of CDMAsimulation of CDMAsimulation of CDMA)

Reading a PGM image file

这是一个对数字信号功放非线性效应的预失真处理的编码，对于初学者有很好的指导和参考意义。(This is to encode a digital signal pre-distortion amplifier nonlinear effects processing, a good guide for beginners and a reference value.)

对于神经网络系统辨识，采用MATLAB进行仿真验证(For neural network system identification, MATLAB is used for simulation verification)

matlab在图像处理中的介绍详细介绍了matlab与图像有关的函数(matlab in image processing matlab introductory details and image-related functions)

加权网络的源代码，对于研究复杂网络模型很有用，可以参考哈^_^(BBV model-weighted network strength and weight distribution are the model of power.)

经典matlab教材《数字信号处理及其matlab实现》电子书(Matlab classic textbook " Digital Signal Processing and its matlab implementation," e-books)

利用三阶模拟BW LP滤波器和双线性变换法，设计数字高通滤波器的matlab代码与结果(Use of third-order analog BW LP filter and bilinear transformation method, the design of digital high-pass filter matlab code and results)

四进制通信系统的进行蒙特卡罗仿真程序，以正交信号为基础。当均值分别等于０，０.１，１.０，２．０时完成１００００个比特的仿真并求出误差概率。绘出理论误码率和蒙特拉罗仿真的差错率并进行比较这俩个结果，并绘出每个均值情况下判决器的１０００个接受信噪比抽样(Four-band communication system to carry out Monte-Carlo simulation procedures to orthogonal signal basis. When the mean, respectively, equivalent to 10,000 completed 0,0.1,1.0,2.0 bit error probability simulation and obtained. Mapping theory and蒙特拉罗BER simulation这俩error rate and compare results, and the mean circumstances of each mapped)

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) )