huiseguanlian

MATLAB中移动通信的仿真的用法，比较基本的，比如信号的产生 加噪等等(MATLAB, the simulation of mobile communications usage, more basic, such as signal plus noise, and so produce)

This is the m file for reading uff file using matlab. I can be easily read uff file in which to analyze the data.

AM RECEIVER based on super heterodyne principle virtually used in all modern radio and television receivers. The approach mainly involves the use of heterodyning or frequency mixing. The signal from the antenna is filtered sufficiently at least to reject the image frequency and possibly amplified. A local oscillator in the receiver produces a sine wave which mixes with that signal, shifting it to a specific intermediate frequency (IF), usually a lower frequency. The IF signals is itself filtered and amplified and possibly processed in additional ways. The demodulator uses the IF signals rather than the original radio frequency to recreate a copy of the original modulation (such as audio). The project is coded in MATLAB.

声音处理的MATLAB程序：DISTEUSQ (calculate euclidean, squared euclidean or mahanalobis distance )

Chinese remainder theorem

FMCW调频连续波雷达的测距测角，阵列信号处理的高分辨率估计，有CDF三角函数曲线/三维曲线图，主要是基于mtlab的程序，信号处理中的旋转不变子空间法，主要为数据分析和统计，ICA（主分量分析）算法和程序，本程序的性能已经达到较高水平。( FMCW frequency modulated continuous wave radar range and angular measurements, High-resolution array signal processing estimates, There CDF trigonometric curve/3D graphs, Mainly based on the mtlab procedures, Signal Processing ESPRIT method, Mainly for data analysis and statistics, ICA (Principal Component Analysis) algorithm and procedures, The performance of the program has reached a high level.)

说明：  利用盲源分离分离混合信号，此算法为FastICA,运行稳定(Separation of mixed signals by blind source separation algorithm)

说明：  用MATLAB做的Fisher线性分类器，处理脑电信号，数据为bci竞赛数据(MATLAB to do with Fisher linear classifier, EEG signal processing, data race data for the bci)

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) )

用Matlab编写的天空之城，分享给大家听听(Matlab prepared Laputa, share them to you)