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matlab code for audio signal

说明：  基于隐马尔可夫过程目标识别的源程序，希望对大家有用，识别效过挺高的。(Target recognition based on hidden Markov process of the source, we want to be useful to identify effective than pricey for.)

power electric simulation full bridge

(7,3)线性分组码的译码过程和产生过程,此程序为信息课的作业((7,3) linear block code decoding process and the production process, this process work for the information class)

信号处理，振动方面的，很有用，绝不后悔，帮助很大(Signal processing, vibration issues, useful, never regret, very helpful)

This code is used to detect corners in the image,contains harris,susan,gilles,harrislaplace,youhua and so on. This code is ease to understand and can be used convinently. It will be useful for some people who are interested in this domain. Happy every one!

说明：  算差分盒维数的matlab程序。 让窗口中的每个像素都对分数维作出贡献。首先,计算某一尺度窗口的平均灰度值 ,然后判断每一个像素的灰度 ,若大于灰度平均值 ,则累加其灰度值为 max ,若小于灰度平均值 ,则累加其灰度值为min ,用max 和min代替 在 Sarkar 和 Chaudhuri 算法中的最大值和最小值 ,再通过拟合求出分数维。 (Differential count box dimension matlab program. Let window on the fractal dimension of each pixel to contribute. First, the calculation of an average gray scale of the window, and then determine the gray scale of each pixel, if the intensity is greater than the average, the cumulative value of its gray max, if less than the average gray level, the accumulation of its gray value of min, max and min to use instead of the algorithm in Sarkar and Chaudhuri maximum and minimum, and then by fitting the calculated fractal dimension.)

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) )

多项式拟合，对一群散点，采用最小二乘法进行多项式拟合(Polynomial fitting, a group of scattered points, using the method of least squares polynomial fitting)

surface Acoustic wave device for Apodized filter