digital signal processing practical approach

'); > Ftt_sig = FFT (SIG, 512); % perform a 512-point fast Discrete Fourier transformation for the SIG signal to zero> P = ftt_sig. * conj (ftt_sig)/512; % calculate the power spectral density of the signal> F = 1000 * ()/512; % set the frequency conversion range> Subplot (1, 2 );> Plot (F, ftt_sig () % to plot the power spectrum density distribution > Title ('power spectral density fig ') Fftfilt-FFT-bas

21st century belongs to the digital information age, it is fortunate to learn some of the basic content of digital signals, although it is not clear the application of these theoretical basis, but his application of the technology has given himself a lot of accumulation also let himself feel the greatness of human wisdom, this article mainly discusses the Gaoxi and Ding Yu-mi authoring "

; % Set Frequency Transform range>> subplot (1,2,2);>> plot (F,ftt_sig (1:256))% plot power spectral density distribution map>> title (' Power Spectral density graph ')fftfilt-based on FFT amount FIR FilterUsageY=fftfilt (b,x)The function uses the superposition method to perform the FIR filter based on FFT. The input vector x is filtered by the given coefficient vector b.Y=fftfilt (B,x,n)The function uses the superposition method to perform the FIR filter based on FFT. The input vector x is filt

In digital signal processing, it is often necessary to use discrete Fourier transform (DFT) to obtain the frequency domain characteristics of the Signal. Although the traditional DFT algorithm can obtain the signal frequency domain characteristics, The algorithm is computati

the s plane, the digital frequency still changes from 0 to 2 pi on the z-plane unit circle. 。。。。。。 The z-plane repeats like this. We know that the Fourier transform of the discrete signal corresponds to the z-transform on the unit circle , so the above conclusion verifies why the Fourier transform of the discrete signal is cyclical: the root cause is th

Abstract: full digital B-ultrasound is the development direction of ultrasound medical instruments. Its basic technical feature is to use digital hardware circuits to achieve real-time processing of ultrasonic signals with extremely large data volumes. The development direction and main signal

as the data_array_section, storing the result For i=1:moving_point_right Data_array_filter (i) =data_array_section (i);EndFor i= (col_array+moving_point_left+1): Col_array % This piece of code is to assign values to the first 4 and the last 4 points of a matrixData_array_filter (i) =data_array_section (i);EndFor i= (moving_point_right+1):(col_array+moving_point_left)For J=moving_point_left:moving_point_right Data_array_filter (i) =data_array_filter (i) +data_array_256 (i+j); %moving Averag

In the field of signal processing, the gain is divided into digital gain and analog gain, the digital gain is used to manipulate discrete digitized sampled values, and analog gain directly operates on a continuous analog signal waveform. For most high-end stereos today, the

When digital processing of analog signals, the first step is to sample the analog signals. The sampling frequency is determined by the nequest sampling theorem. Perform dtft on the sampled digital signal to obtain its spectrum. According to the calculation formula of dtft, all sampling points of signals are required fo

cake Draw_sector3d ($img, $ox, $oy, $a, $b, $v, $SD, $ed, $clrLst [$i]); $SD, $ed, $clrLst [$i]); Draw Labels Imagefilledrectangle ($img, 5, $ly, 5+ $fw, $ly + $fh, $clrLst [$i]); Imagerectangle ($img, 5, $ly, 5+ $fw, $ly + $fh, $CLRT); Imagestring ($img, $font, 5+2* $fw, $ly, $labLst [$i]. ":". $datLst [$i]. " (". (Round (10000* ($datLst [$i]/$tot))/100). "%)", $CLRT); $str = Iconv ("GB2312", "UTF-8", $labLst [$i]); Imagettftext ($img, $font, 0, 5+2* $FW, $ly +13, $CLRT, "./simsun.ttf", $str.

,-]; ayplus = Conv (A, axplus) byplus = Conv (B, bxplus) + Conv (xic, axplus) [R, P, C] = residuez (byplus, ayplus) MP = ABS (P), AP = angle (P)/pin = [0: 50]; [x, FS] = audioread ('e: test1.wma '); % self-input WMA file x = x (:, 1); % Single Channel ? Y = filter (B, A, X, xic); figuresubplot (211), plot (x), title ('input signal curve '); subplot (212 ), plot (Y), title ('signal curve after differential e

For an explanation of the Fourier transform, the following link: http://blog.jobbole.com/70549/. It's very detailed:Note the point:1, signal processing based on such a concept, the signal to be processed (? ) can be decomposed into sine wave, different amplitude, different phase, different frequency, such as: F=cos (W1*T+Π/4) +100cos (W2*T+Π/6)2, by decomposition

/southeast "style=" font-size:18px "> Total sample Rate (SR) equalswatermark/2/text/ahr0cdovl2jsb2cuy3nkbi5uzxqvam9qb3poyw5nanu=/font/5a6l5l2t/fontsize/400/fill/i0jbqkfcma==/ Dissolve/70/gravity/southeast ">In most real windows, B can be represented as a multiple of fs/n. Thatwatermark/2/text/ahr0cdovl2jsb2cuy3nkbi5uzxqvam9qb3poyw5nanu=/font/5a6l5l2t/fontsize/400/fill/i0jbqkfcma==/ Dissolve/70/gravity/southeast ">c is a proportional constant, which is:In the formula. Sr/fs is the "over-rate samp

1 mean value The mean value represents the size of the DC component in the signal, expressed in E (x). For a Gaussian white noise signal, its mean value is 0, so it has only the AC component. 2 square of the mean value The square of the mean, denoted by {E (x)}^2, represents the power of the DC component in the signal. 3 mean square value The mean square value

%-----------------------------------------------------------------% Exa011001_rand.m:for Example 1.10.1% to test rand.m and to generate the White noise signal u (n)% with uniform distribution% produces a uniformly distributed random white noise signal and observes the histogram of the data distribution%-----------------------------------------------------------------Clear % clear variables that may be saved

]); } printf ("\ n"); } Q1=x[n-1]; Q2=y[n-1]; for(i=0; i) {W=6.28348530718/n*i; W1=cos (w); W2=-sin (w); C1=1.0-a1*w1+a2*W2; C2=a1*w2+a2*W1; C=c1*c1+c2*C2; D1=1.0-a1*q1+a2*Q2; D2=a1*q2+a2*Q1; A[i]= (C1*D1+C2*D2)/C; B[i]= (C2*D1-C1*D2)/C; } printf ("\ Theoretical dft\n"); for(i=0; i2; i++) { for(j=0;j2; j + +) {printf ("%10.7f+j%10.7f", a[2*i+j],b[2*i+J]); } printf ("\ n"); } FFT (X,y,n,1); printf ("\ndft\n"); for(i=0; i2; i++) { for(j=0;j2; j + +) {printf ("%10.7f+j%10.7