Y=fft(X, n)returns the n-point DFT. If the length of X is less than n, X is padded with trailing zeros to length n. If the length of X is greater than n, the sequence X is truncated. When X is a matrix, the length of the columns are adjusted in the same manner Y=fft(X, 0, dim)and Y=fit(X, n, dim)applies the FFToperation across the dimension dim Examples A common use of Fourier transforms is to find the frequency components of a signal buried a noisy time domain signal. Consider data sampled at 1000 Hz. Form a signal containing 50 Hz and 120 Hz and corrupt it with some zero-mean random noise t=0:0.001:06 X=sin(2*pi*50*t)+sn(2*pi*120*t) y=x+ 2*randn(size(t)) plo(1000*(1:50)2y(1:50) title('Signal Corrupted with Zero-Mean Random Noise e(milliseconds),) Signal Corrupted with Zero-Mean Random Noise 01520253035404550 time(milliseconds) 2、d Discrete cosine transform(DCT Synta dct(x)Y = fft(X,n) returns the n-point DFT. If the length of X is less than n, X is padded with trailing zeros to length n. If the length of X is greater than n, the sequence X is truncated. When X is a matrix, the length of the columns are adjusted in the same manner. Y = fft(X,[],dim) and Y = fft(X,n,dim) applies the FFT operation across the dimension dim. Examples A common use of Fourier transforms is to find the frequency components of a signal buried in a noisy time domain signal. Consider data sampled at 1000 Hz. Form a signal containing 50 Hz and 120 Hz and corrupt it with some zero-mean random noise: t = 0:0.001:0.6; x = sin(2*pi*50*t)+sin(2*pi*120*t); y = x + 2*randn(size(t)); plot(1000*t(1:50),y(1:50)) title('Signal Corrupted with Zero-Mean Random Noise') xlabel('time (milliseconds)') 0 5 10 15 20 25 30 35 40 45 50 -5 -4 -3 -2 -1 0 1 2 3 4 5 Signal Corrupted with Zero-Mean Random Noise time (milliseconds) 2、dct ——Discrete cosine transform (DCT) Syntax y = dct(x)