Fast Fourier transform of fft function in matlab.
Syntax:-
Y = fft(X) Y = fft(X,n) Y = fft(X,n,dim)
Discprition for fft matlab Function
If x is an vector then it will return the Fourier transform of that vector
Let x is an matrix then fft(X) assume the colum as a vactory and return Fourier transform of each column.
Now let assume X is an multidimensional array then fft(X) treats the values of first array dimension only if the size of that array is not equal to 1 as vector and gives the Fourier transform of each vector
Cases with the fft() MATLAB Function
Y = fft(X,n) returns the n-point DFT.
Case 1
X is an vector and the length of X is lower then n. X will be padded with trailing zeros to length n
Case 2
X is an vector and the length of X is Higher then n. X will be truncated to length n
Case 2
X is an matrix then each column of that matrix treated as in the vector case
Case 2
multidimensional array X is treated as like fft(X) the "first array dimension only if the size of that array is not equal to 1" treated as in the vector case
Y = fft(X,n,dim) gives the Fourier transform along with the dimension dim.
Example Code For ftt() function in MATLAB
Use the help of Fourier transforms to find the frequency components of a signal buried in noise.
Fs = 1000; % Sampling frequency T = 1/Fs; % Sampling period L = 1500; % Length of signal t = (0:L-1)*T; % Time vector
Form a signal containing a 50 Hz sinusoid of amplitude 0.7 and a 120 Hz sinusoid of amplitude 1. S = 0.7*sin(2*pi*50*t) + sin(2*pi*120*t); Corrupt the signal with zero-mean white noise with a variance of 4. X = S + 2*randn(size(t)); Plot the noisy signal in the time domain. It is difficult to identify the frequency components by looking at the signal X(t). plot(1000*t(1:50),X(1:50)) title('Signal Corrupted with Zero-Mean Random Noise') xlabel('t (milliseconds)') ylabel('X(t)')
Compute the Fourier transform of the signal. Y = fft(X); Compute the two-sided spectrum P2. Then compute the single-sided spectrum P1 based on P2 and the even-valued signal length L. P2 = abs(Y/L); P1 = P2(1:L/2+1); P1(2:end-1) = 2*P1(2:end-1); Define the frequency domain f and plot the single-sided amplitude spectrum P1. The amplitudes are not exactly at 0.7 and 1, as expected, because of the added noise. On average, longer signals produce better frequency approximations. f = Fs*(0:(L/2))/L; plot(f,P1) title('Single-Sided Amplitude Spectrum of X(t)') xlabel('f (Hz)') ylabel('|P1(f)|')
Now, take the Fourier transform of the original, uncorrupted signal and retrieve the exact amplitudes, 0.7 and 1.0. Y = fft(S); P2 = abs(Y/L); P1 = P2(1:L/2+1); P1(2:end-1) = 2*P1(2:end-1); plot(f,P1) title('Single-Sided Amplitude Spectrum of S(t)') xlabel('f (Hz)') ylabel('|P1(f)|')
OUTPUT For Example of fft () matlab:-
fft () matlab output |
fftshift matlab |
fourier transform matlab |