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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 a 50 Hz sinusoid of amplitude 0.7 and 120
Hz sinusoid of amplitude 1 and corrupt it with some zero-mean random noise:
Fs = 1000; % Sampling frequency
T = 1/Fs; % Sample time
L = 1000; % Length of signal
t = (0:L-1)*T; % Time vector
% Sum of a 50 Hz sinusoid and a 120 Hz sinusoid
x = 0.7*sin(2*pi*50*t) + sin(2*pi*120*t);
y = x + 2*randn(size(t)); % Sinusoids plus noise
plot(Fs*t(1:50),y(1:50))
title('Signal Corrupted with Zero-Mean Random Noise')
xlabel('time (milliseconds)')
It is difficult to identify the frequency components by looking at the
original signal. Converting to the frequency domain, the discrete Fourier
transform of the noisy signal y is found by taking the
fast Fourier transform (FFT):
NFFT = 2^nextpow2(L); % Next power of 2 from length of y
Y = fft(y,NFFT)/L;
f = Fs/2*linspace(0,1,NFFT/2);

% Plot single-sided amplitude spectrum.
plot(f,2*abs(Y(1:NFFT/2)))
title('Single-Sided Amplitude Spectrum of y(t)')
xlabel('Frequency (Hz)')
ylabel('|Y(f)|')


请教一下上面用红色标注的语句是什么意思？linspace呢？
NFFT的作用是什么？