How to see the frequencies present in a measured signal.

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Gova ReDDy
Gova ReDDy 2014-4-7
评论： Gova ReDDy 2014-4-9
Hello,
I measured a pulse signal sampled with a frequency of 64Hz and want to see the fre-quencies responsible for the signal. Can some explain how to plot the power spectrum using FFT function to see the fre-quecy components responsible for the pulse.The pulse data of 10secs duration is at-tached below.
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Joseph Cheng
Joseph Cheng 2014-4-7
the documentation for fft has examples of how to do it. http://www.mathworks.com/help/matlab/ref/fft.html
The first example can be adapted with the information you gave above.
Gova ReDDy
Gova ReDDy 2014-4-7
Yes,I tried like this and resultant was
a1=data(1,1:640);
plot(a1)
a1_fft = abs(fft(a1));
a1_fft = a1_fft(1:length(a1)/2);
plot(a1_fft);
But from the output pulse signal(posted in the above) it can be understood that it only has 1Hz frequency component but the FFT output shows 1Hz components in larger and also other frequencies of 11 ,21,25,31Hz.
Can someone explain why the other frequency components are also in larger magnitude.

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Image Analyst
Image Analyst 2014-4-7
What make you think a square wave has only one frequency? A sine wave does , but a square wave is made up of an infinite number of sine waves . Hence you get power at multiple frequencies. The FFT of a single square pulse is a sinc function, but when you convolve with a comb function to get a square wave train, you'll get tons of sinc's all adding together. Here are some links with basic info on FFT theory:
Steve's Blog
http://www.cv.nrao.edu/course/astr534/FourierTransforms.html
David Young's File Exchange
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dpb
dpb 2014-4-8
What was your sampling frequency and length? The relationship is
df=1/T
where T is n*dt and n is number of samples and dt the sample rate. From that you can set the frequency axis appropriately for your sample case.
Again, I commend the example at
doc fft
to your attention -- it has exactly the right steps illustrated for a sample case; simply plug your numbers in in the appropriate places. The only difference is they chose to use the power of 2 length FFT, but the same is true if you just use the signal length.

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