## FFT real amplitude value

## FFT real amplitude value

(OP)

Hello,

I want to implement the fast fourier transformation on an

The first question came here, can I use the FFT in an aperiodic singnal?

Once I execute the matlab code I obtain the output of the transformation, which you cant see in the next picture:

As you can see, the first value (DC offset) is correct and it corresponds with the offset of the signal, but the remaining values of the amplitude of the FFT does not corresponf with the real values of the amplitude of the signal, and the FFT notmalization is done. Why this colud be possible?

Thank you!

I want to implement the fast fourier transformation on an

**aperiodic**signal, which you can see in the next picture:The first question came here, can I use the FFT in an aperiodic singnal?

Once I execute the matlab code I obtain the output of the transformation, which you cant see in the next picture:

As you can see, the first value (DC offset) is correct and it corresponds with the offset of the signal, but the remaining values of the amplitude of the FFT does not corresponf with the real values of the amplitude of the signal, and the FFT notmalization is done. Why this colud be possible?

Thank you!

## RE: FFT real amplitude value

Moreover, because it's aperiodic, you probably used a window to kill the aliasing . The FFT implicitly calculates as if your signal were periodic relative to your window, which may result in aliasing and other deleterious effects.

TTFN (ta ta for now)

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## RE: FFT real amplitude value

your amplitudes are fine,

## RE: FFT real amplitude value

## RE: FFT real amplitude value

## RE: FFT real amplitude value

When you seek the spectral content of your data, the constant (or average dc term) is of secondary importance, so you remove it from the figure. What's left is the amplitude spectra absent the dc term.

As one of the earlier responders has suggested, windowing is encouraged, to avoid mis-representing the actual data stream.

Without a proper window, you have basically assumed that your data repeats itself indefinitely. There are a number of excellent texts covering the matter.

## RE: FFT real amplitude value

Unless there is a single dominant frequency, each point of the "real" signal will be a sum of contributions of every wavelength in your FFT. In many cases, you need to run short-term FFTs or Wigner transforms if the frequencies or their amplitudes are not constant over time.

TTFN (ta ta for now)

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## RE: FFT real amplitude value

That pictures represent only two of the vast amount of periods I have, so I understand that I have to use a different window function for each and also a different resolution (fs/N) for each to catch the real amplitude of the frecuencies I want.

## RE: FFT real amplitude value

Luis,

You are currently using the default FFT windowing, that presumes that your data set automatically repeats itself with a period equal to the time interval of the data.

The term "windowing" refers to various means for tapering off the time series to avoid "leakage effects" or frequency content that does not represent your data.

There are a half-dozen "windows" to choose from, if the data is semi-periodic common to fluid movement, Hilbert-Huang transforms are commonly used, but that in itself is anouther topic.

## RE: FFT real amplitude value

And now I implement again the FFT with this signal, and this is the output.

It still doesn't represent the real value of the amplitude, any idea?

## RE: FFT real amplitude value

w(n)=0.5-0.5*cos(2*pi*n/N)

## RE: FFT real amplitude value

What does your autocorrelation look like?

## RE: FFT real amplitude value

Luis,

Interesting that the n=1 component has the magnitude of roughly twice your dc average...

the autocorrelation should show the dc component clearly.

## RE: FFT real amplitude value

Perhaps you should post some of your data.

TTFN (ta ta for now)

I can do absolutely anything. I'm an expert! https://www.youtube.com/watch?v=BKorP55Aqvg

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