Fourier Transform Power Spectrum

In summary, the conversation discusses the results of transforming a sine wave at 10Hz with amplitude 1. The transformed plot shows a spike at 10Hz with amplitude 0.5. As the amplitude of the sine wave is varied, the amplitude of the spike also varies according to the formula A' = A^2/2. This is because power is proportional to A^2 and is averaged over troughs and crests, hence the division by 2. The conversation also mentions the use of LabVIEW to compute the averaged auto power spectrum of a time signal, and references Parseval's theorem for further understanding.
  • #1
cscott
782
1
Input: sine wave at 10Hz, amplitude 1.

After the transform the plot has a spike at 10Hz with amplitude 0.5. If I vary the amplitude of the sine wave I get:

sine amp. - FT spike amp.
1 - 0.5
2 - 2
4 - 8

So it seems A' = A^2/2

Is this because power is proportional to A^2 and it is averaged over trough/crest so division by 2?
 
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  • #2
Are you adding real and imaginary parts?

The power should be the same in both domains.
 
  • #3
Sorry I think I asked my question poorly.

I'm doing this in a lab using LabVIEW and it's doing the FT. When I input a sine wave (vs time) with varied amplitude 'A', I get an output spike of amplitude (A^2)/2 centered at some fixed frequency. Is this because [itex]P \propto A^2[/itex]? Is the half for 'average'?

I'm just trying to make sense of what this VI is doing. All I know is "computes the averaged auto power spectrum of time signal".

Does my data still make no sense?

I'm not directly dealing with imaginary parts...
 
Last edited:
  • #4
OK- you probably forgot to add the power in -ve and +ve frequencies.
 

What is a Fourier Transform Power Spectrum?

A Fourier Transform Power Spectrum is a mathematical tool used in signal processing to analyze the frequency content of a signal. It converts a signal from its original time domain into the frequency domain, allowing the identification of specific frequencies present in the signal.

How is a Fourier Transform Power Spectrum calculated?

A Fourier Transform Power Spectrum is calculated by taking the Fourier transform of a signal, squaring the result, and then plotting the squared magnitude against frequency. This process is repeated for each time interval in the signal, resulting in a graph that shows the power of each frequency present in the signal.

What is the difference between a Fourier Transform and a Fourier Transform Power Spectrum?

A Fourier Transform calculates the amplitude of each frequency component in a signal, while a Fourier Transform Power Spectrum calculates the power of each frequency component. Power takes into account both the amplitude and the frequency of a signal, making the Fourier Transform Power Spectrum a more accurate representation of the signal's frequency content.

What are the applications of a Fourier Transform Power Spectrum?

A Fourier Transform Power Spectrum has a wide range of applications, including signal processing, image processing, speech recognition, and data compression. It is also commonly used in fields such as physics, engineering, and neuroscience for analyzing and interpreting complex signals.

What are the limitations of a Fourier Transform Power Spectrum?

A Fourier Transform Power Spectrum assumes that the signal being analyzed is stationary, meaning that its frequency content does not change over time. It also assumes that the signal is a linear combination of sinusoidal components. These assumptions may not hold true for all signals, leading to potential inaccuracies in the results.

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