Extracting periodicity with Fast Fourier Transform

In summary, the individual is seeking help with extracting the period from a complex discrete signal using a Matlab script. However, the values obtained from the script are incorrect due to a systematic bias. It is suggested to divide by the sample frequency, as there is an inverse relation between period and frequency. However, this results in extremely large values and the individual is unsure if they are using the correct axis values. A suggestion is made to use the formula Period=(Length of the sequence)*dt/index.
  • #1
Ebert87
2
0
Hello all,

I want to extract the period out of a complex discrete signal.

Currently I have the Matlabscript of the attachement.

However, the values I get out of this script are not correct. There is some kind of systematic bias in it.

I think it has something to do with index * samplefrequency.

Anyone an idea what could be wrong?

Thanks in advance!
 

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  • #2
Right, you should divide by sample frequency. Period and frequency have inverse relation.
 
  • #3
Hassan2 said:
Right, you should divide by sample frequency. Period and frequency have inverse relation.

Thanks, I tried it, but now the values are extremely big. Maybe I am using the y-axis value of the frequency domain graph, instead of the x-axis value (which says something about thefrequency of the original signal, right?)?

Getting confused now :confused:
 
  • #4
What are the values of index and Samplefreq?

I think you should use this:

Period=(Length of the sequence)*dt/index
 

1. What is Fast Fourier Transform (FFT)?

Fast Fourier Transform (FFT) is a mathematical algorithm used to extract periodicity from a signal. It is a fast and efficient way to convert a signal from its original domain (typically time or space) to a representation in the frequency domain.

2. How does FFT work?

FFT works by breaking down a signal into its component frequencies. It does this by dividing the signal into smaller segments, performing a series of complex mathematical calculations on each segment, and then combining the results to create a spectrum of the signal's frequencies.

3. What is the significance of extracting periodicity with FFT?

Extracting periodicity with FFT is important in many fields of science and engineering, as it allows us to analyze and understand signals in the frequency domain. This can help us identify patterns, trends, and cycles in data, which can provide valuable insights and aid in making predictions.

4. In what applications is FFT commonly used?

FFT is commonly used in a wide range of applications, including signal processing, audio and video processing, image processing, data compression, and scientific computing. It is also used in fields such as astronomy, physics, and biology for analyzing data from various sources.

5. Are there any limitations to using FFT?

While FFT is a powerful tool for extracting periodicity, it does have some limitations. It assumes that the signal is stationary (i.e. it does not change over time) and that the signal is periodic over the entire time period. It also requires a certain amount of data to be effective, meaning that shorter signals may not provide accurate results.

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