Converting Frequency domain to time domain

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KATARI LOKESH
KATARI LOKESH 2020년 10월 16일
답변: Star Strider 2020년 10월 16일
Hello Experts,
I have csv file which contains Acceleration vs frequency domain data. How do I convert it into Acceleration vs time data using ifft function. Kindly please help me with this as I am new to this.
Thanks

답변 (3개)

Star Strider
Star Strider 2020년 10월 16일
How do I convert it into Acceleration vs time data using ifft function.
Thet depends entirely on what the data are. If you have only magnitude data, then inverting it is impossible.
If you have complex acceleration data, you can transform it using ifft (although it would be necessary to know what the data are and what the frequency vector is). If you have magnitude and phase data, you can convert them to complex frequency domain data, and then use ifft.
If you have ‘one-sided’ complex Fourier transform data (the frequency vector going from 0 to the Nyquist frequenncy), it would be necessary to flip a complex-conjugate version of that and concatenate it to the end of the data you have, then do the ifft. The time vector would then go from 0 the length of the reconstructed vector, with the sampling interval being 1/(2*Nyquist_frequency).

Durganshu
Durganshu 2020년 10월 16일
Hi Katari:
Proceed in the following way:
  1. Import the data from csv file using 'Import Data' tool or load or importdata function. Save the imported data in the form of a matrix.
  2. Use ifft(nameOfVariable) to perform the Inverse Afst fourier transform. If your data contains negative frequencies as well, then you'll haveto use fftshift as well. In that case, the format will be like this: fftshift(ifft(nameOfVariable).
Hope that helps.

KSSV
KSSV 2020년 10월 16일
data = csvread(myfile.csv) ; % also have a look on readtable, xlsread
a = data(:,1) ; % acceleration
f = data(:,2) ; % frequency
t = ifft(f) ; % read about ifft
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KATARI LOKESH
KATARI LOKESH 2020년 10월 16일
Thank you so much, but how do we plot it w.r.t time., my doubt is how to select time and plot this w.r.t time
Kindly suggest
Thanks
KSSV
KSSV 2020년 10월 16일
편집: KSSV 2020년 10월 16일

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