Calculating FFT for discrete values

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SUMMARY

The discussion focuses on calculating the Fast Fourier Transform (FFT) for a discrete set of values: f(n) = (1, 3, 2). The participants clarify that while calculating the Discrete Fourier Transform (DFT) is straightforward, using FFT for only three points may be unnecessary. The symmetry property of the FFT is emphasized, particularly the relationship F(N-k) = F(k)*, which allows for simplifications in calculations. The periodic nature of the exponential function is also discussed, confirming the equivalence of certain angles in the context of FFT calculations.

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  • Understanding of Discrete Fourier Transform (DFT)
  • Familiarity with Fast Fourier Transform (FFT) algorithms
  • Knowledge of complex numbers and Euler's formula
  • Basic principles of signal processing
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Homework Statement


We have a set of values: f(n)=f(0,1,2)=(1,3,2)

so f(0)=1, f(1)=3, f(2)=2, where n=0,1,2the number of values N=3

The question is to calculate the FFT of this signal, the Fourier spectrum the power spectrum and phase spectrum.

I'm not sure concerning FFT. And also about the Fourier Spectrum what should I use to conduct it? the DFT or FFT?

Homework Equations


[/B]
The calculation of DFT for these values.

The Attempt at a Solution



I personally calculated the Discrete Fourier Transform in this way:

$$ F[n]=\sum_{k=0}^{N-1}f(k)e^{-j \frac{2\pi}{N}nk} $$

so I had a set of values: F(0),F(1),F(2)
 
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For only 3 points, it seems unnecessary to try to do the FFT. However, the point of the FFT is to take advantage of symmetry.
F(1) = f(0) +f(1) e^{-j 2pi/N * 1 }+f(2) e^{-j 2pi/N * 2 }
F(2) = f(0) +f(1) e^{-j 2pi/N * 2 }+f(2) e^{-j 2pi/N * 4 }= f(0) +f(1) e^{-j 2pi/N * 2 }+f(2) e^{-j 2pi/N * 1 }
So whay you really have is (for real-valued inputs):
## F(0) = \pmatrix{ f(0), f(1), f(2) } \pmatrix{ 1\\1\\1} , ##
## F(1) = \pmatrix{ f(0), f(1), f(2) } \pmatrix{ 1\\ e^{-j 2\pi/N }\\ e^{ j 2\pi/N }}##
## F(2) = \pmatrix{ f(0), f(1), f(2) } \pmatrix{ 1\\ e^{j 2\pi/N }\\ e^{- j 2\pi/N }} = F(1) ^* ##
As I said, for 3 values, this is more a demonstration of the concept that F(N-k) = F(k)*, which should save you about 1 calculation by just replacing F(1) with its conjugate for F(2).
 
RUber said:
For only 3 points, it seems unnecessary to try to do the FFT. However, the point of the FFT is to take advantage of symmetry.
F(1) = f(0) +f(1) e^{-j 2pi/N * 1 }+f(2) e^{-j 2pi/N * 2 }
F(2) = f(0) +f(1) e^{-j 2pi/N * 2 }+f(2) e^{-j 2pi/N * 4 }= f(0) +f(1) e^{-j 2pi/N * 2 }+f(2) e^{-j 2pi/N * 1 }
So whay you really have is (for real-valued inputs):
## F(0) = \pmatrix{ f(0), f(1), f(2) } \pmatrix{ 1\\1\\1} , ##
## F(1) = \pmatrix{ f(0), f(1), f(2) } \pmatrix{ 1\\ e^{-j 2\pi/N }\\ e^{ j 2\pi/N }}##
## F(2) = \pmatrix{ f(0), f(1), f(2) } \pmatrix{ 1\\ e^{j 2\pi/N }\\ e^{- j 2\pi/N }} = F(1) ^* ##
As I said, for 3 values, this is more a demonstration of the concept that F(N-k) = F(k)*, which should save you about 1 calculation by just replacing F(1) with its conjugate for F(2).

That is a very good and summarized answer. Thank you RUber :)
 
But I still don't understand how e^(-j*2*pi/N *2)= e^(j*2*pi/N). Are these two angles identical??
 
This is the symmetry of the function. ##e^{ix}## is 2pi periodic, so ##e^{ix}=e^{ix+i 2\pi}##
##e^{-j2\pi/N 2+j 2\pi}= e^{-j4\pi/3 +j6\pi/3}=e^{j*2*pi/N}.##
 
RUber said:
This is the symmetry of the function. ##e^{ix}## is 2pi periodic, so ##e^{ix}=e^{ix+i 2\pi}##
##e^{-j2\pi/N 2+j 2\pi}= e^{-j4\pi/3 +j6\pi/3}=e^{j*2*pi/N}.##

OK, thank you again.
 

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