Calculating FFT for discrete values

AI Thread Summary
The discussion centers on calculating the Fast Fourier Transform (FFT) for a discrete set of values, specifically f(n) = (1, 3, 2). Participants explore the relationship between the Discrete Fourier Transform (DFT) and FFT, emphasizing that for a small number of points like three, using FFT may seem unnecessary but can demonstrate symmetry properties. The calculations show that F(1) and F(2) can be related through complex conjugates, illustrating the efficiency of FFT. Additionally, there is clarification on the periodic nature of the exponential function, confirming that e^(-j*2*pi/N * 2) equals e^(j*2*pi/N) due to the periodicity of the function. The conversation concludes with a reinforcement of the importance of understanding these mathematical concepts in 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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