Intuition for imaginary part of Fourier Transformation?

In summary, the Fourier transform of a function where the result in the frequency domain has imaginary components can be represented using an eix term which includes a real and imaginary part. The real part represents the signal while the imaginary part represents the superposition of all the waves. The superposition of waves will give you back a real signal because there are both "positive" and "negative" frequencies, and they have a certain symmetry.
  • #1
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Hi there,

I'm having trouble understanding the Fourier transform of a function where the result in the frequency domain has imaginary components.

For example, if you take the Fourier transform of Sin[t] , the result is
Code:
I Sqrt[\[Pi]/2] DiracDelta[-1 + \[Omega]] - 
 I Sqrt[\[Pi]/2] DiracDelta[1 + \[Omega]]

What does this mean? I can't really graph it, so I am having trouble understanding it.

I can grasp a regular Fourier transform; it simply tells you what components are making up your signal wave. But when an imaginary I is thrown in there, what happens? How can the superposition of all those waves give you the real signal wave?

Does anybody have an intuition they could share?
 
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  • #2
Suppose you have a certain (real) signal which you can represent as a sum of sines and cosines. Using Euler's formula, you can combine each pair of cosine and sine terms into an eix term. Instead of two real coefficients, now you will have a real and imaginary part. One reason to do it this way is that it actually makes the algebra easier!

To inspect the Fourier transform, you can just graph the real and imaginary parts separately (just as you would have graphed the sine and cosine coefficients separately) or you can graph the magnitude and phase separately.

The superposition of waves will give you back a real signal because there are both "positive" and "negative" frequencies, and they have a certain symmetry: if A(x) is the coefficient at frequency x, then A(x) = conj(A(-x)), i.e., if A(x) = a + ib then A(-x) = a - ib.

Check out what you get when you try to put the signal back together, remembering also that cos(x) = cos(-x) and -sin(x) = sin(-x):

(a + bi) * eix + (a - bi) * e-ix = (a + bi) * (cos x + i sin x) + (a - bi) * (cos x - i sin x) = …
 
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  • #3
Wow, that was a terrific explanation. You should consider becoming a teacher!

I believe I have a better understanding now, thanks friend!
 
  • #4
You're welcome!
 

1. What is the imaginary part of the Fourier Transformation?

The imaginary part of the Fourier Transformation is a mathematical concept that represents the complex numbers that are part of the Fourier series. It is denoted by the letter "i" and is used to create a mathematical representation of oscillating signals.

2. Why is the imaginary part important in Fourier Transformation?

The imaginary part is important in Fourier Transformation because it allows for the representation of both amplitude and phase information in a single complex number. This allows for a more efficient and concise representation of signals, making it easier to analyze and manipulate them.

3. How is the imaginary part calculated in Fourier Transformation?

The imaginary part is calculated by taking the Fourier Transform of a signal and separating it into its real and imaginary components. The imaginary part is then calculated using the formula Im(F(x)) = (1/2i)(F(x) - F*(x)), where F(x) is the complex number representation of the signal and F*(x) is the complex conjugate of F(x).

4. What is the relationship between the imaginary part and the Fourier Transform of a signal?

The imaginary part is directly related to the Fourier Transform of a signal as it is a part of the complex number representation of the signal. The Fourier Transform can be thought of as a mapping between the time domain and frequency domain, and the imaginary part represents the frequency components of the signal.

5. How is the imaginary part used in practical applications of Fourier Transformation?

The imaginary part of Fourier Transformation is used in various practical applications such as signal processing, image and audio compression, and filtering. It allows for the separation of signals into their frequency components, making it easier to analyze and manipulate them for various purposes. It is also used in fields such as engineering, physics, and mathematics for solving differential equations and studying oscillations.

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