Fourier Transform (basic table lookup)

In summary, the conjugation rule states that if you want to transform a function from one domain to another, you need to use the inverse function.
  • #1
FrogPad
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0
I have a practice exam I'm going through, and I am stumped on one of the basic problems.

How is this a transform pair?

[tex] 10 X(jt) [/tex] <-----> [tex] 20 \pi x (-\omega) [/tex]

I don't see how one can make this relation. What is the [tex] 10 X (jt) [/tex].

thanks in advance
 
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  • #2
Divide both sides by 10 to simplify!

X(jt)<-----> 2pi x(-w)

Still seems wrong to me. The 2pi is normally included in the defn. of the FT.
What's j ?
 
  • #3
I'll post the exact question. I don't understand the X(jt) as I posted. Note: [tex] j = \sqrt{-1} [/tex]

437273077_752ab8a123_o.jpg
 
  • #4
OK, I got it- but I don't like the j notation. Everyone else writes

x(t) <-----> X(omega)

Define FT[x(t)]= Int[e^-iwt x(t)] dt
FT-1[X(w)]=(1/2pi)Int[e^iwt X(w)] dw

(you may use a different convention, but the prefactors of FT and FT-1 must multiply to (1/2pi) no matter what convention you use)Start with FT[x(t)]=X(w)

Int [ e^-iwt x(t)] dt = X(w)

substitute w=t', t=w'

Int [ e^-iw't' x(w') ] dw' =X (t')

which is the same as writing

Int [ e^-iwt x(w) ] dw =X (t)

Substitute w -> -w

-(1/2pi) Int [ e^iwt x(-w) ] dw =1/(2pi) X (t)

The LHS is the inverse FT, which I'll call FT-1

-FT-1 [x(-w)]=1/2pi X(t)

Take 2pi to other side
X(t)=-2pi FT-1 [x(-w)]

FT both sides

FT[ X(t) ] =-2pi x(-w)

X(t) <----> - 2pi x(-w)

I seem to have an extra minus sign- but who cares? You can have fun checking if it's me or the teacher who messed it up.

Edit- the minus sign comes from changing the limits from +ininity,-infinity to -infinity,+infinity in the integral, when w-> -w

You can then turn the integral 'the right way up' with the introduction of a minus sign.
That always trips me up.
 
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  • #5
The conjugation rule is :

[itex]
g\left( t \right) = \overline {f\left( t \right)} \Rightarrow G\left( \omega \right) = \overline {F\left( { - \omega } \right)}
[/itex]- Warren
 
  • #6
chroot said:
The conjugation rule is :

[itex]
g\left( t \right) = \overline {f\left( t \right)} \Rightarrow G\left( \omega \right) = \overline {F\left( { - \omega } \right)}
[/itex]


- Warren

Is that a proof?
 
  • #7
Oops, actually, I read this too quickly. The example given is a form of the inversion rule:

[itex]
g\left( t \right) = F\left( t \right) \Rightarrow G\left( \omega \right) = f\left( { - \omega } \right)
[/itex]

And no, it's not a "proof," it's an rule of the so-called Fourier transform calculus.

- Warren
 

What is the Fourier Transform?

The Fourier Transform is a mathematical tool used to analyze and decompose a complex signal into its individual frequency components. It converts a function of time into a function of frequency, allowing us to better understand the behavior of a signal.

What is the basic table lookup method for calculating the Fourier Transform?

The basic table lookup method for calculating the Fourier Transform involves using a pre-computed table of values to determine the frequency components of a signal. This method is commonly used for discrete signals and involves looking up the values of the signal at specific time intervals and multiplying them by the corresponding complex exponential values from the table.

What is the difference between the Fourier Transform and the Inverse Fourier Transform?

The Fourier Transform converts a signal from the time domain to the frequency domain, while the Inverse Fourier Transform converts a signal from the frequency domain back to the time domain. The two transforms are inverses of each other and allow us to switch between representations of a signal.

What are some applications of the Fourier Transform?

The Fourier Transform has many practical applications in various fields such as signal processing, image processing, and data analysis. It is used in audio and image compression, filtering and noise reduction, and in understanding the frequency components of a signal in order to make predictions or detect anomalies.

What are some limitations of the Fourier Transform?

The Fourier Transform is limited in its ability to accurately represent signals with sharp edges or sudden changes, as it assumes that the signal is periodic and continuous. It also cannot capture information about the time or order of events in a signal, making it less suitable for certain types of data analysis.

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