Mastering Fourier Transform: Solving a Tricky Integral with Expert Tips

In summary, the conversation discusses a problem with finding the Fourier transform of a function involving a Gaussian integral. The person posting had difficulty solving it and was seeking help. The solution given by the book is sqrt(pi)ae^(-a^2*w^2/4) and the person is wondering if anyone knows how to solve it by completing the square.
  • #1
RyanA1084
4
0
Hi all, I had this problem for homework and it stumped me. It's too late to get points for it, but I'd like to know for future reference. I posted in the homework help forum but figured I'd try here too.

Find the Fourier transform F(w)=integral from -infinity to infinity of f(t)e^(i*w*t)dt

f(t)=e^(-t^2/a^2)

i=sqrt(-1) w=omega=constant a=constant

This looks sort of like a gaussian integral:

integral of e^(-a*x^2)dx=sqrt(pi/a)

but I couldn't see how to do it...

The answer given by the book is sqrt(pi)ae^(-a^2*w^2/4)

Anyone know how to do this??
 
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  • #2
Complete the square..
 
  • #3


Hi there,

First of all, don't feel discouraged if you struggled with this problem. The Fourier transform can be a tricky concept to grasp, especially when it involves integrals. But with some expert tips, you'll be able to master it in no time.

To solve this integral, we can use the following property of the Fourier transform:

F(f(t)) = 1/√(2π) ∫f(t)e^(-iwt)dt

Substituting in the given values, we get:

F(f(t)) = 1/√(2π) ∫e^(-t^2/a^2)e^(-iwt)dt

Now, we can use the Gaussian integral you mentioned and substitute it in for the e^(-t^2/a^2) term. This gives us:

F(f(t)) = 1/√(2π) ∫sqrt(pi/a)e^(-(t^2/a^2 + iwt))dt

Next, we can use the property of exponential functions that e^(ab) = (e^a)^b. This gives us:

F(f(t)) = 1/√(2π) ∫sqrt(pi/a)e^(-(t/a + iw)^2)dt

Now, we can use the substitution u = t/a + iw and du = dt/a. This gives us:

F(f(t)) = 1/√(2π) ∫sqrt(pi/a)e^(-u^2)du

And finally, we can use the Gaussian integral again to solve this integral. This gives us the final result:

F(f(t)) = sqrt(pi)ae^(-a^2*w^2/4)

I hope this helps and gives you a better understanding of how to approach Fourier transforms. Remember to always break down the problem into smaller, more manageable steps and use properties and substitutions to your advantage. Keep practicing and you'll become a master in no time!
 

FAQ: Mastering Fourier Transform: Solving a Tricky Integral with Expert Tips

What is the Fourier Transform and why is it important?

The Fourier Transform is a mathematical tool used to analyze the frequency components of a signal. It allows us to break down a complex signal into simpler sinusoidal components, making it easier to understand and manipulate. It is important in a variety of fields such as signal processing, image and audio analysis, and quantum mechanics.

How does the Fourier Transform work?

The Fourier Transform works by decomposing a signal into a sum of sinusoidal functions with different frequencies, amplitudes, and phases. This is done by taking the integral of the signal multiplied by a complex exponential function. The resulting function gives us the frequency components of the original signal.

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. In other words, the Fourier Transform breaks down a signal into its frequency components, while the Inverse Fourier Transform combines these components to recreate the original signal.

What are some real-world applications of the Fourier Transform?

The Fourier Transform has many practical applications, including image and audio compression, speech recognition, data analysis, and filtering. It is also widely used in medical imaging, radar and sonar systems, and astronomy.

Are there any limitations or drawbacks to using the Fourier Transform?

The Fourier Transform has some limitations, such as assuming that the signal is periodic and continuous, which may not always be true in real-world scenarios. It also requires a large amount of computation for complex signals. Additionally, the Fourier Transform cannot provide information about the time at which a signal occurs, only the frequency components. However, these limitations can be overcome by using other variations of the Fourier Transform or alternative methods of signal analysis.

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