Convolution of gaussian functions

In summary: After applying the inverse Gaussian formula, the result should be:##f_s(\lambda) = \frac{1}{\sqrt{2}}e^{\frac{-\lambda^2}{4s}}##
  • #1
ElijahRockers
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Homework Statement


Recall that we have defined the Gaussian ##f_s## by ##f_s (t) = \sqrt{s}e^{-st^2}## and shown that ##\hat{f_s}(\lambda) = \frac{1}{\sqrt{2}}e^{\frac{-\lambda^2}{4s}}##.

Show that ##f_3 \ast f_6 (t) = \sqrt{\pi}f_{1/2}(t) = \sqrt{\pi/2}e^{-t^{2}/2}##

The Attempt at a Solution



Not sure what's wrong with my approach, but I'm getting ##i## in both of my attempts answers, and besides that my answers are no where near close to the correct answer. Each of the pages represents a single attempt. I first tried multiplying the Fourier transforms of both functions then taking the inverse, and when that didn't work, I tried using the definition of convolution.

Image is attached but resized is hard to read... full size is here
 

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  • #2
ElijahRockers said:

Homework Statement


Recall that we have defined the Gaussian ##f_s## by ##f_s (t) = \sqrt{s}e^{-st^2}## and shown that ##\hat{f_s}(\lambda) = \frac{1}{\sqrt{2}}e^{\frac{-\lambda^2}{4s}}##.

Show that ##f_3 \ast f_6 (t) = \sqrt{\pi}f_{1/2}(t) = \sqrt{\pi/2}e^{-t^{2}/2}##

The Attempt at a Solution



Not sure what's wrong with my approach, but I'm getting ##i## in both of my attempts answers, and besides that my answers are no where near close to the correct answer. Each of the pages represents a single attempt. I first tried multiplying the Fourier transforms of both functions then taking the inverse, and when that didn't work, I tried using the definition of convolution.

Image is attached but resized is hard to read... full size is here
On the left hand page, you're fine down to here:
Capture_ERockers_1.PNG

The line you have after is in error.
It would have been better pull out only ##\displaystyle\ e^{-2t^2} \,,\,## leaving an integrand of ##\displaystyle\ e^{\displaystyle-(9\tau^2-12\tau\,t+4t^2)} \ ## .

The exponent is the negative of a perfect square. Use a substitution to solve the integral.

On the right hand page, you have the following:
Capture_ERockers_2.PNG

Therefore, simply multiply ##\displaystyle\ \hat{f_3}(\lambda) \hat{f_6}(\lambda) \ ## . Then simply transform back using:
##\hat{f_s}(\lambda) = \frac{1}{\sqrt{2}}e^{\frac{-\lambda^2}{4s}}##​
in reverse.
 
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  • #3
Hmmm... transforming back using the inverse Gaussian formula should be rather straightforward, but I don't see where the pi comes from in the final answer.

EDIT: Also the teacher has made a correction to the problem, the answer should be ##f_3 \ast f_6 = \sqrt{\pi} f_2 (t) = \sqrt{\pi/2}e^{-2t^2}## which still doesn't make sense, but I'm inclined to believe he meant ##\sqrt{\pi} f_2 (t) = \sqrt{2\pi}e^{-2t^2}##
 
  • #4
ElijahRockers said:
Hmmm... transforming back using the inverse Gaussian formula should be rather straightforward, but I don't see where the pi comes from in the final answer.

EDIT: Also the teacher has made a correction to the problem, the answer should be ##f_3 \ast f_6 = \sqrt{\pi} f_2 (t) = \sqrt{\pi/2}e^{-2t^2}## which still doesn't make sense, but I'm inclined to believe he meant ##\sqrt{\pi} f_2 (t) = \sqrt{2\pi}e^{-2t^2}##
I forgot, transforming back, there is a normalization factor to consider.
 
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Related to Convolution of gaussian functions

1. What is the concept of convolution in mathematics?

Convolution is a mathematical operation that combines two functions to create a third function. It is often used in signal processing and image analysis to describe the output of a system when given an input.

2. How are Gaussian functions related to convolution?

Gaussian functions are often used as the input and output functions in convolution. This is because they have a bell-shaped curve that represents a wide range of natural phenomena, making them useful for modeling various processes.

3. What does the convolution of two Gaussian functions represent?

The convolution of two Gaussian functions represents the combined effect of the two functions on a given system. It can be thought of as a smoothing or blurring operation, where the output function is a blend of the two input functions.

4. How is the convolution of Gaussian functions calculated?

The convolution of two Gaussian functions is calculated by multiplying the two functions together and then integrating the result over all possible values. This integration can be done analytically or numerically, depending on the complexity of the functions.

5. What are some real-world applications of convolution of Gaussian functions?

The convolution of Gaussian functions has many practical applications, including image and signal processing, noise reduction, and data smoothing. It is also commonly used in statistics and probability to model natural phenomena and to estimate probabilities.

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