Extra Credit-discrete Fourier transform

In summary, the conversation discusses using the discrete Fourier transform to find the normal coordinates for an equation derived in Problem Set 4, Problem 2. The solution for the problem can be found in the provided link and involves using an infinite Fourier transform and eigenfrequencies. The student is struggling with applying this concept and is seeking help.
  • #1
intelwanderer
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Homework Statement


Show that the normal coordinates for the equation we derived in Problem Set 4, Problem 2 are given by the discrete Fourier transform of an infinite series and the eigenfrequencies corresponding to each k.

http://www.ph.utexas.edu/~asimha/PHY315/Solutions-4.pdf

The solution to the problem we are solving for is given above-Problem 2

q'' = ω(2qj - qj+1 - qj-1)


Homework Equations



I'd assume we'd use the infinite Fourier transform given in the problem statement.

Ʃexp(2∏ijk)q(t) from j = negative∞ to ∞

That's all I can think of at the moment.


The Attempt at a Solution



OK, this is an extra credit problem, so naturally, it's harder than the rest of the HW. We don't have to use this kind of math on a regular basis, hence I'm more than a little lost on how to get started. I went with this over the TA weeks ago(I was curious on how you got the real solution), but was too dumb to write down what he said. I'm sure if I got a little help, I'll remember...
 
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  • #2
I know that the Fourier transform of an infinite series is given by the expression above, and that it can be used to solve equations such as the one we are dealing with. I think that the eigenfrequencies would come in as part of the Fourier transform, but I'm not sure how to go about applying them here. Any help would be appreciated!
 

1. What is a discrete Fourier transform (DFT)?

A discrete Fourier transform is a mathematical algorithm that converts a discrete-time signal into a sequence of complex numbers representing the signal's frequency components. It is commonly used in signal processing and data analysis to identify the frequencies present in a given signal.

2. How does a discrete Fourier transform work?

A DFT works by breaking down a signal into a series of sinusoids with different frequencies and amplitudes. It then calculates the complex amplitudes of each sinusoid, which represent the signal's frequency components. These amplitudes can be used to reconstruct the original signal.

3. What is the difference between a discrete Fourier transform and a fast Fourier transform?

A fast Fourier transform (FFT) is a more efficient algorithm for calculating a DFT. While a DFT has a time complexity of O(n^2), an FFT has a time complexity of O(nlogn). This makes it much faster for analyzing signals with large numbers of data points.

4. What are some applications of the discrete Fourier transform?

The DFT has many applications in various fields such as signal processing, image processing, data compression, and data analysis. It is commonly used to identify and filter out noise from signals, to analyze the frequency components of a signal, and to compress data by removing redundant information.

5. How is the discrete Fourier transform related to the continuous Fourier transform?

The discrete Fourier transform is a discrete version of the continuous Fourier transform, which is a mathematical operation that decomposes a continuous-time signal into its frequency components. The DFT is a discrete approximation of the continuous Fourier transform, and it becomes more accurate as the number of data points increases.

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