1D discrete fourier transform

In summary, the problem involves creating a single slit with a width of 10 and height of 1 in a computing coursework. The DFT of this single slit function is then calculated and the real part and amplitude of the transform are plotted. The Fourier transform is approximated as a sum over discrete values, and the height of the slit is later halved and the calculation is repeated. The question also considers the possibility of using a phase function as the transmission function for the slit.
  • #1
Jezza
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Homework Statement


[/B]
This is a computing coursework problem. (There is a reasonably long theory preamble).

Create a single slit centred on the origin (the centre of your array) width 10 and height 1. The array containing the imaginary parts will be zero and the array containing the real parts will be 1 for the 10 elements at either side of the centre of the array and zero otherwise. (This constitutes the function f(x) in DFT equation shown below.)

Calculate the DFT of this single slit function and plot the real part and the amplitude of the transform.

Homework Equations


[/B]
The (1D) Fourier transform can be approximated as a sum over discrete values

[tex]
F(u) = \frac{1}{2N} \sum_{x=-N}^{N-1} \left( f(x) e ^ {-\frac{\pi i x u}{N}} \right)
[/tex]

Where [itex]i[/itex] is the imaginary unit.

The Attempt at a Solution



Where does the 'height' of the slit come into a 1D problem? I would write it off as ignorable information, but for the fact that I'm later asked to halve the height of the slit and repeat the calculation. The only thing I can think of is to halve the intensity of the source, but I can't help feeling the consequences of that are trivial and so not worth the trouble.
 
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  • #2
how would you set up a triangle function for the transmission of the slit
 
  • #3
I'm afraid I don't understand your question. The suggested representation of the slit's transmission is as a square function.
 
  • #4
I feel I might not have explained the question very well. The array represents a discrete real space where the element is the value of [itex]f(x_j)[/itex]. [itex]x_j = j\delta[/itex] where [itex]\delta[/itex] is an arbitrary grid spacing and [itex]j[/itex] is the index of the element.
 
  • #5
what if the transmission function was a phase function, how would you represent that
 
  • #6
I'm not quite sure what a phase function is, but assuming this is the kind of phase function you're talking about https://en.wiktionary.org/wiki/phase_function, I would map [itex]\theta[/itex] to array index in the same way the question leads me to map [itex]x[/itex] to array index.
 
  • #7
So to answer your previous question, I suppose I would have the centre of my array looking something like:

{0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 0.8, 0.6, 0.4, 0.2, 0.0}
 
  • #8
Jezza said:
So to answer your previous question, I suppose I would have the centre of my array looking something like:

{0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 0.8, 0.6, 0.4, 0.2, 0.0}
So, now you know how to adjust the amplitude transmitted by the slit. Sure, it is trivial, but the question posed now allows you to put a hole host of transmission functions into the DFT.
 
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1. What is a 1D discrete fourier transform?

A 1D discrete fourier transform is a mathematical operation that converts a signal in the time domain into its frequency domain representation. It is commonly used in signal processing and data analysis to identify the individual frequencies present in a signal.

2. How is a 1D discrete fourier transform different from a regular fourier transform?

A 1D discrete fourier transform operates on discrete, or sampled, data points, whereas a regular fourier transform operates on continuous data. This means that a 1D discrete fourier transform can only be applied to data that is evenly spaced in time.

3. What are some applications of 1D discrete fourier transform?

1D discrete fourier transform is commonly used in audio and image processing, as well as in areas such as astronomy and physics. It can be used to analyze signals and identify specific frequencies present in a signal, which can be useful in tasks such as noise reduction and pattern recognition.

4. How is a 1D discrete fourier transform calculated?

A 1D discrete fourier transform can be calculated using a mathematical algorithm called the fast fourier transform (FFT). This algorithm breaks down the signal into smaller segments and performs calculations on each segment to determine the frequency components. The results are then combined to create the frequency domain representation of the signal.

5. Are there any limitations to using a 1D discrete fourier transform?

Yes, there are some limitations to using a 1D discrete fourier transform. It can only be applied to data that is evenly spaced in time, and it assumes that the signal is periodic (repeats itself over time). It also has difficulty identifying frequencies that are close together, known as frequency resolution, and can be affected by noise in the data.

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