How do I interpret the units of my discrete convolution results?

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In summary, the conversation is about the process of discrete convolution and how to interpret the results in terms of units. The final "length" or domain of the convoluted function is determined by adding the lengths of the two original functions and subtracting one. It is suggested to center the convoluted data around the original data and to be cautious of contamination from padding when interpreting the results.
  • #1
csand
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I'm having trouble understanding the results of a discrete convolution. I have two functions:

1) High resolution spectra
2) Gaussian curve

The point of this operation for me is put the high resolution data in terms of the lower resolution represented by the guassian curve (filter)

I convolve one with the other, and the results seem reasonable. However I do not understand how to interpret my units.

It is my understanding that the final "length" or domain of my convoluted function will be:

len(1) + len(2) - 1.

So if I'm dealing in wavelengths or such units for my original function (1), how do I now map my new convoluted values to wavelengths? The domain of my convoluted function is bigger than either domain other the two original functions.

Do I "center" my convoluted data around the original data (1) and trim the values on the outside?

My understanding of this is limited so I hope the context to the question I've asked is understandable. I can try to elaborate if more information is required.

Thanks in advance,
Chris
 
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  • #2
I'm going to assume that l1 is the length of your spectrum, l2 the length of your Gaussian kernel, and that l2 is odd. In that case, point i in your original spectrum corresponds to point i + (l2-1)/2 in the convolved spectrum. (So, yes, if you center the old on the new, you'll have the right correspondence.) Also, the length you give, l = l1 + l2 - 1, indicates that the real data are being padded at the ends when you do your convolution. You should not trust the end of the convolved spectrum. Only the central l1 - l2 + 1 values of the convolved data are free from contamination by the padding.
 
  • #3
Thanks for the reply,
-C
 

Related to How do I interpret the units of my discrete convolution results?

1. What is a discrete convolution?

A discrete convolution is a mathematical operation that combines two discrete signals by multiplying them and summing the products. It is often used in signal processing and image filtering.

2. How is discrete convolution different from continuous convolution?

Discrete convolution is used for discrete signals, which are signals that are only defined at certain points in time or space. Continuous convolution is used for continuous signals, which are defined at every point in time or space.

3. What is the purpose of using discrete convolution?

Discrete convolution is commonly used for smoothing or blurring images, removing noise from signals, and detecting patterns or features in data. It is also used in engineering and physics for solving differential equations and simulating systems.

4. What are some common applications of discrete convolution?

Discrete convolution has a wide range of applications, including image and audio processing, speech recognition, pattern recognition, and time series analysis. It is also used in computer vision, medical imaging, and seismic data processing.

5. Can discrete convolution be easily computed?

Yes, discrete convolution can be easily computed using a variety of techniques, such as the direct method, the fast Fourier transform (FFT), or the overlap-add method. The choice of method depends on the specific application and the size of the signals being convolved.

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