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Inverse filtering to undo a convolution blows up

A better approach would be to apply some method to solve problems on the form $$\min_v \|Mv - d\|$$ or possibly $$\min_v \|M(v+d) - d\|$$ Where $M$ is the Gaussian convolution operation, $d$ is the ...
mathreadler's user avatar
0 votes
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Maximally Decimated Polyphase Channelizer Help

I also posted this question on dsprelated.com and got a great answer. The signals are down-converted by the center frequency of each channel. So a tone at the center frequency will be a DC value with ...
rtclark's user avatar
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1 vote

piecewise linear sqrt in python and C output comparison

the numerical differences between your Python and C implementation might simply be that Python's floating point type is a double precision one, whereas your C uses single precision. but I dont know ...
Marcus Müller's user avatar
2 votes

piecewise linear sqrt in python and C output comparison

Well, before they close the question, there are better ways to compute the square root (and other transcendentals) that a big table of piecewise-continuous lines. Even if execution time is important. ...
robert bristow-johnson's user avatar
1 vote

Detecting and fixing clipped positive waveforms

This is possible to detect programmatically (python, numpy, librosa) Yes. There are bunch of different ways: If your signal is known to be DC free (which would be expected from most audio sources and ...
Hilmar's user avatar
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3 votes

Inverse filtering to undo a convolution blows up

However, in both cases, the result blows up to infinity when I take the IFFT. The result blows up before that, when you do division by 0: add a small constant to <...
Jdip's user avatar
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0 votes

Signal response amplitude depends on the time interval in simulation

Reading your question and comments, and testing your code, my understanding of your problem is that you do not understand why you need to normalise the transformed signal so that the amplitudes in the ...
Stephen's user avatar
  • 336
0 votes

How do you properly cut out negative frequencies from FFT of a real signal if it reduces sample size?

You mention magnitudes. You need to consider the complex numbers. For a real input vector, the $0^{th}$ bin will be real. Then for bins $0 < n < N/2$, the values will be complex, so each will ...
TimWescott's user avatar
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0 votes

How do you properly cut out negative frequencies from FFT of a real signal if it reduces sample size?

By removing the negative frequency components, you're not really losing any information because they are just the complex conjugate of the positive frequency components, so they're trivial to recover. ...
Stephen's user avatar
  • 336
2 votes
Accepted

What is the type of blurring in such an image?

Assuming we're dealing with a linear filter, we can use deconvolution to find the kernel. Usually, deconvolution operations estimate the original input image $f$ in the operation $f*h=g$, given the ...
Cris Luengo's user avatar
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