Is there any good everyday application of a convolution of 3 one-dimensional functions? How does that work? Trying to get some intuition around it. Something that maybe looks like:
F ⚬ G ⚬ H= ∫ ∫ ∫f ⚬ g ⚬ h
(I imagine there’d be 3 params for the pairs fg, gh, fh...?)
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Crap I suspect it would actually be a 3x3 matrix to model what I’m thinking of due to autoconvolution and asymmetry. So
[ff fg fh
gf gg gh
hf hg hh]
Roughly: stream of 3 interacting time series events
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(1) response function for your instrument, (2) stimulus (approx. gaussian), (3) pump
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Radio signals are all sine waves modulated (i.e. convoluted) together. Spread spectrum convolutes a signal (f) via frequency modulation (g) across multiple frequencies (h).
This avoids interference or detection on a single spectrum: it would have to hit all of (h) to affect it.
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