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Graph signal sampling under arbitrary signal priors

Graph signal sampling under arbitrary signal priors
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Compressive Sampling for Signal Classification

2006 Fortieth Asilomar Conference on Signals, Systems and Computers, 2006
Compressive sampling (CS), also called compressed sensing, entails making observations of an unknown signal by projecting it onto random vectors. Recent theoretical results show that if the signal is sparse (or nearly sparse) in some basis, then with high probability such observations essentially encode the salient information in the signal.
Haupt, J., Castro, R.M., Nowak, R., Fudge, G., Yeh, A.   +4 more
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Signal reconstruction from the undersampled signal samples

Optics Communications, 2006
Abstract It is well-known from the celebrated Shannon sampling theorem for bandlimited signals that if the sampling rate is below the Nyquist rate, aliasing takes place and the original signal cannot be reconstructed back by simply passing the signal samples through an ideal lowpass filter. However, researchers such as Stern and Gori have shown the
Kamalesh Kumar Sharma, Shiv Dutt Joshi
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