Chaotic communications in the presence of noise

By R. Sherman, J. Gullicksen. 1993

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In Information Theory

Citations in the corpus

Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit (2017, methodology): “The Shannon–Nyquist sampling theorem (Nyquist 1928; Shannon 1949) limits the ability of deconvolution techniques in removing the effect of the PSF, particularly in the presence of noise (Magain, Courbin & Sohy 1998; Courbin 1999; Starck, Pantin & Murtagh 2002).”
Near-optimal sparse fourier representations via sampling (2002, unknown): “We restrict ourselves to the \classical" result: the Shannon sampling theorem [14].”
Fourier interpolation on the real line (2017, background): “The classical Whittaker-Shannon interpolation formula (see [19], [15]) states that if the Fourier transform f̂ is supported in [−w/2, w/2], then (1) f(x) = ∑”
Optimal binning of X-ray spectra and response matrix design (2016, background): “The Shannon (1949) sampling theorem states the following: Let f (x) be a continuous signal.”
Robust Recovery of Signals From a Structured Union of Subspaces (2008, background): “Undoubtedly, the sampling theorem that had the most impact on signal processing and communications is that associated with Whittaker, Kotel´nikov, and Shannon [1], [2].”
Wavelets and Subband Coding (2013, background): “For example, in a point-to-point transmission using a known, time-invariant channel such as a telephone line, one can design the best possible channel coding method to approach channel capacity, that is, achieve a rate R in bits/sec such that R ≤ C where C is the channel capacity [258].”
Nonuniform Sampling and Reconstruction in Shift-Invariant Spaces (2001, background): “[109] M. Unser, A. Aldroubi, and M. Eden, Polynomial spline signal approximations: Filter design and asymptotic equivalence with Shannon’s sampling theorem, IEEE Trans.”
Deconvolution with Correct Sampling (1997, background): “Sampling The sampling theorem (Shannon 1949, Press et al. 1989) determines the maximal sampling interval allowed so that an entire function can be reconstructed from sampled data.”
Structured Compressed Sensing: From Theory to Applications (2011, background): “This fundamental result is often attributed in the engineering community to Shannon–Nyquist [11], [12], although it dates back to earlier works by Whittaker [13] and Koteln´ikov [14].”
A Theory for Sampling Signals From a Union of Subspaces (2008, unknown): “Some concrete cases of H include: in Examples 2 and 3 for piecewise polynomials, H = L 2 (D), where D = [0] [1] (or [0] [1] 2 for 2-”