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O. Morean

dblp:80/1711 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1986
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Information theory · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information theory › signal processing › sampling theory
band-limited signal prediction
0.011986
Robust prediction of band-limited signals from past samples · IEEE Trans. Inf. Theory 1986
Information theory › signal processing › sampling theory
sampling and reconstruction
0.011986
Robust prediction of band-limited signals from past samples · IEEE Trans. Inf. Theory 1986
Information theory › estimation theory
signal estimation
0.011986
Robust prediction of band-limited signals from past samples · IEEE Trans. Inf. Theory 1986

Methods — techniques the papers use, named apart from their topics

nyquist-rate sampling · 0.0geometric convergence analysis · 0.0
YearPublicationVenuePosition
1986 Robust prediction of band-limited signals from past samples
abstract
For a complex-valued deterministic signal of finite energy band-limited to the normalized frequency band|w| \leq \piexplicit coefficients\{a_{kn}\}are found such that for anyTsatisfying0 < T \leq 1/2,\left| f(t)-\sum^{2n}_{k=1}a_{kn}f(t - kT)\right| \leq E_{f}\cdot \beta^{n}whereE_{f}is the signal energy and\beta \doteq 0.6863. Thus the estimate off(t)in terms of2npast samples taken at a rate equal to or in excess of twice the Nyquist rate converges uniformly at a geometric rate tof(t)on(- \infty , \infty). The suboptimal coefficients\{a_{kn}\}have the desirable property of being pure numbers independent of both the particular band-limited signal and of the selected sampling rate1/T. It is also shown that these same coefficients can be used to estimate the value ofx(t)of a wide-sense stationary random process in terms of past samples.
John L. Brown Jr., O. Morean
IEEE Trans. Inf. Theory2