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Digital Signal Processing
Article . 2022 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
SSRN Electronic Journal
Article . 2022 . Peer-reviewed
Data sources: Crossref
https://dx.doi.org/10.48550/ar...
Article . 2022
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
DBLP
Article . 2022
Data sources: DBLP
DBLP
Article . 2022
Data sources: DBLP
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Signal Reconstruction from Noisy Multichannel Samples

Authors: Dong Cheng; Xiaoxiao Hu; Kit Ian Kou;

Signal Reconstruction from Noisy Multichannel Samples

Abstract

We consider the signal reconstruction problem under the case of the signals sampled in the multichannel way and with the presence of noise. Observing that if the samples are inexact, the rigorous enforcement of multichannel interpolation is inappropriate. Thus the reasonable smoothing and regularized corrections are indispensable. In this paper, we propose several alternative methods for the signal reconstruction from the noisy multichannel samples under different smoothing and regularization principles. We compare these signal reconstruction methods theoretically and experimentally in the various situations. To demonstrate the effectiveness of the proposed methods, the probability interpretation and the error analysis for these methods are provided. Additionally, the numerical simulations as well as some guidelines to use the methods are also presented.

31 pages, 6 figures

Related Organizations
Keywords

FOS: Computer and information sciences, Mathematics - Classical Analysis and ODEs, Computer Science - Information Theory, Information Theory (cs.IT), Classical Analysis and ODEs (math.CA), FOS: Mathematics, 39A12, 11R52, 41A05, 12E05

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green