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Signal reconstruction with generalized sampling

Yamamoto, Kaoru LU ; Nagahara, Masaaki and Yamamoto, Yutaka (2018) 56th IEEE Annual Conference on Decision and Control, CDC 2017 In 56th IEEE Conference on Decision and Control, CDC, 2017 p.6253-6258
Abstract
This paper studies the problem of reconstructing continuous-time signals from discrete-time uniformly sampled data. This signal reconstruction problem has been studied by the authors in various contexts, and led to a new signal processing paradigm. The crux there is to employ a physically realizable signal generator model, and design an (sub)optimal filter via H-infinity (C+) optimal sampled-data control theory. The present paper extends this framework to the situation where sampling is more general having a generalized sampling kernel. It is more consistent with a more general framework, for example, wavelet signal expansion, and can lead to a more general applications. We give a general setup along with a solution via... (More)
This paper studies the problem of reconstructing continuous-time signals from discrete-time uniformly sampled data. This signal reconstruction problem has been studied by the authors in various contexts, and led to a new signal processing paradigm. The crux there is to employ a physically realizable signal generator model, and design an (sub)optimal filter via H-infinity (C+) optimal sampled-data control theory. The present paper extends this framework to the situation where sampling is more general having a generalized sampling kernel. It is more consistent with a more general framework, for example, wavelet signal expansion, and can lead to a more general applications. We give a general setup along with a solution via fast-sample/fast-hold approximation. A simulation is presented to illustrate the result. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
56th IEEE Conference on Decision and Control, CDC, 2017
pages
6 pages
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
56th IEEE Annual Conference on Decision and Control, CDC 2017
external identifiers
  • scopus:85046161591
DOI
10.1109/CDC.2017.8264601
language
English
LU publication?
yes
id
d99c17e4-324c-4eb4-aebf-54dda9ab7cd8
date added to LUP
2018-02-16 12:17:34
date last changed
2018-05-20 04:40:36
@inproceedings{d99c17e4-324c-4eb4-aebf-54dda9ab7cd8,
  abstract     = {This paper studies the problem of reconstructing continuous-time signals from discrete-time uniformly sampled data. This signal reconstruction problem has been studied by the authors in various contexts, and led to a new signal processing paradigm. The crux there is to employ a physically realizable signal generator model, and design an (sub)optimal filter via H-infinity (C+) optimal sampled-data control theory. The present paper extends this framework to the situation where sampling is more general having a generalized sampling kernel. It is more consistent with a more general framework, for example, wavelet signal expansion, and can lead to a more general applications. We give a general setup along with a solution via fast-sample/fast-hold approximation. A simulation is presented to illustrate the result.},
  author       = {Yamamoto, Kaoru and Nagahara, Masaaki and Yamamoto, Yutaka},
  booktitle    = {56th IEEE Conference on Decision and Control, CDC, 2017},
  language     = {eng},
  pages        = {6253--6258},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Signal reconstruction with generalized sampling},
  url          = {http://dx.doi.org/10.1109/CDC.2017.8264601},
  year         = {2018},
}