Signal reconstruction from sampled data using neural network

A. Sudou; P. Hartono; R. Saegusa; S. Hashimoto

doi:10.1109/NNSP.2002.1030082

Signal reconstruction from sampled data using neural network

A. Sudou, P. Hartono, R. Saegusa, S. Hashimoto

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

For reconstructing a signal from sampling data, the method based on Shannon's sampling theorem is usually employed. The reconstruction error appears when the signal does not satisfy the Nyquist condition. This paper proposes a new reconstruction method by using a linear perceptron and multilayer perceptron as FIR filter. The perceptron, which has weights obtained by learning when adapting the original signal, suppresses the difference between the reconstructed signal and the original signal even when the Nyquist condition does not stand. Although the proposed method needs weight data, the total data size is much smaller than the ordinary sampling method, as the most suitable reconstruction filter is exclusively adapted to the given sampling data.

Original language	English
Title of host publication	Neural Networks for Signal Processing - Proceedings of the IEEE Workshop
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	707-715
Number of pages	9
Volume	2002-January
ISBN (Print)	0780376161
DOIs	https://doi.org/10.1109/NNSP.2002.1030082
Publication status	Published - 2002
Event	12th IEEE Workshop on Neural Networks for Signal Processing, NNSP 2002 - Martigny, Switzerland Duration: 2002 Sept 6 → …

Other

Other	12th IEEE Workshop on Neural Networks for Signal Processing, NNSP 2002
Country/Territory	Switzerland
City	Martigny
Period	02/9/6 → …

ASJC Scopus subject areas

Electrical and Electronic Engineering
Artificial Intelligence
Software
Computer Networks and Communications
Signal Processing

Access to Document

10.1109/NNSP.2002.1030082

Cite this

Sudou, A, Hartono, P, Saegusa, R & Hashimoto, S 2002, Signal reconstruction from sampled data using neural network. in Neural Networks for Signal Processing - Proceedings of the IEEE Workshop. vol. 2002-January, 1030082, Institute of Electrical and Electronics Engineers Inc., pp. 707-715, 12th IEEE Workshop on Neural Networks for Signal Processing, NNSP 2002, Martigny, Switzerland, 02/9/6. https://doi.org/10.1109/NNSP.2002.1030082

@inproceedings{339716b410604bd28153d1fd712bae6e,

title = "Signal reconstruction from sampled data using neural network",

abstract = "For reconstructing a signal from sampling data, the method based on Shannon's sampling theorem is usually employed. The reconstruction error appears when the signal does not satisfy the Nyquist condition. This paper proposes a new reconstruction method by using a linear perceptron and multilayer perceptron as FIR filter. The perceptron, which has weights obtained by learning when adapting the original signal, suppresses the difference between the reconstructed signal and the original signal even when the Nyquist condition does not stand. Although the proposed method needs weight data, the total data size is much smaller than the ordinary sampling method, as the most suitable reconstruction filter is exclusively adapted to the given sampling data.",

author = "A. Sudou and P. Hartono and R. Saegusa and S. Hashimoto",

year = "2002",

doi = "10.1109/NNSP.2002.1030082",

language = "English",

isbn = "0780376161",

volume = "2002-January",

pages = "707--715",

booktitle = "Neural Networks for Signal Processing - Proceedings of the IEEE Workshop",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "12th IEEE Workshop on Neural Networks for Signal Processing, NNSP 2002 ; Conference date: 06-09-2002",

}

TY - GEN

T1 - Signal reconstruction from sampled data using neural network

AU - Sudou, A.

AU - Hartono, P.

AU - Saegusa, R.

AU - Hashimoto, S.

PY - 2002

Y1 - 2002

N2 - For reconstructing a signal from sampling data, the method based on Shannon's sampling theorem is usually employed. The reconstruction error appears when the signal does not satisfy the Nyquist condition. This paper proposes a new reconstruction method by using a linear perceptron and multilayer perceptron as FIR filter. The perceptron, which has weights obtained by learning when adapting the original signal, suppresses the difference between the reconstructed signal and the original signal even when the Nyquist condition does not stand. Although the proposed method needs weight data, the total data size is much smaller than the ordinary sampling method, as the most suitable reconstruction filter is exclusively adapted to the given sampling data.

AB - For reconstructing a signal from sampling data, the method based on Shannon's sampling theorem is usually employed. The reconstruction error appears when the signal does not satisfy the Nyquist condition. This paper proposes a new reconstruction method by using a linear perceptron and multilayer perceptron as FIR filter. The perceptron, which has weights obtained by learning when adapting the original signal, suppresses the difference between the reconstructed signal and the original signal even when the Nyquist condition does not stand. Although the proposed method needs weight data, the total data size is much smaller than the ordinary sampling method, as the most suitable reconstruction filter is exclusively adapted to the given sampling data.

UR - http://www.scopus.com/inward/record.url?scp=84953738891&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84953738891&partnerID=8YFLogxK

U2 - 10.1109/NNSP.2002.1030082

DO - 10.1109/NNSP.2002.1030082

M3 - Conference contribution

AN - SCOPUS:84953738891

SN - 0780376161

VL - 2002-January

SP - 707

EP - 715

BT - Neural Networks for Signal Processing - Proceedings of the IEEE Workshop

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 12th IEEE Workshop on Neural Networks for Signal Processing, NNSP 2002

Y2 - 6 September 2002

ER -

Signal reconstruction from sampled data using neural network

Abstract

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this