Signal reconstruction from sampled data using neural network

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

    研究成果: Conference contribution

    1 被引用数 (Scopus)

    抄録

    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.

    本文言語English
    ホスト出版物のタイトルNeural Networks for Signal Processing - Proceedings of the IEEE Workshop
    出版社Institute of Electrical and Electronics Engineers Inc.
    ページ707-715
    ページ数9
    2002-January
    ISBN(印刷版)0780376161
    DOI
    出版ステータスPublished - 2002
    イベント12th IEEE Workshop on Neural Networks for Signal Processing, NNSP 2002 - Martigny, Switzerland
    継続期間: 2002 9 6 → …

    Other

    Other12th IEEE Workshop on Neural Networks for Signal Processing, NNSP 2002
    CountrySwitzerland
    CityMartigny
    Period02/9/6 → …

    ASJC Scopus subject areas

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

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