Signal reconstruction from sampled data using neural network

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

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Citations (Scopus)


    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 languageEnglish
    Title of host publicationNeural Networks for Signal Processing - Proceedings of the IEEE Workshop
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Number of pages9
    ISBN (Print)0780376161
    Publication statusPublished - 2002
    Event12th IEEE Workshop on Neural Networks for Signal Processing, NNSP 2002 - Martigny, Switzerland
    Duration: 2002 Sept 6 → …


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

    ASJC Scopus subject areas

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


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