Discrimination of Cylinders with Different Wall Thicknesses using Neural Networks and Simulated Dolphin Sonar Signals

Lars Nonboe Andersen, Whitlow Au, Jan Larsen, Lars Kai Hansen

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    Abstract

    This paper describes a method integrating neural networks into a system for recognizing underwater objects. The system is based on a combination of simulated dolphin sonar signals, simulated auditory filters and artificial neural networks. The system is tested on a cylinder wall thickness difference experiment and demonstrates high accuracy for small wall thickness differences. Results from the experiment are compared with results obtained by a false killer whale (pseudorca crassidens).
    Original languageEnglish
    Title of host publicationProceedings of the IEEE Workshop on Neural Networks for Signal Processing IX
    Place of PublicationPiscataway
    PublisherIEEE
    Publication date1999
    Pages477-486
    ISBN (Print)0-7803-5673-x
    DOIs
    Publication statusPublished - 1999
    Event1999 IEEE Workshop on Neural Networks for Signal Processing IX - Madison, WI, United States
    Duration: 23 Aug 199925 Aug 1999
    Conference number: 9
    http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6375

    Workshop

    Workshop1999 IEEE Workshop on Neural Networks for Signal Processing IX
    Number9
    CountryUnited States
    CityMadison, WI
    Period23/08/199925/08/1999
    Internet address

    Bibliographical note

    Copyright 1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

    Cite this

    Andersen, L. N., Au, W., Larsen, J., & Hansen, L. K. (1999). Discrimination of Cylinders with Different Wall Thicknesses using Neural Networks and Simulated Dolphin Sonar Signals. In Proceedings of the IEEE Workshop on Neural Networks for Signal Processing IX (pp. 477-486). IEEE. https://doi.org/10.1109/NNSP.1999.788167