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Surface identification by acoustic reflection characteristics using delay spectrometry and artificial neural networks

Pathirana, Pubudu N. and Zaknich, Anthony 1997, Surface identification by acoustic reflection characteristics using delay spectrometry and artificial neural networks, in Proceedings of the 1997 IEEE International Conference on Neural Networks, IEEE, Piscataway, N.J., pp. 31-36, doi: 10.1109/ICNN.1997.611630.

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Title Surface identification by acoustic reflection characteristics using delay spectrometry and artificial neural networks
Author(s) Pathirana, Pubudu N.ORCID iD for Pathirana, Pubudu N. orcid.org/0000-0001-8014-7798
Zaknich, Anthony
Conference name Neural Networks. IEEE International Conference (1997 : Houston, Texas)
Conference location Houston, Texas
Conference dates 1997/06/09 - 1997/06/12
Title of proceedings Proceedings of the 1997 IEEE International Conference on Neural Networks
Publication date 1997
Start page 31
End page 36
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Summary The identification of surfaces using incident sound waves is associated with a variety of different applications including, sonar, seabed scanning and medical ultrasound imaging. The biologically innocuous nature, applicability, and simplicity involved in generation and measurement, makes sound inherently a more attractive agent for most applications. Time delay spectrometry can be employed as a way of isolating a desired reflected signal from other reflections dramatically increasing the signal to noise ratio of the receiver of a neural network based classification system. A surface classification system with the analysis of its performance will be introduced in this paper as a successful implementation of the proposed methodology.
Language eng
DOI 10.1109/ICNN.1997.611630
Field of Research 090609 Signal Processing
Socio Economic Objective 0 Not Applicable
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©1997, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30096286

Document type: Conference Paper
Collection: School of Engineering
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