Discrimination Of Cylinders With Different Wall Thicknesses Using Neural Networks And Simulated Dolphin Sonar Signals

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...

Full description

Bibliographic Details
Main Authors: Lars Nonboe Andersen, Whitlow Au, Jan Larsen, Lars Kai Hansen
Other Authors: The Pennsylvania State University CiteSeerX Archives
Format: Text
Language:English
Published: 1999
Subjects:
Killer Whale
Online Access:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.54.1765
id ftciteseerx:oai:CiteSeerX.psu:10.1.1.54.1765
record_format openpolar
spelling ftciteseerx:oai:CiteSeerX.psu:10.1.1.54.1765 2023-05-15T17:03:29+02:00 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 The Pennsylvania State University CiteSeerX Archives 1999 application/postscript http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.54.1765 en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.54.1765 Metadata may be used without restrictions as long as the oai identifier remains attached to it. ftp://eivind.imm.dtu.dk/dist/1999/nonboe.nnsp99.ps.gz text 1999 ftciteseerx 2016-01-08T10:59:26Z 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). Text Killer Whale Unknown
institution Open Polar
collection Unknown
op_collection_id ftciteseerx
language English
description 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).
author2 The Pennsylvania State University CiteSeerX Archives
format Text
author Lars Nonboe Andersen
Whitlow Au
Jan Larsen
Lars Kai Hansen
spellingShingle Lars Nonboe Andersen
Whitlow Au
Jan Larsen
Lars Kai Hansen
Discrimination Of Cylinders With Different Wall Thicknesses Using Neural Networks And Simulated Dolphin Sonar Signals
author_facet Lars Nonboe Andersen
Whitlow Au
Jan Larsen
Lars Kai Hansen
author_sort Lars Nonboe Andersen
title Discrimination Of Cylinders With Different Wall Thicknesses Using Neural Networks And Simulated Dolphin Sonar Signals
title_short Discrimination Of Cylinders With Different Wall Thicknesses Using Neural Networks And Simulated Dolphin Sonar Signals
title_full Discrimination Of Cylinders With Different Wall Thicknesses Using Neural Networks And Simulated Dolphin Sonar Signals
title_fullStr Discrimination Of Cylinders With Different Wall Thicknesses Using Neural Networks And Simulated Dolphin Sonar Signals
title_full_unstemmed Discrimination Of Cylinders With Different Wall Thicknesses Using Neural Networks And Simulated Dolphin Sonar Signals
title_sort discrimination of cylinders with different wall thicknesses using neural networks and simulated dolphin sonar signals
publishDate 1999
url http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.54.1765
genre Killer Whale
genre_facet Killer Whale
op_source ftp://eivind.imm.dtu.dk/dist/1999/nonboe.nnsp99.ps.gz
op_relation http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.54.1765
op_rights Metadata may be used without restrictions as long as the oai identifier remains attached to it.
_version_ 1766057359505883136

Similar Items