Classification of underwater signals using wavelet-based decompositions
By: Ozhan Duzenli
Publisher: Naval Postgraduate School; Available from National Technical Information Service
Published: 1998
Place of publication: Monterey, Calif; Springfield, Va
Language: eng
Number of pages: 166
Description: This thesis investigates the application of wavelet decompositions to classification applications. Two feature      extraction tools are considered: Local Discriminant Bases scheme (LDB) and Power method. Several      dimension reduction schemes including a newly proposed one called the Mean Separator neural network (MS      NN) are discussed. Two types of classifiers are investigated and compared: Classification Trees (CT) and      Back-propagation neural network (BP NN). Classification experiments conducted on synthetic and real-world      underwater signals show that: (1) the Power feature extraction method is more robust to time synchronization      issues than the LDB scheme is; (2) the MS NN scheme is a successful dimension reduction scheme that may be      used with both LDB and Power feature extraction methods; and (3) the BP NN is a more powerful classifier      than CT as it has fewer constraints than CT in partitioning the feature input space.
Notes: "June 1998."

Thesis advisor(s): Monique P. Farques, Ralph D. Hippenstiel.

Thesis (M.S. in Electrical Engineering) Naval Postgraduate School, June 1998.

Includes bibliographical references (p. 161-163)

Approved for public release; distribution unlimited.

Also available online.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat reader.

Turkish Navy author.

dk/dk cc:9116 9/2/98.