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dc.contributor.authorChakraborty, B.
dc.contributor.authorMahale, V.
dc.contributor.authorNavelkar, G.S.
dc.contributor.authorDesai, R.G.P.
dc.date.accessioned2008-02-12T04:38:06Z
dc.date.available2008-02-12T04:38:06Z
dc.date.issued2006
dc.identifier.citationOceans`06 IEEE Asia Pacific, 16-19 May 2006, Singapore, 4 pp.
dc.identifier.urihttp://drs.nio.org/drs/handle/2264/843
dc.description.abstractIn this paper seafloor classifications system based on artificial neural network (ANN) has been designed. The ANN architecture employed here is a combination of Self Organizing Feature Map (SOFM) and Linear Vector Quantization (LVQ1). Currently acquired echo-waveform data acquired using single beam echo-sounder from twelve seafloor sediment locations from central part of the western continental shelf of India is analyzed and performance of the classifier is presented in this paper.
dc.language.isoen
dc.publisherIEEE Singapore Section Secretariat
dc.rightsCopyright [2006]. It is tried to respect the rights of the copyright holders to the best of the knowledge. If it is brought to our notice by copyright holder that the rights are voilated then the item would be withdrawn.
dc.subjectechosoundind
dc.subjectacoustic equipment
dc.subjectechosounders
dc.subjectbackscatter
dc.subjectcontinental margins
dc.subjectocean floor
dc.subjectsediments
dc.subjectperformance assessment
dc.titleSeafloor classification using acoustic backscatter echo-waveform - Artificial neural network applications
dc.typeConference Article


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