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A Novel System for Seafloor Classification Using Artificial Neural Network (ANN) Hybrid Layout with the Use of Unprocessed Multi-Beam Backscatter Data.

Implementing Organization

CSIR-National Institute of Oceanography (CSIR-NIO), Goa

Project Overview

A system for on-line (i.e. real-time) seafloor classification uses multi-beam acoustic device mounted beneath a ship's hull attached to an r.m.s. estimator module through a beam former module.The multi-beam backscatter r.m.s. data comprises a back. The novel system for seafloor classification uses artificial neural network (ANN) hybrid layout with the use of unprocessed multi-beam backscatter data. Its a real-time seafloor roughness classifier using backscatter data after training the self-organized mapping (SOM) network and learning vector quantization (LVQ) network wherein, the system has the unique capability for the combined use of unsupervised SOM followed by supervised LVQ to achieve a highly improved performance in the roughness classification.
Funding Organization
Funding Organization

Quick Information
Area of Research
Physical Sciences
Status
Technology Demonstration
Output
No. of Research Paper
00
Technologies (If Any)
00
No. of PhD Produced
N/A
Startup (If Any)
00
No. of Patents
Filed :00
Grant :00
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