Browsing by Author "De, C."
Now showing items 1-5 of 5
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Acoustic characterization of seafloor sediment employing a hybrid method of neural network architecture and fuzzy algorithm
De, C.; Chakraborty, B. (IEEE, 2009)Seafloor sediment is characterized acoustically in the western continental shelf of India using the echo features extracted from normal incidence single-beam echo sounder backscatter returns at 33 and 210 kHz. The seafloor ... -
Estimation of mean grain size of seafloor sediments using neural network
De, C.; Chakraborty, B. (Springer, 2012)The feasibility of an artificial neural network based approach is investigated to estimate the values of mean grain size of seafloor sediments using four dominant echo features, extracted from acoustic backscatter data. ... -
Model-based seafloor characterization employing multi-beam angular backscatter data - A comparative study with dual-frequency single beam
Haris, K.; Chakraborty, B.; De, C.; Desai, R.G.P.; Fernandes, W.A. (Acoustical Society of America, 2011)Sediment geoacoustic inversion results are estimated employing a multi-beam (MB) echo-sounding system operable at 95 kHz. To characterize the western continental shelf of India (off Goa) seafloor, MB backscatter signals ... -
Preference of echo features for classification of seafloor sediments using neural networks
De, C.; Chakraborty, B. (Springer, 2010)Selection of a set of dominant echo features to classify seafloor sediments using a multilayer perceptron neural network is investigated at two acoustic frequencies (33 and 210 kHz). Several sets of inputs with different ... -
Seafloor characterization using time-dependent acoustic backscatter: Study at Arabian Sea
Chakraborty, B.; De, C. (IEEE, 2008)This paper presents application of `inversion modeling` technique to echo shape data acquired shallow water regions off Goa. The estimated sediment and seafloor roughness parameter for two different frequencies (33 and 210 ...