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- W2068755573 abstract "The paper presents a method of lake bottom sediment classification by artificial neural networks (ANN) using wideband echo signals. The samples to be classified were acquired by experiments at Lake Geneva. There are five types of sediments, namely, silt, rocks, pebbles, sand, and a mixture of sand and gravel. The pattern features are extracted from spectra of echo signals in subband energy expression. Different subband divisions for frequency‐domain feature extraction are compared and it is shown that the contant Q method provides better results in comparison with the constant bandwidth method. Using the constant Q method in association with a BP‐type locally connected neural network, 85.1% correct classification in average has been achieved for a testing data set. Wideband echo signals have outstanding superiority for classification in comparison with narrow‐band signals. It contains more information representing the physical and architectural features of targets. The neural network ultilizes the informa..." @default.
- W2068755573 created "2016-06-24" @default.
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- W2068755573 date "1995-01-01" @default.
- W2068755573 modified "2023-10-18" @default.
- W2068755573 title "Classification of lake bottom sediments by neural networks using wideband echo signals" @default.
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