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Methodologies for characterizing ultrasonic transducers using neural network and pattern recognition techniques

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2 Author(s)
Obaidat, M.S. ; Dept. of Electr. Eng., City Coll. of New York, NY, USA ; Abu-Saymeh, D.S.

System hardware for characterizing ultrasonic transducers and the associated data acquisition software and characterizing algorithms are considered. The hardware consists mainly of a workstation computer, a receiver/pulser with gated peak detector, various monitoring devices, a microcomputer-based 3D positioning controller, and an A/D converter. The characterization algorithms are based on neural network and pattern recognition techniques. It is found that artificial neural network techniques provide far better classification results than the pattern recognition techniques. A multilayer backpropagation neural network which provides a classification accuracy of 94% is developed. Two other multilayer neural networks-sum-of-products and a newly devised neural network called hybrid sum-of-products-have a classification accuracy of 90% and 93%, respectively. The most successful pattern recognition technique for this application is found to be the perceptron, which provides a classification accuracy of 77%

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Industrial Electronics, IEEE Transactions on  (Volume:39 ,  Issue: 6 )