On the characterization of ultrasonic transducers using pattern recognition

This correspondence is concerned with presenting a methodology for characterizing ultrasonic transducers using pattern recognition techniques. An apparatus was developed to collect focus, frequency spectrum, impulse response, and diameter parameters. Six different pattern recognition techniques were applied to classify 83 different transducers. These include: K-means, minimum distance, perceptron, potential function, cosine measure, and Bayes´ classifier. Moreover, two dimensionality reduction techniques, the K-L transform and the Fisher multiple discriminant, were applied to reduce the feature space. It was found that the K-means was the most successful classification algorithm. Very close behind in performance was the combination of K-L transform with minimum distance classifier. The latter reduced the feature space by 44 percent with only 6 percent misclassification error more than K-means. The potential function scheme did not converge to a solution in a time equal to 60 times what was required for the other algorithms. Results of the classification, dimensionality reduction, comparison and validation are presented to highlight the advantages and limitations of the investigated techniques