WPTER: wavelet packet transform for efficient pattern recognition of signals

In the present work, we propose a novel algorithm based on the Wavelet Packet Transform (WPT) for pattern recognition of signals, which operates both feature selection and classification at the same time: Wavelet Packet Transform for Efficient pattern Recognition of signals (WPTER). The distinctive characteristics of WPTER with respect to the previously proposed algorithms for the WPT-based classification of signals consist mainly of two aspects: (1) a Classification Ability criterion is introduced into the procedure for selection of the best discriminant basis; (2) the signals are reconstructed in the original domain by using only the selected wavelet coefficients, which allow for chemical interpretation of the results. gorithm was first tested on an artificial (simulated) set of signals, consisting of a number of subsequent peaks, partially overlapped to each other, with added noise and baseline drift, simulating a three-class system. Then, it was applied to a data set consisting of X-ray diffractograms on fired tiles subjected to different firing cycles, aiming at discriminating the different firing methods on the basis of the phase composition. In both cases, satisfactory classifications were achieved.