Local eigenfunctions based suboptimal wavelet packet representation of contaminated chaotic signals

We report a suboptimal wavelet packet representation (SWPR) of signals eman ating from a chaotic attractor contaminated by low levels of noise. Our met hod-geared towards choosing a suboptimal scaling function to parsimoniously represent the signal-involves extracting local eigenfunctions using artifi cial ensembles generated from a psendo-probability space, and using the ext racted local eigenfunctions to develop a suboptimal scaling function. The a pplication of our novel representation method to actual acoustic emission ( AE) signals, sampled as time-series data (TSD) from the turning process, re veals the superiority of these methods over the existing signal representat ions.