Discrimination and clustering of earthquakes and explosions based on NDWT

A New Signal Processing Approach for Discrimination of EEG Recordings

“…These techniques have strong theoretical foundations and can be applied in non-stationary time series. However, they are generally too complicated and have their own restrictions [14]. For instance, in methods based on time-varying spectra, the appropriate choice of bandwidth and window length or some parametric model for spectral densities are required [5].…”

“…Feature extraction techniques are mostly heuristic, that is, in each problem the researcher finds the quantities which best visualize the difference between time series groups. Maharaj and Alonso [15], Fryzlewicz and Ombao [16], Maharaj and Alonso [17] and Yeganegi et al [14] used different features based on wavelet transform to discriminate time series models. Wavelet transform provides a simple and effective solution to many time series discrimination problems.…”