Abstract
Learning in non-stationary/evolving environments requires methods able to process and deal with non-stationary streams. In this paper we propose a novel algorithm providing a time-frequency decomposition of time-variant signals. Outcoming signals can be used to identify anomalous events/patterns or extract features associated with the time-variance of the signal, precious information for any consequent learning action. The paper extends the Hilbert-Huang Transform notoriously used to deal with time-variant signals by introducing (i) a new Empirical Mode Decomposition that identifies the number of frequency modes of the signal and (ii) an extension of the Hilbert Transform that eliminates negative frequency-values in the time-frequency spectrum. The effectiveness of the proposed Transform has been tested on both synthetic and real time-variant signals acquired by a real-world intelligent system for landslide monitoring.
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Alippi, C., Qi, W., Roveri, M. (2018). An Improved Hilbert-Huang Transform for Non-linear and Time-Variant Signals. In: Esposito, A., Faudez-Zanuy, M., Morabito, F., Pasero, E. (eds) Multidisciplinary Approaches to Neural Computing. Smart Innovation, Systems and Technologies, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-319-56904-8_11
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DOI: https://doi.org/10.1007/978-3-319-56904-8_11
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