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Education and Innovation in Embedded Systems Design

USI Università della Svizzera italiana, USI Faculty of Informatics, Advanced Learning and Research Institute USI Università della Svizzera italiana USI Faculty of Informatics USI Advanced Learning and Research Institute
TitleAn improved Hilbert-Huang Transform for non-linear and time-variant signals
Publication TypeConference Paper
Year of Publication2016
AuthorsAlippi, C., Q. Wen, and M. Roveri
Conference Name26th Italian Workshop on Neural Networks (WIRN 2016)
Date Published05/2016
Conference LocationVietri sul Mare, Salerno, Italy
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 identies 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.