@conference {18513,
	title = {An improved Hilbert-Huang Transform for non-linear and time-variant signals},
	booktitle = {26th Italian Workshop on Neural Networks (WIRN 2016)},
	year = {2016},
	month = {05/2016},
	pages = {1-8},
	address = {Vietri 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.
},
	author = {Alippi, Cesare and Wen, Qi and Roveri, Manuel}
}
