Leontios J. Hadjileontiadis (IEEE S’87–M’98–SM’11) was born in Kastoria (π-1966), Greece. He received the Diploma Degree in Electrical Engineering in 1989 and the Ph.D. Degree in Electrical and Computer Engineering in 1997, both from the Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece. He also received a Diploma Degree in Musicology, AUTH, in 2011, and the Ph.D. Degree in Music Composition from the University of York, York, U.K., in 2004. His research interests include advanced signal processing, machine learning, biomedical engineering, affective computing, active and healthy ageing and biomusic composition. He has a vast experience in project management, coordinating so far European and UAE projects of >US$10.000.000. Prof. Hadjileontiadis has been awarded, amongst other awards, as innovative researcher and champion faculty from Microsoft, USA (2012), the Silver Award in Teaching Delivery at the Reimagine Education Awards (2017-2018), and the Healthcare Research Award by the Dubai Healthcare City Authority Excellence Awards (2019). He is a Senior Member of IEEE.
Swarm Decomposition: A Pray-Predator Approach [English]
Signal decomposition aims at extracting and separating signal components from composite signals, which should preferably be related to semantic units. This is extended to separation of single components from mixed signals, where the composite signal consists of a sample-wise superposition from multiple components. Various approaches have been proposed in the literature, such as wavelet-based multiresolution analysis, synchro-squeezing transform, ensemble empirical mode decomposition, empirical wavelet transform, trying to take into consideration the embedded characteristics of the time series related to nonstationarity and nonlinear harmonic interactions. In this keynote, a recently introduced signal decomposition, namely Swarm Decomposition (SwD), will be presented. The main idea behind the SwD is the pray-predator relationship, where the signal to be decomposed is the pray and the swarm is the predator. Theoretical justifications, comparative analysis and practical examples will be presented, along with further extensions of the SwD in the case of multivariate signal decomposition.