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  • Andreas Angelou ,
  • Georgios Apostolidis ,
  • Leontios Hadjileonitiadis
Andreas Angelou
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Georgios Apostolidis
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Leontios Hadjileonitiadis
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Signal decomposition techniques aim to break down nonstationary signals into their oscillatory components, serving as a preliminary step in various practical signal processing applications. This has motivated researchers to explore different strategies, yielding several distinct approaches. A wellknown optimization-based method, the Variational Mode Decomposition (VMD), relies on the formulation of an optimization problem, utilizing constant bandwidth Wiener filters. However, this poses limitations in constant bandwidth and the need for constituent count. In this paper, a new method, namely Dynamic Bandwidth VMD (DB-VMD), is proposed to generalize VMD by addressing the Wiener filter limitations through enhancement of the optimization problem with an additional constraint. Experiments in synthetic signals highlight DB-VMDâ\euro™s noise robustness and adaptability in comparison to VMD, paving the way for many applications, especially when the analyzed signals are contaminated with noise.