Home
The team makes fundamental and applied research in the analysis of complex natural signals using paradigms and methods from Statistical Physics such as: scale invariance, predictabilit y universal ity classes We study the parameters related to common statistical organization in different complex signals and systems, we derive new types of sparse and compact representations and machine learning approaches We are also developing tools for the analysis of complex signals that better match the statistical and geometrical organisation inside these data: as a typical example, we cite the evaluation of cascading properties of physical variables inside complex signals.