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20032018
most citedUniversal analytic properties of noise. Introducing the J-Matrix formalism

26 citations · 73 across the 16 of their papers we have counts for

collaborators

17 papers

q-bio.QM2018

Discrete structure of the brain rhythms

Luca Perotti, Justin DeVito, Daniel Bessis +1

Neuronal activity in the brain generates synchronous oscillations of the Local Field Potential (LFP). The traditional analyses of the LFPs are based on decomposing the signal into…

physics.ed-ph2018★ 3 cited

Motion of a superconducting loop in an inhomogeneous magnetic field: a didactic experiment

Marco Giliberti, Luca Perotti, Lucio Rossi

We present an experiment conductive to an understanding of both Faraday's law and the properties of the superconducting state. It consists in the analysis of the motion of a superc…

math.NA2018

Matrix methods for Padé approximation: numerical calculation of poles, zeros and residues

Luca Perotti, Michal Wojtylak

A representation of the Padé approximation of the -transform of a signal as a resolvent of a tridiagonal matrix is given. Several formulas for the poles, zeros and residues…

physics.data-an2017★ 1 cited

Recovery of the starting times of delayed signals

Luca Perotti, Daniel Vrinceanu, Daniel Bessis

We present a new method to locate the starting points in time of an arbitrary number of (damped) delayed signals. For a finite data sequence, the method permits to first locate the…

math.DS2015★ 11 cited

Extreme value laws for fractal intensity functions in dynamical systems: Minkowski analysis

Giorgio Mantica, Luca Perotti

Typically, in the dynamical theory of extremal events, the function that gauges the intensity of a phenomenon is assumed to be convex and maximal, or singular, at a single, or at m…

math-ph2015★ 1 cited

Truncations of a class of pseudo-Hermitian operators

Maxim Derevyagin, Luca Perotti, Michal Wojtylak

We consider the class of non-Hermitian operators represented by infinite tridiagonal matrices, selfadjoint in an indefinite inner product space with one negative square. We approxi…