R
Rajarshi Roy
Researcher at University of Maryland, College Park
Publications - 202
Citations - 12838
Rajarshi Roy is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Laser & Chaotic. The author has an hindex of 54, co-authored 198 publications receiving 11814 citations. Previous affiliations of Rajarshi Roy include University of Rochester & National Institute of Standards and Technology.
Papers
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Communication with Chaotic Lasers
TL;DR: The experimental demonstration of chaotic communication with an optical system is described, using an erbium-doped fiber ring laser to produce chaotic light and embedded in the larger chaotic carrier and transmitted to a receiver system where the message was recovered from the chaos.
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Observation of stochastic resonance in a ring laser.
TL;DR: The first observation of stochastic resonance in an optical device, the bidirectional ring laser, is reported and the addition of injected noise can lead to an improved signal-to-noise ratio.
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Experimental observation of chimeras in coupled-map lattices
Aaron M. Hagerstrom,Thomas E. Murphy,Rajarshi Roy,Philipp Hövel,Philipp Hövel,Iryna Omelchenko,Iryna Omelchenko,Eckehard Schöll +7 more
TL;DR: In this paper, experimental realization of a coupled-map lattice reveals dynamical states displaying coexisting spatial domains of coherence and incoherence, and phase-locking behavior can, in theory, coexist with incoherent dynamics.
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Neuronal avalanches imply maximum dynamic range in cortical networks at criticality
TL;DR: In this article, the authors show that cortical networks that generate neuronal avalanches benefit from a maximized dynamic range, i.e., the ability to respond to the greatest range of stimuli.
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Cluster synchronization and isolated desynchronization in complex networks with symmetries
TL;DR: A new framework and techniques are presented and techniques for the analysis of network dynamics that shows the connection between network symmetries and cluster formation are developed that could guide the design of new power grid systems or lead to new understanding of the dynamical behaviour of networks ranging from neural to social.