P
Peter A. Robinson
Researcher at University of Sydney
Publications - 495
Citations - 17549
Peter A. Robinson is an academic researcher from University of Sydney. The author has contributed to research in topics: Plasma oscillation & Wave packet. The author has an hindex of 61, co-authored 489 publications receiving 16034 citations. Previous affiliations of Peter A. Robinson include NASA Headquarters & University of Colorado Boulder.
Papers
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Dynamics of beam-driven Langmuir and ion-acoustic waves including electrostatic decay
TL;DR: In this article, the evolution of the Langmuir and ion-acoustic wave spectra is investigated numerically in time, position, and wave number space, and spontaneous emission is considered.
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Field structure of collapsing wave packets in 3D strong Langmuir turbulence.
TL;DR: A simple model is constructed for the electric fields in the collapsing wave packets found in 3D simulations of driven and damped isotropic strong Langmuir turbulence, accounting for the distribution of wave-packet shapes observed in the simulations, particularly the predominance of oblate wave packets.
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First test of stochastic growth theory for Langmuir waves in Earth's foreshock
Iver H. Cairns,Peter A. Robinson +1 more
TL;DR: In this article, the first test of whether stochastic growth theory (SGT) can explain the detailed characteristics of Langmuir-like waves in Earth's foreshock was presented.
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Markov analysis of sleep dynamics.
TL;DR: A new approach, based on a Markov transition matrix, is proposed to explain frequent sleep and wake transitions during sleep, and shows that the statistics of sleep can be constructed via a single Markov process and that durations of all states have modified exponential distributions, in contrast to recent reports of a scale-free form for the wake stage and an exponential form for
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BOLD responses to stimuli: dependence on frequency, stimulus form, amplitude, and repetition rate.
Peter A. Robinson,P.M. Drysdale,H. Van der Merwe,E. Kyriakou,M.K. Rigozzi,B. Germanoska,Chris Rennie +6 more
TL;DR: There can be widely differing proportionalities between BOLD and peak activity, and this is the likely reason for the low level of correspondence seen experimentally between ERP sources and BOLD measurements and that non-BOLD measurements, such as ERPs, can be used to correct for this effect to obtain improved activity estimates.