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Carlos H. Muravchik
Researcher at National University of La Plata
Publications - 159
Citations - 3249
Carlos H. Muravchik is an academic researcher from National University of La Plata. The author has contributed to research in topics: GNSS applications & Inverse problem. The author has an hindex of 22, co-authored 159 publications receiving 2912 citations. Previous affiliations of Carlos H. Muravchik include National Scientific and Technical Research Council & Yale University.
Papers
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Posterior Cramer-Rao bounds for discrete-time nonlinear filtering
TL;DR: A mean-square error lower bound for the discrete-time nonlinear filtering problem is derived based on the van Trees (1968) (posterior) version of the Cramer-Rao inequality and is applicable to multidimensional nonlinear, possibly non-Gaussian, dynamical systems.
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Nonlinear control of a permanent magnet synchronous motor with disturbance torque estimation
TL;DR: A sensorless nonlinear control scheme for controlling the speed of a permanent magnet synchronous motor driving an unknown load torque through an extended nonlinear observer avoiding the use of mechanical sensors is introduced.
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A nonlinear reduced order observer for permanent magnet synchronous motors
TL;DR: In this article, a nonlinear reduced-order observer for speed and rotor position estimation in permanent magnet synchronous motors (PMSMs) is proposed, based on a model of the motor represented by stationary two-axes coordinates.
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Cortical deficits of emotional face processing in adults with ADHD: Its relation to social cognition and executive function
Agustín Ibáñez,Agustin Petroni,Hugo Urquina,Fernando Torrente,Teresa Torralva,Esteban Hurtado,Raphael Guex,Alejandro Blenkmann,Leandro Beltrachini,Carlos H. Muravchik,Sandra Baez,Marcelo Cetkovich,Mariano Sigman,Alicia Lischinsky,Facundo Manes +14 more
TL;DR: This is the first report to reveal an adult ADHD-specific impairment in the cortical modulation of emotion for faces and an association between N170 cortical measures and ToM and EF.
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Estimating brain conductivities and dipole source signals with EEG arrays
TL;DR: This paper applies the maximum-likelihood and maximum a posteriori (MAP) techniques to simultaneously estimate the layer conductivity ratios and source signal using EEG data, and uses the classical 4-sphere model to approximate the head geometry, and assumes a known dipole source position.