8,586 research outputs found
Sequential Monte Carlo samplers for semilinear inverse problems and application to magnetoencephalography
We discuss the use of a recent class of sequential Monte Carlo methods for
solving inverse problems characterized by a semi-linear structure, i.e. where
the data depend linearly on a subset of variables and nonlinearly on the
remaining ones. In this type of problems, under proper Gaussian assumptions one
can marginalize the linear variables. This means that the Monte Carlo procedure
needs only to be applied to the nonlinear variables, while the linear ones can
be treated analytically; as a result, the Monte Carlo variance and/or the
computational cost decrease. We use this approach to solve the inverse problem
of magnetoencephalography, with a multi-dipole model for the sources. Here,
data depend nonlinearly on the number of sources and their locations, and
depend linearly on their current vectors. The semi-analytic approach enables us
to estimate the number of dipoles and their location from a whole time-series,
rather than a single time point, while keeping a low computational cost.Comment: 26 pages, 6 figure
Robust Gravitational Wave Burst Detection and Source Localization in a Network of Interferometers Using Cross Wigner Spectra
We discuss a fast cross-Wigner transform based technique for detecting
gravitational wave bursts, and estimating the direction of arrival, using a
network of (three) non co-located interferometric detectors. The performances
of the detector as a function of signal strength and source location, and the
accuracy of the direction of arrival estimation are investigated by numerical
simulations.Comment: accepted in Class. Quantum Gravit
State-space solutions to the dynamic magnetoencephalography inverse problem using high performance computing
Determining the magnitude and location of neural sources within the brain
that are responsible for generating magnetoencephalography (MEG) signals
measured on the surface of the head is a challenging problem in functional
neuroimaging. The number of potential sources within the brain exceeds by an
order of magnitude the number of recording sites. As a consequence, the
estimates for the magnitude and location of the neural sources will be
ill-conditioned because of the underdetermined nature of the problem. One
well-known technique designed to address this imbalance is the minimum norm
estimator (MNE). This approach imposes an regularization constraint that
serves to stabilize and condition the source parameter estimates. However,
these classes of regularizer are static in time and do not consider the
temporal constraints inherent to the biophysics of the MEG experiment. In this
paper we propose a dynamic state-space model that accounts for both spatial and
temporal correlations within and across candidate intracortical sources. In our
model, the observation model is derived from the steady-state solution to
Maxwell's equations while the latent model representing neural dynamics is
given by a random walk process.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS483 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Mode-balancing far field control of light localization in nanoantennas
Light localization is controlled at a scale of lambda/10 in the harmonic
regime from the far field domain in a plasmonic nanoantenna. The nanoantenna
under study consists of 3 aligned spheres 50 nm in diameter separated by a
distance of 5 nm. By simply tuning the orientation of an incident plane wave,
symmetric and antisymmetric mode-balancing induces a strong enhancement of the
near field intensity in one cavity while nullifying the light intensity in the
other cavity. Furthermore, it is demonstrated that the dipolar moment of a
plasmonic particle can be fully extinguished when strongly coupled with a dimer
of identical nanoparticles. Consequently, optical transparency can be achieved
in an ultra-compact symmetric metallic structure
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