8,460 research outputs found
Reconstruction of piecewise constant layered conductivities in electrical impedance tomography
This work presents a new constructive uniqueness proof for Calder\'on's
inverse problem of electrical impedance tomography, subject to local Cauchy
data, for a large class of piecewise constant conductivities that we call
"piecewise constant layered conductivities" (PCLC). The resulting
reconstruction method only relies on the physically intuitive monotonicity
principles of the local Neumann-to-Dirichlet map, and therefore the method
lends itself well to efficient numerical implementation and generalization to
electrode models. Several direct reconstruction methods exist for the related
problem of inclusion detection, however they share the property that "holes in
inclusions" or "inclusions-within-inclusions" cannot be determined. One such
method is the monotonicity method of Harrach, Seo, and Ullrich, and in fact the
method presented here is a modified variant of the monotonicity method which
overcomes this problem. More precisely, the presented method abuses that a PCLC
type conductivity can be decomposed into nested layers of positive and/or
negative perturbations that, layer-by-layer, can be determined via the
monotonicity method. The conductivity values on each layer are found via basic
one-dimensional optimization problems constrained by monotonicity relations.Comment: 12 pages, 1 figur
Monotonicity Analysis over Chains and Curves
Chains are vector-valued signals sampling a curve. They are important to
motion signal processing and to many scientific applications including location
sensors. We propose a novel measure of smoothness for chains curves by
generalizing the scalar-valued concept of monotonicity. Monotonicity can be
defined by the connectedness of the inverse image of balls. This definition is
coordinate-invariant and can be computed efficiently over chains. Monotone
curves can be discontinuous, but continuous monotone curves are differentiable
a.e. Over chains, a simple sphere-preserving filter shown to never decrease the
degree of monotonicity. It outperforms moving average filters over a synthetic
data set. Applications include Time Series Segmentation, chain reconstruction
from unordered data points, Optical Character Recognition, and Pattern
Matching.Comment: to appear in Proceedings of Curves and Surfaces 200
High-order conservative finite difference GLM-MHD schemes for cell-centered MHD
We present and compare third- as well as fifth-order accurate finite
difference schemes for the numerical solution of the compressible ideal MHD
equations in multiple spatial dimensions. The selected methods lean on four
different reconstruction techniques based on recently improved versions of the
weighted essentially non-oscillatory (WENO) schemes, monotonicity preserving
(MP) schemes as well as slope-limited polynomial reconstruction. The proposed
numerical methods are highly accurate in smooth regions of the flow, avoid loss
of accuracy in proximity of smooth extrema and provide sharp non-oscillatory
transitions at discontinuities. We suggest a numerical formulation based on a
cell-centered approach where all of the primary flow variables are discretized
at the zone center. The divergence-free condition is enforced by augmenting the
MHD equations with a generalized Lagrange multiplier yielding a mixed
hyperbolic/parabolic correction, as in Dedner et al. (J. Comput. Phys. 175
(2002) 645-673). The resulting family of schemes is robust, cost-effective and
straightforward to implement. Compared to previous existing approaches, it
completely avoids the CPU intensive workload associated with an elliptic
divergence cleaning step and the additional complexities required by staggered
mesh algorithms. Extensive numerical testing demonstrate the robustness and
reliability of the proposed framework for computations involving both smooth
and discontinuous features.Comment: 32 pages, 14 figure, submitted to Journal of Computational Physics
(Aug 7 2009
Efficient implementation of finite volume methods in Numerical Relativity
Centered finite volume methods are considered in the context of Numerical
Relativity. A specific formulation is presented, in which third-order space
accuracy is reached by using a piecewise-linear reconstruction. This
formulation can be interpreted as an 'adaptive viscosity' modification of
centered finite difference algorithms. These points are fully confirmed by 1D
black-hole simulations. In the 3D case, evidence is found that the use of a
conformal decomposition is a key ingredient for the robustness of black hole
numerical codes.Comment: Revised version, 10 pages, 6 figures. To appear in Phys. Rev.
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