11,081 research outputs found
One-dimensional symmetry and Liouville type results for the fourth order Allen-Cahn equation in R
In this paper, we prove an analogue of Gibbons' conjecture for the extended
fourth order Allen-Cahn equation in R N , as well as Liouville type results for
some solutions converging to the same value at infinity in a given direction.
We also prove a priori bounds and further one-dimensional symmetry and rigidity
results for semilinear fourth order elliptic equations with more general
nonlinearities
Path Integral and the Induction Law
We show how the induction law is correctly used in the path integral
computation of the free particle propagator. The way this primary path integral
example is treated in most textbooks is a little bit missleading.Comment: 5 latex pages, no figure
An error estimate of Gaussian Recursive Filter in 3Dvar problem
Computational kernel of the three-dimensional variational data assimilation
(3D-Var) problem is a linear system, generally solved by means of an iterative
method. The most costly part of each iterative step is a matrix-vector product
with a very large covariance matrix having Gaussian correlation structure. This
operation may be interpreted as a Gaussian convolution, that is a very
expensive numerical kernel. Recursive Filters (RFs) are a well known way to
approximate the Gaussian convolution and are intensively applied in the
meteorology, in the oceanography and in forecast models. In this paper, we deal
with an oceanographic 3D-Var data assimilation scheme, named OceanVar, where
the linear system is solved by using the Conjugate Gradient (GC) method by
replacing, at each step, the Gaussian convolution with RFs. Here we give
theoretical issues on the discrete convolution approximation with a first order
(1st-RF) and a third order (3rd-RF) recursive filters. Numerical experiments
confirm given error bounds and show the benefits, in terms of accuracy and
performance, of the 3-rd RF.Comment: 9 page
Advances in surface EMG signal simulation with analytical and numerical descriptions of the volume conductor
Surface electromyographic (EMG) signal modeling is important for signal interpretation, testing of processing algorithms, detection system design, and didactic purposes. Various surface EMG signal models have been proposed in the literature. In this study we focus on 1) the proposal of a method for modeling surface EMG signals by either analytical or numerical descriptions of the volume conductor for space-invariant systems, and 2) the development of advanced models of the volume conductor by numerical approaches, accurately describing not only the volume conductor geometry, as mainly done in the past, but also the conductivity tensor of the muscle tissue. For volume conductors that are space-invariant in the direction of source propagation, the surface potentials generated by any source can be computed by one-dimensional convolutions, once the volume conductor transfer function is derived (analytically or numerically). Conversely, more complex volume conductors require a complete numerical approach. In a numerical approach, the conductivity tensor of the muscle tissue should be matched with the fiber orientation. In some cases (e.g., multi-pinnate muscles) accurate description of the conductivity tensor may be very complex. A method for relating the conductivity tensor of the muscle tissue, to be used in a numerical approach, to the curve describing the muscle fibers is presented and applied to representatively investigate a bi-pinnate muscle with rectilinear and curvilinear fibers. The study thus propose an approach for surface EMG signal simulation in space invariant systems as well as new models of the volume conductor using numerical methods
The Zeta Function Method and the Harmonic Oscillator Propagator
We show how the pre-exponential factor of the Feynman propagator for the
harmonic oscillator can be computed by the generalized -function method.
Besides, we establish a direct equivalence between this method and Schwinger's
propertime method.Comment: 12 latex pages, no figure
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