1,170 research outputs found
A new form of the Kerr solution
A new form of the Kerr solution is presented. The solution involves a time
coordinate which represents the local proper time for free-falling observers on
a set of simple trajectories. Many physical phenomena are particularly clear
when related to this time coordinate. The chosen coordinates also ensure that
the solution is well behaved at the horizon. The solution is well suited to the
tetrad formalism and a convenient null tetrad is presented. The Dirac
Hamiltonian in a Kerr background is also given and, for one choice of tetrad,
it takes on a simple, Hermitian form.Comment: 8 pages, LaTeX, no figures. Corrected and improved version. To appear
in Phys. Rev.
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Calculating the Rotor Between Conformal Objects
Abstract: In this paper we will address the problem of recovering covariant transformations between objects—specifically; lines, planes, circles, spheres and point pairs. Using the covariant language of conformal geometric algebra (CGA), we will derive such transformations in a very simple manner. In CGA, rotations, translations, dilations and inversions can be written as a single rotor, which is itself an element of the algebra. We will show that the rotor which takes a line to a line (or plane to a plane etc) can easily be formed and we will investigate the nature of the rotors formed in this way. If we can recover the rotor between one object and another of the same type, a useable metric which tells us how close one line (plane etc) is to another, can be a function of how close this rotor is to the identity. Using these ideas, we find that we can define metrics for a number of common problems, specifically recovering the transformation between sets of noisy objects
Astrometric Effects of Gravitational Wave Backgrounds with non-Luminal Propagation Speeds
A passing gravitational wave causes a deflection in the apparent astrometric positions of distant stars. The effect of the speed of the gravitational wave on this astrometric shift is discussed. A stochastic background of gravitational waves would result in a pattern of astrometric deflections which are correlated on large angular scales. These correlations are quantified and investigated for backgrounds of gravitational waves with sub- and super-luminal group velocities. The statistical properties of the correlations are depicted in two equivalent and related ways: as correlation curves and as angular power spectra. Sub-(super-)luminal gravitational wave backgrounds have the effect of enhancing (suppressing) the power in low-order angular modes. Analytical representations of the redshift-redshift and redshift-astrometry correlations are also derived. The potential for using this effect for constraining the speed of gravity is discussed
Fast directional continuous spherical wavelet transform algorithms
We describe the construction of a spherical wavelet analysis through the
inverse stereographic projection of the Euclidean planar wavelet framework,
introduced originally by Antoine and Vandergheynst and developed further by
Wiaux et al. Fast algorithms for performing the directional continuous wavelet
analysis on the unit sphere are presented. The fast directional algorithm,
based on the fast spherical convolution algorithm developed by Wandelt and
Gorski, provides a saving of O(sqrt(Npix)) over a direct quadrature
implementation for Npix pixels on the sphere, and allows one to perform a
directional spherical wavelet analysis of a 10^6 pixel map on a personal
computer.Comment: 10 pages, 3 figures, replaced to match version accepted by IEEE
Trans. Sig. Pro
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Pantomimic Gestures for Human-Robot Interaction
This work introduces a pantomimic gesture interface, which classifies human hand gestures using unmanned aerial vehicle (UAV) behaviour recordings as training data. We argue that pantomimic gestures are more intuitive than iconic gestures and show that a pantomimic gesture recognition strategy using micro UAV behaviour recordings can be more robust than one trained directly using hand gestures. Hand gestures are isolated by applying a maximum information criterion, with features extracted using principal component analysis (PCA) and compared using a nearest neighbour classifier. These features are biased in that they are better suited to classifying certain behaviours. We show how a Bayesian update step accounting for the geometry of training features compensates for this, resulting in fairer classification results, and introduce a weighted voting system to aid in sequence labelling.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TRO.2015.247595
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