22 research outputs found
Gravity Probe B: Final Results of a Space Experiment to Test General Relativity
Gravity Probe B, launched 20 April 2004, is a space experiment testing two
fundamental predictions of Einstein's theory of General Relativity (GR), the
geodetic and frame-dragging effects, by means of cryogenic gyroscopes in Earth
orbit. Data collection started 28 August 2004 and ended 14 August 2005.
Analysis of the data from all four gyroscopes results in a geodetic drift rate
of -6,601.8+/- 18.3 mas/yr and a frame-dragging drift rate of -37.2 +/- 7.2
mas/yr, to be compared with the GR predictions of -6,606.1 mas/yr and -39.2
mas/yr, respectively (`mas' is milliarc-second; 1mas = 4.848 x 10-9 rad)
Phenomenology of the Lense-Thirring effect in the Solar System
Recent years have seen increasing efforts to directly measure some aspects of
the general relativistic gravitomagnetic interaction in several astronomical
scenarios in the solar system. After briefly overviewing the concept of
gravitomagnetism from a theoretical point of view, we review the performed or
proposed attempts to detect the Lense-Thirring effect affecting the orbital
motions of natural and artificial bodies in the gravitational fields of the
Sun, Earth, Mars and Jupiter. In particular, we will focus on the evaluation of
the impact of several sources of systematic uncertainties of dynamical origin
to realistically elucidate the present and future perspectives in directly
measuring such an elusive relativistic effect.Comment: LaTex, 51 pages, 14 figures, 22 tables. Invited review, to appear in
Astrophysics and Space Science (ApSS). Some uncited references in the text
now correctly quoted. One reference added. A footnote adde
Comparative stability analysis and performance of magnetic controllers for bias momentum satellites
Painting Scene Recognition Using Homogenous Shapes
Abstract. This paper addresses the problem of semantic analysis of paintings by automatic detection of the represented scene type. The so-lution comes as an incipient effort to fill the gap already stated in the literature between the low level computational analysis and the high level semantic dependent human analysis of paintings. Inspired by the way humans perceive art, we first decompose the image in homogenous regions, follow by a step of region merging, in order to obtain a painting description by the extraction of perceptual features of the dominant ob-jects within the scene. These features are used in a classification process that discriminates among 5 possible scene types on a database of 500 paintings