An Algorithm for Motion Parameter Direct Estimate

Motion estimation in image sequences is undoubtedly one of the most studied research fields, given that motion estimation is a basic tool for disparate applications, ranging from video coding to pattern recognition. In this paper a new methodology which, by minimizing a specific potential function, directly determines for each image pixel the motion parameters of the object the pixel belongs to is presented. The approach is based on Markov random fields modelling, acting on a first-order neighborhood of each point and on a simple motion model that accounts for rotations and translations. Experimental results both on synthetic (noiseless and noisy) and real world sequences have been carried out and they demonstrate the good performance of the adopted technique. Furthermore a quantitative and qualitative comparison with other well-known approaches has confirmed the goodness of the proposed methodology

Systematic inference of the long-range dependence and heavy-tail distribution parameters of ARFIMA models

Long-Range Dependence (LRD) and heavy-tailed distributions are ubiquitous in natural and socio-economic data. Such data can be self-similar whereby both LRD and heavy-tailed distributions contribute to the self-similarity as measured by the Hurst exponent. Some methods widely used in the physical sciences separately estimate these two parameters, which can lead to estimation bias. Those which do simultaneous estimation are based on frequentist methods such as Whittle’s approximate maximum likelihood estimator. Here we present a new and systematic Bayesian framework for the simultaneous inference of the LRD and heavy-tailed distribution parameters of a parametric ARFIMA model with non-Gaussian innovations. As innovations we use the α-stable and t-distributions which have power law tails. Our algorithm also provides parameter uncertainty estimates. We test our algorithm using synthetic data, and also data from the Geostationary Operational Environmental Satellite system (GOES) solar X-ray time series. These tests show that our algorithm is able to accurately and robustly estimate the LRD and heavy-tailed distribution parameters

Estimation based control of flexible systems-sensorless wave based technique

This paper presents an algorithm for parameters and positions estimation of lumped flexible systems. As soon as the parameters and the positions are estimated they can be used to design virtual sensors that can be moved along the system to estimate the position of any lumped mass keeping the system free from any attached sensors. The virtual sensors are nothing but a chain of estimators that are connected at the end of each other, starting with two actuator’s measurements and ending up with system parameters and all the system lumped positions. An estimation Based PID controller is presented based on the feedback of the virtual sensor’s estimates instead of the actual measurement

Astrometric orbit of a low-mass companion to an ultracool dwarf

Little is known about the existence of extrasolar planets around ultracool dwarfs. Furthermore, binary stars with Sun-like primaries and very low-mass binaries composed of ultracool dwarfs show differences in the distributions of mass ratio and orbital separation that can be indicative of distinct formation mechanisms. Using FORS2/VLT optical imaging for high precision astrometry we are searching for planets and substellar objects around ultracool dwarfs to investigate their multiplicity properties for very low companion masses. Here we report astrometric measurements with an accuracy of two tenths of a milli-arcsecond over two years that reveal orbital motion of the nearby L1.5 dwarf DENIS-P J082303.1-491201 located at 20.77 +/- 0.08 pc caused by an unseen companion that revolves about its host on an eccentric orbit in 246.4 +/- 1.4 days. We estimate the L1.5 dwarf to have 7.5 +/- 0.7 % of the Sun's mass that implies a companion mass of 28 +/- 2 Jupiter masses. This new system has the smallest mass ratio (0.36 +/- 0.02) of known very low-mass binaries with characterised orbits. With this discovery we demonstrate 200 micro-arcsecond astrometry over an arc-minute field and over several years that is sufficient to discover sub-Jupiter mass planets around ultracool dwarfs. We also show that the achieved parallax accuracy of < 0.4 % makes it possible to remove distance as a dominant source of uncertainty in the modelling of ultracool dwarfs.Comment: 9 pages, 8 figures, accepted for publication in Astronomy and Astrophysics. The reduced astrometry data will be made publically available through the CD

Feedback cooling of atomic motion in cavity QED

We consider the problem of controlling the motion of an atom trapped in an optical cavity using continuous feedback. In order to realize such a scheme experimentally, one must be able to perform state estimation of the atomic motion in real time. While in theory this estimate may be provided by a stochastic master equation describing the full dynamics of the observed system, integrating this equation in real time is impractical. Here we derive an approximate estimation equation for this purpose, and use it as a drive in a feedback algorithm designed to cool the motion of the atom. We examine the effectiveness of such a procedure using full simulations of the cavity QED system, including the quantized motion of the atom in one dimension.Comment: 22 pages, 17 figure