Method and apparatus for optical modulating a light signal Patent

Method and apparatus for optically modulating light or microwave bea

Optically induced free carrier light modulator

Signal carrier laser beam is optically modulated by a second laser beam of different frequency acting on a free carrier source to which the signal carrier laser is directed. The second laser beam affects the transmission characteristics of the free carrier source to light from the signal carrier laser, thus modulating it

Discharge coefficients for thick-plate orifices

Investigation enables more accurate prediction of coolant flows within internally cooled turbine blades and vanes. The data is applicable for predicting flows in complex flow passages

The Effects of Alpha Particle Bombardment in Liquids

Abstract Not Provided

Prototype selection for parameter estimation in complex models

Parameter estimation in astrophysics often requires the use of complex physical models. In this paper we study the problem of estimating the parameters that describe star formation history (SFH) in galaxies. Here, high-dimensional spectral data from galaxies are appropriately modeled as linear combinations of physical components, called simple stellar populations (SSPs), plus some nonlinear distortions. Theoretical data for each SSP is produced for a fixed parameter vector via computer modeling. Though the parameters that define each SSP are continuous, optimizing the signal model over a large set of SSPs on a fine parameter grid is computationally infeasible and inefficient. The goal of this study is to estimate the set of parameters that describes the SFH of each galaxy. These target parameters, such as the average ages and chemical compositions of the galaxy's stellar populations, are derived from the SSP parameters and the component weights in the signal model. Here, we introduce a principled approach of choosing a small basis of SSP prototypes for SFH parameter estimation. The basic idea is to quantize the vector space and effective support of the model components. In addition to greater computational efficiency, we achieve better estimates of the SFH target parameters. In simulations, our proposed quantization method obtains a substantial improvement in estimating the target parameters over the common method of employing a parameter grid. Sparse coding techniques are not appropriate for this problem without proper constraints, while constrained sparse coding methods perform poorly for parameter estimation because their objective is signal reconstruction, not estimation of the target parameters.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS500 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

The effect of parallel static and microwave electric fields on excited hydrogen atoms

Motivated by recent experiments we analyse the classical dynamics of a hydrogen atom in parallel static and microwave electric fields. Using an appropriate representation and averaging approximations we show that resonant ionisation is controlled by a separatrix, and provide necessary conditions for a dynamical resonance to affect the ionisation probability. The position of the dynamical resonance is computed using a high-order perturbation series, and estimate its radius of convergence. We show that the position of the dynamical resonance does not coincide precisely with the ionisation maxima, and that the field switch-on time can dramatically affect the ionisation signal which, for long switch times, reflects the shape of an incipient homoclinic. Similarly, the resonance ionisation time can reflect the time-scale of the separatrix motion, which is therefore longer than conventional static field Stark ionisation. We explain why these effects should be observed in the quantum dynamics. PACs: 32.80.Rm, 33.40.+f, 34.10.+x, 05.45.Ac, 05.45.MtComment: 47 pages, 20 figure

Implantable acoustic-beacon automatic fish-tracking system

A portable automatic fish tracking system was developed for monitoring the two dimensional movements of small fish within fixed areas of estuarine waters and lakes. By using the miniature pinger previously developed for this application, prototype tests of the system were conducted in the York River near the Virginia Institute of Marine Science with two underwater listening stations. Results from these tests showed that the tracking system could position the miniature pinger signals to within + or - 2.5 deg and + or - 135 m at ranges up to 2.5 km. The pingers were implanted in small fish and were successfully tracked at comparable ranges. No changes in either fish behavior or pinger performance were observed as a result of the implantation. Based on results from these prototype tests, it is concluded that the now commercially available system provides an effective approach to underwater tracking of small fish within a fixed area of interest

Industrial structural geology : principles, techniques and integration : an introduction

The authors wish to acknowledge the generous financial support provided in association with this volume to the Geological Society and the Petroleum Group by Badley Geoscience Ltd, BP, CGG Robertson, Dana Petroleum Ltd, Getech Group plc, Maersk Oil North Sea UK Ltd, Midland Valley Exploration Ltd, Rock Deformation Research (Schlumberger) and Borehole Image & Core Specialists (Wildcat Geoscience, Walker Geoscience and Prolog Geoscience). We would like to thank the fine team at the Geological Society’s Publishing House for the excellent support and encouragement that they have provided to the editors and authors of this Special Publication.Peer reviewedPublisher PD

Semi-supervised Learning for Photometric Supernova Classification

We present a semi-supervised method for photometric supernova typing. Our approach is to first use the nonlinear dimension reduction technique diffusion map to detect structure in a database of supernova light curves and subsequently employ random forest classification on a spectroscopically confirmed training set to learn a model that can predict the type of each newly observed supernova. We demonstrate that this is an effective method for supernova typing. As supernova numbers increase, our semi-supervised method efficiently utilizes this information to improve classification, a property not enjoyed by template based methods. Applied to supernova data simulated by Kessler et al. (2010b) to mimic those of the Dark Energy Survey, our methods achieve (cross-validated) 95% Type Ia purity and 87% Type Ia efficiency on the spectroscopic sample, but only 50% Type Ia purity and 50% efficiency on the photometric sample due to their spectroscopic follow-up strategy. To improve the performance on the photometric sample, we search for better spectroscopic follow-up procedures by studying the sensitivity of our machine learned supernova classification on the specific strategy used to obtain training sets. With a fixed amount of spectroscopic follow-up time, we find that deeper magnitude-limited spectroscopic surveys are better for producing training sets. For supernova Ia (II-P) typing, we obtain a 44% (1%) increase in purity to 72% (87%) and 30% (162%) increase in efficiency to 65% (84%) of the sample using a 25th (24.5th) magnitude-limited survey instead of the shallower spectroscopic sample used in the original simulations. When redshift information is available, we incorporate it into our analysis using a novel method of altering the diffusion map representation of the supernovae. Incorporating host redshifts leads to a 5% improvement in Type Ia purity and 13% improvement in Type Ia efficiency.Comment: 16 pages, 11 figures, accepted for publication in MNRA