A General-applications Direct Global Matrix Algorithm for Rapid Seismo-acoustic Wavefield Computations

A new matrix method for rapid wave propagation modeling in generalized stratified media, which has recently been applied to numerical simulations in diverse areas of underwater acoustics, solid earth seismology, and nondestructive ultrasonic scattering is explained and illustrated. A portion of recent efforts jointly undertaken at NATOSACLANT and NORDA Numerical Modeling groups in developing, implementing, and testing a new fast general-applications wave propagation algorithm, SAFARI, formulated at SACLANT is summarized. The present general-applications SAFARI program uses a Direct Global Matrix Approach to multilayer Green's function calculation. A rapid and unconditionally stable solution is readily obtained via simple Gaussian ellimination on the resulting sparsely banded block system, precisely analogous to that arising in the Finite Element Method. The resulting gains in accuracy and computational speed allow consideration of much larger multilayered air/ocean/Earth/engineering material media models, for many more source-receiver configurations than previously possible. The validity and versatility of the SAFARI-DGM method is demonstrated by reviewing three practical examples of engineering interest, drawn from ocean acoustics, engineering seismology and ultrasonic scattering

Enhanced Lactic Acid Production from Cheese Whey with Nutrient Supplement Addition

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is an Invited Paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 5 (2003): A.E. Ghaly, M.S.A. Tango, and M.A. Adams. Enhanced Lactic Acid Production from Cheese Whey with Nutrient Supplement Addition. Vol. V. May 2003

Object Perception for Intelligent Vehicle Applications: A Multi-Sensor Fusion Approach

International audienceThe paper addresses the problem of object perception for intelligent vehicle applications with main tasks of detection, tracking and classification of obstacles where multiple sensors (i.e.: lidar, camera and radar) are used. New algorithms for raw sensor data processing and sensor data fusion are introduced making the most information from all sensors in order to provide a more reliable and accurate information about objects in the vehicle environment. The proposed object perception module is implemented and tested on a demonstrator car in real-life traffics and evaluation results are presented

Batch Reinforcement Learning for Optimizing Longitudinal Driving Assistance Strategies

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.International audiencePartially Autonomous Driver's Assistance Systems (PADAS) are systems aiming at providing a safer driving experience to people. Especially, one application of such systems is to assist the drivers in reacting optimally so as to prevent collisions with a leading vehicle. Several means can be used by a PADAS to reach this goal. For instance, warning signals can be sent to the driver or the PADAS can actually modify the speed of the car by braking automatically. An optimal combination of different warning signals together with assistive braking is expected to reduce the probability of collision. How to associate the right combination of PADAS actions to a given situation so as to achieve this aim remains an open problem. In this paper, the use of a statistical machine learning method, namely the reinforcement learning paradigm, is proposed to automatically derive an optimal PADAS action selection strategy from a database of driving experiments. Experimental results conducted on actual car simulators with human drivers show that this method achieves a significant reduction of the risk of collision

Spatial Cluster Detection for Repeatedly Measured Outcomes while Accounting for Residential History

Spatial cluster detection has become an important methodology in quantifying the effect of hazardous exposures. Previous methods have focused on cross-sectional outcomes that are binary or continuous. There are virtually no spatial cluster detection methods proposed for longitudinal outcomes. This paper proposes a new spatial cluster detection method for repeated outcomes using cumulative geographic residuals. A major advantage of this method is its ability to readily incorporate information on study participants relocation, which most cluster detection statistics cannot. Application of these methods will be illustrated by the Home Allergens and Asthma prospective cohort study analyzing the relationship between environmental exposures and repeated measured outcome, occurrence of wheeze in the last 6 months, while taking into account mobile locations

A low cost scheme for high precision dual-wavelength laser metrology

A novel method capable of delivering relative optical path length metrology with nanometer precision is demonstrated. Unlike conventional dual-wavelength metrology which employs heterodyne detection, the method developed in this work utilizes direct detection of interference fringes of two He-Ne lasers as well as a less precise stepper motor open-loop position control system to perform its measurement. Although the method may be applicable to a variety of circumstances, the specific application where this metrology is essential is in an astrometric optical long baseline stellar interferometer dedicated to precise measurement of stellar positions. In our example application of this metrology to a narrow-angle astrometric interferometer, measurement of nanometer precision could be achieved without frequency-stabilized lasers although the use of such lasers would extend the range of optical path length the metrology can accurately measure. Implementation of the method requires very little additional optics or electronics, thus minimizing cost and effort of implementation. Furthermore, the optical path traversed by the metrology lasers is identical with that of the starlight or science beams, even down to using the same photodetectors, thereby minimizing the non-common-path between metrology and science channels.Comment: 17 pages, 4 figures, accepted for publication in Applied Optic

Numerical Techniques for Scattering from Submerged Objects

To represent the final results in terms of matrices, one expands all appropriate physical quantities in terms of partial wave basis states. This includes expansions for the incident and scattered fields and the surface quantities. The method then utilizes the Huygen-Poincare integral representation for both the exterior and interior solutions, leading to the required matrix equations. One thus deals with matrix equations, the complexity of which depends on the nature of the problem. It is shown that in general a transition matrix T can be obtained relating the incident field A with the scattered field f having the form T = PQ(-1), where f = TA. The structure of Q can be quite complicated and can itself be composed of other matrix inversions such as arise from layered objects. Recent improvements in this method appropriate for a variety of physical problems are focused on, and on their implementation. Results are outlined from scattering simulations for very elongated submerged objects and resonance scattering from elastic solids and shells. The final improvement concerns eigenfunction expansions of surface terms, arising from solution of the interior problem, obtained via a preconditioning technique. This effectively reduces the problem to that of obtaining eigenvalues of a Hermitian operator. This formalism is reviewed for scattering from targets that are rigid, sound-soft, acoustic, elastic solids, elastic shells, and elastic layered objects. Two sets of the more interesting results are presented. The first concerns scattering from elongated objects, and the second to thin elastic spheroids

Determining Parameters of Cool Giant Stars by Modeling Spectrophotometric and Interferometric Observations Using the SAtlas Program

Context: Optical interferometry is a powerful tool for observing the intensity structure and angular diameter of stars. When combined with spectroscopy and/or spectrophotometry, interferometry provides a powerful constraint for model stellar atmospheres. Aims: The purpose of this work is to test the robustness of the spherically symmetric version of the Atlas stellar atmosphere program, SAtlas, using interferometric and spectrophotometric observations. Methods: Cubes (three dimensional grids) of model stellar atmospheres, with dimensions of luminosity, mass, and radius, are computed to fit observations for three evolved giant stars, \psi Phoenicis, \gamma Sagittae, and \alpha Ceti. The best-fit parameters are compared with previous results. Results: The best-fit angular diameters and values of \chi^2 are consistent with predictions using Phoenix and plane-parallel Atlas models. The predicted effective temperatures, using SAtlas, are about 100 to 200 K lower, and the predicted luminosities are also lower due to the differences in effective temperatures. Conclusions: It is shown that the SAtlas program is a robust tool for computing models of extended stellar atmospheres that are consistent with observations. The best-fit parameters are consistent with predictions using Phoenix models, and the fit to the interferometric data for \psi Phe differs slightly, although both agree within the uncertainty of the interferometric observations.Comment: 5 pages, 6 figures, Accepted for publication in A&A as a Research Not