25 research outputs found
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Dispersive wave processing: a model-based solution
Wave propagation through various media represents a significant problem in many applications in acoustics and electromagnetics especially when the medium is dispersive. We post a general dispersive wave propagation model that could easily represent many classes of dispersive waves and proceed to develop a model-based processor employing this underlying structure. The general solution to the model-based dispersive wave estimation problem is developed using the Bayesian maximum a posteriori approach which leads to the nonlinear extended Kalman filter processor
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Imaging Sciences Workshop Proceedings
This report contains the proceedings of the Imaging Sciences Workshop sponsored by C.A.S.LS., the Center for Advanced Signal & Image Sciences. The Center, established primarily to provide a forum where researchers can freely exchange ideas on the signal and image sciences in a comfortable intellectual environment, has grown over the last two years with the opening of a Reference Library (located in Building 272). The Technical Program for the 1996 Workshop include a variety of efforts in the Imaging Sciences including applications in the Microwave Imaging, highlighted by the Micro-Impulse Radar (MIR) system invented at LLNL, as well as other applications in this area. Special sessions organized by various individuals in Speech, Acoustic Ocean Imaging, Radar Ocean Imaging, Ultrasonic Imaging, and Optical Imaging discuss various applica- tions of real world problems. For the more theoretical, sessions on Imaging Algorithms and Computed Tomography were organized as well as for the more pragmatic featuring a session on Imaging Systems
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Imaging Sciences Workshop, Proceedings, November 15-16, 1995
Welcome to the Imaging Sciences Workshop sponsored by C.A.S.I.S., the Center for Advanced Signal & Image Sciences. Many programs at LLNL use advanced signal and image processing techniques, and the Center was established to encourage the exchange of ideas and to promote collaboration by individuals from these programs. This Workshop is an opportunity for LLNL personnel and invited speakers from other organizations not only to present new work, but, perhaps more importantly, to discuss problems in an informal and friendly setting. This year marks the opening of the CASIS Reference Library in Building 272, and we encourage all attendees to stop by for a look and to make use of it in the future. The Technical Program covers a wide variety of applications at LLNL including physical systems for collecting data and processing techniques for recovering and enhancing images. We hope that you enjoy the presentations, and we encourage you to participate in the discussions. Thanks for attending
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Signal and imaging sciences workshop proceedings
Papers are presented in the areas of: Medical Technologies; Non-Destructive Evaluation; Applications of Signal/Image Processing; Laser Guide Star and Adaptive Optics; Computational Electromagnetic, Acoustics and Optics; Micro-Impulse Radar Processing; Optical Applications; TANGO Space Shuttle
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Imaging sciences workshop
This workshop on the Imaging Sciences sponsored by Lawrence Livermore National Laboratory contains short abstracts/articles submitted by speakers. The topic areas covered include the following: Astronomical Imaging; biomedical imaging; vision/image display; imaging hardware; imaging software; Acoustic/oceanic imaging; microwave/acoustic imaging; computed tomography; physical imaging; imaging algorithms. Selected papers are indexed separately for inclusion in the Energy Science and Technology Database
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Nuclear waste repository characterization: a spatial estimation/identification approach
This paper considers the application of spatial estimation techniques to a groundwater aquifer and geological borehole data. It investigates the adequacy of these techniques to reliably develop contour maps from various data sets. The practice of spatial estimation is discussed and the estimator is then applied to a groundwater aquifer system and a deep geological formation. It is shown that the various statistical models must first be identified from the data and evaluated before reasonable results can be expected
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Internal wave signal processing: A model-based approach
A model-based approach is proposed to solve the oceanic internal wave signal processing problem that is based on state-space representations of the normal-mode vertical velocity and plane wave horizontal velocity propagation models. It is shown that these representations can be utilized to spatially propagate the modal (depth) vertical velocity functions given the basic parameters (wave numbers, Brunt-Vaisala frequency profile etc.) developed from the solution of the associated boundary value problem as well as the horizontal velocity components. These models are then generalized to the stochastic case where an approximate Gauss-Markov theory applies. The resulting Gauss-Markov representation, in principle, allows the inclusion of stochastic phenomena such as noise and modeling errors in a consistent manner. Based on this framework, investigations are made of model-based solutions to the signal enhancement problem for internal waves. In particular, a processor is designed that allows in situ recursive estimation of the required velocity functions. Finally, it is shown that the associated residual or so-called innovation sequence that ensues from the recursive nature of this formulation can be employed to monitor the model`s fit to the data
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Signal processing methods for MFE plasma diagnostics
The application of various signal processing methods to extract energy storage information from plasma diamagnetism sensors occurring during physics experiments on the Tandom Mirror Experiment-Upgrade (TMX-U) is discussed. We show how these processing techniques can be used to decrease the uncertainty in the corresponding sensor measurements. The algorithms suggested are implemented using SIG, an interactive signal processing package developed at LLNL
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Advanced array techniques for unattended ground sensor applications
Sensor arrays offer opportunities to beam form, and time-frequency analyses offer additional insights to the wavefield data. Data collected while monitoring three different sources with unattended ground sensors in a 16-element, small-aperture (approximately 5 meters) geophone array are used as examples of model-based seismic signal processing on actual geophone array data. The three sources monitored were: (Source 01). A frequency-modulated chirp of an electromechanical shaker mounted on the floor of an underground bunker. Three 60-second time-windows corresponding to (a) 50 Hz to 55 Hz sweep, (b) 60 Hz to 70 Hz sweep, and (c) 80 Hz to 90 Hz sweep. (Source 02). A single transient impact of a hammer striking the floor of the bunker. Twenty seconds of data (with the transient event approximately mid-point in the time window.(Source 11)). The transient event of a diesel generator turning on, including a few seconds before the turn-on time and a few seconds after the generator reaches steady-state conditions. The high-frequency seismic array was positioned at the surface of the ground at a distance of 150 meters (North) of the underground bunker. Four Y-shaped subarrays (each with 2-meter apertures) in a Y-shaped pattern (with a 6-meter aperture) using a total of 16 3-component, high-frequency geophones were deployed. These 48 channels of seismic data were recorded at 6000 and 12000 samples per second on 16-bit data loggers. Representative examples of the data and analyses illustrate the results of this experiment
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Transient plasma estimation: a noise cancelling/identification approach
The application of a noise cancelling technique to extract energy storage information from sensors occurring during fusion reactor experiments on the Tandem Mirror Experiment-Upgrade (TMX-U) at the Lawrence Livermore National Laboratory (LLNL) is examined. We show how this technique can be used to decrease the uncertainty in the corresponding sensor measurements used for diagnostics in both real-time and post-experimental environments. We analyze the performance of algorithm on the sensor data and discuss the various tradeoffs. The algorithm suggested is designed using SIG, an interactive signal processing package developed at LLNL