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  • 1
    Publication Date: 2013-08-31
    Description: Coded-aperture imaging is a technique for imaging sources that emit high-energy radiation. This type of imaging involves shadow casting and not reflection or refraction. High-energy sources exist in x ray and gamma-ray astronomy, nuclear reactor fuel-rod imaging, and nuclear medicine. Of these three areas nuclear medicine is perhaps the most challenging because of the limited amount of radiation available and because a three-dimensional source distribution is to be determined. In nuclear medicine a radioactive pharmaceutical is administered to a patient. The pharmaceutical is designed to be taken up by a particular organ of interest, and its distribution provides clinical information about the function of the organ, or the presence of lesions within the organ. This distribution is determined from spatial measurements of the radiation emitted by the radiopharmaceutical. The principles of imaging radiopharmaceutical distributions with coded apertures are reviewed. Included is a discussion of linear shift-variant projection operators and the associated inverse problem. A system developed at the University of Arizona in Tucson consisting of small modular gamma-ray cameras fitted with coded apertures is described.
    Keywords: INSTRUMENTATION AND PHOTOGRAPHY
    Type: NASA, Langley Research Center, Visual Information Processing for Television and Telerobotics; p 33-45
    Format: application/pdf
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  • 2
    Publication Date: 2019-07-10
    Description: Computational grids provide users with many possible places to execute their applications. We wish to help users select where to run their applications by providing predictions of the execution times of applications on space shared parallel computers and predictions of when scheduling systems for such parallel computers will start applications. Our predictions are based on instance based learning techniques and simulations of scheduling algorithms. We find that our execution time prediction techniques have an average error of 37 percent of the execution times for trace data recorded from SGI Origins at NASA Ames Research Center and that this error is 67 percent lower than the error of user estimates. We also find that the error when predicting how long applications will wait in scheduling queues is 95 percent of mean queue wait times when using our execution time predictions and this is 57 percent lower than if we use user execution time estimates.
    Keywords: Cybernetics, Artificial Intelligence and Robotics
    Format: text
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