Actions for Neural networks for calibration tomography

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Neural networks for calibration tomography

Author
Decker, Arthur
Published
Jun 1, 1993.
Physical Description
1 electronic document
Online Version

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Unclassified, Unlimited, Publicly available.
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Summary
Artificial neural networks are suitable for performing pattern-to-pattern calibrations. These calibrations are potentially useful for facilities operations in aeronautics, the control of optical alignment, and the like. Computed tomography is compared with neural net calibration tomography for estimating density from its x-ray transform. X-ray transforms are measured, for example, in diffuse-illumination, holographic interferometry of fluids. Computed tomography and neural net calibration tomography are shown to have comparable performance for a 10 degree viewing cone and 29 interferograms within that cone. The system of tomography discussed is proposed as a relevant test of neural networks and other parallel processors intended for using flow visualization data.
Other Subject(s)
Collection
NASA Technical Reports Server (NTRS) Collection.
Note
Document ID: 19930017717.
Accession ID: 93N26906.
E-7857.
NAS 1.15:106352.
NASA-TM-106352.
1993 International Symposium on Optics, Imaging, and Instrumentation; 11-16 Jul. 1993; San Diego, CA; United States.
Terms of Use and Reproduction
No Copyright.
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