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Optical Engineering

Low-power hardware implementation of artificial neural network strain detection for extrinsic Fabry-Pérot interferometric sensors under sinusoidal excitation
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Paper Abstract

Artificial neural networks are studied for use in estimating strain in extrinsic Fabry-Pérot interferometric sensors. These networks can require large memory spaces and a large number of calculations for implementation. We describe a modified neural network solution that is suitable for implementation on relatively low cost, low-power hardware. Moreover, we give strain estimates resulting from an implementation of the artificial neural network algorithm on an 8-bit 8051 processor with 64 kbytes of memory. For example, one of our results shows that for 2048 samples of the transmittance signal, the presented neural network algorithm requires around 24,622 floating point multiplies and 35,835 adds, and where the data and algorithm fit within the 64-kbyte memory.

Paper Details

Date Published: 1 November 2009
PDF: 9 pages
Opt. Eng. 48(11) 114402 doi: 10.1117/1.3259359
Published in: Optical Engineering Volume 48, Issue 11
Show Author Affiliations
Kyle K. Mitchell, St. Louis Univ. (United States)
William J. Ebel, St. Louis Univ. (United States)
Steve E. Watkins, Missouri Univ. of Science and Technology (United States)


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