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

Kyle K. Mitchell; William J. Ebel Sr.; Steve E. Watkins

doi:10.1117/1.3259359

1 November 2009 Low-power hardware implementation of artificial neural network strain detection for extrinsic Fabry-Pérot interferometric sensors under sinusoidal excitation

Kyle K. Mitchell, William J. Ebel Sr., Steve E. Watkins

Author Affiliations +

Optical Engineering, Vol. 48, Issue 11, 114402 (November 2009). https://doi.org/10.1117/1.3259359

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.

©(2009) Society of Photo-Optical Instrumentation Engineers (SPIE)

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Kyle K. Mitchell, William J. Ebel Sr., and Steve E. Watkins "Low-power hardware implementation of artificial neural network strain detection for extrinsic Fabry-Pérot interferometric sensors under sinusoidal excitation," Optical Engineering 48(11), 114402 (1 November 2009). https://doi.org/10.1117/1.3259359

Published: 1 November 2009

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