Low-power Hardware Implementation Of Artificial Neural Network Strain Detection For Extrinsic Fabry-Pérot Interferometric Sensors Under Sinusoidal Excitation

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 64kbytes 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.

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

artificial neural networks; fiber optic strain sensors; health monitoring; smart structures; vibration testing

International Standard Serial Number (ISSN)

1560-2303; 0091-3286

Document Type

Article - Journal

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2023 Society of Photo-optical Instrumentation Engineers, All rights reserved.

Publication Date

01 Dec 2009

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