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

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

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

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

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摘要

Artificial neural networks are studied for use in estimating strain in extrinsic Fabry-Perot 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.

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来源
《Optical Engineering》 |2009年第11期|114402-1-114402-9|共9页
作者
Kyle Mitchell; William J. Ebel; Steve E. Watkins;
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作者单位

St. Louis University, Center of Sensors and Sensors Systems, Department of Electrical and Computer Engineering, St. Louis, Missouri 63103;

University of Missouri-Rolla, Department of Electrical and Computer Engineering, Applied Optics Laboratory, Rolla, Missouri 65409-0040;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类计量学;
关键词
fiber optic strain sensors; artificial neural networks; vibration testing; health monitoring; smart structures;

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

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