Abstract
We present an artificial neural-network- (NN-) based smart interface framework for sensors operating in harsh environments. The NN-based sensor can automatically compensate for the nonlinear response characteristics and its nonlinear dependency on the environmental parameters, with high accuracy. To show the potential of the proposed NN-based framework, we provide results of a smart capacitive pressure sensor (CPS) operating in a wide temperature range of 0 to 
. Through simulated experiments, we have shown that the NN-based CPS model is capable of providing pressure readout with a maximum full-scale (FS) error of only 
over this temperature range. A novel scheme for estimating the ambient temperature from the sensor characteristics itself is proposed. For this purpose, a second NN is utilized to estimate the ambient temperature accurately from the knowledge of the offset capacitance of the CPS. A microcontroller-unit- (MCU-) based implementation scheme is also provided.
Article PDF
Similar content being viewed by others
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
About this article
Cite this article
Patra, J.C., Ang, E.L., Chaudhari, N.S. et al. Neural-Network-Based Smart Sensor Framework Operating in a Harsh Environment. EURASIP J. Adv. Signal Process. 2005, 498294 (2005). https://doi.org/10.1155/ASP.2005.558
Received:
Revised:
Published:
DOI: https://doi.org/10.1155/ASP.2005.558