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Neural-Network-Based Smart Sensor Framework Operating in a Harsh Environment

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.

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Correspondence to Jagdish C. Patra.

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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.

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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

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  • DOI: https://doi.org/10.1155/ASP.2005.558

Keywords and phrases

  • intelligent sensors
  • artificial neural networks
  • autocompensation