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Open Access

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

  • Jagdish C. Patra1Email author,
  • Ee Luang Ang1,
  • Narendra S. Chaudhari2 and
  • Amitabha Das1
EURASIP Journal on Advances in Signal Processing20052005:498294

Received: 11 February 2004

Published: 30 March 2005


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.

Keywords and phrases

intelligent sensorsartificial neural networksautocompensation

Authors’ Affiliations

Division of Computer Communications, School of Computer Engineering, Nanyang Technological University, Singapore, Singapore
Division of Information Systems, School of Computer Engineering, Nanyang Technological University, Singapore, Singapore


© Patra et al. 2005