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

Author(s): Patra Jagdish C | Ang Ee Luang | Chaudhari Narendra S | Das Amitabha

Journal: EURASIP Journal on Advances in Signal Processing
ISSN 1687-6172

Volume: 2005;
Issue: 4;
Start page: 498294;
Date: 2005;
Original page

Keywords: intelligent sensors | artificial neural networks | autocompensation

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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