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

• Main Authors: Chaudhari Narendra S, Patra Jagdish C, Ang Ee Luang, Das Amitabha
• Format: Article
• Language: English
• Published: SpringerOpen 2005-01-01
• Series: EURASIP Journal on Advances in Signal Processing
• Subjects: intelligent sensors; artificial neural networks; autocompensation
• Online Access: http://dx.doi.org/10.1155/ASP.2005.558

<p/> <p>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 <inline-formula><graphic file="1687-6180-2005-498294-i1.gif"/></inline-formula>. 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 <inline-formula><graphic file="1687-6180-2005-498294-i2.gif"/></inline-formula> 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.</p>