dc.contributor.authorPatra, Jagdish Chandra
dc.contributor.authorAng, Ee Luang
dc.contributor.authorChaudhari, Narendra Shivaji
dc.contributor.authorDas, Amitabha
dc.identifier.citationPatra, J. C., Ang, E. L., Chaudhari, N. S., & Das, A. (2005). Neural-Network-Based Smart Sensor Framework Operating in a Harsh Environment. EURASIP Journal on Applied Signal Processing, 2005(4), 558-574.en_US
dc.description.abstractWe 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 250° C. 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 ±1.0% 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.en_US
dc.format.extent28 p.en_US
dc.relation.ispartofseriesEURASIP journal on applied signal processingen_US
dc.rights© 2005 Jagdish C. Patra et al.en_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation
dc.titleNeural-network-based smart sensor framework operating in a harsh environmenten_US
dc.typeJournal Article
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.description.versionPublished versionen_US

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