Neural-Network-Based Smart Sensor Framework Operating in a Harsh Environment
<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, wi...
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Online Access: | http://dx.doi.org/10.1155/ASP.2005.558 |
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doaj-5f93eb5001fb4f9fb9fb32f89a670ed02020-11-24T21:40:51ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802005-01-0120054498294Neural-Network-Based Smart Sensor Framework Operating in a Harsh EnvironmentChaudhari Narendra SPatra Jagdish CAng Ee LuangDas Amitabha<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>http://dx.doi.org/10.1155/ASP.2005.558intelligent sensorsartificial neural networksautocompensation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chaudhari Narendra S Patra Jagdish C Ang Ee Luang Das Amitabha |
spellingShingle |
Chaudhari Narendra S Patra Jagdish C Ang Ee Luang Das Amitabha Neural-Network-Based Smart Sensor Framework Operating in a Harsh Environment EURASIP Journal on Advances in Signal Processing intelligent sensors artificial neural networks autocompensation |
author_facet |
Chaudhari Narendra S Patra Jagdish C Ang Ee Luang Das Amitabha |
author_sort |
Chaudhari Narendra S |
title |
Neural-Network-Based Smart Sensor Framework Operating in a Harsh Environment |
title_short |
Neural-Network-Based Smart Sensor Framework Operating in a Harsh Environment |
title_full |
Neural-Network-Based Smart Sensor Framework Operating in a Harsh Environment |
title_fullStr |
Neural-Network-Based Smart Sensor Framework Operating in a Harsh Environment |
title_full_unstemmed |
Neural-Network-Based Smart Sensor Framework Operating in a Harsh Environment |
title_sort |
neural-network-based smart sensor framework operating in a harsh environment |
publisher |
SpringerOpen |
series |
EURASIP Journal on Advances in Signal Processing |
issn |
1687-6172 1687-6180 |
publishDate |
2005-01-01 |
description |
<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> |
topic |
intelligent sensors artificial neural networks autocompensation |
url |
http://dx.doi.org/10.1155/ASP.2005.558 |
work_keys_str_mv |
AT chaudharinarendras neuralnetworkbasedsmartsensorframeworkoperatinginaharshenvironment AT patrajagdishc neuralnetworkbasedsmartsensorframeworkoperatinginaharshenvironment AT angeeluang neuralnetworkbasedsmartsensorframeworkoperatinginaharshenvironment AT dasamitabha neuralnetworkbasedsmartsensorframeworkoperatinginaharshenvironment |
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1725924313592233984 |