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Updated: Aug 18, 2026

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
Published on: December 10, 2014
Auto-compensation of nonlinear influence of environmental parameters on the sensor characteristics using neural
Jagdish Chandra Patra1, Ee Luang Ang, Amitabha Das
1School of Computer Engineering, Nanyang Technological University, Singapore 639798. aspatra@ntu.edu.sg
Abstract:
Usually the environmental parameters influence the sensor characteristics in a nonlinear manner. Therefore obtaining correct readout from a sensor under varying environmental conditions is a complex problem. In this paper we propose a neural network (NN)-based interface framework to automatically compensate for the nonlinear influence of the environmental temperature and the nonlinear-response characteristics of a capacitive pressure sensor (CPS) to provide correct readout. With extensive simulation studies we have shown that the NN-based inverse model of the CPS can estimate the applied pressure with a maximum error of +/- 1.0% for a wide temperature variation from 0 to 250 degrees C. A microcontroller unit-based implementation scheme is also proposed.
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