Adaptive stochastic classifier for noisy pH-ISFET measurements

Authors:
Tong Boon Tang;Hsin Chen;Alan F. Murray
Affiliations:
School of Engineering and Electronics, The University of Edinburgh, Edinburgh, UK;School of Engineering and Electronics, The University of Edinburgh, Edinburgh, UK;School of Engineering and Electronics, The University of Edinburgh, Edinburgh, UK
Venue:
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Year:
2003

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Abstract

Sensor drift is an inevitable measurement problem and is particularly significant in the long term. The common practice is to have an autocalibration facility (including standard buffers or accurate integrated actuators) mounted on the monitoring system. However, this approach may not be feasible when the monitoring system is miniaturized to the size of a capsule. In this paper, we develop an adaptive stochastic classifier using analogue neural computation to produce constantly-reliable classification for noisy pH-ISFET measurements. This classifier operates at the signal-level fusion and auto-calibrates its parameters to compensate the sensor drift, with simple learning rules. The ability of the classifier to operate with a drift of 85% of the pH-ISFET's full dynamic range is demonstrated. This sensor fusion highlights the potential of neural computation in miniaturized multisensor analytical microsystems such as Lab-in-a-Pill (LIAP) for long-term measurements.