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Adaptive, integrated sensor processing to compensate for drift and uncertainty: a stochastic 'neural' approach

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3 Author(s)
T. B. Tang ; Sch. of Eng. & Electron., Univ. of Edinburgh, UK ; H. Chen ; A. F. Murray

An adaptive stochastic classifier based on a simple, novel neural architecture - the Continuous Restricted Boltzmann Machine (CRBM) is demonstrated. Together with sensors and signal conditioning circuits, the classifier is capable of measuring and classifying (with high accuracy) the H+ ion concentration, in the presence of both random noise and sensor drift. Training on-line, the stochastic classifier is able to overcome significant drift of real incomplete sensor data dynamically. As analogue hardware, this signal-level sensor fusion scheme is therefore suitable for real-time analysis in a miniaturised multisensor microsystem such as a Lab-in-a-Pill (LIAP).

Published in:

IEE Proceedings - Nanobiotechnology  (Volume:151 ,  Issue: 1 )