Abstract
Sensor drift is an inevitable measurement problem and is particularly significant in the long term. The common practice is to have an auto-calibration 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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Tang, T.B., et al: Towards a Miniature Wireless Integrated Multisensor Microsystem for Industrial and Biomedical Applications. IEEE Sensors Journal: Special Issue on Integrated Multisensor Systems and Signal Processing 2(6) (December 2002) 628–635
Rashid, M.H.: Microelectronics Circuit Analysis and Design. Boston: PWS Publishing Company (1999)
Bergveld, P.: Development, operation, and application of the ion-sensitive field effect transistor as a tool for electrophysiology. IEEE Trans. on Biomedical Eng. 19 (1972) 342–351
Chen, H., Murray, A.F.: A Continuous Restricted Boltzmann Machine with a Hardware-Amenable Learning Algorithm. Proc. of the International Conference on Artificial Neural Networks (2002) 358–363
Hinton, G.E.: Training Products of Experts by Minimizing Contrastive Divergence. Neural Computation 14(8) (2002) 1771–1800
Smolensky, P.: Information processing in dynamic systems: Foundations of harmony theory. Parallel Distributed Processing: Explorations in the Microstructure of Cognition 1 (1986) 195–281
Murray, A.F.: Novelty detection using products of simple experts — a potential architecture for embedded systems. Neural Networks 14 (2001) 1257–1264
Chen, H., Murray, A.F.: A Continuous Restricted Boltzmann Machine with an Implementable Training Algorithm. IEE Proc. of Vision, Image, Signal Processing, in press (2003)
Lei, W., et al: Integrated micro-instrumentation for dynamic monitoring of the gastro-intestinal tract. Proc. of the 2nd Annual International IEEE EMB Special Topic Conference on Microtechnologies in Medicine and Biology (2002) 219–222
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tang, T.B., Chen, H., Murray, A.F. (2003). Adaptive Stochastic Classifier for Noisy pH-ISFET Measurements. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_76
Download citation
DOI: https://doi.org/10.1007/3-540-44989-2_76
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40408-8
Online ISBN: 978-3-540-44989-8
eBook Packages: Springer Book Archive