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Multisensor Fusion for Low-Power Wireless Microsystems

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Perception-Action Cycle

Part of the book series: Springer Series in Cognitive and Neural Systems ((SSCNS))

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Abstract

This chapter addresses the use of artificial neural network (ANN) as a form of multisensor fusion for low-power microsystems in wireless sensor networks. The ANN is configured to perform local preprocessing and early clustering/classification of high-dimensional sensory signals. This chapter reviews the use of ANNs applied to fuse electrochemical sensory data, and the status of state-of-the-art VLSI neural hardware is presented. The hardware-amenability of these neural algorithms creates an opportunity to integrate multiple sensors and their data fusion within a single silicon chip, thus miniaturizing the physical size of microsystems and improving the signal integrity of measurements. Besides the operation of early classification, several other practical issues (i.e., stochastic noise, time-dependent drift, and biofouling) of electrochemical sensors are also discussed. Subsequently, a multisensor microsystem named Lab-in-a-Pill is used as a case study. We demonstrate how to implement an ANN to perform early classification and thus to autocalibrate an array of electrochemical sensors online. The chapter concludes with some discussion and future research directions.

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Notes

  1. 1.

    CI can be defined as the phenomenon that occurs when later training disrupts results of previous training and is characterized by the inability to incrementally learn sets of training patterns. CI is readily observed in studies of backpropagation. This phenomenon is also referred to as sequential learning and sometimes lifelong learning (Sarkaria 2004).

  2. 2.

    Lab-on-a-Chip is a technology that integrates complex laboratory sensors and sample-handling capabilities onto a glass or silicon plate (Figeys and Pinto 2000).

  3. 3.

    SoC is an electronics design methodology that integrates data analysis, instrumentation, and communication capabilities onto a single piece of silicon (Martin and Chang 2001). The reuse of pre-developed electronic circuit modules is an essential component of the SoC concept.

  4. 4.

    This input noise is external, not the purposely injected N j .

  5. 5.

    In this context, “reconstruction” refers to the ability of the CRBM to converge and regenerate the distribution of training data at the visible layer with its trained weights after several Gibbs-sampling steps, disregard to what the initial states of the visible neurons were.

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  1. School of Engineering, The University of Edinburgh, King’s Buildings, Mayfield Road, Edinburgh, EH9 3JL, UK

    Tong Boon Tang

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  1. Tong Boon Tang
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  1. Department of Psychology, Boston University, Boston, 02215, USA

    Vassilis Cutsuridis

  2. Dept. Computing Science, University of Stirling, Stirling, FK9 4LA, United Kingdom

    Amir Hussain

  3. King's College London, Dept. Mathematics, University of London, London, WC2R 2LS, United Kingdom

    John G. Taylor

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Tang, T.B., Murray, A.F. (2011). Multisensor Fusion for Low-Power Wireless Microsystems. In: Cutsuridis, V., Hussain, A., Taylor, J. (eds) Perception-Action Cycle. Springer Series in Cognitive and Neural Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-1452-1_22

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