Multisensor Fusion for Low-Power Wireless Microsystems

  • Tong Boon Tang
  • Alan F. Murray
Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS)


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.


Artificial Neural Network Hide Neuron Cellular Neural Network Stochastic Noise Bayesian Belief Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Authors and Affiliations

  1. 1.School of EngineeringThe University of EdinburghEdinburghUK

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