Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly
One shot associative memory method for distorted pattern recognition
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Distributed Multi-Feature Recognition Scheme for Greyscale Images
Neural Processing Letters
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
EUC'05 Proceedings of the 2005 international conference on Embedded and Ubiquitous Computing
Review: Multi-agent systems for protecting critical infrastructures: A survey
Journal of Network and Computer Applications
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Rapid anomaly detection for smart grid infrastructures through hierarchical pattern matching
International Journal of Security and Networks
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The computational properties of a wireless sensor network (WSN) have been investigated by implementing a fully distributed pattern recognition algorithm within the network. It is shown that the set up allows a physical object to develop a capability, which to some extent may be considered similar to our sense of touch, with the WSN acting as an artificial nervous system in this regard. The effectiveness of the algorithm is inspected by comparing the outputs from the sensors with the stress patterns generated through a simple finite element model and then stored within the network. It is shown that the test object could successfully differentiate between its internal stress states resulting from the changes to its external loading conditions. Suitability of the algorithm is discussed with respect to the data storage requirement per node of the WSN.