Demonstrating principal component aggregation for distributed spatial pattern recognition
Yann‐Aël Le Borgne, Ann Nowé, Kris Steenhaut, Etro Lab, Gianluca Bontempi · 2010
The Principal Component Aggregation has recently been proposed as a versatile distributed information extraction technique for sensor networks [3]. This demonstration illustrates its use for a network-level pattern recognition task. Four different patterns, or events, may be sensed by light measurements of a network of 27 nodes. The sens measurements are fused on the fly along a routing tree up to the base station, where the monitored pattern is recognized by a prediction algorithm.