Demo Abstract: Demonstrating Principal Component Aggregation for Distributed Spatial Pattern Recognition

机译：演示摘要：演示用于分布式空间模式识别的主成分聚合

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摘要

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 sensor 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.

机译：最近，提出了主成分聚合作为用于传感器网络的通用分布式信息提取技术[3]。此演示说明了其在网络级模式识别任务中的用法。可以通过27个节点的网络的光照测量来感测四个不同的模式或事件。传感器的测量值沿着路由树动态融合到基站，在基站中，预测算法可以识别出所监视的模式。

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来源
《9th ACM/IEEE international conference on information processing in sensor networks 2010》|2010年|p.430-431|共2页
会议地点 Stockholm(SE);Stockholm(SE)
作者
Yann-Ael Le Borgne; Kris Steenhaut; Gianluca Bqntempi;
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作者单位

Ann Nowe Computational Modeling Lab Vrije Universiteit Brussel Brussels, Belgium;

Etro Lab Vrije Universiteit Brussel Brussels, Belgium;

Machine Learning Group Universite Libre de Bruxelles Brussels, Belgium;

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会议组织
原文格式 PDF
正文语种 eng
中图分类信息处理（信息加工）;
关键词
algorithms; experimentation;

机译：算法;实验;

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Demo Abstract: Demonstrating Principal Component Aggregation for Distributed Spatial Pattern Recognition

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