Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
First principal components analysis: a new side channel distinguisher
ICISC'10 Proceedings of the 13th international conference on Information security and cryptology
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A mobile robot system usually has multiple sensors of various types. In a dynamic and unstructured environment, information processing and decision making using the data acquired by these sensors pose a signi.cant challenge. Kalman .lter- based methods have been developed for fusing data from various sensors for mobile robots. However, the Kalman .lter methods are computationally intensive. Markov and Monte Carlo methods are even less e.cient than Kalman .lter methods. In this paper, we present an alternative method based on principal component analysis (PCA) for processing the data acquired with multiple sensors.