22 May 2015 Virtual sensor tracking using byzantine fault tolerance and predictive outlier model for complex tasks recognition
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Abstract
A general nonparametric technique is proposed for the analysis of multi-resolution and multivariate feature space to isolate faulty sensors. The basic overlap function of the technique is an existing one-dimensional fault-detection Brooks-Iyengar algorithm which uses weighted precision and accuracy for static data. We prove the dual of the existing overlap function can isolate the measurement intervals in the multi-dimensional feature space for both labelled and unlabeled publicly available datasets. It is shown that computable complexity of learning the feature space increases linearly with the size of the input. The experimental results showed that by using mean average precision of all sensors using ensemble model for dynamic events. The proposed algorithm performed well in the presence of noise across many static and dynamic action recognition datasets.
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Vasanth Iyer, Sachin Shetty, "Virtual sensor tracking using byzantine fault tolerance and predictive outlier model for complex tasks recognition", Proc. SPIE 9478, Modeling and Simulation for Defense Systems and Applications X, 94780F (22 May 2015); doi: 10.1117/12.2179406; https://doi.org/10.1117/12.2179406
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