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    UAV-based Semi-Autonomous Data Acquisition and Classification

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    Date
    2018
    Author
    Said A.B.
    Mohamed A.
    Elfouly T.
    Abualsaud K.
    Harras K.
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    Abstract
    In the context of mobile Health (mHealth) applications, data are prone to several sources of contamination which would lead to false interpretation and misleading classification results. In this paper, a robust deep learning approach with low rank model is proposed to classify mHealth vital signs. Further-more, we propose using the Schatten-p norm instead of the classic nuclear norm since it has shown better recovery performance for several applications. We conduct a comprehensive study where we compare our method to the state-of-art methods and evaluate its performance with respect to the key system parameters. Our findings show indeed that combining deep network with dictionary learning model is effective for vital signs classification even in presence of 50% corruption with 8% improvement over the closest performance.

    DOI/handle
    http://dx.doi.org/10.1109/IWCMC.2018.8450434
    http://hdl.handle.net/10576/13225
    Collections
    • Computer Science & Engineering [‎634 ‎ items ]

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