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• W2783227304 abstract "In this paper, the possibility of using an emerging approach, namely device-free sensing (DFS) technology, for classification of human activities is investigated. To fully evaluate this approach, several samples have been collected in an outdoor open-field environment. Using the collected data along with a classifier, a high-order cumulant (HOC) based feature extraction algorithm is investigated. To demonstrate the improvement of using this algorithm, the classical approach that is based on received-signal strength (RSS) is chosen as a benchmark. The experiment results demonstrated that the classification accuracy of the proposed algorithm is better than the classical approach by at least 15%. In addition, the reliability of the presented approach due to variation of training samples and signal-to-noise ratio (SNR) are also carefully tested using experimentally recorded samples, so that a good reliability can be ensured." @default.
• W2783227304 created "2018-01-26" @default.
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• W2783227304 date "2017-09-01" @default.
• W2783227304 modified "2023-09-26" @default.
• W2783227304 title "Device-free sensing for classification of human activities using high-order cumulant algorithm" @default.
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• W2783227304 doi "https://doi.org/10.1109/iscit.2017.8261203" @default.
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