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- W3047032776 abstract "This study proposes a personal identification technique that applies machine learning with a two-layered convolutional neural network to spectrogram images obtained from radar echoes of a target person in motion. The walking and sitting motions of six participants were measured using an ultrawideband radar system. Time-frequency analysis was applied to the radar signal to generate spectrogram images containing the micro-Doppler components associated with limb movements. A convolutional neural network was trained using the spectrogram images with personal labels to achieve radar-based personal identification. The personal identification accuracies were evaluated experimentally to demonstrate the effectiveness of the proposed technique." @default.
- W3047032776 created "2020-08-10" @default.
- W3047032776 creator A5087021066 @default.
- W3047032776 date "2020-08-05" @default.
- W3047032776 modified "2023-09-23" @default.
- W3047032776 title "Personal Identification Using Ultrawideband Radar Measurement of Walking and Sitting Motions and a Convolutional Neural Network" @default.
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- W3047032776 doi "https://doi.org/10.13140/rg.2.2.36193.53600" @default.
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