Motion-based user identification works reliably inside one XR application but generalizes poorly across different applications.
In: 2024 IEEE 25th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp
2 Pith papers cite this work. Polarity classification is still indexing.
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Environment-conditioned parametric regression on 12-month indoor LoRaWAN data reduces cross-validated RMSE from 8.23 dB to 7.38 dB and lowers the fade margin needed for 99% reliability from ~28 dB to 25.73 dB.
citing papers explorer
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Motion-Based User Identification across XR and Metaverse Applications by Deep Classification and Similarity Learning
Motion-based user identification works reliably inside one XR application but generalizes poorly across different applications.
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Environment-Aware Indoor LoRaWAN Path Loss: Parametric Regression Comparisons, Shadow Fading, and Calibrated Fade Margins
Environment-conditioned parametric regression on 12-month indoor LoRaWAN data reduces cross-validated RMSE from 8.23 dB to 7.38 dB and lowers the fade margin needed for 99% reliability from ~28 dB to 25.73 dB.