Browsing by Author "Li, Shujing"

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    An adaptive energy efficient MAC protocol for RF energy harvesting WBANs
    (IEEE, 2022-11-17) Hu, Juncheng; Xu, Gaochao; Hu, Liang; Li, Shujing; Xing, Yang
    Continuous and remote health monitoring medical applications with heterogeneous requirements can be realized through wireless body area networks (WBANs). Energy harvesting is adopted to enable low-power health applications and long-term monitoring without battery replacement, which have drawn significant interest recently. Because energy harvesting WBANs are obviously different from battery-powered ones, network protocols should be designed accordingly to improve network performance. In this article, an efficient cross-layer media access control protocol is proposed for radio frequency powered energy harvesting WBANs. We redesigned the superframe structure, which can be rescheduled by the coordinator dynamically. A time switching (TS) strategy is used when sensors harvest energy from radio frequency signals broadcast by the coordinator, and a transmission power adjustment scheme is proposed for sensors based on the energy harvesting efficiency and the network environment. Energy efficiency can be effectively improved that more packets can be uploaded using limited energy. The length of the energy harvesting period is determined by the coordinator to balance the channel resources and energy requirements of sensors and further improve the network performance. Numerical simulation results show that our protocol can provide superior system performance for long-term periodic health monitoring applications.
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    ItemOpen Access
    Driver steering behaviour modelling based on neuromuscular dynamics and multi‑task time‑series transformer
    (Springer, 2024-01-11) Xing, Yang; Hu, Zhongxu; Mo, Xiaoyu; Hang, Peng; Li, Shujing; Liu, Yahui; Zhao, Yifan; Lv, Chen
    Driver steering intention prediction provides an augmented solution to the design of an onboard collaboration mechanism between human driver and intelligent vehicle. In this study, a multi-task sequential learning framework is developed to predict future steering torques and steering postures based on upper limb neuromuscular electromyography signals. The joint representation learning for driving postures and steering intention provides an in-depth understanding and accurate modelling of driving steering behaviours. Regarding different testing scenarios, two driving modes, namely, both-hand and single-right-hand modes, are studied. For each driving mode, three different driving postures are further evaluated. Next, a multi-task time-series transformer network (MTS-Trans) is developed to predict the future steering torques and driving postures based on the multi-variate sequential input and the self-attention mechanism. To evaluate the multi-task learning performance and information-sharing characteristics within the network, four distinct two-branch network architectures are evaluated. Empirical validation is conducted through a driving simulator-based experiment, encompassing 21 participants. The proposed model achieves accurate prediction results on future steering torque prediction as well as driving posture recognition for both two-hand and single-hand driving modes. These findings hold significant promise for the advancement of driver steering assistance systems, fostering mutual comprehension and synergy between human drivers and intelligent vehicles.