Zobrazeno 1 - 10
of 334
pro vyhledávání: '"Wang Shengbo"'
Autor:
Tang, Chenyu, Zhang, Ruizhi, Gao, Shuo, Zhao, Zihe, Zhang, Zibo, Wang, Jiaqi, Li, Cong, Chen, Junliang, Dai, Yanning, Wang, Shengbo, Juan, Ruoyu, Li, Qiaoying, Xie, Ruimou, Chen, Xuhang, Zhou, Xinkai, Xia, Yunjia, Chen, Jianan, Lu, Fanghao, Li, Xin, Wang, Ninglli, Smielewski, Peter, Pan, Yu, Zhao, Hubin, Occhipinti, Luigi G.
At-home rehabilitation for post-stroke patients presents significant challenges, as continuous, personalized care is often limited outside clinical settings. Additionally, the absence of comprehensive solutions addressing diverse rehabilitation needs
Externí odkaz:
http://arxiv.org/abs/2411.19000
Autor:
Tang, Chenyu, Gao, Shuo, Li, Cong, Yi, Wentian, Jin, Yuxuan, Zhai, Xiaoxue, Lei, Sixuan, Meng, Hongbei, Zhang, Zibo, Xu, Muzi, Wang, Shengbo, Chen, Xuhang, Wang, Chenxi, Yang, Hongyun, Wang, Ningli, Wang, Wenyu, Cao, Jin, Feng, Xiaodong, Smielewski, Peter, Pan, Yu, Song, Wenhui, Birchall, Martin, Occhipinti, Luigi G.
Wearable silent speech systems hold significant potential for restoring communication in patients with speech impairments. However, seamless, coherent speech remains elusive, and clinical efficacy is still unproven. Here, we present an AI-driven inte
Externí odkaz:
http://arxiv.org/abs/2411.18266
In this paper we investigate the tractability of robust Markov Decision Processes (RMDPs) under various structural assumptions on the uncertainty set. Surprisingly, we show that in all generality (i.e. without any assumption on the instantaneous rewa
Externí odkaz:
http://arxiv.org/abs/2411.08435
Autor:
Wang, Shengbo, Li, Xuemeng, Ding, Jialin, Ma, Weihao, Wang, Ying, Occhipinti, Luigi, Nathan, Arokia, Gao, Shuo
Memristive associative learning has gained significant attention for its ability to mimic fundamental biological learning mechanisms while maintaining system simplicity. In this work, we introduce a high-order memristive associative learning framewor
Externí odkaz:
http://arxiv.org/abs/2410.16734
Optical flow is crucial for robotic visual perception, yet current methods primarily operate in a 2D format, capturing movement velocities only in horizontal and vertical dimensions. This limitation results in incomplete motion cues, such as missing
Externí odkaz:
http://arxiv.org/abs/2409.15345
Autor:
Wang, Shengbo, Pei, Jingfang, Li, Cong, Li, Xuemeng, Tao, Li, Nathan, Arokia, Hu, Guohua, Gao, Shuo
Neuromorphic devices, with their distinct advantages in energy efficiency and parallel processing, are pivotal in advancing artificial intelligence applications. Among these devices, memristive transistors have attracted significant attention due to
Externí odkaz:
http://arxiv.org/abs/2408.15140
Overparameterized stochastic differential equation (SDE) models have achieved remarkable success in various complex environments, such as PDE-constrained optimization, stochastic control and reinforcement learning, financial engineering, and neural S
Externí odkaz:
http://arxiv.org/abs/2407.10065
We explore the control of stochastic systems with potentially continuous state and action spaces, characterized by the state dynamics $X_{t+1} = f(X_t, A_t, W_t)$. Here, $X$, $A$, and $W$ represent the state, action, and exogenous random noise proces
Externí odkaz:
http://arxiv.org/abs/2406.11281
Autor:
Wang, Shengbo, Fang, Mingchao, Song, Lekai, Li, Cong, Zhang, Jian, Nathan, Arokia, Hu, Guohua, Gao, Shuo
Artificial nociceptors, mimicking human-like stimuli perception, are of significance for intelligent robotics to work in hazardous and dynamic scenarios. One of the most essential characteristics of the human nociceptor is its self-adjustable attribu
Externí odkaz:
http://arxiv.org/abs/2406.09304
Autor:
Wang, Shengbo, Li, Cong, Pu, Tongming, Zhang, Jian, Ma, Weihao, Occhipinti, Luigi, Nathan, Arokia, Gao, Shuo
Publikováno v:
2024 IEEE Biomedical Circuits and Systems Conference (BioCAS)
Memristive neuromorphic systems are designed to emulate human perception and cognition, where the memristor states represent essential historical information to perform both low-level and high-level tasks. However, current systems face challenges wit
Externí odkaz:
http://arxiv.org/abs/2406.00378