Search Results for author: Rui Du

Found 9 papers, 3 papers with code

An Overview on IEEE 802.11bf: WLAN Sensing

no code implementations20 Oct 2023 Rui Du, Haocheng Hua, Hailiang Xie, Xianxin Song, Zhonghao Lyu, Mengshi Hu, Narengerile, Yan Xin, Stephen McCann, Michael Montemurro, Tony Xiao Han, Jie Xu

To resolve this issue, a new Task Group (TG), namely IEEE 802. 11bf, has been established by the IEEE 802. 11 working group, with the objective of creating a new amendment to the WLAN standard to meet advanced sensing requirements while minimizing the effect on communications.

Quantization

Fundamental Limits and Optimization of Multiband Sensing

no code implementations21 Jul 2022 Yubo Wan, An Liu, Rui Du, Tony Xiao Han

Then, a metric called the statistical resolution limit (SRL) that provides a resolution limit is employed to investigate the fundamental limits of delay resolution.

Networked Sensing in 6G Cellular Networks: Opportunities and Challenges

no code implementations1 Jun 2022 Liang Liu, Shuowen Zhang, Rui Du, Tong Xiao Han, Shuguang Cui

This article will discuss about the possibility of exploiting the future sixth-generation (6G) cellular network to realize ISAC.

Mapping evolving population geography in China

1 code implementation4 Mar 2022 Lei Dong, Rui Du, Yu Liu

China's demographic changes have important global economic and geopolitical implications.

A Gauss-Seidel projection method with the minimal number of updates for stray field in micromagnetic simulations

no code implementations21 Jan 2021 Panchi Li, Zetao Ma, Rui Du, Jingrun Chen

However, GSPM-BDF2 updates the stray field only once per time step, leading to an efficiency improvement of about $60\%$ than the state-of-the-art GSPM for micromagnetic simulations.

Numerical Analysis Numerical Analysis

Enforcing exact boundary and initial conditions in the deep mixed residual method

1 code implementation4 Aug 2020 Liyao Lyu, Keke Wu, Rui Du, Jingrun Chen

In theory, boundary and initial conditions are important for the wellposedness of partial differential equations (PDEs).

Numerical Analysis Numerical Analysis

Quasi-Monte Carlo sampling for machine-learning partial differential equations

no code implementations5 Nov 2019 Jingrun Chen, Rui Du, Panchi Li, Liyao Lyu

Often, a deep neural network outperforms classical numerical methods in terms of both accuracy and efficiency.

BIG-bench Machine Learning Numerical Integration

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