Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Zhang, Beibei"'
Publikováno v:
IEEE TRANSACTIONS ON BROADCASTING, VOL. 69, NO. 4, DECEMBER 2023
Orbital angular momentum (OAM) and rate splitting (RS) are the potential key techniques for the future wireless communications. As a new orthogonal resource, OAM can achieve the multifold increase of spectrum efficiency to relieve the scarcity of the
Externí odkaz:
http://arxiv.org/abs/2407.21478
Publikováno v:
IEEE WIRELESS COMMUNICATIONS LETTERS, VOL. 13, NO. 4, APRIL 2024
For users in hotspot region, orbital angular momentum (OAM) can realize multifold increase of spectrum efficiency (SE), and the flying base station (FBS) can rapidly support the real-time communication demand. However, the hollow divergence and align
Externí odkaz:
http://arxiv.org/abs/2407.21479
Autor:
Wu, Sifan, Liu, Zhenguang, Zhang, Beibei, Zimmermann, Roger, Ba, Zhongjie, Zhang, Xiaosong, Ren, Kui
Human motion copy is an intriguing yet challenging task in artificial intelligence and computer vision, which strives to generate a fake video of a target person performing the motion of a source person. The problem is inherently challenging due to t
Externí odkaz:
http://arxiv.org/abs/2406.16601
Autor:
Zhang, Beibei, Xiang, Tian, Mao, Chentao, Zheng, Yuhua, Li, Shuai, Niu, Haoyi, Xi, Xiangming, Bai, Wenyuan, Gao, Feng
Time-jerk optimal trajectory planning is crucial in advancing robotic arms' performance in dynamic tasks. Traditional methods rely on solving complex nonlinear programming problems, bringing significant delays in generating optimized trajectories. In
Externí odkaz:
http://arxiv.org/abs/2403.17353
The escalating size of Deep Neural Networks (DNNs) has spurred a growing research interest in hosting and serving DNN models across multiple devices. A number of studies have been reported to partition a DNN model across devices, providing device pla
Externí odkaz:
http://arxiv.org/abs/2312.04025
Autor:
Gao, Yuche, Zhang, Beibei
With the proliferation of video data in smart city applications like intelligent transportation, efficient video analytics has become crucial but also challenging. This paper proposes a semantics-driven cloud-edge collaborative approach for accelerat
Externí odkaz:
http://arxiv.org/abs/2309.15435
Autor:
Yang, Yuheng, Chen, Haipeng, Liu, Zhenguang, Lyu, Yingda, Zhang, Beibei, Wu, Shuang, Wang, Zhibo, Ren, Kui
Action recognition has long been a fundamental and intriguing problem in artificial intelligence. The task is challenging due to the high dimensionality nature of an action, as well as the subtle motion details to be considered. Current state-of-the-
Externí odkaz:
http://arxiv.org/abs/2306.07576
Autor:
Wang, Ran, Zhang, Beibei
We study a large deviation principle for a reflected stochastic partial differential equation on infinite spatial domain. A new sufficient condition for the weak convergence criterion proposed by Matoussi, Sabbagh and Zhang ({\it Appl. Math. Optim.}
Externí odkaz:
http://arxiv.org/abs/2207.06697
We study Freidlin-Wentzell's large deviation principle for one dimensional nonlinear stochastic heat equation driven by a Gaussian noise: $$\frac{\partial u^\varepsilon(t,x)}{\partial t} = \frac{\partial^2 u^\varepsilon(t,x)}{\partial x^2}+\sqrt{\var
Externí odkaz:
http://arxiv.org/abs/2205.13157
Autor:
Wang, Ran, Zhang, Beibei
In this paper, we establish a large deviation principle for the stochastic generalized Ginzburg-Landau equation driven by jump noise. The main difficulties come from the highly non-linear coefficient. Here we adopt a new sufficient condition for the
Externí odkaz:
http://arxiv.org/abs/2111.08292