Zobrazeno 1 - 10
of 1 301
pro vyhledávání: '"So, Hing Cheung"'
One-bit sampling has emerged as a promising technique in multiple-input multiple-output (MIMO) radar systems due to its ability to significantly reduce data volume and processing requirements. Nevertheless, current detection methods have not adequate
Externí odkaz:
http://arxiv.org/abs/2403.06756
Autor:
Xiong, Wenxin, Chen, Yuming, He, Jiajun, Shi, Zhang-Lei, Hu, Keyuan, So, Hing Cheung, Leung, Chi-Sing
This short communication addresses the problem of elliptic localization with outlier measurements. Outliers are prevalent in various location-enabled applications, and can significantly compromise the positioning performance if not adequately handled
Externí odkaz:
http://arxiv.org/abs/2401.15619
With the emerging environment-aware applications, ubiquitous sensing is expected to play a key role in future networks. In this paper, we study a 3-dimensional (3D) multi-target localization system where multiple intelligent reflecting surfaces (IRSs
Externí odkaz:
http://arxiv.org/abs/2310.15574
To alleviate the bias generated by the l1-norm in the low-rank tensor completion problem, nonconvex surrogates/regularizers have been suggested to replace the tensor nuclear norm, although both can achieve sparsity. However, the thresholding function
Externí odkaz:
http://arxiv.org/abs/2310.06233
This paper presents a novel loss function referred to as hybrid ordinary-Welsch (HOW) and a new sparsity-inducing regularizer associated with HOW. We theoretically show that the regularizer is quasiconvex and that the corresponding Moreau envelope is
Externí odkaz:
http://arxiv.org/abs/2310.04762
Autor:
Wang, Zhi-Yong, So, Hing Cheung
Applying half-quadratic optimization to loss functions can yield the corresponding regularizers, while these regularizers are usually not sparsity-inducing regularizers (SIRs). To solve this problem, we devise a framework to generate an SIR with clos
Externí odkaz:
http://arxiv.org/abs/2310.04954
Autor:
Wang, Zhi-Yong, So, Hing Cheung
M-estmators including the Welsch and Cauchy have been widely adopted for robustness against outliers, but they also down-weigh the uncontaminated data. To address this issue, we devise a framework to generate a class of nonconvex functions which only
Externí odkaz:
http://arxiv.org/abs/2310.04953
We tackle the network topology inference problem by utilizing Laplacian constrained Gaussian graphical models, which recast the task as estimating a precision matrix in the form of a graph Laplacian. Recent research \cite{ying2020nonconvex} has uncov
Externí odkaz:
http://arxiv.org/abs/2309.00960
The target sensing/localization performance is fundamentally limited by the line-of-sight link and severe signal attenuation over long distances. This paper considers a challenging scenario where the direct link between the base station (BS) and the
Externí odkaz:
http://arxiv.org/abs/2307.09232
Spectrum sensing in cognitive radio necessitates effective monitoring of wide bandwidths, which requires high-rate sampling. Traditional spectrum sensing methods employing high-precision analog-to-digital converters (ADCs) result in increased power c
Externí odkaz:
http://arxiv.org/abs/2306.13558