Zobrazeno 1 - 10
of 249
pro vyhledávání: '"Liu, Hongshan"'
Recent advancements in Deep Learning (DL) for Direction of Arrival (DOA) estimation have highlighted its superiority over traditional methods, offering faster inference, enhanced super-resolution, and robust performance in low Signal-to-Noise Ratio (
Externí odkaz:
http://arxiv.org/abs/2405.02788
Autor:
Pathiravasan, Chathurangi H, Zhang, Yuankai, Trinquart, Ludovic, Benjamin, Emelia J, Borrelli, Belinda, McManus, David D, Kheterpal, Vik, Lin, Honghuang, Sardana, Mayank, Hammond, Michael M, Spartano, Nicole L, Dunn, Amy L, Schramm, Eric, Nowak, Christopher, Manders, Emily S, Liu, Hongshan, Kornej, Jelena, Liu, Chunyu, Murabito, Joanne M
Publikováno v:
Journal of Medical Internet Research, Vol 23, Iss 1, p e24773 (2021)
BackgroundeCohort studies offer an efficient approach for data collection. However, eCohort studies are challenged by volunteer bias and low adherence. We designed an eCohort embedded in the Framingham Heart Study (eFHS) to address these challenges a
Externí odkaz:
https://doaj.org/article/f6819a15ea004bedbbac24e3eb73074e
Autor:
Liu, Hongshan, Qin, Tong, Gao, Zhen, Mao, Tianqi, Ying, Keke, Wan, Ziwei, Qiao, Li, Na, Rui, Li, Zhongxiang, Hu, Chun, Mei, Yikun, Li, Tuan, Wen, Guanghui, Chen, Lei, Wu, Zhonghuai, Liu, Ruiqi, Chen, Gaojie, Wang, Shuo, Zheng, Dezhi
This article presents a comprehensive study on the emerging near-space communications (NS-COM) within the context of space-air-ground-sea integrated network (SAGSIN). Specifically, we firstly explore the recent technical developments of NS-COM, follo
Externí odkaz:
http://arxiv.org/abs/2401.00283
We introduce an interpretable deep learning approach for direction of arrival (DOA) estimation with a single snapshot. Classical subspace-based methods like MUSIC and ESPRIT use spatial smoothing on uniform linear arrays for single snapshot DOA estim
Externí odkaz:
http://arxiv.org/abs/2309.07411
Autor:
Huang, Ziyi, Liu, Hongshan, Zhang, Haofeng, Li, Xueshen, Liu, Haozhe, Xing, Fuyong, Laine, Andrew, Angelini, Elsa, Hendon, Christine, Gan, Yu
Segment anything model (SAM) has emerged as the leading approach for zero-shot learning in segmentation tasks, offering the advantage of avoiding pixel-wise annotations. It is particularly appealing in medical image segmentation, where the annotation
Externí odkaz:
http://arxiv.org/abs/2308.00883
There is a significant need for the generation of virtual histological information from coronary optical coherence tomography (OCT) images to better guide the treatment of coronary artery disease. However, existing methods either require a large pixe
Externí odkaz:
http://arxiv.org/abs/2307.12138
Optical coherence tomography (OCT) has stimulated a wide range of medical image-based diagnosis and treatment in fields such as cardiology and ophthalmology. Such applications can be further facilitated by deep learning-based super-resolution technol
Externí odkaz:
http://arxiv.org/abs/2307.11130
Histopathological analysis is crucial in artery characterization for coronary artery disease (CAD). However, histology requires an invasive and time-consuming process. In this paper, we propose to generate virtual histology staining using Optical Coh
Externí odkaz:
http://arxiv.org/abs/2211.06737
Autor:
Liu, Hongshan, Li, Xueshen, Bamba, Abdul Latif, Song, Xiaoyu, Brott, Brigitta C., Litovsky, Silvio H., Gan, Yu
Optical coherence tomography (OCT) has become increasingly essential in assisting the treatment of coronary artery disease (CAD). Image-guided solutions such as Percutaneous Coronary Intervention (PCI) are extensively used during the treatment of CAD
Externí odkaz:
http://arxiv.org/abs/2211.06728
Autor:
Liu, Hongshan, Aderon, Colin, Wagon, Noah, Bamba, Abdul Latif, Li, Xueshen, Liu, Huapu, MacCall, Steven, Gan, Yu
American football games attract significant worldwide attention every year. Identifying players from videos in each play is also essential for the indexing of player participation. Processing football game video presents great challenges such as crow
Externí odkaz:
http://arxiv.org/abs/2204.13809