no code implementations • 6 Nov 2023 • Huifa Li, Jie Fu, Zhili Chen, Xiaomin Yang, Haitao Liu, XinPeng Ling
Single-cell RNA sequencing (scRNA-seq) is important to transcriptomic analysis of gene expression.
no code implementations • 29 Mar 2023 • Lu Lu, Yi Yu, Zongsheng Zheng, Guangya Zhu, Xiaomin Yang
Two Andrew's sine estimator (ASE)-based robust adaptive filtering algorithms are proposed in this brief.
no code implementations • 19 Mar 2022 • Lu Lu, Yi Yu, Rodrigo C. de Lamare, Xiaomin Yang
We propose a novel M-estimate conjugate gradient (CG) algorithm, termed Tukey's biweight M-estimate CG (TbMCG), for system identification in impulsive noise environments.
no code implementations • 19 Oct 2021 • Lu Lu, Kai-Li Yin, Rodrigo C. de Lamare, Zongsheng Zheng, Yi Yu, Xiaomin Yang, Badong Chen
Most of the literature focuses on the development of the linear active noise control (ANC) techniques.
no code implementations • 1 Oct 2021 • Lu Lu, Kai-Li Yin, Rodrigo C. de Lamare, Zongsheng Zheng, Yi Yu, Xiaomin Yang, Badong Chen
Active noise control (ANC) is an effective way for reducing the noise level in electroacoustic or electromechanical systems.
no code implementations • 3 Jul 2020 • Shipeng Fu, Zhen Li, Jun Xu, Ming-Ming Cheng, Zitao Liu, Xiaomin Yang
Knowledge distillation is a standard teacher-student learning framework to train a light-weight student network under the guidance of a well-trained large teacher network.
1 code implementation • 9 Jul 2019 • Qilei Li, Zhen Li, Lu Lu, Gwanggil Jeon, Kai Liu, Xiaomin Yang
The rapid development of deep learning (DL) has driven single image super-resolution (SR) into a new era.
Ranked #18 on Image Super-Resolution on BSD100 - 4x upscaling
no code implementations • 27 May 2019 • Lihua Jian, Xiaomin Yang, Zheng Liu, Gwanggil Jeon, Mingliang Gao, David Chisholm
For the fusion stage, first, the trained model is utilized to extract the intermediate features and compensation features of two source images.
4 code implementations • CVPR 2019 • Zhen Li, Jinglei Yang, Zheng Liu, Xiaomin Yang, Gwanggil Jeon, Wei Wu
In this paper, we propose an image super-resolution feedback network (SRFBN) to refine low-level representations with high-level information.