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Tianyu Xu - IEEE Xplore Author Profile
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Tianyu Xu

Publications
4
Citations
42
Publications by Year
20212024
Co-Authors:
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Tianyu Xu

Affiliation

School of Electronic and Information Engineering
Soochow University
Suzhou, China

Publication Topics

Biography

Tianyu Xu received the BS degree from Jiangnan University. She is currently working toward the MSc degree with the Soochow University. Her research interests include information extraction, data mining and natural language processing.(Based on document published on 6 July 2023).
Publications
4
Citations
42
Publications by Year
20212024
Co-Authors:
Show All Co-Authors (74)
Author's Published Works

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Double-Threshold Energy Detection in Cognitive VLC Systems with LED Nonlinearity

Tianyu Xu;Xiaodi You;Zile Jiang;Jian Chen;Changyuan Yu;Mingyi Gao;Gangxiang Shen

2024 IEEE/CIC International Conference on Communications in China (ICCC)
Year: 2024 | Conference Paper |
In cognitive visible light communication (CVLC) systems, the nonlinearity of light-emitting diodes (LEDs) poses a challenge to the spectrum sensing accuracy. To address this challenge, we propose a double-threshold energy detection (DTED) scheme for CVLC systems under LED nonlinearity conditions. This scheme incorporates an uncertainty region, offering secondary users (SUs) increased opportunities...Show More

Double-Threshold Energy Detection in Cognitive VLC Systems with LED Nonlinearity

Tianyu Xu;Xiaodi You;Zile Jiang;Jian Chen;Changyuan Yu;Mingyi Gao;Gangxiang Shen

Year: 2024 | Conference Paper |

Evidence Reasoning and Curriculum Learning for Document-Level Relation Extraction

Tianyu Xu;Jianfeng Qu;Wen Hua;Zhixu Li;Jiajie Xu;An Liu;Lei Zhao;Xiaofang Zhou

IEEE Transactions on Knowledge and Data Engineering
Year: 2024 | Volume: 36, Issue: 2 | Journal Article |
Cited by: Papers (1)
Document-level Relation Extraction (RE) is a promising task aiming at identifying relations of multiple entity pairs in a document. Compared with the sentence-level counterpart, it has raised two significant challenges: a) In most cases, a relational fact can be adequately expressed via a small subset of sentences from the document, namely evidence. But the traditional method cannot model such str...Show More

Evidence Reasoning and Curriculum Learning for Document-Level Relation Extraction

Tianyu Xu;Jianfeng Qu;Wen Hua;Zhixu Li;Jiajie Xu;An Liu;Lei Zhao;Xiaofang Zhou

Year: 2024 | Volume: 36, Issue: 2 | Journal Article |

NTIRE 2023 Challenge on 360° Omnidirectional Image and Video Super-Resolution: Datasets, Methods and Results

Mingdeng Cao;Chong Mou;Fanghua Yu;Xintao Wang;Yinqiang Zheng;Jian Zhang;Chao Dong;Gen Li;Ying Shan;Radu Timofte;Xiaopeng Sun;Weiqi Li;Zhenyu Zhang;Xuhan Sheng;Bin Chen;Haoyu Ma;Ming Cheng;Shijie Zhao;Wanwan Cui;Tianyu Xu;Chunyang Li;Long Bao;Heng Sun;Huaibo Huang;Xiaoqiang Zhou;Yuang Ai;Ran He;Renlong Wu;Yi Yang;Zhilu Zhang;Shuohao Zhang;Junyi Li;Yunjin Chen;Dongwei Ren;Wangmeng Zuo;Renlong Wu;Yi Yang;Zhilu Zhang;Shuohao Zhang;Junyi Li;Yunjin Chen;Dongwei Ren;Wangmeng Zuo;Zhenyu Zhang;Qian Wang;Weiqi Li;Xuhan Sheng;Bin Chen;Hao-Hsiang Yang;Yi-Chung Chen;Zhi-Kai Huang;Wei-Ting Chen;Yuan-Chun Chiang;Hua-En Chang;I-Hsiang Chen;Chia-Hsuan Hsieh;Sy-Yen Kuo;Zebin Zhang;Jiaqi Zhang;Yuhui Wang;Shuhao Cui;Junshi Huang;Li Zhu;Shuman Tian;Wei Yu;Bingchun Luo

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2023 | Conference Paper |
Cited by: Papers (24)
This report introduces two high-quality datasets Flickr360 and ODV360 for omnidirectional image and video super-resolution, respectively, and reports the NTIRE 2023 challenge on 360° omnidirectional image and video super-resolution. Unlike ordinary 2D images/videos with a narrow field of view, omnidirectional images/videos can represent the whole scene from all directions in one shot. There exists...Show More

NTIRE 2023 Challenge on 360° Omnidirectional Image and Video Super-Resolution: Datasets, Methods and Results

Mingdeng Cao;Chong Mou;Fanghua Yu;Xintao Wang;Yinqiang Zheng;Jian Zhang;Chao Dong;Gen Li;Ying Shan;Radu Timofte;Xiaopeng Sun;Weiqi Li;Zhenyu Zhang;Xuhan Sheng;Bin Chen;Haoyu Ma;Ming Cheng;Shijie Zhao;Wanwan Cui;Tianyu Xu;Chunyang Li;Long Bao;Heng Sun;Huaibo Huang;Xiaoqiang Zhou;Yuang Ai;Ran He;Renlong Wu;Yi Yang;Zhilu Zhang;Shuohao Zhang;Junyi Li;Yunjin Chen;Dongwei Ren;Wangmeng Zuo;Renlong Wu;Yi Yang;Zhilu Zhang;Shuohao Zhang;Junyi Li;Yunjin Chen;Dongwei Ren;Wangmeng Zuo;Zhenyu Zhang;Qian Wang;Weiqi Li;Xuhan Sheng;Bin Chen;Hao-Hsiang Yang;Yi-Chung Chen;Zhi-Kai Huang;Wei-Ting Chen;Yuan-Chun Chiang;Hua-En Chang;I-Hsiang Chen;Chia-Hsuan Hsieh;Sy-Yen Kuo;Zebin Zhang;Jiaqi Zhang;Yuhui Wang;Shuhao Cui;Junshi Huang;Li Zhu;Shuman Tian;Wei Yu;Bingchun Luo

Year: 2023 | Conference Paper |

EVSRNet: Efficient Video Super-Resolution with Neural Architecture Search

Shaoli Liu;Chengjian Zheng;Kaidi Lu;Si Gao;Ning Wang;Bofei Wang;Diankai Zhang;Xiaofeng Zhang;Tianyu Xu

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2021 | Conference Paper |
Cited by: Papers (17)
With the development of convolutional neural networks (CNN), the super-resolution results of CNN-based method have far surpassed traditional method. In particular, the CNN-based single image super-resolution method has achieved excellent results. Video sequences contain more abundant information compare with image, but there are few video super-resolution methods that can be applied to mobile devi...Show More

EVSRNet: Efficient Video Super-Resolution with Neural Architecture Search

Shaoli Liu;Chengjian Zheng;Kaidi Lu;Si Gao;Ning Wang;Bofei Wang;Diankai Zhang;Xiaofeng Zhang;Tianyu Xu

Year: 2021 | Conference Paper |

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