Ya Tu

2.4k total citations · 2 hit papers
25 papers, 1.7k citations indexed

About

Ya Tu is a scholar working on Artificial Intelligence, Aerospace Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Ya Tu has authored 25 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Artificial Intelligence, 9 papers in Aerospace Engineering and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Ya Tu's work include Wireless Signal Modulation Classification (19 papers), Radar Systems and Signal Processing (6 papers) and Digital Media Forensic Detection (5 papers). Ya Tu is often cited by papers focused on Wireless Signal Modulation Classification (19 papers), Radar Systems and Signal Processing (6 papers) and Digital Media Forensic Detection (5 papers). Ya Tu collaborates with scholars based in China, United States and Bangladesh. Ya Tu's co-authors include Yun Lin, Shiwen Mao, Zheng Dou, Yun Lin, Lei Chen, Haojun Zhao, Changbo Hou, Bin Tang, Zhaoyue Zhang and Meiyu Wang and has published in prestigious journals such as IEEE Access, IEEE Transactions on Vehicular Technology and IEEE Internet of Things Journal.

In The Last Decade

Ya Tu

24 papers receiving 1.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Contour Stella Image and Deep Learning for Signal Recognition in the Physical Layer

2020 258 citations Yun Lin, Ya Tu et al. IEEE Transactions on Cognitive Communications and Networking profile →
Large-scale real-world radio signal recognition with deep learning

2021 196 citations Ya Tu, Yun Lin et al. Chinese Journal of Aeronautics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Ya Tu China	16	1.3k	433	429	282	243	25	1.7k
Yun Lin China	21	1.1k 0.9×	471 1.1×	361 0.8×	253 0.9×	183 0.8×	139	1.6k
Zheng Dou China	15	785 0.6×	437 1.0×	315 0.7×	166 0.6×	246 1.0×	99	1.4k
Hongji Huang China	9	484 0.4×	1.1k 2.5×	463 1.1×	258 0.9×	269 1.1×	19	1.6k
Jinlong Sun China	18	406 0.3×	567 1.3×	537 1.3×	68 0.2×	188 0.8×	53	1.3k
Zhutian Yang China	21	375 0.3×	846 2.0×	675 1.6×	79 0.3×	356 1.5×	81	1.4k
Iyad Dayoub France	21	568 0.4×	1.0k 2.4×	317 0.7×	217 0.8×	417 1.7×	125	1.6k
Marion Berbineau France	20	331 0.3×	942 2.2×	668 1.6×	123 0.4×	377 1.6×	134	1.7k
Zhen Yang China	28	523 0.4×	1.9k 4.4×	461 1.1×	275 1.0×	810 3.3×	235	2.8k
Yiwei Song China	10	391 0.3×	785 1.8×	299 0.7×	242 0.9×	247 1.0×	41	1.3k

Countries citing papers authored by Ya Tu

Since Specialization

Citations

This map shows the geographic impact of Ya Tu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ya Tu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ya Tu more than expected).

Fields of papers citing papers by Ya Tu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ya Tu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ya Tu. The network helps show where Ya Tu may publish in the future.

Co-authorship network of co-authors of Ya Tu

This figure shows the co-authorship network connecting the top 25 collaborators of Ya Tu. A scholar is included among the top collaborators of Ya Tu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ya Tu. Ya Tu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Zeng, Yi, Yang Yu, John Tan, et al.. (2024). AIR-Bench 2024: A Safety Benchmark Based on Risk Categories from Regulations and Policies. 1(1). 2 indexed citations

2.

Zeng, Yi, Yang Yu, John Tan, et al.. (2024). AIR-Bench 2024: A Safety Benchmark Based on Risk Categories from Regulations and Policies. 1(1).

3.

Tu, Ya, et al.. (2023). C-SRCIL: Complex-valued Class-Incremental Learning for Signal Recognition. 2 indexed citations

4.

Tu, Ya, et al.. (2023). Class-Incremental Learning for Recognition of Complex-Valued Signals. IEEE Transactions on Cognitive Communications and Networking. 10(2). 417–428. 10 indexed citations

5.

Tu, Ya, et al.. (2023). Edge-Enabled Modulation Classification in Internet of Underwater Things Based on Network Pruning and Ensemble Learning. IEEE Internet of Things Journal. 11(8). 13608–13621. 3 indexed citations

6.

Tu, Ya, Yun Lin, Haoran Zha, et al.. (2021). Large-scale real-world radio signal recognition with deep learning. Chinese Journal of Aeronautics. 35(9). 35–48. 196 indexed citations breakdown →

7.

Lin, Yun, Haojun Zhao, Xuefei Ma, Ya Tu, & Meiyu Wang. (2020). Adversarial Attacks in Modulation Recognition With Convolutional Neural Networks. IEEE Transactions on Reliability. 70(1). 389–401. 168 indexed citations

8.

Zhang, Sicheng, Yun Lin, Ya Tu, & Shiwen Mao. (2020). Electromagnetic signal modulation recognition technology based on lightweight deep neural network. Journal of Communications. 41(11). 12. 16 indexed citations

9.

Tu, Ya, Yun Lin, Changbo Hou, & Shiwen Mao. (2020). Complex-Valued Networks for Automatic Modulation Classification. IEEE Transactions on Vehicular Technology. 69(9). 10085–10089. 190 indexed citations

10.

Lin, Yun, Ya Tu, Zheng Dou, Lei Chen, & Shiwen Mao. (2020). Contour Stella Image and Deep Learning for Signal Recognition in the Physical Layer. IEEE Transactions on Cognitive Communications and Networking. 7(1). 34–46. 258 indexed citations breakdown →

11.

Lin, Yun, et al.. (2019). A Node Self-Localization Algorithm With a Mobile Anchor Node in Underwater Acoustic Sensor Networks. IEEE Access. 7. 43773–43780. 42 indexed citations

12.

Lin, Yun, et al.. (2019). Dynamic Channel Allocation for Multi-UAVs: A Deep Reinforcement Learning Approach. 1–6. 12 indexed citations

13.

Tu, Ya, et al.. (2019). A Data-Driven Deep Neural Network Pruning Approach Towards Efficient Digital Signal Modulation Recognition. 55. 1–5. 5 indexed citations

14.

Tu, Ya & Yun Lin. (2019). Deep Neural Network Compression Technique Towards Efficient Digital Signal Modulation Recognition in Edge Device. IEEE Access. 7. 58113–58119. 62 indexed citations

15.

Lin, Yun, Jie Wang, Ya Tu, Lei Chen, & Zheng Dou. (2019). Time-Related Network Intrusion Detection Model: A Deep Learning Method. 1–6. 29 indexed citations

16.

Tu, Ya, Zhen Zhang, Yibing Li, Chao Wang, & Yihan Xiao. (2019). Research on the Internet of Things Device Recognition Based on RF-Fingerprinting. IEEE Access. 7. 37426–37431. 46 indexed citations

17.

Zhang, Chunsheng & Ya Tu. (2018). A Solution to Mongolian Database Application System Based on WPF. Journal of Physics Conference Series. 1069. 12055–12055. 1 indexed citations

18.

Yu, Ling, et al.. (2018). Spectrum Prediction Based on Taguchi Method in Deep Learning With Long Short-Term Memory. IEEE Access. 6. 45923–45933. 80 indexed citations

19.

Li, Yibing, et al.. (2018). An Ensemble Learning Method for Wireless Multimedia Device Identification. Security and Communication Networks. 2018. 1–9. 23 indexed citations

20.

Lin, Yun, Ya Tu, Zheng Dou, & Zhiqiang Wu. (2017). The application of deep learning in communication signal modulation recognition. Journal of Bioresource Management. 1–5. 36 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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