Ling-Yuan Hsu

1.3k citations
44 papers · 1.0k indexed · h-index 21
Topics
Advanced Steganography and Watermarking Techniques (29 papers)Digital Media Forensic Detection (29 papers)Chaos-based Image/Signal Encryption (9 papers)
Partner nations
TaiwanChinaSaudi Arabia

In The Last Decade

Ling-Yuan Hsu

42 papers receiving 973 citations

Peers

Ling-Yuan Hsu
Comparison fields: 5 of 56
  • Computer Vision and Pattern Recognition 795
  • Signal Processing 166
  • Media Technology 150
  • Management Science and Operations Research 102
  • Artificial Intelligence 79
Replace Bing Su with:
Bing Su China
Abhishek Sharma India
Boyue Wang China
Puming Wang China
Jingdong Xu China
Sami Tabbane Tunisia
Tian Xia United States
Chengming Zou China
Ling-Yuan Hsu relative to Bing Su China Bing Su's profile →
Citations per field
00.5×5.8×
Bing Su · 1×
Citations per year

Countries citing papers authored by Ling-Yuan Hsu

Since Specialization
Citations

This map shows the geographic impact of Ling-Yuan Hsu'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 Ling-Yuan Hsu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ling-Yuan Hsu more than expected).

Fields of papers citing papers by Ling-Yuan Hsu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ling-Yuan Hsu. 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 Ling-Yuan Hsu. The network helps show where Ling-Yuan Hsu may publish in the future.

Co-authorship network of co-authors of Ling-Yuan Hsu

This figure shows the co-authorship network connecting the top 25 collaborators of Ling-Yuan Hsu. A scholar is included among the top collaborators of Ling-Yuan Hsu 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 Ling-Yuan Hsu. Ling-Yuan Hsu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 6
2 11
3 4
4 76
5 3
6
Improving DWT-DCT-Based Blind Audio Watermarking Using Perceptually Energy-Compensated QIM
1
7 52
8 13
9 6
10 5
11 27
12 1
13 72
14 29
15 50
16 28
17 35
18 11
19 30
20 82

About Ling-Yuan Hsu

Ling-Yuan Hsu is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Media Technology, having authored 44 papers that have together received 1.0k indexed citations. Recurring topics across this work include Advanced Steganography and Watermarking Techniques (29 papers), Digital Media Forensic Detection (29 papers) and Chaos-based Image/Signal Encryption (9 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (795 citations), Media Technology (150 citations) and Signal Processing (166 citations). Ling-Yuan Hsu has collaborated with scholars based in Taiwan, China and Saudi Arabia. Frequent co-authors include Hwai-Tsu Hu, Shi‐Jinn Horng, Jui‐Lin Lai, Rong‐Jian Chen, Ray-Shine Run, Tzong‐Wann Kao, I‐Hong Kuo, Y-H. Chen, Muhammad Khurram Khan and Chu Yu. Their work appears in journals such as Expert Systems with Applications, IEEE Access and Sensors.

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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