T.S. Huang

1.5k citations
23 papers · 1.0k · 1 hit paper · h-index 10

Impact in

Papers in

T.S. Huang

22 papers receiving 963 citations

Hit Papers

Age Synthesis and Estimation via Faces: A Survey 2010 · 477 citations
4770+5+10Years since publication100200300400

Peers

T.S. Huang
Comparison fields: 5 of 101
  • Computational Mathematics 20
  • Computer Vision and Pattern Recognition 636
  • Signal Processing 215
  • Cancer Research 137
  • Oncology 96
Replace Hairong Lv with:
Hairong Lv China
Joong Kyu Kim South Korea
Masahiko Itoh Japan
Yu Ji China
Ce Wang China
Jia-Lin Shen Taiwan
Fazly Salleh Abas Malaysia
Fangfang Li China
Sha Hu Sweden
Scott Gigante United States
T.S. Huang relative to Hairong Lv China Hairong Lv's profile →
Citations per field
00.5×8.8×
Hairong Lv · 1×
Citations per year

Countries citing papers authored by T.S. Huang

Since Specialization
Citations

This map shows the geographic impact of T.S. Huang'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 T.S. Huang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T.S. Huang more than expected).

Fields of papers citing papers by T.S. Huang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by T.S. Huang. 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 T.S. Huang. The network helps show where T.S. Huang may publish in the future.

Co-authors

The 25 scholars most cited alongside T.S. Huang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with T.S. Huang Line = papers co-authored together T.S. Huang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Age Synthesis and Estimation via Faces: A Survey
Hit paper breakdown →
2010477
2 2014229
3 200873
4 200366
5 200538
6 200426
7 199217
8 199013
9 202011
10 198910
11 19939
12 19898
13 20035
14 20054
15 19904
16 19903
17 19923
18 19892
19 20112
20 20241

About T.S. Huang

T.S. Huang is a scholar working on Materials Chemistry, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics and Signal Processing, having authored 23 papers that have together received 1.0k indexed citations. Recurring topics across this work include Semiconductor Quantum Structures and Devices (4 papers), Music and Audio Processing (4 papers), Solidification and crystal growth phenomena (4 papers), Video Analysis and Summarization (4 papers), ZnO doping and properties (3 papers), Semiconductor materials and interfaces (3 papers), Speech and Audio Processing (3 papers) and Face recognition and analysis (3 papers). The work is most often cited by research in Computational Mathematics (20 citations), Computer Vision and Pattern Recognition (636 citations), Signal Processing (215 citations), Cancer Research (137 citations) and Oncology (96 citations). T.S. Huang has collaborated with scholars based in Taiwan, United States and Australia. Frequent co-authors include Yun Fu, Guodong Guo, Ajay Divakaran, Ziyou Xiong, R. Radhakrishnan, Chi-Hung Lin, Muh Hwa Yang, Ziyu Xiong, Regunathan Radhakrishnan and Yutong Guo. Their work appears in journals such as Journal of Crystal Growth, Materials Science and Engineering B, Nature Cell Biology, Semiconductor Science and Technology and NAR Genomics and Bioinformatics.

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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