K.-M. Yang

476 citations
11 papers · 321 indexed · h-index 4

K.-M. Yang

9 papers receiving 301 citations

Peers

K.-M. Yang
Comparison fields: 5 of 17
  • Signal Processing 278
  • Computer Vision and Pattern Recognition 285
  • Hardware and Architecture 28
  • Computer Networks and Communications 23
  • Computational Theory and Mathematics 8
Replace Tsu-Ming Liu with:
Tsu-Ming Liu Taiwan
H.-J. Stolberg Germany
M.J. Garrido Spain
Dajiang Zhou Japan
M. Hamada Japan
José Luís Güntzel Brazil
P. Mundur United States
Catherine Lamy–Bergot France
Jeongnam Youn United States
Michael Isnardi United States
K.-M. Yang relative to Tsu-Ming Liu Taiwan Tsu-Ming Liu's profile →
Citations per field
00.5×2.7×
Tsu-Ming Liu · 1×
Citations per year

Countries citing papers authored by K.-M. Yang

Since Specialization
Citations

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

Fields of papers citing papers by K.-M. Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by K.-M. Yang. 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 K.-M. Yang. The network helps show where K.-M. Yang may publish in the future.

Co-authorship network

The 13 scholars most cited alongside K.-M. Yang, 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 K.-M. Yang Line = papers co-authored together K.-M. Yang links everyone, so they are left out of the graph.

All Works

11 of 11 papers shown
#Work
1 200313
2 20030
3 20033
4 20021
5 20022
6 199240
7 19901
8 19901
9 1989247
10
FRACTAL BASED IMAGE C ODING S CHEME USING PEANO SCAN
19881
11 198812

About K.-M. Yang

K.-M. Yang is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Hardware and Architecture, having authored 11 papers that have together received 321 indexed citations. Recurring topics across this work include Video Coding and Compression Technologies (9 papers), Advanced Data Compression Techniques (7 papers), Digital Filter Design and Implementation (3 papers), Advanced Image Processing Techniques (2 papers), Advanced Vision and Imaging (2 papers), Advancements in PLL and VCO Technologies (1 paper), Embedded Systems Design Techniques (1 paper) and Retinal Imaging and Analysis (1 paper). The work is most often cited by research in Signal Processing (278 citations), Computer Vision and Pattern Recognition (285 citations) and Hardware and Architecture (28 citations). K.-M. Yang has collaborated with scholars based in United States and Canada. Frequent co-authors include M.-T. Sun, Long Wu, Masashi Maruyama, Hiroshi Fujiwara, M.L. Liou, Michael Mills, Lyndia C. Wu, Lance Wu, Ming–Ting Sun and Didier J. LeGall. Their work appears in journals such as IEEE Transactions on Circuits and Systems for Video Technology, Signal Processing Image Communication, IEEE Transactions on Circuits and Systems, International Conference on Acoustics, Speech, and Signal Processing and Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE.

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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