Sun‐Yuan Kung

15.9k citations
381 papers · 10.9k indexed · 3 hit papers · h-index 46
Topics
Neural Networks and Applications (60 papers)Blind Source Separation Techniques (45 papers)Speech and Audio Processing (35 papers)
Journals
Journal of Biological ChemistrySHILAP Revista de lepidopterologíaBioinformatics

In The Last Decade

Sun‐Yuan Kung

352 papers receiving 10.3k citations

Hit Papers

Principal Component Neural Networks: Theory and Applications1996202620062016199620042005100200300400500

Peers

Sun‐Yuan Kung
Comparison fields: 5 of 185
  • Electrical and Electronic Engineering 2.6k
  • Computer Vision and Pattern Recognition 2.3k
  • Signal Processing 2.3k
  • Computer Networks and Communications 2.2k
  • Artificial Intelligence 2.1k
Replace José M. F. Moura with:
José M. F. Moura United States
Pascal Frossard Switzerland
Antonio Ortega United States
J. Rissanen United States
Albert Benveniste France
C.S. Burrus United States
Jon Bentley United States
Horst D. Simon United States
João P. Hespanha United States
K.R. Rao United States
Sun‐Yuan Kung relative to José M. F. Moura United States José M. F. Moura's profile →
Citations per field
00.5×1.5×1.8×
José M. F. Moura · 1×
Citations per year

Countries citing papers authored by Sun‐Yuan Kung

Since Specialization
Citations

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

Fields of papers citing papers by Sun‐Yuan Kung

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sun‐Yuan Kung. 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 Sun‐Yuan Kung. The network helps show where Sun‐Yuan Kung may publish in the future.

Co-authorship network of co-authors of Sun‐Yuan Kung

This figure shows the co-authorship network connecting the top 25 collaborators of Sun‐Yuan Kung. A scholar is included among the top collaborators of Sun‐Yuan Kung 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 Sun‐Yuan Kung. Sun‐Yuan Kung 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
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From green computing to big-data learning: A kernel learning perspective.
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TIMING ANALYSIS AND DESIGN OPTIMIZATION OF VLSI DATA FLOW ARRAYS.
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About Sun‐Yuan Kung

Sun‐Yuan Kung is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 381 papers that have together received 10.9k indexed citations. Recurring topics across this work include Neural Networks and Applications (60 papers), Blind Source Separation Techniques (45 papers) and Speech and Audio Processing (35 papers). The work is most often cited by research in Signal Processing (2.3k citations), Hardware and Architecture (790 citations) and Computer Vision and Pattern Recognition (2.3k citations). Sun‐Yuan Kung has collaborated with scholars based in United States, Hong Kong and China. Frequent co-authors include Konstantinos Diamantaras, T. Kailath, Man‐Wai Mak, Philip A. Chou, Yunnan Wu, M. Morf, Shibiao Wan, Xinying Zhang, Shang‐Hung Lin and Andreas F. Molisch. Their work appears in journals such as Journal of Biological Chemistry, SHILAP Revista de lepidopterología 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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