Ya Jun Yu
- Signal Processing top 1%
- Digital Filter Design and Implementation 46
- Computational Mechanics top 5%
- Advanced Adaptive Filtering Techniques 29
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- Image and Signal Denoising Methods 16
- Advanced Data Compression Techniques 12
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- Numerical Methods and Algorithms 10
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- Analog and Mixed-Signal Circuit Design 6
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- Low-power high-performance VLSI design 5
- Advancements in PLL and VCO Technologies 5
- Co-authors
- Yong Ching LimYaw Chyn LimT. SaramäkiXin LouPramod Kumar MeherSang Yoon ParkKok Lay TeoRobert Bregović
In The Last Decade
Ya Jun Yu
51 papers receiving 655 citations
Peers
Comparison fields: 5 of 40
- Signal Processing 594
- Computational Mechanics 331
- Computer Vision and Pattern Recognition 296
- Computational Theory and Mathematics 167
- Biomedical Engineering 169
Countries citing papers authored by Ya Jun Yu
This map shows the geographic impact of Ya Jun Yu'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 Jun Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ya Jun Yu more than expected).
Fields of papers citing papers by Ya Jun Yu
This network shows the impact of papers produced by Ya Jun Yu. 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 Jun Yu. The network helps show where Ya Jun Yu may publish in the future.
Co-authorship network
The 22 scholars most cited alongside Ya Jun Yu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 5 | |
| 2 | 2019 | 1 | |
| 3 | 2017 | 11 | |
| 4 | 2017 | 5 | |
| 5 | 2016 | 14 | |
| 6 | 2015 | 2 | |
| 7 | 2015 | 13 | |
| 8 | 2013 | 5 | |
| 9 | 2012 | 30 | |
| 10 | 2011 | 4 | |
| 11 | 2011 | 17 | |
| 12 | 2011 | 14 | |
| 13 | 2009 | 13 | |
| 14 | 2006 | 12 | |
| 15 | 2005 | 36 | |
| 16 | 2004 | 15 | |
| 17 | A width-recursive depth-first tree search approach for the design of discrete coefficient perfect reconstruction lattice filter bank. | 2003 | 2 |
| 18 | 2003 | 18 | |
| 19 | 2003 | 18 | |
| 20 | 2003 | 1 |
About Ya Jun Yu
Ya Jun Yu is a scholar working on Signal Processing, Computational Mechanics and Computer Vision and Pattern Recognition, having authored 51 papers that have together received 695 indexed citations. Recurring topics across this work include Digital Filter Design and Implementation (46 papers), Advanced Adaptive Filtering Techniques (29 papers), Image and Signal Denoising Methods (16 papers), Advanced Data Compression Techniques (12 papers), Numerical Methods and Algorithms (10 papers), Analog and Mixed-Signal Circuit Design (6 papers), Low-power high-performance VLSI design (5 papers) and Advancements in PLL and VCO Technologies (5 papers). The work is most often cited by research in Signal Processing (594 citations), Computational Mechanics (331 citations) and Computer Vision and Pattern Recognition (296 citations). Ya Jun Yu has collaborated with scholars based in Singapore, China and Finland. Frequent co-authors include Yong Ching Lim, Yaw Chyn Lim, T. Saramäki, Xin Lou, Pramod Kumar Meher, Sang Yoon Park, Kok Lay Teo, Robert Bregović, Say Wei Foo and Wenbin Ye. Their work appears in journals such as IEEE Transactions on Circuits and Systems I Regular Papers, IEEE Transactions on Signal Processing, IEEE Transactions on Circuits & Systems II Express Briefs, IEEE Access and Signal Processing.
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.