Ya Jun Yu

52 papers receiving 688 citations

Peers

Ya Jun Yu
Comparison fields: 5 of 43
  • Signal Processing 623
  • Computational Mechanics 340
  • Computer Vision and Pattern Recognition 303
  • Computational Theory and Mathematics 188
  • Biomedical Engineering 182
Replace K.L. Ho with:
K.L. Ho Hong Kong
Elizabeth Elias India
Per Löwenborg Sweden
Takehiro Moriya Japan
S.A. White United States
R. Mahesh Singapore
O. Herrmann Germany
Shaik Rafi Ahamed India
K. M. Tsui Hong Kong
Juha Yli‐Kaakinen Finland
Ya Jun Yu relative to K.L. Ho Hong Kong K.L. Ho's profile →
Citations per field
00.5×2.6×
K.L. Ho · 1×
Citations per year

Countries citing papers authored by Ya Jun Yu

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authors

The 23 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.

Border = papers with Ya Jun Yu Line = papers co-authored together Ya Jun Yu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 52 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201086
2 200762
3 200545
4 200539
5 201232
6 200732
7 200930
8 201328
9 201420
10 201420
11 200318
12 201118
13 201018
14 200318
15 200216
16 201616
17 201116
18 201616
19 200415
20 200913

About Ya Jun Yu

Ya Jun Yu is a scholar working on Signal Processing, Computational Mechanics, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Biomedical Engineering, having authored 52 papers that have together received 737 indexed citations. Recurring topics across this work include Digital Filter Design and Implementation (47 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 (7 papers), Advancements in PLL and VCO Technologies (6 papers) and Low-power high-performance VLSI design (5 papers). The work is most often cited by research in Signal Processing (623 citations), Computational Mechanics (340 citations), Computer Vision and Pattern Recognition (303 citations), Computational Theory and Mathematics (188 citations) and Biomedical Engineering (182 citations). Ya Jun Yu has collaborated with scholars based in Singapore, China and Finland. Frequent co-authors include Yong Ching Lim, Wen Bin Ye, Yaw Chyn Lim, T. Saramäki, Xin Lou, Pramod Kumar Meher, Sang Yoon Park, Kok Lay Teo, Robert Bregović 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.

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