Fangfa Fu

50 papers receiving 260 citations

Peers

Fangfa Fu
Comparison fields: 5 of 53
  • Hardware and Architecture 81
  • Computer Networks and Communications 105
  • Automotive Engineering 41
  • Computer Vision and Pattern Recognition 69
  • Control and Systems Engineering 40
Replace Seda Ogrenci-Memik with:
Seda Ogrenci-Memik United States
Travis S. Taylor United States
Zhaoyi Wei United States
Ankit Dubey India
Guo‐Shing Huang Taiwan
Trang Hoang Vietnam
Tulshi Bezboruah India
Fangfa Fu relative to Seda Ogrenci-Memik United States Seda Ogrenci-Memik's profile →
Citations per field
00.5×6.7×
Seda Ogrenci-Memik · 1×
Citations per year

Countries citing papers authored by Fangfa Fu

Since Specialization
Citations

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

Fields of papers citing papers by Fangfa Fu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fangfa Fu. 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 Fangfa Fu. The network helps show where Fangfa Fu may publish in the future.

Co-authors

The 25 scholars most cited alongside Fangfa Fu, 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 Fangfa Fu Line = papers co-authored together Fangfa Fu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 59 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201551
2 201240
3 200723
4 201912
5 201010
6 20188
7 20108
8 20048
9 20227
10 20096
11 20176
12 20185
13 20205
14 20105
15 20164
16 20204
17 20124
18 20123
19 20233
20 20233

About Fangfa Fu

Fangfa Fu is a scholar working on Computer Networks and Communications, Electrical and Electronic Engineering, Hardware and Architecture, Computer Vision and Pattern Recognition and Nuclear and High Energy Physics, having authored 59 papers that have together received 266 indexed citations. Recurring topics across this work include Interconnection Networks and Systems (21 papers), Parallel Computing and Optimization Techniques (14 papers), Advanced Memory and Neural Computing (11 papers), Embedded Systems Design Techniques (10 papers), Particle Detector Development and Performance (8 papers), CCD and CMOS Imaging Sensors (8 papers), Advanced Vision and Imaging (7 papers) and Analog and Mixed-Signal Circuit Design (5 papers). The work is most often cited by research in Hardware and Architecture (81 citations), Computer Networks and Communications (105 citations), Automotive Engineering (41 citations), Computer Vision and Pattern Recognition (69 citations) and Control and Systems Engineering (40 citations). Fangfa Fu has collaborated with scholars based in China, Germany and Japan. Frequent co-authors include Jinjin Shi, Jinxiang Wang, Jinxiang Wang, Guang‐Ren Duan, Ming‐Feng Hou, Mingyan Yu, Yang Xu, Cheng Liu, Qingli Zhang and Siyue Sun. Their work appears in journals such as Journal of Instrumentation, IEEE Transactions on Nuclear Science, Microelectronics Reliability, IEEE Transactions on Very Large Scale Integration (VLSI) Systems and Surface and Coatings Technology.

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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