Weiwei Shan

1.4k citations
113 papers · 843 · h-index 15

Impact in

Papers in

Weiwei Shan

100 papers receiving 812 citations

Peers

Weiwei Shan
Comparison fields: 5 of 85
  • Hardware and Architecture 224
  • Signal Processing 103
  • Artificial Intelligence 266
  • Electrical and Electronic Engineering 469
  • Computer Vision and Pattern Recognition 99
Replace Luis Parrilla with:
Luis Parrilla Spain
Santosh Kumar Vishvakarma India
Masoud Rostami United States
Tughrul Arslan United Kingdom
Smith United States
Peter Zipf Germany
Hongyang Jia China
Vikram Suresh United States
Volnei A. Pedroni Brazil
Paul N. Whatmough United States
Weiwei Shan relative to Luis Parrilla Spain Luis Parrilla's profile →
Citations per field
00.5×3.3×
Luis Parrilla · 1×
Citations per year

Countries citing papers authored by Weiwei Shan

Since Specialization
Citations

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

Fields of papers citing papers by Weiwei Shan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 113 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202060
2 202358
3 202051
4 202345
5 201535
6 202229
7 202128
8 200826
9 201924
10 201721
11 202021
12 201920
13 201820
14 202317
15 201916
16 201813
17 201913
18 201713
19 201712
20 202212

About Weiwei Shan

Weiwei Shan is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence, Hardware and Architecture, Biomedical Engineering and Computer Vision and Pattern Recognition, having authored 113 papers that have together received 843 indexed citations. Recurring topics across this work include Low-power high-performance VLSI design (40 papers), Advancements in Semiconductor Devices and Circuit Design (19 papers), Semiconductor materials and devices (18 papers), Advanced Memory and Neural Computing (14 papers), Analog and Mixed-Signal Circuit Design (14 papers), Cryptographic Implementations and Security (13 papers), VLSI and Analog Circuit Testing (12 papers) and Chaos-based Image/Signal Encryption (10 papers). The work is most often cited by research in Hardware and Architecture (224 citations), Signal Processing (103 citations), Artificial Intelligence (266 citations), Electrical and Electronic Engineering (469 citations) and Computer Vision and Pattern Recognition (99 citations). Weiwei Shan has collaborated with scholars based in China, United States and Bangladesh. Frequent co-authors include Jun Yang, Longxing Shi, Hao Cai, Zhipeng Xu, Minhao Yang, Mingoo Seok, Shuai Zhang, Chengjun Wu, Peng Cao and Yongliang Zhou. Their work appears in journals such as IEEE Journal of Solid-State Circuits, IEEE Transactions on Circuits & Systems II Express Briefs, IEEE Transactions on Circuits and Systems I Regular Papers, IEEE Access and Scientific Reports.

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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