Pucha Song

451 citations
26 papers · 345 · h-index 10

Impact in

Papers in

Pucha Song

22 papers receiving 337 citations

Peers

Pucha Song
Comparison fields: 5 of 34
  • Signal Processing 237
  • Computational Mechanics 284
  • Computational Mathematics 3
  • Control and Systems Engineering 66
  • Automotive Engineering 22
Replace Laura Romoli with:
Laura Romoli Italy
Liming Shi China
JaeWook Shin South Korea
Akira Ikuta Japan
Shakeel Ahmed Pakistan
Mohammad Shams Esfand Abadi Iran
Juro Ohga Japan
F. Reed United States
Yanyan Wang China
Michał Meller Poland
Pucha Song relative to Laura Romoli Italy Laura Romoli's profile →
Citations per field
00.5×4.8×
Laura Romoli · 1×
Citations per year

Countries citing papers authored by Pucha Song

Since Specialization
Citations

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

Fields of papers citing papers by Pucha Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 22 scholars most cited alongside Pucha Song, 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 Pucha Song Line = papers co-authored together Pucha Song links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201865
2 201857
3 201954
4 202040
5 201924
6 202016
7 201914
8 202113
9 202312
10 202311
11 20219
12 20068
13 20195
14 20213
15 20233
16 20242
17 20242
18 20202
19 20211
20 20211

About Pucha Song

Pucha Song is a scholar working on Computational Mechanics, Signal Processing, Electrical and Electronic Engineering, Artificial Intelligence and Control and Systems Engineering, having authored 26 papers that have together received 345 indexed citations. Recurring topics across this work include Advanced Adaptive Filtering Techniques (19 papers), Blind Source Separation Techniques (16 papers), Speech and Audio Processing (15 papers), Electrohydrodynamics and Fluid Dynamics (2 papers), Control Systems and Identification (2 papers), Power Transformer Diagnostics and Insulation (2 papers), High voltage insulation and dielectric phenomena (2 papers) and Optical Systems and Laser Technology (1 paper). The work is most often cited by research in Signal Processing (237 citations), Computational Mechanics (284 citations), Computational Mathematics (3 citations), Control and Systems Engineering (66 citations) and Automotive Engineering (22 citations). Pucha Song has collaborated with scholars based in China. Frequent co-authors include Haiquan Zhao, Bing Liu, Yingying Zhu, Xiangping Zeng, Feng Zhao, Long Shi, Pengfei Li, Lijun Zhou, Zhongfu Tan and Jinghua Ye. Their work appears in journals such as IEEE Transactions on Circuits & Systems II Express Briefs, Symmetry, IEEE Transactions on Dielectrics and Electrical Insulation, Mechanical Systems and Signal Processing and IEEE Transactions on Systems Man and Cybernetics Systems.

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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