T. Nishimura

457 citations
30 papers · 321 · h-index 12

Impact in

Papers in

T. Nishimura

27 papers receiving 307 citations

Peers

T. Nishimura
Comparison fields: 5 of 61
  • Computational Mechanics 125
  • Mechanical Engineering 100
  • Fluid Flow and Transfer Processes 15
  • Biomedical Engineering 67
  • Control and Systems Engineering 34
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P. Lotton France
Pierrick Lotton France
K. Uno Ingard United States
Takashi Sasaki Japan
Richard Charles United States
Guillaume Pénelet France
V. Easwaran India
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Citations per field
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Citations per year

Countries citing papers authored by T. Nishimura

Since Specialization
Citations

This map shows the geographic impact of T. Nishimura'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 T. Nishimura with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. Nishimura more than expected).

Fields of papers citing papers by T. Nishimura

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200345
2 200428
3 198626
4 197124
5
CLASSIFICATION OF SINGLE TRIAL EEG SIGNALS BY A COMBINED PRINCIPAL + INDEPENDENT COMPONENT ANALYSIS AND PROBABILISTIC NEURAL NETWORK APPROACH
200324
6 199622
7 200720
8 200315
9 199213
10 199911
11 200111
12 199511
13 201511
14 20069
15 19879
16 20208
17 20107
18 19976
19 20085
20 20124

About T. Nishimura

T. Nishimura is a scholar working on Computational Mechanics, Control and Systems Engineering, Artificial Intelligence, Electrical and Electronic Engineering and Atomic and Molecular Physics, and Optics, having authored 30 papers that have together received 321 indexed citations. Recurring topics across this work include Fluid Dynamics and Turbulent Flows (5 papers), Fluid Dynamics and Vibration Analysis (4 papers), Control Systems and Identification (3 papers), Iterative Learning Control Systems (3 papers), Combustion and flame dynamics (3 papers), Neural Networks and Applications (3 papers), Silicon Carbide Semiconductor Technologies (2 papers) and Wind and Air Flow Studies (2 papers). The work is most often cited by research in Computational Mechanics (125 citations), Mechanical Engineering (100 citations), Fluid Flow and Transfer Processes (15 citations), Biomedical Engineering (67 citations) and Control and Systems Engineering (34 citations). T. Nishimura has collaborated with scholars based in Japan, India and United Kingdom. Frequent co-authors include Y. Akasaka, Yuta Matsumoto, Yuichi CHIDA, Yoshiharu Kawamura, T. Oi, Hisashi Miyashita, Jianting Cao, Yoichi Miyawaki, Ryo Furukawa and Yoshiyuki Ishihara. Their work appears in journals such as Heat and Mass Transfer, Experiments in Fluids, International Journal of Heat and Mass Transfer, IEEE Transactions on Magnetics and Solid-State Electronics.

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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