Takuya Kida

788 citations
58 papers · 334 · h-index 11

Impact in

Papers in

Takuya Kida

47 papers receiving 307 citations

Peers

Takuya Kida
Comparison fields: 5 of 43
  • Hardware and Architecture 84
  • Artificial Intelligence 197
  • Computational Theory and Mathematics 76
  • Computational Mechanics 66
  • Computer Networks and Communications 50
Replace Laurent Alonso with:
Laurent Alonso France
Yuan Tang China
Erik Sintorn Sweden
Daniel Etiemble France
A. Benkrid United Kingdom
Robert Heaphy United States
William McLendon United States
Akira Maruoka Japan
Lars Nyland United States
Takuya Kida relative to Laurent Alonso France Laurent Alonso's profile →
Citations per field
00.5×6.5×
Laurent Alonso · 1×
Citations per year

Countries citing papers authored by Takuya Kida

Since Specialization
Citations

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

Fields of papers citing papers by Takuya Kida

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 58 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200339
2 200235
3 200225
4 196721
5 200917
6 200916
7 200316
8 200312
9
Speeding Up String Pattern Matching by Text Compression: The Dawn of a New Era
200110
10 200910
11 201010
12 20029
13 20118
14 19807
15 20026
16 20216
17 19986
18 20096
19
Two Dimensional Pattern Matching for JPEG Images
20085
20 19745

About Takuya Kida

Takuya Kida is a scholar working on Artificial Intelligence, Computational Mechanics, Computational Theory and Mathematics, Signal Processing and Hardware and Architecture, having authored 58 papers that have together received 334 indexed citations. Recurring topics across this work include Algorithms and Data Compression (22 papers), Network Packet Processing and Optimization (8 papers), Cellular Automata and Applications (8 papers), Fluid Dynamics and Vibration Analysis (8 papers), DNA and Biological Computing (7 papers), Natural Language Processing Techniques (7 papers), Fluid Dynamics and Turbulent Flows (6 papers) and Data Management and Algorithms (4 papers). The work is most often cited by research in Hardware and Architecture (84 citations), Artificial Intelligence (197 citations), Computational Theory and Mathematics (76 citations), Computational Mechanics (66 citations) and Computer Networks and Communications (50 citations). Takuya Kida has collaborated with scholars based in Japan, Canada and France. Frequent co-authors include Ayumi Shinohara, Setsuo Arikawa, Masayuki Takeda, Y. Ueda, Yusuke Shibata, Tetsuya Matsumoto, Manabu Iguchi, Hiroki Arimura, Gonzalo Navarro and Hiroshi Sakamoto. Their work appears in journals such as Journal of Fluid Mechanics, The Quarterly Journal of Mechanics and Applied Mathematics, Journal of Applied Mechanics, Computer Methods in Applied Mechanics and Engineering and Theoretical Computer Science.

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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