Toshiyuki ASAKURA
- Computer Vision and Pattern Recognition top 5%
- Control and Systems Engineering top 5%
- Artificial Intelligence
- Media Technology top 5%
- Mechanical Engineering
- Co-authors
- Mamoru MinamiSheng ZhangXiaoli XuShoji HayashiHidekazu SuzukiTakeo OhnishiOsamu HiroseTakashi Kobayashi
- Topics
- Robotic Path Planning Algorithms (17 papers)Neural Networks and Applications (15 papers)Fault Detection and Control Systems (12 papers)
In The Last Decade
Toshiyuki ASAKURA
61 papers receiving 458 citations
Peers
Comparison fields: 5 of 53
- Computer Vision and Pattern Recognition 234
- Control and Systems Engineering 215
- Artificial Intelligence 80
- Media Technology 75
- Mechanical Engineering 67
Countries citing papers authored by Toshiyuki ASAKURA
This map shows the geographic impact of Toshiyuki ASAKURA'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 Toshiyuki ASAKURA with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Toshiyuki ASAKURA more than expected).
Fields of papers citing papers by Toshiyuki ASAKURA
This network shows the impact of papers produced by Toshiyuki ASAKURA. 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 Toshiyuki ASAKURA. The network helps show where Toshiyuki ASAKURA may publish in the future.
Co-authorship network of co-authors of Toshiyuki ASAKURA
This figure shows the co-authorship network connecting the top 25 collaborators of Toshiyuki ASAKURA. A scholar is included among the top collaborators of Toshiyuki ASAKURA based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Toshiyuki ASAKURA. Toshiyuki ASAKURA is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | 0 | |
| 4 | 2 | |
| 5 | 3 | |
| 6 | 22 | |
| 7 | 0 | |
| 8 | Finger spelling recognition using neural network with pattern recognition model | 8 |
| 9 | 3 | |
| 10 | 2 | |
| 11 | 23 | |
| 12 | 1 | |
| 13 | 1 | |
| 14 | 3 | |
| 15 | 3 | |
| 16 | 3 | |
| 17 | 2 | |
| 18 | 1 | |
| 19 | 12 | |
| 20 | 8 |
About Toshiyuki ASAKURA
Toshiyuki ASAKURA is a scholar working on Control and Systems Engineering, Computer Vision and Pattern Recognition and Industrial and Manufacturing Engineering, having authored 75 papers that have together received 510 indexed citations. Recurring topics across this work include Robotic Path Planning Algorithms (17 papers), Neural Networks and Applications (15 papers) and Fault Detection and Control Systems (12 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (234 citations), Control and Systems Engineering (215 citations) and Media Technology (75 citations). Toshiyuki ASAKURA has collaborated with scholars based in Japan, China and Germany. Frequent co-authors include Mamoru Minami, Sheng Zhang, Xiaoli Xu, Shoji Hayashi, Hidekazu Suzuki, Sheng Zhang, Takeo Ohnishi, Osamu Hirose, Takashi Kobayashi and Yunsheng Li. Their work appears in journals such as Measurement, Advanced Robotics and Industrial Robot the international journal of robotics research and application.
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.