Fernando Cladera
Impact in
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- Robotic Path Planning Algorithms
- Advanced Vision and Imaging
- Aerospace Engineering top 10%
- Robotics and Sensor-Based Localization
- UAV Applications and Optimization
Papers in
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- Advanced Memory and Neural Computing 4
- CCD and CMOS Imaging Sensors 2
- Ferroelectric and Negative Capacitance Devices 1
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- Age of Information Optimization 2
- Co-authors
- Camillo J. Taylor (7 shared papers)Vijay Kumar (7 shared papers)Xu Liu (3 shared papers)Steven W. Chen (2 shared papers)Ian D. Miller (3 shared papers)Chao Qu (2 shared papers)Trey Smith (2 shared papers)M. Ani Hsieh (4 shared papers)
- Journals
- IEEE Robotics and Automation Letters (3 papers)IEEE Micro (1 paper)OCEANS 2022, Hampton Roads (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesFinlandBrazil
In The Last Decade
Fernando Cladera
14 papers receiving 223 citations
Peers
Comparison fields: 5 of 48
- Computer Vision and Pattern Recognition 97
- Aerospace Engineering 104
- Geology 17
- Environmental Engineering 32
- Computer Networks and Communications 34
Countries citing papers authored by Fernando Cladera
This map shows the geographic impact of Fernando Cladera'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 Fernando Cladera with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fernando Cladera more than expected).
Fields of papers citing papers by Fernando Cladera
This network shows the impact of papers produced by Fernando Cladera. 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 Fernando Cladera. The network helps show where Fernando Cladera may publish in the future.
Co-authors
The 25 scholars most cited alongside Fernando Cladera, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 65 | |
| 2 | 2022 | 37 | |
| 3 | 2023 | 35 | |
| 4 | 2022 | 30 | |
| 5 | 2022 | 16 | |
| 6 | 2020 | 15 | |
| 7 | 2024 | 10 | |
| 8 | 2024 | 8 | |
| 9 | 2024 | 6 | |
| 10 | 2024 | 4 | |
| 11 | 2022 | 3 | |
| 12 | 2025 | 1 | |
| 13 | 2015 | 1 | |
| 14 | 2025 | 1 |
About Fernando Cladera
Fernando Cladera is a scholar working on Electrical and Electronic Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition, Aerospace Engineering and Ocean Engineering, having authored 14 papers that have together received 232 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (4 papers), Robotics and Sensor-Based Localization (3 papers), Age of Information Optimization (2 papers), UAV Applications and Optimization (2 papers), Advanced Neural Network Applications (2 papers), CCD and CMOS Imaging Sensors (2 papers), Ferroelectric and Negative Capacitance Devices (1 paper) and Medical Image Segmentation Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (97 citations), Aerospace Engineering (104 citations), Geology (17 citations), Environmental Engineering (32 citations) and Computer Networks and Communications (34 citations). Fernando Cladera has collaborated with scholars based in United States, Finland and Brazil. Frequent co-authors include Camillo J. Taylor, Vijay Kumar, Xu Liu, Steven W. Chen, Ian D. Miller, Chao Qu, Trey Smith, M. Ani Hsieh, Kostas Daniilidis and T. W. Donnelly. Their work appears in journals such as IEEE Robotics and Automation Letters, IEEE Micro, OCEANS 2022, Hampton Roads and arXiv (Cornell University).
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.