Matteo Matteucci
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- Robotic Path Planning Algorithms 17
- Advanced Vision and Imaging 15
- Advanced Neural Network Applications 12
- Human-Computer Interaction top 1%
- Cognitive Neuroscience top 2%
- EEG and Brain-Computer Interfaces 17
- Media Technology top 1%
- Signal Processing top 5%
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- Robotics and Sensor-Based Localization 32
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- Autonomous Vehicle Technology and Safety 17
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- Indoor and Outdoor Localization Technologies 15
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- Modular Robots and Swarm Intelligence 12
- Co-authors
- Sara ComaiAnna Maria BianchiS. CeruttiMarco CanniciAndrea BonariniAva ValiChiara PlizzariMartín O. Méndez
- Partner nations
- ItalyGermanySwitzerland
In The Last Decade
Matteo Matteucci
234 papers receiving 3.9k citations
Hit Papers
Peers
Comparison fields: 5 of 172
- Computer Vision and Pattern Recognition 1.0k
- Human-Computer Interaction 246
- Cognitive Neuroscience 798
- Media Technology 263
- Signal Processing 250
Countries citing papers authored by Matteo Matteucci
This map shows the geographic impact of Matteo Matteucci'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 Matteo Matteucci with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matteo Matteucci more than expected).
Fields of papers citing papers by Matteo Matteucci
This network shows the impact of papers produced by Matteo Matteucci. 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 Matteo Matteucci. The network helps show where Matteo Matteucci may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Matteo Matteucci, 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 | 2025 | 1 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 2 | |
| 4 | 2024 | 7 | |
| 5 | 2024 | 0 | |
| 6 | 2024 | 5 | |
| 7 | 2024 | 2 | |
| 8 | 2024 | 3 | |
| 9 | 2023 | 8 | |
| 10 | 2023 | 5 | |
| 11 | 2023 | 5 | |
| 12 | 2022 | 16 | |
| 13 | 2021 | 74 | |
| 14 | 2021 | 15 | |
| 15 | 2019 | 4 | |
| 16 | 2018 | 8 | |
| 17 | 2017 | 8 | |
| 18 | Brain Control of a Smart Wheelchair | 2008 | 17 |
| 19 | EVOLUTIONARY LEARNING OF RICH NEURAL NETWORKS IN THE BAYESIAN MODEL SELECTION FRAMEWORK | 2004 | 5 |
| 20 | A novel model to rule behavior interaction | 2004 | 3 |
About Matteo Matteucci
Matteo Matteucci is a scholar working on Computer Vision and Pattern Recognition, Human Factors and Ergonomics and Software, having authored 246 papers that have together received 4.0k indexed citations. Recurring topics across this work include Robotics and Sensor-Based Localization (32 papers), Autonomous Vehicle Technology and Safety (17 papers), Robotic Path Planning Algorithms (17 papers), EEG and Brain-Computer Interfaces (17 papers), Indoor and Outdoor Localization Technologies (15 papers), Advanced Vision and Imaging (15 papers), Advanced Neural Network Applications (12 papers) and Modular Robots and Swarm Intelligence (12 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.0k citations), Human-Computer Interaction (246 citations) and Cognitive Neuroscience (798 citations). Matteo Matteucci has collaborated with scholars based in Italy, Germany and Switzerland. Frequent co-authors include Sara Comai, Anna Maria Bianchi, S. Cerutti, Marco Cannici, Andrea Bonarini, Ava Vali, Chiara Plizzari, Martín O. Méndez, Luca Mainardi and Andréa Zanchi.
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.