Joaquin Sitte

1.1k citations
90 papers · 842 · h-index 13

Impact in

Papers in

Joaquin Sitte

78 papers receiving 782 citations

Peers

Joaquin Sitte
Comparison fields: 5 of 109
  • Artificial Intelligence 412
  • Computer Vision and Pattern Recognition 230
  • Signal Processing 88
  • Control and Systems Engineering 162
  • Management Science and Operations Research 86
Replace Kaoru Hirota with:
Kaoru Hirota Japan
Liang Hu China
A. Meystel United States
Alan N. Steinberg United States
Stephen I. Gallant United States
Leon Reznik United States
Bernhard Moser Austria
Irene Macaluso Ireland
Narendra S. Chaudhari India
Xiaoqin Zeng China
Joaquin Sitte relative to Kaoru Hirota Japan Kaoru Hirota's profile →
Citations per field
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Citations per year

Countries citing papers authored by Joaquin Sitte

Since Specialization
Citations

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

Fields of papers citing papers by Joaquin Sitte

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 90 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1991111
2 200696
3 199271
4 199360
5 200251
6 200051
7 201234
8 199823
9 200523
10
Advances in Robotics
200923
11 201021
12 200915
13 200915
14
The Parameter-Less SOM Algorithm
200312
15 200912
16 20059
17 20069
18 20129
19 20058
20 19988

About Joaquin Sitte

Joaquin Sitte is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering, Mechanical Engineering and Aerospace Engineering, having authored 90 papers that have together received 842 indexed citations. Recurring topics across this work include Neural Networks and Applications (28 papers), Modular Robots and Swarm Intelligence (12 papers), Robotic Path Planning Algorithms (11 papers), Robotics and Sensor-Based Localization (11 papers), Control Systems and Identification (8 papers), Advanced Image and Video Retrieval Techniques (8 papers), Fuzzy Logic and Control Systems (7 papers) and Evolutionary Algorithms and Applications (7 papers). The work is most often cited by research in Artificial Intelligence (412 citations), Computer Vision and Pattern Recognition (230 citations), Signal Processing (88 citations), Control and Systems Engineering (162 citations) and Management Science and Operations Research (86 citations). Joaquin Sitte has collaborated with scholars based in Australia, Germany and United States. Frequent co-authors include Shlomo Geva, Erik Berglund, Renate Sitte, Petra Winzer, Ulrich Rückert, Frédéric Maire, Ulf Witkowski, Kazuyuki Murase, Jacky Baltes and Ching‐Chang Wong. Their work appears in journals such as Neurocomputing, Autonomous Robots, Neural Computation, IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans and IEEE Control Systems.

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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