Patrick Garda

85 papers receiving 381 citations

Peers

Patrick Garda
Comparison fields: 5 of 81
  • Acoustics and Ultrasonics 4
  • Hardware and Architecture 32
  • Computer Vision and Pattern Recognition 91
  • Signal Processing 47
  • Artificial Intelligence 115
Replace Alastair D. McAulay with:
Alastair D. McAulay United States
Clark S. Lindsey Sweden
Karlheinz Ochs Germany
Lionel Lacassagne France
Glenn Gulak Canada
Abdulkadir Akın Switzerland
Mark E. Lasher United States
K. Strohbehn United States
Csaba Rekeczky Hungary
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Citations per field
00.5×4.5×
Alastair D. McAulay · 1×
Citations per year

Countries citing papers authored by Patrick Garda

Since Specialization
Citations

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

Fields of papers citing papers by Patrick Garda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 98 papers — load more, or switch the sort, to bring in the rest.

#Work
1 198829
2 200824
3 201021
4 199917
5 200313
6 200812
7 201612
8 201710
9 199610
10 198710
11 19989
12 20089
13 19918
14 19908
15 20168
16 20157
17 20087
18 20037
19 19907
20 20017

About Patrick Garda

Patrick Garda is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing and Computer Networks and Communications, having authored 98 papers that have together received 403 indexed citations. Recurring topics across this work include Neural Networks and Applications (22 papers), Advanced Memory and Neural Computing (18 papers), CCD and CMOS Imaging Sensors (13 papers), Advanced Data Compression Techniques (11 papers), Neural Networks and Reservoir Computing (11 papers), Cellular Automata and Applications (11 papers), Image and Signal Denoising Methods (9 papers) and Embedded Systems Design Techniques (8 papers). The work is most often cited by research in Acoustics and Ultrasonics (4 citations), Hardware and Architecture (32 citations), Computer Vision and Pattern Recognition (91 citations), Signal Processing (47 citations) and Artificial Intelligence (115 citations). Patrick Garda has collaborated with scholars based in France, Germany and Japan. Frequent co-authors include Eric Belhaire, F. Devos, Pierre Chavel, Olivier Romain, Andréa Pinna, Yves Cansi, Stéphanie Muller, Lionel Lacassagne, Guo‐Neng Lu and Bertrand Granado. Their work appears in journals such as Electronics Letters, Optics Letters, IEEE Journal of Solid-State Circuits, Optics Communications and Informatics.

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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