J.A.G. Nijhuis

1.1k citations
38 papers · 418 indexed · h-index 10
Journals
IEEE Micro (1 paper)Revue d intelligence artificielle (1 paper)The Journal of VLSI Signal Processing Systems for Signal Image and Video Technology (1 paper)

In The Last Decade

J.A.G. Nijhuis

33 papers receiving 372 citations

Peers

J.A.G. Nijhuis
Comparison fields: 5 of 45
  • Media Technology 184
  • Computer Vision and Pattern Recognition 196
  • Artificial Intelligence 207
  • Information Systems 87
  • Software 13
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Citations per field
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Citations per year

Countries citing papers authored by J.A.G. Nijhuis

Since Specialization
Citations

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

Fields of papers citing papers by J.A.G. Nijhuis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by J.A.G. Nijhuis. 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 J.A.G. Nijhuis. The network helps show where J.A.G. Nijhuis may publish in the future.

Co-authorship network

The 21 scholars most cited alongside J.A.G. Nijhuis, 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 J.A.G. Nijhuis Line = papers co-authored together J.A.G. Nijhuis links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 200630
2
Optimal decomposition of large-neighborhood CNN templates
20040
3 200422
4 20031
5 20022
6 20022
7 20021
8 20022
9 20025
10 20022
11
Mapping a Trained Neural Network on Hardware.
19992
12 19995
13 19987
14
Speech-driven dialing
19981
15
Cascade learning for FIR-TDNNs.
19952
16
Implementing a DSP Kernel for Online Dynamic Handwritten Signature Verification Using the TMS320 DSP Family
199513
17 1995120
18 19900
19 198912
20 198919

About J.A.G. Nijhuis

J.A.G. Nijhuis is a scholar working on Artificial Intelligence, Media Technology, Computational Theory and Mathematics, Signal Processing and Computer Vision and Pattern Recognition, having authored 38 papers that have together received 418 indexed citations. Recurring topics across this work include Neural Networks and Applications (24 papers), Advanced Memory and Neural Computing (8 papers), Neural Networks Stability and Synchronization (5 papers), Vehicle License Plate Recognition (4 papers), Fuzzy Logic and Control Systems (4 papers), Handwritten Text Recognition Techniques (4 papers), Cellular Automata and Applications (4 papers) and Autonomous Vehicle Technology and Safety (3 papers). The work is most often cited by research in Media Technology (184 citations), Computer Vision and Pattern Recognition (196 citations), Artificial Intelligence (207 citations), Information Systems (87 citations) and Software (13 citations). J.A.G. Nijhuis has collaborated with scholars based in Netherlands, Germany and Switzerland. Frequent co-authors include Lambert Spaanenburg, Marco Sinnema, Michel A. Westenberg, Josien P. W. Pluim, Sybren Deelstra, Jan Bosch, John F. MacGregor, Walter Jansen, B. Höfflinger and André van Schaik. Their work appears in journals such as IEEE Micro, Revue d intelligence artificielle, The Journal of VLSI Signal Processing Systems for Signal Image and Video Technology, University of Groningen research database (University of Groningen / Centre for Information Technology) and IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications.

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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