Daniel Graupe

117 papers receiving 3.3k citations

Peers

Daniel Graupe
Comparison fields: 5 of 166
  • Cognitive Neuroscience 1.0k
  • Signal Processing 480
  • Cellular and Molecular Neuroscience 666
  • Computational Mathematics 18
  • Biomedical Engineering 1.2k
Replace Sridhar Krishnan with:
Sridhar Krishnan Canada
Ganesh R. Naik Australia
Yingchun Zhang China
Javier Escudero United Kingdom
Francesco Carlo Morabito Italy
Z. Jane Wang Canada
Yuki Hagiwara Singapore
Tomás Ward Ireland
Heung‐Il Suk South Korea
Patrick van der Smagt Germany
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Citations per field
00.5×2.7×
Sridhar Krishnan · 1×
Citations per year

Countries citing papers authored by Daniel Graupe

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Graupe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 127 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1997318
2 2004281
3 1975274
4 2013272
5 1982172
6 1978162
7 2007148
8 1978126
9
Punctured Convolutional Codes of Rate (n - 1)/n and Simplified Maximum Likelihood Decoding
1979123
10 200185
11 198583
12 201377
13 198977
14 197571
15 198770
16 199867
17 200764
18 199557
19 200950
20 201046

About Daniel Graupe

Daniel Graupe is a scholar working on Biomedical Engineering, Cognitive Neuroscience, Artificial Intelligence, Signal Processing and Control and Systems Engineering, having authored 127 papers that have together received 3.5k indexed citations. Recurring topics across this work include Muscle activation and electromyography studies (34 papers), EEG and Brain-Computer Interfaces (29 papers), Neural Networks and Applications (21 papers), Neuroscience and Neural Engineering (20 papers), Blind Source Separation Techniques (17 papers), Control Systems and Identification (16 papers), Fault Detection and Control Systems (13 papers) and Advanced Adaptive Filtering Techniques (12 papers). The work is most often cited by research in Cognitive Neuroscience (1.0k citations), Signal Processing (480 citations), Cellular and Molecular Neuroscience (666 citations), Computational Mathematics (18 citations) and Biomedical Engineering (1.2k citations). Daniel Graupe has collaborated with scholars based in United States, Israel and United Kingdom. Frequent co-authors include Kate H. Kohn, Vivek Nigam, Hubert Kordylewski, A.A. Beex, Daniela Tuninetti, Michael A. Wincek, Konstantin V. Slavin, Ishita Basu, Aaron S. Field and Jason H. Moore. Their work appears in journals such as International Journal of Systems Science, IEEE Transactions on Automatic Control, Neurological Research, IEEE Transactions on Biomedical Engineering and Automatica.

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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