Tomas Gänsler

1.6k citations
34 papers · 1.0k · h-index 16

Impact in

Papers in

Tomas Gänsler

31 papers receiving 978 citations

Peers

Tomas Gänsler
Comparison fields: 5 of 44
  • Signal Processing 953
  • Computational Mechanics 955
  • Computational Mathematics 10
  • Cognitive Neuroscience 112
  • Computer Vision and Pattern Recognition 97
Replace Luis A. Azpicueta-Ruiz with:
Luis A. Azpicueta-Ruiz Spain
Michael M. Goodwin United States
Maria G. Jafari United Kingdom
Alexander Stenger Germany
André Gilloire France
Feiran Yang China
S. Tressens Argentina
Yutaka Kaneda Japan
Shmulik Markovich‐Golan Israel
M. Berouti United States
Tomas Gänsler relative to Luis A. Azpicueta-Ruiz Spain Luis A. Azpicueta-Ruiz's profile →
Citations per field
00.5×2.7×
Luis A. Azpicueta-Ruiz · 1×
Citations per year

Countries citing papers authored by Tomas Gänsler

Since Specialization
Citations

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

Fields of papers citing papers by Tomas Gänsler

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 16 scholars most cited alongside Tomas Gänsler, 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 Tomas Gänsler Line = papers co-authored together Tomas Gänsler links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2001400
2 2000113
3 199683
4 201247
5 200645
6 201143
7 200135
8 200235
9 200529
10 200127
11 200226
12 200225
13 200420
14 199819
15 199618
16 200215
17 199815
18 200610
19 20027
20 20066

About Tomas Gänsler

Tomas Gänsler is a scholar working on Signal Processing, Computational Mechanics, Control and Systems Engineering, Computer Vision and Pattern Recognition and Cognitive Neuroscience, having authored 34 papers that have together received 1.0k indexed citations. Recurring topics across this work include Advanced Adaptive Filtering Techniques (26 papers), Speech and Audio Processing (25 papers), Blind Source Separation Techniques (19 papers), Acoustic Wave Phenomena Research (3 papers), Image and Signal Denoising Methods (2 papers), Control Systems and Identification (2 papers), Target Tracking and Data Fusion in Sensor Networks (2 papers) and Hearing Loss and Rehabilitation (2 papers). The work is most often cited by research in Signal Processing (953 citations), Computational Mechanics (955 citations), Computational Mathematics (10 citations), Cognitive Neuroscience (112 citations) and Computer Vision and Pattern Recognition (97 citations). Tomas Gänsler has collaborated with scholars based in Sweden, Canada and United States. Frequent co-authors include Jacob Benesty, M. M. Sondhi, Steven L. Gay, Dennis R. Morgan, S.L. Gay, G. Salomonsson, P. Eneroth, Maria Hansson, Silviu Ciochină and Constantin Paleologu. Their work appears in journals such as Signal Processing, IEEE Transactions on Speech and Audio Processing, IEEE Transactions on Communications, IEEE Transactions on Biomedical Engineering and IEEE Transactions on Audio Speech and Language Processing.

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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