Thorben Moos

440 citations
29 papers · 277 · h-index 10

Impact in

Papers in

Thorben Moos

25 papers receiving 274 citations

Peers

Thorben Moos
Comparison fields: 5 of 24
  • Hardware and Architecture 172
  • Artificial Intelligence 238
  • Computer Vision and Pattern Recognition 119
  • Signal Processing 56
  • Electrical and Electronic Engineering 49
Replace George Provelengios with:
George Provelengios United States
Mario Kirschbaum Austria
Bilgiday Yuce United States
Hassen Mestiri Tunisia
Olivier Bronchain Belgium
Chun Guo China
Marcin Rogawski United States
Mark Zhao United States
Ekawat Homsirikamol United States
Ryad Benadjila France
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Citations per field
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Citations per year

Countries citing papers authored by Thorben Moos

Since Specialization
Citations

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

Fields of papers citing papers by Thorben Moos

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 18 scholars most cited alongside Thorben Moos, 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 Thorben Moos Line = papers co-authored together Thorben Moos links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201934
2 202134
3 201926
4 202126
5 201719
6 202118
7 202118
8 201915
9 201912
10 202311
11 20199
12 20248
13 20236
14 20236
15 20216
16 20196
17 20195
18 20174
19 20223
20 20233

About Thorben Moos

Thorben Moos is a scholar working on Artificial Intelligence, Hardware and Architecture, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Signal Processing, having authored 29 papers that have together received 277 indexed citations. Recurring topics across this work include Physical Unclonable Functions (PUFs) and Hardware Security (21 papers), Cryptographic Implementations and Security (21 papers), Chaos-based Image/Signal Encryption (12 papers), Security and Verification in Computing (9 papers), Coding theory and cryptography (4 papers), Advanced Malware Detection Techniques (2 papers), Advancements in Semiconductor Devices and Circuit Design (2 papers) and Integrated Circuits and Semiconductor Failure Analysis (2 papers). The work is most often cited by research in Hardware and Architecture (172 citations), Artificial Intelligence (238 citations), Computer Vision and Pattern Recognition (119 citations), Signal Processing (56 citations) and Electrical and Electronic Engineering (49 citations). Thorben Moos has collaborated with scholars based in Germany, Belgium and United States. Frequent co-authors include Amir Moradi, François‐Xavier Standaert, Tobias Schneider, Shahram Rasoolzadeh, Gregor Leander, Naghmeh Karimi, Pierrick Méaux, Yosuke Todo, Maria Eichlseder and Miroslav Knežević. Their work appears in journals such as IACR Transactions on Cryptographic Hardware and Embedded Systems, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Applied Sciences, Lecture notes in computer science and Maryland Shared Open Access Repository (USMAI Consortium).

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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