William C. Headley

50 papers receiving 579 citations

Peers

William C. Headley
Comparison fields: 5 of 67
  • Signal Processing 101
  • Artificial Intelligence 295
  • Computer Networks and Communications 158
  • Aerospace Engineering 162
  • Electrical and Electronic Engineering 285
Replace Fanggang Wang with:
Fanggang Wang China
Jérôme Lacan France
Zhefu Wu China
Juan F. Valenzuela‐Valdés Spain
Ali Rıza Ekti United States
Tamoghna Roy United States
Christo Kurisummoottil Thomas France
Ali Görçin Türkiye
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Citations per year

Countries citing papers authored by William C. Headley

Since Specialization
Citations

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

Fields of papers citing papers by William C. Headley

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by William C. Headley. 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 William C. Headley. The network helps show where William C. Headley may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 53 papers — load more, or switch the sort, to bring in the rest.

#Work
1
SmartCampus: A user-centric testbed for Internet of Things experimentation
201360
2 200859
3 201853
4 201839
5 201736
6 201036
7 201935
8 200534
9 201328
10 200720
11 202216
12 202114
13 200813
14 200813
15 201912
16 201811
17 200111
18 201910
19 20079
20 20118

About William C. Headley

William C. Headley is a scholar working on Electrical and Electronic Engineering, Aerospace Engineering, Artificial Intelligence, Computer Networks and Communications and Atomic and Molecular Physics, and Optics, having authored 53 papers that have together received 604 indexed citations. Recurring topics across this work include Wireless Signal Modulation Classification (15 papers), Radar Systems and Signal Processing (11 papers), Cognitive Radio Networks and Spectrum Sensing (8 papers), Photonic and Optical Devices (6 papers), Distributed Sensor Networks and Detection Algorithms (6 papers), Blind Source Separation Techniques (5 papers), Optical Coherence Tomography Applications (5 papers) and Adversarial Robustness in Machine Learning (4 papers). The work is most often cited by research in Signal Processing (101 citations), Artificial Intelligence (295 citations), Computer Networks and Communications (158 citations), Aerospace Engineering (162 citations) and Electrical and Electronic Engineering (285 citations). William C. Headley has collaborated with scholars based in United States, United Kingdom and Italy. Frequent co-authors include Claudio R. C. M. da Silva, Alan J. Michaels, R. Michael Buehrer, Alexander Gluhak, Lauren J. Wong, Michele Nati, Steven C. Hauser, Ryan Gerdes, Jiabin Yu and Rahim Tafazolli. Their work appears in journals such as IEEE Open Journal of the Communications Society, IEEE Communications Letters, Journal of Modern Optics, IEEE Transactions on Mobile Computing and Journal of Materials Science Materials in Electronics.

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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