Brian J. d’Auriol

44 papers receiving 528 citations

Peers

Brian J. d’Auriol
Comparison fields: 5 of 65
  • Computer Networks and Communications 399
  • Artificial Intelligence 130
  • Electrical and Electronic Engineering 86
  • Computer Vision and Pattern Recognition 78
  • Sociology and Political Science 75
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Citations per year

Countries citing papers authored by Brian J. d’Auriol

Since Specialization
Citations

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

Fields of papers citing papers by Brian J. d’Auriol

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Brian J. d’Auriol. 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 Brian J. d’Auriol. The network helps show where Brian J. d’Auriol may publish in the future.

Co-authorship network of co-authors of Brian J. d’Auriol

This figure shows the co-authorship network connecting the top 25 collaborators of Brian J. d’Auriol. A scholar is included among the top collaborators of Brian J. d’Auriol based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Brian J. d’Auriol. Brian J. d’Auriol is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 1
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Viewer Perception of Superellipsoid-based Accelerometer Visualization Techniques.
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5
PBR: Priority Based Routing in Multi-Sink Sensor Networks.
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6
A Research Framework Model to Guide Both Broad and Focused Research into Ubiquitous Sensor Networks.
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7 4
8 2
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A Finite State Machine Model to Support the Visualization of Complex Dynamic Systems.
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A TransDimension Visualization Model for Complex Dynamic System Visualizations.
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A Relational Model for Visualizing Codon Usage and Palindrome Distributions in Genome Sequences.
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12
A Free Space Optical Bus Parallel Model Framework.
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Network Vertical Intrusion Model (NetVIM).
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A Computation-Communication Sequencing Model for Intrusion Detection Systems.
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A Conceptual Space Model for Intrusion Detection.
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A State Transition Model Case Study for Intrusion Detection Systems.
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Optical Bus Communication Modeling and Simulation.
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About Brian J. d’Auriol

Brian J. d’Auriol is a scholar working on Computer Networks and Communications, Hardware and Architecture and Computer Vision and Pattern Recognition, having authored 49 papers that have together received 574 indexed citations. Recurring topics across this work include Energy Efficient Wireless Sensor Networks (14 papers), Data Visualization and Analytics (8 papers) and Optical Network Technologies (8 papers). The work is most often cited by research in Computer Networks and Communications (399 citations), Artificial Intelligence (130 citations) and Computer Vision and Pattern Recognition (78 citations). Brian J. d’Auriol has collaborated with scholars based in South Korea, United States and Australia. Frequent co-authors include Hassan Jameel, Riaz Ahmed Shaikh, Heejo Lee, Young-Jae Song, Sungyoung Lee, Sungyoung Lee, Young-Tack Park, La The Vinh, Joo‐Youn Cho and Shuncheng Lee. Their work appears in journals such as Sensors, Pattern Recognition Letters and IEEE Transactions on Parallel and Distributed Systems.

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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