Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
String-Stable CACC Design and Experimental Validation: A Frequency-Domain Approach
2010717 citationsGerrit Naus, Jeroen Ploeg et al.IEEE Transactions on Vehicular Technologyprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Gerrit Naus'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 Gerrit Naus with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gerrit Naus more than expected).
This network shows the impact of papers produced by Gerrit Naus. 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 Gerrit Naus. The network helps show where Gerrit Naus may publish in the future.
Co-authorship network of co-authors of Gerrit Naus
This figure shows the co-authorship network connecting the top 25 collaborators of Gerrit Naus.
A scholar is included among the top collaborators of Gerrit Naus 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 Gerrit Naus. Gerrit Naus is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Edwards, Thomas L., Kanmin Xue, Maarten Beelen, et al.. (2018). A first-in-man trial assessing robotic surgery inside the human eye to perform a subretinal injection. Investigative Ophthalmology & Visual Science. 59(9). 5936–5936.1 indexed citations
5.
Faridpooya, Koorosh, Jan C. van Meurs, Koenraad A. Vermeer, et al.. (2018). Evaluation of OCT vs surgeon guided robotic manipulation in a simulated vitreoretinal model. Acta Ophthalmologica. 96. 11–12.1 indexed citations
Smet, Marc D. de, Gerrit Naus, Koorosh Faridpooya, & Marco Mura. (2018). Robotic-assisted surgery in ophthalmology. Current Opinion in Ophthalmology. 29(3). 248–253.53 indexed citations
8.
Faridpooya, Koorosh, Koenraad A. Vermeer, Marc D. de Smet, et al.. (2018). Evaluation of OCT versus surgeon guided robotic manipulation in a simulated vitreoretinal model. 59(9). 5930–5930.3 indexed citations
9.
MacLaren, Robert E., Thomas L. Edwards, Kanmin Xue, et al.. (2017). Results from the first use of a robot to operate inside the human eye. Investigative Ophthalmology & Visual Science. 58(8). 1185–1185.1 indexed citations
Smet, Marc D. de, et al.. (2014). Micrometer-precision penetration motion in robot-assisted vitreoretinal surgery. Investigative Ophthalmology & Visual Science. 55(13). 2323–2323.3 indexed citations
14.
Naus, Gerrit, et al.. (2013). Robot assistance for micrometer precision in vitreoretinal surgery. Investigative Ophthalmology & Visual Science. 54(15). 5808–5808.22 indexed citations
15.
Naus, Gerrit, et al.. (2010). String-Stable CACC Design and Experimental Validation: A Frequency-Domain Approach. IEEE Transactions on Vehicular Technology. 59(9). 4268–4279.717 indexed citations breakdown →
16.
Naus, Gerrit, René van de Molengraft, & Jeroen Ploeg. (2009). Cooperative adaptive cruise control. Munich Personal RePEc Archive (Ludwig Maximilian University of Munich). 7 Suppl. 120–19.2 indexed citations
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.