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Using convolutional neural networks to detect GNSS multipath

Overview of attention for article published in Frontiers in Robotics and AI, May 2023
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age and source (59th percentile)

Mentioned by

twitter
2 X users

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
6 Mendeley
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Title
Using convolutional neural networks to detect GNSS multipath
Published in
Frontiers in Robotics and AI, May 2023
DOI 10.3389/frobt.2023.1106439
Pubmed ID
Authors

Anthony Guillard, Paul Thevenon, Carl Milner

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 17%
Researcher 1 17%
Unknown 4 67%
Readers by discipline Count As %
Computer Science 1 17%
Unknown 5 83%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 May 2023.
All research outputs
#18,049,656
of 26,388,722 outputs
Outputs from Frontiers in Robotics and AI
#1,159
of 1,811 outputs
Outputs of similar age
#244,700
of 412,310 outputs
Outputs of similar age from Frontiers in Robotics and AI
#16
of 44 outputs
Altmetric has tracked 26,388,722 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,811 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.5. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 412,310 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.