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An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection

Overview of attention for article published in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, January 2024
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1 X user

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3 Mendeley
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Title
An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection
Published in
IEEE Transactions on Very Large Scale Integration (VLSI) Systems, January 2024
DOI 10.1109/tvlsi.2024.3356161
Pubmed ID
Authors

Renas Ercan, Yunjia Xia, Yunyi Zhao, Rui Loureiro, Shufan Yang, Hubin Zhao

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X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 67%
Unknown 1 33%
Readers by discipline Count As %
Engineering 2 67%
Unknown 1 33%
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 19 February 2024.
All research outputs
#22,938,588
of 25,576,801 outputs
Outputs from IEEE Transactions on Very Large Scale Integration (VLSI) Systems
#1,386
of 1,408 outputs
Outputs of similar age
#269,876
of 332,934 outputs
Outputs of similar age from IEEE Transactions on Very Large Scale Integration (VLSI) Systems
#1
of 1 outputs
Altmetric has tracked 25,576,801 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,408 research outputs from this source. They receive a mean Attention Score of 3.8. This one is in the 1st percentile – i.e., 1% 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 332,934 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them