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A robust modulation classification method using convolutional neural networks

Overview of attention for article published in EURASIP Journal on Advances in Signal Processing, March 2019
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About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
1 tweeter

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
10 Mendeley
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Title
A robust modulation classification method using convolutional neural networks
Published in
EURASIP Journal on Advances in Signal Processing, March 2019
DOI 10.1186/s13634-019-0616-6
Authors

Siyang Zhou, Zhendong Yin, Zhilu Wu, Yunfei Chen, Nan Zhao, Zhutian Yang

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 30%
Student > Ph. D. Student 2 20%
Other 1 10%
Student > Bachelor 1 10%
Researcher 1 10%
Other 1 10%
Unknown 1 10%
Readers by discipline Count As %
Engineering 6 60%
Computer Science 2 20%
Physics and Astronomy 1 10%
Unknown 1 10%

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 20 October 2019.
All research outputs
#8,871,556
of 14,155,721 outputs
Outputs from EURASIP Journal on Advances in Signal Processing
#164
of 313 outputs
Outputs of similar age
#188,393
of 315,492 outputs
Outputs of similar age from EURASIP Journal on Advances in Signal Processing
#2
of 3 outputs
Altmetric has tracked 14,155,721 research outputs across all sources so far. This one is in the 24th percentile – i.e., 24% of other outputs scored the same or lower than it.
So far Altmetric has tracked 313 research outputs from this source. They receive a mean Attention Score of 2.9. This one is in the 39th percentile – i.e., 39% 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 315,492 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.