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Attention Score in Context
| Title |
Complexity Analysis of Iterative Basis Transformations Applied to Event-Based Signals
|
|---|---|
| Published in |
Frontiers in Neuroscience, June 2018
|
| DOI | 10.3389/fnins.2018.00373 |
| Pubmed ID | |
| Authors |
Sio-Hoi Ieng, Eero Lehtonen, Ryad Benosman |
| Abstract |
This paper introduces an event-based methodology to perform arbitrary linear basis transformations that encompass a broad range of practically important signal transforms, such as the discrete Fourier transform (DFT) and the discrete wavelet transform (DWT). We present a complexity analysis of the proposed method, and show that the amount of required multiply-and-accumulate operations is reduced in comparison to frame-based method in natural video sequences, when the required temporal resolution is high enough. Experimental results on natural video sequences acquired by the asynchronous time-based neuromorphic image sensor (ATIS) are provided to support the feasibility of the method, and to illustrate the gain in computation resources. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 14 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 3 | 21% |
| Student > Ph. D. Student | 3 | 21% |
| Professor | 2 | 14% |
| Student > Bachelor | 1 | 7% |
| Lecturer | 1 | 7% |
| Other | 1 | 7% |
| Unknown | 3 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 5 | 36% |
| Computer Science | 4 | 29% |
| Physics and Astronomy | 2 | 14% |
| Unknown | 3 | 21% |
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 22 June 2018.
All research outputs
#17,292,294
of 25,382,440 outputs
Outputs from Frontiers in Neuroscience
#8,070
of 11,542 outputs
Outputs of similar age
#220,471
of 341,509 outputs
Outputs of similar age from Frontiers in Neuroscience
#179
of 229 outputs
Altmetric has tracked 25,382,440 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 11,542 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one is in the 24th percentile – i.e., 24% 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 341,509 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 229 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.