Demo Data from EDBT 2026 / reviewers in the wild / expert
Jonathan Decavel-Bueff
dblp:268/1564
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer networks
1 paper |
Wireless sensing and localization · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wireless sensing and localization
ranging |
0.4 | 1 | 2020 | Demo Abstract: Collision Prediction from Pairwise Ranging · IPSN 2020 |
Methods — techniques the papers use, named apart from their topics
range measurement · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Demo Abstract: Collision Prediction from Pairwise RangingabstractThe ability to predict, and thus react to, oncoming collisions among a set of mobile agents is a fundamental requirement for safe autonomous movement, both human and robotic. This demonstration tests a pairwise method in which two agents collect repeated range measurements and predict if they will collide. Compared to methods which use GPS or TDOA to track each agent and then predict collisions, this method does not rely on infrastructure or a fixed coordinate system. However, the accurate prediction of future pairwise range, and thus collision prediction, is highly sensitive to noise and changes in velocity. This prototype can be used to provide intuition for the method’s strengths and weaknesses. Alemayehu Solomon Abrar, Neal Patwari, Jonathan Decavel-Bueff |
IPSN | 3 |