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Luc Marechal

dblp:65/4161 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2014
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author

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 · 50% Internet of things and sensor networks · 50%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wireless sensing and localization › magnetic sensing
magnetic localization
0.212014
Optimal spatial design of non-invasive magnetic field-based localization systems · ICRA 2014
Internet of things and sensor networks
sensor placement
0.212014
Optimal spatial design of non-invasive magnetic field-based localization systems · ICRA 2014
Medical and health informatics
computer-assisted intervention
0.112014
Optimal spatial design of non-invasive magnetic field-based localization systems · ICRA 2014

Methods — techniques the papers use, named apart from their topics

numerical simulation · 0.4genetic algorithm · 0.4
YearPublicationVenuePosition
2014 Optimal spatial design of non-invasive magnetic field-based localization systems
abstract
Magnetic localization systems based on passive permanent magnets (PM) are of great interest due to their ability to provide non-contact sensing and without any power requirement for the PM. Medical procedures such as ventriculostomy can benefit greatly from real-time feedback of the inserted catheter tip. While the effects of the number of sensors on the localization accuracy in such systems has been reported, the spatial design of the sensor layout has been largely overlooked. Here in this paper, a framework for determining an optimal sensor assembly for enhanced localization performance is presented and investigated through numerical simulations and direct experiments. Two approaches are presented: one based on structured grid configuration and the other derived using Genetic Algorithms. Simulation results verified by experiments strongly suggest that the layout of the sensors not only has an effect on the localization accuracy, but also has an effect far more pronounced than improvements brought by increasing the number of sensors.
Luc Marechal, Shaohui Foong, Shuoyu Ding, Dushyanth Madhavan, Kristin L. Wood, Vaibhav Patil, Conor J. Walsh
ICRA1