Jean-Ochin Abrahamians

dblp:139/3550 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 2014
0000-0002-2623-5460ORCID · corroborated

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-authorApplied, interdisciplinary, general and emerging computing · 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.

Artificial intelligence
1 paper
Robot manipulation · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%
Human-computer interaction and pervasive computing
1 paper
Immersive interaction · 100%

Topics — the 2 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation
micro/nano robotics
0.212014
A Nanorobotic System for In Situ Stiffness Measurements on Membranes · IEEE Trans. Robotics 2014
Robotics › Robot manipulation › micro/nano manipulation
nanomanipulation
0.212014
A Nanorobotic System for In Situ Stiffness Measurements on Membranes · IEEE Trans. Robotics 2014

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

tuning-fork-based dynamic force sensing · 0.6scanning electron microscopy · 0.69-DOF nanomanipulation · 0.6
YearPublicationVenuePosition
2014 A Nanorobotic System for In Situ Stiffness Measurements on Membranes
abstract
In order to characterize the mechanical behavior of fragile resonant microelectromechanical systems (MEMS)/nanoelectromechanical systems (NEMS), nondestructive measurements are required. In this paper, a cartography of local stiffness variations on a suspended micromembrane is established for the first time, by a tuning-fork-based dynamic force sensor inside a scanning electron microscope (SEM). Experiments are conducted individually on a batch of InP membranes 200 nm thin, using a 9-degree-of-freedom (dof) nanomanipulation system, complemented with virtual reality and automation tools. Results provide stiffness values in the range of a few newton per meter, with variations in a single sample depending on the membrane models.
Jean-Ochin Abrahamians, Bruno Sauvet, Jerome Polesel-Maris, Rémy Braive, Stéphane Régnier
IEEE Trans. Robotics1
2013 Robotic in situ stiffness cartography of InP membranes by dynamic force sensing
abstract
Typical methods of measuring mechanical properties at the micro-scale are destructive, and do not allow proper characterisation on resonant MEMS/NEMS. In this paper, a cartography of local stiffness variations on a suspended micromembrane is established for the first time, by a tuning-fork-based dynamic force sensor inside a SEM. Experiments are conducted on InP membranes 200nm thin, using a 9-DoF nano-manipulation system, complemented with virtual reality and automation tools. Results provide stiffness values ranging from 0.6 to 3 N/m on a single sample.
Jean-Ochin Abrahamians, Bruno Sauvet, Jerome Polesel-Maris, Rémy Braive, Stéphane Régnier
IROS1