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Gershon Dublon

dblp:24/7571 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none

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

Human-computer interaction and ubiquitous computing · 1Applied, interdisciplinary, general and emerging computing · 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.

Human-computer interaction and pervasive computing
1 paper
Personal fabrication and tangible interfaces · 87% Haptics and multimodal interaction · 13%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Personal fabrication and tangible interfaces › digital fabrication
3d printing
0.212016
Cilllia: 3D Printed Micro-Pillar Structures for Surface Texture, Actuation and Sensing · CHI 2016
Personal fabrication and tangible interfaces
interactive artifacts
0.212016
Cilllia: 3D Printed Micro-Pillar Structures for Surface Texture, Actuation and Sensing · CHI 2016

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

fabrication platform · 0.2
YearPublicationVenuePosition
2016 Cilllia: 3D Printed Micro-Pillar Structures for Surface Texture, Actuation and Sensing
abstract
This work presents a method for 3D printing hair-like structures on both flat and curved surfaces. It allows a user to design and fabricate hair geometries that are smaller than 100 micron. We built a software platform to let users quickly define the hair angle, thickness, density, and height. The ability to fabricate customized hair-like structures not only expands the library of 3D-printable shapes, but also enables us to design passive actuators and swipe sensors. We also present several applications that show how the 3D-printed hair can be used for designing everyday interactive objects.
Jifei Ou, Gershon Dublon, Chin-Yi Cheng, Felix Heibeck, Karl D. D. Willis, Hiroshi Ishii 0001
CHI2
2015 EMI Spy: Harnessing electromagnetic interference for low-cost, rapid prototyping of proxemic interaction
abstract
We present a wearable system that uses ambient electromagnetic interference (EMI) as a signature to identify electronic devices and support proxemic interaction. We designed a low cost tool, called EMI Spy, and a software environment for rapid deployment and evaluation of ambient EMI-based interactive infrastructure. EMI Spy captures electromagnetic interference and delivers the signal to a user's mobile device or PC through either the device's wired audio input or wirelessly using Bluetooth. The wireless version can be worn on the wrist, communicating with the user;s mobile device in their pocket. Users are able to train the system in less than 1 second to uniquely identify displays in a 2-m radius around them, as well as to detect pointing at a distance and touching gestures on the displays in real-time. The combination of a low cost EMI logger and an open source machine learning tool kit allows developers to quickly prototype proxemic, touch-to-connect, and gestural interaction. We demonstrate the feasibility of mobile, EMI-based device and gesture recognition with preliminary user studies in 3 scenarios, achieving 96% classification accuracy at close range for 6 digital signage displays distributed throughout a building, and 90% accuracy in classifying pointing gestures at neighboring desktop LCD displays. We were able to distinguish 1- and 2-finger touching with perfect accuracy and show indications of a way to determine power consumption of a device via touch. Our system is particularly well-suited to temporary use in a public space, where the sensors could be distributed to support a popup interactive environment anywhere with electronic devices. By designing for low cost, mobile, flexible, and infrastructure-free deployment, we aim to enable a host of new proxemic interfaces to existing appliances and displays
Nan Zhao 0008, Gershon Dublon, Nicholas Edward Gillian, Artem Dementyev, Joseph A. Paradiso
BSN2