Demo Data from VLDB 2026 / reviewers in the wild / expert
Josh Tabor
dblp:167/5966
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2015
—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% | |
| Human-computer interaction and pervasive computing
1 paper |
Interaction techniques and input · 77% Ubiquitous computing and smart environments · 23% |
Topics — the 3 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Interaction techniques and input
mobile interaction |
0.2 | 1 | 2015 | Extending Mobile Interaction Through Near-Field Visible Light Sensing · MobiCom 2015 |
Wireless sensing and localization › human sensing
finger localization |
0.2 | 1 | 2015 | Extending Mobile Interaction Through Near-Field Visible Light Sensing · MobiCom 2015 |
Wireless sensing and localization › optical sensing
visible light sensing |
0.2 | 1 | 2015 | Extending Mobile Interaction Through Near-Field Visible Light Sensing · MobiCom 2015 |
Methods — techniques the papers use, named apart from their topics
light propagation/reflection model · 0.4LED and light sensor · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Extending Mobile Interaction Through Near-Field Visible Light SensingabstractMobile devices are shrinking their form factors for portability, but user-mobile interaction is becoming increasingly challenging. In this paper, we propose a novel system called Okuli to meet this challenge. Okuli is a compact, low-cost system that can augment a mobile device and extend its interaction workspace to any nearby surface area. Okuli piggybacks on visible light communication modules, and uses a low-power LED and two light sensors to locate user's finger within the workspace. It is built on a light propagation/reflection model that achieves around one-centimeter location precision, with zero run-time training overhead. We have prototyped Okuli as an Android peripheral, with a 3D-printed shroud to host the LED and light sensors. Our experiments demonstrate Okuli's accuracy, stability, energy efficiency, as well as its potential in serving virtual keyboard and trackpad applications. Chi Zhang 0018, Josh Tabor, Xinyu Zhang 0003 |
MobiCom | 2 |