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Peter McAughan

dblp:243/0052 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—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 · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wireless sensing and localization
acoustic sensing
0.412019
Surface++: A Scalable and Self-sustainable Wireless Sound Sensing Surface · MobiSys 2019
Wireless sensing and localization
backscatter sensing
0.412019
Surface++: A Scalable and Self-sustainable Wireless Sound Sensing Surface · MobiSys 2019
Wireless sensing and localization › RF sensing
passive RF sensing
0.412019
Surface++: A Scalable and Self-sustainable Wireless Sound Sensing Surface · MobiSys 2019
Wireless sensing and localization
wireless sensing
0.412019
Surface++: A Scalable and Self-sustainable Wireless Sound Sensing Surface · MobiSys 2019
Interaction techniques and input
gesture input
0.112019
Surface++: A Scalable and Self-sustainable Wireless Sound Sensing Surface · MobiSys 2019

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

modular fabrication · 0.8analog backscatter · 0.8
YearPublicationVenuePosition
2019 Surface++: A Scalable and Self-sustainable Wireless Sound Sensing Surface
abstract
We present Surface++, which leverages our previous work SATURN, a self-powered flexible acoustic sensor, and ZEUSSS, a passive wireless sound communication technique using analog backscatter, to create a scalable and self-sustainable wireless sound sensing surface. Our new prototype allows for large area acoustic sensing using modular fabrication techniques with the promise of being fully printable. A single small Surface++ patch can be used to extend voice and gesture input for everyday surfaces, while our more sensitive Surface++ modular array allows for large-area context sensing and localization.
Nivedita Arora, Qiuyue Xue, Dhruva Bansal, Peter McAughan, Ryan A. Bahr, Diego Osorio, Alanson P. Sample, Thad Starner, Gregory D. Abowd
MobiSys4