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Chet Gnegy

dblp:138/3134 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021

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
Physical-layer communications · 46% Internet of things and sensor networks · 30% Wireless networking · 23%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Physical-layer communications › spread spectrum
direct-sequence spread spectrum
0.312018
Ultrasonic Communication Using Consumer Hardware · IEEE Trans. Multim. 2018
Internet of things and sensor networks › underwater sensor networks › underwater communication › acoustic communication
near-ultrasonic communication
0.312018
Ultrasonic Communication Using Consumer Hardware · IEEE Trans. Multim. 2018
Physical-layer communications
spread spectrum
0.312018
Ultrasonic Communication Using Consumer Hardware · IEEE Trans. Multim. 2018
Wireless networking
wireless network protocols
0.312018
Ultrasonic Communication Using Consumer Hardware · IEEE Trans. Multim. 2018

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

pseudorandom code · 0.3orthogonal sine modulation · 0.3
YearPublicationVenuePosition
2022 Listening with Googlears: Low-Latency Neural Multiframe Beamforming and Equalization for Hearing Aids
Samuel J. Yang, Scott Wisdom, Chet Gnegy, Richard F. Lyon, Sagar Savla
INTERSPEECH3
2018 Ultrasonic Communication Using Consumer Hardware
abstract
We have implemented a near-ultrasonic communication protocol in the 18.5-20 kHz band, which is inaudible to most humans, using commodity smartphone speakers and microphones to transmit and receive signals. The protocol described in this paper is a component of Google's Nearby platform, where near-ultrasound signals are used to establish copresence between nearby devices by transmitting a short token. High-frequency sound does not pass through walls (most energy is reflected), so identified devices are constrained to approximately the same room, “within earshot” of one another. Our protocol has a raw data rate of 94.5 b/s, and we find in real indoor environments that transmission between mobile devices is reliable at 2 m distance and often works at 10 m. We use direct-sequence spread spectrum modulation, which makes it highly robust to multipath, motion, and narrowband noise. We use a 127-chip pseudorandom code, repeating once per data symbol, and modulate its amplitude with orthogonal sine waveforms encoding 4-bit symbol values. We add the orthogonal sines to a constant “pedestal,” which is inefficient in an information-theoretic sense, but makes synchronization easier. We describe a robust and computationally efficient transmitter and receiver implementations and show experiments on real and simulated data.
Pascal Getreuer, Chet Gnegy, Richard F. Lyon, Rif A. Saurous
IEEE Trans. Multim.2