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Hsiao-Ming Lu

dblp:224/2387 · 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

Artificial intelligence and machine learning · 1 · 1 since 2021Computer 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
Internet of things and sensor networks · 77% Physical-layer communications · 23%

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

TopicWeightPapersLastEvidence papers
Internet of things and sensor networks
backscatter communication
0.312018
In-body backscatter communication and localization · SIGCOMM 2018
Physical-layer communications › propagation
signal propagation
0.112018
In-body backscatter communication and localization · SIGCOMM 2018
YearPublicationVenuePosition
2022 A deep LSTM autoencoder-based framework for predictive maintenance of a proton radiotherapy delivery system
Tai Dou, Benjamin Clasie, Nicolas Depauw, Tim Shen, Robert Brett, Hsiao-Ming Lu, Jacob B. Flanz, Kyung-Wook Jee
Artif. Intell. Medicine6
2018 In-body backscatter communication and localization
abstract
Backscatter requires zero transmission power, making it a compelling technology for in-body communication and localization. It can significantly reduce the battery requirements (and hence the size) of micro-implants and smart capsules, and enable them to be located on-the-move inside the body. The problem however is that the electrical properties of human tissues are very different from air and vacuum. This creates new challenges for both communication and localization. For example, signals no longer travel along straight lines, which destroys the geometric principles underlying many localization algorithms. Furthermore, the human skin backscatters the signal creating strong interference to the weak in-body backscatter transmission. These challenges make deep-tissue backscatter intrinsically different from backscatter in air or vacuum. This paper introduces ReMix, a new backscatter design that is particularly customized for deep tissue devices. It overcomes interference from the body surface, and localizes the in-body backscatter devices even though the signal travels along crooked paths. We have implemented our design and evaluated it in animal tissues and human phantoms. Our results demonstrate that ReMix delivers efficient communication at an average SNR of 15.2 dB at 1 MHz bandwidth, and has an average localization accuracy of 1.4cm in animal tissues.
Deepak Vasisht, Guo Zhang 0006, Omid Abari, Hsiao-Ming Lu, Jacob Flanz, Dina Katabi
SIGCOMM4