Demo Data from EDBT 2026 / reviewers in the wild / expert
Shengtao Lin
dblp:235/8045
· DBLP profile ↗
4ranked-venue papers
1as first author
3since 2021 · last 2024
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 3 · 1 first-author · 3 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
3 papers |
Optical networks · 58% Physical-layer communications · 42% | |
| Theoretical computer science
1 paper |
Coding theory · 100% |
Topics — the 2 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Optical networks
optical fiber communication |
1.2 | 2 | 2023 | Prediction of fiber Rayleigh scattering responses based on deep learning · Sci. China Inf. Sci. 2023 Optical-pulse-coding phase-sensitive OTDR with mismatched filtering · Sci. China Inf. Sci. 2022 |
Physical-layer communications › radio propagation
rayleigh scattering |
0.7 | 1 | 2023 | Prediction of fiber Rayleigh scattering responses based on deep learning · Sci. China Inf. Sci. 2023 |
Methods — techniques the papers use, named apart from their topics
mismatched filtering · 1.1deep learning · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Radiation build-up and dissipation in Raman random fiber laser
Shengtao Lin, Zinan Wang |
Sci. China Inf. Sci. | 1 |
| 2023 | Prediction of fiber Rayleigh scattering responses based on deep learning
Yongxin Liang, Jianhui Sun, Anchi Wan, Zhenyu Ye, Shengtao Lin, Zinan Wang |
Sci. China Inf. Sci. | 8 |
| 2022 | Optical-pulse-coding phase-sensitive OTDR with mismatched filtering
Yongxin Liang, Zinan Wang, Shengtao Lin, Zijie Qiu, Chunye Liu, Yunjiang Rao |
Sci. China Inf. Sci. | 3 |
| 2019 | Distributed Acoustic Sensing Based on Pulse-Coding Phase-Sensitive OTDRabstractPhase-sensitive optical time-domain reflectometry (Φ-OTDR), which utilizes the phase information of Rayleigh scattered lightwave inside optical fiber, could turn a fiber cable into a massive sensor array for distributed acoustic sensing (DAS), i.e., an emerging infrastructure for Internet of Things. Given a certain fiber length, there are tradeoffs among the sensing bandwidth, the sensitivity, and the spatial resolution. In this paper, the concept of linearization and Golay pulse-coding for heterodyne Φ-OTDR are proposed and experimentally verified for the first time. First, we gave a full theoretical treatment on how an intensity-coded yet phase-retrieved Φ-OTDR can be built up as a fully linear system, therefore a significant enhancement of signal-to-noise ratio becomes viable and the sensing bandwidth equals the four-times averaging case. Second in the proof-of-concept experiment, submeter gauge length and nanostrain resolution were realized with 10 km sensing range, in other words, more than ten thousand sensitive sensing units were realized along the fiber. This paper makes a significant step toward high-performance DAS with orders-of-magnitude performance enhancement. Zinan Wang, Ji Xiong, Yun Fu 0007, Shengtao Lin, Yongxiang Chen, Qingyang Meng, Yunjiang Rao |
IEEE Internet Things J. | 5 |