Demo Data from EDBT 2026 / reviewers in the wild / expert
Luhao Ge
dblp:321/5032
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0002-6221-2530ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 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 graphics and multimedia
1 paper |
Visualization and visual analytics · 100% | |
| Computer networks
1 paper |
Wireless networking · 100% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics
image abstraction |
0.7 | 1 | 2023 | ASTF: Visual Abstractions of Time-Varying Patterns in Radio Signals · IEEE Trans. Vis. Comput. Graph. 2023 |
Methods — techniques the papers use, named apart from their topics
user study · 1.3time segmentation algorithm · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | ASTF: Visual Abstractions of Time-Varying Patterns in Radio SignalsabstractA time-frequency diagram is a commonly used visualization for observing the time-frequency distribution of radio signals and analyzing their time-varying patterns of communication states in radio monitoring and management. While it excels when performing short-term signal analyses, it becomes inadaptable for long-term signal analyses because it cannot adequately depict signal time-varying patterns in a large time span on a space-limited screen. This research thus presents an abstract signal time-frequency (ASTF) diagram to address this problem. In the diagram design, a visual abstraction method is proposed to visually encode signal communication state changes in time slices. A time segmentation algorithm is proposed to divide a large time span into time slices. Three new quantified metrics and a loss function are defined to ensure the preservation of important time-varying information in the time segmentation. An algorithm performance experiment and a user study are conducted to evaluate the effectiveness of the diagram for long-term signal analyses. Ying Zhao 0001, Luhao Ge, Huixuan Xie, Genghuai Bai, Yun Lin 0005 |
IEEE Trans. Vis. Comput. Graph. | 2 |