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Hang Zhao

Researcher at Tsinghua University

Publications -  108
Citations -  19405

Hang Zhao is an academic researcher from Tsinghua University. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 32, co-authored 83 publications receiving 12696 citations. Previous affiliations of Hang Zhao include Zhejiang University & Nvidia.

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Proceedings ArticleDOI

Scalability in Perception for Autonomous Driving: Waymo Open Dataset

TL;DR: In this paper, a large scale, high quality, and diverse dataset for self-driving data is presented, consisting of LiDAR and camera data captured across a range of urban and suburban geographies.
Proceedings ArticleDOI

Through-Wall Human Pose Estimation Using Radio Signals

TL;DR: A deep neural network approach that parses wireless signals in the WiFi frequencies to estimate 2D poses through walls despite never trained on such scenarios, and shows that it is almost as accurate as the vision-based system used to train it.
Proceedings ArticleDOI

28 GHz millimeter wave cellular communication measurements for reflection and penetration loss in and around buildings in New York city

TL;DR: Reflection coefficients and penetration losses for common building materials at 28 GHz show that outdoor building materials are excellent reflectors with the largest measured reflection coefficient of 0.896 for tinted glass as compared to indoor building materials that are less reflective.
Proceedings ArticleDOI

28 GHz propagation measurements for outdoor cellular communications using steerable beam antennas in New York city

TL;DR: The world's first empirical measurements for 28 GHz outdoor cellular propagation in New York City are presented, suggesting that millimeter wave mobile communication systems with electrically steerable antennas could exploit resolvable multipath components to create viable links for cell sizes on the order of 200 m.
Posted Content

Semantic Understanding of Scenes through the ADE20K Dataset

TL;DR: This work presents a densely annotated dataset ADE20K, which spans diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts, and shows that the networks trained on this dataset are able to segment a wide variety of scenes and objects.