skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • Home
  • Search Results
  • Page 1 of 1

Search for: All records

Creators/Authors contains: "Xia, Shengqing"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ; (, HOTMOBILE '24: Proceedings of the 25th International Workshop on Mobile Computing Systems and Applications)
    In this work, we present our attempt to tackle the last-hundred-feet problem for autonomous drone delivery.We take a computer-vision based approach to progressively landing towards a convenient and safe drop-off point at all times (here, at the front/garage door). Specifically, we develop structural semantic segmentation (SSS), a new technique that leverages a single-family house structure to streamline and enhance semantic segmentation in the drop-to-door problem context.We implement SSS into an Android app; Our preliminary evaluation in a residential zone shows SSS is promising to make autonomous drop-to-door in real-time, with no need to wait for slow visual processing. Video demo is available at Youtube [5]. App is released at Github [6]. 
    more » « less
    Full Text Available 
  2. ; ; (, 2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS))
    Edge-assisted video analytics is gaining momentum. In this work, we tackle an important problem to compress video content live streamed from the device to the edge without scarifying accuracy and timeliness of its video analytics. We find that on-device processing can be tuned over a larger configuration space for more video compression, which was largely overlooked. Inspired by our pilot study, we design VPPlus to fulfill the potentials to compress the video as much as we can, while preserving analytical accuracy. VPPlus incorporates two core modules – offline profiling and online adaptation – to generate proper feedback automatically and quickly to tune on-device processing. We validate the effectiveness and efficiency of VPPlususing five object detection tasks over two popular datasets; VPPlus outperforms the state-of-art approaches in almost all the cases. 
    more » « less
    Full Text Available