Shu Yan
Jiangsu University
8 Papers
39 Citations
Shu Yan is an academic researcher from Jiangsu University. The author has contributed to research in topics: Fractal antenna & X-ray photoelectron spectroscopy. The author has an hindex of 3, co-authored 3 publications.
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Papers
Moving object detection based on an improved gaussian mixture background model
Rui Yan,Xuehua Song,Shu Yan +2 more
- 29 Sep 2009
TL;DR: A novel algorithm which synthesizes the methods of background subtraction and adjacent-frame difference and adopts Gaussian mixture model to reduce the impact of background disturbance, and uses adjacent- frame difference for reference is proposed.
17
Study and design of a modified fractal antenna for RFID applications
Lei Cao,Shu Yan,Hanhua Yang +2 more
- 29 Sep 2009
TL;DR: The size of the Minkowski fractal reader antenna composed of three layers is much smaller than a convention rectangular patch antenna, it has medium bandwidth, small size, low power loss, which is more satisfactory to the requirement of handheld RFID reader systems.
15
Enhanced Map-Aided GPS/3D RISS Combined Positioning Strategy in Urban Canyons
Xiang Song,Chun-xiao Ren,Hui Qiong Jiang,Li-Ping Li,Wei Wu,Ling Li,Shu Yan,Bingyu Zhang,Jiaen Wu +8 more
TL;DR: A Kalman filtering (KF) tightly coupled method is proposed to fuse the 3D RISS with GPS information and to achieve the preliminary positioning and the effectiveness of the strategy is proved by field test.
7
Investigation and Design of a Modified Aperture-Couple Fractal Antenna for RFID Applications
Hanhua Yang,Shu Yan,Ling Chen,Hongbei Shi +3 more
- 03 Aug 2008
TL;DR: The proposed miniaturization structure of modified square fractal antenna has good impedance and radiation characteristics over the required bandwidth and is suitable to use for RFID as the reader antenna.
7
A Robust Detection Method for Multilane Lines in Complex Traffic Scenes
TL;DR: A simple but robust multilane detection method that achieves similar or better performance compared with several state-of-the-art methods, the F1 score of the proposed method in the normal test set and most challenge test sets is better than other algorithms, which verifies the effectiveness of the proposal.