Professional:                                                                                                     

Master Supervisor

Education:

Hao Shan received the M.S. degrees in the College of Mechanical and Electrical Engineering from Beijing Institute of Technology, Beijing, China, in 2005, and the Ph.D. degree in the Department of Aerospace from Tsinghua University, Beijing, China, in 2010. He was a Visiting Scholar with ‘‘Le CosmoStat Laboratoire, Département d’Astrophysique, CEA Saclay, Paris, France” in 2016. He is currently a researcher in Xinjiang Astronomical Observatory, Chinese Academy of Sciences.

Contact:

shanhao@xao.ac.cn

+86 13579842480

Research Interests:

Radio astronomical signal processing,

Pulsar timing and gravitational wave detection,

Sparse representation and compressed sensing,

Signal denoising, dictionary learning, wavelets, curvelets

Selected Publications:

1. H. Shan. CS-GAC: Compressively Sensed Geodesic Active Contours. Pattern Recognition, 146(2024), 110007.

2. H. Shan, J. Yuan, N. Wang, Z. Wang. Compressed Sensing Based RFI Mitigation and Restoration for Pulsar Signals. APJ, 935(2), 117–124, 2022.

3. H. Shan. Robust RFI excision for pulsar signals by a novel nonlinear M-type estimator with an application to pulsar timing. APJ, 952(1), 70–82, 2023.

4. H. Shan, M. Jiang, J. Yuan, X. Yang, W. Yan, Z. Wang, N. Wang. Sparsity and M-Estimators in RFI Mitigation for Typical Radio Astrophysical Signals. Universe, 9, 488, 20pp, 2023.

5. H. Shan, L. Cui, X. Y. Hong, X. Liu, N. Chang. Wavelet based tone mapping (TM) enhancement to a detection system for faint and compact sources in HDR and large FOV radio scenes, Astronomy and Computing, 42, 100684, 15pp, 2023.

6. H. Shan, X. Wang, J. Yuan, J. Nie, N. Liu, N. Wang. Wavelet based recognition for pulsar signals. Astronomy and Computing, 11(6), 55–63, 2015.

7. H. Shan, J. Ma, H. Yang. Comparisons of wavelets, contourlets and curvelets in seismic denoising. Journal of Applied Geophysics, 69(2), 103–115, 2009.

8. H. Shan, C. He, N. Wang. MCA aided geodesic active contours for image segmentation with textures. Pattern Recognition Letters, 45(1), 235–243, 2014.

9. H. Shan, J. Ma. Curvelet-based geodesic snakes for image segmentation with multiple objects. Pattern Recognition Letters, 31(5), 355–360, 2010.