Associate Professor Department of Computer Science and Engineering   Research Group

Dr. Qi Hao is an associate professor in computer science and engineering at Southern University of Science and Technology. He is an expert in intelligent sensing and unmanned autonomous systems. His research areas include bionic intelligent sensor design, intelligent sensing, machine learning, and unmanned autonomous systems. He received the Ph.D degree from Duke Univ., Durham, NC, USA in 2006, and B.E. and M.E. degrees from Shanghai Jiao Tong Univ., China in 1994 and 1997, respectively, all in Electrical and Computer Engineering. His postdoctoral training was achieved in the Center for Visualization and Virtual Environment at The Univ. of Kentucky. He was an assistant professor in the Dept. of Electrical and Computer Engineering at The Univ. of Alabama, Tuscaloosa, AL, USA. He has published 35 SCI journal papers and 54 EI conference proceedings. He is the co-editor of one book in intelligent sensor networks. After joining the SUSTech, he was PI of NSFC, Shenzhen Science and Technology Innovation Committee, the Nanshan District Pilot Team, SUSTech-Haylion Center for Intelligent Transportation, Intel Master Research Agreement Project, and two Education Reform Project from Guangdong province and Shenzhen city. He has severed as a panelist for US NSF panels and US department of energy ARPA-e program.

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Intelligent Sensing, Machine Learning, Unmanned Autonomous Systems.


Speaker Courses: Machine Learning, Intelligent Robots

Co-speaker courses: Computer System Design and Application, Introduction to Computers, Basic Computer Programming

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1、S. Wang, M. Wen, M. Xia, R. Wang, Q. Hao, Y. Wu, “Angle aware user cooperation for secure massive MIMO in Rician fading channel,” IEEE Journal on Selected Areas in Communications, vol. 38, no. 9, pp. 2182 – 2196, Jun. 2020.

2、R. Ma, G. Lan, Q. Hao, “Enabling Cognitive Pyroelectric Infrared Sensing: From Reconfigurable Signal Conditioning to Sensor Mask Design,” IEEE Transactions on Industrial Informatics, vol. 16, no. 7, Jul, 2020, pp. 4436 – 4446, Sep. 2019.

3. R. Ma, G. Lan, Q. Hao*, “Enabling Cognitive Pyroelectric Infrared Sensing: From Reconfigurable Signal Conditioning to Sensor Mask Design”, IEEE Transactions on Industrial Informatics,Early Access, 2019.

4. S. Wang, F. Jiang, R. Ma, and Q. Hao*, “Development of UAV based target tracking and recognition systems”, IEEE Transactions on Intelligent Transportation Systems,Early Access,2019.

5. F. Jiang, F. Navan, Q. Hao*, “Design, implementation and evaluation of a neural network-based quadcopter UAV system”, IEEE Transactions on Industrial Electronics, Volume: 67, Issue: 3, pp.2076 – 2085, Mar. 2020.

6. F. Jiang and Q. Hao*, “Pavilion: bridging photo-realism and robotics,” Proc. of IEEE International Conference on Robotics and Automation (ICRA), May 2019.

7. Q. Miao, F. Hu, Q. Hao*, “Deep learning for intelligent wireless networks: a comprehensive survey”, IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp.2595-2621, Jun. 2018.

8. R. Ma, Q. Hao*, X. Hu, and C. Wang, “Space Coding Schemes for Multiple Human Localization with Fiber-optic Sensors”, IEEE Sensors Journal, vol. 18, no. 11, pp.4643-4653, Jun. 2018.

9. R. Ma, F. Hu, and Q. Hao*, “Active compressive sensing via pyroelectric infrared sensor for human situation recognition,” IEEE Trans. Syst., Man, and Cyber.:Systems, vol. 47, no. 12, pp. 3340-3350, Dec. 2017.

10. J. Lu, T. Zhang, Q. Sun, F. Hao, and Q. Hao*, “Binary compressive tracking,” IEEE Trans. on Aerospace and Electronic Systems, vol. 53, no. 4, pp.1755-1768, Aug. 2017.

11. F. Hu, and Q. Hao*, “Cyber-physical system with virtual reality for intelligent motion recognition and training,” IEEE Trans. Syst., Man, and Cyber: Systems, vol. 47, no. 2, pp. 347-363, Feb. 2017.

12. Q. Hao*, “Binary sensing and perception for human behavior study,” Proc. of IEEE ChinaSIP, Chengdu, Jul. 2015, pp. 368-372.

13. F. Hu, Q. Hao*, and K. Bo, “A survey on software defined networking (SDN) and openflow: from concept to implementation,” IEEE Comm. Surveys and Tutorials, vol. 16, no. 4, pp. 2181-2206, Nov. 2014.

14. Y. Wang, K. Liu, Q. Hao, D. L. Lau, and L. G. Hassebrook, “Robust active stereo vision using Kullback-Leibler divergence,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 34, no. 3, pp. 548-563, Mar. 2012.

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