Editor Profile
Guo-Zhao Liao
Supervisor: Xiaofeng Gong
Present
Dalian
China
Dalian University of Technology
Guo-Zhao Liao, born on January 11, 1995, in Zhaoqing City, Guangdong Province, is a dedicated scholar and researcher specializing in Signal and Information Processing. Currently enrolled as a second-year doctoral student at the School of Information and Communication Engineering, Dalian University of Technology, Guo-Zhao's academic journey is marked by a profound interest in the practical applications of theoretical concepts within radar systems.
His research focus lies in tensor decomposition and its integration into multistatic MIMO radar technology for parameter estimation, target localization, and tracking. Guo-Zhao’s passion for enhancing radar systems' efficiency and accuracy was ignited during his undergraduate years when he actively participated in a project led by Professor Zhizhuang Liu and Professor Bengang Bao to develop an intelligent wheelchair. This experience not only presented technical challenges but also instilled in him values of empathy and social responsibility.
Guo-Zhao’s dedication to research excellence was evident as he played a pivotal role in securing funding and contributing significantly to the success of the project, leading to the recognition through Chinese utility model patents. His academic pursuits flourished during his master's studies under the mentorship of Professor Wing-Kuen Ling, focusing on audio and biomedical signal processing. During this period, Guo-Zhao published papers in esteemed journals like Innovation and Research in Biomedical Engineering and actively contributed to projects such as the National Natural Science Foundation research.
Currently, Guo-Zhao is furthering his research endeavors as a doctoral student under the guidance of Professor Xiaofeng Gong, exploring the complexities of tensor signal processing in multistatic MIMO radar systems. His recent paper presentation at ICASSP 2024 underscores his commitment to advancing knowledge in this specialized field.
Looking ahead, Guo-Zhao aims to pioneer new algorithms for radar target localization and tracking, leveraging innovative tensor decomposition techniques. His research ambitions extend to investigating Multistatic Nonuniform Rectangular Arrays Target Localization and Bistatic Nonuniform Rectangular Arrays Target Tracking, with the goal of making significant advancements in radar technology.
In summary, Guo-Zhao Liao’s academic journey exemplifies a relentless pursuit of excellence in research and innovation. Equipped with a solid foundation in both theoretical and applied research, he is poised to make meaningful contributions to the scientific community within the realm of signal and information processing.
- Guozhao Liao, Bingo Wing-Kuen Ling and Kang-Gao Pang, “Grouping intrinsic mode functions and residue for pathological classifications via electroglottograms,” Innovation and Researh in Biomedical Enginnering (IRBM), vol. 44, no. 2, pp. 100740, 2023. (SCI)
- Yuxin Lin, Bingo Wing-Kuen Ling, Caijun Li and Guozhao Liao, “Multivariate two dimensional singular spectrum analysis based fusion method for four view image based object classification,” online avalaible in Multimedia Tools and Applications, 2023. (SCI)
- Kang-Gao Pang, Tai-Chiu Hsung, Guozhao Liao, Wing-Kuen Ling, Alex Ka-Wing Law and Winnie W.S. Choi, “Obstructive sleep apnea detection using speech signals with high frequency components,” Journal of Communications, vol. 17, no. 1, pp. 49-55, 2022. (EI)
- Lingyue Hu, Kailong Zhao, Xueling Zhou, Bingo Wing-Kuen Ling and Guozhao Liao, “Empirical mode decomposition based multi-modal activity recognition,” Sensors, vol. 20, no. 21, pp. 6055, 2020. (SCI)
- Guo-Zhao Liao, Xiao-Feng Gong, and Qiu-Hua Lin, “Multistatic MIMO Radar Target Localization via A Novel Double Coupled Canonical Polyadic Decomposition Algorithm,” IEEE Transactions on Signal Processing (under preparation for submission).
- Guozhao Liao, Bingo Wing-Kuen Ling and Ringo Wai-Kit Lam, “A Comfortable Mouth Breathing Assisted Training Methodology Based on Empirical Mode Decomposition and Random Forest before Nasal Surgery,” Multimedia Tools and Applications. (under second round of review).
