Cameras can perceive the appearance, color, and shape information of objects in the environment and are widely used in object classification, object detection, object segmentation, object tracking and other fields. However, it has drawbacks in recognizing the distance and speed of an object. Radar (refers to millimeter-wave radar in this project) uses the Doppler principle to measure the distance and speed of the object, and is not affected by external factors in the weather. Therefore, the combination of camera and radar can realize the perception of all-round attributes of the object and can meet the needs of working in all-weather conditions. At present, with the rapid development of deep learning technology, multi-sensor fusion has attracted more and more attention. However, the technology of radar-camera fusion is far from mature, especially the multi-modal object association, object detection and tracking technology in the moving environment, which has also become a bottleneck restricting the wide application of radar-camera fusion technology. This project takes radar-camera fusion as the research object and conducts systematic research on the perception of the environment by the radar-camera, the processing and understanding of sensor information, and the mapping of perception information to the decision. Besides, this project applies the method of deep learning to the fusion of radar and camera to establish a unified deep learning model to analyze the data of radar and camera. In addition, this project will also be combined with actual scenarios to develop products of radar-camera integration in intelligent transportation, assisted driving, consumer electronics, security monitoring, robots and other industries.
For more information about doctoral scholarship and PhD programme at Xi’an Jiaotong-Liverpool University (XJTLU): Please visit
http://www.xjtlu.edu.cn/en/study-with-us/admissions/entry-requirements
http://www.xjtlu.edu.cn/en/admissions/phd/feesscholarships.html
Requirements:
The candidate should have a first class or upper second class honours degree, or a master’s degree (or equivalent qualification) in Mathematics, physics, computer, automation and other related professional background
Strong knowledge background in machine learning
Evidence of good spoken and written English is essential. The candidate should have an IELTS score of 6.5 or above, if the first language is not English. This position is open to all qualified candidates irrespective of nationality.
Degree:
The student will be awarded a PhD degree from the University of Liverpool (UK) upon successful completion of the program.
Supervisor Profile:
Principal Supervisor: Steven Guan (Sheng-Uei Guan) received his BSc. from Tsinghua University and M.Sc. & Ph.D. from the University of North Carolina at Chapel Hill.
He is currently an Honorary Professor at University of Liverpool & also a Professor at Xi'an Jiaotong-Liverpool University (XJTLU). He served the head of department position at XJTLU for 4.5 years, creating the department from scratch and now in shape. Before joining XJTLU, he was a tenured professor and chair in intelligent systems at Brunel University, UK.
Prof. Guan has worked in a prestigious organization for several years, serving as a design engineer, project leader, and department manager. After leaving the industry, he joined the academia for three and half years. He served as deputy director for the Computing Center and the chairman for the Department of Information & Communication Technology. Later he joined the Electrical & Computer Engineering Department at National University of Singapore as an associate professor for 8 years.
Prof. Guan’s research interests include: machine learning, computational intelligence, big data analytics, mobile commerce, modeling, networking, personalization, security, coding theory, and pseudorandom number generation. He has published extensively in these areas, with 140+ journal papers and 200+ book chapters or conference papers. He has chaired, delivered keynote speech for 100+ international conferences and served in 190 international conference committees and 20+ editorial boards.
JITRI co-supervisor:
Dr. Yutao Yue received his Bachelor's degree of applied physics from University of Science and Technology of China, master and PhD degrees of computational physics from Purdue University of USA. He then served as team leader of Guangdong “Zhujiang Plan” 3rd Introduced Innovation Scientific Research Team, senior scientist and Chief Human Resources Officer of Shenzhen Kuang-Chi Group, etc.
His research interest include computational modeling and artificial intelligence, radar vision fusion, electromagnetic fields, etc. He has been engaged in frontier technology research and development and industrialization for 20 years. He has co-invented 354 granted Chinese patents, 18 USA patents, and 7 EU patents. He has led 6 major research projects with a total funding of nearly 150 million RMB. He has advised 13 postdoc research fellows, published over 20 papers, and received multiple awards including Wu Wenjun Artificial Intelligence Science and Technology Award. He has been received by General Secretary Xi Jinping due to outstanding achievements.
How to Apply:
Interested applicants are advised to email [Email Address Removed] and [Email Address Removed] the following documents for initial review and assessment (please put the project title in the subject line).
- CV
- Two reference letters with company/university letterhead
- Personal statement outlining your interest in the position
- Proof of English language proficiency (an IELTS score of 6.5 or above)
- Verified school transcripts in both Chinese and English (for international students, only the English version is required)
- Verified certificates of education qualifications in both Chinese and English (for international students, only the English version is required)
- PDF copy of Master Degree dissertation (or an equivalent writing sample) and examiners reports available
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