2022 2nd International Conference on Digital Signal and Computer Communications (DSCC 2022)

Keynote Speakers


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Professor  Georgios Theodoropoulos

Southern University of Science and Technology, China


Georgios Theodoropoulos is a Chair Professor in the Department of Computer Science and Engineering at SUSTech, Shenzhen, China. He joined SUSTech from Durham University in the UK, where he was the Executive Director of the Institute of Advanced Research Computing and held a Chair in the School of Engineering and Computing Sciences. Prior to that he was a Senior Research Scientist with the IBM Research where he played a strategic role in the establishment of IBM’s Exascale Systems research roadmap for Europe at the company’s Smart City Technology Center in Dublin. He has held a honorary Chair at the Trinity College Dublin and senior posts at the Nanyang Technological University, Singapore and the University of Birmingham, UK. As a Director of one of the UK’s National e-Science Centres, he made strategic contributions to UK’s e-infrastructures programmes for physical and social sciences and humanities. He is a Chartered Engineer and holds a PhD from the University of Manchester in the UK, where he was mentored by Stephen Furber, the principal designer of the ARM processor that powers much of the world’s mobile computing and embedded systems today.

Speech Title: Cognitive Digital Twins: The Next Frontier of info-Symbiotic Systems

Abstract: 

Info-symbiotic systems provide a unique proven paradigm to construct integrated ecosystems of systems, models and data and enable trustworthy, explainable, holistic and contextual analytics at a grand scale.  Recent years have witnessed an explosion of utilisation of info-symbiotic systems, in the form of Digital Twins, in a wide range of domains, from manufacturing and health to smart cities.  Incorporating intelligence and cognition in a Digital Twin will unlock the full potential of this disruptive technology, providing seamless integration and info-symbiotic collaboration between the physical and virtual worlds and capturing the increasing complexity and uncertainty of the problems society is facing.  The talk will outline a roadmap towards cognitively rich Digital Twins, discussing challenges, opportunities and some concrete examples.



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Professor Ning Sun

Nankai University, China 


Ning Sun received the B.S. degree in measurement & control technology and instruments from Wuhan University, Wuhan, China, in 2009, and the Ph.D. degree in control theory and control engineering from Nankai University, Tianjin, China, in 2014. He is currently an IEEE Senior Member. He is currently a Professor with the Institute of Robotics and Automatic Information Systems, Nankai University, Tianjin, China. He was awarded the prestigious Japan Society for the Promotion of Science (JSPS) Postdoctoral Fellowship for Research in Japan (Standard). His research interests include intelligent control for mechatronic/robotic systems with emphasis on (industrial) applications. Dr. Sun received the Wu Wenjun Artificial Intelligence Excellent Youth Award in 2019, the China 10 Scientific and Technological Developments in Intelligent Manufacturing (2nd achiever) in 2019, the First Class Prize of Wu Wenjun Artificial Intelligence Natural Science Award in 2017, the First Class Prize of Tianjin Natural Science Award in 2018, the Golden Patent Award of Tianjin in 2017, the IJCAS (International Journal of Control, Automation, and Systems) Academic Activity Award in 2018 and 2019, the Outstanding Ph.D. Dissertation Award from the Chinese Association of Automation (CAA) in 2016, etc. He serves as an Associate Editor (editorial board member) for several journals, including IEEE ACCESS, Frontiers in Neurorobotics, International Journal of Control, Automation, and Systems, IET Cyber-Systems & Robotics, Transactions of the Institute of Measurement and Control, International Journal of Precision Engineering and Manufacturing, etc. Dr. Sun has been an Associate Editor of the IEEE Control Systems Society (CSS) Conference Editorial Board (including ACC, IEEE CDC) since July 2019, and he is/was an Associate Editor for IEEE ICRA 2021 and IEEE/RSJ IROS 2020.


Speech Title: Modeling, Processing, and Intelligent Control for Pneumatic Artificial Muscle-Actuated Robotic Systems


Abstract:  

With the rapid development of rehabilitation robots and the growing demands for human-robot interaction, modeling and intelligent control of pneumatic artificial muscle (PAM) robots have increasingly attracted the attention of many researchers. It is a challenging research topic to overcome the effects of PAMs’ inherent defects (e.g., high nonlinearities, hysteresis, time-varying characteristics, etc.), despite the merits of lightness, safety, and high power-to-weight/volume ratios of PAMs. To this end, we aim to achieve accurate modeling and advanced control for PAM robots, which may contribute to their further theoretical research and practical applications. Specifically, for single-PAM robots, there exist some difficulties as follows: 1) PAM systems are susceptible to unknown external disturbances due to their high nonlinearities, creep, hysteresis, etc. 2) PAM robots usually suffer from parameter uncertainties and unmodeled dynamics. 3) The ultimate control inputs (corresponding to the pressurized air) of PAM robots should be constrained to be nonnegative. To solve these problems, we propose a disturbance estimation-based nonlinear control method, a neuroadaptive control method with system uncertainties, and an adaptive control method with unidirectional input constraints, respectively. Further, for multi-PAM robots, the following issues should be considered: 1) Since torques/forces are generated by air pressure and are not the ultimate control inputs, the torque models of PAM robots are not direct and effective. 2) To ensure safety, the system state variables (e.g., contracted lengths of muscles, ranges of robots’ movements, etc.) are usually limited. To this end, we propose an accurate dynamic modeling method and a nonlinear control method with overshoot constraints, respectively. Some future research directions will also be discussed.



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Professor Jian Yao

Wuhan University, China.


Yao is a professor, Doctoral Supervisor, Distinguished Professor of "Chutian Scholar" Program of Hubei Province, Discipline Development Leader of the School of Remote Sensing Information Engineering of Wuhan University, a candidate of the National Major Talent Project A-type Youth Project, a member of the Strategic Talent Training Program of Changsha, a high-end talent of the 3551 Entrepreneurship and Innovation Program of Wuhan, a student of the Class 2019 of Baidu Alpha College, a leader of the Artificial Intelligence College of Guangdong Open University, director of the Artificial Intelligence Application Innovation Center of Guangdong Open University, dean of Research Institute of Desauto Technology (Shenzhen) Co., Ltd., Professor of Xiamen University of Aeronautics and Astronautics, distinguished researcher of Songhua River Thousand People Industry Research Institute, director of Wuhan University Computer Vision and Remote Sensing Lab (WHU-CVRS Lab), director of 3D Big Data Artificial Intelligence Innovation Research Center of Wuhan University, incumbent Member of the Chinese Society of Artificial Intelligence, member of the Computer Vision Professional Committee of the Chinese Computer Society (CCF), member of the Imaging Detection and Perception Committee of the Chinese Society of Image and Graphics, member of the Machine Vision Specialty Committee of the Chinese Society of Image and Graphics, member of 3D Vision Specialist Committee of the Chinese Society of Image and Graphics, member of the Big Data and Artificial Intelligence Working Committee of the Chinese Society of Surveying and Mapping, and director of the New Overseas Chinese Professionals Association of Hubei Province and Wuhan City. In April 2012, he was introduced to the School of Remote Sensing and Information Engineering of Wuhan University as a discipline development leader and has been a faculty member since then. He was invited as a specially-appointed professor of “Hubei Scholar” Program of Hubei in 2013. He has participated in many large-scale projects such as the EU's sixth and seventh framework plans, as well as cooperation projects with the International Atomic Energy Agency. In recent years, he has published over 130 papers on international journals including Pattern Recognition, Computer Vision & Image Understanding, International Journal of Robotics Research, IEEE Transactions on Image Processing, ISPRS Journal of Photogrammetry and Remote Sensing, IEEE Transactions on Geoscience and Remote Sensing (TGRS) and CVPR, applied for over 70 IPs and patents, with 30 authorized by the Chinese government. He has long been a reviewer of top journals and conference proceedings. After joining Wuhan University, Prof. Yao has established the WHU-CVRS Lab, which now consists of four advisors, one postdoctoral fellows, 30 PhDs and master students. Prof. Yao has chaired a series of research projects at national and provincial levels, including national key research programs, programs of the 973 Project, and the National Natural Science Fund. Meanwhile, in collaboration with well-known enterprises like Tencent, Huawei and Alibaba, he has initiated a range of joint research programs and talent training projects. 


Speech Title: Challenging issues and key technologies for multi-image fusion

Abstract:  

Multi-image fusion refers to the fusion of images captured with different characters, different time, different viewpoints or different resolutions for the same scene, aiming at generating a new image containing more information. The image fusion technology is widely used in remote sensing, medical, civil and other fields. However, they are ineffective in practical applications when there are challenging issues such as changes in content, differences in viewpoints, and differences in exposure among images. So, in this talk, I will present some recent key technologies and advances in multi-source image fusion and high-resolution wide-field imaging in multi-image fusion. Then I will share with you a series of representative research work done by our team in multi-image fusion and its applications, including image stitching, video stitching, image stitching and fusion, 3D texture mapping, single image super-resolution, multi-focus image fusion, and etc. Finally, I will discuss with you some of the open issues and problems involving multi-image fusion.



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Professor Yan Zhao

Jilin University, China 


Yan Zhao, professor, doctoral supervisor and vice president of school of communication engineering, Jilin University. She was a postdoctoral researcher at the Technical University of Tampere, Finland in 2003, a postdoctoral in Vienna University of Technology, Austria in 2008, and a visiting professor in University of Ottawa, Canada in 2013. She was the winner of the 9th Jilin Provincial Youth Science and technology award. She is the standing director of China Society of Image and Graphics, secretary general of Jilin Society of Image and Graphics. She has presided over a number of national projects, published more than 90 academic papers, and authorized more than 20 international and domestic invention patents. Her main research interests include image and video coding, virtual-reality fusion technology, true three dimensional light field imaging.


Speech Title: Virtual-reality fusion technology in augmented reality

Abstract:  

Augmented reality (AR) can be widely used in industrial maintenance, military, medical, entertainment, education, tourism, online video communication and historic site restoration and cultural heritage protection etc. With the maturity of AR technology, the integration of AR and industry is more and more in-depth. Virtual-real fusion is the key technology of augmented reality and the effect of fusion directly affects the user experience of augmented reality. This report will introduce illumination estimation methods and virtual-reality fusion technologies based on illumination consistency and geometric consistency.