2024 4th International Conference on Computer Graphics, Image and Virtualization(ICCGIV 2024)
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Prof. Shuwen Xu, IEEE Senior Member, Xidian University, China

Biography: Shuwen Xu, an IEEE Senior Member, hails from Huangshan city in Anhui, China. He attained his B.Eng. and Ph.D. degrees in electronic engineering from Xidian University, Xi’an, China, in 2006 and 2011, respectively. Following his education, he joined the National Laboratory of Radar Signal Processing at Xidian University. 

In 2017 and 2018, Dr. Xu served as a visiting professor at McMaster University in Canada. Currently, he holds the position of professor at the National Laboratory of Radar Signal Processing at Xidian University. Additionally, he serves as the vice director of the National Collaborative Innovation Center of Information Sensing and Understanding and Director of the Radar Signal Processing and Data Processing Department within the same institution.

Dr. Xu's research interests encompass radar target detection, statistical learning, and SAR image processing. He is actively involved in advancing these fields and contributing to the academic community through his research and teaching endeavors.

Keynote Speech Title:

Adaptive Matched Target Detection in Sea Clutter


Abstract :

In this report, we propose optimum and near-optimum adaptive coherent detectors of radar targets in compound-Gaussian clutter with generalized inverse Gaussian texture. The target amplitude and the speckle covariance matrix are modeled as unknown quantities to be estimated. On the basis of the two-step generalized likelihood ratio test (GLRT) and the estimate of the speckle covariance matrix, the optimum coherent detector and its adaptive version are designed. It is demonstrated that the proposed optimum coherent detector contains three common detectors, which are the optimum K detector (OKD), the generalized likelihood ratio test linear-threshold detector (GLRT-LTD), and  the generalized likelihood ratio test detector for compound-Gaussian clutter with inverse Gaussian texture (GLRT-IG). The proposed near-optimum coherent detector contains two common detectors, the GLRT-LTD and the alpha-MF detector in K-distributed clutter, and has a comparable detection performance of the near-optimum detector in CG-IG clutter which was proposed before. Theoretical analysis and numerical experiments illustrate that the proposed two detectors for CG-GIG clutter have the constant false alarm ratio (CFAR) property relative to the estimate of the speckle covariance matrix and Doppler steering vector. Moreover, the detection performance of the two coherent detectors are evaluated by the simulated and real data. 



Prof. Weigang Hou, Chongqing University of Posts and Telecommunications, China

Biography:Weigang Hou, an esteemed Member of IEEE, earned his Ph.D. in information and communication systems from Northeastern University in Shenyang, China, in 2013. In 2012, he undertook the role of Associate Researcher in the Department of Computer Science at City University of Hong Kong, Hong Kong. Currently, he serves as a distinguished Full Professor at Chongqing University of Posts and Telecommunications in Chongqing, China.

With a robust academic profile, Dr. Hou has contributed significantly to the scholarly landscape, producing more than 120 publications. Notably, over 80 of these publications bear his name as the first or corresponding author, showcased in eminent platforms such as IEEE magazines, reputable journals, and distinguished conferences. His research expertise centers around the realm of optical networks on chips, demonstratingWeigang Hou, a distinguished Member of IEEE, obtained his Ph.D. in information and communication systems from Northeastern University in Shenyang, China, in 2013. In 2012, he held the position of Associate Researcher at the Department of Computer Science at City University of Hong Kong in Hong Kong. Presently, he serves as a highly respected Full Professor at Chongqing University of Posts and Telecommunications in Chongqing, China.

Dr. Hou's academic contributions are showcased through more than 120 publications to his name. Impressively, over 80 of these papers have been published with him as the first or corresponding author, appearing across a range of prestigious platforms including IEEE magazines, transactions, journals, and renowned conferences. His research endeavors predominantly focus on the cutting-edge domain of optical networks on chips, reflecting his expertise and significant impact in this specialized field.



Prof. Xin Xu, IEEE Senior Member, Wuhan University of Science and Technology, China

Biography: Xin Xu received B.S. and Ph.D. degrees in computer science and engineering from Shanghai Jiao Tong University, Shanghai, China, in 2004 and 2012, respectively. He is currently a full professor at the School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China. His research deals with image processing, computer vision, and deep learning. More specifically, his research areas focus on building a hierarchical person-identification architecture, including detection and recognition for nighttime surveillance scenarios. He has published more than 150 high-level papers in academic journals and at academic conferences. His publication was selected as the cover paper of the journal International Journal of Intelligent Systems in 2022. He was shortlisted as the Best Paper Finalist of the IEEE International Conference on Multimedia and Expo (ICME) 2021.



Assoc. prof. Hoshang Kolivand, IEEE Senior member, Liverpool John Moores University, UK

Biography: Hoshang Kolivand is an accomplished researcher in the field of computer science, specializing in AI and mixed reality. He received his MS degree in applied mathematics and computer science from Amirkabir University of Technology, Iran, in 1999. He went on to pursue his PhD at the Media and Games Innovation Centre of Excellence (MaGIC-X) at Universiti Teknologi Malaysia, completing it in 2013. Dr. Kolivand further honed his expertise in augmented reality through a postdoctoral program at UTM in 2014. With a rich academic background, Hoshang has previously served as a lecturer at Shahid Beheshti University, Iran, and later as a senior lecturer at Universiti Teknologi Malaysia. Currently, he holds the position of Reader (Associate Professor) in AI and Mixed Reality at Liverpool John Moores University and is also a Visiting Professor at Bharath Institute of Higher Education and Research (BIHER, Chennai, India. Throughout his career, Hoshang has made significant contributions to the academic community. He has authored numerous articles in international journals, conference proceedings, and technical papers, including book chapters. Additionally, he has published several books on object-oriented programming and mathematics.

Dr. Kolivand is recognized as a senior member of IEEE and actively contributes to various conferences and international journals as a Technical Program Committee (TCP) member. His dedication to advancing the field of computer science and his passion for research continues to drive his pursuit of knowledge and innovation.

Keynote Speech Title:

Breaking Boundaries with AI: Current and Future of Mixed Reality 


Abstract :

In this talk, we delve into the profound impact of AI on Mixed Reality, uncovering the latest advancements and groundbreaking innovations that are breaking the boundaries of digital experiences. From sophisticated real-time simulations to personalized virtual environments, explore how AI's integration with Mixed Reality is driving unprecedented immersion and transforming the way we perceive and interact with the virtual world. Join us as we unravel the limitless possibilities and implications of this transformative fusion.



Assoc. prof. Ata Jahangir Moshayedi, IEEE Senior member, Jiangxi University of Science and Technology, China

Biography: Dr. Ata Jahangir Moshayedi, an Associate Professor at Jiangxi University of Science and Technology in China, holds a PhD in Electronic Science from Savitribai Phule Pune University in India. He is a distinguished member of IEEE and ACM, as well as a Life Member of the Instrument Society of India and a Lifetime Member of the Speed Society of India. Additionally, he contributes to the academic community as a valued member of various editorial teams for international conferences and journals. Dr. Moshayedi's academic achievements are marked by a portfolio of over 90 papers published across esteemed national and international journals and conferences. In addition to his scholarly publications, he has authored three books and is credited with two patents and nine copyrights, emblematic of his pioneering contributions to the field. His research interests include robotics and automation, sensor modelling, bio-inspired robots, mobile robot olfaction, plume tracking, embedded systems, machine-vision-based systems, virtual reality, machine vision, artificial intelligence.

Keynote Speech Title:

Visionary Integration: Enhancing AGVs with Vision Systems and Machine Perception


Abstract :

Service robots represent a transformative application of robotics that profoundly impacts human life, spanning domains from healthcare to industry. These robots serve as lifesavers and support systems, alleviating humans from strenuous tasks and repetitive work that might compromise accuracy in job execution. According to ISO 8373:2012, service robots encompass two main types: personal service robots, designed for use outside manufacturing, and professional service robots, catering to non-commercial and commercial purposes. These robots operate on a spectrum from semi-autonomous to fully autonomous, gradually gaining acceptance as invaluable human assistants across diverse applications and professions. Industries are increasingly integrating service robots into their production lines, marking a pivotal shift within the context of the industrial revolutions. The first revolution brought mechanization, followed by the second revolution powered by electricity. Industry 4.0, however, intertwines digital and internet technologies, propelling further innovation and evolution in the realm of technology. Within this discourse, the focus narrows to AGV (Automated Guided Vehicles) and MIR (Mobile Industrial Robots) as exemplary service robots. The discussion delves into the modeling steps and simulation processes involved in their creation. Additionally, it scrutinizes the performance of designed AGVs employing various algorithms. This analysis aims to serve as a guide for researchers, offering insights and practical implementations for diverse control systems within modeled systems.