Keynote Speakers

Prof. Yudong Zhang,IEEE Senior Member

Informatics, University of Leicester, UK

Title: Artificial Intelligence for Infectious Disease Diagnosis

Abstract:COVID-19 is a pandemic disease that caused more than 6.89 million deaths until 13/April/2023. X-ray and CT scans are two popular chest imaging techniques used in radiology to get detailed images of the chest noninvasively for diagnostic purposes. Traditional manual analysis of X-ray or CT-based scans is tedious and error-prone. To solve the problem, our lab develops and applies new ML theories and techniques, such as advanced pooling-based networks, graph convolutional networks, attention neural networks, weakly supervised networks, etc. We also use cloud computing techniques to run our developed app on the remote server to help doctors in the suburban area. Two other chest-related infectious diseases: secondary pulmonary tuberculosis and community-acquired pneumonia, will be covered in this talk.

Prof. Hong Huang, IEEE Member

Chongqing University, China

Huang Hong is a Professor with the Image Information Processing Laboratory and Director of the Department of Measurement and Control Technology and Instruments, , Chongqing University, China. He also is a senior member of IEEE, Associate editor of Frontier in Oncology, Guest editor of Remote Sensing, and the experts of Ministry of Science and Technology, National Natural Science Foundation of China

His main research activities are in the fields of image processing, pattern recognition, and remote sensing. Dr. Huang is a Reviewer of over 20 international journals, including IEEE Trans. Geoscience And Remote Sensing, IEEE Trans. Cybernetics, ISPRS, and IEEE Trans. Medical Imaging.

Prof. Qinmin Yang

Zhejiang University, China

Title: Applications of Eigenvalues and Eigenvectors in Data Mining

Abstract: Wind energy has been considered to be a promising alternative to current fossil-based energies. Large-scale wind turbines have been widely deployed to substantiate the renewable energy strategy of various countries. In this talk, challenges faced by the control community for highly reliable and efficient exploitation of wind energy are discussed. Advanced controllers are designed to (partially) overcome problems, such as uncertainty, intermittence, and intense dynamics. Theoretical results and attempts for practice are both present.

Prof. Cunhua Pan (H Index: 40)

Southeast University, China

Title: Twotimescale Transmission Design for RIS-aided Massive MIMO Systems

Abstract: Massive multiple-input multiple-output (MIMO) technology is an essential technique to provide extremely high network throughput in current and future communication systems. However, to achieve such high throughput, hundreds of antennas should be equipped at the base station (BS), which raises the issues of high cost and energy consumption. On the other hand, reconfigurable intelligent surface (RIS), also known as intelligent reflecting surface (IRS), has been proposed as a revolutionary technology to support high data rates while maintaining a low cost and energy consumption. In this talk, I will introduce the RIS-aided massive MIMO systems with Rician fading for both users-RIS and RIS-BS channels. I will present the closed-form approximate achievable rate expressions, and then provide a comprehensive analysis of the interplay between the RIS and the conventional massive MIMO systems, including the power scaling laws, the asymptotic achievable rate, and the impact of critical systems parameters. I also present an optimization method to use statistical CSI to optimize the RIS phase shifts for both the sum user rate maximization and minimum user rate maximization problems. Extensive simulations are provided to characterize the gains by integrating RIS into massive MIMO networks.

Research field: signal processing in wireless communication, including transmission design, channel estimation, and positioning, synaesthesia integration, AI-assisted communication, etc.

Assoc Prof. Chang Liu, Guangdong University of Technology

Title：Research on Blockchain-Enabled Data Sharing Technology in Internet of Vehicles 

Abstract: The security of data sharing is critical to enabling efficient inter-vehicle data sharing and improving transportation efficiency in the Internet of Vehicles (IoV) environment. However, the current centralized security management models are inadequate to meet the timeliness and security requirements of data sharing. While blockchain technology can potentially solve these problems, applying it to IoV data sharing poses challenges such as low efficiency and high computational costs. To tackle these difficulties, we propose an IoV architecture based on a consortium blockchain and conduct research for data privacy, data validity, and consensus efficiency on three key aspects. First, we propose a data privacy protection technique that combines federated learning and blockchain, which effectively prevents data privacy breaches and data poisoning attacks, improves data validity, and preserves data privacy. Second, we propose a consensus node selection scheme based on multiple factors such as vehicle trust value, communication range, and computing power, and a data sharing incentive mechanism based on evolutionary game theory. Third, we propose a two-layered and multi-sharded blockchain architecture and an optimized PBFT consensus protocol based on BLS signature technology to address the efficiency challenges in data verification consensus.