| Professor Miaowen WenIEEE Senior Member, South China University of Technology, China Bio: Miaowen Wen received the Ph.D. degree from Peking University, Beijing, China, in 2014. From 2019 to 2021, he was the Hong Kong Scholar with the Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong. He is currently a Professor with South China University of Technology, Guangzhou, China. He has authored or co-authored two books and more than 200 journal articles. His research interests include wireless and molecular communications. He was a recipient of the IEEE Asia-Pacific (AP) Outstanding Young Researcher Award in 2020. He was the Winner of the Data Bakeoff Competition (Molecular MlMO) at the IEEE Communication Theory Workshop (CTW), Selfoss, Iceland. He served as an Editor/a Guest Editor of IEEE Transactions on Communications, IEEE Journal on Selected Areas in Communications, and IEEE Journal of Selected Topics in Signal Processing. He is an Editor/a Senior Editor of IEEE Transactions on Wireless Communications, IEEE Transactions on Molecular, Biological, and Multi-scale Communications, and IEEE Communications Letters. Speech Title：Affine Frequency Division Multiplexing (AFDM): Principle, implementation, and challenges Abstract: Sixth-generation (6G) are expected to introduce new usage scenarios, along with enhanced and new capabilities. In 6G networks, high-mobility communications that enjoy high reliability and operate at high carrier frequencies will play a critical role. However, orthogonal frequency division multiplexing, widely used in current wireless networks, suffers from significant performance degradation in high-mobility scenarios, since frequency dispersion resulting from Doppler shifts destroys the subcarrier orthogonality and causes inter-carrier interference. Recently, affine frequency division multiplexing (AFDM) has emerged as a promising waveform, which can achieve the optimal diversity order over doubly dispersive channels. After revisiting the characteristics of doubly dispersive channels, this talk introduces the fundamentals of AFDM, and then some implementation issues of AFDM with our proposed solutions. Finally, some open challenges of AFDM are discussed. |
| Dr. Ata Jahangir MoshayediIEEE Senior Member, Associate Professor at DGUT-CNAM Institute, Dongguan University of Technology,Smart Structural Health Monitoring & Control Laboratory Dongguan University of Technology Dongguan, China Bio:Ata Jahangir Moshayedi,holds a Ph.D. in Electronics from Pune University, India. He is a senior member of IEEE, Memeber of ACM, CCF and a Life Member of the Instrument Society of India, and a Life Member of Speed society, in India. Dr. Moshayedi’s contributions extend beyond academia, as he actively participates in various IEEE conferences, serving in roles such as Keynote speake and technical Chair, reviewer, and editorial team member for conferences and international journals. His scientific achievements are substantial, with over 100 articles published in prestigious national and international journals. Alongside his research publications, he has authored Four books and holds two patents and 16 copyrights, showcasing his pioneering contributions. Notably, his latest book, "Unity in Embedded System Design and Robotics: A Step-by-Step Guide", published by CRC Press, is recognized as the first book to integrate VR, robotics, and embedded systems. His primary research interests include robotics and automation, Health monitoring, sensor modeling and biomimetic robotics, mobile robot olfaction and plume tracking, embedded systems, machine vision-based systems, virtual reality, cognitive science, and bio-inspired systems. Speech Title：Respiratory Parameter Estimation Using Pharyngeal Phonetics And Machine Learning: Breaking Free From Spirometry Abstract: Traditional spirometry, while essential for diagnosing respiratory diseases, is constrained by its invasiveness and dependence on patient exertion, rendering it unsuitable for many high-risk individuals. This work presents a paradigm-shifting alternative: a non-contact lung function assessment that leverages pharyngeal phonetics and machine learning. By analyzing respiratory acoustics without forceful exhalation, our method eliminates key risks and discomforts. It unlocks new possibilities for telemedicine and decentralized monitoring, allows for high-frequency testing, and employs advanced analytics to detect incipient lung impairment. Ultimately, this technology promises to democratize respiratory care by offering a safer, more scalable, and readily deployable solution through standard consumer devices. |
| Professor Thippa Reddy GadekalluIEEE Senior Member, Zhejiang A&F University, China Bio: Thippa Reddy Gadekallu (IEEE Senior Member) received the bachelor’s degree in computer science and engineering from Nagarjuna University, India, in 2003, the master’s degree in computer science and engineering from Anna University, Chennai, Tamil Nadu, India, in 2011, and the Ph.D. degree from Vellore Institute of Technology, Vellore, Tamil Nadu, in 2017. He is currently with the Division of Research and Development, Lovely Professional University, Phagwara, India, the Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon, and the Center of Research Impact and Outcome, Chitkara University, Punjab, India. He has more than 14 years of experience in teaching. He has more than 150 international/national publications in reputed journals and conferences.His research interests include machine learning, the Internet of Things, deep neural networks, blockchain, and computer vision. He is an editor of several publishers, such as Springer, Hindawi, Plosone, Scientific Reports (Nature), and Wiley. He also acted as a guest editor in several reputed publishers, such as IEEE, Elsevier, Springer, Hindawi, and MDPI. He was recently recognized as one among the top 2% scientists in the world as per the survey conducted by Elsevier, in 2021. Speech Title：Agentic AI Security in 6G Networks Abstract: The convergence of agentic AI and 6G networks marks a turning point in network security. Agentic AI systems are autonomous agents that perceive, remember, plan, and act within the network itself - making them powerful tools but also introducing complex security challenges. This talk provides a structured examination of how attackers can weaponize agentic AI across radio access, open RAN, and digital twin layers, while also showing how the internal components of these agents become vulnerable to manipulation. The presentation concludes with defenses and governance frameworks needed to build agentic systems that are autonomous yet trustworthy, emphasizing that security must be built into the fabric of how these agents think and act. |
