The compression of real-world scanned 3D human dynamic meshes is an emerging research area.  The inherent depth and multitude of viewing angles enabled by 3D dynamic meshes contribute to an enriched experience across various applications, such as telepresence, virtual reality, and 3D digital streaming. Unlike synthesized dynamic meshes with fixed topology, such as those created using graphic design software, scanned dynamic meshes often not only have varying topology across frames but also scan defects such as holes and outliers, increasing the complexity of prediction and compression. Additionally, human meshes often combine rigid and non-rigid motions, making accurate prediction and encoding significantly more difficult compared to objects that exhibit purely rigid motion. 

This talk presents a compression method designed for real-world scanned human dynamic meshes, leveraging embedded key nodes. The temporal motion of each vertex is formulated as a distance-weighted combination of transformations from neighboring key nodes, requiring the transmission of solely the transformations of the sparse key nodes.  To improve rate-distortion performance, we integrate semantic segmentation of body parts into the codec, allowing region-specific transformation modeling.  This allows deformation-rich regions to use affine transformations, while retaining simpler rigid models elsewhere.  This bimodal method achieves more accurate mesh deformations, especially in sequences involving complex non-rigid motion, without compromising compression efficiency in simpler regions.

Pamela Cosman is a Distinguished Professor of Electrical and Computer Engineering (ECE) at UC San Diego. She received her B.S. from the California Institute of Technology in 1987 and Ph.D. in Electrical Engineering from Stanford University in 1993.  After postdocs at Stanford University and the University of Minnesota, she joined the faculty of UC San Diego in 1995. Her administrative and editorial positions have included Director of the Center for Wireless Communications, Associate Dean for Students of the Jacobs School, and Editor-in-Chief of the IEEE Journal on Selected Areas in Communications. She became the Chair of the ECE Department in 2024. She has published over 300 papers on image/video processing and wireless communications, as well as an advice book for girls contemplating STEM careers (Free to Choose STEM, IEEE eBooks), and two children’s books that introduce engineering and math concepts through fiction. Prof. Cosman is a Fellow of the IEEE, the holder of the Jack Keil Wolf Endowed Chair in Electrical Engineering, and winner of national and regional leadership awards from the Electrical and Computer Engineering Department Heads Association (ECEDHA), the Athena Foundation, and the San Diego County Engineering Council.  

InfAs data-intensive applications such as generative AI and high-performance computing accelerate, optical interconnect architectures face fundamental bottlenecks in bandwidth scaling, power efficiency, and system integration. This talk presents recent advances in silicon photonics enabling 1.6 T and 3.2 Tbps optical engines for high-performance computing. Key topics include high-power O-band DFB lasers, high-speed driver/TIA ICs, and heterogeneous integration for Co-Packaged Optics (CPO). The session will also discuss Taiwan’s collaborative SiPh ecosystem and its roadmap toward low-power, AI-driven optical interconnects.ormation is not yet available

Dr. Chun-Nien Liu received his Ph.D. in Photonics from National Sun Yat-sen University in 2015 and is currently an Associate Professor at National Chung Hsing University. His research focuses on silicon photonics, LiDAR systems, optoelectronic packaging, and heterogeneous integration technologies for high-speed optical communication.

He leads multiple NSTC flagship projects, including “Road to Next-Generation 3.2 Tbps Si-Photonics Optical Engines”, and collaborates with industrial partners such as TSMC, Foxconn, and Taiwan Color Optics. Dr. Liu’s team has developed advanced SiPh transceiver modules, CPO architectures, and AI-driven photonic design frameworks that bridge academic research and semiconductor industry applications.

There is much excitement among engineers and scientists engaged in research & development of artificially intelligent systems.  They have successfully  resolved many challenging technical problems and have demonstrated the practical viability of  autonomous driving. These are major milestones in  engineering and a clear harbinger of a transformative new era of moving goods, supplies, and  people from point A to point B. Yet, along with these accomplishments come many new challenges  that are not only of a technical nature, but also of a broader social, legal, and even “ethical” nature.  Such issues become more urgent and important as collisions and accidents involving self-driving  or semi-autonomous vehicles occur more often – injuring and even killing humans in the real  world. A key challenge that needs to be addressed is making sure that the artificially engineered  automobiles and humans cohabit in a harmonious, safe, and secure manner. For researchers this  provides the exciting opportunity to pursue important problems from a broad range of topics in  distributed perception, cognition, planning, and control. We will present a “Human Centered” approach for the development of highly automated vehicle technologies. We will also  present a brief sampling of contributions in the development of systems and algorithms to perceive  situational criticalities, predict intentions of intelligent agents, and plan/execute actions for safe &  smooth maneuvers and control transitions. We will highlight major research milestones in the  autonomous vehicles area and discuss issues that require deeper, critical examination and careful  resolution to assure safe, trustworthy, and robust operation of these highly complex systems in the real  world. 

Mohan Trivedi received his PhD in Electrical Engineering from Utah State University in 1979, after completing undergraduate work in India. He has published extensively and has edited over a dozen volumes including books, special issues, video presentations, and conference proceedings. Trivedi is a recipient of the Pioneer Award and the Meritorious Service Award from the IEEE Computer Society; and the Distinguished Alumnus Award from Utah State University. He is a Fellow of the International Society for Optical Engineering (SPIE). He is a founding member of the Executive Committee of the UC System-wide Digital Media Innovation Program (DiMI). Trivedi is also Editor-in-Chief of Machine Vision & Applications (http://link.springer-ny.com/link/service/journals/00138/index.htm).

Spirometry is a crucial assessment for detecting respiratory obstructive diseases such as chronic obstructive pulmonary disease (COPD). While there are several respiratory trainers and spirometers available on the market, incorrect blowing techniques may render key metrics (e.g., forced vital capacity (FVC), forced expiratory volume in first second (FEV1), and Peak Expiratory Flow (PEF)) unreliable, necessitating supervised testing by healthcare professionals in hospital settings. This talk will introduce a portable respiratory training system, integrating hardware for receiving airflow information through airflow sensors and software designed using a real-time development platform to present the spirometry test interface and an interactive game interface for training. This system categorizes common blowing waveforms into six types using machine learning models and dynamic time warping (DTW) algorithm. Based on the classification results, users are directed to appropriate respiratory training sessions to help them master correct testing techniques.

Chih-Yu Wen (Senior Member, IEEE) received the B.S.E.E. and first M.S.E.E. degrees from the National Cheng Kung University, Tainan, Taiwan, in 1995 and 1997, respectively, and the second M.S.E.E. and Ph.D. degrees from the University of Wisconsin-Madison, Madison, WI, USA, in 2002 and 2005, respectively, all in electrical engineering. He joined the Department of Electrical Engineering, National Chung Hsing University, Taichung, Taiwan, in 2006, where he is currently a Lifetime Distinguished Professor. His current research interests include wireless communications, biomedical signal processing for health monitoring, and distributed networked sensing and control. He is a member of the Chinese Institute of Engineers. He was a recipient of the National Innovation Awards–Institute for Biotechnology and Medicine Industry, in 2016, 2018, 2019, 2020, and 2021, for contributions to remote rehabilitation and smart medical devices. He and coauthors were also recipients of Best Paper and Oral Presentation Awards, such as the 2020 Taiwan Telecommunications Annual Symposium Best Paper Award, the 2021 IEEE ECBIOS Best Paper Award, and the 2025 IEEE ICCE-TW Best Oral Presentation Award at "Smart Systems and Its Applications" Session. Since 2018 and 2025, he has been Associate Editors of IET Signal Processing and International Journal of Fuzzy Systems, respectively.