Conference Speakers

 

Prof. Soochang Park
Chungbuk National University (CBNU), Korea

Speech Title: Reliable AIoT-based Virtual Sensing for Macro-to-Micro Scale Seamless Monitoring of Hyper-local Urban Environments

Abstract: Urban resilience against heat islands and extreme weather demands reliable hyper-local environmental data, yet sparse official stations and costly dense IoT deployments fail to deliver sufficient spatial resolution and trustworthiness. To address this, we design an environmental standard model that quantifies surrounding contextual influences on sensing areas and propose an automated recognition method to extract these states from multi-source observations. Building on the standardized representation, an AI-based mapping framework aligns similar environmental conditions across CCTV networks, weather stations, and sparse micro-scale IoT sensors. Cross-modal knowledge distillation and spatial statistical learning then transfer physical knowledge to vision-only models, enabling accurate estimation of both visible and invisible micro-climate variables. The resulting virtual sensors, derived primarily from existing CCTV infrastructure, achieve cost-effective and trustworthy macro-to-micro scale seamless monitoring for urban resilience.

Biodata: Dr. Soochang Park received his Ph.D. in Computer Engineering from Chungnam National University in 2011. He was a postdoctoral researcher at Rutgers University (2012–2013), a research engineer at Télécom SudParis, France (2013–2015), and a research associate at the Hong Kong University of Science and Technology (2015–2017). He joined Chungbuk National University in 2017 as an Assistant Professor and was promoted to Associate Professor in 2021. At Chungbuk National University, he also served as Head of the Business Incubation Center (2017–2018) and Head of the Department of Computer Engineering (2023–2025). In 2025, he was a Visiting Professor at the University of New South Wales, Australia. Dr. Park was elevated to IEEE Senior Member in October 2025. He currently serves as Vice Chair of the IEEE Consumer Technology Society Technical Committee on Consumer Communications Networks & Connectivity (2025–2026). His career reflects over 13 years of contributions to academic research, leadership, and global collaboration.

Prof. Nobuo Funabiki
Okayama University, Japan

Speech Title: A 3D Modelling Method from Panoramic Images for Indoor Environment Digital Twin and Its Application to CCTV Camera Placement

Abstract: In this talk, I introduce a 3D modelling method for an indoor environment digital twin from its 360° panoramic images using 3D point cloud and Gaussian splatting technologies with visual simultaneous localization and mapping (VSLAM) framework. Digital twin is a popular technology to represent a real world in a virtual world using a 3D model, enabling us to monitor, simulate, analyze, and optimize the performance of a real-world system by programs. Then, I show its application to a closed-circuit television (CCTV) camera placement optimization. CCTV cameras are deployed worldwide to monitor movements of humans and other objects to improve the efficiency and safety of our societies. Optimal camera locations for effective surveillance are determined by combining ray-casting visibility analysis and a greedy optimization algorithm in the 3D model, maximizing visual coverage while minimizing blind spots and avoiding excessive overlap between camera views.

Biodata: Nobuo Funabiki received the B.S. and Ph.D. degrees in mathematical engineering and information physics from the University of Tokyo, Japan, in 1984 and 1993, respectively. He received the M.S. degree in electrical engineering from Case Western Reserve University, USA, in 1991. From 1984 to 1994, he was with the System Engineering Division, Sumitomo Metal Industries, Ltd., Japan. In 1994, he joined the Department of Information and Computer Sciences at Osaka University, Japan, as an assistant professor, and became an associate professor in 1995. He stayed at University of California, Santa Barbara, in 2000-2001, as a visiting researcher. In 2001, he moved to the Department of Communication Network Engineering (currently, Department of Information and Communication Systems) at Okayama University as a professor. His research interests include computer networks, optimization algorithms, educational technology, and web application systems. He was the chairman at IEEE Hiroshima Section in 2015 and 2016. He was a vice president for conferences in 2023 and 2024, and is a member of Board of Governors (BoG) in 2025-2027 at IEEE Consumer Technology Society (CTSoC). He is a member of IEEE, IEICE, and IPSJ.

Prof. Chih-Peng Fan
National Chung Hsing University, Taiwan

Speech Title: Design of a Visual Support System for Yoga Self-Practice by Deep Learning-based Human Pose Estimation and Skeleton Tracking Technologies

Abstract: The rising popularity of home fitness has boosted demand for real-time, precise AI-based yoga coaching systems. To assist beginners with yoga self-practice. In this keynote talk, the OpenPose based yoga self-practice assistance system for dynamic and static yoga by angle-based poses matching and pose difficulty estimation on the NVIDIA Jetson Nano platform is introduced. The developed system uses the OpenPose Body25 model to extract the important information related to body key joints by evaluating the accuracy of users’ poses against yoga instructor demonstrations with User-Instructor synchronization on the basis of joint angle differences and total distance between adjacent video frames, and then the user’s feedback with fuzzy based scoring strategy will be processed simultaneously. To prevent overly difficult yoga poses from causing injuries to beginners, the developed system includes a difficulty assessment feature that allows users to select poses according to their ability. The proposed system evaluates pose difficulty from the front and side views by estimating angular velocity, body area, body bending direction, flexibility requirements, and range of motion on the basis of joint angles and vectors. Then the information of developed Yoga difficulty estimation levels is integrated into the developed yoga self-practice system.

By using OpenPose for keypoint extraction, the system executes a novel multifeature weighted cosine similarity algorithm to evaluate poses across three dimensions: global position, local structural variation, and joint angles. Adaptive normalization strategies are employed to eliminate body proportion discrepancies and mirroring errors. Furthermore, the system tracks score variations over time to quantify core stability and pose maintenance ability. For enhanced interaction, a dual-channel multimodal guidance mechanism is incorporated, utilizing adaptive skeletal rendering and dynamic joint enlargement to minimize body occlusion and highlight errors. Additionally, a nonblocking asynchronous multilingual text-to-speech module with priority scheduling and cooldown mechanisms is integrated to prevent cognitive overload. Experimental results indicate that the proposed system effectively suppresses detection jitter and identifies errors in both overall poses and specific body parts. Crucially, the outputs align perfectly with expert subjective rankings, offering a low-cost, robust, and highly precise AI solution for home fitness.

Biodata: Chih-Peng Fan received the B.S., M.S., and, and Ph.D. degrees, all in electrical engineering, from National Cheng Kung University, Taiwan, in 1991, 1993 and 1998, respectively. During October 1998 to January 2003, he was a design engineer at Computer and Communications Research Laboratories (CCL), Industrial Technology Research Institute (ITRI), Hsinchu, Taiwan. In 2003, he joined the Department of Electrical Engineering at National Chung Hsing University in Taiwan as an Assistant Professor. He became a full Professor in 2013. He has more than 110 publications, including technical journals, technical reports, book chapters, and conference papers. His teaching and research interests include deep-learning based digital image processing and pattern recognition, digital video coding and processing, digital baseband transceiver design, VLSI design for digital signal processing, and fast prototype of DSP systems with FPGA and embedded SOC platform. He served and is serving as: General Chair of ICCE-TW 2018; Executive Conference Chair of ICCE 2024; Advisory Chair of ICCE 2025; TPC Chair of ICCE 2027; Conference Chair of ICCE ICCT Pacific 2026; TPC Chair of ICCE ICCT Pacific 2027; IEEE Transactions on Consumer Electronics -Associate Editor (2022-2025); Member of Editorial Board of Journal of Real-Time Image Processing (2021-Now); IEEE CTSoc Representative at the IEEE Systems Council's AdCom (2020-2021); IEEE CTSoc Representative at the IEEE Sensors Council's AdCom (2023-Now); Secretary of IEEE CTSoc Sensors and Actuators (SEA) TC (The first term); Chair of IEEE CTSoc Sensors and Actuators (SEA) TC (2025-Now); He is a member of Taiwan IC Design Society (TICD), IEICE, and IEEE.

Prof. Hiroshi Fujinoki
Southern Illinois University, USA

Speech Title: TBA

Abstract: Ransomwares have been spreading rapidly and show no sign of slowing down, especially as the average ransom demand has continued to increase in recent years. Ransomware attacks continue to intensify by bypassing conventional static or dynamic detection-based security measures and inflicting irreparable harm on critical organizational data. To change the game, we propose a new fail-safe approach to preventing damage from ransomwares. This means that even if a ransomware successfully takes our production systems hostage, the new approach will still protect the production systems, freeing us from the threats posed by ransomwares.

In this talk, we will introduce a backup-based defense mechanism that transparently and continuously secures production data against ransomwares while ensuring controlled growth in the number of backup copies. Our approach focuses on a “just-in-time” backup strategy—termed In-Operation Off-Site Backups—that performs continuous and verifiable file duplication at each update and is combined with a detection mechanism that can halt malicious encryption attempts as soon as they are detected. The solution leverages Bloom filters to control the number of backup copies while maintaining a low false-negative miss-detection rate for malicious activities by ransomwares—on the order of 10⁻⁸—providing extremely high confidence that unauthorized data modifications will be detected before damage spreads. By employing fake fields, locality-aware thresholds, and fine-tuned probabilistic data structures, the system distinguishes malicious activities from legitimate ones, even preventing denial-of-service threats from causing harmful, unnecessarily frequent backups. The new approach ensures the safety of the protected systems so that, even when it fails to detect ransomwares, the production data remain safe.

Simulation results demonstrate that this approach can achieve highly reliable detection and sustainable storage utilization. The proposed method fills a crucial gap in ransomware protection by ensuring that backups remain both secure and manageable, thereby significantly mitigating the risk of catastrophic data loss.

Biodata: December 2000: Earned a Ph. D. degree, Computer Science, Univ. of South Florida, Tampa, FL). August 2001: Appointed an Assistant Professor, the Department of Computer Science, Southern Illinois University Edwardsville, IL, USA.Current: Appointed a Professor, the Department of Computer Science, Southern Illinois University Edwardsville, IL, USA. 2012-2013: An advisory technical committee member: US-TRANSCOM in the U.S. Air Force. 2016-current: A technical adviser/the system administrator of IDOT (Illinois Department of Transportation)-D8 public inter-state traffic information announcement system. 2026, 2024: a keynote chair, ICCE (International Conference on Consumer Electronics). 2024: a keynote chair, ICCCI (International Conference on Computer Communication and the Internet). Invited as a guest speaker: Wuhan University (Wuhan, China), Nagoya Institute of Technology (Nagoya, Japan), Okayama University (Okayama Japan), Okayama University of Science (Okayama, Kapan), Missouri Department of Transportation (MO, USA). Primary Research: routing in large-scale computer networks, network security, and network protocols

Invite Speakers

 

Dr. Li-Hsiang Shen
National Central University (NCU), Taiwan

Speech Title: Engineering 6G Wireless Environments: From Intelligent Surfaces to Flexible MIMO and Movable Metasurfaces
Abstract: The emerging paradigm of wireless environment engineering is reshaping future 6G wireless networks. Reconfigurable intelligent surfaces (RIS), fluid antenna/element systems (FAS/FES), and movable antennas/elements (MA/ME) have attracted significant attention due to their ability to manipulate radio propagation, extend coverage, mitigate channel fading, and improve spectral and energy efficiency. By intelligently controlling electromagnetic waves, RIS enables advanced communication and sensing functionalities, while recent developments have further evolved RIS into multi-functional RIS (MF-RIS) architectures, including STAR-RIS, dual STAR-RIS (D-STAR), double-sided STAR-RIS (DS-STAR), and energy-harvesting RIS. These technologies provide seamless 360-degree coverage and bidirectional signal control using a single metasurface. From a practical perspective, RIS deployment has progressed from intelligent deployment on auto-guided vehicles (AGVs) to integration with unmanned aerial vehicles (UAVs) and low-Earth orbit (LEO) satellites, enabling flexible space-air-ground networks. Beyond static surfaces, FAS/FES and MA/ME introduce spatial reconfigurability through dynamic element repositioning and non-uniform array configurations, creating additional spatial diversity and enhanced beamforming capabilities. Their integration with RIS further gives rise to fluid and movable metasurfaces. Looking ahead, six-dimensional movable antennas (6DMA) and six-dimensional movable metasurfaces (6DMM), jointly optimizing 3D position and 3D orientation, offer unprecedented flexibility for wireless environment control. Combined with artificial intelligence (AI) and advanced sensing technologies, these innovations are paving the way toward fully programmable wireless environments, seamlessly integrating communications, sensing, localization, and computing in future 6G networks.

Biodata: Li-Hsiang Shen received Ph.D. degree from the Institute of Communication Engineering, National Chiao Tung University (NCTU), Hsinchu, Taiwan, in 2020. Since February 2024, he has been an Assistant Professor with the Department of Communication Engineering, National Central University (NCU), Taoyuan, Taiwan. From 2018 to 2019, he was a Visiting Scholar with the Next Generation Wireless Research Group of the Department of Electrical and Computer Engineering (ECE), University of Southampton, U.K. From 2021 to 2023, he was a Postdoc with ECE, National Yang Ming Chiao Tung University (NYCU), Hsinchu, Taiwan. In 2023, he was a Visiting Scholar with California PATH, Berkeley DeepDrive, University of California, Berkeley (UCB), USA. In 2025, he was a Visiting Professor with the ECE, University of Toronto, Ontario, Canada. His research interests include wireless broadband in 5G/6G, space-air-ground integrated network (SAGIN), low Earth orbit (LEO), multi-functional reconfigurable intelligent surfaces (RIS), integrated sensing, computing and communications, and machine/deep learning for wireless networks.

Dr. Shen was the recipient of Ph. D Scholarship from NCTU and from Industry-Academic Elite Program. He was rewarded the first prize of Broadcom Foundation Asia Pacific Workshop in 2019. In 2021, he was rewarded IEEE Best PhD Thesis Award, NYCU Outstanding Ph.D. Research, and Phi Tau Phi Scholastic Honor Society of Taiwan. In 2022, he was rewarded National Science and Technology Council (NSTC) FutureTech Award, NSTC Postdoctoral Research Abroad Program, and NSTC Postdoctoral Research Award. In 2024 and 2025, he was rewarded NCU Rising Stars three times. In 2025, he was rewarded Wen-Yuan Pan Foundation Exploration Research Award. In 2026, he received a best paper award in Taiwan Telecommunications Annual Symposium and a best poster award of NSTC project.