KEYNOTE SPEAKERS

 


Prof. Yan-qing Lu
Nanjing University, China

 

Speech Title: Liquid Crystal Based Dynamic Edge Detection and Imaging  
Abstract: Optical edge detection, as an efficient means of data compression and feature extraction, holds great potential in fields of machine vision and biomedical imaging. However, conventional optical edge detection devicesexhibit fixed functionality once fabricated, lacking dynamic tunability, which limits their application in complex scenarios. Liquid crystals (LCs), as soft matter with excellent stimulus-responsiveness and multidimensional light manipulation capabilities, provide an ideal platform for realizing dynamically tunable optical edge detection. In this presentation, I will introduce our recent research advances in LC-tunable dynamic edge detection including wavelength-selective edge detection, dynamic electrical switching of the edge orientation and an electrically tunable heralded single-photon imaging platform. These works reveal the immense application potential of “soft mattonics” in intelligent optical information processing, particularly in optical computing and quantum imaging.

Bio: Yan-qing Lu received both his BS and Ph.D. degrees from Physics department, Nanjing University, China, in 1991 and 1996 respectively. Then he stayed in the same University as a lecture (1996) and associate professor (1998). He worked in Academia and Industry in the United States from 2000 to 2006, where he developed a serial of liquid crystal based fiber-optic devices with his colleagues in Chorum Tech., CREOL, UCF and EZconn. Corp. He is currently a Changjiang distinguished professor at Nanjing University and a Fellow of Optica, Fellow of COS (Chinese Optical Society) and Fellow of CSOE (Chinese Society for Optical Engineering). He currently serves as the director of Chinese Liquid Crystal Society, the executive editor-in-chief for Chinese Optics Letters. His research interests include softmatter photonics (Soft mattonics), nanophotonics and nonlinear optics. He is the author or co-author of over 400 peer-reviewed papers in Science, Nat. Photon., Nat. Nano., Nat. Commun., Sci. Adv., PNAS, PRL, Light Sci. Appl. etc..



Prof. Nikolay Petrov
ITMO University, Russia, Harbin Institute of Technology, China

 

Speech Title: Continuous-Wave THz Phase Imaging: Engineering Constraints in System Design, Algorithms and Applications  
Abstract: Terahertz (THz) imaging offers significant capabilities for inspecting internal structures and material properties invisible to conventional optical machine vision systems. However, the transition from physical measurement to visual data relies heavily on computational phase retrieval and system design factors often overlooked in literature. To address this disparity, priority is given to electronic continuous-wave sources coupled with homodyne and direct intensity detection, offering a pragmatic balance between system complexity and phase information retrieval. Critical components required for practical measurements are reviewed, including a structured classification of sources and detection approaches. Engineering constraints, such as Noise Equivalent Power (NEP) characterization and data acquisition protocols, are analyzed alongside iterative phase retrieval algorithms. Applications across diverse sectors, including telecommunications, biology, medicine, cultural heritage, food inspection, security, and industry, are examined through theoretical analysis and practical examples.

Bio: Nikolay V. Petrov received his PhD degree in Optics from ITMO University, Russia. In 2016 he established a digital and display holography laboratory. In 2019 he gained a doctorate (Dr. habil.) degree in optics and became a Leading Professor in 2021, and main Researcher in 2023. In 2023-2024 he was Visiting Professor in Qingdao Innovation and Development Center of Harbin Engineering University, and currently he is a Chair Professor in Harbin Institute of Technology. Dr. Petrov was the recipient of the Russian Federation Government Prize in Education in 2010 and is a winner of several other prizes. He is a topical editor of the following journals: Light: Advanced Manufacturing and Applied Optics, and have the Outstanding Editor award of Light: Advanced Manufacturing in 2021 and 2023.

 

Prof. Yibin Tian
Shenzhen University, China

 

Speech Title: Towards Intelligent Monitoring and Inspection of Large-Scale Factories: A Ground–Air Collaborative Multi-Agent System with Multi-Modal Perception and Robotic Manipulation  
Abstract: Modern industrial facilities require autonomous inspection systems capable of operating reliably in complex, large-scale, and potentially hazardous environments. We present a ground–air collaborative multi-agent, multi-modal factory inspection and monitoring system that integrates unmanned aerial vehicles (UAVs) with heterogeneous ground mobile robots, including a 4-leg–wheeled hybrid AGV and an onboard 6-axis robotic arm, to achieve intelligent factory inspection.

The proposed system exploits the complementary strengths of aerial and ground platforms. UAVs provide rapid, wide-area coverage and access to elevated spaces, while the 4-leg–wheeled AGV combines wheeled efficiency with legged mobility to traverse uneven terrain, stairs, and obstacles. The integrated 6-axis robotic arm enables close-range inspection, precise sensor positioning, contact-based measurements, and manipulation tasks such as valve checking and probe deployment. To support robust perception, the system fuses multi-modal sensing, including RGB, thermal and 3D vision, temperature and tactile sensing, as well as radiation detection, enabling reliable detection of structural defects, thermal anomalies, radiation leakage, and equipment degradation under challenging conditions.

A hierarchical perception and data-fusion framework is developed to achieve spatiotemporal alignment and semantic understanding across modalities and platforms. Cooperative task allocation and motion planning strategies coordinate heterogeneous agents, allowing dynamic role assignment, redundancy-aware sensing, and adaptive re-tasking in response to detected anomalies. Shared semantic maps and inter-agent communication facilitate consistent situational awareness and precise fault localization.

Bio: Yibin Tian is a Professor at Shenzhen University. He received his PhD from the University of California, Berkeley. His research activities focus on computational imaging, multimodal intelligent sensors, multimodal perception and sensor fusion, machine learning, and intelligent robotics. He has led or undertaken major projects funded by the National Natural Science Foundation of China, and the Department of Science and Technology of Guangdong Province. He has 11 years of cutting-edging technology R&D and management experience in Silicon Valley, and has served as a Senior Scientist, Principal Engineer and Chief Engineer at leading and pioneering companies in the optoelectronic sensing and imaging industry. Products he developed have been deployed by more than 20 Fortune 500 companies, including Apple, Microsoft, Intel, TSMC, Samsung, and Huawei etc. He has published over 50 academic papers, holds more than 40 granted U.S. and Chinese patents. He has managed more than 10 multinational interdisciplinary projects in optoelectronic imaging and sensing with successful commercialization, overseeing cumulative R&D funding exceeding RMB 100 million.