ICASIS 2024
2nd International Conference on Advanced Sensing and Intelligent Systems
Kunming, China | June 22-23, 2024


Prof. Pingyi Fan
Tsinghua University, China

Dr. Pingyi Fan is a professor of the Department of Electronic Engineering of Tsinghua University. He received Ph.D. degree at the Department of Electronic Engineering of Tsinghua University in 1994. From 1997 to 1999, he visited the Hong Kong University of Science and Technology and the University of Delaware in the United States. He also visited many universities and research institutes in the United States, Europe, Japan, Hong Kong and Singapore. He has obtained many research grants, including national 973 Project, 863 Project, mobile special project and the key R&D program, national natural funds and international cooperation projects. He has published more than 190 SCI papers (more than 130 IEEE journals), and 4 academic books. He also applied for more than 30 national invention patents, 5 international patents and. He won seven best paper awards of international conferences, including IEEE ICC2020 and Globecom 2014, and received the best paper award of IEEE TAOS Technical Committee in 2020, the excellent editor award of IEEE TWC (2009), etc. He has served as the editorial board member of several Journals, including IEEE and MDPI. He is currently the editorial board member of Open Journal of Mathematical Sciences, the deputy director of China Information Theory society, the co-chair of China's 6G-ANA TG4, and the chairman of Network and Communication Technology Committee of IEEE ChinaSIP. His current research interests are in 6G wireless communication network and machine learning, semantic information theory and generalized information theory, big data processing theory, intelligent network and system detection, etc.
Title: Advances of Digital Image Lossless Compression and Its Applications in Semantic Communications
Abstract: Metaverse and digital twins are the promising technologies. They may bring great impacts to people’s working modes and daily life. Among them, the most important information media are digital images or videos. In this talk, we mainly focus on the new developments of digital image lossless compression. We first review the theoretical methods and its industrial standards on digital image compressions and then present key challenges for digital image processing, including the estimations and its implementations to approximate the entropy of digital images. Later on, we introduce soft compression, a new approach for the digital image lossless compression, and prove that it can approach the entropy of the digital images in theory. Various experiments on some popular image databases demonstrate its performance, compared to the known JPEG, PNG, JPEG-2000 etc. We also discuss its potential applications in some typical scenarios including semantic communications. Finally, we point out some promising research directions.



Prof. David Greenhalgh
University of Strathclyde, UK

Professor David Greenhalgh gained a PhD from the University of Cambridge in 1984 and worked at Imperial College, London from 1984 to 1986. He also has a first class Honours degree in Mathematics and a distinction in Part III Mathematics. He is am currently a member of the Population Modelling and Epidemiology Research Group at Strathclyde and has been a member of staff at Strathclyde in the Departments of Mathematics, Statistics and Modelling Science and Mathematics and Statistics since 1986. He is currently (since 2017) a full professor in the Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK, Postgraduate Director (Mathematics and Statistics) at Strathclyde and also Associate Editor of Journal of Biological Systems. He has published over 110 publications in international refereed journals, supervised over 20 MPhil and PhD research students and been on the editorial board of eighteen international journals. In 2015 he was awarded a two year (2015-2017) Leverhulme Trust Research Fellowship grant (50K RF-2015-88) as PI to study mathematical modelling of vaccination against dengue. He has also been involved in collaboration with Malaysia to mathematically model a mosquito trap to control dengue and won a 187K grant from the Newton Fund to do this in 2016. His main research interests are in mathematical and statistical epidemiology but he has also done some work in genetic algorithms and signal and image processing.
Title: Convergence Criteria for Genetic Algorithms
Abstract: In this talk we discuss convergence properties for genetic algorithms. By looking at the effect of mutation on convergence, we show that by running the genetic algorithm for a sufficiently long time we can guarantee convergence to a global optimum with any specified level of confidence. We obtain an upper bound for the number of iterations necessary to ensure this, which improves previous results. Our upper bound decreases as the population size increases. We produce examples to show that in some cases this upper bound is asymptotically optimal for large population sizes. Finally we discuss implications of these results for optimal coding of genetic algorithms.



Prof. Jianbin Qiu
Harbin Institute of Technology, China
IEEE Fellow

Jianbin Qiu received the B.Eng. and Ph.D. degrees in Mechanical and Electrical Engineering from the University of Science and Technology of China, Hefei, China, in 2004 and 2009, respectively. He also received the Ph.D. degree in Mechatronics Engineering from the City University of Hong Kong, Kowloon, Hong Kong, in 2009.
He is currently a Full Professor at the School of Astronautics, Harbin Institute of Technology, Harbin, China. He was an Alexander von Humboldt Research Fellow at the Institute for Automatic Control and Complex Systems, University of Duisburg-Essen, Duisburg, Germany. His current research interests include intelligent and hybrid control systems, signal processing, and robotics.
Prof. Qiu is a Fellow of IEEE and serves as the chair of the IEEE Industrial Electronics Society Harbin Chapter, China. He is an Associate Editor of IEEE Transactions on Fuzzy Systems, IEEE Transactions on Cybernetics, and IEEE Transactions on Industrial Informatics.
Title: Adaptive Output-Feedback Boundary Control of Distributed Parameter Systems
Abstract: Distributed parameter systems, which are described by partial differential equations, widely exist in aerospace engineering, bioengineering, chemical engineering, and electrical engineering. Over the past decades, the control issues for distributed parameter systems have attracted considerable attention. In particular, the output-feedback adaptive control of distributed parameter systems is very challenging due to limited sensor measurements, unknown spatially varying parameters, and infinite-dimensional coupled dynamics. This talk will introduce some recent results on output-feedback adaptive boundary control for several classes of distributed parameter systems. The basic tools include observer canonical form, swapping identifier, and infinite-dimensional backstepping approach.



Prof. Mohamad Sawan
Chair Professor, Westlake University, Hangzhou, China
Emeritus Professor, University of Montreal, Canada
IEEE Fellow
Fellow of the Royal Society of Canada (FRSC)
Fellow of the Canadian Academy of Engineering (FCAE)

Mohamad Sawan is Chair Professor in Westlake University, Hangzhou, China, and Emeritus Professor in Polytechnique Montreal, Canada. He is founder and director of the Center of Excellence in Biomedical Research on Advanced Integrated-on-chips Neurotechnologies (CenBRAIN Neurotech) in Westlake University, Hangzhou, China. He received the Ph.D. degree from University of Sherbrooke, Canada. Dr. Sawan was Editor-in-Chief of the IEEE Transactions on Biomedical Circuits and Systems (2016-2019). He is founder of the Polystim Neurotech Laboratory. He hosted the 2016 IEEE International Symposium on Circuits and Systems, and the 2020 IEEE International Medicine, Biology and Engineering Conference (EMBC). He was a Canada Research Chair in Smart Medical Devices (2001-2015), and was leading the Microsystems Strategic Alliance of Quebec, Canada (1999-2018). Dr. Sawan published more than 1000 peer reviewed papers and many books and patents. He received several awards such as Chinese Government Friendship, Hangzhou Outstanding Talent, Shanghai International Collaboration, the Queen Elizabeth II Golden Jubilee Medal, etc. Dr. Sawan is Life Fellow of the IEEE, Fellow of the Royal Society of Canada, Fellow of the Canadian Academy of Engineering, and “Officer” of the National Order of Quebec.
Title: Intelligent Closed-loop Systems to Recover Neurodegenerated Functions
Abstract: Main neurodegenerative diseases remain lacking appropriate research attention and adequate progress due to the complexity of brain constitution and functioning in both anatomy and physiology levels. Recently, increase interest of bioinspired optoelectronics brain-computer interfaces is becoming promising approach to grasp the mechanisms and then to record achievements in this field. Also, recent artificial intelligence learning algorithms and neuromorphic hardware architectures represent appropriate approaches to detect biomarkers and to implement efficient electronics-optics sensors and actuators.
This talk covers multi sensing approaches and systems-on-chips design and tests for intelligent wearable and implantable closed-loop neuromodulation devices. They are built around sensing and activation blocks and used to regulate specific vital functions. They are intended for monitoring and manipulating either signals or cells and study neural diseases. They are microdevices dealing with multidimensional design challenges such as harvesting energy, low-power and high-data rate links, etc. Case studies include various neurorecording and stimulation methods for brain diseases such as epilepsy, stroke, and addictions.



Prof. Jiangzhou Wang
University of Kent, UK
IEEE Fellow, IET Fellow
Fellow of the Royal Academy of Engineering, UK (FREng)
International Member of the Chinese Academy of Engineering

Jiangzhou Wang (IEEE Fellow) is a Professor at the University of Kent, U.K. His research interest is in mobile communications. He has published over 400 papers and 4 books. He was a recipient of the 2022 IEEE Communications Society Leonard G. Abraham Prize and the 2012 IEEE Globecom Best Paper Award. Professor Wang is a Fellow of the Royal Academy of Engineering, U.K., Fellow of the IEEE, and Fellow of the IET. He was the Technical Program Chair of the 2019 IEEE International Conference on Communications (ICC2019), Shanghai, the Executive Chair of the IEEE ICC2015, London, and the Technical Program Chair of the IEEE WCNC2013.



Prof. Xianbin Wang
Western University, Canada
IEEE Fellow
Fellow of the Canadian Academy of Engineering (FCAE)
Fellow of the Engineering Institute of Canada (FEIC)

Dr. Xianbin Wang is a Professor and a Tier-1 Canada Research Chair at Western University, Canada. His current research interests include 5G/6G technologies, Internet of Things, communications security, machine learning, and intelligent communications. He has more than 500 highly cited journals and conference papers, in addition to over 30 granted and pending patents and several standard contributions.
Professor Wang is a Fellow of IEEE, Fellow of the Canadian Academy of Engineering and a Fellow of the Engineering Institute of Canada. He has received many prestigious awards and recognitions, including the IEEE Canada R. A. Fessenden Award, Canada Research Chair, Engineering Research Excellence Award at Western University, Canadian Federal Government Public Service Award, Ontario Early Researcher Award, and nine Best Paper Awards. He was involved in many IEEE conferences, including GLOBECOM, ICC, VTC, PIMRC, WCNC, CCECE, and CWIT, in different roles, such as General Chair, TPC Chair, Symposium Chair, Tutorial Instructor, Track Chair, Session Chair, and Keynote Speaker. He serves/has served as the Editor-in-Chief, Associate Editor-in-Chief, and editor/associate editor for over ten journals. He was the Chair of the IEEE ComSoc Signal Processing and Computing for Communications (SPCC) Technical Committee and is currently serving as the Central Area Chair for IEEE Canada.



Prof. Kun Yang
University of Essex, UK
IEEE Fellow, IET Fellow, ACM Distinguished Member
Member of Academia Europaea (MAE)

Kun Yang received his PhD from the Department of Electronic & Electrical Engineering of University College London (UCL), UK. He is currently a Chair Professor in the School of Computer Science & Electronic Engineering, University of Essex, UK, leading the Network Convergence Laboratory (NCL). He is also an affiliated professor of Nanjing University, China. His main research interests include wireless networks and communications, future Internet and edge computing. In particular he is interested in energy aspects of future communication systems and AI for wireless. He has managed research projects funded by UK EPSRC, EU FP7/H2020, and industries. He has published 400+ papers and filed 20 patents. He serves on the editorial boards of a number of IEEE journals (e.g., IEEE ComMag, TNSE, TVT). He is a Deputy Editor-in-Chief of IET Smart Cities Journal. He has been a Judge of GSMA GLOMO Award at World Mobile Congress – Barcelona since 2019. He was a Distinguished Lecturer of IEEE ComSoc (2020-2021).
He is a Member of Academia Europaea (MAE), a Fellow of IEEE, a Fellow of IET and a Distinguished Member of ACM.
Title: AI-enabled Self-driving Communication Networks
Abstract: Modern Artificial Intelligence (AI) has proven to be a powerful enabler that has gained success in many vertical fields. There is a clear evidence of determined effort in the communication and network community to explore the AI power to deliver 6G mobile network’s promises of being faster, greener and smarter. This talk starts with a brief introduction of 6G mobile communication systems, and then looks into how new AI technologies, and in particular machine learning, come into play in 6G from different perspectives. It covers new trends in 6G communication research such as data-driven communication system design, semantic communications, digital twin networks (DTN), and large model for wireless networks. One major objective of these researches is to achieve self-driving communication networks where lengthy standardization of such as communication waveforms or protocol design can be somehow reduced or even eliminated, thus enabling 6G to self-drive to versatile requirements from vertical industries.



Prof. Lie-Liang Yang
University of Southampton, UK
IEEE Fellow, IET Fellow, AAIA Fellow

Lie-Liang Yang is the professor of Wireless Communications in the School of Electronics and Computer Science at the University of Southampton, UK. He received his MEng and PhD degrees in communications and electronics from Northern (Beijing) Jiaotong University, Beijing, China in 1991 and 1997, respectively, and his BEng degree in communications engineering from Shanghai TieDao University, Shanghai, China in 1988. He has research interest in wireless communications, wireless networks and signal processing for wireless communications, as well as molecular communications and nano-networks. On these research topics, he has graduated 35 PhD students and currently supervises 5 PhD students, and has also supervised 150+ master projects. He has published 400+ research papers in journals and conference proceedings, authored/co-authored four books and also published 10+ book chapters. The details about his research publications can be found at https://www.ecs.soton.ac.uk/people/llyang. He is a fellow of the IEEE, IET and the AAIA, and was a distinguished lecturer of the IEEE VTS. He served as an associate editor to various journals, and is currently a senior editor to the IEEE Access and a subject editor to the Electronics Letters. He also acted different roles for organization of conferences.



Prof. Shuanghua Yang
University of Reading, UK
IET Fellow, IEEE Senior Member

Shuang-Hua Yang received his BSc degree in instrument and automation and the MSc degree in process control from the China University of Petroleum (Huadong), Beijing, China, in 1983 and 1986, respectively, and the PhD degree in intelligent systems from Zhejiang University, Hangzhou, China, in 1991. He is currently professor and the Head of Department of Computer Science at University of Reading, UK, and the Director of the Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet, based at the Southern University of Science and Technology, China. His research interests include cyber-physical systems, the Internet of Things, wireless network-based monitoring and control, and safety-critical systems. He is a fellow of IET and InstMC, UK, and a senior member of IEEE. He was awarded a Doctor of Science, degree, a higher doctorate degree, in 2014 from Loughborough University to recognize his scientific achievement in his academic career. He was awarded the 2010 Honeywell Prize by the Institute of Measurement and Control in the UK in recognition of his contribution to home automation research. He is also an Associate Editor of IET Cyber-Physical Systems: Theory and Applications.
Title: Accident identification and localization for water distribution networks
Abstract: Efficient management of water distribution networks (WDNs) is crucial for ensuring water quality and mitigating losses from contamination and leaks. Traditional methods for contamination source identification (CSI) encounter computational challenges, while leak detection techniques struggle with multi-leak scenarios, and localization methods face challenges in achieving precise real-time results. To address these limitations, we propose DLGEA, a novel framework integrating deep learning and evolutionary algorithms for CSI, and a leak detection and localization framework via online change-point detection and leak sensitivity modeling. DLGEA utilizes a deep neural network (DNN) model trained on simulated contamination events to guide evolutionary algorithms by optimising the search space, thus enhancing CSI efficiency. The proposed leak detection and localization framework integrates 1D-convolutional auto-encoder (1D-CAE) with an online changepoint detector, sequentially discounting normalized maximum likelihood (SDNML), for real-time detection in multi-leak scenarios. Leveraging leak sensitivity modeling and 1D-CAE, it enhances localization accuracy while minimizing computational cost. Evaluation on benchmark datasets demonstrates the superior performance of our frameworks, showcasing its potential for enhancing WDN management and security.