The development of cloud computing and the Internet of Things (IoT) makes the network boundary generalization, the conventional perimeter-based architecture defense is hard to realize, and the traditional gateway identity and access control system is difficult to deal with new threats. The zero-trust architecture does not trust the enterprise network behind the firewall to be secure, assumes vulnerabilities, and verifies each resource request as if it came from an uncontrolled network. No matter where the request comes from or what resource is accessed, zero-trust takes the principle of "never trust, always verify".
The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of zero-trust technology and network information security. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.
Lightweight identity authentication protocol, dynamic access control, Secure information transmission, Cryptographic protocols, Trust assessment, Software defined boundary
HAITAO XU, received the B.S. degree in communication engineering from Sun Yat-Sen University, China, in 2007, the M.S. degree in communication system and signal processing from University of Bristol, UK, in 2009, and the Ph.D. degree in communication and information system from University of Science and Technology Beijing, China, in 2014.
From 2014 to 2016, he was with the Department of Communication Engineering, University of Science and Technology Beijing, Beijing, China, as a Post-doc. Currently, he is a professor with the Department of Communication Engineering, University of Science and Technology Beijing, Beijing, China. His research interests include information security, wireless resource allocation and management, wireless communications and networking, dynamic game and mean field game theory, big data analysis, and security. Dr. Xu has co-edited a book titled Security in Cyberspace and co-authored over 50 technical papers. He has been serving in the organization teams of some international conferences, e.g. CCT2014, CCT2015.
XIAOBIN XU is a Lecturer at the Beijing University of Technology. He obtained his Ph.D. at Beijing University of Posts and Telecommunications in 2014. His current research areas include Internet of Things and space-terrestrial integrated networks.
Title: Mobile Edge Artificial Intelligence for 6G
6G is expected to support a variety of intelligent services, including smart cities, autonomous systems, industrial Internet of Things (IoT), and metaverse, achieving the vision of connected intelligence. Although effective for intelligence distillation, centralized learning requires the transfer of massive data from mobile devices to the server, which leads to both network congestion and privacy leakage. Edge AI featured by supporting model training and inference at the network edge has the potential to enhance the effectiveness and trustworthiness of 6G networks. However, to enable scalable and trustworthy edge AI, many challenging issues need to be tackled, for which advanced communication/networking techniques, low-complexity resource allocation strategies, holistic systems architectures should be developed. Moreover, as sensing, communication, computation, and intelligence should be well integrated, the research on edge AI involves multiple academic disciplines, including machine learning, wireless communications, operation research, and etc.
The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and industry. The other goal is to show the latest research results in the field of edge AI. We encourage prospective authors to submit related distinguished research papers on the subject of mobile edge AI for 6G. Both theoretical and experimental contributions are welcome.
Federated learning and analytics; Edge inference;Communication-efficient edge training; Edge AI for intelligent radio resource allocation; Experimental testbeds for edge AI; Edge learning models, algorithms and architectures; Wireless network techniques for edge training; Model compression for edge inference.
YUANMING SHI received the B.S. degree in electronic engineering from Tsinghua University, Beijing, China, in 2011. He received the Ph.D. degree in electronic and computer engineering from The Hong Kong University of Science and Technology (HKUST), in 2015. Since September 2015, he has been with the School of Information Science and Technology in ShanghaiTech University, where he is currently a tenured Associate Professor. He visited University of California, Berkeley, CA, USA, from October 2016 to February 2017. Dr. Shi is a recipient of the 2016 IEEE Marconi Prize Paper Award in Wireless Communications, and the 2016 Young Author Best Paper Award by the IEEE Signal Processing Society. He is an editor of IEEE Transactions on Wireless Communications and IEEE Journal on Selected Areas in Communications. His research areas include optimization, statistics, machine learning, wireless communications, and their applications to 6G, IoT, and edge AI.
ZHOU YONG received the B.Sc. and M.Eng. degrees from Shandong University, Jinan, China, in 2008 and 2011, respectively, and the Ph.D. degree from the University of Waterloo, Waterloo, ON, Canada, in 2015. From Nov. 2015 to Jan. 2018, he worked as a postdoctoral research fellow in the Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, Canada. He is currently an Assistant Professor in the School of Information Science and Technology, ShanghaiTech University, Shanghai, China. He was the track co-chair of IEEE VTC 2020 Fall and the general co-chair of IEEE ICC 2022 workshop on edge artificial intelligence for 6G. His research interests include edge AI, 6G, and Internet of Things.
YOULONG WU obtained his B.S. degree in electrical engineering from Wuhan University, Wuhan, China, in 2007. He received the M.S. degree in electrical engineering from Shanghai Jiaotong University, Shanghai, China, in 2011. In 2014, he received the Ph.D. degree at Telecom ParisTech, in Paris, France. In December 2014, he worked as a postdoc at the Institute for Communication Engineering, Technical University Munich (TUM), Munich, Germany. In 2017, he joined the School of Information Science and Technology at ShanghaiTech University. He obtained the TUM Fellowship in 2014 and is an Alexander von Humboldt research fellow.His research interests in Communication Theory, Information Theory and its applications e.g., coded caching, distributed computation, and machine learning.
DINGZHU WEN received Bachelor degree and Master degree from Zhejiang University in 2014 and 2017, respectively, and received Ph. D. degree from The University of Hong Kong in 2021. Subsequently, he joined ShanghaiTech University. Currently, he is an assistant professor at School of Information Science and Technology. His research interests include task-oriented communications, edge intelligence, integrated sensing, computation, and communication, over-the-air computation, and in-band full-duplex communications.
Title: Integration of Communications and Sensing towards 6G Networks
The evolution from 5G to 6G networks embracing many new emerging technologies, among which integration of communications and sensing (ISAC) brings new vigor in the research community. Specifically, integration of communications and sensing motivates multiple promising research topics empowering functions of the mobile networks. For example, integration of communications and sensing enable dual functions of data communications together with radar for object detection, which can sufficiently make use of the precious wireless resources. From the wide sense, the sensing functions can provide valuable information to enhance the performances of communications, and the innovative design for communications systems can significantly assist sensing functions. By harmonizing the originally competing relationship between sensing and communications, smarter networks can be devised in supporting diverse services nowadays as well as inspiring new applications in future. Moreover, the concept of ISAC is not limited to sensing and communications. The interaction between storage, computing, sensing, and communications will become the inevitable tendency to equip 6Gnetworkswithcomprehensiveintelligence. In this respect, this workshop intends to embrace the efforts and contributions from both researchers and practitioners in relevant areas to raise discussions regarding the techniques, applications, implementations, etc., related to ISACwiththe generic sense. We solicit high-quality original research papers addressing the topics including, but not limited to:
Architecture study and design of ISAC;
Information theoretical analysis on ISAC;
Physical layer technique for ISAC, including the waveform, coding, signal, etc.;
Channel measurement and modeling for ISAC;
Antenna and antenna array design for ISAC;
Spectrum analysis and management for ISAC;
Network protocols towards ISAC;
Co-design of ISAC with existing standards, such as 5G, WLAN, etc.;
Co-design of ISAC in sub-6G and mmWave communications;
Resource allocation and management for ISAC;
Security and privacy issues for ISAC;
Physical layer security technique for ISAC;
ISAC with reconfigurable intelligent surfaces;
ISAC in space-air-ground integrated networks;
ISAC in vehicular to everything (V2X) networks;
ISAC with artificial intelligence-based approaches;
Prototype or implementation of ISAC;
Field test or simulation of ISAC;
Hardware design and system of ISAC.
Selected high-quality papers can be recommended to special issues of SCI-indexed journals. Note that the recommended papers still need to go through rigorous peer review processes and the journal version of the paper needs to have evident improvement and expansion over the conference version.
QINGHE DU received the B.S. and M.S. degrees from Xi’an Jiaotong University, China, and the Ph.D. degree from Texas A&M University, USA. He is currently a Professor with School of Information and Communications Engineering Department, Xi’an Jiaotong University. His research interests widely cover the area of wireless communications and networking with emphases on 5G/6G, statistical QoS provisioning, secure wireless transmissions, IoT, machine learning and big data over wireless networks. He has published over 100 technical papers. He received the Best Paper Awards of IEEE GLOBECOM 2007, China Communications 2017 and 2020, IEEE/CIC ICCC 2021, and IEEE COMCOMAP 2019, respectively. He serves and has served as an Associate Editor of IEEE COMMUNICATIONS LETTERS, an Area Editor of KSII Transactions on Internet and Information Systems, an Editor of Electronics.
XIAO TANG received the B.S. degree in information engineering and Ph.D. degree in information and communication engineering from Xi’an Jiaotong University, Xi’an, China, in 2011 and 2018, respectively. He is currently an Associate Professor with the Department of Communication Engineering, Northwestern Polytechnical University, Xi’an, China. His research interests include wireless communications and networking, game theory, and physical layer security. He has published over 50 technical papers in refereed internal journals and conferences. He has served as Guest Editors for multiple international journals and served as a Co-Chair for IEEE VTC Spring 2022 Workshop on IoT/CPS-Security.
CHAO XU received the B.S. degree in electronic information engineering and the Ph.D. degree in information and communication engineering from Xidian University, Xi'an, China, in 2009 and 2015, respectively. He was a Postdoctoral Researcher from 2015 to 2017 with the School of Telecommunications Engineering, Xidian University. He is currently an Associate Professor with the School of Information Engineering, Northwest A&F University, Yangling, China, and employed as a researcher with the Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling. He was recognized as an Exemplary Reviewer of the IEEE WIRELESS COMMUNICATIONS LETTERS in 2020. His current research interests include AoI analysis and optimization, and machine learning for wireless communications and Internet of Things (IoT) networks.
Feng Ke received the B.S., M.S., and Ph.D. degrees in electronic engineering from South China University of Technology (SCUT), China. He is currently an Associate Professor with the School of Electronic and Information Engineering, SCUT, China and serves as the Vice Director for the Intelligent Communication Network and Computer Engineering Technology Engineering Center of SCUT. His research interests include communications and information theory, wireless body area networks, edge computing and machine learning for wireless networks. He received the best paper awards of IEEE ICCC 2019. He has published over 60 technical papers and obtained more than 20 authorized patents.
Title: Optical Wireless Communication and Power Transfer
The ground-breaking optical wireless communication and power transmission techniques have gained significant attention from both academia and industrial experts in recent decades. Powering remote systems through laser diodes to either operate devices or recharge batteries offers several benefits. Remote laser diodes can remove burden of carrying extra batteries and can also reduce mission time by removing battery swap-time and charging. Particularly, laser power transfer (LPT) has been getting attention to power Unmanned Aerial vehicles (UAVs). However, there are several critical concerns such as misalignment, harsh atmospheric conditions and low transfer efficiency.
This workshop aims to bring together the research accomplishments provided by leading researchers from academia and the industrial experts. To further promote the development of these areas, we invite researchers to contribute original research manuscripts as well as review articles on recent advances in the design and performance analysis of innovative systems that employ the concepts of OWC and OWPT. Articles dedicated to the integration of these concepts with emerging Intelligent Reflecting Surfaces (IRS) or UAVs are highly preferred.
Optical Wireless Communication (OWC), Optical Wireless Power Transfer (OWPT), Laser Power Transfer (LPT), Simultaneous Wireless Information and Power Transfer (SWIPT), Solar Cell
Syed Agha Hassnain Mohsan has Doctoral degree in Marine Information Science and Engineering from Ocean College, Zhejiang University. He is serving as Guest Editor for Sensors and Micromachines. He has worked as a peer reviewer for Chinese Optics Letter, Optical and Quantum Electronics, Drones, Sensors, Energies, Electronics and several other SCI/SCIE journals. He has served as an invited speaker and TPC member for several International Conferences. His research interests include Optical Wireless Communication, Wireless Power Transfer, UWSNs, IoUT, NOMA, IRS, and 5G/6G technology. He has published more than 40 papers in OSA, Springer Nature, IEEE, SPIE, MDPI and Elsevier etc.
Title: Non-Orthogonal Multiple Access for 6G
Due to the limited spectrum resource, multiple access design is one of the most challenging problems for each generation of wireless networks. Among the multiple access families, non-orthogonal multiple access (NOMA) is a promising technique since it accommodates multiple users over the same resource block, thus enhancing the connectivity and spectrum efficiency. Recall the fact that the communication requirements in 6G are stringent, it force the current NOMA to evolve into the next generation with the aid of new techniques.
This workshop focuses on attracting novel and solid contributions on the emerging topic of NOMA for 6G. Both theoretical and more applied contributions are solicited, covering, but not necessarily limited to, the following topics:
Fundamental limits and performance analysis of NOMA
Advanced channel coding and modulation for NOMA
NOMA for ultra-reliable low-latency communication (URLLC)
XIDONG MU received the Ph.D. degree in Information and Communication Engineering from the Beijing University of Posts and Telecommunications (BUPT), Beijing, China, in 2022. He is currently a Postdoctoral Researcher with the School of Electronic Engineering and Computer Science, Queen Mary University of London, U.K. His research interests include non-orthogonal multiple access, IRSs/RISs aided communications, integrated sensing and communications, and optimization theory. He received the Exemplary Reviewer Certificate of the IEEE Transactions on Communications in 2020. He serves the Conference Symposium and Workshop Officer for IEEE ComSoc Next Generation Multiple Access Emerging Technology Initiative (NGMA-ETI).
Tianwei Hou received Ph.D. degree from Beijing Jiaotong University (BJTU) in 2021. He was a visiting scholar at Queen Mary University of London (QMUL) (Sep. 2018-Nov. 2020). Since 2021, he has been an associate professor at BJTU. Dr. Hou’s current research interests include next generation multiple access (NGMA), reconfigurable intelligent surface (RIS) aided communications, UAV communications, multiuser multiple-input multiple-output(MIMO) communications, and stochastic geometry. He received the Exemplary Reviewer of the IEEE COMMUNICATIONLETTERS and the IEEE TRANSACTIONSON COMMUNICATIONS in 2018 and 2019. He has served as a TPC Member for many IEEE conferences, such as GLOBECOM,VTC,etc.Hehasservedasaco-chairforIEEE2022-FallVTCworkshop.Hehas served as a publicity officer of IEEE Next Generation Multiple Access Emerging Technologies Initiatives (ETI).
Data and applications security and privacy have rapidly expanded as a research field with many important challenges to be addressed. With rapid global penetration of the Internet and smartphones and the resulting productivity and social gains, the world is becoming increasingly dependent on its cyber infrastructure. Criminals, spies and predators have learned to exploit this landscape much quicker than defenders have advanced in their technologies. Security and Privacy have become an essential concern of applications and systems throughout their lifecycle. Security concerns have rapidly moved up the software stack as the Internet and web have matured. The security, privacy, functionality, cost and usability tradeoffs necessary in any practical system can only be effectively achieved at the data and application layers. This special session provides a dedicated venue for high-quality research in this arena and seeks to foster a community with this focus on cyber security.
Topics included but not limited to:
- Application-layer security policies - Access control for applications - Access control for databases - Data-dissemination controls - Data forensics - Data leak detection and prevention - Enforcement-layer security policies - Privacy-preserving techniques - Private information retrieval - Search on protected/encrypted data - Secure auditing
- Cyber threat modelling and analysis
- IoT security
- Blockchain and security
- Cloud-edge security and privacy
- Cryptography and cryptoanalysis
- Steganography and steganalysis
- Big data security and privacy
- Machine learning and AI in cyber security; - Security and privacy in healthcare - Security and privacy in the Internet of Things - Security policies for databases - Social computing security and privacy - Social networking security and privacy - Trust metrics for applications, data, and users - Usable security and privacy - Web application security
Dr S. SHITHARTH completed his PhD in the Department of Computers Science & Engineering, Anna University. He is currently pursuing his Postdoc at The University of Essex. He has worked in various institutions with a teaching experience of eight years. Now, he is working as an Associate Professor at Kebri Dehar University, Ethiopia. He has published in more than 45 International Journals and 20 International & National conferences. He has even published four patents in IPR. He is also an active member of IEEE Computer Society and in 5 more professional bodies. He is also a member of the International Blockchain organization. He is a certified Hyperledger expert and certified blockchain developer. His current research interests include Cyber Security, Blockchain, Critical Infrastructure & Systems, Network Security & Ethical Hacking. He is an active researcher, reviewer and editor for many international journals.
Tianbo Wang received the Ph.D. degree from the Beihang University. He is currently an associate professor with the School of Cyber Science and Technology, Beihang University. He has participated in several National Natural Science Foundations and other research projects as a Contributor. His research interests include network and information security, intrusion detection technology, system security. and information countermeasure. He has published a number of papers, such as IEEE Transactions on Dependable and Secure Computing (TDSC), IEEE Transactions on Information Security and Forensics (TIFS)，IEEE Transactions on Mobile Computing and ACM Computing Surveys (CSUR) . He also served as TPC member for some International Conferences, such as ISPA 2019 and Trustcom 2022.
As the emerging and promising candidates for 6G, visible light communications, and other emerging wireless optical techniques are earning increasing attention and investigation from both the wireless communication and the optical communication communities. These various and distinct hybrid techniques open the novel research opportunities for numerous application scenarios, including but not including but not limited to indoor wireless communications, indoor positioningand sensing, wireless backhaul, vehicular applications, underwater wireless applications, satellite applications, underground wireless applications, high speed train applications, healthcare wireless applications, retro reflection communications applications, unmanned aerial vehicular wireless applications, and other developing fields.
The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. The main goal is to show the latest research works in the field of visible light communications & positioning, hybrid optical wireless techniques, especially for empowering 6G development. We encourage prospective authors to submit related research papers on the following subjects: Emerging Wireless Optical Communications, Visible Light Communications & Positioning & Sensing, Hybrid Optical Wireless, Free Space Optics, Radio Over Fiber, Thz Communications, and B5G&6G.
Emerging Wireless Optical Techniques, Visible Light Communications, Wireless Optical Communications, Wireless Optical Positioning, Wireless Optical Sensing, Free Space Optics, Radio Over Fiber, Thz Communications, 6G
JUPENG DING received the M.Sc. and Ph. D. degree from the Communication, Beijing University of Posts and Telecommunications (BUPT), Beijing, China. In 2013, he joined Key Laboratory of Wireless-Optical Communications, Chinese Academy of Sciences, Optical Wireless Communication and Network Center, School of Information Science and Technology, University of Science and Technology of China, Hefei, China. In 2017, he worked in one world top 500 enterprises. Then he joined Xinjiang University working on optical wireless communication. His current research interests include visible light communication, optical wireless links, B5G&6G mobile systems & networks, and free space optics.
Now he is an Associate Professor in College of Information Science and Engineering, Xinjiang University. He has published more than 50 journal and conference papers and holds more than 20 national and international patents, in most of which he worked as the first author, the first author & corresponding author, or the first inventor. He works as one active reviewer of numerous high level journals including IEEE Wireless Communications Magazine, IEEE Communications Magazine, IEEE Communications Letters, IEEE Wireless Communications Letters, IEEE Journal of Lightwave Technology, Elsevier Optics Communications, OSA Optics Letters, and OSA Optics Express. He is a senior member of Chinese Institute of Electronics, Chinese Optical Society, and China Institute of Communications.
Title: Impact of Cyber Twin and Blockchain Innovations for Industrial Applications Utilizing AI
As Within the current era, the concept of cyber twin innovation has been developing as a progressed stage for distinctive applications. -is the work emphasizing analyzing the impact of cyber twin innovation for fabricating hardware in Industry 4.0 applications by understanding three distinctive basic goals? For the proposed conception the unused framework shown is distinguished for integrating tri objective cases with a manufactured insights calculation. In expansion, high-security measures are too consolidated utilizing blockchain technology which is one essential necessity for mechanical applications for making genuine twins. Both frameworks' demonstration and algorithm have been combined for giving an effective performance in real-time employing a physical substance. The effectiveness of the proposed model is tried with a sensor model and reenacted with four scenarios where the anticipated demonstration gives way better performance for more than 72% when compared with existing techniques.
Artificial intelligence, Blockchain, Cyber twin, Industry 4.0, Wireless networks
AMRUTH RAMESH THELKAR, received the Bachelor of Engineering(B.E.) degree in Electrical & Electronics Engineering from Visvesvaraya Technological University, India in 2006, the Master of Technology(M.Tech), degree in Electronics & Communication Engineering: Industrial Electronics, from Sri Jayachamarajendra College of Engineering, Mysore, Karnataka - Visvesvaraya Technological University, India, in 2008, and the Ph.D. degree in Electrical & Electronics Engineering: Control and Solar, Information system from Shri .JJT University(In collaboration with NIE Mysore, Visvesvaraya Technological University-Belgaum, Karnataka) – Rajasthan, India, in 2015.
From 2009 to 2015, he was with the NIE-Institute of Technology and Sri Venkateshwara College Engineering, Visvesvaraya Technological University Belgaum, Karnataka, India, as a Faculty. Currently, he is a professor with the Department of Electrical & Computer Engineering Department(Control & instrumentation Stream): Jimma Institute of technology, Jimma University, Ethiopia. His research interests include information security, wireless sensor resource and Advanced control applications, wireless communications and networking, dynamic system analysis, UAV, AI and cyber security. Dr. Amruth Ramesh Thelkar has contributed a research work for book titled Advanced practical approaches to web mining techniques and applications-Design of wireless IoT sensor node and platform for fire detection and Authored, Correspondence, co-authored over 40 technical papers. He has been serving in the organization teams of some international conferences, Session chair & International Research advisory committee member(“International Conference on Computation, Data Intelligence and Security (ICC-DIS)-2021” at G H Raisoni College of Engineering and Management, Pune). • National Research advisory committee member for ICIRST-21)” (IFERP)),Addids ababa ,Ethiopia. • Reviewer for the journals of Elesvier, applied science and engineering (Jase)/ CMCComputers, Hindawi, IJECE, Materials & Continua, USA/Hindawi Journal/Oriental journal of computer science & Technology (OJCST).(Scopus Indexed, SCI & Web of sciences). • Worked as coordinator in establishment of Center of Excellence research department at NIEIT, Mysore. • Worked as coordinator in MOU with Centre for Wind Energy Technology (CWET), Chennai.
Title: Intelligent UAV-assisted Wireless Data Networks
Unmanned aerial vehicles (UAVs) can provide a promising solution for collecting sensory data of the geo-distributed ground devices in human-unfriendly environments, enhancing network scalability and connectivity. For the data communication, the drone can patrol and physically approach a ground device to achieve a line-of-sight (LoS) air-ground connection, thereby enabling a high data rate under all terrains and saving the transmission energy of the ground devices.
We aims to organize this workshop to attract new and solid applications and technologies on the UAV-assisted wireless data networks with artificial intelligence techniques, such as machine learning, deep learning, and deep reinforcement learning, in Internet of Things (IoT), smart cities, cyber-physical systems, vehicular networks, 5G/6G networks, mobile computing systems, etc
Unmanned aerial vehicles, data communications, IoT, security and privacy, energy harvesting, trajectory planning, resource management, machine learning, deep learning, deep reinforcement learning.
Kai Li, received the B.E. degree from Shandong University, China, in 2009, the M.S. degree from The Hong Kong University of Science and Technology, Hong Kong, in 2010, and the Ph.D. degree in computer science from The University of New South Wales, Sydney, NSW, Australia, in 2014.
Dr Li is currently a Senior Research Scientist with CISTER research center, Porto, Portugal. He is a CMU-Portugal Research Fellow, which is jointly supported by Carnegie Mellon University (CMU), USA, and FCT, Portugal. In June 2022, he was a Visiting Research Scholar with CyLab Security and Privacy Institute at CMU, Pittsburgh, PA. Prior to this, he was a Postdoctoral Research Fellow with the SUTD-MIT International Design Centre, The Singapore University of Technology and Design, Singapore, from 2014 to 2016. He was a Visiting Research Assistant with ICT Centre, CSIRO, Australia, from 2012 to 2013. From 2010 to 2011, he was a Research Assistant with Mobile Technologies Centre, The Chinese University of Hong Kong, Hong Kong.
Dr. Yong Niu is the Associate Professor of State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
Journal Paper Reviewer of IEEE Journal on Selected Areas in Communications, IEEE Communications Magazine,IEEE Transactions on Mobile Computing, IEEE Transactions on Wireless Communications,IEEE Transactions on Vehicular Technology, IEEE Transactions on Communications, IEEE Transactions on Green Communications and Networking, IEEE Internet of Things Journal, IEEE Transactions on Network Science and Engineering, IEEE Transactions on Cognitive Communications and Networking, IEEE Communications Letters, Mobile Networks and Applications, IEEE Access,Wireless Communications and Mobile Computing.
Attendee of IEEE 802 Plenary Session, Waikoloa, July 2015.
Title: Machine Vision andautonomous driving
Machine vision is an important branch of computer science, involving many fields such as computer, image processing, pattern recognition, artificial intelligence, signal processing, optical electromechanical integration and so on. It is widely used in engineering practice, such as product appearance inspection, defect detection, assembly integrity detection, texture recognition, face recognition, tracking and positioning, unmanned driving, etc. Autonomous driving can help prevent traffic accidents by changing the basic driving mode of cars, liberate people from a large amount of driving time and reduce carbon emissions, and it will also become a part of road traffic. The required data is provided by the camera and sensor, which are processed by the computer in a fraction of a second. Autonomous vehicle in a full sense rely on the cooperation of artificial intelligence, visual computing, radar, monitoring devices and global positioning system, so that computers can operate motor vehicles automatically and safely without any human.
The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of machine vision technology and autonomous driving. We encourage prospective authors to submit related distinguished research papers on thefollowing subject: originaltechnology and practical case on machine vision technology and autonomous driving.
DONGYUAN GE, received the M.S. degree in Mechanical design and theory from Wuhan University of Technology, China, in 2004, and the Ph.D. degree in mechanical manufacturing and automation from South China University, China, in 2013.
Currently, he is a research fellowshipwith the College of mechanical and automotive engineering, Guangxi University of Science and Technology, China. His research interests include machine vision, machine learning, and intelligent transportation.
Title:Innovative AIoT Technology Development and Applications
Looking forward to the industry trend in 2022, AI and IoT will rapidly merge and evolve into Intelligent IoT (AIoT). Under the trend of the Internet of Things, the amount of data in each application field will become larger and larger in the future. The AioT processor must be able to perform the calculation results in an instant and efficient manner, and the overall system construction efficiency can be generated. AIoT's high efficiency and high flexibility must rely on perfect system design. The application of IoT is rapidly expanding. The materials and components used in the future will increase rapidly. In response to this trend, system verification must also be improved. The system emphasizes high integration, so not only the chip, material, and system architecture must be fully verified. The role of communication in the Internet of Things is also very important. The application environment of the Internet of Things is different. Designers must choose the appropriate communication standard for their system characteristics. With the accelerated introduction of intelligent applications in various fields, the industrialization of AIoT has taken shape. The industry has begun to actively develop in the past years, and the development of AIoT is still worth our expectation.
Industry 4.0, AIoT, Wireless Sensor Networks, Cloud Computing, IoT(Internet of Things), Artificial Intelligence.
Wen-Tsai Sung is working with the Department of Electrical Engineering, National Chin-Yi University of Technology as a Distinguishedprofessor and Dean of Academic Affairs. He received a PhD and MS degree from the Department of Electrical Engineering, National Central University, Taiwan in 2007 and 2000. He has won the 2009 JMBE Best Annual Excellent Paper Award and the dragon thesis award that sponsor is Acer Foundation. His research interests include Wireless Sensors Network, Data Fusion, System Biology, System on Chip, Computer-Aided Design for Learning, Bioinformatics, and Biomedical Engineering. He has published a number of international journal and conferences article related to these areas. Currently, he is the chief of Wireless Sensors Networks Laboratory. At present, he serves as the Editor-in-Chief in three international journals: International Journal of Communications (IJC), Communications in Information Science and Management Engineering (CISME) and Journal of Vibration Analysis, Measurement, and Control (JVAMC), he also serves as the other international journals in Associate-Editor and Guest Editor (IET Systems Biology)
Title:Metaverse in Business
According to Wikipedia, in futurism and science fiction, the metaverse is a hypothetical iteration of the Internet as a single, universal and immersive virtual world that is facilitated by the use of virtual reality (VR) and augmented reality (AR) headsets. In colloquial use, a metaverse is a network of 3D virtual worlds focused on social connection. Research firm Gartner predicted that 25% of people will spend at least one hour a day in the metaverse by 2026. And with metaverse technology platforms predicted to become a tremendous market - up to the $800 billion by 2024, according to a December 2021 Bloomberg report. Metaverse will transform the way people interact with other people, the way people interact in the digital worlds. The metaverse is a vision of the future that allows for a massive set of opportunities for consumers to interact with businesses and vice versa. Metaverse will transform entire industries, such as: business operations, new revenue streams, advertising, branding and marketing opportunities, enhanced customer experiences immersive entertainment, enhanced education and training.
Professor Dr. Dimiter Velev is with the Department of Information Technologies and Communications at the University of National and World Economy (UNWE), Sofia, Bulgaria. He is also the Director of the Science Research Center for Disaster Risk Reduction at UNWE. Prof. Velev is a member of the International Federation for Information Processing (IFIP), in which he is the Vice-chair of IFIP TC5 New Activities and Interdisciplinary Research and the Secretary of IFIP WG 5.15 Information Technology in Disaster Risk Reduction. Since January 2022 Prof. Velev is the chair of the IFIP Domain Committee on Quantum Computing. Prof. Velev’s main areas of academic and R&D interest are Information Technology, Web Science, Cloud Computing, Mobile Computing, Online Social Networks, Integrated Information Systems for Disaster Management, Artificial Intelligence, Cybersecurity, XR, QC.
Title:Practicality of self-tuning α-βfilter with unknown parameter
In order to study the practicability of the self-tuning α-β filter with unknown parameters,taking the target tracking system as an example, theoretical analysis and experimental verification of the practicability of the filter are carried out in different situations. When the parameter estimation is stable and convergent, the self-tuning α-β filter is convergent and effective in infinite time, and has practicality; when the parameter estimation does not converge, the self-tuningα-β filter is effective and practicalbefore the definite collapse point. A quick judgment method of elementary function for predicting the moment of collapse is proposed, which effectively expands the practical range of the self-tuningα-βfilter.
HENG LI is an associate professor at Fuyang Normal University. His research areas are self-tuning filter and system identification.He won the prize of Anhui Province Teaching Achievement Award in 2021, won the Fuyang Normal University Teaching Achievement Award in 2019, and was named Fuyang Normal University Teaching Achievement Award in 2019.
Title:Nature-inspired algorithms, improvements, and applications
Along with the development of science and technology of our world, we are facing more and more complicated problems in both dimensionality and scalability. Analytical solutions might be no longer accessible, on the contrary, stochastic methods play more important role in such conditions. Being metaheuristic, the nature-inspired algorithms have been proved to be efficient and easy to find the best solutions for most complicated problems. More than three hundred of them have been proposed, yet none of them could solve all of the existed problems causing the No Free Lunch (NFL) theorem. We are still under demand of new algorithms, even their improvements. And more applications were introduced to verify their capabilities.
The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of nature-inspired algorithms and understand how they work in both benchmark functions and the real-world engineering problems, including their improvements in capabilities. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.
Zheng-Ming Gao, received his B.S. degree in Rocket launching engineering in 2002, M.S. degree in rocket engine in 2005, Ph.D. in nuclear engineering in 2010 from Xi'an Research Institute of High Technology. From 2010 to 2018, he served as an engineer in Baoji Research Institute of High Technology. From 2018, he served as a lecture with Jingchu University of technology. His research interests include the nature-inspired algorithms and design optimization.
Mr. GAO is now served as Member of Youth Editorial Committee of Journal of Ordnance Equipment Engineering, and Member of the Youth Work Committee of CAAI, Chairman of Jingmen Greenby Network Technology Co., Ltd. He has finished eight major national defense projects, one provincial natural research project, six City Hall level projects. He has published more than eighty papers, sixties of them have been indexed in SCI/EI, he also occupied more than 50 patents and 40 software copyrights, he has published seven monographs by now. He is now the leader of the “Research team of machine learning and its applications of Jingchu university of technology”, chairman with an institute of intelligent information technology, Hubei Jingmen industrial technology research institute; chairman with an institute of intelligent computation technology, Jingchu university of technology.
Mr. GAO has been serving in the organization teams of some international conferences, e.g. MLISE2021, MLMI2022, ISSCEIC2022.
Title:Multisource Information Fusion in a Complex and Uncertain Environment
With the development of science and technology, more and more sensors are used to detect data to affect all aspects of people's lives. Therefore, the detection accuracy of the sensor has also been improved again and again, but the comprehensive processing of the detection results of multiple sensors is not enough, which will also cause errors in comprehensive research and judgment. The research on multi-source information fusion has made considerable progress in recent years, not only in the field of real numbers, but also in multi-source information fusion and even quantum information processing in the field of complex numbers.
Evidence theory, Complex evidence theory, Uncertain information modeling and decision-making, Quantum information processing, Sensor data fusion
Fuyuan Xiao is a Professor with the School of Big Data and Software Engineering, Chongqing University, Chongqing, China. Dr. Xiao has published many papers in prestigious journals and conferences, including IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Information Sciences, and so on. Her current research interests include information fusion, uncertain information modeling and decision, intelligent information processing, and quantum information processing. Dr. Xiao also serves as a reviewer for several prestigious journals, such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Cybernetics, and IEEE Internet of Things Journal, etc. She has been serving in the organization teams of some international conferences, e.g. FSDM 2022, PCCNT 2023, DMBD 2022 and IEEE ICA 2022.
Yong Deng received the Ph.D. degree in precise instrumentation from Shanghai Jiao Tong University, Shanghai, China, in 2003. He has been a Full Professor with the Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, Chengdu, China. He has published more than 100 articles in refereed journals, such as Decision Support Systems, the European Journal of Operational Research, and Scientific Reports. His research interests include evidence theory, decision-making, information fusion, and complex system modeling. He served as a Program Member of many conferences, such as the International Conference on Belief Functions. He served as a member of many editorial boards, such as an Academic Editor of PLOS One. He served as a Reviewer for more than 30 journals, such as the IEEE Transactions on Fuzzy Systems. He has received numerous honors and awards, including the Elsevier Highly Cited Scientist in China for the period of 2014–2022.
Masayoshi Aritsugi is a Professor of Big Data Science and Technology, Division of Informatics and Energy, Faculty of Advanced Science and Technology, Kumamoto University, Japan. He received his D.E. degree in computer science and communication engineering from Kyushu University, Japan, in 1996. His research interests include database systems and parallel/distributed data processing. He was a recipient of IEEE COMPSAC Best Paper Award in 2015 and IEEE International Conference on Signal Processing Best Paper Award in Image Processing and Understanding in 2016. He is a senior member of IEICE and IPSJ, and a member of ACM, IEEE, and DBSJ.
Title:Construction and Metrological Analysis for Multi-index Evaluation Systems
This workshop mainly carries out the quality evaluation of traditional Chinese medicine, food and drug quality evaluation, pharmaceutical analytical informatics, chemical analysis, food analysis, determination and quality analysis of agricultural product ingredients, food quality and safety risk assessment, tea composition analysis, statistics, chemometrics, data analysis, food and drug safety and evaluation, scientific research on inspection technology, and technical work related to food and drug quality standards, etc. The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry and solicit high-quality original research papers addressing the topics.
Quality evaluation of traditional Chinese medicine, Food and drug quality evaluation, Drug analysis informatics, Chemical analysis, Food analysis, Agricultural product composition determination and quality analysis, Food quality and safety risk assessment, Tea composition analysis, Statistics, Chemometrics, Data analysis
Professor Libing Zhou is a professor of Guangxi Science & Technology Normal University since August 2019, a professor of Guizhou Institute of Technology from 2013 to 2019, an associate professor of Qianxinan Vocational and Technical College for Nationalities from 2008 to 2012, and an associate professor of Bozhou Vocational and Technical College from 2012 to 2013.He is mainly engaged in multi-disciplinary cross-integration research such as quality evaluation of traditional Chinese medicine, food and drug quality evaluation, drug analysis informatics, chemical analysis, food analysis, agricultural product composition determination and quality analysis, food quality and safety risk assessment, tea composition analysis, statistics, chemometrics, data analysis, etc. At present, he is a part-time project evaluation expert of the Ministry of Science and Technology of the People's Republic of China, and an evaluation expert of the expert database of multi-provincial science and technology departments. He presided over and participated in many scientific research projects and published over 100 papers. 7 SCI journal papers were accepted in 2022, and 5 were currently published. In recent years, more than 20 series of papers are published every year.
Title: Image Processing and Computer Vision based on Machine learning and Deep learning
Visual analysis and machine learning are two important techniques in most academic, industrial, business, and medical applications. Visual analysis including image/video processing and computer vision systems is closely related to various fields, such as automatic navigation, intelligent robots and smart healthcare, etc. Machine learning has obtained great success in vision, graphics, natural language processing, gaming, and controlling.
Lei Chen received the B.Sc. and M.Sc. degrees in electrical engineering from Shandong University, Jinan, China, and the Ph.D. degree in electrical and computer engineering from University of Ottawa, Ontario, Canada. He is currently an Associate Professor with the School of Information Science and Engineering, Shandong University, China. His research interests include image processing and computer vision, visual quality assessment and pattern recognition, machine learning and artificial intelligence. He was the principal investigator of projects granted from the National Natural Science Foundation of China, National Natural Science Foundation of Shandong Province, China Postdoctoral Science Foundation, etc. He has published more than 30 papers on top international journals and conferences in recent years including IEEE TIP, Signal Process., ICME, etc. He was awarded the Future Plan for Young Scholars of Shandong University. He served for the ICIGP 2021, ICIGP 2022, IoTCIT 2022, and MLCCIM 2022 as Technical Co-Chair or Publicity Co-Chair.
Title: Holographic Communication: Application Scenarios, Enabling Technologies, and System Architectures
With the evolution of the mobile communication systems towards the sixth generation or 6G, it is anticipated that holographic communication will come into reality, holographic data will be transmitted, distributed, and accessed through the network, and holographic scenarios and applications will abound here and there in society. For example, holographic telepresence will allow people to beam themselves to a location thousands of miles away and interact with people there. However, in order to shape up holographic communication, it requires to overcome a whole bunch of challenges on all fronts, such as the generation of hologram, transmission of the holographic data, and rendering for display.
Recently, the learning based methods has emerged as a paradigm shift from the conventional simulation / optimization-based methods for computer-generated hologram, achieving a promising trade-off between quality and runtime. The holographic projection systems have constantly improvd with larger and faster spatial light modulators and phased array photonic integrated circuit. In addition, the superb performance of ultra-high bandwidth and extremely-low latency, which 6G is supposed to provide, will largely satisfy the demand of these holographic applications for immersive experience with massive amounts of data and real-time interactions.
Challenging but promising, it seems that holographic communication will make a huge impact on the society in the near future, fully taking advantage of the ubiquitous intelligence, distributed computing power, and extremely high network performance of 6G. In this respect, this workshop seeks to bring together academic researchers, industrial practitioners, and individuals working on this exciting research areas to share their innovative ideas, latest findings, and bold visions, and discuss the potential use cases, open research problems, technical challenges, and solution methods in the context.
Holographic Communication, Computer Generated Hologram, Spatial Light Modulator, Metaverse, AR/VR/XR, 6G, Edge Computing, Cloud Computing, Internet of Things, Artificial Intelligence.
Fengyou Sun received his PhD degree in Information Security and Communication Technology from NTNU–Norwegian University of Science and Technology, Trondheim, Norway in 2020. He is currently working at the Department of Computer Science and Technology, Zhejiang Normal University. His research interests include applied probability and stochastic process, especially queueing theory and stochastic network calculus, and their applications to wireless channels and computer networks. Recently, he has been doing research on the smart utilization of the wireless channel resources, like stochastic dependence, and the emergence of disruptive applications and the corresponding traffic engineering mechanisms, such as holographic communication.
Title: Underwater Communication Methods and Technologies
Telecommunication technologies are crucial for the whole world, as we live interconnected on a daily basis. Radiofrequency and optical waves are examples of these technologies, but, unlike their use on land, their applicability in underwater environments is highly challenging due to the intrinsic characteristics of water. The importance of communication between two underwater devices lies in the speed and capacity to analyze information in real time in a receiver. This can be applied in different applications, e.g., autonomous underwater robots, coastal or reef monitoring stations, etc
Electronics, data transmission, underwater communication, telecommunications.
Nixon Jimenez, Delond, received the B.S. degree in Mechatronics engineering from Universidad Tecnologica Centroamericana, Honduras, in 2022. From Feb. 2022 to the current date, he is the professor for mechatronics at Instituto Tecnológico de Excelencia Educativa, and Lab Instructor at Universidad Tecnologica Centroamericana. His research interests include but not limited to robotics, telecommunications and artificial intelligence. He has published an article titled “Computer vision neural network using YOLOv4 for underwater fish video detection In Roatan, Honduras”.
Title: Drone-aided network intelligence techniques for disaster management
In disasters such as earthquakes, fires or floods, the rapid, effective and reliable communication and emergency response could greatly alleviate economic loss and save lives. However, the communication network infrastructures tend to be collapsed or limited functioning in such circumstances. Drones as flying robots, with high mobility and agility, can assist as flying base stations or mobile relays to serve a disaster area out of the reach of the cellular networks.
Most existing drone-aided communication network systems for disaster management support only a single drone feature with limited capacity or naively operate multiple drones manually by pilots, experiencing the issues of non-cooperation, long delay bottleneck, energy consuming and limited adaptability to dynamic disaster environment. There are important unaddressed technological issues in the literature, regarding the drone-user interaction for path planning, drone-drone cooperation for sustainable service provision, and onboard energy allocation for balancing both hovering time and service capacity according to energy consumption and dynamic user demands.
Aware of such limitations, a future drone network should be intelligent enough to provide fast and reliable communication services to rescuers and victims in case of cellular coverage collapse. Researchers from academia and practitioners from the industry are invited to submit their cutting-edge original research and review articles on drone-aided network intelligence techniques in disasters.
XIAO ZHANG, obtained his PhD degree from City University of Hong Kong in computer science in 2016. Currently, he is associate professor with South-Central University for Nationalities, Wuhan, China. He was a visiting scholar/researcher with Utah State University, Utah, USA and University of Lethbridge, Alberta, Canada. During 2016-2019, he was a Postdoc Research Fellow at Singapore University of Technology and Design. He has published over 40 papers on flagship journals and conferences, such as IEEE Trans. on Cybernetics, IEEE Trans. on Mobile Computing. One of them was awarded as Second Prize of Outstanding Research Paper in 2020 by Computer Academy of Guangdong in China. He is the founding Chair of International Workshop on Mobile Computing, Algorithms and Network Economics (MCANE) and Editor of two UAV networking-related academic journals (Frontiers in Space Technologies and Frontiers in Communications and Networks). He is a regular reviewer for IEEE JSAC, IEEE/ACM Trans. on Networking, IEEE TMC, IEEE Wireless Communication, IEEE Communications Letters, INFOCOM 2018, MobiHoc 2018. He served as TPC member of IFIP Networking 2021, IEEE GLBOECOM 2020, WCNC 2019, 2020, 2021, ICC 2018 Workshop-UAVs in 5G. In addition, he has been invited to give talks at the renowned international conferences, including EMO 2015, ISAAC 2016, GLBOECOM 2017, 2019, 2020, CEC 2020.
Title:Research on Human behavior Sensing Technology based on WIFI signal
With the development of computing technology, the machine-centered computing mode is shifting to the human-centered computing mode. The future development direction is to make people become a part of the computing link, promote the integration of the physical world and the information world, and realize high-level human-computer interaction. Accurate perception and interpretation of human behavior are essential technical support. In recent years, with the increasing number of WiFi hotspots deployed and the wide application of WiFi in the field of perception, especially positioning, human behavior sensing technology based on WiFi signal has attracted extensive attention. Its basic principle is that when WiFi signal meets the human body in the process of transmission, phenomena such as reflection, refraction, diffraction and scattering occur, which will cause disturbance to the normal propagation of the signal.
By analyzing the received signal and detecting the characteristics of signal disturbance change, the state of the human body encountered in the process of signal transmission can be perceived. WiFi behavior sensing is based on existing communication devices and makes use of WiFi signals widely existing in the environment, which has good universality and scalability. Perception method with the traditional human behavior, such as computer vision perception technology, infrared sensing technology and special sensors technology, based on the WiFi signal perception technology of human behavior with non line-of-sight, passive perception (do not need to carry sensing), low cost, easy deployment, without being limited by the light conditions, strong expansibility and so on a series of advantages.
Bin Chen, Associate professor, Master degree of Beijing University of Posts and Telecommunications, visiting scholar of Peking University, presided over the Science and Technology Fund project of Yunnan Provincial Education Department and the Science and Technology Department Project. Published academic papers are: the time sensitive collaborative filtering algorithm based on restrictive random walk, the application of artificial intelligence technology in the field of network security, network data protection based on space confusion nearest neighbor query methods, multi-node network time series data similarity measure algorithm and data applications, computer network attack grey evaluation model and algorithm, etc. More than 20 academic papers. Such as neural network algorithm analysis in computer network model, computer network information security protection strategy and its key technologies, etc., participated in the construction of the digitalization project of Lijiang City Government in Yunnan Province. Lijiang ancient town, including "smart mind", lijiang, epidemic prevention and control platform, lijiang area monitoring for the rational use of drugs and prescriptions circulation service platform, lijiang national health information platform construction projects of all digital platform for the local digital construction put forward the professional opinion, especially in view of the system platform of user privacy, information security, network security issues such as effective feasible solutions are put forward. He has been serving in the organization teams of some international conferences, e.g. Imodern electronic technology,IoTBDSC 2022, CECNet2022.
Yijin Shi , Associate professor, graduated from Yunnan University. Since 2008, he has worked in Lijiang Institute of Culture and Tourism, and is now the Deputy Director of Scientific Research Department. Visiting Scholar of Peking University, "One Hundred Digital Informatization Talents of Lijiang", "Reserve Talents of Young and Middle-Aged Academic and Technical Leaders of Lijiang". Of the 15 papers published, 2 were searched by EI and 1 by CPCI. Publication of a textbook (deputy editor). He has presided over and participated in more than 10 scientific research projects, applied for 3 patents and 2 software copyrights. Over the past five years, more than 300,000 yuan of scientific research experience has been obtained. In 2014, he was awarded the "Excellence in Scientific Research Award of Peking University". His research interests include computer application technology and artificial intelligence.
Title:Advanced Deep Learning with Applications in Precision Medicine
Precision medicine combines biomedicine and bioinformatics and deploys data-driven machine learning techniques for data analysis. The multi-modal complex medical big data with the intertwined feature relationships need to be tackled using novel statistics methods instead of traditional statistical trials.Deep learning was once considered a “black box”. However, it works better than simple statistical methods and traditional machine learning. Recently, big data-driven deep learning techniques have developed rapidly and achieved impressive performance in several fields, including imaging, automatic speech recognition, and bioinformatics. Precision medicine is becoming an increasingly important application. In particular, interpretable deep learning neural networks have been well explored recently, showing great potential to provide more insights into the disease mechanisms.
Advanced deep learning models in health informatics，Feature representation learning algorithms in disease diagnosis，Medical image processing with deep learning，Server and database construction
Tao Zhou, Prof. Dr. Tao Zhou is an professor at School of Computer Science and Engineering, North Minzu University, Yinchuan, China. He is also a Ph.D. supervisor in Institute for Medical Informatics at University of Huaqiao, Fuzhou, China. He obtained his PhD degree from Computer Science and technology, Northwestern Poly-technology Univ. in China. His research interests include pattern recognition, machine learning, machine vision, medical image analysis. He has published over 200 academic papers, in which, as first and corresponding authors, he has published 3 books, 2 ESI Papers . Furthermore, he has obtained 4 authorized patents.
Title:Affective Computing Based on Computer Vision
Affective computing is a rapidly growing multidisciplinary field that explores how technology can understand the emotional state of human, how interaction between humans and technologies can be impacted by affect, how systems can be designed to utilize affect to enhance capabilities, and how sensing and affective strategies can transform human and computer interaction. Image and video are capable of carrying huge resourceful cues of human affection, including facial expression, posture, action, and gaze etc. Computer vision based affective computing explores the cues of human affection carried by image and video, and develops algorithms and systems to recognize, interpret, process, simulate, and utilize human affects.
The aim of this workshop is to bring together the latest research advancement and emerging applications of computer vision based affective computing to spark future work. Interest topics include, but not limited to:
Algorithms and features for the recognition of affective state from face and body gestures
Methods for multi-modal recognition of affective state
Tools and methods of annotation for provision of emotional corpora
Dong Zhang received his B.S.E.E. and M. S. degrees from Nanjing University, China, in 1999 and 2003, respectively, and Ph.D. degree from Sun Yat-sen University, China, in 2009. From 2008 to 2009, he was as a visiting scholar at the department of Electrical and Computer Engineering of Brigham Young University, Utah, U.S.A. He is currently an associate professor of the School of Electronics and Information Technology, Sun Yat-sen University. His research interests include image processing, computer vision, affective computing, and information hiding. He serves and served a guest editor of Electronics, MDPI, and a co-editor of International Journal of Advanced Robotic Systems.
Title:Resource Allocation for sensor system
The current smart city contains many sensor device, such as the sensors for data collection. Each device has its own resource requirements. We are interesting to better performance, lower costs and lessened environmental impact. Thus, some higher efficiency resource allocation techniques are necessary for mono-static/multi-static sensor systems.
The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. We encourage prospective authors to submit related distinguished research papers on the subject of the following topics (but not limited to):
Sensors deployment for targets detection and tracking.
Task planning for multi-function sensor/sensor network.
Sensor resource allocation for target tracking.
Phase unwrapping and its application.
Frequency sharing for multi-function sensor/sensor network.
Radar based target detection and recognition technology
Spectral efficiency of reconfigurable intelligent surface aided sensor
Millimeter wave radar intelligence perception and imaging
Tianxian Zhang, received the B.S. and Ph.D. from University of Electronic Science and Technology of China (UESTC) in 2009 and 2015, respectively. He is currently a professor with the School of Information and Communication Engineering, UESTC. His main research interests include radar signal processing, multi-function waveform design, multi-objective optimization. He has published more than 30 scientific articles in refereed journals, such as IEEE Transactions on Signal Processing, IEEE Transactions on Geoscience and Remote Sensing, IEEE Transactions on Aerospace and Electronic Systems, etc.
Xiaoping Li, received his master and Ph.D. degrees in communication and information engineering from Xi’an Jiaotong University. He worked as an associated professor at the School of Mathematical Sciences, University of Electronic Science and Technology of China. His research interests include signal processing, coding theory. He participated in the National Natural Science Foundation and Chinese National Programs for High Technology Research and Development, etc. He published more than 20 papers.
Xueting Li received the B.S. and Ph.D. degrees from the University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2013 and 2020, respectively. She is currently an assistant research fellow with Sichuan University. Her research interests include signal detection, multi-sensor resource management, and multi-function integrated system resource optimization.
Title:Research of Rail Transit Dispatching System Based on Multi-network Fusion
According to the requirements of multi-level rail transit and multi-network integration in metropolitan area, multi-level rail transit should have the characteristics of public transportation and network operation. Therefore, the signal dispatching system of multi-level rail transit should also be designed according with the operational requirements from public transportation and multi-network. In this paper, a design scheme of signal operational dispatching system for multi-network fusion is presented, based on the integration of National Railway mainline CTC (Centralized Train Control) dispatching system and urban rail transit ATS (Automatic Train Supervision) dispatching system. This paper puts forward the scheme of rail transit signal dispatching system for multi-network integration and independent from train control system for the first time, which provides a top-level design for the real integration of mainline and urban rail transit dispatching system in the future.
Multi-level rail transit, multi-network fusion, dispatching system
HUAXIANG LIU, received the B.S. degree in transport engineering from Hefei University of Technology, China, in 2004, and the M.S. degree in transportation planning and management from Tongji University, China, in 2007.
Mr. Liu has long been engaged in product development and project management of urban rail train control system and railway signal system. He is currently a senior engineer and has presided over and participated in the design of signal system for many urban rail and national railway lines, and has received a number of related fields of scientific and technological progress awards, invention patents and published papers.
YUE DAI is a technical product certification supervisor of Casco. Signal Ltd., and is also a professional auditor in the signal system field of China Railway product certification industry. She obtained her B.S. degree in network engineering from Nanjing University of Posts and Telecommunications, in 2011, and the M.S. degree in engineering in software engineering from the University of Science and Technology of China, in 2015.
DONGHAI WANG, obtained his B.S. degree in automatic control from Beijing Jiaotong University, in 2002, and the M.S. degree in traffic information engineering and control from Beijing Jiaotong University, in 2005. His current research areas include urban rail transit automatic train control system, automatic train driving and OCC intellgence dispatching system.
Title:Research on multi-source heterogeneous big data application methods, models and algorithms
With the continuous development of information technology, digital economy has become an important driving force of modern social development. Information technologies such as big data, Internet of Things, artificial intelligence and blockchain are conducive to improving the planning, decision-making and management capabilities of agro-industrial production through the analysis, modeling and visualization of big data on product production, operation and sales. This theme is oriented to the major needs of digital economy development, combining IoT and AI, researching the core algorithm of intelligent data collection and edge computing for the whole process of product production, realizing intelligent control, precise operation and scientific management of agro-industry; based on deep learning algorithm, using multi-source heterogeneous data of product production and sales, researching product production optimization prediction model and precise marketing data decision model; using digital twin Using digital twin technology, we will study the visualization model of product production, help production managers and researchers to realize the digital generation of products, and provide modern basic means for the digital economy; integrate the Internet of Things with blockchain and other technologies to achieve credible and controllable product quality based on the data information of the whole process of the whole industry chain, and provide effective means for product quality and safety management.
Main submission scope.
1. Production data intelligent collection and edge computing core algorithm design
2. Prediction and decision modeling of product production and marketing big data
3. Key technologies of crop breeding for high-throughput phenotype detection
4. Intelligent system for product authentication and production information based on blockchain technology
Computer visualization modeling, machine learning, artificial intelligence optimization algorithms, blockchain technology, Internet of things
Wenlong Yi, PhD, is an associate professor and master's student supervisor. He graduated from Saint Petersburg Electrotechnical University (one of the first "Priority 2030" universities in Russia). His main research interests include: natural scene visualization modeling, machine learning, and blockchain technology. He is currently working in the School of Software of Jiangxi Agricultural University and the Key Laboratory of Agricultural Information Technology of Jiangxi Higher Education Institution. He is also a member of the Intelligent Agriculture Committee of the Chinese Society of Artificial Intelligence, a senior member of the Chinese Computer Society, a professional member of IEEE, a correspondence reviewer of the National Natural Science Foundation of China, and a dissertation reviewer of the Degree Center of the Ministry of Education. He has taught "C Programming" and "Data Structures" for undergraduate students and "Big Data in Agriculture" for graduate students. He has hosted one National Natural Science Foundation of China (NSFC) and nine provincial-level projects. He has published more than 40 research papers as the first or corresponding author, including 20 SCI and EI retrievals; published one monograph and one undergraduate textbook; been granted four national invention patents as the first inventor and more than 30 software copyrights; actively participated in academic exchange activities of famous universities and research institutions at home and abroad, and made more than 10 presentations in academic conferences at home and abroad.
Title:Integration of Satellite-Aerial-Terrestrial Networks
Due to the seamless connectivity and high data rate, Aerial and Space Communication has been viewed as a key element to bring real-time, higher capacity communication and wider coverage in the connection and deployment of a plethora of applications such as smart grids, Internet-of-Things (IoT), wireless sensor networks, space-based cloud for big data, and vehicular ad-hoc networks. It is viewed as a key element in emergency rescue for earthquakes and fire disasters, and transoceanic communication that current terrestrial communications cannot cover.
Aerial and Space Communication is the key point of the Beyond 5 Generation (B5G) networks, which have already attracted much attention. Nevertheless, owing to the inherent nature of satellite broadcasting and coverage of huge areas, Aerial and Space Communications can easily be exposed to various security issues. Secure information transmission has aroused extensive interest from the wireless communications community in order to prevent eavesdroppers from taking advantage of the broadcast nature of the radio propagation medium to intercept confidential messages.
The theme of this topic is to investigate secrecy transmission in Aerial and Space Communication’, and potential topics may include, but are not limited to the following:
Security protocol for aerial and space communications
B5G network safety measures
Developments in cloud storage security
Interception prevention in aerial and space communications
Developments in radio propagation mediums
Security concerns and studies on radio communication
Satellite broadcasting and security research
Satellite UAV-assisted Communications
RIS-based Satellite Communications
Covert Satellite Communications
Security protocol for aerial and space communications; B5G network safety measures; Developments in cloud storage security; Interception prevention in aerial and space communications; Developments in radio propagation mediums; Security concerns and studies on radio communication; Satellite broadcasting and security research; Satellite UAV-assisted Communications
KEFENG GUO received his B.S. degree from Beijing Institute of Technology, Beijing, China, in 2012, the M.S. degree from PLA University of Science and Technology, Nanjing, China, in 2015 and the Ph.D. degree in Army Engineering University of PLA in 2018. He is a Lecturer in School of Space Information, Space Engineering University. He is also the associate professor in the College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics. He has authored or coauthored nearly 60 research papers in international journals and conferences. His research interests focus on cooperative relay networks, MIMO communications systems, multiuser communication systems, satellite communication, hardware impairments, cognitive radio, NOMA technology and physical layer security. He was the Guest Editor for the special issue onIntegration of Satellite– Aerial– Terrestrial Networks of Sensors.
Dr. Guo has been the TPC member of many IEEE sponsored conferences, such as IEEE ICC, IEEE GLOBECOM and IEEE WCNC.
HAIFENG SHUAI received the B.S. degree from Tianjin University of Science and Technology, Tianjin, China, in 2017, and the M.S. degree from Space Engineering University, Beijing, China, in 2019, where he is currently pursuing the Ph.D. degree. His research interests include satellite-terrestrial networks, non-orthogonal multiple access, wireless communication systems, and multiuser communication systems.
RUI LIU received the B.S. degree from Space Engineering University, Beijing, China, in 2019, where he is currently pursuing the Ph.D. degree. He has authored or coauthored nearly 20 research papers in international journals and conferences. His research interests include on satellite-terrestrial networks, cognitive radio systems, NOMA technology, cooperative relay networks, wireless communication systems, and multiuser communication systems.
Title: Advanced Radar Signal Processing Technology for Remote Sensing
Radar is an important tool for long-distance detection and perception, and has important applications in many key fields such as sensing space, earth and sea. The increasingly complex environment and targets have put forward higher requirements for radar signal processing technology. Radar signal processing includes waveform design, clutter recognition and suppression, target detection, classification and recognition, etc. Improvements in radar performance rely on fine-grained processing at each step. In recent years, the rapid development of advanced signal processing theories and methods has brought many possibilities to the update of radar signal processing technology. Therefore, it is very necessary and meaningful to study and discuss the advanced radar signal processing technology.
Radar signal processing, Waveform design, Clutter recognition and suppression, Target detection, Target classification, Target recognition
Shuwen Xu (IEEE Senior Member), received the B.Eng. and Ph.D. degrees, both in electronic engineering, from Xidian University, Xi’an, China, in 2006 and 2011, respectively. He worked at the National Laboratory of Radar Signal Processing, Xidian University, after that. He worked as a visiting professor in Mcmaster University in 2017 and 2018, Canada. He is currently a professor with the National Laboratory of Radar Signal Processing, Xidian University. He is also the vice director of National Collaborative Innovation Center of Information Sensing and Understanding and the Director of radar signal processing and data processing Department. His research interests are in the fields of radar target detection, statistical Learning, and SAR image processing. He has published more than 100 related academic papers, and holds more than 40 national patents.
JIAN XUE, received the B.Eng. and Ph.D. degrees, both in electronic engineering, from Xidian University, Xi’an, China, in 2015 and 2020, respectively. He is currently an Associate Professor at the School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications. His research interests include radar clutter suppression, radar target detection, machine learning, and intelligent radar signal processing.
Sainan Shi, received the B.S. degree in electrical engineering and the Ph.D. degree in information and signal processing from Xidian University, Xi’an, China, in 2013 and 2018, respectively. She is currently an associate professor with School of Electronic and Information Engineering, Nanjing University of Information Science and Technology. Her research interests include radar signal processing and weak target detection in sea clutter.
Title: Reconfigurable Intelligent Surface for B5G & 6G
Reconfigurable intelligent surface (RIS) or intelligent reflecting surface (IRS) is recently recognized as one of the most promising technologies to effectively increase coverage and improve communication performance for future wireless communications beyond 5G and 6G (B5G & 6G). Specifically, the RIS is a planar surface consisting of an array of passive reflecting elements, each of which can independently induce a controllable reflection coefficient on the incident signal. By installing an RIS on the facade of buildings, an additional virtual line of sight (LoS) link can be established between the base station (BS) and the user. By judiciously adjusting the phase shifts of the reflecting elements, the reflected signal can be constructively superimposed with the signal from the direct path to enhance the desired signal power or destructively mitigate unfavourable signals such as multi-user interference or signal leakage to eavesdroppers. The RIS is less expensive to deploy than conventional active transmitters since its reflecting elements only passively reflect the incoming signal without any signal processing operations and does not require costly and power-hungry radio frequency (RF) chains. Moreover, it is lightweight and has limited layer thickness, so it can be easily integrated into the environment. Despite the above-mentioned appealing advantages of RIS, it also brings some new signal processing challenges such as channel estimation, robust transmission design, angle/position estimation, distributed algorithm design.
Zhengyu Zhu (Senior Member, IEEE) received the Ph.D. degree in information engineering from Zhengzhou University, Zhengzhou, China, in 2017. From October 2013 to October 2015, he visited the Communication and Intelligent System Laboratory, Korea University, Seoul, South Korea, to conduct a collaborative research as a Visiting Student. He is currently an associate professor with Zhengzhou University. He served as an Associate Editor for the JOURNAL OF COMMUNICATIONS AND NETWORKS, the WIRELESS COMMUNICATIONS AND MOBILE COMPUTING, and the PHYSICAL COMMUNICATIONS from 2021.His research interests include information theory and signal processing for wireless communications such as B5G/6G, Intelligent reflecting surface, the Internet of Things, machine learning, millimeter wave communication, UAV communication, physical layer security, convex optimization techniques, and energy harvesting communication systems.
Title: End-to-End Deep Clustering for Social Media Analysis
Mining user-centric social media data streams, i.e. social networks and user-generated multimedia content, offers great opportunities for clustering in unstructured data organization and knowledge discovery. Whereas, the big volume, dynamic, and heterogeneous social media data post challenges in terms of scalability, online learning capability, multimodal information fusion, and robustness to noise and parameter settings.
An integration of deep learning and clustering is seemingly a promising solution where the deep neural networks perform end-to-end feature extraction and the clustering algorithms find optimized data partitions. However, existing methods usually make them in two stages, and it is still an open challenge to learn the features under clustering objectives.
This workshop aims to sort out novel solutions of end-to-end deep clustering and their practical applications for social media analysis. Related topics are not limited to:
Deep features for clustering
Deep subspace clustering algorithms
Adaptive resonance theory and deep learning
Contrastive learning and clustering
User community detection with deep clustering
Social media indexing with end-to-end deep clustering
Multimodal end-to-end deep clustering
Keywords: Clustering, Deep learning, subspace learning, adaptive resonance theory, contrastive learning，Social media analysis
Lei Meng has been Professor in the School of Software of Shandong University since 2020. He was selected into the talent program of Shandong Taishan Scholars, Shandong Outstanding Youth Science Foundation (Overseas), Shandong University Qilu Young Scholars, and won the honorary titles of Chinese Academy of Engineering Frontier Outstanding Young Scholars, ACM Jinan Branch Academic Rising Star, and Shandong Outstanding Youth in Artificial Intelligence. He has published a Springer monograph, more than 50 papers in TKDE, MM, AAAI and other top journal conferences in the field of artificial intelligence, and obtained 7 international and national invention patents. He builds the Multimedia Mining, Reasoning and Generation (MMRC) laboratory to carry out pioneering innovation research in multimedia understanding, cross-modal reasoning, neural rendering, digital twinning and other directions applied for smart family and smart society governance. MMRC has been selected into the "20 New Universities" introduction innovation team plan in Jinan. As the project leader, he presided over 5 projects above the provincial and ministerial level, such as the National key research and development project and the National Natural Science Foundation Youth Fund, with a total research fund of more than 5 million Yuan. He served as the Associate Editor of Applied Soft Computing, the first district journal of Chinese Academy of Sciences, the executive member of Multi-media Committee of China Computer Society (CCF), and the vice chairman of Jinan Sub-Forum of CCF YOCSEF.