Workshops
Workshop 1 : Cybersecurity, Cyberspace Governance, Security Big Data, Artificial intelligence, International Strategy for Cyberspace
Title 1：Cybersecurity, Cyberspace Governance, Security Big Data, Artificial intelligence, International Strategy for Cyberspace
Keywords: Medical Electronics. Medical Imaging. Intelligent Systems. Image and Vision. Artificial Intelligence. Machine Learning. Pulmonary/lung disease
Summary: Current Cyberspace is increasingly becoming pervasive, complex, and ever-evolving due to factors like enormous growth in the number of network users, continuous appearance of network applications, increasing amount of data transferred, and diversity of user behaviors. Understanding traffic and behaviors in such networks is a difficult yet vital task for network management but recently also for cybersecurity purposes. Security big data analysis can, for example, enable the analysis of the spreading of malicious software and its capabilities or can help to understand the nature of various network threats including those that exploit users’ behavior and other users’ sensitive information. On the other hand, cyberspace governance can help to assess the effectiveness of the existing countermeasures or contribute to building new, better ones. Recently, cyber security big data analysis has been utilized in the area of economics of cybersecurity e.g. to assess ISP “badness” or to estimate the revenue of cybercriminals.
This workshop aims 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 cybersecurity technology and understand how governance strategy can influence it. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews. 

Chairs:
Dr. Jian Gong, University of Electronic Science and Technology of China, China
 
Jian Gong received a Ph.D. degree in pattern recognition and intelligent systems from Xidian University. He is a post-doctoral researcher at University of Electronic Science and Technology of China. His research interests include signal detection and parameter estimation in array signal processing. He has presided over 11 national or provincial projects with National Natural Science Foundation of China, the China Postdoctoral Science Foundation, and the Natural Science Foundation of Shaanxi Province. Based on these projects, he has published more than 30 papers and the monographs" Estimation of target angle of MIMO radar in a complex electromagnetic environment".


Dr. Yiduo Guo, Xidian University, China

Yiduo Guo received a Ph.D. degree in electronic science and technology from Air Force Engineering University. He is a full-time Post-Doctoral Research Fellow with the National Laboratory of Radar Signal Processing, Xidian University. His research interests include space-time adaptive processing and MIMO radar signal processing. He has presided over 6 national or provincial projects with National Natural Science Foundation of China, the China Postdoctoral Science Foundation, and the Natural Science Foundation of Shaanxi Province. Based on these projects, he has published more than 40 papers and the monographs "Joint estimation of multi-parameters with bistatic MIMO radar ".
Workshop 2 : Machine Learning and Optimization for Radar Signal Processing
Title 1：Machine Learning and Optimization for Radar Signal Processing
Keywords: coming soon
Summary: Optimization has been playing an essential role in modern radar systems for decades to achieve high accuracy and robustness. With the rapid development of machine learning (ML), radar researchers have started seeking data-driven methods to tackle the increasingly complex electromagnetic environment. In the latest ten years, ML methods have sparked a large amount of research across multiple disciplines including signal processing, data mining and communications. Key techniques such as matrix/tensor factorization, convolutional neural networks and reinforcement learning, etc., have been advancing many classical and timely radar signal processing problems, e.g., target detection and localization, beamforming and radar imaging – to name just a few. This special session aims to bring together researchers and experts from the broader signal processing, data mining, and machine learning communities to address existing and arising challenges in radar signal processing.

The session aims at bringing together top experts from the radar signal processing community using machine learning and optimization as a ‘bridge’. Communicating with experts across different disciplines can oftentimes spark new ideas, discover new applications, and inspire exciting future work. The organizers have a unique position for putting together such a program: They have been working on the disciplines of interest for many years, and they are well-connected in the field of machine learning and optimization methods. The invited contributors are also leading experts or rising stars. This makes cross-disciplinary collaboration likely to happen. Exciting interactions and collaborations are expected to be nurtured during the proposed event. Topics of interest include but are not limited to:
· MIMO Radar
· Array Signal Processing
· Synthetic Aperture Radar Processing
· Radar Target Detection using Deep Learning
· Radar Parameter Estimation using Deep Learning
· Radar Target Recognition
· Radar Waveform Design
· Convex/Non-Convex Optimization
· Structured Tensor/Matrix Optimization
· Deep Convolutional Neural Network
· Recurrent Neural Network
· Reinforcement Learning
· Attention/Transformer Networks
· Supervised/Unsupervised Learning Methods
· Sparse Recovery/Compressive Sensing
· Low-Rank Representation
· Manifold Learning
· Dual Functional Radar and Communication
· Transfer Learning


Chair:
Session Chair 1:
Dr. Cai Wen, Northwest University, McMaster University, China

Dr. Wen received his B.S. degree in electrical engineering and Ph. D. degree in signal and information processing from Xidian University, Xi’an, P.R. China, in 2009 and 2014, respectively. He is currently a full-time Post-Doctoral Research Fellow with the Department of Electrical and Computer Engineering, McMaster University, Hamilton, Canada. He is also an Associate Professor with the School of Information Science and Technology, Northwest University, Xi’an, China. He was selected into the Postdoctoral International Exchange Program of China (top 120) in 2019. His research interests include sensor array signal processing, MIMO radar signal processing, and integrated sensing and communication.


Session Chair 2:
Dr. Yan Huang, Southeast University, China


Dr. Huang received his B.S. degree in electrical engineering and Ph. D. degree in signal and information processing from Xidian University, Xi’an, P.R. China, in 2013 and 2018, respectively. He is currently an Associate Professor at the State Key Lab of Millimeter Waves, School of Information Science and Engineering, Southeast University. Dr. Huang's research interests include machine learning, synthetic aperture radar, image processing, and remote sensing. 



Session Chair 3:
Assoc. Prof. Guimei Zheng, Air Force Engineering University, China

Guimei Zheng was born in Fujian, China in 1987. In 2009 and 2014, he received a B. Eng. degree in biomedical engineering and a Ph. D. degree in signal and information processing from Xidian University, China, respectively. Dr. Zheng was with the Department of Electronic Engineering, Tsinghua University, Beijing 100084, China, as a full-time Post- Doctoral Research Fellow in 2015-2017. He is currently an Associate Professor at the Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, China. His research interests lie in MIMO radar and vector sensor array signal processing. 



Workshop 3 : Artificial Intelligence based Array Signal Processing and Its Applications
Title 1：Artificial Intelligence based Array Signal Processing and Its Applications
Keywords: Sonar/Radar Signal Processing, Machine/Deep Learning for Array Signal and Processing, Artificial Intelligence in Sensor Networks, Microphone Array Signal Processing
Summary: Nowadays, artificial intelligence is largely used to face complex modeling, prediction, and recognition tasks in different research fields. The application of artificial intelligence methods to array signal processing has encountered a big interest in the scientific community in the last decade, with a wide diversification of research topics in relationship with radar and sonar.

The focus is on suitably processing signals from sensors, with solutions such as nonlinear and non-Gaussian signal processing methodologies combined with convex and non-convex optimization, and sensor-based machine learning/deep learning neural networks. In recent years, many algorithms for array signal processing have incorporated some form of intelligence as part of their framework in solving a problem. These algorithms have been widely used on purpose. The aim of this workshop is therefore to provide the most recent advances on the application of novel artificial intelligence algorithms to a wide range of array signal processing tasks in real acoustic environments.
 
This workshop aims 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 signal processing technology. The focus of the workshop will be on a broad range of array signal processing such as sensors, signal, and artificial intelligent processing involving the introduction and development of new advanced theoretical and practical algorithms. Original research and review articles are welcome.

Chair:
Session Chair 1:
Dr. Qi Liu, South China University of Technology, China
Qi Liu received his Ph.D. degree in Electrical Engineering from City University of Hong Kong, Hong Kong, China, in 2019. His research interests broadly lie in multidimensional signal processing, optimization methods, and neuromorphic computing with applications to DOA estimation, robust sparse recovery, spiking neural networks, matrix completion, keyword spotting, image denoising inpainting, as well as MIMO radar. From 2018 to 2019, he was a Visiting Scholar at Department of Electrical and Computer Engineering, University of California, Davis, CA, USA. During this period, he was invited to give talks at University of California, San Diego and California Institute of Technology, respectively. From 2019 to 2022, he worked as a Research Fellow in Department of Electrical and Computer Engineering, National University of Singapore, Singapore. Currently, He is a Professor at the School of Future Technology, South China University of Technology, Guangzhou International Campus. He was the recipient of the “Best Paper Award” at the 2019 IEEE International Conference on Signal, Information, and Data Processing. Dr. Liu serves as the Associate Editor for Digital Signal Processing.

Session Chair 2:
Dr. Hui Cao, Wuhan University of Technology, China


Hui Cao received a Ph.D. degree in electronic engineering from the City University of Hong Kong in October 2017. He is currently a Lecturer with the School of Information Engineering, Wuhan University of Technology. His research interests include statistical signal processing, parameter estimation, and source localization.



Workshop 4 : Enabling Technologies for 6G Wireless Communications
Title 1：Enabling Technologies for 6G Wireless Communications
Keywords: 6G. Wireless Communications. Communication Technology
Summary: With the deployment of the fifth generation (5G) wireless communication systems in full swing, the sixth generation (6G) wireless communications systems have attracted extensive attention from both academia and industry, devoting to identifying critical drivers, performance requirements, and technological innovations. Recently, many advanced techniques have been explored to support the requirements of 6G wireless communications. These technologies include TeraHertz communications, mmWave communications, visible light communications, unmanned aerial vehicle communications, cell-free communications, intelligent reflecting surface communications, space-air-ground-sea integrated communication, holographic communications, covert wireless communications, joint communication and sensing, artificial intelligence, digital twin, wireless power transfer, wireless brain-computer interface, Internet of everything, cloud XR, etc.

This workshop aims to show the latest research results in the field of enabling technologies of 6G wireless communications. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews. 

Chair:
Assoc. Prof. Jin-Yuan Wang, Nanjing University of Posts and Telecommunications, China
Jin-Yuan Wang received his Ph.D. degree in Information and Communication Engineering from the National Mobile Communications Research Laboratory, Southeast University, Nanjing, China, in 2015. From January 2016 to June 2019, he was a Lecturer with the Nanjing University of Posts and Telecommunications, Nanjing, China, where he has been an Associate Professor since July 2019. His current research interest is 5G/6G wireless communications. He has authored/co-authored over 100 journal/conference papers. Dr. Wang is currently serving as the Youth Editorial Board Member of JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY, and the Topic Editor of SENSORS. He has been a Workshop Chair, Track Chair, or Technical Program Committee Member of many international conferences. He also serves as a reviewer for many international journals, such as IEEE JSAC, IEEE TWC, IEEE TCOM, and IEEE TVT.



Workshop 5 : Theory and Technology of Multi-domain Cooperative Localization of Wireless Signals in Complex Scenarios
Title 1：Theory and Technology of Multi-domain Cooperative Localization of Wireless Signals in Complex Scenarios
Keywords: Wireless Positioning, Cooperative Localization, Complex Scenarios, Multi-domain Cooperation
Summary: Wireless signal positioning technology has been widely used in many industrial and information technology fields such as wireless communication, smart city, radio astronomy, aerospace, seismic survey, automatic driving, national defense security, and so on, and is playing an increasingly important role in them. However, in actual physical scenarios, the electromagnetic signal and channel environment are extremely complex, and many factors restrict the performance of wireless signal localization. Multi-domain cooperative processing is an important way to improve the performance of wireless signal localization in a complex electromagnetic environment.

This workshop is dedicated to publishing research papers related to "Theory and technology of multi-domain cooperative localization of wireless signals in complex scenarios", aiming at publishing innovative and cutting-edge theories and applications in this field. Potential topics include but are not limited to the following:
(1)Theory and technology of multi-system cooperative positioning.
(2)Theory and technology of multiple-target cooperative localization.
(3)Direct position determination technique by using signal waveform information.
(4)Wireless signal positioning and detection integration technology.
(5)Wireless signal positioning and identification integration technology.
(6)Theory and technology of cooperative localization in wireless sensor networks.
(7)Theory and technology of wireless cooperative positioning based on machine learning.
(8)Theoretical performance analysis for wireless cooperative localization.

Chairs:

Session Chair 1:
Assoc. Prof. Ding Wang, Information Engineering University, China
Ding Wang received his bachelor's, master's, and doctor's degrees from Information Engineering University in July 2004, July 2007, and December 2011 respectively. From January 2015 to January 2018, he worked as a postdoctoral researcher at Information Engineering University. He worked as an associate professor and doctoral supervisor in the School of Information Systems Engineering, Information Engineering University. He was supported by National Natural Science Foundation of China (Grant No. 62171469, Grant No. 62071029 and Grant No. 61772548), China Postdoctoral Science Foundation (Grant No. 2016M592989), Key Scientific and Technological Research Project in Henan Province (Grant No. 192102210092). His research interests include wireless signal cooperative localization and array signal processing. As the first/corresponding author, he published more than fifty papers in journals indexed by SCI, and as the first author, he published five monographs. He won two second-class prizes and four third-class prizes for provincial and ministerial scientific and technological progress and won the top article award in outstanding S&T Journals of China (F5000) in 2020.

Session Chair 2:
Dr. Jiexin Yin, Information Engineering University, China
Jiexin Yin received her bachelor's, master's, and doctor's degrees from Information Engineering University in June 2011, June 2014, and June 2018 respectively. She worked as a member in the College of Information Systems Engineering, Information Engineering University, and as a postdoctoral researcher at Information Engineering University. She was supported by the National Natural Science Foundation of China (Grant No. 61901526), China Postdoctoral Science Foundation (Grant No. 2019M663997), and the military special projects. Her research interests include wireless signal cooperative localization and array signal processing. As the first/corresponding author, she published more than thirty papers in journals indexed by SCI, and she published three monographs. She won one second-class prize and three third-class prizes for provincial and ministerial scientific and technological progress, and PLA excellent master's and doctoral dissertations. She won the 7th China Youth Scientific and Technological Innovation Award, and "Xiaoping Scientific and Technological Innovation Team" as the team leader.



Workshop 6 : Sparse/Tensor Signal Processing for Radars and Communications 
Title 1：Sparse/Tensor Signal Processing for Radars and Communications 
Keywords: Array Signal Processing, Sensor Array, Tensors, Sparse Signal Processing
Summary: Modern communication and radar systems explore multiple diversities, for instance, spatial diversity, temporal diversity, frequency diversity, polarization diversity. Consequently, the transmitted and/or received signals in communication and radar systems always exhibit a multidimensional structure. In particular, modern communication and radar systems consist of large-scale transmitting/receiving antennas and high speed sampling modules, which leads to a massive volume of data being processed. On the other hand, the inherent characteristics-of-interesting in the array data are often (or approximately) sparse and low rank. Traditional matrix-based signal processing methods do not fully capture such a tensor nature. In comparison, tensor algebra offers fundamental advantages over its matrix counterparts concerning identifiability and uniqueness. Sparse/low-rank tensor signal processing techniques have been applied in the field of radar and communication systems to mitigate the effect of environmental noise, multipath propagation, channel inconsistency of receivers and various interferences. These methods can achieve high target detection performance, suppress the background noise and deal with multipath effects, as well as handle multi-dimensional signals. However, they may also suffer from high computational complexity and model mismatch, etc. Thus, new models and algorithms based on tensor decompositions are needed to improve the performance of communication and radar systems.

This workshop is intended to solicit high-quality contributions in recent advances for sparse/low-rank tensor signal processing in radar and communication systems. Authors are invited to submit original papers presenting new theoretical and/or application-oriented research including algorithms, models, technology and applications.


Chairs:

Session Chair 1:
Assoc. Prof. Fangqing Wen, China Three Gorges University, China
Fangqing Wen was born in 1988. He received his B.S. degree in electronic engineering from Hubei University of Automotive Technology, Shiyan, China, in 2011, and a postgraduate degree in 2013 from the College of Electronics and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China, where he received his Ph.D. degree in 2016. From October 2015 to April 2016, he was a visiting scholar with the University of Delaware, USA. Since 2021, he has been with the College of Computer and Information Technology, China Three Gorges University, China, where he is currently an associate professor. He is a senior member of Chinese Institute of Electronics and a senior member of IEEE. His research interests include MIMO radar, array signal processing, and compressive sensing.


Session Chair 2:
Assoc. Prof. Liangtian Wan, Dalian University of Technology, China

Liangtian Wan received the B.S. degree and the Ph.D. degree in the College of Information and Communication Engineering from Harbin Engineering University, Harbin, China, in 2011 and 2015, respectively. He has been a Research Fellow at the School of Electrical and Electrical Engineering, Nanyang Technological University, Singapore from 2015 to 2017. He is currently an Associate Professor at the School of Software, Dalian University of Technology, China. He has around 70 published papers, such as IEEE TITS, IEEE Wireless Com, IEEE IoTJ, IEEE TII, IEEE TIE, IEEE ICASSP and so on. His current research interests are mainly in the areas of array signal processing, social network analysis, big data, wireless sensor networks, and compressed sensing and its applications. 


Session Chair 3:
Dr. Han Wang, City University of Macau, China
Han Wang (Senior Member, IEEE) received his B.S. degree in electrical engineering from Hubei Minzu University, China, in 2009, the M.S. and Ph.D. degree in information and communication system from Hainan University, Haikou, China, in 2013 and 2017, respectively. He has worked in China Mobile Jiangxi branch as a network engineer for one year （July 2013-Aug. 2014）. He is currently a Postdoctoral Research Fellow at City University of Macau. He is the author of over 30 papers published in related journals. His current research interests include intelligent communication, filter-bank multicarrier communications and information theory. He has served as a reviewer over 20 international SCI journals, including the IEEE Transactions on Communications, IEEE Transactions on Industrial Informatics, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Vehicular Technology, IEEE Wireless Communication Letters, and IEEE Access. He is also the Lead Editor for Special Issue on Artificial Intelligence for Next-Generation Wireless Networks of MOBILE INFORMATION SYSTEMS, Lead Editor for Special Issue on AI-Driven Intelligent Sensor Networks: Key Enabling Theories, Architectures, Protocols, and Techniques of JOURNAL OF SENSORS, Guest Editor for Special Issue on Artificial Intelligence in E-Healthcare and M-Healthcare of JOURNAL OF HEALTHCARE ENGINEERING, Guest Editor for Special Collection on Advanced Machine Learning for Future Internet of Things of 5G Networks in INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS.

Workshop 7 : Wireless Communication and Machine Learning: Architecture, Algorithms, and Applications  
Title 1：Wireless Communication and Machine Learning: Architecture, Algorithms, and Applications
Keywords: Wireless computing, Sensor Data, IoT, Machine Learning, Wireless Sensing
Summary: Internet of Things (IoT) and wireless communication enable the interconnection between billions of devices such as industrial machines, sensors, processes. Users exchange data without any central coordination which enables tremendous applications.

However, it is still an existing challenge for handling large amounts of data since the storage, the processing is limited. Therefore, machine learning and artificial intelligence have become the most promising combination with IoT for better use, storage, and avoiding uncertainty management in decision making. AI in IoT plays a significant role in data collected from IoT devices and wireless signals. For proper utilization of this diverse type of data collected from sensors and wireless channels will offer an efficient solution for the development of products and services to achieve the user’s expectation from different sectors. Machine learning methods play a significant to increase the quality of the data collected from different IoT devices. Despite the various advantages of the integration of AI with different intelligent systems for various industrial applications, the appropriate application of AI poses several challenges concerning data quality, data volume, integration, and accuracy of the inferences drawn from the collected data. In recent decades, machine learning (ML) based methods and technologies have emerged in AI and the convergence of ML and IoT will complement each other to produce a greater impact and availability of different services including healthcare, building monitoring, infant and elder service, and power sectors.

This workshop aims to 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 IoT, AIoT, mobile sensing, and wireless data analysis. We encourage the original and unpublished research and review papers on the subject of heretical approaches and practical case reviews. 


Chairs:

Session Chair 1:
Prof.  Zhenjiang Tan, Jilin Normal University, China

Zhengjiang Tan received his Ph.D. degree from the Chinese Academy of Sciences, Changchun Institute of Optics, Fine Mechanics and Physics in 2003. Currently, he is a professor at Jilin Normal University. His research interests include network security, privacy protection, and network applications. He has published over 30 articles in SCI/EI international conference proceedings and journals.





Session Chair 2:
Dr. Hongyu Sun, Jilin Normal University, China


Hongyu Sun received her Ph.D. from Jilin University, Changchun, China in 2017. From Jan. 2015 to Sep. 2016, she was a visiting scholar at University of Maryland, Baltimore County. Currently, she is an assistant professor at Jilin Normal University. Her research interests include the Internet of Things (IoT), RF-based vision, wireless communications, and mobile computing. She has published over 20 articles in international conference proceedings and journals indexed by SCI/EI.



Session Chair 3:
Prof. Jingqing Jiang, Inner Mongolia Minzu University, China


Jingqing Jiang received her Ph.D. from Jilin University, Changchun, China in 2007. From Sep. 2012 to Sep. 2013, she was a visiting scholar at University of Missouri Columbia, USA. Currently, she is a professor at Inner Mongolia Minzu University. Her research interests include pattern recognition, computational intelligence and green computing. She has published over 30 articles in SCI/EI international conference proceedings and journals.




Session Chair 4:
Dr. Mingyu Bai, Inner Mongolia Minzu University, China



Mingyu Bai is a lecturer at College of Computer Science and Technology in Inner Mongolia Minzu University. His research interests are related to several areas, such as parallel computing, cloud computing, computer network, and network security. He has published several research papers in national and international journals, conference proceedings.




Workshop 8 : Swarm Intelligence  
Title 1： Swarm Intelligence; Nature-inspired Algorithms
Keywords: Swarm Intelligence, Nature-inspired Algorithms, Physics-based Optimization Algorithms; Evolutionary Algorithms; Swarm-based Algorithms; Human-based Optimization Algorithms
Summary: Along with the development of science and technology in our world, we are facing more and more complicated problems in both dimensionality and scalability. Analytical solutions might be not accessible now, and stochastic methods play a more important role in such conditions. Swarm intelligence, or the nature-inspired algorithms, have been proposed for more than dozens of decades, and there have been proposed more than two hundred of them, yet none of them could solve all of the existed problems causing the No Free Lunch (NFL) rule. We are still in demand of new algorithms, even their improvements.

This workshop aims 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 swarm intelligence and understand how the world 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. 

Chair:

Dr. Zhengming Gao, Jingchu University of Technology, Jingmen, China; Institute of Intelligent Information Technology, Hubei Jingmen Industrial Technology Research Institute, Jingmen, China

Zhengming Gao is associate professor at Jingchu University of Technology. He received his D.-Eng. degree in 2010. He now serves as a faculty member with School of computer engineering, Jingchu University of Technology, Member of the Youth Working Committee of the Chinese Association of Artificial Intelligence, Chairman of Jingmen Greenby Network Technology Co., Ltd. He has finished eight major national defense projects, one provincial natural research project, four City Hall level projects. He has published more than eighty papers, of which sixties of them having been indexed in SCI/EI, he also occupied more than 50 patents and 40 software copyrights, he has published six 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. And he is now focusing on intelligent information technology and development.

Workshop 9 : Machine Learning, Blockchain, and Cryptography  
Title 1： Swarm Intelligence; Nature-inspired Algorithms
Keywords: Privacy, Big Data, Security, Blockchain
Summary: Machine learning and Blockchain technologies can mitigate the issues such as slow access of medical data, patient agency, system interoperability, patient agency, improved data quantity and quality of medical research. Blockchain technology is easy to store information in such a way that doctors can see a patient’s entire medical history, but researchers only see statistical data instead of any personal information. The strong consensus mechanisms, decentralization, ultra-secure and immutable ledgers of Blockchain technologies have tremendous potential to rebalance and improve machine learning algorithms.

This workshop aims to bring together the research accomplishments provided by researchers from academia and the Medical field. The other goal is to show the latest research results in the field of machine learning, blockchain and cryptography, involving the introduction and development of new advanced theoretical and practical algorithms, or applying blockchain and machine learning for the enhancement of the e-healthcare system. Original research and review articles are welcome.

Chair:
Assoc. Prof. Guangfu Wu, Jiangxi University of Science and Technology, China

Guangfu Wu was born in Yushan, Jiangxi Province, China, in 1977. He received the B.S. degree in mathematics education from Wuyi University, Guangdong, in 2000, the M.S. degree from the School of Mathematical Sciences, Xiamen University, Xiamen, in 2008, and the Ph.D. degree from School of Information Science and Engineering, Xiamen University, in 2012. Since 2016, he has been an Associate Professor with the School of Information Engineering, Jiangxi University of Science and Technology. He is the author of more than ten articles, and more than ten inventions. Dr. Wu became a member (M) of the Chinese Association for Cryptologic Research (CACR) in 2014. His research interests include coding theory and cryptography, blockchain, and artificial intelligence. He was supported by National Natural Science Foundation of China (Grant No. 11461031), key project of Jiangxi Provincial department of education(Grant No. GJJ170492), State Key Laboratory of Cryptology, ( Grant No.MMKFKT202123 ).


Workshop 10 : Urban Surveillance Radar: The Fundament and Its Applications
Title 1：Urban Surveillance Radar: The Fundament and Its Applications
Keywords: Urban Surveillance Radar, Target Localization, Human Activity Recognition, NLOS Detection
Summary: 
Information collection with surveillance radar is a valuable ability in urban environments. Herein, the surveillance radars have the capacities to provide inferences on building structures as well as the presence, behavior, and vital signs of human targets, even when they are around a corner, both indoors and outdoors. Due to the complex electromagnetic phenomena in urban environments, such as diffraction and multipath reflection, etc., accurate information acquisition of targets in the urban environment is a challenging problem. Advanced theory and technology are highly desired to improve the sensing ability of the urban surveillance radar.

This workshop is dedicated to publishing research papers related to "Urban Surveillance Radar: The fundament and its applications", aiming at publishing the state of art of the most recent progress in urban environment sensing techniques and approaches. Potential topics include but are not limited to the following:
  • Through-the-wall radar
  • Radar based non-line-of-sight target detection
  • Radar based human activity recognition
  • Vital-sign monitoring with radar
  • Indoor target localization with radar
  • Vehicle-mounted millimeter wave radar


Chairs:

Session Chair 1:
Assoc. Prof. Shisheng Guo, University of Electronic Science and Technology of China, China

Shisheng Guo received the B.S. degree in communication engineering from the Nanchang Hangkong University, Nanchang, China, in 2013, and the Ph.D. degree in signal and information processing from the University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2019. He is currently an Associate Research Fellow with the School of Information and Communication Engineering, UESTC. He was supported by the National Natural Science Foundation of China (Grant No. 62001091). His research interests include through-the-wall radar and radar based NLOS target detection. He is a Member of IEEE, the Session Co-chair of ICAUS 2021, ICAUS 2022, and 2019 ICCAIS.




Session Chair 2:
Prof. Yong Jia, Chengdu University of Technology, China

Yong Jia received his master's and doctor's degrees from University of Electronic Science and Technology of China in June 2010 and June 2014 respectively. He was a Visiting Researcher at the Center for Advanced Communications, Villanova University. Currently, he is a professor at Chengdu University of Technology, and he also is a candidate for academic and technical leadership of Sichuan Province. He was supported by the National Natural Science Foundation of China (Grant No. 61501062) and Sichuan Science and Technology Program (Grant No. 2019YFG0097 and Grant No. 2022YFS0531). His research interests include through-the-wall radar detection, non-line-of-sight target detection, and radar based human activity recognition. As the first/corresponding author, he published thirteen papers on journals indexed by SCI. He won one first-class prize of Sichuan Science and Technology Progress Award.



Session Chair 3:
Dr. Yang Yang, Tianjin University, China


Yang Yang received his doctor's degree from Tianjin University in June 2019. He worked as an assistant professor at Tianjin University. He was supported by National Natural Science Foundation of China (Grant No. 62101378 and Grant No. 62171318), and National Key R&D Program of China (Grant No. 2021YFE0204200). His research interests include Deep Learning and micro-Doppler-based human detection and recognition. As the first/corresponding author, he published more than ten papers on journals indexed by SCI. He won three first-class prizes and one second-class prize for provincial and ministerial scientific and technological progress.


Workshop 11 : Image Processing and Pattern Recognition
Title 1：Image Processing and Pattern Recognition
Keywords: Image Processing And Analysis, Pattern Recognition, Computer Vision, Image Retrieval, Machine Learning, Knowledge Representation, Feature Engineering
Summary: 
Image processing and pattern recognition use computer technology and mathematical methods to carry out scientific research on the representation of image and video information, encoding and decoding, image segmentation, image quality evaluation, target detection and recognition and stereo vision. The main research contents include pattern recognition and security monitoring of image and video, medical and material image processing, evolutionary algorithm, artificial intelligence, rough set and data mining. It is widely used in face recognition, fingerprint recognition, optical character recognition, natural language processing and information management systems in many fields.

This workshop aims to bring together the research accomplishments of image processing and pattern recognition provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of image processing and pattern recognition. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews. 

Chairs:

Prof. Huimin Lu, Changchun University of Technology, China
She graduated from Xi'an Jiaotong University and obtained her master’s and doctor’s degree in Computer Science and Technology in 2005 and 2010 respectively. She completed her research at the Postdoctoral Programme in Computer Science and Technology of Jilin University in 2014, and she was a visiting scholar at the University of Missouri-Columbia, the USA from 2016-2018. She is a professor at the School of Computer Science and Engineering of Changchun University of Technology, and a doctoral supervisor in Statistics, Data Science and Artificial Intelligence, and a master's supervisor in Computer Science and Technology, and Electronic Information. Her research interests include Artificial Intelligence and Application, Data Analysis and Mining, Biometric Recognition, Computer Vision and other fields. She has participated in several National Natural Science Foundation, and National 863 Program projects, and she also has presided over several scientific research projects such as the National Natural Science Foundation of China, the Natural Science Foundation of Jilin Province, key R & D, major R & D, and industrial technology R & D of Jilin Province. Based on these projects, she has published more than 50 academic papers indexed by SCI and EI and obtained more than 10 patents and software copyrights. Moreover, she also got the Jilin Provincial Science and Technology Progress Award and the State Federation of Commerce Science and Technology Progress Award.

Workshop 12 : Ubiquitous Sensing----Indoor Detection, Positioning, Communications and More 
Title 1：Ubiquitous Sensing----Indoor Detection, Positioning, Communications and More 
Keywords: Indoor Sensing, Vital Signs Detection, People Counting, Gait Recognition, Microdoppler Feature, Person Identification
Summary: 
Smart sensing of the indoor environment is the premise of building a smart home. For example, Heating, Ventilation, and Air Conditioning (HVAC) control sensing systems based on smart sensors are the key point of building low-carbon intelligent residential buildings. Due to the advantages of protecting privacy, being immune to different light conditions, being able to detect both stationary and moving objects, radio-based WIFI and radar have attracted much attention in the field of indoor perception. However, the indoor environment is complex and changeable, which makes indoor detection, positioning and communication by a great challenge. In particular, the influences from indoor multipath, nonhumans targets (e.g., pets, fans, curtains, sweeping robots, etc.), and random movement of limbs, seriously affect the performance of indoor detection, positioning, communications. How to effectively eliminate these interferences has become a bottleneck in the development of indoor detection, positioning, communications.

This workshop will address key technical problems in indoor detection, positioning, and communication, and will focus on the following issues (but not limited to them):
  1. Monitoring of vital signs based on radar and WIFI and its application;
  2. People positioning, people counting, behavior analysis and identification;
  3. Characteristic analysis of common indoor nonhuman moving objects (pets, fans, curtains, sweeping robots, etc.) based on WIFI and radar;
  4. Cancellation of indoor multipath interference and cognition of indoor environment;
  5. Integrated sensing and communication waveform design;
  6. Channel measurement and modeling of indoor environment;
  7. Network architectures and communication protocols;
  8. High-precision integration of sensing, localization and communication-based machine learning/AI/big data;
  9. Integration of sensing, localization and communication based on collaboration.

Chairs:

Session Chair 1:
Assoc. Prof. Chundi Zheng, Foshan University, China

Chundi Zheng received a Ph.D. degree in Information and Communication Engineering from Tsinghua University, Beijing, China, in 2013. He is currently an Associate Professor with the School of Electronic Information Engineering, Foshan University, China. His research interests include radar signal processing, indoor detection, array signal processing, vital sign monitoring, and sparse recovery. He is also the head of a 77GHz radar indoor monitoring product project.




Session Chair 2:
Assoc. Prof. Ningyan Guo, Beijing University of Posts and Telecommunications, China
Ningyan Guo received a Ph.D. degree in Instrument Science and Opto-electronics Engineering from Beihang University, Beijing, China, in 2014. From 2010 to 2012, she was a Visiting Scholar at Department of Aerospace Engineering, University of Colorado Boulder, Boulder, Colorado, USA. From 2014 to 2019, she was a communication systems engineer in the Beijing Research Institute of Mechanical and Electrical Technology. From 2019 to 2021, she was a postdoctoral researcher with the Department of Electronic Engineering, Tsinghua University. Since 2022, she has been an Associate Professor with the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China. Her current research interests include localization algorithms, high-precision positioning techniques, and integrated sensing and communication.


Session Chair 3:
Assis. Prof. Tianyao Huang, Tsinghua University, China

Tianyao Huang received a B.S. degree in 2009 in telecommunication engineering from the Harbin Institute of Technology, Heilongjiang, China, and the Ph.D. degree in 2014 in electronics engineering from the Tsinghua University, Beijing, China, respectively. From 2014 to 2017, he was a radar researcher in Aviation Industry Corporation of China (AVIC). Since July 2017, he has joined Intelligent Sensing Lab, Department of Electronic Engineering, Tsinghua University, as an assistant professor. His current research interests include signal processing, compressed sensing, and joint radar communications system design.
Workshop 13 : Advanced Signal Processing Method For Anti-Jammer Application
Title 1：Advanced Signal Processing Method For Anti-Jammer Application
Keywords: Anti-Jamming, Multiple-Sensor Array Radar, Waveform Diversity, Multi-Dimensional Signal Processing
Summary: 
With the development of electronic system and signal processing theory, it becomes increasingly competitive between modern radar and electronic jammer in recent years. Jamming signal can be modulated in multi-dimensions with space-time coupled manner, which is widely used in defense and civil applications to protect its friends. On the other side, modern radars are developed for performance enhancement within dense electromagnetic jamming environment by using new strategies including waveform diversity and/or agility, sophisticated design of signal recovery, knowledge-based adaptive processing, advanced learning-based processing framework, and so on.

This session is dedicated to the anti-jamming challenge in the radar community, aiming at publishing innovative and cutting-edge theories and applications in this field. Potential topics include but are not limited to the following:
(1) waveform optimization for multiple-input multiple-output radar
(2) advanced coding and frequency/time/coding diverse array radar
(3) waveform agility for jamming mitigation
(4) sparse recovery and compressive sensing based processing method
(5) environment sensing feedback for anti-jamming method

Chairs:

Session Chair 1:
Assoc. Prof. Jingwei Xu, Xidian University, China

Jingwei Xu received a B.S. degree in electronic engineering, and a Ph.D. degree in signal and information processing, both from Xidian University, China, in 2010 and 2015, respectively.  From 2015/12 to 2018/07, he was a lecturer at National Lab of Radar Signal Processing at Xidian University where he now serves as Associate Professor.  From 2017/12 to 2019/12, he was a Postdoctoral Fellow in “Hong Kong Scholar Program” at the City University of Hong Kong. His research interests include radar system modeling, multi-sensor array signal processing, space-time adaptive processing, multiple-input multiple-output radar, and waveform diverse array radar. He has published more than 100 scientific articles in refereed journals, such as IEEE Transactions on Signal Processing, IEEE Transactions on Aerospace and Electronic Systems, etc.




Session Chair 2:
Assoc. Prof. Lan Lan, Xidian University, China
Lan Lan was born in Xi'an, China in 1993. She received her B.S. degree in Electronic Engineering, and a Ph.D. degree in signal and information processing, both from Xidian University, Xi'an, China, in 2015 and 2020, respectively. She has been a visiting Ph. D student at the University of Naples Federico II, Naples, Italy, from July 2019 to July 2020. She is currently a Tenure-track Associated Professor at the National Laboratory of Radar Signal Processing, Xidian University from August 2020. Her research interests include frequency diverse array radar systems, MIMO radar signal processing, target detection, and ECCM. She was a recipient of the Excellent Paper Award at the CIE 2016 International Conference on Radar. She is elected to the Youth Talent Promotion Project supported by China Association for Science and Technology in 2022. She is currently with the editorial boards of Digital Signal Processing.


Session Chair 3:
Assoc. Prof. Yanhong Xu, Xi'an University of Science and Technology, China

Yanhong Xu was born in Shandong Province, China, in 1989. She received a B.S. degree in electronic engineering and a Ph.D. degree in electromagnetic field and microwave technology from Xidian University, Xi’an, China, in 2012, and 2017, respectively. From 2018/01 to 2019/12, she was a Lecturer with the School of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an, China. In 2019/12, she was elected as an Associate Professor in the same institute. During the period from 2018/02 to 2020/02, she is a Postdoctoral Fellow in the State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong. Her research interests include array antenna theory, frequency diverse array, wideband antenna and millimeter wave antenna technology. She was elected as an expert of “Thousand Talents Plan” (Young) of Shaanxi Province in 2020.
Workshop 14 : Recent Advances in Indoor Localization
Title 1：Recent Advances in Indoor Localization
Keywords: Indoor Localization, Simultaneous Localization, and Mapping (SLAM), Machine Learning
Summary: 
Indoor localization has been a hot research topic in the previous decades. Owing to the absence of the GPS signal, a practical approach for indoor localization is to utilize sensor networks. However, owing to the complex propagation channel in indoor environments, there is still room for improving the robustness and stability of the current indoor localization methods. Traditional research mainly focuses on the localization of the indoor object. Recent advances in millimeter wave (mmwave) communications and massive MIMO technology enable simultaneous localization and mapping (SLAM). Moreover, the advances in machine learning make the processing of complex localization problems using a large amount of data possible. 
The workshop aims at publishing original research works on the topic of recent advances in indoor localization. Potential topics include but are not limited to:  
  1. SLAM in indoors;
  2. Massive MIMO for indoor localization;
  3. Indoor localization for IoT applications;
  4. Machine learning methods for indoor localization;
  5. Multi-sensor fusion for indoor localization;
  6. Indoor navigation for robots;
  7. Multi-object localization in indoors;
  8. Indoor rigid body localization.

Chairs:

Session Chair 1:
Prof. Gang Wang, Ningbo University, China
Gang Wang received a B.Eng. degree from Shandong University, Jinan, China, and a Ph.D. degree from Xidian University, Xi’an, China, both in electronic engineering, in 2006 and 2011, respectively. He joined Ningbo University, Ningbo, China, in January 2012, where he is currently a full Professor. From January 2014 to April 2014, he was a Research Associate at the Chinese University of Hong Kong, Hong Kong, China. From June 2018 to June 2019, he was a Visiting Scholar at the University of Missouri, Columbia, USA. His research interests are in the areas of target localization and tracking in wireless networks, array signal processing, and robust ellipse fitting in image processing. Dr. Wang serves as Handling Editor for Digital Signal Processing (Elsevier) and Signal Processing (Elsevier), and Associate Editor for IEEE Transactions on Aerospace and Electronic Systems. He is a member of the IEEE Signal Processing Society Sensor Array and Multichannel (SAM) Technical Committee. He is a Senior Member of the IEEE.

Session Chair 2:
Assoc. Prof. Yangong Zheng, Ningbo University, China

Yangong Zheng is an Associate Professor in the Faculty of Electrical Engineering and Computer Science, Ningbo University, China. He received his Ph.D. from the School of Electronic Science and Technology of Dalian University of Technology in 2014. During 2011-2013, he worked as a visiting scholar in Chemistry Department of The Ohio State University in USA. Currently, his research interests focus on the gas sensor, electronic nose, and gas recognition based on machine learning algorithms. Dr. Zheng serves as Guest Editor for Sensors, Chemosensors, Elctronic in MDPI, and Frontiers in sensors (Frontiers). He is a member of IEEE Sensors Council and IEEE Transmitter.




Session Chair 3:
Assoc. Prof. Ye Tian, Ningbo University, China


Ye Tian received his B.Eng. and Ph.D. degrees from Jilin University, Changchun, China, both in information and communication engineering, in 2009 and 2014, respectively. He won a Huawei scholarship in 2013 and was selected as a young top talent by the Hebei Provincial Department of Education in 2016. He joined Ningbo University in March 2021, where he is currently an Associate Professor. He has published more than 30 international peer-reviewed journal/conference papers and more than 10 patents. His research interests include array signal processing, target localization, and source localization using massive MIMO arrays.

Workshop 15 : Machine Learning and Optimization for Edge Computing
Title 1：Machine Learning and Optimization for Edge Computing
Keywords: Edge Computing, Machine Learning, Network Optimization, Resource Allocation, Signal Processing, Content Delivery
Summary: 
According to the Visualization Network Index released by Cisco, the global mobile traffic has grown at a compound annual growth rate of 46% in the past five years. Therefore, how to achieve low-latency content transmission by effectively allocating heterogeneous network resources is a key issue to be solved urgently in future wireless networks. AI-assisted edge computing can be studied to effectively overcome problems such as high latency and traffic load caused by cloud computing. However, realizing cooperative allocation and intelligent optimization of network resources in edge computing is challenging, and there are still many important open research problems.

This workshop aims 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 edge computing. The focus of the workshop will be on a broad range of edge computing such as resource allocation, content delivery, signal processing, machine learning, and network optimization involving the introduction and development of new advanced theoretical and practical algorithms. Original research and review articles are welcome.

Chairs:

Session Chair 1:
Assoc. Prof. Chao Fang, Beijing University of Technology, China
Chao Fang received his B.S degree in Information Engineering from Wuhan University of Technology, Wuhan, China, in 2009, and his Ph.D. degree from the State Key Laboratory of Networking and Switching Technology in Information and Communication Engineering from Beijing University of Posts and Telecommunications, Beijing, China, in 2015. He joined the Beijing University of Technology in 2016 and now is an associate professor. In 2019, he was selected for Beijing Nova Program of Science and Technology. Moreover, he was a visiting scholar at University of Technology Sydney, Commonwealth Scientific and Industrial Research Organization, Hong Kong Polytechnic University, Kyoto University, and Muroran Institute of Technology. He is a senior member of IEEE and served as the Session Chairs of ICC NGN’2015 and ICCC NMNRM’2021, and Poster Co-Chair of HotICN’2018. He also served on the Technical Program Committee of ISCIT’2016-2019, GreenCom’2019-2020, ICCC’2020-2021, ICCAIS’2019, and so on. His current research interests include future networks, intelligent network control, and edge computing.

Session Chair 2:
Assoc. Prof. Zhuwei Wang, Beijing University of Technology, China

Zhuwei Wang received his B.S. and Ph.D. degrees from the Beijing University of Posts and Telecommunications, Beijing, China, in 2005 and 2011, respectively. He was a Visiting Scholar in Department of Electronic and Computer Engineering at University of California at San Diego from Oct. 2008 to Oct. 2010, and a Postdoctoral Research Fellow in Department of Electrical Engineering at Columbia University in the City of New York from Oct. 2012 to Oct. 2014. Currently, he is an Associate Professor at Beijing University of Technology, Beijing, China. His research interests include Networked Control Systems, Edge Intelligence, Optimization Design, and Real-time Applications.





Session Chair 3:
Assoc. Prof. Zhihao Qu, Hohai University, China


Zhihao Qu received his B.S degree from Nanjing University in 2009, and his Ph.D. degree from Nanjing University in 2018. He joined Hohai University in 2019 and now is an associate professor. Moreover, he was a visiting scholar of Hong Kong Polytechnic University from 2019 to 2020. He is a member of IEEE and served as the Publication Chair of ICPADS 2020. He also served on the Technical Program Committee of GlobeCom’2022, ICA3PP’2021, WASA’2021, INFOCOM workshop ICCN’2021, and ICFC’2020. His current research interests include federated learning, edge computing, and distributed machine learning.

Workshop 16 : Multimedia Signal Analysis and Processing
Title 1：Application of Information Technology in the Field of Human Health Diagnosis and Treatment
Keywords: Multimedia Signal Processing, Biosignal, Physiological Index, Health Diagnosis, Disease Treatment
Summary: 
With the acceleration of the aging process of society, both individuals and governments face a heavy economic burden and manpower shortage problem in the health care field. Conducting health treatment more quickly and effectively is thus an important but daunting task, where combining information technology and biosignals seems to be a meaningful detection and analytical approach. Biosignal is defined as any signal in living beings that can be continually measured and monitored. Multimodal biosignals can effectively help diagnose different potential health problems in the body. Among them, ECG is for diagnosing heart-related diseases and disorders such as sudden cardiac arrest, cardiovascular diseases, etc. EEG measures biological potential generated by the neuronal activity of the brain, which is more complex than ECG. Recently, EEG signal analysis has been used to analyze various hidden diseases of the brain, including determining whether a test user is suffering from depression or identifying early-stage undetectable mental illnesses such as Parkinson's disease. Multimedia signal processing and analysis based on video or voice can detect human physiological indexes. 

On the other hand, the growing development of information technology can potently help to process biological information or establish efficient and accurate health treatment countermeasures. 

This workshop aims to bring together the research on health measurement and treatment by multimedia signal processing. Research findings can show the up-to-date technical advances in the industry. We encourage prospective authors to submit related distinguished research papers on the subject of: application of information technology in the field of human health diagnosis and treatment. 

Chair:

Assoc. Prof.  Zhuozheng Wang, Beijing University of Technology of China, China
Zhuozheng Wang received a Ph.D. degree in Circuit and System from Beijing University of Technology. He is a national public visiting scholar of Michigan State University and an expert on the Intelligent Building and Building Automation Committee of China Automation Society. His research area is EEG signal processing by using deep learning. He has presided over the Beijing Outstanding Talents Project, the National Natural Science Foundation of China, the Science and Technology Planning Project of the Beijing Municipal Education Commission, the Beijing Natural Science Foundation, and participated in numerous related projects such as the Science and Technology Planning Project of the Beijing Municipal Education Commission. Based on these projects, he has published dozens of papers and national invention patents in recent years. He has also served as an advisor for numerous competitions and has received numerous corresponding awards.


Workshop 17 : Advanced RF and mm-Wave Circuits and Systems for 5G/6G Systems
Title 1：Electronics Engineering
Keywords: Circuits and Systems, Transceiver Front-End, Power Amplifiers, Linearization Technique, Signal Processing, Integrated Design Method.
Summary: 
High performance RF and mm-wave circuits and systems are highly required to boost the deployment of 5G/6G systems. This workshop calls for works demonstrating the most recent progress and contributions to design methods of RF and mm-wave circuits and systems for 5G/6G application. This workshop will focus on but not limited to the following parts. (1) Advanced RF and mm-wave transceiver front-end architectures. (2) Novel design methods for highly-efficient wideband and multi-band power amplifiers. (3) Linearization techniques of RF nonlinear circuits, including analog and digital predistortion methods. (4) Signal processing techniques for complex modulated signals. (5) Integrated design methods for RF and mm-wave circuits and systems based on advanced technologies. 

Chair:

Session Chair 1:
Assoc. Prof.  Weimin Shi, Associate Professor, Chongqing University, Chongqing, China
Weimin Shi received the Ph.D. degree from the University of Electronic Science and Technology of China, Chengdu, China, in 2019. From 2019 to 2021, he was a Post-Doctoral Research Fellow with the Department of Electrical and Computer Engineering, The Hong Kong University of Science and Technology (HKUST), Hong Kong. He is currently an Associate Professor with the School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China. His current research interests include board level power amplifiers, MMIC power amplifiers, and CMOS millimeter-Wave integrated circuit design. He has published more than 40 papers on top journals in recent years including IEEE TMTT, IEEE TIE, IEEE TCAS1, etc. He won the first place of the 2018 IEEE International Microwave Symposium Student Design Competition ‘14th High Efficiency Power Amplifier’.



Session Chair 2:
Assoc. Prof.  Yong Gao, University of Electronic Science and Technology of China, Chengdu, China

Yong Gao received the Ph.D degree in electromagnetic field and microwave technology from the University of Electronic Science and Technology of China, Chengdu (UESTC), PR China, in 2019. From 2019 to 2021, he was a full-time Post-Doctor Researcher in electromagnetic fields and microwave technology with UESTC. Since 2021, he was working as an associate researcher fellow with UESTC. His research interests include nonlinear dielectric properties of high power microwave material, interaction mechanism between high power microwave and materials, high power devices feature parameter extraction, passive intermodulation test technology, design of microwave devices, and microwave integrated circuits. He has published more than 20 papers on top journals in recent years including IEEE TMTT, IEEE TCAS2, IEEE AWPL, etc.


Workshop 18 : Advanced Image Systems & Techniques
Title 1：Advanced Image Systems & Techniques
Keywords: Robotic Vision and Industry; Medical Image Visualization Analysis and Processing; Imaging Devices and Techniques; Novel applications of image sensors; Augmented Reality and Computer Vision; Cognitive Vision and Artificial Intelligence
Summary:The design principles, development, and applications of new imaging technologies and computer visualization techniques are explored by scientists and engineers over the years. The use of machine learning and artificial intelligence to analyze and interpret imaging data is rapidly changing the global economy, experiencing an unparalleled integration of science and technology. The scope of the workshop is to explore, advance and generate new knowledge on multifaceted imaging design principles, systems, and techniques, with application in medical/industrial field. The workshop aims to bridge multidisciplinary areas with healthcare, industry, robotics, Internet of Things (IoT) and artificial intelligence with emerging imaging trends that would lead ultimately to novel systems, technologies, standards, metrology and system.

Chair:
Prof. Qi Wang, School of Life Science, Tiangong University, China
 
Qi Wang received B.S. and Ph.D. Degrees from the school of Electrical and Automation Engineering at Tianjin University in 2009 and 2012, respectively. She was a visiting scholar at the University of Edinburgh in 2020. She is currently a professor in the school of Life Science, Tiangong University, China. Her research interests include medical imaging, process tomography and intelligent information processing.
Workshop 19 : Artificial Intelligence and Its Cross-domain Applications
Title 1：Artificial Intelligence and Its Cross-domain Applications
Keywords: Intelligent architecture, Agricultural crop phenomics, Medical rehabilitation robotics, Virtual reality, art
Summary:
Artificial Intelligence is a new technical science that researches and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence. Research in this area includes robotics, speech recognition, image recognition, natural language processing, and expert systems. Since the birth of artificial intelligence, its theory and technology are increasingly mature, with the development of human society from information to intelligence, the application of artificial intelligence technology is deep into all walks of life in human society. The application fields of Artificial Intelligence include computer vision, natural language processing, intelligent robots, deep learning, data mining, etc. Specific application scenarios include but are not limited to intelligent architecture, agricultural crop phenomics, medical rehabilitation robots, virtual reality, art, etc.

The workshop aims to bring together research findings from academia and industry. Another goal is to showcase the latest research results of AI in the fields of intelligent architecture, agricultural crop phenomics, medical rehabilitation robotics, virtual reality, art, and other cross-applications. Prospective authors are encouraged to submit relevant outstanding research papers, including reviews of theoretical approaches and practical cases.

Chair:
Prof. Wenli Zhang, Beijing University of Technology, China

Wenli Zhang is a professor at the Faculty of Information Science, Beijing University of Technology. Her research interests include Computer vision and pattern recognition; Application research of artificial intelligence technology in agricultural crop phenoomics, UAV inspection, intelligent architecture, bionic rehabilitation prosthesis, and other interdisciplinary fields. She presided over and  participated in several key projects supported by Ministry of Education, Ministry of Science and Technology, Beijing Science and Technology Commission, and Beijing Natural Science Foundation.  She published 20 papers indexed by SCI/Ei on Chinese core journals, She declared nearly 30 international/national invention patents, utility models.
Workshop 20 : RF/microwave/mm-wave Circuits and Systems
Title 1：Substrate Integrated Circuits and Antennas
Keywords: Substrate Integrated Circuits, Substrate Integrated Suspended Line (SISL), Substrate Integrated Waveguide (SIW), RF/microwave/mm-wave Front-End Circuits, Passive Circuits, Antennas, and Antenna Array
Summary:
RF, Microwave, and mm-wave circuits are widely used in modern communications such as 5G communication, satellite, and radars. It is the most significant trend to make these circuits and systems low cost, small size, lightweight, and high performance. The properties of transmission lines, such as losses, size, etc., play vital roles in these circuits and systems. In recent years, substrate integrated transmission lines including substrate integrated suspended line (SISL), substrate integrated waveguide (SIW), substrate integrated coaxial line (SICL), etc. have been widely used in high-performance circuits and systems.

This workshop aims to invite worldwide researchers to present their latest work on RF/microwave/mm-wave substrate integrated circuits. Potential topics include but are not limited to the following:
(1) Substrate integrated circuits including substrate integrated suspended line (SISL), substrate integrated waveguide (SIW), substrate integrated coaxial line (SICL), etc.
(2) RF/microwave/mm-wave Front-End Circuits
(3) Passive circuits including filters, multiplexers, couplers, dividers, baluns, magic-Ts, phase shifters, etc.
(4) Antennas elements and antenna arrays, antenna feeding networks.

Chair:

Session Chair 1:

Dr. Yongqiang Wang, Tianjin University, China
Yongqiang Wang (Member, IEEE) received the B.S. and Ph.D. degrees from the University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2014 and 2019, respectively. From 2019 to 2020, he was a post-doctoral researcher with The Chinese University of Hong Kong, Hong Kong. He is currently a full-time Associate Professor with the School of Microelectronics, Tianjin University, Tianjin, China. Dr. Wang was selected for the Young Elite Scientists Sponsorship Program by CAST in 2022. He has authored or co-authored 58 journals and conference articles and holds 16 patents granted. His current research interest includes microwave passive circuit design and its applications. Dr. Wang was the finalist for the Student Innovation Competition of iWEM in 2014, the Advanced Practice Paper Competition (APPC) of IEEE MTT-S IMS in 2017, and IWS in 2019. He was a recipient of the Best Student Paper of UCMMT2018, NCMMW2017, and NCMMW2019, where he twice received the Excellent Paper Award supported by the Education Development Foundation of Lin Weigan. He is a reviewer for the IEEE Transactions on Microwave Theory and Techniques, IEEE Transactions on Industrial Electronics, IEEE Transactions on Circuits and Systems I: Regular Papers, IEEE Transactions on Antennas and Propagation, IEEE Transactions on Components, Packaging and Manufacturing Technology, IEEE Transactions on Electron Devices, IEEE Microwave and Wireless Components Letters, IET Microwaves, Antennas & Propagation, and Electronics Letters.

Session Chair 2:

Dr. Ningning Yan, Tianjin University, China

Ningning Yan received the B. E and Ph. D. degrees from University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2012 and 2019, respectively. From 2016 to 2017, she was a joint Ph.D. student Scholar at the Applied Electromagnetics Laboratory, University of Houston. From 2019 to 2022, she was an assistant professor at Tianjin University, Tianjin, China. She is currently an associate professor in the School of Microelectronics, Tianjin University, Tianjin, China. Dr. Yan has authored or co-authored more than 50 journal and conference articles. Her current research interests include substrate integrated suspended line (SISL) antennas, dielectric resonator antennas, Yagi antennas, leaky-wave antennas, multiband antennas, antenna arrays, and feeding networks.




Workshop 21 : Semi, Self- and Unsupervised Learning for Image Understanding
Title 1：Semi, Self- and Unsupervised Learning for Image Understanding
Keywords: Image Understanding, Deep Learning, Algorithms and Applications of Image Processing, Semi, self- and unsupervised learning
Summary:
Deep learning strategies achieve outstanding successes in computer vision tasks. They reach the best performance in a diverse range of tasks such as image classification, object detection, or semantic segmentation. However, the current strategies rely heavily on a huge amount of labeled data. In many real-world problems, it is not feasible to create such an amount of labeled training data. Therefore, it is common to incorporate unlabeled data into the training process to reach equal results with fewer labels. This special workshop aims to introduce and discuss unsupervised, semi-supervised, self-supervised, or weakly-supervised methods of image understanding and the new application. We encourage prospective authors to submit related distinguished research papers on the subjects of image classification, image segmentation, object detection and recognition, object tracking, activity recognition, and other applications of image understanding.

Chair:

Session Chair 1:

Prof. Rongruan Chen, Hunan University of Technology and Business, China

He received his Ph.D. degree in photogrammetry and remote sensing from Wuhan University, China, in 2010. He is currently a professor in the School of Resource and Environment, Hunan University of Technology and Business. He is a young backbone teacher in Hunan Province and a Hunan Province 121 Innovative Talents. He presided over one national natural science foundation project and six provincial scientific research projects, published more than 20 papers, and gained two national invention patents and six software copyrights. His current research interests include artificial intelligence and image processing.



Session Chair 2:

Assoc. Prof. Shaonian Huang, Hunan University of Technology and Business, China


She received her B.S. degree in computer science from Hunan Normal University, China, in 2000, and her M.S. and Ph.D. degrees in computer science from Central South University, China, in 2006 and 2018, respectively. She is currently an Associate Professor with the School of Computer, Hunan University of Technology and Business, China. Her current research interests include computer vision, machine learning, crowd behavior analysis, and modeling.





Workshop 22 : Low Power Consumption and Reliable Wireless Communications for IoT applications
Title 1：Low Power Consumption and Reliable Wireless Communications for IoT applications
Keywords: Image Understanding, Deep Learning, Algorithms and Applications of Image Processing, Semi, self- and unsupervised learning
Summary:
Wireless communication technologies play an important role in today’s society. For that reason, wireless systems are almost ubiquitous and can now be found in many application areas, especially for IoT applications. However, due to the restrictions of limited power supply and hardware resources, the optimization of power consumption in wireless communication protocol becomes a critical issue in the IoT area. Meanwhile, reliability and security are also two main concerns when applying the traditional wireless communication technologies in the IoT areas, especially for the healthcare circumstances. Therefore, a founding pillar of the IoT concept and the growing market is the availability of low-cost, low-power, and reliable devices with wireless technologies.

This workshop aims 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 low power consumption and reliable wireless communication technologies for IoT applications and its sub-areas such as NB-IoT, Wireless sensors networks, Body area networks, industry IoT, IoT security methods, and so on. We encourage prospective authors to submit related distinguished research papers on the subjects of both: theoretical approaches and practical case reviews. 

Topics of interest include but are not limited to wireless communications, low power communication scheme and implementation, communication signal processing, and communication security.

Chair:

Session Chair 1:

Assoc. Prof. Junchao Wang, Chongqing University, China


Dr. Junchao Wang received a B.E. degree in Microelectronics from Chongqing University of Posts and Telecommunications, Chongqing, China, in 2013, an M.S. degree in Electrical Engineering from Illinois Institute of Technology, Chicago, US, in 2015, and a Ph.D. degree in Electrical Engineering from McGill University, Montreal, Canada, in 2019. Currently, he is an Associate Professor with the Department of Microelectronics and Communication Engineering, Chongqing University, China. His current research interests include Body Area Network, stochastic computing, and low power VLSI.



Session Chair 2:

Assist. Prof. Kaining Han, University of Electronic Science and Technology of China, China


Dr. Kaining Han received a B.E. degree and Ph.D. degree in communication engineering from the University of Electronic Science and Technology of China, Chengdu, China, in 2014 and 2019. He was a Graduate Research Trainee with the Department of Electrical and Computer Engineering of McGill University, Montreal, Canada. Currently, he is an Assistant Professor with the National Key Lab of Science and Technology on Communications, University of Electronic Science and Technology of China. His research interests include high-speed low-power DSP technology with VLSI, wireless body area networks, and stochastic computing based system designs.





Workshop 23 : Artificial intelligence technology and Application
Title 1：Artificial intelligence technology and Application
Keywords: Artificial Intelligence, Machine Learning, Deep Learning， Representation Learning, Transfer Learning, Knowledge representation, automatic reasoning and search methods, machine learning and knowledge acquisition, knowledge processing system, natural language understanding, computer vision, intelligent robot, intelligent city, intelligent transportation, intelligent agriculture

Summary:
Artificial intelligence has undergone rapid development in recent years and has been successfully applied to real-world problems such as economic, social, life, and other industries discovery and design. n the future, the field of artificial intelligence will continue to make rapid progress, and language, sound, and vision technologies and multimodal solutions will make great progress, completely changing "human efficiency".This seminar will discuss the advantages of current deep learning applications, the comprehensive application of artificial intelligence with computer vision, deep learning and big data as the core, as well as the application prospect and development trend of the next generation of artificial intelligence. The method based on deep learning has begun to solve some basic problems in industry, agriculture and other fields. Some methodological advances, such as deep neural networks, message passing models, hybrid de novo design and other innovative machine learning paradigms, may become commonplace and help solve some of the most challenging problems. Open data sharing and model development will play a central role in using artificial intelligence to promote smart city, smart transportation, smart agriculture and design.

This workshop aims to bring together the latest research progress of academic and industry researchers, such as artificial intelligence methods, machine learning, and deep learning models, especially in knowledge representation, automatic reasoning and search methods, machine learning and knowledge acquisition, knowledge processing systems, natural language understanding, computer vision, intelligent robots discovery and design areas. We encourage prospective authors to submit related distinguished research papers on this subject.

Chair:

Dr. Qiudong  Yu, Tianjin University of Technology and Education, China
Dr. Qiudong Yu , Professor of computer science and doctoral supervisor, member of the young scientist Club of China Electronics Society, expert on the "national training program" of the Ministry of education, expert on project technical evaluation of Tianjin Science and Technology Bureau and Tianjin Agricultural and Rural Committee, expert of digital campus construction specification of Vocational Colleges of the Ministry of education, director of Tianjin automation society, and honorary president of Tianjin high tech Enterprise Association. With nearly 20 years of computer software and hardware development experience and theoretical foundation, he has deeply integrated computer technology with the production of various industries, and specializes in the research on the combination of computer digital technology such as Internet of things, informatization, automation and intelligence with the application fields such as agriculture and vocational education. He has successively presided over and completed dozens of national projects, provincial and ministerial science and technology plan projects and enterprise horizontal entrusted projects, and accumulated more than 100 scientific research achievements. Won one first prize in Tianjin Science and technology progress award, three second prizes in Tianjin Science and Technology Progress Award (two of which were presided over), and young and middle-aged backbone innovative talents in Colleges and universities in Tianjin.





Workshop 24 : Multichannel signal detection and filtering
Title 1 : Adaptive Signal Detection and Filtering 
Keywords: Adaptive Detection, Adaptive Filtering, Array Signal Processing, Beamforming, Constant False Alarm Rate (CFAR), Frequency Diverse Array(FDA), Multiple-Input Multiple-Output (MIMO) Radar.
Summary：With the increase in computation power and advances in hardware design, the received data for sensor systems are usually multichannel, namely, vector-valued or even matrix-valued. Moreover, the frequency diversity, polarization diversity, or waveform diversity can also lead to the multichannel form of the received data. The multichannel data contain more information, compared with the single-channel data. It is more convenient to use the multichannel data model to characterize the correlated properties between data in different channels. Using these correlated properties, one can design a filter, whose output signal-to-noise(SNR) is often higher than that for single-channel data. Similarly, utilizing the data correlation, one can devise a detector, which has superior detection performance to a detector for single-channel data. Detection and filtering are two fundamental problems in the signal proceeding. Adaptivity is the key point for multichannel signal processing since the clutter spectral properties are usually unknown and need to be estimated. 

This workshop aims to show the latest research results in adaptive signal detection and filtering. Potential topics of interest include, but are not limited to: adaptive detection or filtering with limited training data, adaptive detection or filtering in heterogeneous environments, adaptive detection or filtering in sea clutter, adaptive detection filtering in interference/jammer, MIMO radar/FDA radar detection or filtering, DOA, beamforming, etc. 

Chair: Prof. Weijian Liu, Wuhan Electronic Information Institute, China
Weijian Liu received the B.S. degree in information engineering and M.S. degree in signal and information processing, both from Wuhan Radar Academy, Wuhan, China, and the Ph.D. degree in information and communication engineering from National University of Defense Technology, Changsha, China, in 2006, 2009, and 2014, respectively. Now he is an associate professor at Wuhan Electronic Information Institute. He published more than 100 papers indexed by SCI. His research interests include multichannel signal detection, statistical and array signal processing. He is a handling editor or an associate editor with Signal Processing, Digital Signal Processing, and Circuits, Systems, and Signal Processing.

Workshop 25 : X as a Service
Title 1 : SaaS, PaaS, IaaS, EaaS  
Keywords: Adaptive Detection, Adaptive Filtering, Array Signal Processing, Beamforming, Constant False Alarm Rate (CFAR), Frequency Diverse Array(FDA), Multiple-Input Multiple-Output (MIMO) Radar.
Summary： Recently, service computing has become an increasingly popular topic in both industry and academia, in virtue of its remarkably high performance and strong support for various new personalized services. A service is a self-describing software module that is published, located, and invoked on the web. Nowadays, enterprises
may focus on their core functions and find services on the web to fulfill their tasks. By combining different services, users may fulfill their complex business requirements, this is the service composition problem. Through the cloud, everything will be delivered as a service, from computing power to business processes to personal interactions. This workshop aims 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 service and service computing. We encourage prospective authors to submit related research papers on the subject of both: theoretical approaches and practical case reviews.

Chairs

Session Chair 1:

Dr. Jing Li, Shandong University of Technology, China


Jing Li is an Associate Professor at School of Computer Science and Technology, Shandong University of Technology, Shandong, China. She received her Doctoral degree in Computer Science at Concordia University, Montreal, Canada. Her main research interests are service computing, edge computing, and service composition.






Session Chair 2:

Dr. Ming Zhu, Shandong University of Technology, China



Ming Zhu is an Assistant Professor at School of Computer Science and Technology, Shandong University of Technology, Shandong, China. He received his Ph.D. degree in Computer Science at Concordia University, Montreal, Canada. His research interests are related to service computing, cloud computing, and service selection.


Workshop 26 : Modulation Recognition of Radar Signal based on Machine Learning
Title 1 : Modulation Recognition of Radar Signal based on Machine Learning  
Keywords: Modulation Recognition. Radar Signal Processing. Artificial Intelligence. Machine Learning
Summary: 
With the advancement of information industry, electronic warfare has gradually become the main mode of operation of modern war. As the key technology of electronic reconnaissance, radar signal modulation recognition occupies a core position in electronic warfare. At present, the environment of electronic warfare is often complex and changeable, and radar signal modulation recognition is becoming more and more difficult. With the development of artificial intelligence, deep learning has gradually emerged in the field of radar signal modulation and recognition. However, the non-cooperative nature makes it difficult to collect radar data and can not meet the requirements of training for the number of samples. Therefore, how to realize the accurate recognition of radar signal modulation under the condition of a small number of samples has become an urgent technical problem to be solved.

This workshop aims to collect the research results provided by academic and industry researchers, focusing on introducing new advanced theories and practical algorithms into the field of radar signal modulation recognition. Original research and review articles are welcome.

Chairs

Session Chair 1:

Assoc. Prof. Jingpeng Gao, Harbin Engineering University, China


Jingpeng Gao received the B.S., M.S., and the Ph.D. degrees in Electrical Information Engineering from Harbin Engineering University (HEU) in 2002, 2007 and 2014 respectively. He has been a lecturer in Harbin Engineering University of China since 2002, became a lecturer in 2007 and became master tutor in 2015. During 2015-2017, he stayed in State Key Laboratory of Computational Mathematical and Experimental Physics, Beijing Institute of Space Long March Vehicle as a post-doctoral. He earned the first and third prizes of teaching instruments and equipment in China. His research interests include electronic countermeasure, machine learning, radar target recognition, and signal detection.






Session Chair 2:

Assoc. Prof. Fang Ye, Harbin Engineering University, China



Fang Ye was born in 1980. She received her Ph. D. degree from Harbin Engineering University in 2006. She is an associate professor at the School of Information and Communication Engineering, Harbin Engineering University. Her research interests are cognitive confrontation and intelligent decision-making.


Workshop 27 : Computer Vision
Title 1 : Image Processing, Intelligent Video Analytics, Artificial Intelligence, Big Data Processing, Intelligent Systems
Keywords : Medical Electronics. Medical Imaging. Intelligent Systems. Image and Vision. Artificial Intelligence. Machine Learning. Deep Learning
Summary:
In recent years, computer vision has gradually become a forward-looking research field recognized by academia and industry and has spawned several highly visible commercial applications such as face recognition and intelligent video surveillance. The research goal of computer vision is to enable computers to have the visual ability of human beings so that they can understand the content of images and understand dynamic scenes. It is expected that the computer can automatically extract the hierarchical semantic concepts contained in visual data such as images and videos, as well as the spatiotemporal correlations between multi-semantic concepts. The research of computer vision is the first step in the intelligentization of computer systems and the bridge to realize artificial intelligence. Many exciting research results are emerging in the field of computer vision. For example, the performance of face recognition, object recognition and classification has approached or even surpassed the human visual system. It can be said that the current development of computer vision has entered a new stage.

This workshop aims 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 computer vision and understand how governance strategy can influence it. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.

Chair: 
Assoc. Prof.  Yunzuo Zhang, Shijiazhuang Tiedao University, China
Yunzuo Zhang received a Ph.D. degree in information and communication engineering from Beijing Institute of Technology. He is an associate professor fellow with School of Information Science and Technology and a doctoral tutor fellow with School of Transportation, Shijiazhuang Tiedao University. His research interests include image processing, artificial intelligence, and big data. He has presided over 8 national or provincial projects with National Natural Science Foundation of China and the Natural Science Foundation of Hebei Province. Based on these projects, he has published more than 30 papers, applied for and authorized 19 national invention patents, and 7 utility model patents, obtained 46 computer software copyrights and published 6 academic monographs, and 2 textbooks.


Workshop 28 : Robotics
Title 1 : Robot Perception, imitation, control, decision
Keywords : Robot, Multi agent, Big Data, Artificial intelligence, Perception, control, decision, ROS, Chip, Design
Summary:
The industrial revolution has reached a new development stage. Robot is the inevitable product of the times. For robots, intelligence is an indispensable and important part, and it is also the mainstream today. Robots need perception, imitation, control, and decision, which depend on huge amounts of data. Deep learning and Reinforcement learning is good at learning model on data. Robots imitate human beings, make decisions and control their behavior of robots by perceiving external information. For example, the camera of a robot is used to capture video information. Sensors collect all kind of data including text, voice, video, and image. The robot analyzes the data collected by sensors, we use multi agent reinforcement learning for robot control. We can make the robot look like a human. There are planning, navigation, and so on. The chip was also an important part of the robot. Foreign countries attached more importance to the study of the chip and its enhancement of it. We mainly deployed the system under the ROS and on the main board of the computer. Finally, we design the robot step by step and make the robot look like people.

This workshop aims 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 robots and understand how some strategies can influence it. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.

Chair: 
Prof. Ming Cao, Nanchang Institute of Science and Technology, China

Ming Cao received his Master's Degree in Statistics at Shenzhen University and worked as a member at Nanchang Institute of Technology. His research interests include Deep Learning, Reinforcement learning, Software Engineering, Numerical Simulation, and Electronics Engineering.



Workshop 29 : Analog and Mixed-Signal IC
Title 1 : Analog and Mixed-Signal IC
Keywords : Analog circuits, Mixed-signal IC, Data converter, IC test
Summary:
The rapid development of Integrated Circuits (IC) fabrication technology and the advance of System-on-a-Chip (SoC) design technology have made it possible to integrate millions of transistors on a single chip including digital and analog components. These mixed-signal solutions are widely used in modern mobile and multimedia devices.
In a typical application of the mixed-signal system, the external analog signals are sensed and changed to digital representations by Analog-to-Digital Converter (ADC), after the digital signal processing, the digital data are translated to our inherently analog world again by Digital-to-Analog Converter (DAC). ADC and DAC are at the systems level, and they typically contain amplifiers, sample-and-hold circuits, low-pass filters, etc.

This workshop aims to bring together the research accomplishments provided by researchers from academia and the industry and to show the latest research results in the field of analog and mixed-signal IC. We encourage prospective authors to submit related distinguished research papers on the subjects: data converter, comparator, operational amplifier, etc.

Chair: 
Assoc. Prof. Jun Yuan, Chongqing University of Posts and Telecommunications, China

He received B.E. and M.E. degrees in Electrical Engineering in 2006, 2009 respectively, from Southwest Jiaotong University, China. And then in 2012 he received D.Eng. Degree from Kochi University of Technology, Japan. Then he joined School of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, China. His areas of research interests are analog-digital mixed signal IC design, DFT research and noise processing IC design.



Workshop 30 : Computer Vision Technologies for Smart City Applications
Title 1 : Computer Vision Technologies for Smart City Applications  
Keywords: Smart City, Computer vision, Deep Learning, Machine Intelligence
Summary: 
Smart Cities demand highly scalable and connected technologies to operate at multiple distributed locations. Recent advances in Computer Vision such as Edge AI and Deep Learning combine AI vision with IoT. These new technologies make it possible to handle the huge amount of complex visual data and enable fast processing, robustness by decentralization, and scalability of real-world computer vision systems. In the past two decades, smart city solutions have emerged, enabled by technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Deep Learning, and Cloud Computing. They offer vast potential to address infrastructural, societal, and pandemic challenges. With smart technologies, communities can improve energy distribution, streamline trash collection, decrease traffic congestion, improve air quality, and more with help from smart, connected sensing systems.

The other goal is to bring together the research accomplishments provided by researchers from academia and the industry and show the latest computer vision technologies in the field of Smart City. We encourage prospective authors to submit related distinguished research papers on the subject of Computer Vision Technologies for Smart City(CVT for SC). Please name the title of the submission email with “paper title+ CVT for SC”.

Topics of interest: 
  • Perimeter monitoring and Person detection,
  • Detect violent and dangerous situations,
  • Action Recognition for Vandalism Detection,
  • Compliance Monitoring and Inspection,
  • Suicide prevention in public spaces,
  • Crowd disaster avoidance application,
  • Protection of Critical Infrastructures,
  • Weapon detection and reporting,
  • Social Distancing monitoring in public places,
  • Automated mask detection,
  • Hygiene Compliance Control,
  • Healthcare monitoring,
  • Smart city traffic monitoring


Chairs

Session Chair 1:

Dr. Mingliang Gao, Shandong University of Technology, China

Mingliang Gao received his Ph.D. degree in Communication and Information Systems from Sichuan University. He is now an associate professor at the Shandong University of Technology. He was a visiting lecturer at the University of British Columbia during 2018-2019. He established the Brighten Vision Group and enjoyed immensely working with students and researchers. He has been the principal investigator for a variety of research funding, including the National Natural Science Foundation, China Postdoctoral Foundation, National Key Research Development Project, etc. His research interests include computer vision, machine learning, and intelligent optimal control. He has published over 100 journal/conference papers in IEEE, Springer, Elsevier, and Wiley.






Session Chair 2:

Dr. Qilei Li,  Queen Mary University of London, United Kingdom



Qilei (Kevin) Li is a second-year Ph.D. student in Computer Science, Queen Mary University of London, supervised by Prof. Shaogang (Sean) Gong. Previously, he received the M.S. degree from Sichuan University in 2020. His research interests include computer vision and deep learning, particularly focusing on person ReID, and video/image enhancement. He is a student member of IEEE, and he serves as a reviewer for Information Fusion, IEEE TIM, IEEE Access, Concurrency, and Computation: Practice and Experience, and Multimedia System.


Workshop 31 : Navigation and Communication Fusion Technology
Title 1 : Research on Multi-source Fusion Navigation, Navigation, and Communication Fusion Technology
Keywords : Navigation and Position Perception. Navigation and Communication Fusion Technology. Indoor positioning. Multi-source integrated navigation. Information fusion
Summary:
With the rapid development of location and navigation technology, location-based services bring better and better user experience to people. However, a single navigation source often can not meet the accuracy requirements of users and is not robust and reliable, and to solve the problem that a single indoor positioning technology can not meet people's positioning requirements in a complex environment, multi-source integrated navigation technology is proposed. Compared with traditional single navigation source, multi-source integrated navigation can make full use of the advantages of each navigation source and provide the best location navigation service. Multi-source information fusion and multi-source fusion navigation technology are analyzed and summarized, and the commonly used multi-source fusion algorithms and performance evaluation of multi-source fusion navigation are described.

Chair: 
Prof. Yuanfa Ji, Guilin University of Electronic Technology
Yuanfa Ji received his doctorate from the National Astronomical Observatories of the Chinese Academy of Sciences. He is a post-doctoral fellow at the Yunnan Astronomical Observatories of the Chinese. He works as a professor at Guilin University of Electronic Technology. His research interests include satellite communications, satellite navigation, real-time dynamic positioning and navigation receivers. He is also a reviewer of journals such as Journal of Astronautics and Journal of Beihang University. In recent years, he has presided over and participated in many scientific research projects, such as National Natural Science Foundation of China, SME Innovation Fund of Ministry of Science and Technology, and published more than 100 academic papers, including more than 20 papers indexed by EI or SCI. Currently researching projects: National Natural Science Foundation of China, National Defense Pre-research project, National major special project - Beidou Demonstration Application project, and many other practical projects.


Workshop 32 : Machine Learning
Title 1 : Application of Machine Learning in Various Domains
Keywords : Machine Learning, Applications, Learning Theory, Smart Cities, Artificial Intelligence, Neural Network, Classification and Regression, Clustering, Time Series, Probabilistic Methods
Summary:
This workshop aims to bring together the researchers working in the area of machine learning under one umbrella and disseminate the applications of machine learning in various domains. Machine learning is a buzzword in today's technology, and it is expanding at a rapid pace. We are using machine learning in our daily life even without knowing it. Be it Google Maps, Google Assistant, or Alexa, each one of us is using machine learning in one or the other way. To explore and broaden the horizon of machine learning field, papers are invited from prospective authors from academia and industry on the cited topic. The workshop will give authors a platform to interact and share their work with other researchers.

Chair: 
Assoc. Prof. Bhavya Deep, Bhaskaracharya College of Applied Sciences, University of Delhi, India
He is skilled in Lecturing, Curriculum Development, and Research. Dr. Bhavya Deep has a demonstrated history of working in the field of academics and research. Having more than two decades of work experience, he is an awardee of “Most Significant Research Outcome” and “Best Display Award” for two sponsored projects of the University of Delhi, India. He is a Departmental Coordinator, DBT Star College Scheme, Government of India since 2017, and worked in various senior positions at the college and university level. His research interests include Cloud Computing, Recommender Systems, Machine Learning, and the Internet of Things.


Workshop 33 : Advanced Detection and Estimation Theories with Applications in Sensors
Title 1 : Advanced Detection and Estimation Theories with Applications in Sensors
Keywords : Detection, Hypothesis testing, Estimation, Target tracking, Information fusion, Radar target detectionDetection, Hypothesis testing, Estimation, Target tracking, Information fusion, Radar target detection
Summary:
Detection and estimation are important bases for information processing of various sensors such as radar and sonar, which are widely used in the field of sensor sensing, e.g., target detection, localization, tracking, identification and information fusion. In recent years, various new types of targets have achieved rapid development, the detection environment faced by sensors continues to be complex, and the comprehensive requirements of intelligent sensing have also prompted the transformation of sensors to integrated multifunctional information systems. Traditional detection and estimation methods based on probabilistic statistics can hardly meet the multi-task requirements of modern sensors. Meanwhile, advanced theories or methods such as information geometry, variational inference, finite set statistics, joint detection and tracking, message-passing, and deep learning emerge as a promising solvers to detection and estimation problems in sensors and have shown excellent application prospects.
This workshop aims at providing opportunities for academic exchange and collaboration for researchers interested in detection and estimation. We encourage original papers presenting new theoretical and/or application-oriented research. Potential topics include but are not limited to the following:
  1. Simultaneous detection and estimation methods 
  2. Joint target detection and tracking 
  3. Track-before-detect for radar targets detection
  4. Hybrid state estimation and its application in sensors
  5. Variational inference and its application in target tracking
  6. Target Detection and tracking using random finite set theory 
  7. Information geometry detection 
  8. Feature-aided target detection and tracking
  9. Detection fusion and estimation fusion
  10. Message passing based on multiple target tracking
  11. Deep learning based target detection and tracking


Session Chair 1 : 
Dr. Daikun Zheng, Air Force Early Warning Academy, China

Dr. Daikun Zheng received his M.S. and Ph.D. degree in information and communication engineering from Air Force Early Warning Academy (AFEWA) in 2012 and 2016, respectively. He is currently an assistant professor with AFEWA. He has presided over several national and military research projects. He has authored or co-authored more than 30 scientific articles in refereed journals, such as Optics Express, Signal Processing, etc. His research interests include signal processing for new radar system, target detection and tracking, information fusion, and intelligent sensing technology.




Session Chair 2 : 
Assoc. Prof. Hong Xu, Xidian University, China



Hong Xu was born in Sichuan Province, China, in 1991. He received a B.S. degree in electronic countermeasures engineering and an M.S. degree in information and communication engineering, both from Wuhan Radar Academy, Wuhan, China, and a Ph.D. degree in information and communication engineering from Navy University of Engineering, Wuhan, China, in 2013, 2015, and 2020, respectively. He is currently a tenure-track associated professor at the Hangzhou Institute of Technology, Xidian University. His current research interests include adaptive state estimation, multiple target tracking, and multiple sensor information fusion, etc.



Workshop 34 : Security Detection
Title 1 : Security Detection: Dangerous Goods Detection
Keywords : Security Detection, Feature Extraction, Public Security, Dangerous Goods Classification
Summary:
With the rapid economic development and the accelerated flow of personnel and materials, the demand for passengers and logistics has grown exponentially in recent years. However, as a carrier with a frequent flow of people and a high concentration of high-value items, various means of transportation are facing huge challenges. Among them, the illegal transportation and use of dangerous goods have seriously affected public safety. The existing security inspection methods all have shortcomings such as high cost, high radiation, and the need for close contact inspection, which cannot meet the needs of security inspection in the modern transportation industry. Therefore, the use of new means and new methods to achieve accurate detection of dangerous goods is the most popular research topic in the field of security inspection today.

This workshop is dedicated to publishing research papers related to "Safety Detection: Dangerous Goods Detection" to showcase the latest research results in the field of safety inspection technology. Potential topics include but are not limited to the following:
(1) Classification of flammable liquids
(2) Liquid dangerous goods detection
(3) Machine learning method for dangerous goods detection
(4) A new design method for the classification of dangerous goods with high precision and low loss
(5) Multi-sample dangerous goods classification detection


Chair: 
Prof. Dongmei Zhou, Chengdu University of Technology, China

Dongmei Zhou is a professor at the School of Mechanical and Electrical Engineering, Chengdu University of Technology. She received a bachelor's degree from Sichuan University in 1995 and a master's degree in signal and information processing from Chengdu University of Technology in June 2002. As the first/corresponding author, she has published 9 papers indexed by SCI , 4 papers indexed by EI, and 2 Chinese core papers, and obtained 4 domestic and foreign invention patents. Her current research interests include multimedia information processing such as digital signals and images.


Workshop 35 : Broadcast, Video, Infrared & Image Processing
Title 1 : Edge Video Analysis and Compression
Keywords : edge computing, video analysis, video compression, hardware acceleration
Summary:
In recent years, enormous intelligent cameras have been utilized for various applications, including surveillance, autonomous vehicles, quality detection, etc. With more data being created by video content and analytics, machine vision comes up with new requirements and challenges, including data storage, network bandwidth, security, and instantaneity. To address the above-mentioned issues, the compression and storage of surveillance videos have become an increasingly hot research topic to reduce the requirements of data storage and network bandwidth. The video data analysis is also moved from the cloud to the edge to improve real-time performance. However, the computing capability is usually insufficient in edge or end devices. Therefore, light-weighted networks and dedicated hardware accelerators are under intensive development to reduce the computing energy while maintaining the quality of the data analysis and compression. 

This workshop will provide an interchange forum to show the latest research results including system optimization, AI algorithm, computer architecture, and circuit design for edge video analysis and compression. Moreover, it will offer an opportunity for academic and industrial attendees to interact and explore collaborations.

Chair: 
Prof. Kejie Huang, Zhejiang University, China
Kejie Huang  received the Ph. D. degree from the Department of Electrical Engineering, National University of Singapore (NUS), Singapore, in 2014. He has been a Tenure-Track Professor with the College of Information Science Electronic Engineering, Zhejiang University (ZJU) since 2016. Prior to joining ZJU, he has spent five years at the IC design industry, including Samsung and Xilinx, two years in the Data Storage Institute, Agency for Science Technology and Research (A*STAR), and another three years in the Singapore University of Technology and Design (SUTD), Singapore. He has authored or coauthored more than 40 scientific articles in international peer-reviewed journals and conference proceedings. He holds four granted international patents, and more than 10 pending ones. His research interests include circuits and architecture design for in-memory computing, in CMOS image sensor computing, image/video compression and generation, and deep learning accelerator design. Currently he is undertaking five 10-million-level projects and one Key Program of National Natural Science Foundation of China. He is a senior IEEE member and currently serves as the Associate Editor of the IEEE TRANSACTIONS ON CIRCUITS ANDSYSTEMS-PART II: Express Briefs.


Workshop 36: ADSP: Advanced Digital Signal Process
Title 1：Sparse Representation in Signal Processing
Keywords : Sparse, Signal Processing, Compressive Sensing, Imaging, Representation
Summary:According to CS (Compressive Sensing) theory, the exact recovery of an unknown sparse signal can be achieved from limited measurements by solving a sparsity constrained optimization problem. Furthermore, this method possesses super-resolving ability, overcoming the limitation imposed by bandwidth and synthetic aperture. Because of that, many signal-processing problems of current interest can be cast as the separation of a low-rank signal of interest from a sparse signal of outliers. Such a low-rank/sparse representation (LRSR) has found extensive use across a myriad of signal processing applications over the last decade.
 

Chair:Prof. Bo Pang, National University of Defense Technology, China

Bo Pang was born in Anhui Province, China, in 1984. He received the B.S. M.S. and Ph. D. degrees at the College of College of Electronic Science and Technology, National University of Defense Technology, Changsha, China, in 2007, 2009 and 2014. He is now an associate professor in the College of Electronic Science of National University of Defense Technology. His current research interests include Radar Signal Processing, Polarimetric Radar Information Processing, Synthetic Aperture Radar Imaging and Interpretation, etc.
To be announced.