Keynote Speaker 1: Prof. Shuwen Xu, Xi'an University of Electronic Science and Technology, China

 

Brief Introduction: Xu Shuwen, Ph.D. in Engineering, professor, doctoral supervisor, has long been engaged in research on sea clutter detection and weak target detection by sea radar. Currently, he is the deputy director of the Institute of Electronic Engineering (National Key Laboratory of Radar Signal Processing) at Xi'an University of Electronic Science and Technology, the deputy director of the National Information Perception Collaborative Innovation Center, the leader of the Shaanxi Provincial Youth Innovation Team, and a provincial-level talent recipient. Currently, he is a senior member of IEEE and a subject evaluation expert of the Ministry of Education. Served as an editorial board member for journals such as Journal of Electronics and Information, Journal of Radar, System Engineering and Electronic Technology, and Signal Processing. Formerly a fully funded visiting professor for CSC at Mcmaster University in Canada and a temporary researcher at China Electronics Corporation.

 

Research Area: Sea clutter Porter sensing and related research on weak target detection by sea radar

 

Title: Research on Adaptive Target Detection Technology in Sea Clutter Background

 

Abstract: Most of the Earth's area is covered by the ocean, and events that occur below, above, and above the ocean greatly affect our lives. Remote sensing and surveillance at sea are very important. Since its invention in the 1930s, radar has played a crucial role in remote sensing and surveillance. This report introduces the generation mechanism of sea clutter, the amplitude statistical model of sea clutter, the reflection coefficient of sea clutter, and the empirical model from the perspective of signal processing. Finally, it introduces various optimal detectors designed based on different sea clutter statistical models in remote sensing detection, and prospects the development of sea clutter detection and remote sensing detection technology.

 

 

 

 

Keynote Speaker 2: Prof. Ende Wang, Shenyang Ligong University, China

 

Brief Introduction: He used to be a researcher of Shenyang Institute of Automation, Chinese Academy of Sciences, a doctoral supervisor of the University of Chinese Academy of Sciences, one of the first special post researchers of Chinese Academy of Sciences, and a member of the Youth Innovation Promotion Association of Chinese Academy of Sciences.

 

Research Area: Optoelectronic guidance and countermeasures

 

Title: Optoelectronic guidance anti-interference technology

 

Main Content: 1. Laser semi-active guidance anti-interference Anti sunlight, backscatter, deception, high repetition rate. 2. Infrared imaging anti-interference, Features, Firestacks, Directional Lasers. 3. Composite guidance anti-interference, Composite guidance method and multi information fusion anti-interference

 

 

 

 

Keynote Speaker 3: Prof. Zhenliu Zhou, Shenyang Institute of Engineering, China

 

Brief Introduction: Dr. Zhou, professor, graduated from the computer network and security major of the University of the Chinese Academy of Sciences in June 2008 and obtained a doctor's degree. Liaoning Province's "Hundred and Ten Thousand Talents Project" is a high-level talent, a leading high-level talent in Shenyang, an honorary member of the Shenyang Branch Forum of the China Computer Society Youth Science and Technology Forum, a judicial appraiser of electronic evidence from the Shenyang Security Bureau, an external expert from the Liaoning Provincial Department of Security Digital Forensics Center, a member of the National University Big Data Education Alliance Council, and the director of the Shenyang Energy Internet Intelligent Perception and Security Technology Key Laboratory, The main research direction is computer networks and information security.

 

Research Area: Computer network, information security

 

Title: Intelligent Perception Technology and Development Trends for Network Security

 

Abstract: This report introduces the development process of network security perception technology, combined with the current new forms of cyberspace technology, introduces the background, significance, and role of research on network security intelligent perception. It introduces intelligent perception technology from multiple aspects such as intelligent perception architecture, functional structure, and key technologies. Finally, it analyzes and predicts the future development direction and trend of network security intelligent perception technology.

 

 

 

 

Keynote Speaker 4: Associate Researcher Xi Chen, Tsinghua University, China

 

Brief Introduction: Dr. Chen Xi, Ph.D. in Engineering, Associate Researcher at the National Center for Information Technology at Tsinghua University. He graduated with a doctoral degree from the Department of Electronic Engineering at Tsinghua University in 2016. His main research interests include integrated navigation of satellite communication constellations, large-scale opportunistic signal navigation, and inference problems in global fusion navigation. Teacher Chen Xi is a member of IEEE, a member of the China Electronics Society, a communication evaluation expert of the Natural Science Foundation, and an expert in the National Graduate Education Evaluation and Monitoring Expert Database.

 

Research Area: integrated navigation of satellite communication constellations, large-scale opportunistic signal navigation, and inference problems in global fusion navigation

 

Title: Satellite communication constellation integrated navigation

 

Abstract: With the vigorous development of broadband communication constellations, there has been a significant increase in satellite signals. If navigation can be utilized, it will greatly improve the accuracy and completeness of navigation and positioning. In this study, we report on the progress of broadband communication constellation integrated navigation related work, including (1) the theoretical field of satellite communication constellation integrated navigation; (2) Innovative arrival time estimation methods and Doppler estimation methods for integrated wideband signals with conduction; (3) Experimental results based on iridium stars.

 

 

 

 

Keynote Speaker 5: Researcher Feng Qi, Shenyang Institute of Automation, Chinese Academy of Sciences, China

 

Brief Introduction: Dr. Qi Feng, researcher, doctoral supervisor. Received a master's degree from the Catholic University of Leuven in Belgium in September 2005 and a doctoral degree from the Catholic University of Leuven in Belgium in March 2011. Researcher and doctoral supervisor of Shenyang Institute of Automation of the Chinese Academy of Sciences, distinguished researcher of the Chinese Academy of Sciences, Class A of the Hundred Talents Plan of the Chinese Academy of Sciences, hundred levels of the Hundred Thousand Talents Project of Liaoning Province, young top talents of Liaoning Province, and leading talents of Shenyang City. Currently serving as an expert in the national high-tech field theme expert group, director of the Liaoning Province Terahertz Imaging Perception Key Laboratory, director of the Academic Committee of the Guangdong Province Metamaterial Microwave RF Key Laboratory, member of the Academic Committee of the Ministry of Education Terahertz Technology Key Laboratory, member of the Academic Committee of the Key Laboratory of Optoelectronic Information Processing of the Chinese Academy of Sciences, and standing member of the Terahertz Biophysics Branch of the Chinese Biophysics Society.

 

Research Area: Microwave, laser, radar, acoustics

 

Title: Terahertz imaging sensing technology

 

Abstract: As the last electromagnetic band to be developed, terahertz contains rich spectral information that corresponds to the composition of matter; And in terms of imaging, it exhibits characteristics different from previous imaging technologies, which is a good compromise between penetration and resolution; This has brought new development opportunities for imaging technology. This report will introduce the research progress of the team in recent years from three aspects: imaging theory, core devices, and imaging systems, including perspective imaging and gas sensing technologies related to intelligent driving, non-destructive testing, and other related applications.

 

 

 

 

Keynote Speaker 6: A. Prof. Zhuo Wang, Shenyang Ligong University, China

 

Brief Introduction: Dr. Wang Zhuo is an associate professor and master's supervisor at Shenyang University of Technology. His main research interests include machine learning and anomaly detection. I graduated with a bachelor's degree in Computer Software from Xi'an University of Electronic Science and Technology in 1992, and a master's degree in Computer Software and Theory from Northeast University in 2004. Published over 30 papers in academic journals and international conferences both domestically and internationally. Served as a reviewer for internationally renowned journals such as Knowledge based Systems, Information Sciences, and Information Fusion

 

Research Area: Machine learning, anomaly detection

 

Title: False comment detection: past, present, and future

 

Abstract: With the continuous development of e-commerce, online shopping has become the preferred way for people to choose goods or services. However, due to the openness of online product reviews, the platform's review of user reviews is not strict. Driven by profits, illegal merchants or individuals may intentionally post false reviews, resulting in a decrease in the credibility of online product reviews and seriously affecting customers' judgment of product quality. Due to the difficulty of human identification of false comments, it is difficult to construct large-scale annotated datasets, which poses a huge challenge for machine learning methods to identify false comments. This report first reviews the research methods of false comment detection technology for online products in recent years, including supervised and unsupervised methods. Then, it summarizes the current mainstream technologies for false comment detection. Finally, combined with the latest research progress in machine learning, it looks forward to the future development direction of false comment detection technology. The report will also introduce various false comment detection methods proposed by the research group in recent years, and share research methods and insights on false comment detection issues.

 

 

 

 

Keynote Speaker 7: A. Prof. Yong Dai, Shenyang Ligong University, China

 

Brief Introduction: In the past three years, A. Prof. Yong Dai has led one basic scientific research general project of the Liaoning Provincial Department of Education, one school level research initiation project, one horizontal scientific research project of "unveiling and leading", and participated in three provincial, ministerial, and national scientific research projects; Published more than 20 academic papers in domestic and international academic journals, including more than 10 indexed by SCI and EI.

 

Research Area: Robot control and navigation technology, autonomous vehicle related technology

 

Title: Research on Low Cost Vision SLAM Technology for autonomous vehicle

 

Abstract: SLAM (simultaneous localization and mapping) is a technology that enables simultaneous localization and mapping in unknown environments. This technology only relies on the machine's own sensors and does not require any external information sources, achieving stable positioning work in unknown working environments. SLAM has become a rigid requirement in the field of autonomous driving. SLAM can be classified according to sensors, and common SLAM sensors can be classified into the following categories: laser sensors, RGB D sensors, and structured light vision sensors. Currently, laser and RGB D SLAM technologies are very mature, but their costs are relatively high. In today's increasingly fierce competition of automatic driving, how to quickly seize the market and enable vehicles to provide low-cost, high-precision SLAM technology that can be applied to autonomous vehicle has become the top priority. Due to its low production cost, structured light visual sensors have become a key research direction in the field of autonomous driving in major enterprises, institutions, and research institutes of universities.

 

 

 

 

Keynote Speaker 8: Prof. Pascal Lorenz, University of Haute Alsace Colmar, France

 

Brief Introduction: Pascal Lorenz received his M.Sc. and Ph.D. degrees from the University of Nancy, France, in 1990 and 1994, respectively. From 1990 to 1995, he was a research engineer with WorldFIP Europe and Alcatel-Alsthom, Boulogne-Billan-court, France. His research interests include QoS, wireless networks, and high-speed networks. He was a Technical Editor on the IEEE Communications Magazine Editorial Board from 2000 to 2006. He has been a Technical Editor of IEEE Network since 2015 and IEEE Transactions on Vehicular Technology since 2017. He is an Associate Editor for the International Journal of Communication Systems (Wiley), the Journal on Security and Communication Networks (Wiley), the International Journal of Business Data Communications and Networking, and the Journal of Network and Computer Applications (Elsevier). He is an IARIA Fellow and a member of many international program committees. He has organized many conferences, chaired several technical sessions, and given tutorials at major international conferences. He was an IEEE Com-Soc Distinguished Lecturer from 2013 to 2014.

 

Research Area: Telecommunication network topology, telecommunication security, cloud computing

 

Title: Advanced architectures of Next Generation Wireless Networks

 

Abstract: Internet Quality of Service (QoS) mechanisms are expected to enable wide spread use of real time services. New standards and new communication architectures allowing guaranteed QoS services are now developed. We will cover the issues of QoS provisioning in heterogeneous networks, Internet access over 5G networks and discusses most emerging technologies in the area of networks and telecommunications such as IoT, SDN, Edge Computing and MEC networking. We will also present routing, security, baseline architectures of the inter-networking protocols and end-to-end traffic management issues.

 

 

 

 

Keynote Speaker 9: A. Prof. Обходский Артём, Tomsk State University, Russia

 

Brief Introduction: A technology and software solution has been developed for measuring the electromagnetic parameters of the electrical and physical devices currently produced in Russia. As a leader, I have over 13 years of experience in research projects.
Published over 100 scientific papers. Successfully completed over 10 R&D project leaders and executors within the framework of the Federal Research and Development Plan in the priority areas of scientific and technological development in the Russian Federation.

 

Research Area: Telecommunication network topology, telecommunication security, cloud computing

 

Title: A multi-sensor complex for diagnosing diseases through exhaust gas samples

 

Abstract: Developed and manufactured for various diseases. Multi sensor complexes for molecular diagnosis; The complex consists of a set of semiconductor sensors and implements a neural network. Network data processing algorithm; Trained artificial neural networks can identify and distinguish patients with pathological processes. The composite molecular patterns contained in exhaled air samples and healthy exhaled air samples. Diagnosis time average not Over 5 minutes.

 

 

 

 

Keynote Speaker 10: A. Prof. Nasir Saeed, United Arab Emirates University (UAEU), UAE

 

Brief Introduction: Nasir Saeed (Senior Member, IEEE) his M.Sc. degree in Satellite Navigation from the Polito di Torino, Italy, in 2012. He received his Ph.D. in Electronics and Communication Engineering from Hanyang University, Seoul, South Korea, in 2015. He was an Assistant Professor with the Department of Electrical Engineering at IQRA National University, Peshawar, from 2015 to 2017. From July 2017 to December 2020, he was a Postdoctoral Research Fellow with the Communication Theory Laboratory at King Abdullah University of Science and Technology (KAUST), KSA. He is currently an Associate Professor with the Department of Electrical and Communication Engineering at United Arab Emirates University (UAEU), Al Ain, UAE. He has published more than 70 international journal and conference articles. He is also a Senior Member of IEEE and Associate Editor of IEEE Wireless Communications Letters. His current research interests include non-conventional communication networks, Heterogenous vertical networks, multi-dimensional signal processing, and localization.

 

Research Area: Digital Signal Processing, Satellite Communication, RF Engineering

 

Title: AquaTech Unleashed: Opportunities and Challenges in the Internet of Underwater Things

 

Abstract: The Internet of Underwater Things technology can be established using various underwater wireless communications technologies, including acoustic, radio frequency (RF), magnetic induction, and optical. Each of these technologies has its pros and cons; for example, acoustic technology reaches longer distances but is bandwidth limited, while underwater optical wireless communications (UOWCs) can support higher data rates at the cost of short ranges. This talk will highlight opportunities and challenges faced by Internet of Underwater Things Technology and present future research directions.

 

 

 

 

Keynote Speaker 11: A. Prof. Zeeshan Kaleem, COMSATS University Islamabad, Pakistan

 

Brief Introduction: Zeeshan Kaleem received his B.S. and M.S. Electronics Engineering from the University of Engineering and Technology (UET), Peshawar and Hanyang University, Korea in 2007 and 2010, respectively. He received his Ph.D. in Electronics Engineering Department from Inha University in 2016. From 2010 to 2012, he was a lecturer at Namal College, Pakistan (an associate college of the University of Bradford, UK). Since March 2016, He is working as an assistant professor in the Electrical and Computer Engineering Department, COMSATS University Islamabad, Wah Campus, Pakistan.

 

Research Area: Cellular Communicationm Communication Engineering, Telecommunications Engineering

 

Title: Artificial Intelligence to Detect Radio Frequency Signatures of Drones: State-of-the-art & Research Challenges

 

Abstract: In the rapidly evolving landscape of drone technology, the utilization of Artificial Intelligence (AI) for detecting radio frequency (RF) signatures emitted by drones has gained significant traction. This keynote presentation dives into the state-of-the-art advancements and persisting research challenges in this domain. The keynote will commence with an exploration of the latest AI-powered techniques that integrate machine learning and deep learning with RF signal processing to achieve accurate and efficient drone detection. However, the journey is accompanied by research challenges that demand attention. The variability in RF signatures due to diverse drone types and communication protocols poses a considerable hurdle. Moreover, the influence of environmental factors on RF propagation necessitates adaptive AI models. Real-time detection capabilities and the ethical implications of widespread drone surveillance form additional dimensions to be addressed. As a keynote speaker, this presentation will unravel these complexities and emphasize the need for adaptable AI models to accommodate evolving drone technology and RF emissions. By outlining the current progress in terms of simulation results and critical research directions, it aims to guide researchers and practitioners toward refining AI-driven RF drone detection systems. Ultimately, the keynote discusses the role of AI in enhancing security and safety in an area dominated by drones.

 

 

 

 

Keynote Speaker 12: Prof. Zhijun Zhang, South China University of Technology, China

 

Brief Introduction: Zhang Zhijun, a professor of South China University of Technology. Prof. Zhang is National top young talents, IEEE CIS (Computational Intelligence) Chairman of Guangzhou Chapter and IEEE Senior member. The director of the bionic intelligent robot laboratory of South China University of Technology, the director of the Hongqian Intelligent Human Machine Interaction Joint Laboratory of South China University of Technology, the director of the Tianxia Valley Artificial Intelligence and Digital Agriculture Joint Laboratory of South China University of Technology, the high-level talent scholar of South China University of Technology, the outstanding youth of Guangdong Province, and the top young talents of Guangdong Province in scientific and technological innovation. He has been engaged in neural network, machine learning, big data analysis, control optimization, swarm unmanned system and robot research for a long time. He is an editorial board member of the SCI journal Electronics, an executive editor in chief of the Global Journal of Neural Science, an associate editor in chief of the International Journal of Robotics and Control, and a reviewer of more than 20 international SCI professional journals, Evaluator of China National Foundation Committee and Guangdong Provincial High tech Enterprise. Zhang Zhijun has published more than 100 papers in important international journals (such as nature sub journal, IEEE Transactions) and conferences, 83 papers in SCI journals, and edit 2 English works. The total number of citations is 2998. It has accepted more than 100 invention patents and authorized 46 invention patents. Presided over or participated in more than 30 national key R&D projects, NSFC and horizontal projects.

 

Research Area: Robots, neural networks, machine learning, virtual reality, and human-computer interaction

 

Title: Vary Parameter Recurrent NeuralNetwork Applied to Intelligent Robots

 

Abstract: Everything in nature changes with time is eternal and absolute, while stationary is only relative. Inspired by this fundamental law of nature and based on the neurodynamic approach, Dr. Zhijun Zhang designed and proposed a varying-parameter recurrent neural network. Various forms of varyingparameter recurrent neural networks are designed and derived, and it is theoretically demonstrated that the network has the property of super-exponential convergence in solving time-varying problems and robot motion planning problems. In solving noise-containing problems, this model can effectively suppress noise and has obvious advantages over similar methods. The network model can effectively overcome the limitations of the existing methods in terms of slow convergence and weak robustness in solving time-varying, nonlinear, underdetermined, and multi-solution problems of robot systems in complex environments, and has the advantages of high solution accuracy, fast error convergence, and robustness. In practical systems, this method can be applied to robot motion planning, natural human-robot interaction and flight controller design and many other aspects.