Spoof surface plasmon polariton (SSPP) or designer surface plasmon polariton is the lower-frequency analogue to surface plasmon polariton (SPP) in optics, which can mediate surface electromagnetic waves propagating along corrugated metallic surfaces at microwave or millimeter-wave frequencies. Similar to SPP in optics, SSPP features strong field confinement capability and flexible manipulation of electromagnetic waves at a subwave length scale. It has inspired many interesting works in recent years, e.g. ultra-thin and flexible circuits, new hybrid-mode transmission lines, filters, etc. Moreover, SSPP attracted progressive attentions due to its potential applications in electromagnetic wave radiation and antenna design.
Prof. Dongfang Guan, National University of Defense Technology, China
Dongfang Guan was born in Henan Province, China in 1988. He received the B. S. and Ph. D. degrees at College of Communications Engineering of PLA University of Science and Technology in China in 2011 and 2016 respectively. He is now a lecturer at College of Electronic Science of National University of Defense Technology. His current research interests include microstrip antennas, array antenna, SIW technology, spoof surface plasmons and metamaterials.
Workshop 2 :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.
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. Please name the title of the submission email with “paper title_workshop title”.
Prof. Weijian Liu,Wuhan Electronic Information Institute, China
Weijian Liu received a Ph. D. degree in information and communication engineering at National University of Defense Technology in 2014. He is currently a Lecturer at Wuhan Electronic Information Institute. As the first author or corresponding author, he published 64 papers indexed by SCI. He participated in projects supported by National Natural Science Foundation, National Defense Key Laboratory, Foundation Natural Science Foundation of Hubei Province, etc. His current research interests include multichannel signal detection, statistical and array signal processing. He is a senior member of IEEE, and servers as an Associate Editor for the Circuits, Systems, and Signal Processing, and a youthful member of the editorial board of Journal of Terahertz Science and Electronic Information Technology.
Prof. Ningbo Liu,Naval Aviation University, China
Ningbo Liureceived a Ph. D. degree in information and communication engineering from Naval Aviation University and worked as a post-doctoral in the second academy of China Aerospace Science and Industry Corporation. He now works in the Information Fusion Institute of the Naval Aviation University. His research interest is intelligent signal processing and radar target detection. He participated in projects supported by National Natural Science Foundation projects, the field fund projects of the Equipment Development Department and the science and technology commission, Equip advance research projects, China postdoctoral fund project, etc. He won the first prize of provincial and ministerial science and technology progress award. He published a scholarly monograph 'Fractal Theory and Application in Radar Target Detection' and papers on IET Radar Sonar & Navigation, IEEE Geoscience, Remote Sensing Letter, Signal Processing, Journal of Physics. The papers were cited more than 400 times. He holds 10 authorized patents and 4 software copyrights.
Prof. Lei Zuo,Xidian University, China
Lei Zuo received the Ph. D. degree at Xidian University in 2014. He is currently an associate professor at the National Laboratory of Radar Signal Processing of Xidian University. He hosted projects supported by National Natural Science Foundation, National Laboratory Foundation, Natural Science Foundation of Shanxi Province, etc. His current research interests include target detection and target identification.
Workshop 3 : ADSP: Advanced Digital Signal Process
Title 1：Sparse Representation in Signal Processing
Keywords : Sparse, Signal Processing, Compressive Sensing, Imaging, Representation
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.
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.
With the development of the Internet of things technology and the Internet of things sensing node equipment, the perception node equipment is gradually changing to a variety of types and isomerization. How to manage network globally and allocate network resources as a whole has become a new research hot spot. At the same time, large-scale data transmission in the network also involves data security issues. For example, heterogeneous network convergence can guarantee the interconnection of various private networks and large-scale applications. In hybrid adaptive network, users can access multiple communication networks (commercial communication network, enterprise private network or comprehensive utilization of these two networks) at the same time, which will improve the performance, flexibility and affordability of the existing network. On the other hand, this multi network flexibility provides deterrent capability. It is easy for users to switch between communication networks, which increases the time, workload and cost of attacks. So, it has stronger security.
The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of hybrid adaptive network and understand how security technology can influence it. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case design.
Prof. Jinnan Zhang, Beijing University of Posts and Telecommunications, China
Jinnan Zhang received a Ph.D. degree and now is an associate professor inBeijing Universityof Postsand Telecommunications. He worked as a member at the State Key Laboratory of Information Photonics and Optical Communications. His research interests include Cloud Collaboration basedon Internetof Things, Intelligent Perception, Blockchain, Key Technologies of High-speed Communication System.He participated in the National High Technology Research and Development Program("863"Program) of China, Key Project of National Natural Science Foundation of China (NSFC), National Program on Key Basic Research Project (973 Program), Beijing Municipal Natural Science Foundation. Based on these projects, he published many academic papers.
Keywords : edge computing, video analysis, video compression, hardware acceleration
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.
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.
Optical network has been deployed in many scenes such as mobile backhaul, access network, metro network, core network, data center network, sensor network, etc. Since optical network covers a wide area and is composed of a large number of easily damaged optical components, it is highly susceptible to natural disasters or man-made destruction. Natural disasters such as earthquake, tsunami, hurricane, are most common and destructive failures due to those broad geographical coverage and very great destructiveness. To design a disaster-resilient optical network, various survivability technologies have been developed. However, most of proposed survivability technologies merely rely on optical network itself that it is difficult to deal with widespread link failures, network islanding, and even network paralysis, caused by severe natural disasters or man-made destructions. In 2008, the 7.8-magnitude Wenchuan earthquake in China leads to around 30,000 km of fiber optic cables cut and 4000 telecom offices becoming ineffective. Rescuers cannot contact the outside world merely using optical networks that brings great difficulty to rescue works. Besides using optical network to achieve uninterrupted traffic transmission, other mediums such as photonic millimeter-wave, satellite, long term evolution (LTE), etc., are also used to recover interrupted traffic in optical network. Satellites which are located at space area over 400 kilometers are disaster-independent with the TON. In other words, even if serious disasters occur on the earth, satellites can run properly. Moreover, it can provide homogeneous spatial light-paths for interrupted light-paths in optical network and avoids complex transformations of communication protocol, signal type, signal rate, etc. The satellite network provides a promising method to improve the survivability of TONs against disasters. Using heterogeneous media to achieve disaster-resilient survivability becomes an important research focus.
The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of disaster-resilient survivability of large-scale optical networks based on heterogeneous media. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews. Please name the title of the submission email with "paper title_workshop title".
Prof. Xin Li,Information Photonics and Optical Communications (IPOC), China
Xin Li received a Ph.D. degree in communication and information system from Beijing University of Posts and Telecommunications (BUPT) in 2014. He is currently an associate professor in the State Key Laboratory of Information Photonics and Optical Communications (IPOC). His research interests include networks designing, planning, the traffic control and resource allocations, network survivability, optical network security, artificial intelligence, etc. He has been actively undertaking several national projects, published more than 80 journals and refereed conferences, and authorized 10 patents. He was granted the first prize of technology invention award by the Chinese institute of electronics in 2017. He was granted the first prize of cooperation innovation award by the China industry-university-research institute collaboration association in 2016.
Title 1：Recent Advances in Edge Computing, Networking and Storage for Smart Cities
Keywords：Smart Cities, Edge/Fog computing, Edge storage, Internet of Things
A smart city is a framework, predominantly composed of the advanced information and communication technologies, which enables citizens to realize high quality of life via smart services. In the concrete, citizens engage with smart city ecosystems in various ways using smartphones, global positioning systems, smart sensors, and connected cars and homes. The abundant smart devices must reliably and availably transmit a diverse set of data obtained by capturing a physical aspect of the environment to deliver the cloud for processing. However, the cloud has bandwidth limitations when trying to deal with such a number of connections. Edge computing is a viable solution to address the limitations of cloud computing for enabling real-time smart cities environments. Edge computing, networking and storage are making the storage and computing closer to edge devices, enabling many citizen-oriented applications in smart cities. There are many important technical challenges, including reliable distributed storage for high-speed mobile devices, low-latency networking in complex and heterogeneous urban environments, information processing and computing with varied quality requirements, algorithms and protocols for better computing and communication service, the support for emerging applications including Internet of Things, intelligent traffic, smart home, and more.
The aim of this workshop is bring together the leading researchers and developers from both academia and industry to discuss and present their latest research and innovations on the theory, algorithms and system technologies that can substantially impact existing edge computing and networking or lead to novel future developments for smart cities. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.
Prof. Chunguo Li, Southeast University, China
Chunguo Li received the bachelor's degree from Shandong University in 2005, and Ph.D. degree from Southeast University in 2010. In July 2010, he joined the Faculty of Southeast University, Nanjing China, where he is currently an Advisor of Ph.D candidates and Full Professor. From June 2012 to June 2013, he was the Post-Doctoral researcher with Concordia University, Canada. From July 2013 to August 2014, he was with the DSL laboratory of Stanford University as visiting associate professor. From August 2017 to July 2019, he was the adjunct professor of Xizang Minzu University under the supporting Tibet program organized by China National Human Resources Ministry.
He is an IET Fellow, IEEE Computational Intelligence Society (CIS) Nanjing Chapter Chair, and IEEE Senior Member. His research interests are in wireless communications and cyberspace security, and machine learning/artificial intelligence based image/video signal processing. He has published over one hundred international journal papers with over one thousand citations. He received six best conference paper awards and six province awards of science and technology progress. He serves some international journals such as IET Communications as associate editor. He serves some international conferences as organizer/session chair in long term.
Prof. Jinjin Zhang,Nanjing Audit University, China
Jinjin Zhang received a Ph.D. degree in technology of computer application from Nanjing University of Aeronautics and Astronautics in 2011. In May 2011. He is the vice dean of the school of information engineering of Nanjing Audit University. His research interests are in formal methods and logic in computer science.
He participated in the National Natural Science Foundation, National Key Projects, and Natural Science Foundation of Henan Province. He has published Information and Computation, Formal Methods in Computer Science, and so on. He has been invited to serve as a reviewer in several journals and international conferences, e.g., Chinese Journal of Computers, Computer Research and Development, and Computer Science.
Prof. Haibo Dai,Nanjing University of Posts and Telecommunications, China
Haibo Dai received the Ph.D degree in electrical engineering from Southeast University, Nanjing, China, in 2017. Since February 2018, he has been a faculty member in the School of Internet of Things, Nanjing University of Posts and Telecommunications. His current research interests include wireless resource allocation and management, vehicle-to-everything communications, unmanned aerial vehicle communications, optimization in space-air-ground integrated networks, game theory and artificial intelligence in 5G networks and beyond. He participated in the National Natural Science Foundation, National Science and Technology Major Project of China, China Postdoctoral Science Foundation. Based on these projects, he published many academic papers.
Prof. Wandong Xue,Qinghai University, China
Wandong Xue, received a Master Degree in Computer Science from Lanzhou university of Technology, He worked as a member at Qinghai University. His research interests include Intelligent transportation, artificial intelligence, embedded and natural language.
He participated in two projects of National Natural Science Foundation, which are the project of the "Chunhui Program" of the Ministry of Education and the project of Qinghai Provincial Science and Technology Program. The first applicant was approved as the Youth Research Fund Project of Qinghai University in 2020, etc.
He has published four EI papers, one SCI paper, and three soft works. His first inventor has obtained three invention patents.
Title 1：Emerging Antennas and circuits for 5G Applications
Keywords：5G mobile communication, Antennas, Microwave, Millimeter wave, RF components
With the blooming of wireless technologies for communications, fifth-generation (5G) mobile networks have become one of the hottest topics in recent years. For any 5G wireless devices and systems, antennas, filters, amplifiers, mixers, and so forth are crucial components for the RF frontend. However, most conventional designs have difficulty satisfying the stringent requirements of the 5G mobile communications on bandwidth, radiation pattern, size, and cost. For example, antennas and circuits for implantable systems and Internet of Things are desired to be with new frequency band, low profile, compact size, low cost, and easy integration. Thus, the demand for different types of novel and high-performance antennas and circuits is increasing exponentially.
This workshop is intended to propose and discuss the design of microwave and millimeter-wave antennas and circuits regarding the 5G mobile communications. The objective of this workshop is to establish the state of the art for the most relevant problems in RF components and to search for novel efficient concepts and designs in the related areas. It is expected to address and report recent attractive topics in antennas and circuits for the 5G techniques. The desired purpose of the workshop is to make a contribution to 5G mobile communications in both academic and industrial areas. We invite investigators to submit original research articles and reviews to this workshop.
Prof. Gang Zhang,Nanjing Normal University (NNU), China
Gang Zhang, received his PhD degree in electronics and information engineering at Nanjing University of Science and Technology (NUST), Nanjing, China, in 2017. He is currently working at School of Electrical and Automation Engineering, Nanjing Normal University (NNU), Nanjing, China. In Sep. 2013 to Oct. 2014, he was a visiting scholar with the School of Information Technology and Electrical Engineering, University of Queensland, Australia. Dr. Zhang is a session chair in 2020 Cross Strait Radio Science & Wireless Technology Conference (CSRSWTC 2020) and a reviewer for several top-tier journals. He has been an associate editor of IET ELECTRONICS LETTERS and a Lead Guest Editor of FRONTIERS IN PHYSICS since 2020. His research interests include miniaturized high-performance microwave/millimeter-wave multi-functions integrated passive components, and numerical synthesis methods in electromagnetics.
Prof. Bo Li, Nanjing University of Posts and Telecommunications (NJUPT), China
Bo Li was born in Hunan, China. He received the B.S. and Ph.D. degrees both in communication engineering from Nanjing University of Science and Technology (NJUST), China, in 2006 and 2011, respectively.
From Nov. 2011 to May 2014, he was with Nanyang Technological University (NTU), Singapore, as a Research Fellow. From Jul. 2017 to Jan. 2018, he was with the Faculty of Science and Technology, University of Macau (UM), China, as a Post-Doctoral Fellow. From Jan. 2018 to Apr. 2018, he was with the State Key Laboratory of Terahertz and Millimeter Waves (SKLTMW), City University of Hong Kong, China, as a Visiting Professor. Since June 2014, he joined Nanjing University of Posts and Telecommunications (NJUPT), China, and has been a Professor in the School of Electronic and Optical Engineering, NJUPT, since July 2015. He has authored or co-authored more than 60 papers in international journals and conference proceedings.
Prof. Jinxin Li,Hunan University, China
Jinxin Lireceived the B.S. degree in electronic information science and technology in 2008 and Ph.D degree in electromagnetic fieldand microwave technology in 2017 from the University of Electronic Science and Technology of China(UESTC), Chengdu, China. From 2015 to 2016, he was a Visiting Ph.D. Student at the Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
He is currently an assistant professor at Hunan University since 2017 and a visiting researcher of State Key Laboratory of Millimeter Wave (Southeast University)since 2018. His research interests include antenna arrays,artificial intelligence, array decoupling, array synthesis, metamaterials, and passive devices and circuit.Dr Li is the author or coauthor of more than30 papers published in journals and conference proceedings. He has been served as a reviewer for several international journals and conferences. He is also the young editors of Journal of Liaocheng University.
Title 1 : Data mining and intelligent information extraction
Keywords： Data mining, Information extraction, Artificial intelligence, Big data
With the rapid development of social networks, sensor technology and mobile Internet technology, a large number of multi-category, heterogeneous, and unstructured data have been generated. Mastering and mining big data such as mobile phones, Weibo, and the Internet can perceive crowd activities. It has been widely used in urban building identification, population estimation, public resource allocation and other fields, providing information support for urban planning and management. In addition, big data mining can quickly obtain disaster area information, assess the impact of disasters in real time, and provide data support for the government or relevant departments to make decisions, which is of great significance for rapid emergency rescue.
Since the 1990s, intelligent information extraction methods have emerged. Shallow machine learning such as support vector machines and Boosting has quickly become the mainstream algorithm for remote sensing intelligent information extraction. In recent years, deep learning network models have been continuously improved, and breakthroughs have been made in image recognition and information extraction. The accuracy of many tasks has surpassed the accuracy of manual identification, and it is used for scientific research and development in smart cities, environmental monitoring, disaster information early warning and acquisition, land use monitoring, vegetation monitoring and other industries.
The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of data mining 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. Please name the title of the submission email with “paper title_ workshop title”.
Prof. Xiaoyong Zhang,University of Beijing Information Science and Technology, China
Xiaoyong Zhang, received a Ph.D. degree in Geomorphology and Geographical Information System at Peking University. She is currently worked at the Beijing Key Laboratory of High Dynamic Navigation Technology, University of Beijing Information Science and Technology. Her research interests include Mobile data mining, Intelligent extraction of remote sensing big data, and Disaster Assessment. He participated in the National Natural Science Foundation, Natural Science Foundation of Beijing Province, and National Key Research and Development Project. Based on these projects, she published many academic papers.
Title 1：Integrated Intelligent Permanent Magnetic Vacuum Circuit Breaker
Keywords：permanent magnetic vacuum circuit breaker, integrated, Intelligent, on-line monitoring, IEC61850
This workshop will Introduce Research background and the implementation method product function and the type test result of the Integrated Intelligent permanent magnetic vacuum circuit breaker，which integrated the sensors into the vacuum circuit breaker frame and integrated the protection、measuring、control、monitoring，communication function into an intelligent terminal which puts in the circuit breakers frame. The type test results showed that Integrated Intelligent breaker can achieve On-line Monitoring for Mechanical Characteristics and temperature and electric classis，as well as the protection and control function communication of IEC61850 etc.
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 the permanent magnetic vacuum circuit breaker. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews. Please name the title of the submission email with “paper title_workshp title”.
Prof. Dongyun Dai,Xiamen University of Technology, China
Senior Engineer of Xiamen University of Technology since 2020. She received a master's degree in electronic engineering from Huazhong University of Science and technology in 2007. She worked as an engineer in Xi’an High Voltage Apparatus Research Institute, from 2007 to 2019. She led the project of 12/24kV Intelligent KYN switchgearand VCB and joined in the design of self-adaptation Control and monitoring System of 40.5kV Permanent Magnetic Vacuum Synchronous Current-breaker and 550kV fault current limiter. She is familiar with primary switchgearand secondary intelligent electrical device fusion and its Edge calculation and Reliability, evaluation.
Title 1 : Radar Technologies and Its Signal (or Information) Processing for Meteorological Applications
Keywords：New technologies for meteorological radar, Polarimetric, Fast scanning, Weather surveillance, Suppression for clutters and interference
The meteorological radars play an important part in weather forecasting, monitoring, predicting and managing meteorological disasters, and weather modifications. In the contradiction between quantitative observation requirements for multiple variables, fast scanning and the weather process with complexity, wide range, and changing sharply and quickly, the meteorological radars show great differences with other radars, such as air traffic control radars and military radars. The uniqueness of meteorological radars which accomplished special form in detection principle, system construction, signal processing and information retrieved technology, as well as observation mode and data application method, makes them always play an important role in the radar families. New technologies for radars and their signal processing have also been, are being or will be introduced into meteorological radars. The study of multi-function radars, including weather detection functions, is also underway.
At present, all over the world, a lot of studies for weather radar, cloud radar, radar wind profiler, upper-air wind observation radar cooperated with radiosonde, as well as sodar and lidar, etc. have been accomplished partly, and shown in new radar systems, technologies, processing methods and application models. Many of these had been employed and applied in meteorological and environmental operations and researches. Such a lot of results are reported and communicated, will play a great role in the development of meteorological radars and their applications in meteorology, especially in providing direct data for supporting the disaster (weather disasters such as flash floods, urban floods, and secondary disasters such as landslides) prevention and reduction. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews. Please name the title of the submission email with “paper title_workshp title”.
Prof. Jianxin He, vice president of Chengdu University of Information Technology, China
Prof. Jianxin He, vice president of Chengdu University of Information Technology, Ph.D. Supervisor, Director of the Key Laboratory of Atmospheric Sounding (KLAS), Head of the National Atmospheric Observation Technology Teaching Team, Academic Leader of Meteorological Radar Signal Processing Technology Research Team, Academic Technology Leader of Sichuan Province, Excellent Experts with outstanding contributions in Sichuan Province. Prof. He has been engaged in scientific research on weather radar systems and radar signal processing for a long time, and has improved the signal processing methods and data quality control theory of Doppler and dual-polarization weather radars. Moreover, hehas made great effortsto develop FPGA application in weather radar and has alsoimproved the detection performance and stability of weather radars. The weather radar signal processors developed are widely used in the new generation of weather radar networks, which promotes the wide application and social benefits of radar weather.
In the 21st century, information technology has been rapidly developed and used, but modern network information systems are more open, so there are bound to be many potential security risks. Some people can even set up various passwords and accounts to protect their own security. However, both online fraud and corresponding security measures have shown increasingly higher technological substitutions through technological development. As the basic technology of various advanced technologies, biometrics (including facial recognition, vein recognition, red membrane recognition, eye pattern recognition, fingerprint recognition, voiceprint recognition, gait recognition, handwriting recognition, etc.) can be more secure and effective. Local verification of identity information plays an increasingly important role in Internet security and information authentication. In the recent year of the major global epidemic environment, face recognition and vein recognition are the representative creatures. The rapid development of recognition technology, and the entry of artificial intelligence technology into the field of biometrics, is bound to further improve the accuracy and security of biometrics, and shine in the field of network security.
This workshop aims to bring together the Authentication Technology&Network and Computer Security research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of Authentication Technology&Network and Computer Security, including Software Reliability and Software Testing. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches, algorithms and practical case reviews. Please name the title of the submission email with “paper title_workshop number”.
Prof. Huimin Lu,School of Computer Science and Engineering, Changchun University of Technology, China
Huimin Lu received a Ph.D. degree in Computer Science and Technology from Xi’an Jiaotong University, a postdoctoral of Jilin University, and visiting scholar of University of Missouri - Columbia, USA. She is a professor and Ph.D. Supervisor at Changchun University of Technology. Her research interests include Data Analysis and Mining, Artificial Intelligence, and Biometric Identification.
She participated in the National Natural Science Foundation, and National 863 Program. She also undertook the National Natural Science Foundation, and Key Projects, Key Research and Development Projects, Industrial Technology Research and Development Projects, and Natural Science Foundation of Jilin Province. Based on these projects, she published more than 20 academic papers which were indexed by SCIE and EI. She has also obtained more than 10 patents and the Science and Technology Progress Award of Jilin Province.
Title 1：Robot Perception, imitation, control, decision
Keywords：Robot, Multi agent, Big Data, Artificial intelligence, Perception, control, decision, ROS, Chip, Design
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 the behavior of robots by perceiving external information.For example,the camera of robot is used to capture video information.Sensors collect all kind of data includes 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 the enhancement of it.We mainly deployed the system under the ROS and on the main board of the computer.Finally,we design robot step by step and make 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 robot 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. Please name the title of the submission email with “Robot Perception, imitation, control, decision making”.
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.
Keywords: Signal Processing, Equipment fault diagnosis
Prof. Fan Jiang, China University of Mining and Technology, China
Fan Jiang is associate professor, master tutor, director of the Rotor Dynamics Professional Committee of China Vibration Engineering Society with Ph.D., was selected as the vice president of science and technology of "Jiangsu Science and Technology Association Young Science and Technology Talents Lifting Project" in 2018 and "Double Innovation Plan" in Jiangsu Province in 2017.The main research interests are: big data processing and analysis, intelligent diagnosis and health assessment, intelligent monitoring and control.
As the project leader, he presided over the National Natural Science Foundation of China Youth Fund Project, Jiangsu Natural Science Foundation Youth Fund Project, the first-class support project for Chinese doctoral students, and the Youth Science and Technology Fund Project of China University of Mining and Technology.
Title 1：Heterogeneous Data Fusion and Deep Learning for Smart City
Keywords: Posture Recognition, Edge Computing, Heterogeneous Data Fusion, Indoor Positioning
Smart City, established based on Digital City, Internet of things, and Cloud Computing, is the integration of the real world and the digital world. It has a wide prospect for economic transformation and development, smart urban management, and intelligent services for the public. The upsurge of intellectualization has brought about research hotspots of Smart City including intelligent perception and adaptive feature extraction and fusion of multi-source heterogeneous big data, real-time scheduling and optimization of cloud resources for the complex environment, multi-level integration and visualization of big data based on deep learning and open and fault-tolerant cloud platform architecture for flexible scheduling services, which are widely applied in areas of Intelligent Security, Intelligent Education, Intelligent Care, Intelligent Monitoring, etc.
One aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. And the other goal is to present the latest research results of multi-source heterogeneous big data fusion and its deep learning in the field of Smart City. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case review.
Prof. Hou Qing, College of computer science and technology, Guizhou University, China
Hou Qing, received a doctor’s degree from the Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University in 2017, works as a professor and master's supervisor of Guizhou University, and the general manager of Group Customer Department of China ComService Guizhou Company, as well as a professor-level senior engineer. His research interests include data mining and analysis, image processing and the Internet of Things. He has been engaged in planning consultation, R&D and implementation and software development in communication and information industry for years, and has taken the lead to accomplish more than 100 projects, published 20 papers and obtained over 10 invention patents. He obtained the Title of National Excellent Communication Designer and Labor Model of China Telecom.
Brain like computing is a highly intersected and integrated life science, especially brain science and information technology. Its technical connotation includes a deep understanding of the principle of brain information processing. On this basis, a new processor, algorithm, and system integration architecture are developed and applied to a wide range of fields such as new generation artificial intelligence, big data processing, and human-computer interaction. A brain-like computer is a new computer model which simulates the operation of a brain neural network and has ultra-large-scale pulse real-time communication.
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 Brain-like Computing 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. Please name the title of the submission email with “paper title_workshop number”.
Prof. Hongwei Mo, Harbin Engineering University, China
Hongwei Mo, born in 1973. He is a professor at Automation College of Harbin Engineering University. He got Ph.D. degree from the same university in 2005. He is a visiting Scholar of UCDavis, CA, USA from 10, 2003-10, 2004.
He worked as a lecturer, associate professor, and professor in Automation College, Harbin Engineering University from 1996 till now.
His main research interests includes Natural Computing, Intelligent System, Artificial Intelligence, Intelligence Robot, and UAV. He has published 80 papers on nature inspired computing in international journals and conferences. He is the guest editor of Special issue on Nature inspired computing and applications of Journal of Information Technology Research. He is the author of 5 monographs and he is the editor of "Handbook of Artificial Immune Systems and Nature inspired computing: Applying Complex Adaptive Technologies".
And he is a member of the IEEE Computing Intelligence Society, IEEE Robotics and Automaton Society. He is also the program committee member over 30 international conferences. And he is the organization committees chair of the 2013 International Conference on Swarm Intelligence. He is the program committees chair of 2017 International Conference on Bio-inspired Computing Theories and Applications.
He serves as the associate editor of the International Journal of Computing Intelligence and Pattern Recognition, member of editorial boards of the Journal of Information Technology Research and International Journal of Swarm Intelligence Research, International Journal of Robotics and Automation Technology.
With the development of wireless communication technology, the user’s demand for diversified wireless services is growing rapidly. As this demand increases,the user’s pursuit of higher bandwidth is expected to be higher and higher. However, the spectrum of conventional radio frequency (RF) communications is very scarce. Optical wireless communications (OWC) have been considered as a promising complementary to the overcrowded spectrum of RF transmission. Compared to traditional RF communications, OWC has many advantages, such as license-free operation, immunity to electro-magnetic interference, network security, and tremendous bandwidth. By providing important technical and operational advantages, OWC is receiving increasing attention within the research community. OWC can be implemented in three major bands of infrared, ultraviolet, and visible-light. Therefore, sub-technologies including free-space optical (FSO) communications, visible light communications (VLC), infrared communications, and ultraviolet communications belong to the field of OWC. The OWC systems can be applied to many application areas such as indoor, outdoor, vehicular, terrestrial, underwater, intersatellite, deep-space, etc. At present, OWC faces several challenges in terms of weather effects, channel impairments, eye and skin safety regulations, compatibility with existing networks, mobility, system performance, and transceiver design. These challenges lead to a lot of research activities on OWC and need to be addressed by the scientific community at large.
This workshop aims to bring together the research accomplishments provided by researchers from academia and the industry working in LiFi, VLC, FSO communications, infrared communications as well as ultraviolet communications. Potential topics of interest include, but are not limited to: channel modelling, performance analysis, parameter optimizations, modulation and coding schemes, transceiver design, positioning technique, signal processing, resource allocation, physical layer security, and OWC applications. We encourage prospective authors to submit related distinguished research papers on the subject of both theoretical approaches and practical case reviews. Please name the title of the submission email with “paper title_workshop title”.
Prof. Jin-Yuan Wang,Nanjing University of Posts and Telecommunications, China
Jin-Yuan Wangreceived 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 optical wireless communications.
He has participated in the National Natural Science Foundation of China,the Natural Science Foundation of Jiangsu Province, the Key International Cooperation Research Project, the National Key Research and Development Program of China, etc. Based on these projects, he has authored or co-authored more than 100 journals/conference papers. He is a member of the China Institute of Electronics (CIE), China Institute of Communications(CIC), and Institute of Electrical and Electronics Engineers (IEEE). He has been a Technical Program Committee Member of many international conferences, such as IEEE ICC and WTS. He also serves as a reviewer for many international journals, such as IEEE JSAC, IEEE TWC, IEEE TCOM, and IEEE TVT.
Title 1：Advanced Imaging Technique for Pulmonary Disease
Keywords:Medical Electronics. Medical Imaging. Intelligent Systems. Image and Vision. Artificial Intelligence. Machine Learning. Pulmonary/lung disease
Due to smoking, air pollution, unhealthy lifestyles and other reasons, pulmonary disease has become the world's fourth leading cause of death. Early detection and diagnosis is very important for alleviating the progression of pulmonary disease. Chest imaging can provide more information for the diagnosis of abnormality on the lungs and gradually become the main method for the diagnosis of pulmonary diseases. However, according to the needs of early detection and long-term monitoring of pulmonary diseases, there is still a big gap between the current chest imaging methods and the goal of precision medicine.
This workshop aims to seek opportunities for knowledge exchange, cooperation and collaboration of scientists from both medical and engineering fields. The topics of the workshop include but not limit to (1) the physiological characteristics of lungs, (2) the development mechanism of pulmonary disease, (3) the development status and trends of chest imaging technology, (4) new theories and technologies for pulmonary disease diagnosis and monitoring.
Prof. Jiabin Jia, School of Engineering, The University of Edinburgh, UK
Jiabin Jia received Ph.D. Degrees from the University of Leeds in 2010. Following three years as a Research Fellow, he started to work at the University of Edinburgh in 2013. He is now a Senior Lecture in Electronic and Electrical Engineering at School of Engineering, the University of Edinburgh. He is a senior member of IEEE. His specialize in Agile Tomography research. His research areas include electrical impedance tomography, medical imaging and industrial process multiphase flow dynamics.
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.
The theme of "Big data management decision and information resource management" involves big data decision analysis and cloud computing, data mining and intelligent computing, artificial intelligence and deep learning, etc., explores the reality and necessity of big data and information resource management in the fields of professional construction and discipline development, and provides innovative ideas and methods for big data management decision-making and information resource management The latest international frontier trends, such as practice, fully demonstrate the status quo of big data international research frontier areas, and explore the possibility and reality of international cooperation between universities in the field of professional subjects.
The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of big data decision analysis and cloud computing, data mining and block chain Technology, artificial intelligence and deep learning, 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. Please name the title of the submission email with “paper title_workshp title”.
Prof.Xiaosong Wu, Yunnan University of Finance and Economics, China
Xiaosong Wu, Received a Ph.D. degree in Management Science from , Department of Management and Economics, Kunming University of Science and technology, China, majoring in Management Science and Engineering. Thesis title “Study on the influence of national innovation system to enterprise innovation capability and innovation performance”. Currently Dr. Wu leads the IDII for Informatics and system and the Innovation centre of information school YNUFE. The Centre has 1 staff and 5 Master students, focusing on several key research areas such as software engineering, information visualisation, data mining and system modelling. Professor Wu as a director of IDII in Yunnan University of Finance and Economics. has been active in the research and commercialisation in cooperation governance and service-based systems. He has won 3 external grants and successfully led or leading three major externally funded projects with the role of academic visiting, at a total grant value to CSC and UNESCO of over £16K. Meanwhile He participated in the National Natural Science Foundation, National Key Projects, National Key Research and Development Project, Natural Science Foundation of Yunnan Province, Tackling Key Scientific and Technological Problems in Yunnan Province. Based on these projects, he published many academic papers. Such as, has published over 3 papers in refereed international journals and conferences and 7 book chapters. in China, USA and at International Level (PCT). He has been the Co-chair or PC member of IEEE and IASTED International Conferences.
With the rapid development of artificial intelligence technology, big data technology and image processing involve the most extensive application fields, and are becoming more and more common, complex and evolving. Understanding the flow and behavior in such problems is a difficult and crucial task. One is to understand the development of image processing technology and its application in various scenarios, and the other is big data analysis. Among them, traffic big data is one of the most analytically valuable data fields. With the continuous increase in the number of car drivers, the number of car ownership has increased year by year, the diversity of driving users' behaviors, and the gradual maturity of autonomous driving technology and other factors, in order to optimize the operation mode of the road, intelligent transportation systems have emerged as the times require, and the road operation is moving towards The development of intelligence and automation. Utilize the traffic big data generated by various intelligent road facilities or on-board equipment, such as image acquisition devices, ETC systems and GPS, etc., through traffic big data analysis, on the one hand, it is possible to analyze the settings, functions and defects of each intelligent transportation system, including analysis The detection and verification of the distribution law and distribution position of the intelligent transportation system facilities on the road, as well as the analysis of the abnormal state of the equipment, the correction of the abnormality of the equipment, etc., on the other hand, the analysis and recognition of various attributes and characteristics of the road and vehicles, including analysis and recognition Road speed limit information and road capacity recognition, etc., perceive the traffic situation of the road, and predict the traffic state in a short period of time in the future. The purpose of this seminar is to gather research results provided by academics and industry researchers. Another goal is to showcase the latest research results in the field of big data technology and image processing, and understand the impact of optimization strategies on it. We encourage authors to submit relevant outstanding research papers on the following two topics: theoretical methods and practical case applications.
Prof.Fumin Zou, Fujian University of Technology, China
Fumin Zou, received a Ph.D. degree in Traffic Information Engineering and Control from Central South University. He is currently Secretary-General of Fujian Automotive Networking Industry Alliance, Director of Fujian Key Laboratory of Automotive Electronics and Electric Drive Technology, Director of Digital Fujian Transportation Big Data Research Institute. His research interests include Traffic Information and Control Engineering, Big Data Technology and Artificial Intelligence.
He presided over the National Natural Science Foundation of China, the major scientific and technological projects of Fujian Province and other scientific research projects, and obtained more than 100 national invention patents, including 3 US invention patents. The "backbone transmission network system based on multi-mode Mesh technology" that he presided over has been transferred to a certain department of the General Assembly of the People's Liberation Army; he took the lead in organizing the "Big Data-based Road Transport Intelligent Transportation Information Service System And Its Terminal" has become a national standard. Based on these projects, he published many academic papers. He has published more than 80 academic papers in conferences and journals such as Journal of Railways, Highway Traffic Technology, IEEE ACCESS, etc. He instructed students to win many awards, including the Bronze Award in the Challenge Cup National Competition, and the third prize in the Internet + National Competition.
Intelligent software engineering is a new direction of integration of software engineering and artificial intelligence. The research content of intelligent software engineering is very extensive, including: the development of intelligent software tools to improve the efficiency of software engineering, but also including the construction of highly intelligent software. The operating system is one of the most critical basic software. As the core object of software engineering research, the operating system has experienced decades of development, and has developed several generations of different characteristics of the operating system.
With the development of artificial intelligence, we hope to further explore the integration of intelligent software engineering and operating system. The contents of this discussion group include but are not limited to the following: Research on integration of artificial intelligence and software engineering; research on intelligent software tools; research on intelligent software development; research on integration of artificial intelligence and operating system; research progress of intelligent operating system; research on operation system Research on system intelligence, research and development of intelligent operating system tools, etc.
At the same time, the seminar encourages authors to submit theoretical methods and practical cases of research papers on related topics. Please name the title of the submission email with “paper title_work-shop Of Intelligent Software Engineering and Operating Systems”.
Prof.Jin Zhang，Hunan Normal University，china
Jin Zhang is a professor at Hunan Normal University since 2014. He is in charge of the key lab of intelligent software engineer of Hunan Normal University since 2019. His research interests include software engineering, data mining and artificial intelligence.
Title 1 : Advanced Sensing and Detection Technology
Keywords:FiberSensors; Optics Devices; Optoelectronics Detection; Sensor Design; Sensing Signal Demodulation; Laser Detectors; OpticalIntegration; Interferometers; Micro/Nano-photonics; Industry Measurement; Lab on a Chip; Sensitive Materials
Advanced sensing and detection technologies play an extremely important role in our daily life and production process, which can greatly promote the intelligent development in many fields, including health maintenance, extreme environmental parameter monitoring, environmental pollution control, industry manufacturing and artificial intelligence, etc. Intelligent measurement and control instruments, photoelectric detection technology, laser detection, nano-photonics and optical fiber sensing are the most popular research directions with broad application prospects. In recent years, a variety of intelligent measurement and control instruments have been demonstrated with the miniature size and high performance. They have played a unique role in the fields of automation technology, aerospace, military, biotechnology, and medical treatment. The corresponding devices include detection/analysis technology, robot sensors, and wearable sensors. Photoelectric detection technology has been developed rapidly with the characteristics of high measurement accuracy, high speed, and a high degree of automation, and widely used for non-contact measuring with fast speed and highprecision. Optical fiber sensor has become one of the hotspots in the field of sense due to its advantages oflightweight, small size, high sensitivity, anti-electromagnetic interference, and easy multiplexing to form distributed measurement. Nanophotonics is an interdisciplinary research area combined with nanoscience and photonics. The study on the interaction mechanism of light and matter at the nanometer scale has greatly promoted the development of single-molecule detection technologies with extremely high sensitivity. Additionally, the discovery and application of low-dimensional nanomaterials such as graphene, carbon nanotubes, and quantum dots have greatly enhanced the performance of sensor devices. In this workshop, all kinds of sensor design and detection methods are suggested. Excellent works will be recommended to SCI index journals and win the cash reward.
Prof. Jin Li, College of Information Science and Engineering, Northeastern University, China
Jin Li is an Associate Professor in the College of Information Science and Engineering at Northeastern University, China since Jan. 2016. He was born in the city of Yuncheng, Shan Xi Province, China in Dec. 1983. He earned a Bachelor’s degree in Electronic Science and Technology at Harbin Institute of Technology (HIT), China in 2007, a Master's degree in Physical Electronics at HIT, China in 2009, a Doctor's degree in Physical Electronics at HIT, China in 2013. His research interests include micro/nano-structured photonics, nanomaterials’ nonlinear optics, micro/nanofiber sensors , and integrated optical devices.
As an essential means of industrial innovation, artificial intelligence technology represented by the Internet of Things, big data, and cloud computing is an inevitable trend that is applied in the ecological field. We encourage prospective authors to submit related distinguished research papers on the 2 subjects: theoretical approaches and practical case reviews. Please name the title of the submission email with “paper title_workshop title”.
Prof. Kunrong Hu, School of Southwest Forestry University
Hu Kunrong, Associate professor, Master of Forest Management, head of Department of Data Science and Engineering. He isfocusing on the application research of technologies such as Internet of Things, Big Data, "3S" Technology and Machine Learning in forest management science, forest ecological monitoring, and forest biodiversity protection. The main research is to provide integrated innovation solutions for complex systems oriented to the Internet of Things and big data, and to study the principles and methods of integrated innovation of complex systems.
Prof. Yongke Sun, School of Southwest Forestry University
Yongke Sun was born in Xianyang, Shanxi, China in 1980, received the M.S. degree from Yunnan University, in 2012. He is recently pursuing the Ph.D. degree of Southwest Forestry University. His research interests include big data, network security and computer-aided wood identification.
Prof. Youjie Zhao, received his Master degree from Yangzhou University
Youjie Zhao, received his Master degree from Yangzhou University, Yangzhou, China, in 2007. He is an Associate Professor in the School of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, China. He has authored or coauthored more than 20 peer-reviewed research papers index by SCI. His research interests include bioinformatics and big data.
Prof. Yong Cao , got Ph.D degree from University of Electronic Science and Technology of China on computer application technology in 2010.
Yong Cao (ORCID: 0000-0002-9545-8203) got Ph.D degree from University of Electronic Science and Technology of China on computer application technology in 2010. He has been working as an associate professor at Big Data and Intelligence College in Southwest Forestry University since 2011. His main research interests include complex networks, machine learning, bioinformatics, computer-aided wood identification, nonlinear science and software engineering. He is a co-founder of International Engineering and Technology Institute (IETI) and an expert in the field of computer science.
With the development of machine learning, In particular, the widely successful application of deep learning, cyberspace security t can be solved by machine learning.For example, the detection of inferior chip or hardware Trojan horse, pseudo-base station detection, virtualization security, credit card fraud and so on can be abstracted as classification problems; Device identity authentication, abnormal social network account detection, network intrusion detection and so on can be abstracted as clustering problems; User identity authentication, malicious/abnormal/intrusion detection, forensics analysis, network public opinion and other issues can be abstracted as both classification problems and clustering problems.
Prof. Haixia Long, School of Information Science and Technology Hainan Normal University, China
Haixia Long, Professor, Master's Tutor, Doctor of Engineering, Major in Computer Application. Hainan Nanhai Famous Masters, Hainan Province High-level Top-notch Talents. Three textbooks and two monographs have been published; Presided over 1 National Natural Science Foundation under research, 3 Hainan Natural Science Foundation projects and 1 Hainan Education Department project; As the first finisher, he won one third prize of Hainan Provincial Science and Technology Progress Award, and as the first finisher, he won one third prize of Hainan Provincial University Outstanding Scientific Research Achievement Award; He has published more than 30 research papers.