Workshop #1
Title: Lung Cancer Detection by the Analysis of A Biomarker Toluene Based on Multi-task Simpleformer Model
Lung cancer is one of the most threatening malignancies to human health and life. Early detection and treatment can improve the five-year survival rate. As a key biomarker for lung cancer diagnosis, toluene can be used for breath simulation analysis of lung cancer patients, which can complement the existing lung cancer diagnosis methods and improve the diagnosis rate of lung cancer. In this paper, a multi-task simple transformer model is proposed in the electronic nose system to conduct gas-sensing experiments on toluene in low-concentration environments. Encoder blocks are shared and two tasks are performed simultaneously, achieving fast and accurate target recognition and concentration prediction at low concentrations. The accuracy of the model on the target recognition task is 98.52%, which is better than the K-Nearest Neighbors (KNN) (89.06%) and the Support Vector Machine (SVM) (91.17%). The root mean square error on the concentration prediction task is 0.0369ppm, outperforming the Support Vector Regression (SVR) (0.0898ppm) and the Extreme Gradient Boosting (XGBoost) (0.0725ppm). The experimental results show that the MT-Simpleformer has good prediction performance for the identification and concentration of a lung cancer biomarker (toluene) under dynamic, low-concentration conditions. It provides an effective reference method for rapid, economical, and non-invasive lung cancer screening.
Lung Cancer, Electronic Nose, Multi-task, Transformer, Toluene

 Yatao Yang
Shenzhen University
Yatao Yang received his B.Sc. degree in optical instrumentation engineering from Zhejiang University, China, and Ph.D. degree in fiber optics from Glasgow Caledonia University, U.K.. After B.Sc. he joined Institute of Optics and Electronics, Chinese Academy of Sciences, where he was involved in semiconductor equipment and optoelectronic device development.  In 1996, he was with the University of Leeds, U.K., as a research officer involved in areas of optical fiber laser materials. In 1997, he joined Resonance Ltd., in Canada, where he was developing spectral gas sensors. In 1998, he joined JDSU Corporation, Canada, where he was developing optical fiber devices. In 2000, he joined Chorum Technologies Inc., US, where he was developing optical fiber devices. In 2004, he joined JDSU Corporation, US, where he was developing optical fiber devices and fiber lasers. In 2009, he joined NeoPhotonics Corporation, as VP of R&D, developing optical fiber devices. In2014, he founded Shenzhen Dade Laser Technology Co. , Ltd. In 2017, he was appointed as a Distinguished Professor with the College of Information Engineering, Shenzhen University, China. In 2018, he became the Head of the Smart IoT Center, Shenzhen University. His research interests include optical networking, optical sensors, optical data transmission and data processing, optical nanomaterials, optical-wireless communications, and lasers.
Workshop #2
Title: New Air Interface Technologies
The development of mobile communication technology is changing rapidly.Meanwhile, the Internet of Things(IoT) and artificial intelligence(AI) are two important technologies that have gradually emerged in recent years, attracting widespread attention worldwide. On the one hand, the importance of mobile communication technology for people is self-evident. On the other hand, the Internet of Things and artificial intelligence have gradually penetrated into various fields such as industry, agriculture, healthcare, and daily life.Therefore, how to organically combine them has become the research goal and development direction of the next generation mobile communication technology. This workshop calls for works demonstratingseveral popular new air interface technologiesin next-generation mobile communication.In particular, this workshop will focus on the follows: (1)New multicarrier modulation technologies, such asOTFS,FBMC, etc. They adopt different methods to effectively overcome the serious ICI problem caused by the destruction of the orthogonality of subcarriers in traditional OFDM in fast time-varying channel scenarios.(2) RIS (also known as IRS). As a new type of auxiliary wireless transmission technology, it has technical characteristics such as passive regulation, easy deployment, and low cost, which can demonstrate better performance than relay.(3) NOMA. Faced with the explosive growth of wireless communication traffic and device access, NOMA can achieve better spectral efficiency and network capacity than traditional OMA schemes.(4) Massive MIMO. It utilizes large-scale antennas to provide spatial freedom, significantly improving the spectral and energy efficiency of the system, thereby better facing the future surge in data services and user numbers.

Tianming Ma
Shanghai University of Engineering Science
Tianming Ma works as an associate professor at the Shanghai University of Engineering Science. His main research interests are air interface technologies in B5G/6G communication. He received the Ph.D. degree from Shanghai institute of Microsystem and Information Technology (SIMIT), Chinese Academy of Sciences, Shanghai, China, in 2012. He was a post-doctoral researcher and obtained the first-class general financial grant from the China Postdoctoral Science Foundation in Tsinghua University, Beijing, China from 2012 to 2014.He has presided over the National Natural Science Foundation of China and participated as a major participant in major national science and technology projects. Moreover, he has authored/co-authored over 50 journal/conference papers and authorized 9 Chinese invention patents as the first inventor. He is a senior member of the China Institute of Communications (CIC), a senior member & life member of the Chinese Institute of Electronics (CIE) and a member of the IEEE Communications Society & IEEE Signal Processing Society. He also serves as a reviewer for many international journals, such as IEEE TWC, IEEE TVT, IEEE CNCOMM, IEEE CL, and IEEE SPL. 
Workshop #3
Title: Enhancing Forest Peatland Fire Detection and Groundwater Management Using IoT System
The Workshop on IoT Solutions for Environmental Monitoring aims to bring together researchers, professionals, and enthusiasts in the field of Internet of Things to explore the practical applications of IoT systems in environmental monitoring. This workshop will focus on two critical aspects: the detection and prevention of forest peatland fires and the efficient management of groundwater resources. By leveraging IoT technologies, we can develop innovative solutions to address these environmental challenges and contribute to sustainable ecosystems.This workshop will provide a platform for participants to share their research, experiences, and insights related to IoT-based environmental monitoring. It will encourage collaborative discussions, knowledge exchange, and the exploration of potential solutions to real-world problems. Attendees will have the opportunity to learn from experts in the field, engage in hands-on activities, and develop a deeper understanding of the role IoT plays in environmental conservation.Topics to be covered in the workshop may include:
Introduction to IoT and its relevance in environmental monitoring
Challenges and opportunities in forest peatland fire detection
IoT-enabled sensor networks for early fire detection and alarm systems
Data analytics and machine learning for fire risk assessment in peatland areas
IoT applications for remote monitoring of groundwater levels and quality
Wireless sensor networks for real-time environmental data collection
Integration of IoT systems with cloud platforms for data storage and analysis
Case studies and success stories of IoT implementation in environmental monitoring
We believe that this workshop will foster innovation, collaboration, and the exchange of ideas among researchers, practitioners, and industry professionals. Together, we can explore the transformative power of IoT and work towards creating a more sustainable future.Please name the email title of the submission with “paper title_workshop title”, when sending an email to this workshop.
Internet of Things (IoT); Environmental monitoring; Forest Peatland Fire; Groundwater Management; Sensor Networks; Data Analytics; Machine Learning; Risk Assessment; Remote Monitoring; Wireless Sensor Networks; Data Storage and Analysis; Sustainability.
Lu Li
Universiti Putra Malaysia

Lu Li is a Chinese citizen born and raised south of the Yangtze River in China. He is currently serving as an executive committee of IEEE Malaysia ComSoc/VTS Joint Chapter. His research interests include wireless and mobile communication, optical sensors, biomedical sensors, channel estimation and propagation technology of mmWave, IoT systems, Industrial application, peatland forests, fire prediction and machine learning. Additionally, he has been invited to serve as a reviewer for many international journals indexed by Scopus. He is also a recipient of some international awards, such as the Selangor State Innovation Award (AINS) in 2022, the Best Dissertation Award awarded by the IEEE Malaysia ComSoc/VTS joint Chapter in 2022, etc. He is also a collaborator for projects under international funding bodies, namely the NICT Japan-ASEAN IVO, Asia Pacific Telecommunity (APT) and SEARCA University Consortium (UC) Seed Fund for Collaborative Research Grant.
Workshop #4 [Communication and Security in Smart Grids]
Title: Grid Engages Evolving Interconnection Demands
Emerging communication and cyber security technologies are boosting the smart electric power grids in terms of interconnecting a great number of heterogeneous elements to meet ever-growing demands in quality-of-service, reliability and safeguarding capability. Closely related to the grids, these demands are significantly driving extraordinary researches in the fields of communication network designs, IoT interoperability, machine learning for grids, standardization, and the security of cyber-physical systems. The researches envision new opportunities to turn the grid to be more connective and productive. 
This workshop targets a wide scope that ranges from theory, design, implementation, evaluation and standard in the fields of communication and security in smart grids. 
Topics of interest include (but not limited):  
Communication and networking system
5G and beyond for smart grids
Communication protocols
Machine learning for promoting informatics in smart grids
Cyber anomaly detection
Security and privacy risk assessment and management
Secure and resilient cyber architecture
Security defenses
Security performance analysis for smart grids
Digital twin for cyber-physical systems
Smart Gird, Communications, Security

Boyang Zhou
Associate Researcher/ Research Expert Zhejiang Lab
Boyang Zhou is currently a research expert at Intelligent Network Research Institute of Zhejiang Lab, and an adjunct associate professor at School of Intelligent Science and Technology, Hangzhou Institute for Advanced Study, UCAS. He received his doctoral degree in Computer Science from Zhejiang University in 2014. As PI or core members, he has participated in more than 10 national or provincial projects. Recently, he has published more than 30 papersin IEEE TII, IEEE COMMAG, IEEE TPDS, INFOCOM, ICC, GLOBECOM, ISCC, USENIX ONS, issued / applied 30 patents, as well as leaded several standards. He have received IWQoS and CACD best paper awards. He has leaded the development of Industrial Endogenous Security Testbed, and developed the prototype of disruption resilient transport protocol for power grids. He has been TPC members in 30 international conferences. His research interests include Smart Grid Communications, Future Internet, and Network Security.

Workshop #5
Title: Green Cellular Network and Green User Equipment
With awareness of the harmful effects to the environment and climate change, on-grid brown energy consumption of information and communications technology(ICT) has drawn much attention. Cellular base stations (BSs) are among the major energy guzzlers in ICT, and their contributions to the global carbon emissions increase dramatically. For the user side, user equipment is also estimated to be the leading energy consumer in the coming years, especially with the ongoing worldwide development of Internet of Things. To achieve the carbon peak and neutrality goals, it is a radical energy solution to leverage green (renewable) energy to power modern communication system.
This workshop is to bring together the latest scientific results on green communications provisioned by academia and the industry. We encourage prospective authors to submit related research papers to contribute our sustainable and environment-friendly society.
Green energy prediction
Green energy usage scheduling
Green energy harvesting
Green cellular network
Green communications
Green Internet of Things
Green user equipment
Green Energy, Green Communications, Green Base Station, Green Internet of Things, Green User Equipment

Xilong Liu
Associate Professor
Yunnan University
Xilong Liu received the B.E. degree in telecommunication engineering from Zhengzhou University, Henan, China, in 2011, and the M.S. and Ph.D. degrees in electrical engineering from the New Jersey Institute of Technology, Newark, NJ, USA, in 2013 and 2019, respectively. He is currently an Associate Professor with the School of Information Science and Engineering, Yunnan University, Yunnan, China. His research interests include 5G/6G wireless communications, green communications and networking, drone-assisted networking, device-to-device communications, Internet of Things, and network optimization. He serves as reviewers and TPC members of IEEE premium journals and flagship conferences.
Workshop #6
Title: Digital Signal Processing Technology in High-capacity Optical Fiber Communication
Optical fiber communication is fast becoming a key instrument in communication area. In recent years, with the development of an increasingly digital lifestyle, services that have high requirements for data-carrying capacity, such as high-definition videos and virtual reality, grow rapidly. Therefore, data-carrying capacity is a major issue in optical fiber communication. The data-carrying capacity of optical fiber communication system has been expanded by multiplexing techniques that use wavelength, amplitude, phase, and polarization of light to encode information in past three decades. However, it is still difficult to meet the increasing demand for communication. In order to break through the capacity bottleneck of single-mode fiber, there is an urgent need to develop high capacity optical fiber communication and its key technology.

Sitong Zhou
Beijing Institute of Technology 
Sitong Zhou, received a Ph.D. degree majored in Electronic Science and Technology from Beijing University of Posts and Telecommunications in 2021. After graduation, She entered the postdoctoral workstation of the Beijing Institute of Technology. 
Her research interests focus on high capacity optical fiber communication and digital signal processing combined with machine learning. Based on her researches, she published more than 20 papers in Optics Letters, Optics Express and other journals/conferences.
Workshop #7
Title: Robust, Advanced and Smart UAV Communications and Networks
UAVs are envisioned as a promising technology for ensuring data connection and improving communications efficiency. However, considering the fast-moving speed, dynamic topology and limited resources of UAV network, the quality of communications remains instability. This workshop seeks original unpublished papers focusing on novel architectures and emerging technologies for constituting the robust, advanced and smart UAV communications and networks. Topics of interest for this workshop include but are not limited to:
Network Architecture for UAV communications
Transmission protocols for UAV communication and networks
Cooperative spectrum management and resource allocation for UAV communication and network
Interference management technique for UAV communication and network
Intelligent association for massive UAV clusters
Integrated Sensing, Computation and Communication for UAV network
Security and reliability for UAV communications and network
UAVCommunication and Network, Resource Allocation, Network Association

Simeng Feng
Associate Professor
Nanjing University of Aeronautics and
Simeng Feng, associate researcher and master supervisor of School of Electronic and Information Engineering in Nanjing University of Aeronautics and Astronautics, member of IEEE, senior member of China Communication Society, awarded "Doctor of Entrepreneurship and Innovation" of Jiangsu Province, "Changkong Scholar" of NUAA. She received PhD degree from the University of Southampton whose research interests include wireless optical communication, visible light communication network, UAV cluster network, intelligent information network, etc. She has published international high-level journals in the field of wireless communications, such as IEEE Transactions on Wireless Communications, IEEE Transactions on Communications, etc. She also served as the long-term reviewer of IEEE, OSA and other international authoritative journals. Furthermore, she is the principal investigator of National Natural Science Foundation projects and participated in the research of several national and ministerial projects.
Workshop #8
Title: Resource Allocation For Sensor System
The current smart city contains many sensor device, such as the sensors for data collection. Each device has its own resource requirements. We are interesting to better performance, lower costs and lessened environmental impact. Thus, some higher efficiency resource allocation techniques are necessary for mono-static/multi-static sensor systems.
The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. We encourage prospective authors to submit related distinguished research papers on the subject of the following topics (but not limited to): 
Sensorsdeploymentfor targets detection and tracking.
Task planning for multi-function sensor/sensor network.
Sensor resource allocation for target tracking.
Phase unwrapping and its application.
Frequency sharing for multi-function sensor/sensor network.
Radar based target detection and recognition technology
Spectral efficiency of reconfigurable intelligent surface aided sensor
Millimeter wave radar intelligence perception and imaging
Resource Allocation, Sensors Deployment, Task Planning, Target Detection and Recognition, Phase Unwrapping

Tianxian Zhang
Tianxian Zhang, received the B.S. and Ph.D. from University of Electronic Science and Technology of China (UESTC) in 2009 and 2015, respectively. He is currently a professor with the School of Information and Communication Engineering, UESTC. His main research interests include radar signal processing, multi-function waveform design, multi-objective optimization. He has published more than 30 scientific articles in refereed journals, such as IEEE Transactions on Signal Processing, IEEE Transactions on Geoscience and Remote Sensing, IEEE Transactions on Aerospace and Electronic Systems, etc.

Xiaoping Li
Xiaoping Li, received his master and Ph.D. degrees in communication and information engineeringfrom Xi'an JiaotongUniversity. He worked as an associated professor at the School of Mathematical Sciences, University of Electronic Science and Technology of China. His research interests include signal processing, coding theory. He participated in the National Natural Science Foundation and Chinese National Programs for High Technology Research and Development, etc. He published more than 20 papers.

Xueting Li
Assistant Research Fellow 
Sichuan University
Xueting Li received the B.S. and Ph.D. degrees from the University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2013 and 2020, respectively. She is currently an assistant research fellow with Sichuan University. Her research interests include signal detection, multi-sensor resource management, and multi-function integrated system resource optimization.

Workshop #9
Title: Air-Sea Cross-Domain Communication
The sixth generation mobile communication system (6G), the integrated network of heaven and earth, and the maritime joint operation network all require the establishment of an integrated network across multiple dimensions such as airspace, airspace, region, and sea area to connect node units above and below the sea surface, achieving seamless coverage of information in all time and all areas. Air-sea cross-domain communication technology, as an important foundational support foundation, is an important link in achieving this vision. Due to the different characteristics of two media, air and seawater, as well as the impact of harsh environments, information transmission across media communication channels encounters significant bottlenecks. At present, air-sea cross-domain communication technology mainly adopts cross medium link level direct communication (such as acoustic communication, optical communication, magnetic communication, millimeter wave, etc.), cross medium relay communication, cross medium communication networking, and other methods. This workshop invites the following original papers related to the theme of "air-sea cross-domain communication", including innovative ideas, concepts, new discoveries, new methods, new improvements, and new applications.
Cross Medium Relay Communication, Cross Medium Optical Communication, Cross Medium Magnetic Communication, Cross Medium Communication Networking, Other Air-sea Cross-domain Communication Methods

Zhigang Shang
Harbin Engineering
Zhigang Shang, Ph.D. in Engineering, Professor at Harbin Engineering University, and JKW Youth Support Talent. The main research directions are air sea cross domain communication, cross domain fusion, and cross domain unmanned cluster. In recent five years, he has presided over or participated in more than 10 projects such as the National Natural Science Foundation of China, the State Key Laboratory Fund, XX Major Project, XX Joint Fund, etc. Published over 40 academic papers, including more than 20 included in SCI/EI; 32 national invention patents; Published 7 books. He served as reviewer of nearly ten academic journals, such as IEEE, Signal Processing, and Acoustics Journal, chairman of the branch of the Global Intelligent Control and Automation Conference, member of the EITCE 2020 Organizing Committee, member of the Youth Working Committee of the Institute of Accusations, and one of the leaders of the underwater acoustic engineering for the development of electronic information engineering technology (Blue Book) of the Chinese Academy of Engineering.

Jiaxuan Xie
Tsinghua University
2007-2021: Bachelor of Network Engineering of PLA Institute of Electronic Engineering
2012-2013: Master of Communication Engineering, School of Electronic Engineering, Naval University of Engineering
2019-present: PhD of the Department of Electronics at Tsinghua University

Lu Wang
Associate Professor
Harbin Engineering University
Lu Wang, Ph. D in Engineering, is an associate professor at Harbin Engineering University. She received the Ph.D. degree from Keio University if Japan. She has worked as a research assistant and assistant professor at Osaka University and Keio University in Japan, respectively. Her research interests include radar image interpretation, radar signal processing and etc. Dr. Wang has published over 46 papers in international SCI/EI journals and conferences. In 2019, she received the Science Research Award from Japan Electronic, information and Communication Society. She has led or participated 9 projects such as national-level projects of Japan, key laboratory fund of Japan, A3 Foresight Program of the National Natural Science Foundation of China and etc. She served as a Chair of the MSN 2020 Workshop, the Cup Competition Chair in ChineseCSCW2023, an editorial board member of AJRS, and has served as a program committee member (TPC) for conferences such as IEEE ICC 2021, 2020, WCNC 2021, and Globecom 2020, 2019. She has also served as a reviewer for journals such as IEEE TNSE, IEEE TVT, and IEEE WCL.
Workshop #10
Title: Unveiling the Potential of IoT-Based Intelligent Systems: From Idea to Execution
The rapid evolution of the Internet of Things (IoT) has completely revolutionized our interaction with technology and the world. IoT has led to remarkable transformations across various sectors, from intelligent homes and connected vehicles to industrial automation, agriculture, and healthcare systems. To design and implement an IoT system, it is vital to recognize and grasp the fundamental building blocks of intelligent systems that optimize the utilization of IoT technologies. This workshop aims to provide participants with a comprehensive understanding of IoT-based intelligent system architecture and its practical implementation using ESP32-based IoT development boards, Edge devices and Cloud computing. Our presented intelligent system illustrates the key components utilized in an IoT-based intelligent system to detect anomalies in various sensor readings linked to a machine, enabling early prediction of component failures.
IoT, Intelligent Systems, Anomalies, Cloud Computing

Muhammad Raisuddin Ahmed
Associate Professor
Military Technological College
Dr. Muhammad Raisuddin Ahmed is a Senior Lecturer at the Military Technology College in Muscat, Oman, affiliated with the University of Portsmouth's Oman campus. With a rich academic and research background, Dr. Ahmed has held positions as a Teaching Fellow at the University of Canberra and a research officer at the Australian National University in Australia. He has an impressive educational profile, having completed a Ph.D. at the University of Canberra, a Master of Engineering Studies in Telecommunication and a Master of Engineering Management from the University of Technology, Sydney, Australia and a Bachelor of Engineering (Hons) in Electronics Majoring in Telecommunications from Multimedia University in Malaysia. Dr. Ahmed has authored numerous papers in the fields of Wireless Sensor Networks, Internet of Things, Machine Learning and Artificial Intelligence, Distributed Wireless Communication, and Antenna. His research contributions have been published in high-impact journals and conferences, reflecting his dedication to advancing knowledge and innovation in these domains.
Workshop #11
Title: Resources Management of IoV Based on Different Machine Learning Algorithms
The Internet of Vehicles (IoV) is a network that connects vehicles, infrastructure, and other entities to improve transportation efficiency, safety, and convenience. Resource management in IoV involves efficiently allocating and managing various resources such as network bandwidth, computational power, and energy consumption. Machine learning algorithms can be utilized to optimize and automate resource management in IoV. 
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 Resources management of IoV. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.
Vehicular Network, Resource Allocation and Management, Reinforcement Learning, Intelligent Traffic System

Xiumei Fan
Xi'an University of Technology
Xiumei Fan, received the Ph.D. degree in Communication Control from Northern Jiaotong University of China, Postdoctoral research in the Department of Computer Science at Tsinghua University. At present, she works as a professor at the School of Faculty of Automation and Information Engineering, Xi'an University of Technology. Her main research areas are the Internet of Vehicles, the Intelligent traffic, mobile edge computing and so on. As the Principal Investigator, she has completed two projects of the National Natural Science Foundation of China, the National High-Tech Research and Development Plan (863), the Beijing Natural Science Foundation Project etc. And as the main applicant and researcher, she has participated in the completion of 973 Program, the National Natural Science Foundation key projects of China and NSFC Projects of Inter Cooperation and Exchanges. More than 100 academic papers have been published and 9 patents for authorized inventions have been granted.
Workshop #12
Title: Construction of Big Data Platform in Animal Husbandry and Analysis of Internet of Things Application Technology
With the arrival of the intelligent era, all kinds of intelligent concepts have been proposed and applied in various fields. The application of the relatively advanced big data platform construction and the Internet of Things technology has a great significance in promoting the development of Chinese animal husbandry in an intelligent and scientific direction. This paper analyzes the big data platform construction of animal husbandry and the application technology of the Internet of Things, aiming to provide several valuable references for relevant people.
Animal Husbandry; Big Data; Internet of Things; Artificial Intelligence

Tiantian Zhang
Assistant Research Fellow
Zhejiang University
Dr. Tiantian Zhang works as Assistant research fellow at the Ocean College, Zhejiang University;
He is also work with the Zigong Innovation Center, Zhejiang University.  
Visiting scholar at Ajou University, South Korea;
Visiting scholar at Seoul National University of Science and Technology, South Korea;
Shaoxing City "Hometown of Famous scholars" talent plan innovation talents;
Deputy Secretary-General of Korea International Friendly Cultural Exchange Association;
Deputy chief editorof Research on Internet of Things Communication Technology, China Overseas Chinese Press.
His main research interests are Artificial Intelligent Internet of Things (AIoT) and Mobile Internet of Things (MIoT), Intelligent agriculture, blockchain and Cloud computing. Moreover, he has published 2 SCI papers , 2 National first-class journal of Engineering, 5 Software works , 2 invention patents, 4 applications patents.
Workshop #13
Title: Edge Intelligence Based Cloud-Edge Integrated Networks for Internet of Things
With the rapid development of wireless technologies, wireless networks need to provide user equipments and Internet of Things (IoT) terminals with wide-area intelligent connectivity at anytime and anywhere. Mobile edge networks will face great challenges in their operating. Driven by the rapid development of emerging technologies, it has become the development trend of future communication networks to build an edge intelligence Based Cloud-Edge Integrated networks (CEIN) that integrates cloud computing and edge computing to achieve unified and efficient resource scheduling, network management as well as network control. Due to high latency requirements in task processing and content acquiring, and the inherent characteristics such as low battery supplies of users and IoT devices, edge intelligence can be deployed to CEIN, contributing to providing high-speed computing power. This workshop will focus on the follows (1) In-network intelligent learning, and distributed control for cloud and edge IoT terminals management; (2) Multi-agent reinforcement learning for decision-making of task offloading and resource management; (3) Advanced AI algorithms for network optimization and control.
Reinforcement Learning, IoT, Resource Management, Edge and Cloud Computing

Peng Lin
Associate Professor
Nanjing University of Information Science and Technology
Peng Lin received the Ph.D degrees in communication and information systems from Northeastern University, Shenyang, China in 2021. From 2019 to 2020, he visited the University of British Columbia, Vancouver, Canada and Carleton University, Ottawa, Canada as a visiting scholar. He was introduced to Nanjing University of information science and technology as a Longshan Scholar in 2021. He has published international journals and conferences including IEEE WCM, TWC, TII, TVT, IoTJ, ICCC, etc.He served as the reviewers for many IEEE journals and conferences. His research interests include edge computing, wireless network resource management, digital / network twinning, the application of artificial intelligence algorithms in communication networks and so on.
Workshop #14
Title: Distributed Machine Learning for Internet of Things
Machine learning has been an essential technology in a variety of applications, enabling them to extract knowledge from vast amounts of raw data, gain insight into complex trends, and guide optimization processes. However, traditional machine learning always operates in a centralized manner, placing high demands on the computation, storage, and energy of the learning agent, which is incompatible with emerging IoT devices characterized by limited onboard resources. Distributed learning that leverages the power of multiple IoT devices connected via a network to train learning models in a distributed and parallel manner, has emerged as a promising approach to address this problem. While distributed learning hold great potentials for smart IoT applications, the interaction and collaboration among multiple learning agents present many new research challenges. For example, how to efficiently divide learning tasks among multiple agents in IoT networks with highly dynamic topology. Moreover, learning knowledge and decision sharing in distributed learning may raise big problems of trust and security of connected smart IoT devices. This workshop sets up a platform for collaboration and discussions on both the challenges and opportunities on distributed learning empowered IoT, and prospective authors are encouraged to contribute their original research advances here.
Distributed Learning, Internet of Things, Multi-agent Cooperation, Federated Learning, Transfer Learning

Ke Zhang
Associate Professor
University of Electronic Science and Technology of China
Ke Zhang received the Ph.D. degree in communication and information systems from University of Electronic Science and Technology of China, in 2017. He is currently an associate professor in the School of Information and Communication Engineering, University of Electronic Science and Technology of China. He is the recipient of International Workshop on Communication Technology for Vehicles 2016 Best Paper Award, IEEE ICCT 2021 Best Paper Award, and IEEE SmartIoT 2019 Best Student Paper Award. He is/was the TPC member of IEEE GLOBECOM 2017, 2021 and 2022, IEEE TrustCom 2021, IEEE DigitalTwin 2022, IEEE ICC 2023, and the Local Chair of BlockSys 2022. He served as Guest Editors of IEEE Network and China Communications.
Workshop #15
Title: Advances in Optical Wireless Communications
Given the exponentially increasing mobile data traffic and the challenge of radio frequency (RF) spectrum shortage, substantial research efforts have been invested into exploring the hitherto barely exploited higher-frequency spectrum, such as the optical spectrum, spawning the field of optical wireless communication (OWC). OWC is expected to be applied to a wide range of applications, such as indoor wireless network, underwater communication, vehicular communication and so on. To this end, this workshop aims to serve as a key platform to showcase and disseminate the latest progress and breakthrough in the area of the OWC. Its topics of interest include, but are not limited to:
High-speed OWC transmission techniques 
High-security OWC transmission techniques
OWC Network organization 
Intelligent resource arrangement in OWC 
OWC based high accuracy positioning
Hybrid RF and OWC techniques 
Power line assisted OWC
Underwater optical wireless communications
Optical Wireless Communications, Physical Layer Security, Network Organization, Resources Management, Machine Learning, Heterogeneous Network

Baolong Li
Nanjing University of Information Science and Technology
Baolong Li received the Ph.D. degree in communication and information systems from Southeast University in 2017. He is currently with School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, China. He is the principal investigator of National Natural Science Foundation, Natural Science Foundation of Jiangsu Province, etc. Furthermore, he has published international high-level journals in the field of optical wireless communications, such as IEEE Transactions on Communications, IEEE Transactions on Vehicular Technology, Journal of lightwave Technology, etc.
Workshop #16
Title: Recent Advances and Applications for Medical AI
Medical AI has gradually attracted people's attention recently. People uses smart devices to detect common diseases wherever you are and then provides efficient evidence for the doctor to make a diagnosis. Then doctors can have more clinical reference basis, which contributes to improving the effectiveness of diagnosis and treatment. This workshop focuses on the computer and data science enabled intelligent medicine, including while not limited to the clinical decision making, computer-assisted surgery, telemedicine, drug development, image analysis and computation, and health management.
Smart Healthcare, Medical AI, Intelligent Diagnosis, Computer-aid Treatment

Fei Gu
Assistant Professor
Soochow University
Fei Gu is an assistant professor (Excellent Young Scholar) in school of computer science and technology, Soochow University. He received the Doctor degree of computer architecture from Beihang University in 2019, the MS degree of computer science and technology from Nanjing University of Aeronautics and Astronautics in 2014. During December 2017 to December 2018, he was a joint visiting Ph.D student in McGill University, Montreal, Quebec, Canada. His research area focuses on mobile computing, healthcare, data mining and computer vision. He has pubilsed more than 30 papers on top international conferences and journals in recent years including IEEE IoT, TON, TSC, JNCA, INFOCOM, IWQoS, GLOBALCOM etc. Moreover, he was the principal investigator of projects granted from China Postdoctoral Science Foundation, Jiangsu Postdoctoral Research Foundation, the National Science Foundation of the Jiangsu Higher Education Institutions of China, Suzhou Planning Project of Science and Technology, etc.
Workshop #17
Title: Array-based Wireless Localization for IoT Networks
Array-based wireless localization is a critical technology for IoT applications that utilizes the varying time delays of different array sensors to determine the location of devices. By enabling devices to determine their location without relying on GPS, array-based wireless localization plays an important role in enclosed indoor environments such as factories. The objective of this workshop is to provide a platform for researchers, engineers, and students to exchange ideas and learn from leading experts in the field. We will cover a range of topics related to array-based wireless localization such as 
1. New array-based wireless localization techniques
2. Robust array-based wireless localization 
3. Far/near field array-based wireless localization 
4. Novel AI schemes for array-based wireless localization 
5. Sparse array-based wireless localization
6. Sparse array signal processing 
We welcome paper submissions on the topics mentioned above (but not limited to them). We believe that this workshop will be of great interest to anyone working in the associated research areas. By bringing together experts from academia and industry, we hope to foster collaboration and accelerate the development of new technologies and applications.
Wireless Localization, IoT, Array, Signal Processing

Yaxing Yue
Zhejiang University
Yaxing Yue, received a Ph.D. degree in Electronics Science and Technology from Beijing Institute of Technology. He works as a researcher fellow and post-doctor at the College of Information Science and Electronic Engineering, Zhejiang University. He is also a visiting scholar at Pengcheng Laboratory. His research interests include array signal processing, MIMO wireless communication and radar. He has conducted or participated in sixprojects such asthe National Natural Science FoundationProject of China.

Ying Liu
Zhejiang University
Ying Liu, received a Ph.D. degree in Electronics Science and Technologyfrom Zhejiang University. She works as a post-doctorat the College of Control Science and Engineering, Zhejiang University. Her research interests include array signal processing and wireless localization.

Mengzhi Wang
Zhejiang University
Mengzhi Wang, received a Ph.D. degree in School of Automation from Beijing Institute of Technology. He was also a joint Ph.D. student at the University of Alberta. Now, he works as a post-doctor at the School of Control Science and Engineering, Zhejiang University. His research interests include Cyber-physical System Control, Industrial Control System Security.
He has conducted or participated in the National Natural Science Foundation, National Key Research and Development Project, Zhejiang Province Key Research and Development Project, etc. Based on these projects, he has published more than 10 papers in IEEE Trans, and other journals/conferences.
Workshop #18
Title: Intelligent Transportation for Future Cities
Intelligent transportation for future cities workshop aims to bring key thought leaders, researchers, and practitioners together from various disciplines to explore the most recent technology advances, interdisciplinary approaches, business models, and state-of-the-art techniques. It focuses not only on intelligent transportation systems (ITS) but also on the role and benefit of next-generation transportation systems and mobility trends in the cities of the future.
This workshop invites original papers contributing to the modeling, control, and analysis of transportation systems. Yet, research papers which address any combination of theory, analytical modeling and optimization, numerical simulations, real-world data-driven approaches, experimentation, advanced deployment, and case studies are welcome.
Topics——The following outlines the key topics of interest at this workshop, but are not limited to:  
Transportation networks
Autonomous and Connected (V2X) vehicles
Advanced Public Transportation Management
Crowdsourcing and social transportation 
Artificial Intelligence (AI) and Machine Learning (ML) in ITS
Modeling, control, and simulation
Big data and naturalistic driving datasets and analytics
Vehicle localization and autonomous navigation 
Security, privacy, and safety of transportation systems
Cooperative techniques and systems for intelligent transportation 
Commercial vehicle operations 
Intelligent logistics and Transportation demand management
Multi-modal and public transportation management
COVID-19 and its impact on the transportation system
Intelligent Air, Road, and Rail traffic management systems 
Traffic data mining and knowledge discovery in ITS 
Driver and Traveler Support Systems
Sensing, Detectors, and Actuators
Intelligent Transportation Systems, Future Cities, Internet of Vehicles

Pushpendu Kar
Assistant Professor
University of Nottingham (China Campus)
Dr. Pushpendu Kar is currently working as an Assistant Professor in the School of Computer Science, University of Nottingham (China campus). Before this, he was a Postdoctoral Research Fellow at the Norwegian University of Science and Technology, the National University of Singapore, and Nanyang Technological University. He also worked in different engineering colleges as a lecturer and in the IT industry as a software professional. He has more than 12 years of teaching and research experience as well as one and a half years of industrial experience at IBM. He has completed all his PhD, Master of Engineering, and Bachelor of Technology in Computer Science and Engineering. He also completed Postgraduate Certificate in Higher Education (PGCertHE) in 2023. He was awarded the prestigious Erasmus Mundus Postdoctoral Fellowship from the European Commission, ERCIM Alain Bensoussan Fellowship from the European Union, and SERB OPD Fellowship from the Dept. of Science and Technology, Government of India. He has received the 2020 IEEE Systems Journal Best Paper Award. He has received four research grants for conducting research-based projects, three of them as a Principal Investigator (PI). He also received many travel grants to attend conferences and doctoral colloquiums. He is the author of more than 50 scholarly research papers, which have been published in reputed journals and conferences, and in IT magazines. He has also published two edited books. He is also an inventor of five patents. He has participated in several conference committees, worked as a team member to organize short-term courses, and delivered a few invited talks as well as Keynote Lectures at international conferences and institutions. He is a Senior Member of IEEE and a Fellow of the Higher Education Academy (FHEA), UK. Ningbo Municipal Government, China has recognized him as a High-Level Talent.
Workshop #19
Title: Advanced Power Amplifiers and Its Linearity Methods
With the successful deployment of 5G communication systems, significant progress has been made in data capacity and service quality. At the same time, the operation bands and signal modulation scheme are both increased a lot. As a key block in the wireless transmitter, power amplifier (PA) is required to have wide operation band, high back-off efficiency and good linearity. Though lots of work have been conducted to improve the above performances of PAs in the past years, advanced PA architectures and its linearity methods are still the focus of most attention. This workshop calls for works demonstrating the most recent progress and contributions to design methods and linearization techniques of power amplifiers. This workshop will focus on but not limited to the following parts. (1) Novel deign methods for highly-efficient wideband PAs, including bandwidth extension and efficiency enhancement methods. (2) Wideband or multi-band power amplifiers with active load modulation for the improvement of back-off efficiency. (3) Multi-mode power amplifiers to promote the popularization of 5G/6G technology. (4) The linearization methods of power amplifiers, including analog and digital predistortion methods. Please name the email title of the submission with “paper title_workshop title”, when sending an email to this workshop.
Power Amplifier, Wideband, High Efficiency, Load Modulation

Weimin Shi
Associate Professor
 Chongqing University
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 50 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 on "14th High Efficiency Power Amplifier".

Ce Shen
Assistant Professor
Ce Shen received the B.S. degree in electronic information engineering and the Ph.D. degree in circuits and systems from the University of Electronic Science and Technology of China, Chengdu, China, in 2015 and 2022, respectively.
He is currently an Assistant Researcher with Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China (UESTCYDRI). His current research interests include highly efficient broadband power amplifiers, Doherty power amplifiers, harmonic control technology, transistor nonlinear modeling and digitally assisted RF circuit designs.
Workshop #20
Title: Computer Vision and Its Application in Unmanned Systems
The unmanned systems are widely used in both civilian and military fields, which will change people's way of life and fighting-pattern of war. As one kind of intelligent perception technology for unmanned systems, the computer vision technology has been investigated by a large number of researchers. However, there still exists many environmental factors for application of the computer vision technology, such as heavy rain and fog, uneven illumination of dark space light sources, and high dust, et. al. These environmental factors lead to poor image quality, high difficulty in reconstruction of 3D scenes, and low accuracy in object detection and recognition. As a result, the computer vision technology is hard to be applied in unmanned systems effectively. To promote thetheory andapplication of the computer vision in unmanned systems,this workshop proposes a topic "Computer Vision and Its Application in Unmanned Systems".
Computer Vision, Super-resolution Reconstruction, Pattern Recognition, Three-Dimensional Reconstruction, Machine Learning, Unmanned Systems

Deqiang Cheng
China University of Mining and Technology
Deqiang Cheng, PhD Supervisor. He is currently a Professor of the School of Information and Control Engineering at China University of Mining and Technology, China. He was a visiting scholar at Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education, in Xidian University in 2012. He also was the visiting professor at University of Alberta, Canada in 2014. He was National Coal Youth "May Fourth" Medal Winner, National Advanced Individual at Education of Coal Industry, Advanced Individual (Excellent Education Worker) at Education of Jiangsu Province, "333 Projec" Young and middle-aged Academic and Technological Leader of Jiangsu Province.
His research interests include Image Processing, Computer Vision and Pattern Recognition. As the project leader, he presided 3 projects of the Natural Science Foundation of China and more than 10 provincial and ministerial projects. As first author or corresponding author, He has published 2 Monographs and more than 100 scientific papers. 20 invention patents were granted by CNIPA. 2 invention patents were granted by USPTO. 10 research findings were awarded as provincial and ministerial research results.

Qiqi Kou
China University of Mining and Technology
Qiqi Kou is currently a Lecturer with the School of Computer Science and Technology, China University of Mining and Technology, and he is also the deputy director of the Intelligent Detection and Pattern Recognition Research Center at China University of Mining and Technology. His research interests include image enhancement and restoration, object detection and recognition, image super-resolution reconstruction, pedestrian re-id, etc.
As the project leader, he has undertaken more than 20 projects including national projects and one group standard of China Coal Industry Association. He was invited to serve as the special reviewer of NPL, ESA, MTAP , Journal of Computer Science and other domestic and foreign academic journals , and was also a member of the technical Review Committee of the International Conference ICICSP. He has obtained fourteen authorized invention patents, five provincial and ministerial level scientific and technological progress awards, published more than 40 scientific papers. Furthermore, he was awarded as an Outstanding Young Backbone teacher of China University of Mining and Technology in 2022.

Feixiang Xu
China University of Mining and Technology
Feixiang Xu, received a Ph.D. degree from Jilin University. He is currently a Lecturer of the School of Information and Control Engineering at China University of Mining and Technology, China. He focused on intelligent sensing and control of Unmanned Systems, and he was responsible for two national projects and two school-level projects as a leader. He received the excellent doctor program of Jiangsu Province, the talent training program for young teachers of my university, the excellent doctoral dissertation of Jilin University, et al. He has published a total of 20 academic papers in journals as the first author or corresponding author.
Workshop #21
Title: Advanced Machine Learning Theory for Unmanned Swarm Networks
Unmanned swarm networks have gained significant attention from both academia and industry. Various unmanned platforms, such as Unmanned Aerial Vehicle and Unmanned Vehicles, need to connect to the network for overcoming information isolation and offer diverse services. These high-dynamic unmanned platforms result in a new clan of networks named Mobile Ad Hoc Networks (MANETs).MANET is a dynamic network consisting of mobile nodes that continuously configure and organize themselves, without relying on any fixed infrastructure. It offers excellent mobility and enables connectivity even in remote locations.Dealing with real-time communication and ensuring end-to-end Quality of Service (QoS) guarantees in MANETs is exceptionally challenging due to the constant mobility of nodes. To achieve maximum efficiency and resilience, MANETs need to possess intelligent capabilities to adapt to highly dynamic topologies and diverse QoS requirements.
Recently, advanced machine learning based approaches have been regarded as key enablers for communication networks. Advanced machine learning techniques for network management, operations, and automation improve the way we address networking today. Therefore, machine learning is also believed to hold significant potential in addressing the challenges faced by MANETs, particularly in areas such as intelligent self-organizing networking, adaptive traffic control, and security issues. Machine learning has the capability to extract valuable knowledge from data and adapt effectively to the dynamic environment of MANETs. While researchers and practitioners have started exploring various machine learning techniques to enhance the performance of MANETs, there remain numerous challenges to be tackled. Key questions arise regarding the optimal deployment of machine learning in such a highly dynamic network environment.
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 Advanced Machine Learning Theory for Unmanned Swarm Networks. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.
Advanced Machine Learning, Unmanned Aerial Vehicle, Resource Allocation

Tianle Mai
Beijing Institute of 
Tianle Mai received a Ph.D. degree in the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing. His research interests include unmanned swarm networks, future network architecture, network artificial intelligence, multi-agent system, space-terrestrial integrated network, network resource allocation and dedicated networks. He has published more than 30 papers in prestigious peer-reviewed journals and conferences.

Binghong Liu
Beijing University of Posts and Telecommunications
Binghong Liu received a Ph.D. degree in the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing. In 2023, she joined BUPT, where she is currently a Postdoc with the State Key Laboratory of Networking and Switching Technology. Her research interests include resource allocation in radio access networks, UAV, integration of communication and navigation for satellites.
Workshop #22
Title: Smart Agriculture and Digital Agriculture
Smart agriculture and digital agriculture are advanced stages of agricultural production, integrating various information technologies such as modern computer science, sensors, robots, artificial intelligence, etc., to achieve intelligent perception, early warning, decision-making, analysis, and expert online guidance of agricultural production environment, providing precise planting, visual management, and intelligent decision-making for agricultural production. Smart agriculture and digital agriculture are important ways to achieve modernization of agriculture and rural areas, and are also key drivers for comprehensively promoting rural revitalization.
The following outlines the key topics of interest at this workshop, but are not limited to:  
1. Agricultural sensors
2. Precision agriculture
3. Agricultural Information System
4. Agricultural Internet of Things
5. Agricultural cloud computing
6. Agricultural blockchain
7. Intelligent Equipment and Control
8. Agricultural remote sensing
9. Agricultural big data
10. Artificial Intelligence
11. Traceability of agricultural product quality
12. Agricultural and Rural Modernization Construction
13. Smart Agriculture Education
Smart agriculture, Digital Agriculture, Artificial Intelligence, Internet of Things, Cloud Computing

Xinghua Sun
Hebei North University
Xinghua Sun, received the M.S. degree from the Shanghai Normal University, Shanghai, China, in 2007 and the Ph.D. degree in Environmental Science and Engineering from the Shanghai Normal University, Shanghai, China, in 2021. Now, He is a professor of information science and Engineering College of Hebei North University, China.He participated in the National Science and Technology Research Project, Hebei Provincial Natural Science Foundation etc. Based on these projects, he has authored and co-authored more than 20 papers in refereed journals and conference proceedings. He has served as an editor for Journal of Information Processing Systems.
Workshop #23
Title: Artificial Neural Network Application in Laser Beam Drilling
Artificial neural network is a variant of machine learning that inspired by the working of human brain. To establish a mathematical relationship between design and quality parameters feed forward neural network is the prominent tool. Lately manufacturing industries are centering on neural network for the data prediction of complex manufacturing process. Therefore, study of architecture of neural network is very important before its implementation. 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 artificial neural network applications in advanced machining processes especially in laser based drilling. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.
Artificial Neural Network, Feed Forward, Manufacturing, Laser Drilling

Surendra Kumar Saini
Associate Professor
Poornima College of Engineering Jaipur India
Dr. Surendra Kumar Saini has received Ph.D. degree in Manufacturing Engineering from Motilal Nehru National Institute of Technology Allahabad, India. He is currently working as Associate Professor in Mechanical Engineering Department at Poornima College of Engineering affiliated to Rajasthan Technical University, Rajasthan, India. He has also been worked as Junior & Senior Research Fellow in the ARDB New Delhi sponsored research project. He has more than 13 years of experience in Teaching and Research. His research areas include artificial intelligence based tools application in unconventional machining processes, laser materials processing, additive manufacturing, welding, casting, and statistical techniques. He has authored more than fifty research papers in peer reviewed International and National Journals and Conference proceedings. He has guided M.Tech. Dissertations and research scholars are under process. He is also working as reviewer and editorial board member for International Journals and Conferences. He has delivered expert talks in several International and National events. He has been awarded a number of prizes and honors. He is a Life Member of the Association of Machines and Mechanism.
Workshop #24
Title: Exploring the Opportunities and Challenges of IoT
This workshop proposal aims to delve into the vast realm of the Internet of Things (IoT) and its implications in various domains. The Internet of Things has revolutionized the way we interact with technology, enabling seamless connectivity and data exchange across devices. However, the widespread adoption of IoT also brings forth several challenges that need to be addressed for its successful implementation. This workshop will provide a platform for researchers, industry professionals, and enthusiasts to share their knowledge, experiences, and insights on IoT, fostering discussions on its opportunities, advancements, security, privacy, and potential applications.
IoT Applications; IoT Security; Computer Security

Jinhui Liu
Associate Professor
Northwestern Polytechnical University
Jinhui Liu received the Ph.D. degree in information security from the School of Computer Science, Wuhan University, Wuhan, China, in 2017. She is currently an Associate Professor with Northwestern Polytechnical University, Xi’an, China. Her research interests include post-quantum cryptography and its applications, especially lattice-based digital signatures and secure multiparty computation.
Workshop #25
Title: Quantum Computing in Communication and IoT
Quantum computing has the potential to significantly impact communication and the Internet of Things (IoT) in various ways. Quantum computing can be applied in Cryptography and Security, Quantum Key Distribution (QKD), Optimization and Routing, Sensor Networks and IoT Devices, Machine Learning and Data Analysis, Simulation and Modeling, Traffic Management and Quality of Service (QoS), Network Security Monitoring, etc. quantum computing holds great promise, it is still in its early stages of development, and practical, scalable quantum computers are not yet widely available. The integration of quantum technologies into communication and IoT will depend on advancements in quantum hardware, software, and the development of appropriate quantum algorithms. Nevertheless, quantum computing has the potential to revolutionize these fields by addressing complex problems that are difficult or impossible to solve with classical computers.
Quantum Computing, Communication Technologies, IoT, Security, Key Distribution, Optimization

Amol P. Bhagat
Assistant Professor
Prof Ram Meghe College of Engineering & Management
Dr Amol P. Bhagat, Department of IT, CSE, (Programme Coordinator – Innovation and Entrepreneurship Development Centre, Manager – Business Incubator). He completed his B.E. (Information Technology) from Government College of Engineering, Amravati in the year 2005; with Master's degree in Computer Science and Engineering from Walchand College of Engineering, Sangli in the year 2009 and Ph.D. in Information Technology under the guidance of Dr. Mohammad Atique in the year 2016 and is a research supervisor since 2019. (Notification No. 22/2019 Dated: 14/03/2019). He has 13.5 years of industry and teaching experience. He has guided around 30 UG and 28 PG projects. He has to his credit: 23 Patents filed, 3 patent grants, 89 Research papers published in National and International Journal and 2 books and 9 book chapters published, 6 National/ International Awards. He served as resource person in 100+ programmes such as Workshops, STTPs, FDPs sponsored and funded by IETE, ISTE, IEI, AICTE, DST, CSI, NABARD, Ministry of Agriculture, etc.

Shrikant M. Harle
Assistant Professor
Prof Ram Meghe College of Engineering & Management
Dr. Shrikant M. Harle Assistant Professor in the Department of Civil Engineering Completed graduation from Government College of Engineering, Amravati (India) in 2008, Postgraduation from National Institute of Technology-Rourkela (India) in 2010, completed PhD in 2019 in the field of low volume roads. Joined Larsen & Toubro as Post graduate Engineer Trainee in 2010, promoted as senior design engineer and then Assistant Engineering Manager till 2012. Later joined as Assistant Professor in Department of Civil Engineering Prof Ram Meghe College of Engineering and Management, Badnera. Presently a member of Innovation & Entrepreneurship Development Centre, Incubation centre & Internal Quality Assurance Cell (IQAC). Teaching experience of 7 years and industrial experience of 2.5 years. Published 4 patents, 4 books & more than 75 research papers in reputed international journals & conferences. Reviewer & editorial board member of journals, member of scientific committee of conferences. A keynote speaker and session chair for International conference.