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    Prof. Pascal Lorenz, University of Haute-Alsace, France, France

    Speech Title: Advanced architectures of Next Generation Wireless Networks

    Pascal Lorenz (lorenz@ieee.org) received his M.Sc. (1990) and Ph.D. (1994) from the University of Nancy, France. Between 1990 and 1995 he was a research engineer at WorldFIP Europe and at Alcatel-Alsthom. He is a professor at the University of Haute-Alsace, France, since 1995. His research interests include QoS, wireless networks and high-speed networks. He is the author/co-author of 3 books, 3 patents and 200 international publications in refereed journals and conferences. He was Technical Editor of the IEEE Communications Magazine Editorial Board (2000-2006), IEEE Networks Magazine since 2015, IEEE Transactions on Vehicular Technology since 2017, Chair of IEEE ComSoc France (2014-2020), Financial chair of IEEE France (2017-2022), Chair of Vertical Issues in Communication Systems Technical Committee Cluster (2008-2009), Chair of the Communications Systems Integration and Modeling Technical Committee (2003-2009), Chair of the Communications Software Technical Committee (2008-2010) and Chair of the Technical Committee on Information Infrastructure and Networking (2016-2017). He has served as Co-Program Chair of IEEE WCNC'2012 and ICC'2004, Executive Vice-Chair of ICC'2017, TPC Vice Chair of Globecom'2018, Panel sessions co-chair for Globecom'16, tutorial chair of VTC'2013 Spring and WCNC'2010, track chair of PIMRC'2012 and WCNC'2014, symposium Co-Chair at Globecom 2007-2011, Globecom'2019, ICC 2008-2010, ICC'2014 and '2016. He has served as Co-Guest Editor for special issues of IEEE Communications Magazine, Networks Magazine, Wireless Communications Magazine, Telecommunications Systems and LNCS. He is associate Editor for International Journal of Communication Systems (IJCS-Wiley), Journal on Security and Communication Networks (SCN-Wiley) and International Journal of Business Data Communications and Networking, Journal of Network and Computer Applications (JNCA-Elsevier). He is senior member of the IEEE, IARIA fellow and member of many international program committees. He has organized many conferences, chaired several technical sessions and gave tutorials at major international conferences. He was IEEE ComSoc Distinguished Lecturer Tour during 2013-2014.

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    Assoc. Prof. Ata Jahangir Moshayedi,Jiangxi University of science and Technology, China

    Dr. Ata Jahangir Moshayedi, an Associate Professor at Jiangxi University of Science and Technology in China, holds a PhD in Electronic Science from Savitribai Phule Pune University in India. He is a distinguished member of IEEE and ACM, as well as a Life Member of the Instrument Society of India and a Lifetime Member of the Speed Society of India. Additionally, he contributes to the academic community as a valued member of various editorial teams for international conferences and journals. Dr. Moshayedi's academic achievements are, marked by a portfolio of over 90 papers published across esteemed national and international journals and conferences along with 3 books on robotics (VR and mobile olfaction) and embedded systems. In addition to his scholarly publications, he has authored three books and is credited with two patents and nine copyrights, emblematic of his pioneering contributions to the field. His research interest includes Robotics and Automation/ Sensor modeling/Bio-inspired robot, Mobile Robot Olfaction/Plume Tracking, Embedded Systems / Machine vision-based Systems/Virtual reality, and Machine vision/Artificial Intelligence. Currently, Dr. Moshayedi is actively engaged in pioneering work at Jiangxi University, where he is developing a model for Automated Guided Vehicles (AGVs) and advancing the realm of Food Delivery Service Robots.

    Speech Title: Visionary Integration: Enhancing AGVs with Vision Systems and Machine Perception
    Abstract: Service robots represent a transformative application of robotics that profoundly impacts human life, spanning domains from healthcare to industry. These robots serve as lifesavers and support systems, alleviating humans from strenuous tasks and repetitive work that might compromise accuracy in job execution. According to ISO 8373:2012, service robots encompass two main types: personal service robots, designed for use outside manufacturing, and professional service robots, catering to non-commercial and commercial purposes. These robots operate on a spectrum from semi-autonomous to fully autonomous, gradually gaining acceptance as invaluable human assistants across diverse applications and professions. Industries are increasingly integrating service robots into their production lines, marking a pivotal shift within the context of the industrial revolutions. The first revolution brought mechanization, followed by the second revolution powered by electricity. Industry 4.0, however, intertwines digital and internet technologies, propelling further innovation and evolution in the realm of technology. Within this discourse, the focus narrows to AGV (Automated Guided Vehicles) and MIR (Mobile Industrial Robots) as exemplary service robots. The discussion delves into the modeling steps and simulation processes involved in their creation. Additionally, it scrutinizes the performance of designed AGVs employing various algorithms. This analysis aims to serve as a guide for researchers, offering insights and practical implementations for diverse control systems within modeled systems.

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    Prof. Thomas Hanne, University of Applied Sciences and Arts Northwestern Switzerland, Switzerland

    Thomas Hanne received master's degrees in Economics and Computer Science, and a PhD in Economics. From 1999 to 2007 he worked at the Fraunhofer Institute for Industrial Mathematics (ITWM) as senior scientist. Since then he is Professor for Information Systems at the University of Applied Sciences and Arts Northwestern Switzerland and Head of the Competence Center Systems Engineering since 2012.
    Thomas Hanne is author of more than 200 journal articles, conference papers, and other publications and editor of several journals and special issues. His current research interests include computational intelligence, evolutionary algorithms, metaheuristics, optimization, simulation, multicriteria decision analysis, natural language processing, machine learning, systems engineering, software development, logistics, and supply chain management.

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    Assoc. Prof. Han Huang,South China University of Technology, China

    Dr. Huang is a professor and doctoral supervisor of the School of Software Engineering at South China University of Technology. He is currently serving as an associate editor of IEEE Transactions on Evolutionary Computation (IF: 14.3), Complex & Intelligent Systems (IF: 5.8) and IEEE Transactions on Emerging Topics in Computational Intelligence (IF: 5.3), and Director of Teaching Steering Committee for Software Engineering of Undergraduate Colleges and Universities in Guangdong Province. Prof. Huang has made great contributions to the scholarship on the theories and application of intelligent optimization algorithms. For example, he has proposed a time complexity analysis method of real-world evolutionary algorithms, algorithms for efficient and accurate image matting, a method for automated test case generation based on path coverage, etc. Prof. Huang has hosted more than twenty national and provincial projects. He has published two books, Theory and Practice of Intelligent algorithm and Theory, Methods and Tools for Time Complexity Analysis of Evolutionary Algorithm. He has also published more than 80 papers in IEEE TCYB, IEEE TETC, IEEE TSE, IEEE TEVC, IEEE TIP, IEEE TFS, and Science China, including ESI highly cited papers. As the first inventor, Prof. Huang has 49 invention patents granted in China and seven invention patents granted in the United States. He won China Patent Excellence Award and developed an association standard entitled “Standard for glass-box testing without source code” as the first completer. Additionally, Prof. Huang pays attention to social services. Over the past five years, he has given more than 50 public lectures on science and technology for government offices, primary and secondary schools, CCF, YOCSEF, media, etc. He has been in charge of the development and release of six public software systems such as Unit Test Algorithm Platform www.unittestpc.com.cn, Automatic Structural Equation Modeling System www.autosem.net, Evolutionary Algorithm Time Complexity Analysis System www.eatimecomplexity.net, and Energy Storage Optimization System http://energystorage.autosem.net, which have provided free technical service and support for lots of researchers and engineers.
    Title: Micro-scale searching algorithm and its application
    Abstract: Intelligent optimization algorithm is an important artificial intelligence method which is often used to solve complex black-box optimization problems. From the perspective of the nature of algorithm performance, this report will describe the fundamental reasons and key points of algorithm performance improvement. It will introduce the idea of micro-scale searching algorithm: By determining the effective decision subset of optimization problems, adjust the reasonable allocation of computational resources and achieve effective search in a small space, thus obtaining the optimal solution or high-quality feasible solution of the problem. Based on this algorithmic idea, The report will analyze the micro-scale searching assumptions and performance nature of intelligent optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), ant colony optimization (ACO). It will also summarize and discuss the pitfalls of analyzing performance of evolutionary computing methods. Finally, this report will introduce the applications of micro-scale searching algorithm ideas in industrial software, software engineering, computer vision, digital logistics, etc.

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    Assoc. Prof. Anum Shafiq, Nanjing University of Information Science and Technology, China

    Dr. Anum Shafiq is currently serving as an Associate Professor at the School of Mathematics and Statistics, Nanjing University of Information Science and Technology, China. With a Ph.D. earned in 2016 from Quaid-i-Azam University Islamabad, Pakistan, she has made significant contributions to the field of mathematics and statistics. Prior to her current position, Dr. Shafiq worked as an Assistant Professor at prestigious universities in Pakistan for four years, followed by postdoctoral research at North West University, South Africa, from 2019 to January 2020. Her research interests primarily revolve around numerical methods for boundary value problems, computational techniques employing traditional and heuristic paradigms, Newtonian and non-Newtonian nanofluids flow, microorganisms, and multidisciplinary applications of mathematics. Dr. Shafiq has garnered recognition for her scholarly work, being listed among the Top 2% Scientists worldwide by Stanford University for consecutive years (2022 and 2023). Her extensive publication record includes over 140 research papers in SCI indexed journals, with an impressive research h-index of over 48 and more than 5278 citations according to Web of Sciences. In addition to her research contributions, Dr. Shafiq actively participates as an editorial board member for numerous reputed journals and serves as a potential reviewer for top-ranking research journals. She also shares her expertise as a resource person, delivering invited talks at workshops and conferences both nationally and internationally. Dr. Shafiq's dedication to advancing mathematical research and her significant scholarly achievements underscore her commitment to academic excellence and innovation.

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    Prof. Qiu Daowen, Sun Yat-sen University, China

    My main research outcomes have been in the following areas. (1) Quantum models of computation. (2) Quantum query algorithms. (3) Quantum cryptograpy and quantum communication. (4) Quantum states distinguishablility and quantum states cloning. (5) Theory of computation based on quantum and lattice-valued logic. (5) The applications of fuzzy and probabilistic automata to discrete event systems, focusing on diagnosability and supervisory control.
    We have published over 130 papers in peer-review journals, and over 25 conferences papers. More specifically, (1) we have systematically studied a number of different QFA (quantum finite automata) models, and solved the decidability of equivalence and minimization of these QFA models (D. Qiu, L. Li, X. Zou, P. Mateus, J. Gruska, Acta Informatica, 2011, 48 (5-6): 271-290; P. Mateus, D. Qiu, L. Li, Information and Computation, 2012, 218: 36-53;L. Li, D. Qiu, Theoretical Computer Science, 2008, 403(1): 42-51). Therefore, we have answered the problems of how to decide the equivalence of quantum sequential machines proposed by Professor Gudder, and how to decide the equivalence of MM-1QFA proposed by Professor Gruska. In particular, we have answered the problems of how to minimize QFAs proposed by Moore and Crutchfield. We proposed a model of quantum-classical finite automata, named as one-way quantum finite automata together with classical states (D. Qiu, L. Li, P. Mateus, A. Sernadas, Journal of Computer and System Sciences, 2015, 81(2): 359-375). Also, we have studied some properties of 2QFAC, quantum pushdown automata, and quantum Turing machines. (2) We have proved the characterization of all Boolean functions that can be solved by quantum 1-query algorithm. (3) We have studied quantum states discrimination and quantum cloning machines, and we have derived some bounds on unambiguous discrimination and minimum-error discrimination (some bounds are optimal to a certain extent), and some relationships between unambiguous discrimination and minimum-error discrimination have been clarified. Also, we have established a generic machine model of probabilistic cloning and deleting, and proposed a universal probabilistic deleting machine. (4) We have studied quantum teleportation and superdence coding based on different entangled states (W-states). (5) We have studied semi-quantum cryptography and proved that a semi-quantum key distribution protocol is unconditional security. (6) We have discovered some essential connections between quantum logic and models of computation, and we have established residuated lattice-valued automata theory (D. Qiu, Information and Computation, 2004, 190(2): 179-195). (6) We have established a fundamental framework of the supervisory control for fuzzy discrete event systems (FDES) and developed the supervisory control of probabilistic discrete event systems (PDES), using fuzzy automta and probabilistic automata, respectively.

    Speech Title: Distributed Grover's algorithm together with realization of Oracles
    Abstract: In this talk, we would present two distributed Grover’s algorithms. Our distributed Grover’s algorithms require fewer qubits and have a linear advantage in time complexity compared to the original Grover's algorithm. Also, the number of qubits in our distributed Grover's algorithm is less than that in Grover's algorithm. Finally, we give an efficient algorithm of constructing quantum circuits for realizing the oracle corresponding to any Boolean function with conjunctive normal form (CNF).

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    Prof. Yang Yue, Xi'an Jiaotong University, China.

    Yang Yue received the B.S. and M.S. degrees in electrical engineering and optics from Nankai University, China, in 2004 and 2007, respectively. He received the Ph.D. degree in electrical engineering from the University of Southern California, USA, in 2012. He is a Professor with the School of Information and Communications Engineering, Xi'an Jiaotong University, China. Dr. Yue’s current research interest is intelligent photonics, including optical communications, optical perception, and optical chip. He has published over 270 journal papers (including Science) and conference proceedings with >12,000 citations, one book, six edited books, two book chapters, >60 issued or pending patents, >200 invited presentations (including 1 tutorial, >30 plenary and >60 keynote talks). Dr. Yue is a Fellow of SPIE, a Senior Member of IEEE and Optica. He is an Associate Editor for IEEE Access and Frontiers in Physics, Editor Board Member for four other scientific journals, Guest Editor for >10 journal special issues. He also served as Chair or Committee Member for >100 international conferences, Reviewer for >70 prestigious journals.
    Speech Title: Multiparameter Performance Monitoring for Optical Communications Channels Using Deep Learning
    Abstract: In recent years, deep learning has come to the forefront as a promising technology to aid in multiparameter performance monitoring for optical communications channels. In this talk, we will introduce CNN-based techniques to effectively monitor multiple system performance parameters of optical channels using eye diagram measurements. Experimental results demonstrate this method achieves a prediction accuracy >98% when tasked with identifying the modulation format (QPSK, 8-QAM, or 16-QAM), as well as the optical signal-to-noise ratio (OSNR), roll-off factor (ROF), and timing skew for 32 GBd coherent channels. For PAM-based intensity-modulation direct detection (IMDD) channel eye-diagram-based CNN method maintain >97% identification accuracy for 432 classes under different combinations of probabilistic shaping (PS), ROF, baud rate, OSNR, and chromatic dispersion (CD) by each modulation format. Furthermore, we undertake on an extensive comparison of ResNet-18, MobileNetV3 and EfficientNetV2. Our designed VGG-based model of reduced layers, alongside the lightweight MobileNetV3, demonstrates enhanced cost-effectiveness while maintaining high accuracy.

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    Prof. Xueye Chen,Ludong University, China

    Xueye Chen received his B.S. degrees in mechanical engineering from Northeast Agricultural University (NEAU), Harbin, China, in 2005. He received his M.S. degrees in mechanical engineering from Harbin Institute of Technology (HIT), Harbin, China, in 2007. He received his Ph.D. degrees in mechanical engineering from Dalian University of Technology (DUT), Dalian, China, in 2012. He is currently a professor in College of Transportation, Ludong University (LDU), Yantai, China. His research interests include microfluidics, nanofluidics, design and simulation with multiphysics. He published over 160 journal papers. As of March 2022, his works were cited 2100 times (ISI).
    Speech Title: Design Methods for Micromixers
    Abstract: Micromixers are important functional units of microfluidic chips and have significant application value in fields such as disease monitoring, healthcare, chemical engineering, and life sciences. According to input energy, it is divided into passive micromixers and active micromixers. This article provides common design methods for micromixers, including macro micro model method, fractal design method, topology optimization design method, and the design of electric micromixers, hoping to provide reference for relevant researchers.

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    Assoc. Prof. Shou Feng, Harbin Engineering University, China

    Feng Shou, Associate Professor, Master Supervisor of Harbin Engineering University, Deputy Director of the Key Laboratory of Advanced Ship Communication and Information Technology of the Ministry of Industry and Information Technology, IEEE member, Senior member of the Chinese Society of Communications, Member of the Imaging Detection and Perception Committee of the Chinese Society of Image and Graphics, peer review expert of the National Natural Science Foundation of China, academic dissertation review expert of the Ministry of Education, Visiting Scholar of Indiana University Bloomington, Guest Editor of Remote Sensing, an international authoritative journal in the field of remote sensing. Member of the editorial board of international Journal Frontiers in Imaging, American Journal of Remote Sensing, He also serves as a reviewer for many authoritative academic journals such as IEEE TIP, IEEE TGRS, IEEE GRSL, and Remote Sensing. In the past three years, he has published 20 academic papers as the first/corresponding author in top journals in the field of Remote Sensing such as IEEE TIP, IEEE TGRS, and Remote Sensing, and 2 papers have been selected as ESI highly cited papers. Published 6 conference papers in IGARSS and other international conferences, applied for 8 national invention patents as the first inventor, and has authorized 4. Served as the Branch Chair of IGARSS2020 and IGARSS2021, as a member of the Organizing Committee of IGARSS2023, organized the Community-Contributed Session "CCS.9: Recent Advances in Hyperspectral Image Processing: Methodology and Application ". As a guest editor, he organized 3 special issues in Remote Sensing.

    Speech Title: Recent Advances for High Resolution Remote Sensing Image Change Detection
    Abstract: Remote sensing technology is an important technical means for human beings to perceive the world, and change detection technology has become the mainstream of current research. Change detection is a pixel-level task, which is mainly used for fine extraction and recognition of changed ground object information from bitemporal images. Change detection is the basis for subsequent practical application tasks of remote sensing images and has very important research significance, which is widely used in digital precision agriculture, environmental monitoring, national defense and military strategy and other fields. With the rapid development of artificial intelligence technology, many new change detection methods and algorithms have been proposed. Moreover, rapid advances in these methods have also promoted the application of associated algorithms and techniques to problems in many related fields. This keynote aims to report and cover the latest advances and trends about the Recent Advances for High Resolution Remote Sensing Image Change Detection.

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    Prof. Hongjun Wang, Southwest Jiaotong University, China

    Hongjun Wang, Ph.D. (Postdoctoral), Associate Researcher, PhD supervisor, Assistant to the Dean, He is currently an associate professor with the Key Lab of Cloud Computing and Intelligent Techniques, Southwest Jiaotong University. His research interests include machine learning, data mining and ensemble learning. He has published more than 50 research papers in journals and conferences and he is a member of ACM and CCF.