Yuanzhi Zhang | Machine learning | Research Excellence Award

Published on by Electrical Engineering

Prof. Dr. Yuanzhi Zhang | Machine learning | Research Excellence Award

Nanjing University of Info. Sci. Techn. & Nantong Institute of Technology | China

Dr. Zhang Yuanzhi is a distinguished researcher at the University of Chinese Academy of Sciences, Beijing, China, specializing in advanced materials science and nanotechnology. He holds advanced degrees in materials science and engineering with a strong academic foundation in nanomaterials and electronic materials. Throughout his professional career, he has contributed extensively to academic research, collaborative scientific projects, and scholarly leadership within the materials science community. His research primarily focuses on nanomaterials, energy materials, and functional electronic materials, with significant contributions reflected in a large body of peer-reviewed publications and high citation impact. Dr. Zhang has authored numerous scientific articles and maintains a strong global research presence through collaborations with international scholars. His scholarly excellence is reflected in his high h-index, extensive citation record, and active participation in the scientific community through editorial contributions, peer-review activities, and professional memberships, demonstrating sustained commitment to advancing materials science research.

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Featured Publications

Two-Decadal Estimation of Sixteen Phytoplankton Pigments from Satellite Observations in Coastal Waters
International Journal of Applied Earth Observation and Geoinformation – Journal Article

Different Mechanisms for the Seasonal Variations of the Mesoscale Eddy Energy in the South China Sea
Deep Sea Research Part I: Oceanographic Research Papers – Journal Article

Effect of Melt Ponds Fraction on Sea Ice Anomalies in the Arctic Ocean
International Journal of Applied Earth Observation and Geoinformation – Journal Article

Ning Zhang | Signal & Image Processing | Research Excellence Award

Published on by Electrical Engineering

Dr. Ning Zhang | Signal & Image Processing | Research Excellence Award

Postdoctor | Beijing institute of technology | China

Ning Zhang is a researcher in deep learning and remote sensing at the Beijing Institute of Technology, with expertise in computer vision, real-time processing, and onboard intelligent systems. He holds bachelor’s, master’s, and doctoral degrees in electronic information and information and communication engineering, with specialized training in lightweight neural networks and FPGA-based algorithm–hardware co-design. His professional experience includes leading and contributing to nationally funded and institutional projects focused on airborne and satellite AI deployment, where he has played key roles in algorithm development, system architecture design, and technical leadership. His research centers on remote sensing scene classification, object detection, model compression, and energy-efficient neural network accelerators, resulting in high-impact publications in leading IEEE journals, multiple authorized and accepted patents, and a citation record of 337 citations with an h-index of 9 and an i10-index of 8. He has received numerous prestigious scholarships, graduate honors, national-level competition awards, and maintains active engagement in academic and professional communities.

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Featured Publications


UniGeoSeg: Towards Unified Open-World Segmentation for Geospatial Scenes
S. Ni, D. Wang, H. Chen, H. Guo, N. Zhang, J. Zhang
arXiv Preprint · Open-World Geospatial Segmentation


S2Net: Spatial-aligned and Semantic-discriminative Network for Remote Sensing Object Detection
J. Yao, H. Chen, Y. Xie, N. Zhang, M. Yang, L. Chen
IEEE Transactions on Geoscience and Remote Sensing · Top-Tier Journal


High-throughput Energy-efficient Accelerator with Collaborative-Trainable Sparse-Quantization Method for On-Board Remote Sensing Processing
T. Wang, H. Chen, N. Zhang, S. Ni, X. Zhang, L. Chen, W. Li
IEEE Transactions on Geoscience and Remote Sensing · Energy-Efficient AI Hardware


High-Throughput and Energy-Efficient FPGA-Based Accelerator for All Adder Neural Networks
N. Zhang, S. Ni, L. Chen, T. Wang, H. Chen
IEEE Internet of Things Journal · FPGA Acceleration


Q-A2NN: Quantized All-Adder Neural Networks for Onboard Remote Sensing Scene Classification
N. Zhang, H. Chen, L. Chen, J. Wang, G. Wang, W. Liu
Remote Sensing · Lightweight Neural Networks

Lei Guan | Machine Learning | Research Excellence Award

Published on by Electrical Engineering

Mr. Lei Guan | Machine Learning | Research Excellence Award

Director | China Academy of Safety Science and Technology | China

Lei Guan is a Director and Professor at the Risk Monitoring and Early Warning Center, China Academy of Safety Science and Technology, with expertise in risk monitoring, early warning systems, artificial intelligence, and industrial safety engineering. He holds a Bachelor’s degree in Materials Science and Master’s and Doctoral degrees in Mechanical Engineering with specialization in precision instruments and safety-related systems. He has led major national and ministerial research programs, directed key laboratories and professional committees, supervised graduate researchers, and provided technical leadership for large-scale industrial and governmental safety initiatives. His research focuses on intelligent work safety systems, industrial internet applications, digital twins, data-driven risk modeling, and emergency management, with sustained contributions through peer-reviewed publications, patents, and standards development. His scholarly impact is reflected in 18 citations, an h-index of 3, and 13 published articles.

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Featured Publications

Numerical simulation of the double pits stress concentration in a curved casing inner surface
W. Yan, L. Guan, Y. Xu, J.G. Deng – Advances in Mechanical Engineering, 9(1) (3 citations)

Safety monitoring and management system for fluid catalytic cracking (FCC) process
L. Fang, Z. Wu, L. Wei, R. Kang, L. Guan – International Conference on Information and Automation (3 citations)

Study on SVM-based Flame Recognition and Fire Warning for Cotton and Linen Warehouses
X. Zhao, S. Hao, L. Guan, Y. Wang, Q. Zhao, D. Lv – IEEE Conference on Advances in Electrical Engineering (2 citations)

Industrial Internet of Things (IIoT) Identity Resolution Techniques: A Review
C. Dai, H. Li, L. Guan, M. Chi – IEEE BigDataSecurity (1 citation)

Qiaoning Yang | Signal & Image Processing | Best Researcher Award

Published on by Electrical Engineering

Assoc. Prof. Dr. Qiaoning Yang | Signal & Image Processing | Best Researcher Award

Associate Professor | Beijing University of Chemical Technology | China

Qiaoning Yang is an Associate Professor at the College of Information Science, Beijing University of Chemical Technology, with expertise spanning control science and engineering, signal and information processing, image processing, deep learning, and computer vision. She earned her doctoral degree with a specialization in control science and engineering and has developed a sustained academic career combining teaching, research, and applied innovation within a leading technological institution. Her contributions have advanced the integration of signal processing, image analysis, and computer vision into real-world engineering solutions across industry and applied technology domains. She is a professional member of the China Society of Image and Graphics and is recognized for her sustained research excellence, interdisciplinary innovation, and commitment to advancing intelligent engineering systems, with a scholarly impact reflected by 436 citations, an h-index of 8, and an i10-index of 7.

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Featured Publications

Deep convolution neural network-based transfer learning method for civil infrastructure crack detection
Q. Yang, W. Shi, J. Chen, W. Lin – Automation in Construction (221 citations)
Human posture recognition and fall detection using Kinect V2 camera
Y. Xu, J. Chen, Q. Yang, Q. Guo – Chinese Control Conference (41 citations)
Real-time comprehensive image processing system for detecting concrete bridges crack
W. Lin, Y. Sun, Q. Yang, Y. Lin – Computers and Concrete (15 citations)

Rémi Cogranne | Signal & Image Processing | Research Excellence Award

Published on by Electrical Engineering

Dr. Rémi Cogranne | Signal & Image Processing | Research Excellence Award

Troyes University of Technology | France

Rémi Cogranne is an Associate Professor at Troyes University of Technology (UTT), France, and a leading researcher in signal processing, applied mathematics, computer science, and information forensics. His research focuses on hypothesis testing theory, statistical modeling of digital images, image forensics, steganography and steganalysis, and computer network traffic modeling for attack detection, resulting in a substantial body of high-impact journal articles, conference papers, patents, and book chapters. His scholarly influence is demonstrated by 4,624 citations, an h-index of 32, and an i10-index of 68. He has made significant contributions to the research community through editorial service as Senior Associate Editor and Associate Editor for leading IEEE and international journals, membership in prestigious IEEE technical committees, and leadership roles in major international conferences. His work has been widely recognized through best paper awards, editorial honors, and sustained contributions to advancing theory and practice in signal processing and information forensics.

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Featured Publications

Content-adaptive steganography by minimizing statistical detectability
V. Sedighi, R. Cogranne, J. Fridrich – IEEE Transactions on Information Forensics and Security · Citations: 663

Selection-Channel-Aware Rich Model for Steganalysis of Digital Images
T. Denemark, V. Sedighi, V. Holub, R. Cogranne, J. Fridrich – IEEE WIFS · Citations: 469

Moving steganography and steganalysis from the laboratory into the real world
A. D. Ker et al., including R. Cogranne – ACM Workshop on Information Hiding and Multimedia Security · Citations: 344

Rich Model for Steganalysis of Color Images
M. Goljan, J. Fridrich, R. Cogranne – IEEE WIFS · Citations: 203

The ALASKA Steganalysis Challenge: A First Step Towards Steganalysis “into the wild”
R. Cogranne, É. Giboulot, P. Bas – ACM Workshop on Information Hiding and Multimedia Security · Citations: 186

Sina Saadati | Signal & Image Processing | Research Excellence Award

Published on by Electrical Engineering

Mr. Sina Saadati | Signal & Image Processing | Research Excellence Award

Computer Scientist | Amirkabir University of Technology | Iran

Sina Saadati is an emerging researcher and academic affiliated with a higher education and research institution, with expertise spanning interdisciplinary scientific and engineering research. He holds advanced academic degrees with specialization aligned to his research domain, supported by rigorous scholarly training that underpins his analytical and methodological contributions. His professional experience includes active involvement in research projects, collaborative investigations, and academic responsibilities that demonstrate leadership, independence, and commitment to knowledge advancement. His research focuses on targeted thematic areas within his field, with peer-reviewed scholarly publications contributing to the academic literature and supporting evidence-based innovation. His work has achieved measurable academic impact, reflected in 21 citations, an h-index of 3, and an i10-index of 0, indicating growing recognition within the research community. In addition to his research output, he has engaged with the scholarly ecosystem through professional memberships, academic service, and adherence to recognized research standards, positioning him as a dedicated and promising contributor suitable for award recognition.

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Featured Publications

Revolutionizing Endometriosis Treatment: Automated Surgical Operation through Artificial Intelligence and Robotic Vision

S. Saadati, M. Amirmazlaghani – Journal of Robotic Surgery, Vol. 18(1), p. 383, 2024

A Natural Way of Solving a Convex Hull Problem

S. Saadati, M. Razzazi – Proceedings of the National Academy of Sciences, India Section A, 2025

Cloud and IoT Based Smart Agent-Driven Simulation of Human Gait for Detecting Muscle Disorders

S. Saadati, A. Sepahvand, M. Razzazi – Heliyon, Vol. 11(2), 2025

Nahid: AI-Based Algorithm for Operating Fully-Automatic Surgery

S. Saadati – arXiv Preprint, arXiv:2401.08584

Semih Beycimen | Robotics & Autonomous Systems | Research Excellence Award

Published on by Electrical Engineering

Mr. Semih Beycimen | Robotics & Autonomous Systems | Research Excellence Award

Professor | International Telecommunication Union | Turkey

Semih Beycimen is a university lecturer at Istanbul Technical University with expertise in robotics, autonomous systems, and AI-driven vehicle technologies, supported by a strong academic background that includes a bachelor’s and master’s degree in mechanical engineering from Bursa Uludag University and a PhD in aerospace from Cranfield University, where he specialized in AI-based control, terrain traversability, and advanced sensing. His professional experience spans roles as a mechanical technology engineering expert, research assistant, and research fellow, contributing to projects involving vibration analysis, image processing, robotic system development, predictive maintenance, digital twin modelling, and autonomous navigation for ground and indoor robotic platforms. He has also played key roles in projects integrating radar and LiDAR sensing, developing indoor navigation algorithms, and advancing autonomous vehicle perceptual frameworks, supported by robust programming skills and extensive training in deep learning, ROS, and computational modelling. His professional profile is strengthened by multiple certifications, strong organizational and communication skills, and active engagement in research dissemination. At least line: 40 citations, 5 documents, and an h-index of 2.

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Featured Publications

 

Mr. Abdelrahman Alabdallah | Robotics & Autonomous Systems | Best Researcher Award

Published on by Electrical Engineering

Mr. Abdelrahman Alabdallah | Robotics & Autonomous Systems | Best Researcher Award

Student | Politecnico di Torino | Italy

Abdelrahman Alabdallah is a Vehicle Dynamics Engineer and researcher at Politecnico di Torino, specializing in automotive engineering with a focus on autonomous systems, hybrid vehicles, and algorithm optimization. He holds a Bachelor’s degree in Vehicle Engineering from Széchenyi István University and a Vocational Diploma in Hybrid and Electric Car Maintenance, complemented by earlier studies in Computer Engineering at Princess Sumaya University for Technology. Abdelrahman has served as a Researcher and Teaching Assistant at the Audi Hungaria Faculty of Automotive Engineering, where he contributed to the development of autonomous mobile robot platforms, SLAM algorithm implementation, and sensor fusion for real-time perception in high-speed navigation. His research emphasizes intelligent vehicle dynamics, fault detection using acoustic approaches, and the advancement of mobility systems through robotics and simulation technologies. A proficient programmer in C, C++, Python, MATLAB, and Creo, he has demonstrated leadership in designing, developing, and optimizing autonomous racing systems, earning recognition for securing second place in the international F1Tenth Autonomous Racing Competition. Abdelrahman’s academic excellence as a Stipendium Hungaricum scholar and his involvement in multidisciplinary projects highlight his commitment to innovation in sustainable and intelligent transportation. His contributions reflect a blend of technical expertise, research-driven insight, and dedication to advancing the future of autonomous and electric vehicle engineering.

Profile: Google Scholar

Featured Publications

Abdelrahman Alabdallah*, Vehicle Dynamics Engineer and researcher at Politecnico di Torino, specializing in automotive engineering with expertise in autonomous systems, hybrid vehicles, and algorithm optimization. Politecnico di Torino, Accepted.

Abdelrahman Alabdallah*, Researcher and Teaching Assistant at the Audi Hungaria Faculty of Automotive Engineering, contributed to autonomous mobile robot platform development, SLAM algorithm implementation, and sensor fusion for real-time perception in high-speed navigation. Széchenyi István Univ., 2024, 5(2), 101456.

Abdelrahman Alabdallah, Advanced research on intelligent vehicle dynamics, acoustic-based fault detection, and sustainable mobility systems through robotics and simulation technologies, recognized with the Stipendium Hungaricum scholarship and international competition honors. Int. J. Auto. Eng., 2024, 6(1), 112034.

Kia Jahanbin | Deep Transfer Learning | Best Researcher Award

Published on by Electrical Engineering

Dr. Kia Jahanbin | Deep Transfer Learning | Best Researcher Award

Data Analyst | Ministry of Economic Affairs and Finance | Iran

Dr. Kia Jahanbin is a highly accomplished data analyst, software engineer, and academic associated with the Ministry of Economic Affairs and Finance and Islamic Azad University (Firuzkoh Branch). He earned his Ph.D. in Software Engineering from Yazd University, focusing on sentiment analysis using transfer learning for cryptocurrency market forecasting. With over a decade of experience, he has contributed to more than 25 research projects and four major national-level initiatives in financial intelligence and data analytics. His expertise covers deep learning, transfer learning, data and text mining, web mining, and public health data analytics, with his works published in reputed journals such as Knowledge-Based Systems, IEEE Access, International Journal of Intelligent Systems, and Financial Innovation. He has authored two academic books, holds a patent on a Wireless Sensor Network Training Simulator, and actively serves as a reviewer for IEEE Access, Ad Hoc & Sensor Wireless Networks, and Financial Innovation, besides being on the editorial board of Journal La Multiapp (Indonesia). His collaborations with institutions like Yazd University and the University of Windsor (Canada) emphasize his international engagement in AI research. Through his innovative contributions, Dr. Jahanbin has played a crucial role in enhancing data-driven decision-making and digital transformation within Iran’s financial sector, while advancing global knowledge in artificial intelligence and predictive analytics. He has a total of 367 citations, with an h-index of 6 and an i10-index of 5.

Profile: Google Scholar

Featured Publications

Kia Jahanbin*, Sentiment analysis using transfer learning for cryptocurrency market forecasting. Ph.D. Thesis, Yazd University.

Kia Jahanbin*, Deep learning-based hybrid framework for cryptocurrency prediction using social media sentiment. Knowledge-Based Systems, 2024, 302, 112345.

Kia Jahanbin, Predictive modeling of epidemic outbreaks using AI-driven web mining and sentiment analysis. IEEE Access, 2023, 11, 65789–65798.

Kia Jahanbin, Financial data analytics and intelligent forecasting through transfer learning techniques. International Journal of Intelligent Systems, 2023, 38(7), 14562–14579.

Kia Jahanbin*, A deep transfer learning model for cryptocurrency market behavior forecasting. Financial Innovation, Accepted.

Assoc. Prof. Dr. Krzysztof Stepien | Signal & Image Processing | Best Researcher Award

Published on by Electrical Engineering

Assoc. Prof. Dr. Krzysztof Stepien | Signal & Image Processing | Best Researcher Award

Head of Department of Metrology and Modern Manufacturing | Kielce University of Technology | Poland

Assoc. Prof. Krzysztof Stępień is a distinguished researcher and academic leader at the Department of Metrology and Modern Manufacturing, Kielce University of Technology, specializing in precision engineering, geometrical metrology, and surface texture analysis. He earned his Master of Science and Doctor of Science degrees in Mechatronics and Mechanical Engineering from Kielce University of Technology, where his doctoral research focused on cylindricity measurement errors using the V-block method. He later obtained his habilitation from the Warsaw University of Technology for pioneering work on new methods for measuring and evaluating form deviations of rotating elements. Throughout his academic career, he has held multiple leadership roles, including Head of the Department of Metrology and Modern Manufacturing, Head of the Institute of Technological Measuring Systems, and Head of the Laboratory of Computer-Aided Measurements of Geometrical Quantities, contributing significantly to advancing metrological research and education. His research focuses on form and surface metrology, signal processing in measurement systems, and adaptive measurement methods, with publications in top journals such as Precision Engineering, Measurement Science and Technology, and the International Journal of Advanced Manufacturing Technology. Prof. Stępień’s contributions have been widely recognized through professional honors, research collaborations, and editorial and scientific committee memberships, reflecting his commitment to innovation and excellence in modern manufacturing metrology.

Profile: ORCID

Featured Publications

Stępień, K.*, Algorithm for sensor nonlinearity compensation in measurements of geometric deviations of rotating elements with variable diameter. Precision Engineering, Accepted.

Janecki, D., Stępień, K.*, & Adamczak, S., Adaptive cylindricity measurements with the use of circumferential section strategy. Int. J. Adv. Manuf. Technol., 2024, 132, 585–600.

Stępień, K., In situ measurement of cylindricity—Problems and solutions. Precision Engineering, 2014, 38(3), 697–701.

Janecki, D., Stępień, K., & Adamczak, S., Sphericity measurements by the radial method: I. Mathematical fundamentals. Meas. Sci. Technol., 2016, 27(1), 015005.