Yanxia Liang | Computer Science | Research Excellence Award

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Yanxia Liang | Computer Science | Research Excellence Award

西安邮电大学 | China

Dr. Yanxia Liang is a distinguished researcher and associate professor whose work advances the fields of communication engineering and intelligent information processing. She serves at the Shaanxi Key Laboratory of Information Communication Network and Security and the School of Communication and Information Engineering at Xi’an University of Posts and Telecommunications, where she contributes to both academic research and graduate mentorship. Her expertise spans interference management, radio resource management, and information compression within mobile communication systems, with a particular emphasis on improving the efficiency, reliability, and adaptability of next-generation networks. With a strong background in clustering algorithms, K-means optimization, cluster-head selection, image processing, and advanced compression techniques—including discrete cosine transform, entropy coding, and lossless compression—she has established a diverse research profile bridging theory and practical applications. Her work on imaging data processing, compression ratio optimization, and the design of robust image compression algorithms contributes to the development of faster, more bandwidth-efficient communication technologies. Across her career, she has authored numerous studies that integrate signal processing, resource allocation strategies, and intelligent algorithmic frameworks to address modern challenges in wireless communication environments. She is also engaged in exploring emerging trends in mobile communication systems, aiming to enhance system performance through improved data handling and reduced interference. Her contributions support the broader evolution of smart communication infrastructures, including applications in multimedia transmission, network optimization, and secure information exchange. Recognized for her interdisciplinary approach, Yanxia Liang continues to advance research that connects communication theory with real-world technological demands, making her a vital contributor to the scientific community working toward more efficient and intelligent communication networks.

Profile: Scopus

Featured Publications

Liang, Y., Sun, C., Jiang, J., Liu, X., He, H., & Xie, Y. (2020). An efficiency-improved clustering algorithm based on KNN under ultra-dense network. IEEE Access, 8. IEEE.

Liang, Y., Zhao, S., Liu, X., He, H., Zhao, X., & Wang, H. (2024). A balanced energy-efficient clustering strategy for WSNs. IEEE Sensors Journal, 24(22). IEEE.

He, H., Liang, Y., & Li, S. (2021). Clustering algorithm based on azimuth in mmWave massive MIMO–NOMA system. In 2021 IEEE/CIC International Conference on Communications in China (ICCC Workshops). IEEE.

Liang, Y., Liu, X., Jiang, J., Du, J., Sun, C., & Xie, Y. (2020). A practical dynamic clustering scheme using spectral clustering in ultra-dense network. In 2020 IEEE/CIC International Conference on Communications in China (ICCC Workshops). IEEE.

Liang, Y., Jia, T., Li, N., Liu, X., Jiang, J., Lu, G., & Zhao, M. (2024). Review of static image compression algorithms. In 2024 IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE.

Liu, X., & Liang, Y. (2021). A novel Moore–Penrose-inverse-matrix-based data compression method. In 2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT). IEEE.

Xiaohang Zhao | Computer Science | Best Researcher Award

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Xiaohang Zhao | Computer Science | Best Researcher Award

Dr. Xiaohang Zhao, Changchun Institute of Optics, Fine Mechanics and Physics, China

Xiaohang Zhao is a dedicated Ph.D. candidate in Mechatronic Engineering at the University of Chinese Academy of Sciences (UCAS), affiliated with the esteemed Changchun Institute of Optics, Fine Mechanics and Physics. His research emphasizes cutting-edge infrared imaging and remote sensing technologies, particularly for spaceborne applications. Zhao has demonstrated his scientific innovation through multiple first-author publications in high-impact SCI-indexed journals and the successful filing of four patents. His work addresses critical challenges in image quality, including low-light enhancement, stripe noise removal, and blind deblurring. In addition to his academic research, Zhao has contributed to major national defense projects such as the DXX infrared grating camera and the Geological-1 satellite imaging system. With strong expertise in algorithm development, FPGA hardware design, and detector-driven imaging techniques, he actively explores real-time enhancement solutions in space-based imaging. Zhao combines theoretical rigor with practical engineering, aiming to advance China’s capabilities in aerospace and remote sensing.

Publication Profile

orcid

🎓 Education

Xiaohang Zhao earned his Bachelor of Science degree in Electronic Information Science and Technology from Northeast Normal University, where he gained a strong foundation in signal processing, circuit design, and embedded systems. Following his undergraduate studies, he was admitted to the University of Chinese Academy of Sciences (UCAS) for doctoral research in Mechatronic Engineering, joining the prestigious Changchun Institute of Optics, Fine Mechanics and Physics. At UCAS, Zhao has been engaged in advanced courses and research in optical engineering, infrared imaging, sensor data processing, and mechatronic system design. His academic training includes deep exploration of atmospheric scattering models, hardware-software co-design, and scientific programming for large-scale image processing. Under expert supervision, Zhao continues to sharpen his knowledge through national defense-oriented projects and interdisciplinary collaborations, setting a strong foundation for a future in cutting-edge imaging technology, especially focused on spaceborne and defense-related optical systems.

💼 Experience

Xiaohang Zhao is currently a Ph.D. researcher at the Changchun Institute of Optics, Fine Mechanics and Physics, part of the Chinese Academy of Sciences. His professional work spans both algorithm design and hardware implementation for remote sensing applications. He has developed and implemented advanced image processing techniques—including low-light image enhancement, blind deblurring, and stripe noise removal—for real-time space-based imaging systems. Zhao’s experience includes active involvement in defense and aerospace projects such as the DXX infrared grating camera and the Geological-1 satellite imaging mission. He has also contributed to real-world imaging system development using FPGA platforms, ensuring high-efficiency hardware acceleration. His engineering approach combines deep algorithmic insight with system-level design, detector calibration, and embedded optimization. Zhao has collaborated with multidisciplinary teams, integrating sensor data with advanced image enhancement pipelines and ensuring compliance with strict aerospace-grade performance and reliability standards.

🏆 Honors and Awards

Xiaohang Zhao has been recognized for his outstanding research contributions in infrared and remote sensing imaging with several accolades. His innovative work has led to 4 authorized patents in the field of image enhancement and spaceborne imaging algorithms. He is the first author of 5 SCI-indexed journal articles, including publications in high-impact platforms such as IEEE Sensors Journal and Remote Sensing. His academic excellence earned him multiple research scholarships and commendations from the University of Chinese Academy of Sciences. Zhao has also been selected for participation in key national defense projects, highlighting the practical relevance and strategic importance of his research. His commitment to bridging theoretical development with real-world applications has been recognized through internal awards from the Changchun Institute of Optics for innovation in imaging system design and deployment. These honors underscore his growing reputation in the field of high-performance imaging and optical engineering.

🔬 Research Focus

Xiaohang Zhao’s research centers on infrared and remote sensing image enhancement, with a particular focus on spaceborne systems. His work addresses fundamental challenges in low-light image enhancement, blind deblurring, stripe noise removal, and non-uniform illumination compensation, essential for high-precision satellite and defense imaging. He specializes in image quality enhancement algorithms that are tightly coupled with detector characteristics, enabling real-time implementation through FPGA-based hardware acceleration. Zhao also develops atmospheric scattering models to refine image clarity under complex environmental conditions. His technical portfolio includes detector-driven algorithm optimization, real-time enhancement, and noise-resilient imaging techniques suitable for remote and harsh space environments. Zhao’s applied research contributes directly to national defense projects, including the DXX infrared grating camera and Geological-1 satellite imaging, positioning him as a critical contributor to China’s aerospace imaging capabilities. His future goals include advancing autonomous onboard image correction systems for next-generation satellites.

📚 Publications

  • 📄 Low-Light Image Enhancement Based on Retinex and Adaptive Histogram Equalization for Spaceborne Systems

  • 📄 Stripe Noise Removal in Infrared Images via Dual-Domain Sparse Coding

  • 📄 Blind Image Deblurring for Remote Sensing Using Deep Prior and Motion Estimation

  • 📄 Real-Time FPGA Implementation of Image Enhancement Algorithms for Onboard Satellite Processing

  • 📄 Infrared Image Restoration under Atmospheric Scattering Conditions with Physics-Based Modeling

Lesole Kalake | Computer Science | Best Research Article Award

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Lesole Kalake | Computer Science | Best Research Article Award

Dr. Lesole Kalake, National Department of Health, South Africa

Dr. Lesole Soldaat Kalake is a South African ICT and AI researcher, educator, and database professional with a strong interdisciplinary background in computer science, statistics, and business intelligence. He holds a PhD in Information and Communication Engineering from Shanghai University, China. With professional experience spanning over two decades, he has served in both academia and government, notably at the National Department of Health as a Business Analyst and Assistant Director. He has lectured at various institutions including the University of KwaZulu-Natal, UNISA, and Kobe Institute of Technology in Japan. Dr. Kalake has published extensively in peer-reviewed journals, focusing on multi-object tracking, electronic health system security, and computer vision. He is also an active peer reviewer for IEEE Access and Springer journals. Passionate about applying AI in public sector systems, he is known for his expertise in SQL databases, SAS tools, and machine learning frameworks, and continues to contribute to South Africa’s eHealth transformation.

Publication Profile

scopus

Education

Dr. Kalake earned his PhD in Information and Communication Engineering from Shanghai University, China in 2024. He also holds an MSc in Information Systems from Kobe Institute of Technology, Japan, and a BSc Honours in Applied Population Science from the University of KwaZulu-Natal, where he also completed his BSc in Computer Science and Statistics. His academic credentials further include professional diplomas and certificates: a Business Analysis degree from Desto Pty Ltd, Moderation of Outcomes-Based Assessment from Edutel Pty Ltd, and Assessment of Outcomes-Based Assessment from PC Training Holdings. Dr. Kalake is certified as a SAS Base 9 Programmer, SAS Advanced Programmer, and MCTS in Microsoft SQL Server 2008. These qualifications highlight his strong foundation in analytics, software engineering, and IT systems design, supporting his multidisciplinary contributions in both academia and government sectors, particularly in the realms of digital health and artificial intelligence.

Experience

Dr. Kalake has extensive professional experience in software development, tutoring, business analysis, and database administration. Since 2009, he has served the National Department of Health (South Africa) as an Assistant Director focusing on SQL database management, project coordination, and business intelligence. He previously worked for organizations such as Sasuka Pty Ltd and Gauteng Department of Public Works as a Business Analyst and SAS Developer. In academia, he held roles at the University of KwaZulu-Natal, UNISA, and Kobe Institute of Technology, tutoring in IT and statistics. His work has involved e-Governance coordination (JICA/IDCJ project) and developing reporting systems, security frameworks, and decision-support tools for government and private sectors. He is highly skilled in SAS tools, Microsoft SQL Server, Power BI, and modern AI frameworks like PyTorch and Keras, contributing to a seamless integration of data science into public health and development systems.

Awards and Honors

Dr. Lesole Kalake’s scholarly contributions have garnered international recognition. He has served as a peer reviewer for prestigious journals such as IEEE Access and Springer’s Multimedia Tools and Applications since 2021. His critical reviews have covered advanced topics in federated learning, AI for health diagnostics, and cross-dataset validation for age estimation. As a conference presenter, he co-authored a paper at the AFRICATEK 2017 international conference on the use of 3D facial recognition for secure eHealth authentication, published in Springer. His ongoing government work in pharmaceutical economic evaluations has also contributed to national policy development. Though his academic work is recent, it reflects high-impact innovation, especially in multi-object tracking and real-time computer vision, indicating growing recognition in the AI and public sector technology communities. His combined academic, research, and government contributions position him as a forward-thinking leader in the application of technology for development.

Research Focus

Dr. Kalake’s research lies at the intersection of artificial intelligence, eHealth security, and computer vision. His recent investigations explore real-time multi-object tracking across non-overlapping camera views, aiming to enhance detection and re-identification using deep learning models. He has worked on improving object detection performance by integrating methods like HOG (Histogram of Oriented Gradients) with Convolutional Neural Networks (CNNs). Additionally, he has focused on video processing, smart surveillance, and deep learning algorithms to improve detection quality in constrained environments. In the healthcare domain, he is investigating security frameworks for Electronic Health Record (EHR) systems, proposing models using 3D face recognition, Wi-Fi, and smartphone-based authentication to safeguard patient data. His interdisciplinary focus contributes to advancements in AI-driven diagnostics, public sector information systems, and the digital transformation of health systems. This blend of academic and applied research highlights his commitment to AI for public good.

Publication Top Notes

  • 📄 Analysis Based on Recent Deep Learning Approaches Applied in Real-Time Multi-Object Tracking: Review, IEEE Access, 2021

  • 📄 Enhancing Detection Quality Rate with a Combined HOG and CNN for Real-Time Multiple Object Tracking, Sensors, 2022

  • 📄 Applying Ternion Stream DCNN for Real-Time Vehicle Re-Identification and Tracking, Sensors, 2022

  • 📘 Designing an Electronic Health Security System Framework Using Wi-Fi, Smartphone, and 3D Face Recognition, AFRICATEK 2017, Springer

Chengyuan Zhang | Computer Science | Best Research Article Award

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Chengyuan Zhang | Computer Science | Best Research Article Award

Chengyuan Zhang, Hunan University, China

Dr. Chengyuan Zhang, born in October 1985 in Suining County, Hunan, is an Associate Professor and Ph.D. Supervisor at the College of Computer Science and Electronic Engineering, Hunan University. He also serves as a Guest Professor at the College of Information and Intelligence, Hunan Agricultural University. Dr. Zhang earned his Ph.D. in Computer Science from the University of New South Wales, where he also completed his master’s studies under the guidance of Professors Xuemin Lin and Wenjie Zhang. His professional experience includes academic roles in both China and Australia, notably as a postdoctoral researcher. With nearly 40 SCI-indexed publications and over 1,300 citations, he is widely recognized in the domains of multimedia computing, spatio-temporal data analysis, and machine learning. Dr. Zhang is a frequent reviewer for top-tier journals and has contributed to numerous national and provincial-level research projects. He holds editorial and committee roles in several internationally recognized journals and conferences.

Publication Profile

scopus

Education

Dr. Chengyuan Zhang began his academic journey in Software Engineering at Sun Yat-sen University, earning his bachelor’s degree in 2008. He then advanced to the University of New South Wales (UNSW), one of Australia’s leading research institutions, where he obtained a master’s degree in Computer Science in 2011. His academic excellence led him to pursue a Ph.D. in Computer Science at UNSW from July 2011 to December 2015. During his doctoral studies, he worked under the mentorship of esteemed professors Xuemin Lin and Wenjie Zhang. His research during this period laid the groundwork for his future academic contributions in data mining, multimedia computing, and spatio-temporal data processing. The international education experience at UNSW not only honed his technical expertise but also equipped him with a global research perspective and collaborative mindset, helping him transition smoothly into high-impact academic roles upon returning to China.

Experience

Dr. Chengyuan Zhang currently holds the position of Associate Professor and Deputy Director in the Department of Computer Science at Hunan University. He joined the university in December 2019 and assumed his deputy directorship in November 2023. Previously, he served as a Lecturer at the College of Information Science and Engineering at Central South University from 2016 to 2019. His international experience includes a one-year postdoctoral research position at the University of New South Wales from 2015 to 2016, where he continued his research on large-scale data processing. Over the years, Dr. Zhang has participated in and led various nationally funded projects, especially in multimedia, graph data analysis, and AI-driven spatio-temporal applications. He also contributes significantly to the academic community as an editor and reviewer for top journals such as IEEE TKDE, ACM TOIS, and IEEE TNNLS. His career reflects a balanced combination of research innovation, teaching, and academic leadership.

Awards and Honors

Dr. Chengyuan Zhang has received several recognitions for his scholarly contributions, including the Outstanding Reviewer Award from Pattern Recognition Letters, which highlights his dedication to academic quality and peer review. He serves as Academic Editor for Advances in Multimedia and as a Guest Editor for prestigious journals such as Multimedia Tools and Applications and Mathematics. Dr. Zhang has also contributed as a Reviewer for top-tier academic journals including IEEE TPAMI, IEEE TNNLS, IEEE TKDE, ACM TOIS, and ACM TOMM. In addition, he has served as a Program Committee Member for leading international conferences such as ACM Multimedia and IJCAI-PRICAI. These roles are a testament to his academic credibility and recognition within the global research community. His grant-winning research and participation in national foundations reflect his active role in shaping future advancements in multimedia, data mining, and artificial intelligence.

Research Focus

Dr. Chengyuan Zhang’s research centers on multimedia computing, spatio-temporal multi-modal data analysis, image processing, graph data analysis, and machine learning. His work aims to address complex challenges related to information retrieval, representation learning, and knowledge discovery from large-scale, heterogeneous datasets. Specifically, he focuses on designing efficient algorithms for spatio-temporal queries, cross-modal hashing retrieval, and dynamic image enhancement techniques. His research often integrates deep learning, graph theory, and semantic correlation mining, contributing to both theoretical advancements and real-world applications—especially in areas like intelligent agriculture, social recommendation systems, and wireless sensor networks. With support from multiple National Natural Science Foundation of China (NSFC) grants and Hunan provincial research programs, his work is recognized as both innovative and impactful. He has published nearly 40 papers in top journals and conferences such as IEEE TKDE, ACM TOIS, and ACM TOMM, earning over 1,300 citations and an H-index of 18.

Publication Top Notes

  1. 📖 MvHAAN: Multi-view hierarchical attention adversarial network for person re-identification – World Wide Web, 2024

  2. 📖 Bi-Direction Label-Guided Semantic Enhancement for Cross-Modal Hashing – IEEE TCSVT, 2024

  3. 🖼 Using CNN with Multi-Level Information Fusion for Image Denoising – Electronics, 2023

  4. 🖼 Adaptive Dynamic Shuffle Convolutional Parallel Network for Image Super-Resolution – Electronics, 2024

  5. 📷 Efficient Feature Redundancy Reduction for Image Denoising – World Wide Web, 2024

  6. 🔍 Efficient Maximal Biclique Enumeration on Large Uncertain Bipartite Graphs – IEEE TKDE, 2023

  7. 🔍 Efficient Maximum Edge-Weighted Biclique Search on Large Bipartite Graphs – IEEE TKDE, 2022

  8. 🤖 Robust Sparse Weighted Classification for Crowdsourcing – IEEE TKDE, 2022

  9. 🌐 Multi-Graph Heterogeneous Interaction Fusion for Social Recommendation – ACM TOIS, 2022

  10. 🌐 Scaling High-Quality Pairwise Link-Based Similarity Retrieval on Billion-Edge Graphs – ACM TOIS, 2022

 

Iustina Ivanova | Computer Science | Best Researcher Award

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Mrs. Iustina Ivanova | Computer Science | Best Researcher Award

Mrs. Iustina Ivanova, Fondazione Bruno Kessler, Italy

Mrs. Iustina Ivanova is a researcher at Fondazione Bruno Kessler in Italy, specializing in the application of Artificial Intelligence (AI) in real-world contexts. Her early foundation in software engineering, coupled with her fascination for computer vision, has fueled her pursuit of impactful solutions in diverse fields such as sports and smart agriculture. Notably, her distinction-earning Master’s in AI, focusing on neural networks for object detection, highlights her dedication to advancing cutting-edge technology.

Education:

Her academic journey began with a Specialist degree in Software Engineering from Bauman Moscow State Technical University, Russia (2007-2013), followed by a Master of Science in Artificial Intelligence from the University of Southampton, United Kingdom, which she completed with distinction in 2018. Although she pursued a PhD in Computer Science at the Free University of Bolzano, Italy, from 2019 to 2022, she opted to discontinue her doctoral studies to focus on professional endeavors.

Professional Profiles:

ORCID Profile

Scopus Profile

Professional Experience:

Researcher
Foundazione Bruno Kessler (Italy)
October 2023 – Present
Engaged in advancing Artificial Intelligence in smart agriculture, with a focus on step-ahead forecasting using sensor data. Conducted experiments with machine learning models to enhance prediction accuracy and decision-making in agricultural systems.

Data Science Moderator
Netology Company (Russia)
May 2019 – October 2020
Developed and delivered lectures on Statistics and Mathematics for Data Science as part of the “Data Science” course. Designed accessible educational materials hosted online for wider learning opportunities (Netology Statistics Repository).

Computer Vision Data Scientist
OCRV Company (Russia)
April 2019 – November 2019
Worked on a video-based tracking system for railway operations. Focused on detecting objects and people in video data, measuring working hours, and deploying advanced computer vision algorithms to improve workplace efficiency.

Teacher of Informatics and Mathematics
Repetitor.ru (Russia)
August 2013 – November 2017
Organized and facilitated engaging study sessions to prepare high school students for final exams in informatics and mathematics. Successfully guided approximately 30 students to pass exams and gain university admissions.

Research Interests:

Mrs. Ivanova’s research interests center on computer vision, machine learning, and the integration of AI technologies into diverse domains such as smart agriculture and sports analytics. She has made notable contributions through her research project “Sensors and Data for the Analysis of Sports Activities (SALSA).” This project, focusing on computer vision solutions and recommender systems for sport climbers, resulted in several well-received publications. Her work bridges technology and user experience, demonstrating innovation and practical value in AI-driven applications.

Publications:

Climbing Crags Repetitive Choices and Recommendations
2023-09-14 | Conference paper | DOI: 10.1145/3604915.3610652 | Contributors: Iustina Ivanova

Recommender Systems for Outdoor Adventure Tourism Sports: Hiking, Running and Climbing
2023-07-18 | Journal article | DOI: 10.1007/s44230-023-00033-3 | Contributors: Iustina Ivanova; Mike Wald

How Can We Model Climbers’ Future Visits from Their Past Records?
2023-06-16 | Conference paper | DOI: 10.1145/3563359.3597408 | Contributors: Iustina Ivanova; Mike Wald

Introducing Context in Climbing Crags Recommender System in Arco, Italy
2023-03-27 | Conference paper | DOI: 10.1145/3581754.3584120 | Contributors: Iustina Alekseevna Ivanova; Mike Wald

Map and Content-Based Climbing Recommender System
2022 | Conference paper | DOI: 10.1145/3511047.3536416 | Contributors: Ivanova, I.A.; Buriro, A.; Ricci, F.

Climber Behavior Modeling and Recommendation
2021 | Conference paper | DOI: 10.1145/3450613.3459658 | Contributors: Ivanova, I.

Climbing Route Difficulty Grade Prediction and Explanation
2021 | Conference paper | DOI: 10.1145/3486622.3493932 | Contributors: Andric, M.; Ivanova, I.; Ricci, F.

Knowledge-Based Recommendations for Climbers
2021 | Conference paper | EID: 2-s2.0-85116934926 | Contributors: Ivanova, I.; Andrić, M.; Ricci, F.

Climbing Activity Recognition and Measurement with Sensor Data Analysis
2020 | Conference paper | DOI: 10.1145/3395035.3425303 | Contributors: Ivanova, I.; Andric, M.; Janes, A.; Ricci, F.; Zini, F.

Video and Sensor-Based Rope Pulling Detection in Sport Climbing
2020 | Conference paper | DOI: 10.1145/3422844.3423058 | Contributors: Ivanova, I.; Andric, M.; Moaveninejad, S.; Janes, A.; Ricci, F.

Conclusion:

Mrs. Iustina Ivanova is a strong candidate for the Research for Best Researcher Award, given her impressive contributions to recommender systems and outdoor adventure tourism. Her work is not only academically robust but also highly relevant in practical contexts, addressing modern challenges in personalization and activity recognition.