The Information Engineering University | China

Dr. Tian Gao is a distinguished researcher in the field of remote sensing, specializing in multimodal image matching, Arctic sea ice motion analysis, and image registration for optical and SAR imagery. He completed his graduate studies at PLA Information Engineering University, Zhengzhou, China, focusing on geospatial information and advanced computational methods for Earth observation.Gao has authored 11 peer-reviewed publications, including in top-tier journals such as IEEE Sensors Journal, ISPRS Journal of Photogrammetry and Remote Sensing, and the International Journal of Applied Earth Observation and Geoinformation. His notable contributions include the development of SFA-Net, a SAM-guided focused attention network for multimodal remote sensing image matching, and innovative approaches to sharpened side phase fusion and self-similar adjacent self-convolutional feature registration. Gao’s work also encompasses keypoint-free feature tracking for Arctic sea ice motion retrieval, DEM super-resolution using attention-based and relative depth-guided methods, and GNSS-denied UAV geolocalization. These efforts have advanced both methodological innovation and practical applications in environmental monitoring, geospatial intelligence and disaster response.His research demonstrates extensive collaboration with domestic and international scholars, reflecting interdisciplinary engagement across remote sensing, UAV imaging, and geospatial data analysis. Gao’s publications have collectively received 51 citations, highlighting the growing impact of his work in the scientific community.Beyond methodological contributions Gao’s work has significant societal and environmental relevance enabling improved monitoring of polar ice dynamics, enhancing emergency response through UAV-assisted image stitching and supporting sustainable geospatial intelligence applications. With expertise spanning optical and SAR imagery multimodal data fusion and image registration, Tian Gao continues to contribute to cutting-edge research that bridges academic innovation with real-world solutions in Earth observation and remote sensing.

Profiles: ORCID | Scopus

Featured Publications

1.Wang, Y., Lan, C., Gao, T., Yao, F., & Mu, Z. (2025). Multimodal image matching using sharpened side phase fusion method. IEEE Sensors Journal.

2.Gao, T., Lan, C., Lv, L., Shi, Q., Huang, W., Wang, Y., & Mu, Z. (2025). Robust registration of multimodal remote sensing images using self-similar adjacent self-convolutional feature. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

3.Gao, T., Lan, C., Zhou, C., Zhang, Y., Huang, W., Wang, L., & Wang, Y. (2025). Arctic sea ice motion retrieval from multisource SAR images using a keypoint-free feature tracking algorithm. ISPRS Journal of Photogrammetry and Remote Sensing. Cited By: 1

4.Huang, W., Sun, Q., Guo, W., Xu, Q., Wen, B., Gao, T., & Yu, A. (2025). Multi-modal DEM super-resolution using relative depth: A new benchmark and beyond. International Journal of Applied Earth Observation and Geoinformation.

5.Gao, T., Lan, C., Huang, W., & Wang, S. (2025). SFA-Net: A SAM-guided focused attention network for multimodal remote sensing image matching. ISPRS Journal of Photogrammetry and Remote Sensing.

Tian Gao’s research advances remote sensing and multimodal image analysis, enabling precise monitoring of Arctic sea ice, GNSS-denied UAV navigation, and environmental changes. His work bridges scientific innovation with practical applications, supporting disaster response, geospatial intelligence, and sustainable environmental management globally.

Ahmed Elmekawy | Startups and Industry Applications | Research Excellence Award

Published on 05/12/202505/12/2025 by Computer Vision Awards

Dr. Ahmed Elmekawy | Startups and Industry Applications | Research Excellence Award

Researcher | Saint Petersburg State University | Egypt

Dr. Ahmed Hassan Abdelrahman Elmekawy is a researcher in Condensed Matter Physics, specializing in magnetic nanowires, FORC (First-Order Reversal Curve) analysis, micromagnetic modeling, and nanoscale magnetism. He is affiliated with JINR, St. Petersburg State University, and the Cyclotron Project at the Egyptian Atomic Energy Authority (EAEA). His work bridges theoretical modeling and advanced experimental techniques for understanding magnetic behavior in low-dimensional nanostructures.Dr. Elmekawy has authored 11 scientific publications, accumulating 71 citations, with an h-index of 4 and an i10-index of 3, reflecting his growing visibility and influence in nanomagnetism research. His contributions focus on unraveling magnetization dynamics internal magnetic interactions and structural property relationships in iron and Ni/Cu nanowire arrays which are foundational materials for next-generation spintronic devices magnetic sensors and energy-efficient data storage systems.Among his notable works his publication Magnetic Properties and FORC Analysis of Iron Nanowire Arrays stands as a highly cited study that advanced the interpretation of magnetic interactions through FORC techniques. His subsequent studies including “Magnetic Properties of Ordered Arrays of Iron Nanowires: The Impact of Length and Effect of Interactions and Non-uniform Magnetic States on Magnetization Reversal further deepened scientific understanding of nanoscale magnetism and geometrical effects on magnetization reversal mechanisms.His recent publications in Nano-Structures & Nano-Objects and Journal of Magnetism and Magnetic Materials highlight significant advancements in correlating FORC measurements with micromagnetic simulations demonstrating compatibility between theoretical modeling and experimental observations. These studies provide new frameworks for evaluating internal magnetic interactions in segmented and non-segmented nanowire systems offering new tools for material optimization.In addition to nanomagnetism Dr. Elmekawy has contributed to nuclear physics particularly through work on proton and antiproton scattering from He using Glauber multiple scattering models. His interdisciplinary collaborations span Russia, Egypt, and Europe, showcasing strong international engagement.Dr. Elmekawy’s research contributes to societal and technological innovation by supporting the development of advanced magnetic materials crucial for secure communication systems biomedical imaging energy systems and miniaturized electronic components. His scientific trajectory reflects a commitment to precision collaboration and impactful discovery.

Profile: Googlescholar

Featured Publications

1.Elmekawy, A. H. A., Iashina, E. G., Dubitskiy, I. S., Sotnichuk, S. V., & Bozhev, I. V., et al. (2020). Magnetic properties and FORC analysis of iron nanowire arrays. Materials Today Communications, 25, 101609. Cited By: 26

2.Elmekawy, A. H. A., Iashina, E., Dubitskiy, I., Sotnichuk, S., Bozhev, I., & Kozlov, D., et al. (2021). Magnetic properties of ordered arrays of iron nanowires: The impact of the length. Journal of Magnetism and Magnetic Materials, 532, 167951. Cited By: 20

3.Dubitskiy, I. S., Elmekawy, A. H. A., Iashina, E. G., Sotnichuk, S. V., & Napolskii, K. S., et al. (2021). Effect of interactions and non-uniform magnetic states on the magnetization reversal of iron nanowire arrays. Journal of Superconductivity and Novel Magnetism, 34(2), 539–549. Cited By: 16

4.Mistonov, A. A., Dubitskiy, I. S., Elmekawy, A. H. A., Iashina, E. G., & Sotnichuk, S. V., et al. (2021). Change in the direction of the easy magnetization axis of arrays of segmented Ni/Cu nanowires with increasing Ni segment length. Physics of the Solid State, 63(7), 1058–1064. Cited By: 7

5.Nabiyev, A. A., Mustafayev, I. I., Mehdiyeva, R. N., Nuriyev, M. A., & Andreev, E. V., et al. (2025). Post‐γ‐irradiation effects in nano-SiO2 particle reinforced high-density polyethylene composite films: Structure–property relationships, thermal stability, and degradation. Polymer Composites. Cited By: 1

Dr. Ahmed Elmekawy’s research in magnetic nanowires and FORC analysis advances fundamental understanding of nanoscale magnetism, enabling innovations in spintronics, high-density data storage, and energy-efficient magnetic devices. His interdisciplinary work bridges theory and experiment, contributing to technological development, materials science, and global scientific progress.

Jinxu Zhang | Document Image Analysis | Research Excellence Award

Published on 05/12/2025 by Computer Vision Awards

Dr. Jinxu Zhang | Document Image Analysis | Research Excellence Award

Harbin Institute of Technology | China

Dr. Jinxu Zhang is a researcher at the Harbin Institute of Technology specializing in multimodal understanding, Document Visual Question Answering (DocVQA), and multimodal large language models. His work focuses on advancing key technologies for interpreting complex, multi-form, and multi-page documents, contributing significantly to the fields of document intelligence and machine reading systems.He has completed and continues to contribute to the National Natural Science Foundation of China (NSFC) project on Key Technologies of Multi-form Document VQA. His research outputs include six SCI/Scopus-indexed publications5 , with a total of 41 citations, an h-index of 2, and an i10-index of 2. His contributions appear in top-tier venues such as ACM Multimedia (CCF-A), EMNLP Findings (CCF-B), Information Fusion (SCI, IF 15.5), and IEEE Transactions on Learning Technologies. His notable works CREAM, DocRouter, DocAssistant, and DREAM introduce innovative solutions for hierarchical multimodal retrieval, prompt-guided vision transformers, mixture-of-experts connectors and robust reasoning strategies for document comprehension.Dr. Zhang’s patented work on an intelligent question-answering system for multi-form documents further extends his impact toward practical deployable intelligent document systems. His research achievements emphasize coarse-to-fine retrieval key-region reading step-wise reasoning and efficient multimodal fusion. He also incorporates Reinforcement Learning–based data enhancement and Chain-of-Thought (CoT) construction to improve model reasoning in multi-page document analysis.He actively collaborates with university researchers in multimodal understanding document analysis OCR and deep learning fostering interdisciplinary innovation. His work contributes to building reliable and generalizable document intelligence systems with broad societal applications including education digital governance business automation and large-scale knowledge management.Dr. Zhang continues to advance the frontier of intelligent document analysis through sustained research model innovation and high-impact scholarly contributions.

Profiles: Scopus | Googlescholar

Featured Publications

1.Liu, M., Zhang, J., Nyagoga, L. M., & Liu, L. (2023). Student-AI question cocreation for enhancing reading comprehension. IEEE Transactions on Learning Technologies, 17, 815–826. Cited By: 28

2.Zhang, J., Yu, Y., & Zhang, Y. (2024). CREAM: Coarse-to-fine retrieval and multi-modal efficient tuning for document VQA. In Proceedings of the 32nd ACM International Conference on Multimedia (pp. 925–934). Cited By: 13

3.Zhang, J., Fan, Q., & Zhang, Y. (2025). DocAssistant: Integrating key-region reading and step-wise reasoning for robust document visual question answering. In Findings of the Association for Computational Linguistics: EMNLP 2025 (pp. 3496–3511).

4.Zhang, J., Fan, Q., Yu, Y., & Zhang, Y. (2025). DREAM: Integrating hierarchical multimodal retrieval with multi-page multimodal language model for documents VQA. In Proceedings of the 33rd ACM International Conference on Multimedia (pp. 4213–4221).

5.Zhang, J., & Zhang, Y. (2025). DocRouter: Prompt guided vision transformer and Mixture of Experts connector for document understanding. Information Fusion, 122, Article 103206.

Dr. Zhang’s research advances the global frontier of intelligent document understanding by enabling machines to accurately interpret complex, multi-page documents with human-level reasoning. His innovations in multimodal fusion, retrieval, and robust VQA architectures support breakthroughs in scientific research, digital governance, education, and automated knowledge management. Ultimately, his work drives the development of reliable, scalable, and socially beneficial AI systems that enhance information accessibility and decision-making worldwide.

Tchilabalo Bouyo | Microbiology | Research Impact Award

Published on 05/12/202505/12/2025 by Computer Vision Awards

Dr. Tchilabalo Bouyo | Microbiology | Research Impact Award

University of Lomé | Togo

Dr. Tchilabalo Bouyo is a Biological Engineer and early-career researcher specializing in molecular bacteriology, natural product chemistry, and antimicrobial resistance. With demonstrated experience in laboratory management and applied microbiological research, he has developed strong expertise in DNA extraction, PCR-based detection, and molecular characterization of pathogenic organisms. His analytical proficiency spans RStudio, SPSS, Excel, and GraphPad Prism, enabling robust statistical analysis data visualization and quality assurance in biomedical research. His background includes impactful roles at Centro Investigacion Biomedica and Infirmerie NDE Tokion Séminaire where he contributed to microbiological diagnostics antimicrobial resistance identification and laboratory quality compliance.Bouyo’s research output includes seven scientific documents consisting of journal articles experimental studies and preprints indexed in Scopus Clarivate and Crossref with 1 citation and an h-index of 1 reflecting a growing and internationally visible academic trajectory. A cornerstone of his scholarly contribution is the 2025 article Clarithromycin-resistant Helicobacter pylori in Africa: a systematic review and meta-analysis published in Antimicrobial Resistance Infection Control. This influential work provides comprehensive evidence on antibiotic resistance trends across Africa contributing to global policy discussions and antimicrobial stewardship strategies.In parallel Bouyo has extensively investigated phytochemicals toxicity profiles and antimicrobial properties of medicinal plants widely used in traditional medicine in Togo and West Africa. His studies on species such as Pteleopsis suberosa Piliostigma thonningii Calotropis procera Anchomanes difformis and Tetrapleura tetraptera highlight the therapeutic potential of indigenous flora and support the scientific validation of ethnopharmacological practices. These works have appeared in journals such as Scientific Reports Journal of Applied Biotechnology and Chemical Science International Journal.Collaborating with multidisciplinary teams across Africa Asia and South America Bouyo contributes to global research networks in natural product drug discovery antimicrobial resistance surveillance and evidence-based traditional medicine. His work advances public health by bridging laboratory science cultural knowledge systems and modern biomedical innovation reinforcing his commitment to impactful and socially relevant global health research.

Profiles: Scopus | ORCID

Featured Publications

1. Kossi, K., Komi, K. K., Sandrine, S. T., Bouyo, T., & Tchadjobo, T. (2025). Bioactive molecules isolated from Phyllanthus amarus Schum & Thonn, Caesalpinia bonduc (L.) Roxb, Momordica charantia Linn and Xylopia aethiopica (Dunal) A. Rich used in the Diabeto-Dolvo® recipe in Togo: A review. Chemical Science International Journal.

2. Dossouvi, K. M., Bouyo, T., Sognonnou, S., Ibadin, E. E., Lv, L.-C., Sambe Ba, B., Seck, A., Dossim, S., Sellera, F. P., Camara, M., et al. (2025). Clarithromycin-resistant Helicobacter pylori in Africa: A systematic review and meta-analysis. Antimicrobial Resistance and Infection Control.

3. Bouyo, T., Komi, K. K., Salifou, S. T., Pissang, P., Gbekley, H. E., Hoekou, Y., M’boumba, B. E., Kpatagnon, J. K., Bidjada, B., & Dossouvi, K. M., et al. (2025). Phytochemical and biological studies of the hydroethanolic extract of Pteleopsis suberosa and Piliostigma thonningii used in herbal medicine in Togo. Research Square.

4. Tchakondo, S., Boleti, N., Gamayizome, A. E., Bouyo, T., Gbekley, E. H., Tchacondo, T., & Karou, S. D. (2025). Phytochemical, toxicological, and antimicrobial evaluation of the hydroethanolic extract of Calotropis procera (Ait.) leaves used in traditional medicine in the Maritime Region of Togo: An experimental study. Research Square.

5. Bouyo, T., Komi, K. K., Salifou, S. T., Pissang, P., Gbekley, E. H., Hoekou, Y., M’boumba, B. E., Kpatagnon, J. K., Bidjada, B., Sossou, K., et al. (2025). In vitro phytochemical and biological evaluation of hydroethanolic extract of Anchomanes difformis in Togo. Scientific Reports.

Xuewen Zhou | Machine Learning for Computer Vision | Young Scientist Award

Published on 05/12/202505/12/2025 by Computer Vision Awards

Mr. Xuewen Zhou | Machine Learning for Computer Vision | Young Scientist Award

Master of Engineering | Hubei Normal University | China

Mr. Xuewen Zhou is a developing researcher in medical signal processing, medical image segmentation, and intelligent optimization algorithms, with growing contributions to the fields of biomedical engineering and computational intelligence. Affiliated with Hubei Normal University, his research focuses on designing advanced fractional-order and optimization-driven neural network models to enhance the analysis of physiological signals such as ECG and EEG as well as dermatological image segmentation. With 5 scientific publications, 4 citations, and an h-index of 1, Dr. Zhou is steadily establishing a strong academic presence.Dr. Zhou’s notable achievements include the publication of multiple SCI-indexed journal papers and active participation in leading international conferences. His recent SCI Q2 paper Adaptive Fractional Order Pulse Coupled Neural Networks with Multi-Scale Optimization for Skin Image Segmentation introduces an innovative segmentation framework integrating fractional order optimization with pulse coupled neural networks. The method employs a novel entropy–edge fitness function significantly improving accuracy in skin lesion delineation.Another key contribution is the SCI Q2 paper Improved Sparrow Search Based on Temporal Convolutional Network for ECG Classification where Dr. Zhou explores hybrid fractional order algorithms to optimize ECG recognition. His work rigorously analyzes the influence of positive and negative fractional orders on optimization stability offering valuable insights into next-generation fractional learning systems.In the EI indexed China Automation Congress Dr. Zhou proposed an ECG classification model combining spatial–channel attention networks with an improved RIME optimization algorithm enhancing hyperparameter tuning for complex biomedical patterns. He also contributed to neuromorphic computing through the ICNC paper on FRMAdam iTransformer KAN presenting a fractional order momentum optimizer for EEG and ECG prediction.Dr. Zhou maintains strong collaborations with researchers including Jiejie Chen Ping Jiang Xinrui Zhang Zhiwei Xiao and Zhigang Zeng contributing to interdisciplinary advancements across medical AI fractional order theory and neural computation. His research demonstrates meaningful societal impact by improving early disease detection supporting intelligent diagnostic tools and advancing clinical decision making technologies on a global scale.

Profiles: Scopus | ORCID | ResearchGate

Featured Publications

1.Zhou, X., Chen, J., Jiang, P., Zhang, X., & Zeng, Z. (2026). Adaptive fractional-order pulse-coupled neural networks with multi-scale optimization for skin image segmentation. Biomedical Signal Processing and Control, (February 2026).

2.Zhou, X., Chen, J., Xiao, Z., Zhang, X., Jiang, P., & Zeng, Z. (2026). Improved sparrow search based on temporal convolutional network for ECG classification. Biomedical Signal Processing and Control, (February 2026).

3.Xiao, Z., Chen, J., Zhou, X., Wei, B., Jiang, P., & Zeng, Z. (2025). Monotonic convergence of adaptive Caputo fractional gradient descent for temporal convolutional networks. Neurocomputing, (December 2025).

4.Zhang, X., Chen, J., Zhou, X., & Jiang, P. (2024, December 13). FRMAdam-iTransformer KAN: A fractional order RMS momentum Adam optimized iTransformer with KAN for EEG and ECG prediction. In 2024 International Conference on Neuromorphic Computing (ICNC).

5.Zhou, X., Chen, J., Jiang, P., & Zhang, X. (2024, November 1). Electrocardiogram classification based on spatial-channel networks and optimization algorithms. In 2024 China Automation Congress (CAC).

Dr. Xuewen Zhou’s work advances science and society by developing fractional-order neural systems that significantly enhance the accuracy of biomedical signal and image analysis. His innovations support earlier disease detection, improved diagnostic reliability, and broader global access to intelligent healthcare technologies.

Şifa Gül Demiryürek | Generative Models for Computer Vision | Outstanding Scientist Award

Published on 02/12/202502/12/2025 by Computer Vision Awards

Dr. Şifa Gül Demiryürek | Generative Models for Computer Vision | Outstanding Scientist Award

Lecturer | Aksaray University | Turkey

Dr. Şifa Gül Demiryürek is a researcher specializing in acoustics, dynamics, vibration control, nonlinear structures, and metamaterials, with a growing body of work that bridges fundamental mechanics and applied engineering. Her research focuses on low-frequency broadband vibration damping, nonlinear passive particle dampers, and metamaterial-inspired structures aimed at improving stability, efficiency, and durability in modern mechanical systems.She has authored 11 scientific documents, accumulating 19 citations with an h-index of 3, reflecting the emerging impact of her contributions. Her early work includes the experimental study of thermal-mixing phenomena in coaxial jets published in the Journal of Thermophysics and Heat Transfer demonstrating her multidisciplinary foundation in fluid–thermal interactions. Transitioning toward structural dynamics her doctoral research at the University of Sheffield advanced the understanding of periodically arranged nonlinear particle dampers under low-amplitude excitation providing new insights into damping mechanisms critical for lightweight and high-performance structures.Dr. Demiryürek has collaborated with notable researchers such as A. Krynkin and J. Rongong contributing to recognized venues including DAGA, ACOUSTICS Proceedings, and the Institute of Acoustics. Her studies on metamaterial-based dampers and locally resonating structures highlight innovative strategies for vibration mitigation particularly in the low-frequency regime where traditional dampers are less effective. Her works further expand this direction with investigations on dynamic behavior of thermoplastics and material resonance considerations for wind turbine towers addressing contemporary engineering challenges related to sustainability and structural reliability.In addition to research publications she has contributed educational materials including Introduction to Metamaterials supporting broader knowledge dissemination in emerging engineering domains. Her collaborations in applied mechanics such as the numerical evaluation of electric motorcycle chassis demonstrate a commitment to integrating theoretical advances into practical real-world applications.Through her focused work at the intersection of vibration engineering and metamaterial science Şifa Gül Demiryürek is contributing to next-generation solutions for safer quieter and more efficient mechanical systems with potential societal impact across manufacturing transportation renewable energy and advanced materials engineering.

Profiles: Googlescholar | Scopus | ORCID

Featured Publications

1.Demiryürek, S. G., Kok, B., Varol, Y., Ayhan, H., & Oztop, H. F. (2018). Experimental investigation of thermal-mixing phenomena of a coaxial jet with cylindrical obstacles. Journal of Thermophysics and Heat Transfer, 32(2), 273–283. Cited By: 5

2. Demiryürek, S. G. (2022). Periodically arranged nonlinear passive particle dampers under low-amplitude excitation (Doctoral research, University of Sheffield). Cited By: 3

3. Demiryürek, S. G., & Krynkin, A. (2021). Low-frequency broadband vibration damping using the nonlinear damper with metamaterial properties. In DAGA 2021 Conference Proceedings (pp. 94–96). Cited By: 3

4.Demiryürek, S. G., Krynkin, A., & Rongong, J. (2020). Modelling of nonlinear dampers under low-amplitude vibration. In ACOUSTICS 2020 Proceedings. Cited By: 3

5.Demiryürek, S. G., Krynkin, A., & Rongong, J. (2019). Non-linear metamaterial structures: Array of particle dampers. Universitätsbibliothek der RWTH Aachen. Cited By: 3

Dr. Şifa Gül Demiryürek’s research advances next-generation vibration damping and metamaterial technologies, enabling safer, quieter, and more efficient mechanical systems across industries. Her contributions support innovation in sustainable engineering from wind energy structures to lightweight transportation strengthening global efforts toward resilient, high-performance designs.

Etienne Perre | Human Computer Interaction and Augmented Reality | Best Innovation Award

Published on 02/12/202502/12/2025 by Computer Vision Awards

Prof. Etienne Perre | Human Computer Interaction and Augmented Reality | Best Innovation Award

Professor | Université Grenoble Alpes | France

Prof. Etienne Perret is an internationally recognized scholar in chipless Radio Frequency Identification (RFID), electromagnetic engineering, and microwave systems. With a distinguished research portfolio comprising 345 scientific documents, an h-index of 35, i10-index of 114, and over 5,740 citations, he stands among the foremost contributors to the global advancement of chipless RFID technologies. His pioneering work has shaped modern paradigms in identification, sensing, coding, and microwave-based signal processing.Dr. Perret’s research focuses on the design, optimization, and experimental realization of chipless RFID tags, hybrid and polarization-diverse coding methods UWB reader systems, RF encoding particles and group-delay–based encoding structures. His landmark publications such as hybrid-coded chipless tags, depolarizing tags for robust detection high-capacity polarization-insensitive tags and fully printable paper-based RFID systems are among the most cited works in the field each receiving between 150 and 400 citations. These contributions have provided the theoretical foundation and practical architectures enabling low-cost printable and flexible RFID solutions suitable for large-scale deployment.A notable part of his research explores RF sensing using silicon nanowires leading to breakthroughs in humidity sensing environmental monitoring and multifunctional RFID devices. His work on RCS magnitude-level coding noncommensurate transmission-line all-pass networks and analog signal processing further demonstrates his versatility across high-frequency electronics and electromagnetic engineering.Dr. Perret collaborates extensively with leading researchers including S. Tedjini A. Vena O. Rance, R. Nair, and others across Europe Australia and North America. His co-authored books and contributions to IEEE Magazines and Elsevier volumes continue to guide researchers engineers and industry practitioners.The societal impact of his research is profound enabling sustainable chipless and cost-effective identification technologies for logistics supply chain monitoring smart packaging structural health analysis and IoT sensing. His work accelerates the transition toward greener RFID solutions by eliminating semiconductor chips and promoting scalable printable technologies that benefit both industry and the environment.Dr. Perret remains a leading global voice advancing the science and engineering of next-generation RFID and wireless sensing systems.

Profiles: Googlescholar | Scopus

Featured Publications

1. Vena, A., Perret, E., & Tedjini, S. (2011). Chipless RFID tag using hybrid coding technique. IEEE Transactions on Microwave Theory and Techniques, 59(12), 3356–3364. Cited By: 404

2. Vena, A., Perret, E., & Tedjni, S. (2013). A depolarizing chipless RFID tag for robust detection and its FCC compliant UWB reading system. IEEE Transactions on Microwave Theory and Techniques, 61(8), 2982–2994. Cited By: 274

3.Vena, A., Perret, E., & Tedjini, S. (2012). High-capacity chipless RFID tag insensitive to the polarization. IEEE Transactions on Antennas and Propagation, 60(10), 4509–4515. Cited By: 261

4.Vena, A., Perret, E., & Tedjini, S. (2012). A fully printable chipless RFID tag with detuning correction technique. IEEE Microwave and Wireless Components Letters, 22(4), 209–211. Cited By: 209

5.Gupta, S., Parsa, A., Perret, E., Snyder, R. V., Wenzel, R. J., & Caloz, C. (2010). Group-delay engineered noncommensurate transmission line all-pass network for analog signal processing. IEEE Transactions on Microwave Theory and Techniques, 58(9), 2392–2407. Cited By: 194

The nominee’s pioneering work in Human–Computer Interaction and Augmented Reality bridges the gap between digital and physical worlds, enabling more intuitive and immersive user experiences. Their innovations contribute to advancing global technology, empowering industries, education, and society with next-generation interactive solutions.

Ahmadreza Khodayari | Industrial and Manufacturing Applications | Excellence in Research

Published on 28/11/202528/11/2025 by Computer Vision Awards

Mr. Ahmadreza Khodayari | Industrial and Manufacturing Applications | Excellence in Research

PhD Candidate | The University of Adelaide | Australia

Mr. Ahmadreza Khodayari is a mining engineering researcher whose work integrates rock mechanics, blasting science, fracture mechanics, and machine learning to advance precision modelling and optimization in mining operations. With a Published Documents 8 citation index comprising 110 citations, an h-index of 4, and an i10-index of 4, his contributions span experimentally grounded studies, data-driven prediction models and mechanistic simulations that address key challenges in rock breakage and material flow behaviour.His research portfolio includes several completed and ongoing projects focused on blast modelling rock fracture characterization and artificial intelligence applications in geo-materials engineering. Notable works include the calibration of mechanistic blast models using Ernest Henry Mine datasets the development of machine learning models for predicting blast-induced fragment sizes and advanced Blender Physics Engine simulations to assess sublevel caving (SLC) material flow. He has also executed misfire impact analyses on SLC gravity flow supporting safer and more predictable caving performance. Additionally his studies on AI-driven prediction of concrete and rock fracture toughness contribute to bridging traditional fracture mechanics with modern computational intelligence.Ahmadreza’s publications are featured in respected outlets such as Engineering Fracture Mechanics Theoretical and Applied Fracture Mechanics Steel and Composite Structures and the Journal of Mining and Environment. His 2022 work on predicting mixed-mode fracture toughness using extreme gradient boosting and metaheuristic optimization has accumulated significant citations reflecting strong community interest in AI-enhanced fracture modelling. His earlier experimental studies on freeze–thaw effects in Lushan Sandstone provided valuable insights into strength degradation mechanisms in cold-region geomaterials.He collaborates with researchers from the Lebanese French University Imam Khomeini International University and other international institutions strengthening global knowledge exchange in blasting and rock mechanics. His contributions to major conferences including FragBlast MassMin and ARMA demonstrate active engagement with both scientific and industry practitioners.Through a combination of high-fidelity numerical modelling physics-based simulations and advanced data-driven techniques Ahmadreza’s research aims to enhance fragmentation predictability mine productivity and geomechanical safety. His work continues to shape emerging methodologies in intelligent mining systems contributing to more efficient and sustainable resource extraction practices worldwide.

Profiles: Googlescholar | ORCID | ResearchGate

Featured Publications

1.Fakhri, D., Khodayari, A., Mahmoodzadeh, A., Hosseini, M., Ibrahim, H. H., & Others. (2022). Prediction of mixed-mode I and II effective fracture toughness of several types of concrete using the extreme gradient boosting method and metaheuristic optimization algorithms. Engineering Fracture Mechanics, 276, 108916. Cited By: 39

2.Khodayari, A. R. (2019). Effect of freeze–thaw cycle on strength and rock strength parameters (A Lushan sandstone case study). Journal of Mining and Environment, Cited By: 27

3.Fakhri, D., Mahmoodzadeh, A., Mohammed, A. H., Khodayari, A., Ibrahim, H. H., & Others. (2023). Forecasting failure load of sandstone under different freezing–thawing cycles using Gaussian process regression method and grey wolf optimization algorithm. Theoretical and Applied Fracture Mechanics, 125, 103876. Cited By: 24

4.Hosseini, M., & Khodayari, A. R. (2018). Effects of temperature and confining pressure on mode II fracture toughness of rocks (Case study: Lushan sandstone). Journal of Mining and Environment, 9(2), 379–391. Cited By: 17

5.Khodayari, A., Fakhri, D., Mohammed, A. H., Albaijan, I., Mahmoodzadeh, A., & Others. (2023). The gene expression programming method to generate an equation to estimate fracture toughness of reinforced concrete. Steel and Composite Structures, 48(2), 163–177. Cited By: 3

His research advances intelligent blasting and rock-mass behaviour prediction, enabling safer, more efficient, and data-driven mining practices that strengthen global resource sustainability.

Xiangu Chen | Biomedical and Healthcare Applications | Best Research Article Award

Published on 28/11/202528/11/2025 by Computer Vision Awards

Prof. Xianguo Chen | Biomedical and Healthcare Applications | Best Research Article Award

Professor | Zhejiang University School of Medicine | China

Dr. Xianguo Chen is an active researcher in the field of lung cancer biology, molecular oncology, and precision medicine, with a strong focus on exploring genetic alterations, therapeutic resistance mechanisms, and biomarker-driven clinical translation. Affiliated with the Zhejiang University School of Medicine, Dr. Chen has established a robust research portfolio, contributing 16 scientific publications, accumulating 48 citations, and maintaining an h-index of 4, reflecting consistent scholarly impact within a rapidly evolving biomedical landscape.Dr. Chen’s research spans critical areas of lung adenocarcinoma, non-small cell lung cancer (NSCLC), oncogenic signaling pathways, and clinical molecular diagnostics. His work includes multiple contributions as first author, corresponding author, and co-corresponding author, demonstrating scientific leadership and collaboration across multidisciplinary teams. Notable publications include studies on miR-1293–mediated angiogenesis regulation, carbonic anhydrase 4 as a prognostic biomarker, and the identification of novel RET and ALK fusions in NSCLC, each contributing valuable insights into cancer progression, heterogeneity, and precision-targeted therapy.His commitment to translational oncology is further reflected in several research grants. These include major funded projects focused on acacetin-mediated SMYD2 inhibition and DNA damage repair, KMT3C-driven osimertinib resistance via ENO1-regulated glycolysis, and metabolomic discrimination of pulmonary nodules combined with fecal microbiota transplantation strategies. These funded studies highlight his expertise in integrating molecular biology, bioinformatics, and therapeutic research to address pressing clinical challenges in cancer diagnosis and treatment.In addition to his publication record, Dr. Chen engages in collaborative research involving over 130 co-authors, demonstrating broad interdisciplinary partnerships across medical, molecular, and computational sciences. His recent article on machine learning–based immune prognosis modeling for lung adenocarcinoma extends his contributions into the domain of AI-assisted oncology, reinforcing the relevance of computational technologies in modern cancer research.Dr. Chen’s scientific efforts collectively aim to enhance early cancer detection, refine prognostic tools, and illuminate new molecular targets for therapy. Through his funded projects, high-quality publications, and sustained collaborative activity, he continues to contribute significantly to the advancement of global lung cancer research and its transition toward more personalized, mechanism-driven clinical care.

Profiles: Scopus | ResearchGate

Featured Publication

1.Construction and validation of immune prognosis model for lung adenocarcinoma based on machine learning. (2025). Frontiers in Oncology.

Dr. Xianguo Chen research advances precision oncology by uncovering molecular mechanisms that drive lung cancer progression and therapeutic resistance, enabling more accurate diagnostics and targeted treatment strategies.

Sandip Kaledhonka | Biomedical and Healthcare Applications | Research Excellence Award

Published on 28/11/202528/11/2025 by Computer Vision Awards

Assoc. Prof. Dr. Sandip Kaledhonka | Biomedical and Healthcare Applications | Research Excellence Award

Associate professor | Indian Institute of Technology Bombay | India

Dr. Sandip Kaledhonka is an accomplished structural biologist whose research focuses on time-resolved cryogenic electron microscopy (cryo-EM), ribosome dynamics, and molecular mechanisms underlying protein synthesis. With 38 published research documents 775 citations, an h-index of 10, and an i10-index of 10, he has established a strong global research presence through high-impact publications and sustained collaborations with leading scientists across structural biology biophysics and molecular microbiology.Dr. Kaledhonkar’s research has significantly advanced the understanding of dynamic events in translation initiation, elongation, termination, and ribosome recycling. His landmark work Late steps in bacterial translation initiation visualized using time-resolved cryo-EM published in Nature revealed critical structural intermediates that define the kinetics of ribosomal assembly. He has also contributed foundational methods including the widely used microfluidic spraying-plunging technique for ultrafast sample preparation enabling real-time visualization of rapid biochemical reactions.A notable aspect of his research is the integration of mixing-spraying microfluidics with high-resolution cryo-EM an approach that has provided unprecedented insights into transient conformations of biological macromolecules. His studies on ribosome subunit association release-factor activation and ribosome recycling published in journals such as Structure and Biophysical Journalhave shaped current understanding of translation control and fidelity. His contributions extend to photobiology with influential work on photoactive yellow protein (PYP) focusing on chromophore isomerization protonation hydrogen bonding networks and signaling kinetics.Beyond ribosome biology Dr. Kaledhonkar has collaborated on impactful multidisciplinary research including bacteriophage characterization microbial biofilm reduction structural components of jumbo phages and mechanisms of innate antimicrobial defense involving AAA-ATPases. His recent works further explore methodological innovations in cryo-EM pose estimation extracellular vesicle isolation and enzyme conformational regulation highlighting his broad scientific influence.He has co-authored publications with leading researchers such as Joachim Frank Ziao Fu Bo Chen Måns Ehrenberg and Robert A. Grassucci underscoring a strong record of international collaboration. With expertise spanning structural dynamics microfluidics and time-resolved structural biology Dr. Kaledhonkar’s research continues to contribute to the global advancement of molecular and biomedical sciences offering foundational knowledge that drives future therapeutic and biotechnological innovations.

Profiles: Googlescholar | Scopus

Featured Publications

1. Horst, M. A., Stalcup, T. P., Kaledhonkar, S., Kumauchi, M., Hara, M., & Xie, A. (2009). Locked chromophore analogs reveal that photoactive yellow protein regulates biofilm formation in the deep sea bacterium Idiomarina loihiensis. Journal of the American Chemical Society, 131(47), 17443–17451. Cited By : 61

2. Kaledhonkar, S., Fu, Z., White, H., & Frank, J. (2018). Time-resolved cryo-electron microscopy using a microfluidic chip. In Protein Complex Assembly: Methods and Protocols (pp. 59–71). Humana Press. Cited By : 52

3. Kumauchi, M., Kaledhonkar, S., Philip, A. F., Wycoff, J., Hara, M., Li, Y., & Xie, A. (2010). A conserved helical capping hydrogen bond in PAS domains controls signaling kinetics in the superfamily prototype photoactive yellow protein. Journal of the American Chemical Society, 132(44), 15820–15830. Cited By : 12

4. Das, S., & Kaledhonkar, S. (2024). Physiochemical characterization of a potential Klebsiella phage MKP-1 and analysis of its application in reducing biofilm formation. Frontiers in Microbiology, 15, 1397447. Cited By : 3

5. Ghosh, S., Roy, S., Baid, N., Das, U. K., Rakshit, S., Sanghavi, P., Hajra, D., Das, S., … & (include remaining authors if available). (2025). Host AAA-ATPase VCP/p97 lyses ubiquitinated intracellular bacteria as an innate antimicrobial defence. Nature Microbiology, 1–16. Cited By : 2

Dr. Kaledhonkar’s pioneering time-resolved cryo-EM work reveals molecular events in real time, advancing fundamental understanding of translation mechanisms. His innovations in microfluidic methodology continue to transform structural biology and accelerate discoveries in molecular medicine.