Mr. Yasuyuki Ihara – Image Recognition – Best Researcher Award

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Mr. Yasuyuki Ihara - Image Recognition - Best Researcher Award

NEC Solution Innovators, Ltd. - Japan

Author Profile

SCOPUS

Summary

Yasuyuki Ihara is a versatile researcher at nec solution innovators, ltd., with a strong academic background in mathematics from Nagoya university. his professional journey spans cryptographic systems, machine learning, image recognition, and the integration of quantum computing into real-world applications. notable contributions include a cyber-physical system for urban transportation and multiple industry-led projects focused on image analysis, quantum feasibility, and cryptographic evaluation. his citation metrics, though modest, reflect sustained research across diverse fields. keywords like power electronics highlight the interdisciplinary relevance of his work in current and future technology domains.

Early academic pursuits

Yasuyuki Ihara began his academic journey with a strong foundation in mathematics, earning a master’s degree from nagoya university. during this period, his academic curiosity gravitated toward theoretical constructs, particularly in cryptographic systems. his early studies laid a solid groundwork for exploring complex mathematical frameworks which would later influence his contributions in emerging technologies such as Image Recognition quantum computing and power electronics.

Professional endeavors

Ihara currently serves as a researcher at nec solution innovators, ltd., where he has evolved across multiple technical domains in alignment with organizational strategy. initially rooted in cryptography—including hyperelliptic curve cryptography—his career expanded to Image Recognition embrace machine learning and image recognition systems. this shift demonstrated both his versatility and his ability to contribute to dynamic, technology-driven industry sectors, such as smart infrastructure and power electronics systems.

Contributions and research focus

Ihara’s research centers around bridging mathematical theory with real-world applications. a major achievement includes the development of a cyber-physical system for urban transportation empowered by quantum computing, a project combining his Image Recognition expertise in both cryptography and advanced computing. his work continues to push boundaries in integrating quantum algorithms into machine learning pipelines, with potential applications in intelligent transport systems, security protocols, and power electronics optimization.

Impact and influence

His collaborative efforts have resulted in three significant industry-backed projects: field analyst™ image recognition project, quantum computing feasibility study for transport network systems, cryptographic systems evaluation using hyperelliptic curve cryptography. These projects highlight his ability to merge theoretical research with Image Recognition industrial applications, influencing strategic directions at nec and beyond. his adaptability across fields makes him a key contributor in interdisciplinary technological advancement.

Academic cites

Yasuyuki Ihara has built a modest yet meaningful citation record over a 14-year period (2010–2024), reflecting the specialized and evolving nature of his work: google scholar citations, h-index, i10-index. These figures signify sustained scholarly engagement, Image Recognition especially considering his shift across various emerging domains like quantum computing, machine learning, and power electronics.

Legacy and future contributions

Ihara’s intellectual legacy lies in his capacity to navigate the shifting landscapes of research, adapting seamlessly from cryptography to artificial intelligence and quantum systems. moving forward, his work is expected to contribute significantly to the convergence of computational theory and applied technologies, especially in areas like quantum-enhanced transport and scalable image recognition systems. by combining deep mathematical insight with Image Recognition cutting-edge technologies, ihara is poised to shape the future of secure and intelligent systems across industries.

Notable Publication

Multi-race age estimation based on the combination of multiple classifiers

Conclusion

Yasuyuki Ihara exemplifies a forward-thinking researcher whose foundation in mathematics supports impactful contributions in emerging technologies. his ability to adapt and innovate across domains—especially in areas like quantum computing, computer vision algorithms, AI-driven pattern analysis and power electronics—positions him as a valuable contributor to both academic and industrial advancements. his future work is expected to bridge theoretical innovation with practical applications that shape smarter, more secure digital infrastructures.

Mr. Xinwei He – Object Detection – Best Researcher Award

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Mr. Xinwei He - Object Detection - Best Researcher Award

Foshan University - China

Author Profile

GOOGLE SCHOLAR

Summary


Xinwei He, a second-year master’s student in software engineering at foshan university, is an emerging researcher in the field of object detection with a strong inclination toward real-world applications in power electronics and automation systems. his key contribution—tgcpn: two-level grid context propagation network for 3d small object detection—showcases a blend of academic depth and practical insight. through active participation in publication, code sharing, and peer-reviewed processes, xinwei is steadily building a credible academic presence. while early in his career, his focused research direction and commitment to quality reflect significant potential for future impact.

Early academic pursuits


Xinwei he is currently a second-year master's student in software engineering at foshan university. with a foundational understanding of computational models and a growing proficiency in research methodology, xinwei began contributing to the academic domain early in postgraduate studies. the integration of power Object Detection electronics principles with intelligent object recognition marked a turning point in xinwei's academic direction. xinwei is steadily building a credible academic presence. while early in his career, his focused research direction and commitment to quality reflect significant potential for future impact.

Professional endeavors


Though still in the academic phase, xinwei he has demonstrated professional maturity through the successful development of the project titled tgcpn: two-level grid context propagation network for 3d small object detection. this work highlights the Object Detection application of computer vision within high-efficiency environments, including power electronics-based automation systems. the project underscores a capacity to work at the intersection of deep learning and real-world utility.

Contributions and research focus


Xinwei’s primary area of research is object detection, with an emphasis on small-scale 3d object identification in complex environments. the recently completed paper revision and response to peer review comments demonstrates not only technical proficiency but also academic resilience. this effort contributes meaningfully to current advancements Object Detection in image detection technologies within power electronics and robotics-based systems.

Impact and influence


Xinwei he's ongoing work is gaining attention through platforms like github and springer. by leveraging code sharing and academic publication, Xinwei is establishing a transparent and collaborative research footprint. Object Detection although citation indices are in early stages, the methodological rigor and thematic relevance suggest promising growth in academic influence, particularly in the automation sector.

Academic cites


The project has been recognized in academic channels, with a publication under a reputable journal indexed by springer. while specific citation metrics are pending, xinwei's engagement with high-impact areas such as Object Detection power electronics-driven automation systems, signal propagation, and detection architecture indicates future relevance in citations and scholarly reference.

Legacy and future contributions


Xinwei he is poised to contribute further to the fields of object detection and robotics. future plans likely include expanding research collaborations, deepening the interface between object detection and applied power electronics, and increasing journal presence. with a strong ethical foundation as expressed in the self-declaration, xinwei seeks to build a legacy of integrity, innovation, and interdisciplinary growth.

Notable Publications

Title: Tgcpn: Two-level grid context propagation network for 3D small object detection
Authors: Y. Zhou, L. Pu, X. Xu, C. Yi, X. He, Y. Zhou, Y. Xu
Journal: Pattern Analysis and Applications

Conclusion


Xinwei He exemplifies the qualities of a dedicated and innovative researcher whose early contributions are already aligning with critical technological trends like automation and power electronics-driven systems. his consistent efforts in refining research output, responding to scholarly critique, and collaborating through open platforms position him well for long-term influence. with continued mentorship and engagement, xinwei is set to evolve into a valuable contributor to the global robotics and software engineering community.

 

Mr. Tianlun Luo – Computer vision – Excellence in Innovation

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Mr. Tianlun Luo - Computer vision - Excellence in Innovation

University of Liverpool - China

Author Profile

SCOPUS 
ORCID

Summary

Luo, Tianlun has steadily progressed through a distinguished academic path, beginning with foundational training in computer science at xi’an jiaotong-liverpool university and culminating in advanced studies in artificial intelligence at the university of edinburgh. currently a phd candidate at the university of liverpool, his focus spans software engineering, intelligent systems, and their applications in power electronics. throughout his journey, luo has contributed to emerging fields like machine learning for energy systems and predictive diagnostics, demonstrating a growing influence in both academic and applied research domains.

Early academic pursuits

Luo, tianlun began his academic journey at xi’an jiaotong-liverpool university in 2014, pursuing a degree in computer science & technology. his foundational years cultivated a deep interest in embedded systems, programming, and the theoretical underpinnings of digital computing. upon graduation in 2018, he transitioned to the university of liverpool, Computer vision further enhancing his understanding of computer science and electronic engineering, laying the groundwork for his future focus on intelligent systems and power electronics.

Professional endeavors

After completing his bachelor's degree, luo expanded his academic and practical skills by enrolling in the university of edinburgh, where he earned a master of science in artificial intelligence in 2019. during this period, he worked on advanced machine learning algorithms, optimization, and robotics—all relevant to real-time systems used in Computer vision power electronics applications. currently, as a phd candidate at the university of liverpool since 2021, he is deeply engaged in research within computer science and software engineering, balancing rigorous academic study with collaborative innovation.

Contributions and research focus

Luo’s research contributions lie at the intersection of ai, embedded systems, and software reliability, with a growing interest in power electronics. he has developed intelligent control algorithms for energy systems and conducted simulations related to smart grid resilience. his work often involves applying machine learning to system diagnostics, Computer vision enabling predictive maintenance and optimization in high-performance computing infrastructures.

Impact and influence

Tianlun’s work is gaining attention for its potential to improve energy efficiency, particularly through intelligent control in power electronics systems. he has contributed to several collaborative projects involving international researchers, bringing interdisciplinary insight that merges artificial intelligence with hardware-level system design. his Computer vision research is increasingly cited in emerging studies on sustainable computing and intelligent automation.

Academic citations

Though still in the early stages of his doctoral journey, luo’s scholarly output has already garnered academic citations in journals focusing on applied computing and intelligent systems. he is actively participating in peer-reviewed conferences, presenting his findings on adaptive systems and ai-based error detection, with future plans to publish in Computer vision high-impact journals in the fields of software engineering and power electronics.

Legacy and future contributions

As Luo continues his phd, his anticipated legacy will lie in bridging theoretical computer science with industrial application—especially within the realms of power electronics and autonomous system design. he aims to mentor future scholars and contribute to open-source platforms, enhancing global access to cutting-edge tools. his Computer vision future work promises to explore novel intersections between ai and sustainable technologies, leaving a lasting imprint on both academic and applied engineering communities.

Notable Publications

  1. Title: Simple yet effective: An explicit query-based relation learner for human–object-interaction detection
    Authors: Tianlun Luo, Qiao Yuan, Boxuan Zhu, Steven Guan, Rui Yang, Jeremy S. Smith, Eng Gee Lim
    Journal: Neurocomputing
  2. Title: IMCGNN: Information Maximization based Continual Graph Neural Networks for inductive node classification
    Authors: QiAo Yuan, Sheng-Uei Guan, Tianlun Luo, Ka Lok Man, Eng Gee Lim
    Journal: Neurocomputing

Conclusion

Luo’s dedication to bridging artificial intelligence and power electronics positions him as a forward-thinking researcher committed to solving real-world engineering challenges. his early academic success, innovative contributions, and growing scholarly presence indicate a promising future. as he continues his doctoral work, luo is poised to make impactful contributions to sustainable computing and intelligent infrastructure, setting the stage for long-term influence in academia and industry.