Jun Tang | Computer Science | Best Researcher Award

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Mr. Jun Tang | Computer Science | Best Researcher Award

AI Algorithm Researcher | Chengdu Zhihui Heneng City Technology | China

Mr. Jun Tang is a researcher specializing in intelligent transportation and autonomous driving, with a strong focus on the integration of computer vision and artificial intelligence to enhance vehicular perception and decision making systems. His research primarily explores large vision foundation models and their applications in object detection, scene understanding, and adaptive driving environments. He has contributed to developing advanced detection frameworks that leverage reinforcement learning to improve recognition accuracy, robustness, and real time responsiveness in dynamic traffic conditions. Mr. Tang’s recent interests include prompt-guided object detection methods that utilize natural language and contextual cues to refine visual understanding within autonomous systems. Through his work at Chengdu Zhihui Heneng City Technology, he plays a key role in bridging the gap between theoretical AI models and practical intelligent mobility applications, fostering innovations that advance the safety, efficiency, and scalability of next generation transportation systems. His interdisciplinary approach combines deep learning, machine perception, and cognitive automation, contributing to the development of more adaptive and human like decision making in autonomous vehicles.

Profile: Orcid

Featured Publications

Tang, J., Li, D., Yang, J., Chen, J., & Yuan, R. (2025). Leveraging large visual models for enhanced object detection: An improved SAM-YOLOv5 model. Knowledge-Based Systems, 114757.

Tang, J. (2025, August 29). RT-DETR-based intelligent transportation object detection optimization method and system with prompt mechanism fusion.

Tang, J. (2025, May 27). Object detection method and system based on prompt engineering and regional text description.

Tang, J. (2025, April 11). Quantitative evaluation method and system for multimodal large models.

Tang, J. (2025, January 17). Evaluation method and system for urban governance multimodal large models based on text labeling.

Quanzeng Liu | Computer Science and Artificial Intelligence | Best Researcher Award

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Mr. Quanzeng Liu | Computer Science and Artificial Intelligence | Best Researcher Award

Member Chinese Association of Automation China

Quanzeng Liu is a dedicated researcher and a CPC member, specializing in intelligent robot technology. Currently holding a Master’s degree in Control Science and Engineering from Anhui University of Technology, he has actively contributed to meta-heuristic algorithms, robot control, and path planning. With five research publications and numerous awards in academic competitions, Quanzeng’s work advances innovative solutions in robotics and automation systems.

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Education 🎓

Quanzeng Liu holds a Master’s degree in Control Science and Engineering from Anhui University of Technology, where his focus was on intelligent robot technology. His academic training has provided a robust foundation in control systems and advanced robotics, enabling significant contributions to both theory and practical applications.

Experience 💼

Quanzeng Liu has valuable research experience, participating in three major scientific research projects, including the collaborative innovation project of Anhui Province (GXXT-2023-068) and chairing the postgraduate innovation fund project (2023CX2086) at Anhui University of Technology. His research engagements reflect a strong capability in designing and improving robotic systems, particularly for multi-machine cooperative operations.

Research Interests 🔍

Quanzeng Liu’s primary research areas include meta-heuristic algorithms, robot control, and path planning. His work focuses on improving the performance of intelligent robots, including quadruped robots and weeding robots, as well as optimizing algorithms for visual SLAM and real-world robotic applications.

Awards 🏆

Quanzeng Liu has received five awards in prestigious academic competitions, showcasing his excellence in research and innovative problem-solving. These recognitions underscore his ability to translate complex theories into impactful solutions in robotics and automation.

Publications Top Notes:📚

Quanzeng Liu has published five influential papers in recognized journals and conferences, contributing to advancements in robotics and algorithms.

CMGWO: Grey wolf optimizer for fusion cell-like P systems
Heliyon, 2024. Read here

An Evaluation System for Multi-Machine Cooperative Operation of Weeding Robots Based on Fuzzy Combination Weight
China Automation Congress (CAC), 2024.

Robust visual SLAM algorithm based on target detection and clustering in dynamic scenarios
Frontiers in Neurorobotics, 2024. Read here

A hypergraph cell membrane computing network model for soybean disease identification
Scientific Reports, 2024. Read here

Conclusion ✅

Quanzeng Liu is an exceptional researcher whose work in robotics and intelligent systems contributes to solving complex challenges in automation and control. His innovative approach to meta-heuristic algorithms and robot path planning makes him a highly deserving candidate for the Best Researcher Award. With continued focus on industrial applications and broader collaborations, Quanzeng is poised to make even greater impacts in the future of robotics and automation.

João Oliveira | Computer Science | Best Researcher Award

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Mr. João Oliveira | Computer Science | Best Researcher Award

Researcher Instituto de Telecomunicações Portugal

João Diogo Videira Oliveira is a dedicated researcher in vehicular communications and telematics engineering, currently contributing to advanced research at the Instituto de Telecomunicações in Aveiro, Portugal. With a strong background in computer and telematics engineering, João’s work focuses on enhancing communication protocols and intelligent transportation systems (ITS).

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Education 🎓

  • M.Sc. in Computer and Telematics Engineering (2021–2023)
    Institution: Universidade de Aveiro, Portugal | ua.pt
  • B.Sc. in Computer and Telematics Engineering Sciences (2018–2021)
    Institution: Universidade de Aveiro, Portugal | ua.pt

João has excelled academically, building a robust foundation in vehicular networks and communication protocols.

Experience 🛠️

  • Researcher | Instituto de Telecomunicações, Aveiro, Portugal
    Duration: March 2024 – Present
  • Research Scholarship | Instituto de Telecomunicações, Aveiro, Portugal
    Duration: February 2023 – February 2024

João’s experience involves extensive work on V2X systems, C-ITS protocols, and simulation frameworks like Vanetza and Artery V2X, contributing to innovations in vehicular communication and safety systems.

Research Interests 🔍

João’s primary research interests include:

  • Vehicular Communications (V2X)
  • Communication Protocols for ITS (C-ITS, ITS-G5)
  • Simulation frameworks such as OMNeT++, SUMO, and Artery V2X
  • Automated driving and fault simulation systems

His work addresses challenges in maneuver coordination, safety systems, and intelligent transportation technologies.

Awards & Certifications 🏆

  • Fault Simulation Training | Segula Testcenter, Rodgau (October 2024)
    • Training focused on safety driver responsibilities and practical driving maneuvers involving error scenarios.
    • Developed expertise in automated and partially automated vehicle systems.

Publications Top Notes: 📚

A Maneuver Coordination Analysis Using Artery V2X Simulation Framework (2024)

Reference: Oliveira, J., Vieira, E., Almeida, J., Ferreira, J., & Bartolomeu, P. C. (2024).
Electronics, 13(23), 4813.
Read here: https://doi.org/10.3390/electronics13234813

Cited by: Researchers working on V2X communication protocols and vehicular network safety systems.

Yunfei Zì | Computer Science | Best Researcher Award

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Dr. Yunfei Zì | Computer Science | Best Researcher Award

Researcher Wuhan University of Technology China

Zi Yunfei is a distinguished researcher specializing in voiceprint recognition and artificial intelligence, affiliated with the Wuhan University of Technology. His expertise lies in developing advanced speaker verification systems and acoustic feature extraction methods, especially within IoT contexts. Currently, he is concluding his Ph.D. under the guidance of Professor Xiong Shengwu.

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Google scholar

Education 🎓

  • Ph.D. in Computer Science and Technology (2019–2023) – Wuhan University of Technology
  • M.Eng. in Information and Communication Engineering (2016–2019) – Beijing University of Graphic Arts
  • B.Eng. in Computer Science and Technology (2011–2015) – Northeast Petroleum University

Experience 💼

Zi has led and contributed to various research initiatives, including a Huawei NRE project and significant AI advancements in IoT voiceprint recognition and military voice monitoring. His technical contributions have been instrumental in enhancing acoustic feature extraction and system integration on Huawei’s deep learning platform, MindSpore.

Research Interests 🔍

  • Voiceprint Recognition
  • Short Utterance Speaker Verification
  • Artificial Intelligence & Deep Learning
  • Acoustic Feature Enhancement
  • IoT Smart Services

Awards 🏆

  • Outstanding Academic Achievement Award – Beijing University of Graphic Arts, 2018
  • Outstanding Master’s Thesis Award – Beijing University of Graphic Arts, 2019
  • Huawei Smart Base Future Star Award – Ministry of Education-Huawei, 2021
  • Outstanding Doctoral Thesis Award – Wuhan University of Technology, 2024

Publications Top Notes📚:

Multi-Fisher and Triple-Domain Feature Enhancement-Based Short Utterance Speaker Verification for IoT Smart Service – IEEE Internet of Things Journal (2024) [DOI:10.1109/JIOT.2023.3309659]

Joint Filter Combination-based Central Difference Feature Extraction – Expert Systems with Applications (2023) [DOI:10.1016/j.eswa.2023.120995]

Fisher Ratio-Based Multi-Domain Frame-Level Feature Aggregation – Engineering Applications of Artificial Intelligence (2024) [DOI:10.1016/j.engappai.2024.108063]

Short-Duration Speaker Verification by Joint Filter Superposition – IEEE Transactions on Consumer Electronics (2024) [DOI:10.1109/TCE.2024.3411116]

Aggregating Discriminative Embedding by Triple-Domain Feature Joint Learning – Biomedical Signal Processing and Control (2023) [DOI:10.1016/j.bspc.2023.104703]