Dr. Ning Zhang | Deep Learning | Research Excellence Award

Posted on by Global Sensor Awards

Dr. Ning Zhang | Deep Learning | Research Excellence Award

Dr. Ning Zhang | Deep Learning | Beijing Institute of Technology | China

Dr. Ning Zhang is a male researcher in the field of intelligent sensing and information and communication engineering, with a strong academic and applied background in deep learning–driven sensor systems, onboard artificial intelligence, and edge computing for aerospace and remote sensing platforms. He received his doctoral and master’s training in Information and Communication Engineering from Beijing Institute of Technology and completed his undergraduate education in Electronic Information Engineering at Wuhan University of Technology, forming a solid interdisciplinary foundation that integrates algorithms, hardware architecture, and embedded intelligence. Professionally, Dr. Ning Zhang has served as a project leader and key algorithm and hardware engineer in multiple nationally funded and internationally oriented research projects, including onboard AI systems for small satellites, UAV-based intelligent perception, and dynamic neural network deployment under constrained computing environments.

Citation Metrics (Google Scholar)

400

300

200

100

0

Citations
337

Documents
6

h-index
9

Citations
Documents
h-index

View Google Scholar Profile

Featured Publications

Dr. Zobeir Raisi | Deep Learning | Excellence in Research Award

Posted on by Global Sensor Awards

Dr. Zobeir Raisi | Deep Learning | Excellence in Research Award 

Dr. Zobeir Raisi | Deep Learning | Chabahar Maritime University | Iran

Dr. Zobeir Raisi is a male expert in computer vision, machine learning, and deep learning, specializing in object detection, recognition, tracking, segmentation, 3D human pose estimation, and camera calibration, combining advanced theoretical knowledge with practical and applied research experience. He holds a Ph.D. in Systems Design Engineering from the University of Waterloo, Canada, and both M.E. and B.E. degrees in Electrical Engineering from the University of Sistan & Balouchestan, Iran, reflecting a strong academic foundation and interdisciplinary technical proficiency. Dr. Zobeir Raisi’s professional experience encompasses postdoctoral research focused on automating sports analytics using smartphone cameras, supervising master’s students in camera calibration projects with industry collaboration, and conducting Ph.D.-level research on transformer-based deep learning frameworks for arbitrary-shaped text detection and recognition in complex visual environments. He has contributed as a research assistant in computer vision and machine learning applications for automated assembly and anomaly detection systems, as well as serving as a lecturer and assistant professor at Chabahar Maritime University, teaching courses spanning digital image processing, digital systems, electrical circuits, computer architecture, microcontrollers, and electromagnetics, while mentoring undergraduate and graduate students and managing journal editorial processes.

Citation Metrics (Google Scholar)

450
300
200
100
0

Citations
416

Documents
9

h-index
9

Citations
Documents
h-index

View Google Scholar Profile
 View ORCID Profile

Featured Publications


Machine Learning Algorithms for Signal and Image Processing


– John Wiley & Sons, 2022 · 100+ Citations

Transformer-Based Text Detection in the Wild


– IEEE/CVF CVPR, 2021 · 74 Citations

2D Positional Embedding-Based Transformer for Scene Text Recognition


– Journal of Computational Vision and Imaging Systems, 2020 · 32 Citations

Ms. Leiyao Liao | Deep Learning Awards | Best Researcher Award

Posted on by Global Sensor Awards

Ms. Leiyao Liao | Deep Learning Awards | Best Researcher Award

Ms. Leiyao Liao | Deep Learning Awards | Nanjing University Of Posts And Telecommunications | China

Ms. Leiyao Liao is a distinguished researcher and lecturer at the School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, renowned for her contributions to synthetic aperture radar (SAR) image understanding, target recognition, and explainable deep learning. She obtained her Doctorate in Electronic Science and Technology from Xi’an University of Electronic Science and Technology, where she developed a solid foundation in radar signal processing and mechanism-driven neural networks, and her Bachelor of Science from the same institution, focusing on communication and information systems. In her professional career, Ms. Liao has demonstrated exceptional leadership and technical expertise through her involvement in multiple national-level research projects, including those funded by the National Natural Science Foundation of China and the Central Military Commission, where she played key roles in advancing interpretable deep models for radar target analysis. Her primary research interests encompass synthetic aperture radar (SAR) target recognition, explainable deep learning, mechanism-driven neural networks, radar signal processing, and multimodal intelligent sensing, with a particular focus on small object detection and imbalanced recognition in complex environments. Ms. Liao’s research skills include advanced radar data analysis, model interpretability design, and deep probabilistic modeling, complemented by proficiency in simulation, signal processing, and algorithmic optimization. Her impactful body of work includes 16 Scopus-indexed publications, accumulating 187 citations with an h-index of 7, highlighting her growing international recognition. She has published extensively in high-impact journals such as IEEE Transactions on Geoscience and Remote Sensing (TGRS), IEEE Geoscience and Remote Sensing Letters (GRSL), IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), and IEEE Journal of Selected Topics in Signal Processing (JSTSP). Ms. Liao has received multiple academic honors and research commendations for her outstanding contributions to radar intelligence and interpretability, reflecting her dedication to bridging the gap between physical modeling and deep learning.

Professional Profiles: Scopus

Featured Publications 

  1. Liao, L. (2025). Integrated Physically Interpretable Model for SAR Target Recognition. IEEE Geoscience and Remote Sensing Letters. (Citations: 26)

  2. Liao, L. (2025). Research on Collision Access Method for Satellite Internet of Things Based on Bayliss Window Function. Sensors (Basel, Switzerland). (Citations: 0)

  3. Liao, L. (2024). EMI-Net: Interpretable Deep Network for SAR Target Recognition. IEEE Transactions on Geoscience and Remote Sensing. (Citations: 41)

  4. Liao, L. (2024). Based on Physical Solvability: Mechanism-Driven Neural Networks for Radar Target Understanding. Journal of Electronics. (Citations: 18)

  5. Liao, L. (2022). Interpretable Deep Probabilistic Model for HRR Radar Signal and Its Application to Target Recognition. IEEE Journal of Selected Topics in Signal Processing. (Citations: 52)

  6. Liao, L. (2023). Fusion-Based Multimodal SAR Target Classification Using Explainable Deep Learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. (Citations: 29)

  7. Liao, L. (2023). Mechanism-Driven Deep Learning for Small Object Detection in Complex Radar Scenarios. IEEE Access. (Citations: 21)

Dr. Zhiwei Zhang | Deep Learning Awards | Best Researcher Award

Posted on by Global Sensor Awards

Dr. Zhiwei Zhang | Deep Learning Awards | Best Researcher Award 

Dr. Zhiwei Zhang, AVIC Manufacturing Technology Institute, China

Zhiwei Zhang, is a research engineer specializing in aviation manufacturing technology in China. He holds a bachelor’s and master’s degree in Automation from Shenyang Ligong University and earned his Ph.D. in Instrument Science and Technology from Yanshan University. His research focuses on digital radiographic and industrial CT nondestructive testing, computer vision, and ensemble learning algorithms for additive manufacturing. He has published seven SCI-indexed research papers and holds two authorized patents. Zhiwei Zhang also serves as a reviewer for the Journal of Computational Methods in Sciences and Engineering, reflecting his active contribution to the academic and industrial research community.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award: Zhiwei Zhang

Zhiwei Zhang, a highly skilled research engineer in aviation manufacturing technology, has demonstrated outstanding contributions in the fields of nondestructive testing, computer vision, and ensemble learning for additive manufacturing. His innovative research integrates cutting-edge technologies like digital radiography, industrial CT, and machine learning, addressing critical challenges in the aerospace industry.

🎓 Education

  • 🏫 Bachelor’s Degree in Automation – Shenyang Ligong University

  • 🎓 Master’s Degree in Automation – Shenyang Ligong University

  • 🧪 Ph.D. in Instrument Science and Technology – Yanshan University

💼 Work Experience

  • 👨‍🔧 Research Engineer – Specializing in aviation manufacturing technology in China

  • 🔬 Focus areas include:

    • Digital radiographic and industrial CT nondestructive testing

    • Computer vision

    • Ensemble learning algorithms for additive manufacturing

🏆 Achievements

  • 📄 Published 7 SCI-indexed research papers in high-impact journals

  • 🧾 Granted 2 authorized patents

  • 🧑‍⚖️ Reviewer for the Journal of Computational Methods in Sciences and Engineering

🎖️ Awards & Honors

  • 🏅 Recognized for contributions in nondestructive testing and AI applications in manufacturing
    (Note: Specific award titles not mentioned; can be added if provided.)

Publication Top Notes:

A Hybrid Framework for Metal Artifact Suppression in CT Imaging of Metal Lattice Structures via Radon Transform and Attention-Based Super-Resolution Reconstruction

Complex Defects Detection of 3-D-Printed Lattice Structures: Accuracy and Scale Improvement in YOLO V7

A Prediction Model for Maximum Stress of Additive Manufacturing Lattice Structures Based on Voting-Cascading

Deep convolution IT2 fuzzy system with adaptive variable selection method for ultra-short-term wind speed prediction

An improved meta heuristic IT2 fuzzy model for nondestructive failure evaluation of metal additive manufacturing lattice structure

An improved stacking ensemble learning model for predicting the effect of lattice structure defects on yield stress

Data-driven XGBoost model for maximum stress prediction of additive manufactured lattice structures

Adaptive Defect Detection for 3-D Printed Lattice Structures Based on Improved Faster R-CNN

A Hybrid Model Based on Jensen’s Inequality Theory for 3D Printed Lattice Structures Maximum Stress Prediction

Dr. Mohassin Ahmad | Deep Learning | Best Researcher Award

Posted on by Global Sensor Awards

Dr. Mohassin Ahmad | Deep Learning | Best Researcher Award 

Dr. Mohassin Ahmad, Guru Nanak Institutions, India

Dr. Mohassin Ahmad is an accomplished academic and researcher currently serving as an Assistant Professor in the Department of Electronics and Communication Engineering at Guru Nanak Institutions, Hyderabad, since September 2023. He earned his Ph.D. in Image Forensics from the National Institute of Technology Srinagar in 2024, following an M.Tech in Communication and Information Technology from the same institute and a Bachelor of Engineering degree in Electronics and Communication from the University of Kashmir. Dr. Ahmad has extensive teaching and research experience, including a previous tenure as Assistant Professor at NIT Jammu and Kashmir from 2013 to 2017. His research interests focus on digital image forensics, image tampering detection, and communication systems, with multiple publications in reputed international journals. He has contributed significantly to curriculum development and laboratory setup and is known for his dedication to student mentorship and academic excellence. Dr. Ahmad is also recognized for his Young Researcher Award for work in copy-move forgery detection algorithms. Fluent in English, Urdu, and Kashmiri, he combines strong technical expertise with effective communication and leadership skills.

Professional Profile:

GOOGLE SCHOLAR

ORCID

SCOPUS

Research and Academic Profile

Dr. Mohassin Ahmad has recently completed his PhD in Image Forensics (Electronics & Communication) from NIT Srinagar in 2024. His academic background is solid with a Master’s in Communication & Information Technology and a Bachelor’s in Electronics and Communication, showing a focused trajectory in communication technologies and electronics.

🎓 Education

  • PhD (2024) in Image Forensics (Electronics & Communication) — NIT Srinagar

  • M.Tech (2013) in Communication & Information Technology — NIT Srinagar (77.16%)

  • B.E (2010) in Electronics and Communication — University of Kashmir (79.3%)

💼 Work Experience

  • Assistant Professor, Guru Nanak Institutions, Hyderabad (ECE Dept.) — Since Sept 2023

  • Assistant Professor, Electronics & Communication Department, NIT Jammu & Kashmir — Sept 2013 to Aug 2017

    • Delivered lectures & coordinated courses

    • Established new labs & designed curriculum

    • Guided B.Tech & M.Tech research projects

    • Played key role in framing B.Tech & M.Tech curriculum

    • Mentored students with academic & personal support

🏆 Achievements & Awards

  • Young Researcher Award for paper:
    “A comparative analysis of Copy-Move forgery detection algorithms” — International Journal of Electronic Security and Digital Forensics, 2022

    • RSquarel score of 84, Award ID: RSL014

📚 Selected Research Publications

  • Detection and localization of image tampering with fused features — 2022

  • Comparative analysis of Copy-Move forgery detection algorithms — 2022

  • Novel image tamper detection using optimized CNN and firefly algorithm — 2021

  • Review on Digital Image Forgery Detection Approaches — 2021

  • FPGA implementation of convolution algorithms for image processing — 2019

Publication Top Notes:

Threats to medical diagnosis systems: analyzing targeted adversarial attacks in deep learning-based COVID-19 diagnosis

DS‐Net: Dual supervision neural network for image manipulation localization

A comparative analysis of copy-move forgery detection algorithms

Detection and localization of image tampering in digital images with fused features

A Comparative Analysis of Copy-Move Forgery Detection Algorithms

A novel image tamper detection approach by blending forensic tools and optimized CNN: Sealion customized firefly algorithm

Digital Image Forgery Detection Approaches: A Review

Prof. Jin Ho Suh | Deep Neural Network Awards | Best Researcher Award

Posted on by Global Sensor Awards

Prof. Jin Ho Suh | Deep Neural Network Awards | Best Researcher Award

Prof. Jin Ho Suh, Pukyong National University, South Korea

Dr. Jin-Ho Suh is a distinguished professor and expert in robotics, currently leading the Field Robotics Laboratory (FRLab) within the Major of Mechanical System Engineering at Pukyong National University, South Korea. With a Ph.D. in Control Engineering from the Tokyo Institute of Technology, Japan, and over two decades of academic and professional experience, Dr. Suh has significantly contributed to the fields of robotics and mechanical systems. He has held prominent roles, including Director of the Institute of Control, Robotics, and Systems, and is a Senior Member of IEEE. His leadership extends to national initiatives as the Chairman of the National Core Technology Committee for Robotics in South Korea and as an expert member of the Presidential Advisory Council on Science & Technology.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award: Prof. Jin-Ho Suh

Prof. Jin-Ho Suh, a distinguished researcher in the field of robotics and control engineering, holds a Ph.D. in Control Engineering from the Tokyo Institute of Technology and currently serves as a professor at Pukyong National University in South Korea. His extensive academic and professional experience, combined with significant contributions to the field, makes him an excellent candidate for the Best Researcher Award.

Education 🎓

  • Ph.D. in Control Engineering
    Tokyo Institute of Technology, Japan (Dec 1998 – Mar 2002)
  • Master of Engineering
    Graduate School of Engineering, Pukyong National University, South Korea (Mar 1996 – Feb 1998)
  • Bachelor of Science in Mathematics
    Hanyang University, South Korea (Mar 1989 – Feb 1993)

Work Experience 🛠️

  • Professor (Sep 2018 – Present)
    Major of Mechanical System Engineering, Pukyong National University
  • Senior Member (Nov 2022 – Present)
    Institute of Electrical and Electronics Engineers (IEEE)
  • Director
    • Institute of Control, Robotics, and Systems (Jan 2022 – Present)
    • Korean Society for Precision and Engineering (Jan 2020 – Present)
    • Korea Robotics Society (Jan 2018 – Present)
    • Korean Society for Power System Engineering (Jan 2017 – Present)
  • Chairman of the National Core Technology Committee (Robot) (Nov 2017 – Present)
    Korean Association for Industrial Technology Security
  • Expert Member (Jan 2021 – Present)
    Presidential Advisory Council on Science & Technology, South Korea
  • Adjunct Professor (Dec 2013 – Aug 2018)
    Department of Mechanical Engineering, POSTECH
  • Director of R&D Division (Apr 2006 – Aug 2018)
    Korea Institute of Robotics and Convergence Technology (KIRO)
  • Post-Doctoral Fellow (Jun 2003 – Feb 2006)
    National Research Laboratory, Dong-A University

Achievements 🏆

  • Patents
    • 28 patents (7 international PCT)
  • Publications
    • 14 papers in international journals (13 SCI)
    • 23 papers in domestic journals (15 SCOPUS)
    • 15 papers in international conferences
    • 60 papers in domestic conferences

Awards and Honors 🌟

  • Director Roles in Leading Engineering Societies
    • Institute of Control, Robotics and Systems
    • Korea Robotics Society
    • Korean Society for Precision and Engineering
  • Presidential Advisory Council Member
    • Significant contributions to national robotics and precision engineering strategies.
  • Chairman, National Core Technology Committee (Robot)
    • Recognized leader in industrial robotics technology and security.

Publication Top Notes

Artificial Neural Network for Glider Detection in a Marine Environment by Improving a CNN Vision Encoder

Development of a Multi-Robot System for Pier Construction

Model-Free RBF Neural Network Intelligent-PID Control Applying Adaptive Robust Term for Quadrotor System

Development of Fishcake Gripping and Classification Automation Process Based on Suction Shape Transformation Gripper

Estimation and Control of a Towed Underwater Vehicle with Active Stationary and Low-Speed Maneuvering Capabilities

Adaptive Robust RBF-NN Nonsingular Terminal Sliding Mode Control Scheme for Application to Snake Robot’s Head for Image Stabilization

Development of Recovery System for Underwater Glider

Assoc Prof Dr. Wenlong Hang | Artificial Intelligence Award | Best Researcher Award

Posted on by Global Sensor Awards

Assoc Prof Dr. Wenlong Hang | Artificial Intelligence Award | Best Researcher Award

Assoc Prof Dr. Wenlong Hang, Nanjing Tech University, China

Wenlong Hang holds a Doctor of Engineering degree from Jiangnan University, where he graduated in June 2017, specializing in Light Industry Information Technology. During his doctoral studies, he visited both Hong Kong Polytechnic University and the Shenzhen Institutes of Advanced Technology. Since September 2017, Dr. Hang has been a faculty member at the School of Computer Science and Technology at Nanjing Tech University. His research interests primarily focus on artificial intelligence and machine learning, with a particular emphasis on medical image analysis and EEG signal processing. He has published more than 30 papers in reputable journals and conferences, contributing significantly to semi-supervised learning, federated learning, and EEG classification techniques. His representative works include research on medical image segmentation, reliability-aware semi-supervised frameworks, and domain-generalized EEG classification.

Professional Profile:

Summary of Suitability for Best Researcher Award :

Wenlong Hang is highly suitable for the Best Researcher Award based on his extensive research and contributions in the fields of artificial intelligence, machine learning, and medical image processing. His academic background, with a Doctor of Engineering degree from Jiangnan University, and professional experiences at institutions like Hong Kong Polytechnic University and Shenzhen Institutes of Advanced Technology, demonstrates his deep involvement in advanced technological research.

Education:

  • Doctor of Engineering (Graduated in June 2017)
    • Major: Light Industry Information Technology
    • Institution: Jiangnan University
    • Doctoral Visits: Hong Kong Polytechnic University, Shenzhen Institutes of Advanced Technology

Work Experience:

  • Since September 2017: Faculty Member
    • Position: Professor at the School of Computer Science and Technology
    • Institution: Nanjing Tech University

Research Areas:

  • Artificial Intelligence (AI)
  • Machine Learning (ML)
  • Medical Image Segmentation
  • EEG Classification

Publication top Notes:

CITED: 109
CITED: 109
CITED: 73
CITED: 67
CITED: 34
CITED: 33

 

Mr. Zhongwen Hao | Deep learning Award | Best Researcher Award

Posted on by Global Sensor Awards

Mr. Zhongwen Hao | Deep learning Award | Best Researcher Award 

Mr. Zhongwen Hao, Cranfield University, China

Zhongwen Hao is a Master’s candidate in Aerospace Manufacturing at Cranfield University, UK, and concurrently pursuing a Master of Mechanical Engineering at Nanjing University of Aeronautics and Astronautics, China. He completed his Bachelor’s degree in Electronic Information with a focus on Image Processing from China University of Mining and Technology. His research interests include robot control, visual servoing, image processing, and deep learning. Zhongwen has led notable projects such as visual servoing of robotic arms using deep learning techniques and galaxy image classification. His proficiency in programming with C++, Python, and MATLAB, coupled with his skills in deep learning and image processing, underscores his technical expertise. He has published research on motion prediction and object detection in visual servoing systems. Zhongwen is known for his strong project execution abilities, team spirit, and resilience.

Professional Profile:

Summary of Suitability:

Hao’s research direction aligns well with cutting-edge fields such as robot control, visual servoing, image processing, and deep learning. These areas are highly relevant and significant in contemporary technological advancements. Hao has a solid educational foundation with advanced studies in Aerospace Manufacturing and Mechanical Engineering, complemented by a bachelor’s degree in Electronic Information with a focus on Image Processing. This diverse yet interconnected educational background enhances his research capabilities.

Education

  1. Cranfield University, Bedford, UK
    Master’s Candidate of Aerospace Manufacturing
    Major: Deep Learning and Image Processing
    September 2023 – September 2024
  2. Nanjing University of Aeronautics and Astronautics, Nanjing, China
    Master of Mechanical Engineering
    Major: Mechanical
    September 2022 – June 2025 (Expected)
  3. China University of Mining and Technology, Xuzhou, China
    Bachelor of Electronic Information
    Major: Image Processing
    September 2017 – June 2021

Work Experience

  1. Project Leader
    Research on Visual Servoing of Robotic Arms Based on Deep Learning
    June 2024 – September 2024

    • Led research on target detection using the DETR model, trajectory planning with the PSO algorithm, and motion prediction using BiLSTM and KAN neural networks.
    • Integrated and simulated algorithms in ROS using Gazebo to validate their effectiveness.
  2. Participator
    Galaxy Image Classification Based on Deep Learning
    February 2024 – March 2024

    • Handled image preprocessing and reconstruction, and implemented galaxy image classification using the VIT model, achieving a classification accuracy of 90%.

Publication top Notes:

Motion Prediction and Object Detection for Image-Based Visual Servoing Systems Using Deep Learning