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

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)

Prof. Din-Yuen Chan | Deep Learning | Best Scholar Award

Posted on by Global Sensor Awards

Prof. Din-Yuen Chan | Deep Learning | Best Scholar Award 

Prof. Din-Yuen Chan, National Chiayi University, Taiwan

Din-Yuen Chan is a prominent scholar in electrical engineering with extensive experience in visual signal processing and computer vision. He earned his Ph.D. in Electrical Engineering from National Cheng Kung University, Taiwan, in 1996. A member of the Visual Signal Processing and Communication Technical Committee (VSPC TC) since 2010, he served as the founding director of the Department of Electrical Engineering (2007–2011) and as Dean of the College of Science and Engineering at National Chiayi University (2017–2020). His research spans semantic object detection, video/audio coding, stereoscopic 3D, AI-based pattern recognition, and deep learning neural networks. In the past five years, he has published multiple SCI-indexed journal papers on topics such as stereo matching, instance segmentation, speaker diarization, depth estimation, and autonomous robotics. As a frequent corresponding author, he continues to lead innovations in applied AI and multimedia processing.

Professional Profile:

SCOPUS

Summary of Suitability for the Best Scholar Award

Dr. Din-Yuen Chan has maintained an outstanding academic career for over two decades, contributing significantly to the fields of electrical engineering and computer vision. His long-standing commitment to advancing knowledge is reflected in his leadership roles and consistent research output in areas such as semantic object detection, AI-based pattern recognition, video/audio coding, and stereoscopic 3D.

🎓 Education

  • Ph.D. in Electrical Engineering
    National Cheng Kung University, Taiwan 🇹🇼
    Completed in 1996

💼 Work Experience

  • 🧠 Member, Visual Signal Processing and Communication Technical Committee (VSPC TC)
    Since 2010

  • 🏛️ Founding Director, Department of Electrical Engineering, National Chiayi University
    2007 – 2011

  • 🎓 Dean, College of Science and Engineering, National Chiayi University
    2017 – 2020

🧪 Research Interests

  • 🔍 Computer Vision

  • 🎯 Semantic Object Detection

  • 🎞️ Video/Audio Coding

  • 🤖 AI-based Pattern Recognition

  • 🥽 Stereoscopic 3D

  • 🧠 Deep Learning Neural Networks

🏅 Achievements & Honors

  • ✍️ Published multiple SCI-indexed journal papers in high-impact venues, including:

    • EURASIP Journal on Image and Video Processing

    • IET Computer Vision

    • Multimedia Tools and Applications

    • Applied Sciences

  • ⭐ First or corresponding author in many significant papers on stereo matching, depth estimation, 3D object placement, and speaker diarization.

  • 🤖 Developed a low-cost autonomous outdoor robot with end-to-end deep learning navigation.

  • 🧏 Invented a new speaker-diarization technology using spectral-LSTM.

  • 🎓 Recognized leader in academia for establishing and leading research and administrative departments.

Publication Top Notes:

A new speaker-diarization technology with denoising spectral-LSTM for online automatic multi-dialogue recording

Natural-Prosodic Cross-Lingual Personalized TTS

New Efficient Depth Estimation and Real-Time Object 3D Recognition Models for Humanoid Robotic Environment Understanding

Rational 3D object placement based on deep learning based plane detection

INTEGRATED LIGHT-RESNET AND POOLFORMER NETWORKS FOR SHAPE-PRESERVING LANE DETECTION

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

Masoud DANESHTALAB | deep learning | Best Researcher Award

Posted on by Global Sensor Awards

Prof. Masoud DANESHTALAB | deep learning | Best Researcher Award 

Prof. Masoud DANESHTALAB, Mälardalen University, Sweden.

Masoud Daneshtalab, Ph.D., Docent, Full Professor
Masoud Daneshtalab is a globally recognized scholar and Full Professor at Mälardalen University (MDU), Sweden. With over two decades of academic and professional excellence, he has made significant contributions to computer science and engineering, specializing in dependable systems, AI, and hardware/software co-design. A prolific researcher with an H-index of 35 and over 5,100 citations, Dr. Daneshtalab is included in the prestigious World’s Top 2% Scientists Ranking. He serves as the Scientific Director of Fundamental AI at MDU and collaborates internationally, holding adjunct professorships and contributing to cutting-edge research initiatives.

Professional Profile:

Google Scholar

Suitability of Masoud Daneshtalab for the Best Researcher Award

Dr. Masoud Daneshtalab is a highly suitable candidate for the “Research for Best Researcher Award,” based on his exceptional academic achievements and professional contributions. Here are the key reasons

Education

🎓 Academic Journey

  • Docent (2018): Qualified in Computer Science and Electronics, Mälardalen University, Sweden.
  • Ph.D. (2008–2011): Information and Communication Technology, University of Turku, Finland. Dissertation: Adaptive Implementation of On-Chip Networks under Prof. Hannu Tenhunen.
  • M.Sc. (2004–2006): Computer Engineering, University of Tehran, Iran. Thesis: Low Power Methods in Network-on-Chips under Prof. Ali Afzali-Kusha.
  • B.Sc. (1998–2002): Computer Engineering, Shahid Bahonar University of Kerman, Iran.

Experience

💼 Professional Contributions

  • Scientific Director (2024–Present): Fundamental AI, Mälardalen University, Sweden.
  • Full Professor (2020–Present): Innovation, Design & Engineering, MDU.
  • Adjunct Professor (2019–Present): Computer Systems, Tallinn University of Technology, Estonia.
  • Previous Roles: Associate Professor at MDU (2016–2020), EU Marie Curie Fellow at KTH Royal Institute of Technology (2014–2016), Lecturer at the University of Turku (2011–2014), and Researcher at the University of Tehran (2006–2008).

Research Interests

🔬 Key Areas

  • Optimization and robustness in deep learning models.
  • HW/SW co-design and heterogeneous computing.
  • Dependable systems, memory architectures, and interconnection networks.
  • Cutting-edge projects include sustainable AI, federated learning, and reliable autonomous systems.

Awards

🏆 Recognitions

  • Best Paper Awards: IEEE ECBS (2019), IEEE MCSoC (2018), and multiple HiPEAC Paper Awards (2013–2017).
  • Research Grants: Marie Skłodowska-Curie Fellowship (2014), Nokia Foundation (2009), and others.
  • Top Reviewer: IEEE Transactions on Computers (2013).
  • Fellowships: GETA, Helsinki University of Technology (2008–2011).

Publications

A review on deep learning methods for ECG arrhythmia classification

CITIED: 490

Time-Sensitive Networking in automotive embedded systems: State of the art and research opportunities

CITIED: 147

Routing algorithms in networks-on-chip

CITIED: 136

Smart hill climbing for agile dynamic mapping in many-core systems

CITIED: 125

EDXY–A low cost congestion-aware routing algorithm for network-on-chips

CITIED: 124

Deep Maker: A multi-objective optimization framework for deep neural networks in embedded systems

CITIED: 122