Ms. Congying Sun, Xi’an University of Technology, China

Congying Sun, a native of Xianyang City, Shaanxi Province, is an emerging researcher specializing in control science, engineering, and multi-modal remote sensing technologies. She earned her Bachelor’s degree in Printing Engineering from Xi’an University of Technology in 2022 and is currently pursuing a Master’s degree in Control Science and Engineering at the same institution, expected to graduate in 2025. Her professional experience includes a tenure at the Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, where she played a pivotal role in developing an infrared-visible aircraft image dataset and deploying state-of-the-art infrared small target detection models. Congying has demonstrated her ability to merge academic excellence with practical application through her contributions to national and engineering projects, including research on multi-source collaborative intelligent perception technology for aircraft and non-cooperative multi-target classification and cognition technology.

🧑‍💻 Professional Experience

Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences (July 2023 – August 2024)

📸 Independently created an infrared-visible aircraft image dataset, including camera selection, data collection, and image annotation.

📝 Authored project proposals, research papers, and patents with great precision.

🤖 Developed and deployed infrared small target detection models and multi-modal remote sensing image fusion detection models.

🏅 Achievements

Granted Invention Patents:

📜 Infrared Small Target Detection Method (CN118762159A)
📜 Multi-modal Feature Fusion Target Detection Model and Method (CN118865046A)

💡 Research Interests

🔍 Machine Learning | 🌍 Multi-modal Remote Sensing | 🎯 Target Detection

Congying Sun’s innovative approach, technical expertise, and impactful contributions to cutting-edge research make her a rising star in the field of control science and engineering. 🌟

Publication Top Notes:

Location-Guided Dense Nested Attention Network for Infrared Small Target Detection

MMYFnet: Multi-Modality YOLO Fusion Network for Object Detection in Remote Sensing Images

Prof. Catalin Dumitrescu | Artificial Intelligence Awards | Excellence in Research

Prof. Catalin Dumitrescu, University Politehnica of Bucharest, Romania

Dr. Cătălin Dumitrescu is an Associate Professor and R&D Scientific Adviser specializing in Computing and Artificial Intelligence at the Department of Electronics & Telecommunications, Transportation Engineering Faculty, University Politehnica of Bucharest (UPB), Romania. With a Ph.D. in Digital Signal Processing and Machine Learning from UPB, he possesses extensive expertise in artificial intelligence, machine learning, and digital signal processing, particularly in applications related to defense, cybersecurity, and multimedia security. Dr. Dumitrescu has over 20 years of R&D experience in the defense industry, including roles in machine learning systems for IMINT & SIGINT. He is also a certified expert in Critical Infrastructure Risk Management and Competitive Intelligence.

Professional Profile:

GOOGLE SCHOLAR

Summary of Suitability for Excellence in Research: Dr. Catalin Dumitrescu

Dr. Catalin Dumitrescu exemplifies excellence in research through his extensive expertise, academic credentials, professional experience, and impactful contributions in the fields of Artificial Intelligence, Machine Learning, and Digital Signal Processing, particularly in applications for defense, transportation, and security.

Education

🎓 Ph.D. in Digital Signal Processing & Machine Learning – University Politehnica of Bucharest.
📜 Engineering Degree in Signal and Image Processing – Transportation Engineering Faculty, UPB.
🎓 Postgraduate Degree in International Business & Economics – Bucharest University of Economic Studies.
📑 Certified Expert in:

Critical Infrastructure Risk Management ⚠️
Competitive Intelligence 🧠

Professional Experience

🔹 2015 – Present: Associate Professor, R&D Adviser in AI & Computing, UPB.
🔹 2018 – Present: R&D Consultant, SOLIDUS AI TECH.
🔹 2015 – 2018: Software Systems Architect, UTI GROUP.
🔹 1995 – 2015: R&D Military Officer, Defense Advanced Technology Institute.
🔹 1986 – 1995: Electronics Engineer, Transport Research Institute.

💡 Career Highlights:

20+ years of experience in Machine Learning, AI, and Cyber Defence.
Expertise in IMINT & SIGINT for the defence sector 🛡️.
Development of advanced algorithms and software architecture for signal processing and AI systems.

Research Interests

🔍 Core Areas:

Artificial Intelligence & Machine Learning 🤖
Digital Signal Processing 📡
Neural Networks for Audio & Image Analysis 🎧🖼️
Cyber Security & Forensics 🕵️‍♂️
Cognitive Radio Systems 📻

🔍 Specialized Focus:

Deep Learning for object detection and classification 🖥️
Brain-Computer Interfaces 🧠
EEG, EKG, and EMG signal analysis 📊
Cryptography & Multimedia Security 🔒

Teaching Expertise

📚 Courses include:

Cyber Security & Defence 🔐
Digital Image Processing 📷
Real-Time Signal Processing ⏱️
Multimedia Forensics and Security 🎥

Publication top Notes:

Fuzzy logic for intelligent control system using soft computing applications

CITED:61

Development of an acoustic system for UAV detection

CITED:60

Using brain-computer interface to control a virtual drone using non-invasive motor imagery and machine learning

CITED:21

Aircraft trajectory tracking using radar equipment with fuzzy logic algorithm

CITED:21

Internal Auditing & Risk Management, No. 4 (56)

CITED:17

Monitoring system with applications in road transport

CITED:17

Location-Guided Dense Nested Attention Network for Infrared Small Target Detection

MMYFnet: Multi-Modality YOLO Fusion Network for Object Detection in Remote Sensing Images