Dr. Juan Lei | Sonar Imaging Awards | Best Researcher Award

Dr. Juan Lei | Sonar Imaging Awards | Best Researcher Award

Dr. Juan Lei, Northwestern Polytechnical University, China

Juan Lei was born in Shaanxi, She received her B.S. degree in Electronic Information Science and Technology from Northwest University, Xi’an, China, in 2008, and her M.S. degree from Northwestern Polytechnical University, Xi’an, China, in 2013. Since September 2018, she has been pursuing a Ph.D. at Northwestern Polytechnical University. Her primary research interests include image processing and deep learning, with a particular focus on underwater sonar signal processing. With expertise in Underwater Unmanned Vehicles and on-board sensors, she has been actively engaged in the development of underwater image recognition and segmentation technologies. She also serves as the Deputy General Manager of Xi’an Tianhe Maritime Technology Co. Ltd., a company dedicated to researching and manufacturing underwater robots and sensor-equipped devices for acquiring underwater images and data.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award

Juan Lei has demonstrated a strong commitment to research in the field of image processing, deep learning, and underwater sonar signal processing. Her academic journey, from obtaining a B.S. in Electronic Information Science and Technology to an ongoing Ph.D. at Northwestern Polytechnic University, highlights her dedication to advancing scientific knowledge.

📚 Education:

  • 🎓 B.S. in Electronic Information Science and Technology – Northwest University, Xi’an, China (2008)
  • 🎓 M.S. in [Electronic/Engineering Field] – Northwestern Polytechnic University, Xi’an, China (2013)
  • 🎓 Ph.D. Candidate in [Relevant Field] – Northwestern Polytechnic University, Xi’an, China (2018–Present)

💼 Work Experience:

  • 🏢 Deputy General Manager – Xi’an Tianhe Maritime Technology Co. Ltd.
    🔹 Specialized in underwater robotics and sensor-equipped devices for underwater data acquisition
    🔹 Focused on underwater image recognition and segmentation

🏆 Achievements, Awards & Honors:

  • 🥇 Expertise in image processing & deep learning
  • 🌊 Knowledge of Underwater Unmanned Vehicles (UUVs) & onboard sensors
  • 🎯 Research focus on underwater sonar signal processing
  • 🏅 Contributed to advancements in underwater image recognition & segmentation

Publication Top Notes:

CNN–Transformer Hybrid Architecture for Underwater Sonar Image Segmentation

 

Mr. haodi mei | Ocean Sensors Award | Best Researcher Award

Mr. haodi mei | Ocean Sensors Award | Best Researcher Award 

Mr. haodi mei,  Northwestern Polytechnical University, China

Haodi Mei received his M.S. degree in Signal and Information Processing from the School of Marine Science and Technology at Northwestern Polytechnical University (NPU), Xi’an, China, in 2017. He is currently pursuing a Ph.D. degree at Northwestern Polytechnical University (NPU). His research interests focus on underwater acoustic sensor networks.

Professional Profile:

SCOPUS

Summary of Suitability for Best Researcher Award

Haodi Mei holds an M.S. degree in Signal and Information Processing from the School of Marine Science and Technology at Northwestern Polytechnical University (NPU), Xi’an, China, and is currently pursuing a Ph.D. at the same institution. His research interests focus on underwater acoustic sensor networks (UASN), a critical area for oceanographic data collection, environmental monitoring, and defense applications.

Education:

  • M.S. in Signal and Information Processing, School of Marine Science and Technology, Northwestern Polytechnical University (NPU), Xi’an, China, 2017.
  • Currently pursuing a Ph.D. at Northwestern Polytechnical University (NPU).

Work Experience:

  • (If applicable, please provide details about any relevant work experience or positions held at NPU or other institutions.)

Publication top Notes:

Node Load and Location-Based Clustering Protocol for Underwater Acoustic Sensor Networks

Q Learning-Based Routing Protocol With Accelerating Convergence for Underwater Wireless Sensor Networks

An Adaptive MAC Protocol for Underwater Acoustic Sensor Networks With Dynamic-High Load

Multi-Agent Reinforcement Learning-Based Routing Protocol for Underwater Wireless Sensor Networks With Value of Information

An Adaptive Routing Protocol for Underwater Acoustic Sensor Networks With Ocean Current

Reinforcement Learning-Based Opportunistic Routing Protocol Using Depth Information for Energy-Efficient Underwater Wireless Sensor Networks