Lingzhen Kong

Nanjing University of Science and Technology, Chongqing Technology and Business University

Papers

Total Citations

H-Index

About

Dr. Lingzhen Kong is a researcher at the forefront of biometric security and intelligent fault diagnosis, with a focus on integrating advanced signal processing with machine learning for real-world IoT and industrial applications. Her most cited work, "Gender Classification Based on Spatio-Frequency Feature Fusion of OCT Fingerprint Images in the IoT Environment" (2024, 12 citations), addresses critical privacy and security challenges in interconnected systems by developing a novel biometric identification method that leverages optical coherence tomography (OCT) fingerprint images. This contribution is particularly impactful as it enhances authentication accuracy while safeguarding user data in IoT networks. Additionally, Dr. Kong’s paper "Joint feature enhancement mapping and reservoir computing for improving fault diagnosis performance" (2021, 2 citations) tackles the complexities of industrial robot condition monitoring, proposing a robust FEM-RC framework that effectively filters noise from sensor signals to improve diagnostic reliability. Her work bridges the gap between secure biometrics and predictive maintenance, demonstrating versatility in applying computational methods to both human-centric and industrial challenges. With a growing citation record, Dr. Kong is establishing herself as a key contributor to secure, intelligent systems in the era of pervasive computing.