My research in AI primarily focuses on developing deep learning algorithms for non-cooperative spectrum sensing, adaptive threshold settings for dynamic spectrum allocation, and machine learning applications in wireless communication. I am particularly interested in how AI can optimize spectrum usage, enhance sensor networks, and drive innovations in wireless communications.
Assessment of spectrum sensing using support vector machine combined with principal component analysis
Performance Analysis of the Effect of Nonlinear Low Noise Amplifier for Wideband Spectrum Sensing in the Poisson Field of Interferers
A Deep Convolutional Neural Network Based Transfer Learning Method for Non-Cooperative Spectrum Sensing
I explore the intersection of AI and telecommunications, particularly in the areas of adaptive algorithms for spectrum occupancy, signal processing, and the use of machine learning to improve telecommunication infrastructure and protocols. My work aims to make telecommunication systems more efficient, robust, and capable of handling the demands of the future.
Using cutting-edge machine learning techniques, I research environmental applications, such as detecting river plastic using UAV sensor data and predicting streamflow patterns from snowmelt. This interdisciplinary approach aims to provide innovative solutions for sustainable development and environmental conservation.