Exploring how intelligent systems hold up beyond the environments they were trained in.
This paper investigates domain generalization techniques for WiFi sensing systems, addressing the challenge of maintaining model performance across different environmental conditions. We propose novel adaptation methods that significantly improve cross-domain robustness while maintaining computational efficiency.
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This thesis extends prior work on domain generalization for WiFi sensing, developing comprehensive frameworks that maintain high performance across diverse real-world environments. The research focuses on creating robust, deployable systems with real-time performance optimization.
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Evaluating large language model performance for autonomous marine systems control.
Novel approaches to processing and representing lidar data for machine learning applications.
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