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Decision Automation for Electric Power Network Recovery
We propose a novel decision technique that addresses the massive number of decision choices for large-scale real-world problems; in addition, our method also features an experiential learning component that adaptively determines the utilization of the computational resources based on the performance of a small number of choices.
Controlled Tracking in Urban Terrain: Closing the Loop
We investigate the challenging problem of integrating detection, signal processing, target tracking, and adaptive waveform scheduling with lookahead in urban terrain.
Signal Processing Systems and Control
