Reinforcement Learning (RL) is the study of programs that improve their performance by receiving rewards and punishments from the environment. Most RL methods optimize the discoun...
This paper presents a probing-based method for probabilistic localization in automated robotic assembly. We consider peg-in-hole problems in which a needle-like peg has a single p...
—Thermal control is crucial to real-time systems as excessive processor temperature can cause system failure or unacceptable performance degradation due to hardware throttling. R...
Yong Fu, Nicholas Kottenstette, Yingming Chen, Che...
Recently, a reconfigurable and biologically inspired paradigm based on network-on-chip (NoC) and spiking neural networks (SNNs) has been proposed as a new method of realising an ef...
Snaider Carrillo, Jim Harkin, Liam McDaid, Sandeep...
Robust distributed systems commonly employ high-level recovery mechanisms enabling the system to recover from a wide variety of problematic environmental conditions such as node f...
Charles Edwin Killian, Karthik Nagaraj, Salman Per...