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Daniel O'Neill
Researcher at Stanford University
Publications - 37
Citations - 2509
Daniel O'Neill is an academic researcher from Stanford University. The author has contributed to research in topics: Wireless network & Throughput. The author has an hindex of 16, co-authored 37 publications receiving 2202 citations. Previous affiliations of Daniel O'Neill include Sun Microsystems.
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Journal ArticleDOI
Power Control By Geometric Programming
TL;DR: This work presents a systematic method of distributed algorithms for power control that is geometric-programming-based and shows that in the high Signal-to- interference Ratios (SIR) regime, these nonlinear and apparently difficult, nonconvex optimization problems can be transformed into convex optimized problems in the form of geometric programming.
Proceedings ArticleDOI
Residential Demand Response Using Reinforcement Learning
TL;DR: An online learning application that implicitly estimates the impact of future energy prices and of consumer decisions on long term costs and schedules residential device usage as Markov, but does not assume knowledge of the structure or transition probabilities of these Markov chains.
Proceedings ArticleDOI
QoS and fairness constrained convex optimization of resource allocation for wireless cellular and ad hoc networks
TL;DR: A suite of problem formulations that allocate network resources to optimize SIR, maximize throughput and minimize delay are proposed, by using convex optimization, which accommodate a variety of realistic QoS and fairness constraints.
Journal ArticleDOI
Optimal Demand Response Using Device-Based Reinforcement Learning
TL;DR: This paper formulate a fully automated EMS's rescheduling problem as a reinforcement learning (RL) problem, and argues that this RL problem can be approximately solved by decomposing it over device clusters.
Journal ArticleDOI
Dynamic Control and Optimization of Distributed Energy Resources in a Microgrid
TL;DR: Simulation results for simulations using real-data demonstrate the ability of the optimization framework to respond dynamically in real-time to external conditions while maintaining the functional requirements of all DERs.