@proceedings {2303,
	title = {Deep learning with edge computing for localization of epileptogenicity using multimodal rs-fmri and eeg big data},
	year = {2017},
	publisher = {IEEE},
	author = {Hosseini, Mohammad-Parsa and Tran, Tuyen X and Pompili, Dario and Elisevich, Kost and Soltanian-Zadeh, Hamid}
}
@proceedings {2300,
	title = {Mobee: Mobility-Aware Energy-Efficient Coded Caching in Cloud Radio Access Networks},
	year = {2017},
	publisher = {IEEE},
	author = {Tran, Tuyen X and Kazemi, Fatemeh and Karimi, Esmaeil and Pompili, Dario}
}
@article {2299,
	title = {Mobile Edge Computing: Recent Efforts and Five Key Research Directions},
	journal = {IEEE COMSOC MMTC Commun.-Frontiers},
	year = {2017},
	month = {7},
	pages = {29-34},
	author = {Tran, Tuyen X and Hosseini, Mohammad-Parsa and Pompili, Dario}
}
@article {2302,
	title = {Optimized Deep Learning for EEG Big Data and Seizure Prediction BCI via Internet of Things},
	journal = {IEEE Transactions on Big Data},
	year = {2017},
	pages = {1{\textendash}1},
	publisher = {IEEE},
	author = {Hosseini, Mohammad-Parsa and Pompili, Dario and Elisevich, Kost and Soltanian-Zadeh, Hamid}
}
@proceedings {2304,
	title = {SSFB: Signal-Space-Frequency Beamforming for Underwater Acoustic Video Transmission},
	year = {2017},
	publisher = {IEEE},
	author = {Rahmati, Mehdi and Pompili, Dario}
}
@proceedings {2301,
	title = {Understanding the Computational Requirements of Virtualized Baseband Units using a Programmable Cloud Radio Access Network Testbed},
	year = {2017},
	publisher = {IEEE},
	author = {Tran, Tuyen X and Younis, Ayman and Pompili, Dario}
}
@article {2305,
	title = {UNISeC: Inspection, Separation, and Classification of Underwater Acoustic Noise Point Sources},
	journal = {IEEE Journal of Oceanic Engineering},
	year = {2017},
	publisher = {IEEE},
	author = {Rahmati, Mehdi and Pompili, Dario}
}
@article {103,
	title = {Research Challenges in Computation, Communication, and Context Awareness for Ubiquitous Healthcare},
	journal = {IEEE Communications Magazine},
	volume = {50},
	year = {2012},
	month = {May},
	pages = {92{\textendash}99},
	abstract = {A new paradigm for ubiquitous healthcare characterized by pervasive continuous vital sign data collection, real-time processing of monitored data to derive meaningful physiological parameters, and context-aware data- and patient-centric decision making, is central to deliver personalized healthcare solutions to the elderly and the physically challenged. However, this new paradigm requires real-time processing of wirelessly collected vital signs using inherently complex physiological models and analysis of the processed information under context (e.g., location, ambient conditions, current physical activity) to extract knowledge about the health condition of patients. As the computational capabilities of biomedical sensor nodes are insufficient to run these models, this article presents an innovative resource provisioning framework that organizes and harnesses the computing capabilities of under-utilized electronic devices in the vicinity (e.g., laptops, tablets, PDAs, DVRs, medical terminals) in home and hospital settings. Novel wireless communication solutions for reliable vital sign transmission and algorithms for acquiring context awareness to support this framework are also discussed.},
	author = {H. Viswanathan and Chen, Baozhi and Pompili, Dario}
}
@article {1927,
	title = {Thermal Anomaly Detection in Datacenters},
	journal = {Journal of Mechanical Engineering Science (SAGE)},
	volume = {1},
	year = {2011},
	month = {December},
	pages = {1-14},
	author = {Yang Yuan and E. K. Lee and Pompili, Dario and Junbi Liao}
}
