no code implementations • 12 Feb 2024 • Mohammad Koosha, Nicholas Mastronarde
In this study, we develop a framework based on stochastic geometry to evaluate the statistical characteristics of radio frequency interference (RFI) originating from a large-scale terrestrial Next-G network operating in the same frequency band as an RS satellite.
no code implementations • 13 Dec 2023 • Mohammad Koosha, Nicholas Mastronarde
Second, leveraging stochastic geometry, we assess the feasibility of using this passive band within a large-scale network in LoS of SMAP while ensuring that the error induced on SMAP's measurements due to RFI is below a given threshold.
no code implementations • 18 Jun 2023 • Mohammad Koosha, Nicholas Mastronarde
The National Aeronautics and Space Administration's (NASA) Soil Moisture Active Passive (SMAP) is the latest passive remote sensing satellite operating in the protected L-band spectrum from 1. 400 to 1. 427 GHz.
no code implementations • 27 Nov 2018 • Owen Lahav, Nicholas Mastronarde, Mihaela van der Schaar
Our results demonstrate that ML experts cannot accurately predict which system outputs will maximize clinicians' confidence in the underlying neural network model, and suggest additional findings that have broad implications to the future of research into ML interpretability and the use of ML in medicine.
no code implementations • 21 Nov 2018 • Changhee Lee, Nicholas Mastronarde, Mihaela van der Schaar
Estimating the individual treatment effect (ITE) from observational data is essential in medicine.
no code implementations • 22 Jul 2018 • Nikhilesh Sharma, Nicholas Mastronarde, Jacob Chakareski
Our experiments demonstrate that the proposed algorithm closely approximates the performance of an optimal offline solution that requires a priori knowledge of the channel, captured data, and harvested energy dynamics.
no code implementations • 26 Mar 2018 • Nikhilesh Sharma, Nicholas Mastronarde, Jacob Chakareski
We consider an energy harvesting sensor transmitting latency-sensitive data over a fading channel.
Networking and Internet Architecture
no code implementations • 29 Sep 2010 • Nicholas Mastronarde, Mihaela van der Schaar
The advantages of the proposed online method are that (i) it does not require a priori knowledge of the traffic arrival and channel statistics to determine the jointly optimal power-control, AMC, and DPM policies; (ii) it exploits partial information about the system so that less information needs to be learned than when using conventional reinforcement learning algorithms; and (iii) it obviates the need for action exploration, which severely limits the adaptation speed and run-time performance of conventional reinforcement learning algorithms.