5 citations · 9 across the 2 of their papers we have counts for
3 papers
eess.SP2022★ 5 cited
Data-Driven Modeling of Noise Time Series with Convolutional Generative Adversarial Networks
Adam Wunderlich, Jack Sklar
Random noise arising from physical processes is an inherent characteristic of measurements and a limiting factor for most signal processing and data analysis tasks. Given the recen…
eess.SP2021★ 4 cited
Feasibility of Modeling Orthogonal Frequency-Division Multiplexing Communication Signals with Unsupervised Generative Adversarial Networks
Jack Sklar, Adam Wunderlich
High-quality recordings of radio frequency (RF) emissions from commercial communication hardware in realistic environments are often needed to develop and assess spectrum-sharing t…
astro-ph.EP2019
Estimating dayside effective temperatures of hot Jupiters and associated uncertainties through Gaussian process regression
Emily K. Pass, Nicolas B. Cowan, Patricio E. Cubillos +1
In this work, we outline a new method for estimating dayside effective temperatures of exoplanets and associated uncertainties using Gaussian process (GP) regression. By applying o…