Jacob Rezac

Publications

A Data-Driven Approach to Complex Voxel Predictions in Grayscale Digital Light Processing Additive Manufacturing Using U-nets and Generative Adversarial Networks

July 6, 2023

Author(s)

Jason Killgore, Thomas Kolibaba, Benjamin Caplins, Callie Higgins, Jake Rezac

Machine learning models such as U-nets like the pix2pix conditional generative adversarial network (cGAN) are shown to predict 3D printed voxel geometry in

Radio Spectrum Occupancy Measurements Amid COVID-19 Telework and Telehealth

October 14, 2022

Author(s)

Dan Kuester, Xifeng Lu, Dazhen Gu, Azizollah Kord, Jake Rezac, Katie Carson, Marla L. Dowell, Elizabeth Eyeson, Ari Feldman, Keith Forsyth, Vu Le, John Marts, Mike McNulty, Kyle Neubarth, Andre Rosete, Matthew Ryan, Maija Teraslina

During the COVID-19 pandemic, NIST began a targeted campaign of measurements of averaged power and occupancy rate in the radio spectrum. The purpose was to

Reproducibility Assessment of a Telecommunication Testbed

September 16, 2022

Author(s)

Jeanne Quimby, Jake Rezac, Mary Gregg, Michael Frey, Jason Coder, Anna Otterstetter

Telecommunication testbeds are a fundamental tool in communication research, enabling prototyping and validating new ideas. Unfortunately, these testbeds are

Bi-Criteria Radio Spectrum Sharing with Subspace-Based Pareto Tracing

March 22, 2022

Author(s)

Zachary J. Grey, Susanna Mosleh, Jake Rezac, Yao Ma, Jason Coder, Andrew Dienstfrey

Radio spectrum is a scarce resource. To meet demands, new wireless technologies must operate in shared spectrum over unlicensed bands (coexist). We consider

Monte Carlo Augmented Channel Estimator

March 3, 2022

Author(s)

Alec Weiss, Atef Elsherbeni, Jeanne Quimby, Jake Rezac

We have developed a novel Monte Carlo-augmented channel estimator for millimeter-wave orthogonal frequency division multiplexing systems To better estimate and

View more Publications

Jacob Rezac (Fed)

Publications