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Publications

Search Publications by

Michael Frey (Fed)

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Displaying 1 - 10 of 10

Generative Adversarial Network Performance in Low-Dimensional Settings

April 20, 2021
Author(s)
Felix M. Jimenez, Amanda Koepke, Mary Gregg, Michael R. Frey
A generative adversarial network (GAN) is an artificial neural network with a distinctive training architecture, designed to create examples that faithfully reproduce a target distribution. GANs have recently had particular success in applications

Characterizing LTE User Equipment Emissions Under Closed-Loop Power Control

April 6, 2021
Author(s)
Jason Coder, Aric Sanders, Michael R. Frey, Adam Wunderlich, Azizollah Kord, Jolene Splett, Lucas N. Koepke, Daniel Kuester, Duncan McGillivray, John M. Ladbury
This report presents a laboratory-based characterization of Long Term Evolution (LTE) User Equipment (UE) emissions under closed-loop power control, building on our prior work (NIST TN 2069), which assessed the impact of a large number of factors affecting

Multiple Changepoint Analysis of Noisy Nonlinear Data with an Application to Modeling Crack Growth in Additively Manufactured Titanium

December 5, 2019
Author(s)
Lucas N. Koepke, Jolene D. Splett, Timothy P. Quinn, Nikolas W. Hrabe, Jake T. Benzing, Michael R. Frey
Noisy measurement data pose a challenge for changepoint analysis, especially in the presence of multiple changepoints and when the model is nonlinear. We explore various approaches to estimating changepoints and their standard errors under these conditions

Characterizing LTE User Equipment Emissions: Factor Screening

September 30, 2019
Author(s)
Jason Coder, Adam Wunderlich, Michael R. Frey, Paul T. Blanchard, Dan Kuester, Azizollah Kord, Max Lees, Aric Sanders, Jolene Splett, Lucas N. Koepke, Rob Horansky, Duncan McGillivray, John M. Ladbury, Jeffrey T. Correia, Venkatesh Ramaswamy, Jerediah Fevold, Shawn Lefebre, Jacob K. Johnson, John Carpenter, Mark Lofquist, Keith Hartley, Melissa Midzor
Characterizations of long-term evolution (LTE) user equipment (UE) emissions are a key ingredient in models of interference between wireless cellular networks and other systems that must use the same radio frequency spectrum. This report presents (i) a

Monte Carlo Sampling Bias in the Microwave Uncertainty Framework

June 27, 2019
Author(s)
Michael R. Frey, Benjamin Jamroz, Amanda Koepke, Jake Rezac, Dylan Williams
The Microwave Uncertainty Framework (MUF) is a software suite created, supported, and made publicly available by the Radio Frequency Division of the U.S. National Institute of Standards and Technology. The general purpose of the MUF is to provide automated

Correlation-Based Uncertainty in Loaded Reverberation Chambers

October 1, 2018
Author(s)
Maria G. Becker, Michael R. Frey, Sarah B. Streett, Catherine A. Remley, Robert D. Horansky, Damir Senic
When reverberation chambers are loaded to increase the coherence bandwidth for modulated-signal measurements, a secondary effect is decreased spatial uniformity. We show that an appropriate choice of stirring sequence, consisting of a combination of mode

Measured Emission Spectra of Selected AWS-3 LTE Transmitters

January 16, 2018
Author(s)
Michael R. Frey, Geoffrey Sanders, Jolene Splett, John M. Ladbury, Frank Sanders, Azizollah Kord, Ryan T. Jacobs
Version three of Advanced Wireless Services (AWS-3) radio systems will soon use spectrum that is adjacent to bands currently used by airborne telemetry links at U.S. government test and training ranges (TTRs). It is possible that out-of-band (OoB) and

Software Tools for Uncertainty Evaluation in VNA Measurements: A Comparative Study

June 8, 2017
Author(s)
Gustavo Avolio, Dylan Williams, Michael Frey, Sarah B. Streett, Dominique Schreurs, Andrea Ferrero, Michael Dieudonne
We compared three software tools designed for scattering-parameter measurement uncertainty evaluation. These tools propagate uncertainty to calibrated S-parameters by means of a sensitivity analysis. We also validated the sensitivity analysis with Monte

A Bell Inequality for a Class of Multilocal Ring Networks

Author(s)
Michael R. Frey
Quantum networks with independent sources of entanglement (hidden variables) and nodes that execute joint quantum measurements can create strong quantum correlations spanning the breadth of the network. Understanding of these correlations has to the