Estimation of transmitter I/Q imbalance using convolutional neural networks. - Lauren J. Wong; William C. Headley; Alan J. Michaels (CCWC, 2018)

Clustering Learned CNN Features from Raw I/Q Data for Emitter Identification. - Lauren J. Wong; William C. Headley; Seth Andrews; Ryan M. Gerdes; Alan J. Michaels (MILCOM, 2018)

Developing RFML Intuition: An Automatic Modulation Classification Architecture Case Study. - William H. Clark; Vanessa Arndorfer; Brook Tamir; Daniel Kim; Cristian Vives; Hunter Morris; Lauren J. Wong; William C. Headley (MILCOM, 2019)

Distributed Automatic Modulation Classification with Compressed Data. - Lauren J. Wong; Parker D. White; Michael Fowler; William C. Headley (MILCOM, 2019)

Specific Emitter Identification Using Convolutional Neural Network-Based IQ Imbalance Estimators. - Lauren J. Wong; William C. Headley; Alan J. Michaels (IEEE Access, 2019)

Groundwork for Neural Network-Based Specific Emitter Identification Authentication for IoT. - Jason M. McGinthy; Lauren J. Wong; Alan J. Michaels (IEEE Internet Things J., 2019)

Resilience Improvements for Space-Based Radio Frequency Machine Learning. - Lauren J. Wong; Emily Altland; Joshua Detwiler; Paolo Fermin; Julia Mahon Kuzin; Nathan Moeliono; Abdelrahman Said Abdalla; William C. Headley; Alan J. Michaels (ISNCC, 2020)

Explainable Neural Network-based Modulation Classification via Concept Bottleneck Models. - Lauren J. Wong; Sean McPherson (CCWC, 2021)

Multinomial-Based Decision Synthesis of ML Classification Outputs. - Alan J. Michaels; Lauren J. Wong (MDAI, 2021)

An RFML Ecosystem: Considerations for the Application of Deep Learning to Spectrum Situational Awareness. - Lauren J. Wong; William H. Clark; Bryse Flowers; R. Michael Buehrer; William C. Headley; Alan J. Michaels (IEEE Open J. Commun. Soc., 2021)

Classification of Radio Signals Using Truncated Gaussian Discriminant Analysis of Convolutional Neural Network-Derived Features. - J. B. Persons; Lauren J. Wong; Megan O. Moore; William C. Headley; Michael Fowler (MILCOM, 2022)

Quantifying Raw RF Dataset Similarity for Transfer Learning Applications. - Lauren J. Wong; Sean McPherson; Alan J. Michaels (IEEE Open J. Commun. Soc., 2022)

Transfer Learning for Radio Frequency Machine Learning: A Taxonomy and Survey. - Lauren J. Wong; Alan J. Michaels (Sensors, 2022)

Transfer Learning for RF Domain Adaptation - Synthetic Dataset. - Lauren J. Wong; Sean McPherson; Alan J. Michaels (IEEE DataPort, 2022)

A Real-World Dataset Generator for Specific Emitter Identification. - Braeden P. Muller; Lauren J. Wong; William H. Clark; Alan J. Michaels (IEEE Access, 2023)

Sensitivity Analysis of RFML Applications. - Braeden P. Muller; Lauren J. Wong; Alan J. Michaels (IEEE Access, 2024)

An Analysis of Radio Frequency Transfer Learning Behavior. - Lauren J. Wong; Braeden P. Muller; Sean McPherson; Alan J. Michaels (Mach. Learn. Knowl. Extr., 2024)

Transferring Learned Behaviors between Similar and Different Radios. - Braeden P. Muller; Brennan E. Olds; Lauren J. Wong; Alan J. Michaels (Sensors, 2024)

Assessing the Value of Transfer Learning Metrics for Radio Frequency Domain Adaptation. - Lauren J. Wong; Braeden P. Muller; Sean McPherson; Alan J. Michaels (Mach. Learn. Knowl. Extr., 2024)