24 citations · 40 across the 6 of their papers we have counts for
6 papers
An Analysis of RF Transfer Learning Behavior Using Synthetic Data
Lauren J. Wong, Sean McPherson, Alan J. Michaels
Transfer learning (TL) techniques, which leverage prior knowledge gained from data with different distributions to achieve higher performance and reduced training time, are often u…
Assessing the Value of Transfer Learning Metrics for RF Domain Adaptation
Lauren J. Wong, Sean McPherson, Alan J. Michaels
The use of transfer learning (TL) techniques has become common practice in fields such as computer vision (CV) and natural language processing (NLP). Leveraging prior knowledge gai…
Explainable Neural Network-based Modulation Classification via Concept Bottleneck Models
Lauren J. Wong, Sean McPherson
While RFML is expected to be a key enabler of future wireless standards, a significant challenge to the widespread adoption of RFML techniques is the lack of explainability in deep…
The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications
Lauren J. Wong, William H. Clark, Bryse Flowers +3
While deep machine learning technologies are now pervasive in state-of-the-art image recognition and natural language processing applications, only in recent years have these techn…
Classification of Radio Signals Using Truncated Gaussian Discriminant Analysis of Convolutional Neural Network-Derived Features
J. B. Persons, Lauren J. Wong, W. Chris Headley +1
To improve the utility and scalability of distributed radio frequency (RF) sensor and communication networks, reduce the need for convolutional neural network (CNN) retraining, and…
Emitter Identification Using CNN IQ Imbalance Estimators
Lauren J. Wong, William C. Headley, Alan J. Michaels
Specific Emitter Identification is the association of a received signal to a unique emitter, and is made possible by the naturally occurring and unintentional characteristics an em…