24 citations · 35 across the 8 of their papers we have counts for
8 papers
Large Scale Radio Frequency Wideband Signal Detection & Recognition
Luke Boegner, Garrett Vanhoy, Phillip Vallance +4
Applications of deep learning to the radio frequency (RF) domain have largely concentrated on the task of narrowband signal classification after the signals of interest have alread…
Large Scale Radio Frequency Signal Classification
Luke Boegner, Manbir Gulati, Garrett Vanhoy +5
Existing datasets used to train deep learning models for narrowband radio frequency (RF) signal classification lack enough diversity in signal types and channel impairments to suff…
Reservoir Based Edge Training on RF Data To Deliver Intelligent and Efficient IoT Spectrum Sensors
Silvija Kokalj-Filipovic, Paul Toliver, William Johnson +1
Current radio frequency (RF) sensors at the Edge lack the computational resources to support practical, in-situ training for intelligent spectrum monitoring, and sensor data classi…
Practical Fingerprinting of RF Devices in the Wild
Silvija Kokalj-Filipovic, Luke Boegner, Robert D. Miller
We present a new RF fingerprinting technique for wireless emitters that is based on a simple, easily and efficiently retrainable Ridge Regression (RR) classifier. The RR learns to…
Reservoir-Based Distributed Machine Learning for Edge Operation
Silvija Kokalj-Filipovic, Paul Toliver, William Johnson +1
We introduce a novel design for in-situ training of machine learning algorithms built into smart sensors, and illustrate distributed training scenarios using radio frequency (RF) s…
AutoEncoders for Training Compact Deep Learning RF Classifiers for Wireless Protocols
Silvija Kokalj-Filipovic, Rob Miller, Joshua Morman
We show that compact fully connected (FC) deep learning networks trained to classify wireless protocols using a hierarchy of multiple denoising autoencoders (AEs) outperform refere…