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pro vyhledávání: '"Boegner, Luke"'
Autor:
Boegner, Luke, Vanhoy, Garrett, Vallance, Phillip, Gulati, Manbir, Feitzinger, Dresden, Comar, Bradley, Miller, Robert D.
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 already been detected and extracted from a wideband capture. To encourage br
Externí odkaz:
http://arxiv.org/abs/2211.10335
Autor:
Boegner, Luke, Gulati, Manbir, Vanhoy, Garrett, Vallance, Phillip, Comar, Bradley, Kokalj-Filipovic, Silvija, Lennon, Craig, Miller, Robert D.
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 sufficiently assess model performance in the real world. We introduce the
Externí odkaz:
http://arxiv.org/abs/2207.09918
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 identify devices using bursts of waveform samples, conveniently transf
Externí odkaz:
http://arxiv.org/abs/2105.04492
Autor:
Boegner, Luke, Cho, Yong, Fleming, Nicholas, Gilman, Tyler, Huang, Teng Kuan, King, Kyle, Kruder, Nathaniel, Lafond, Joshua, McLaughlin, Timothy, Noh, Sye Hoon, Poh, William, Ruppel, Emily, Wei, Libby
Bikeshares promote healthy lifestyles and sustainability among commuters, casual riders, and tourists. However, the central pillar of modern systems, the bike station, cannot be easily integrated into a compact college campus. Fixed stations lack the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::34d88a06ec3f735e72ae900fb83f2bd5