Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Joshua Morman"'
Publikováno v:
MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6e5cce075a60a4e9d407176c649dbfe6
https://doi.org/10.1109/milcom55135.2022.10017973
https://doi.org/10.1109/milcom55135.2022.10017973
Publikováno v:
GlobalSIP
The current dataset generation methods for RF Machine Learning (RFML) tasks consist of either completely synthetically generated data or completely raw digitized data from an RF front end. The synthetic datasets are often unrealistic in terms of wave
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::85ad410f9be077e8380d12d79f650d7a
https://doi.org/10.1109/globalsip45357.2019.8969160
https://doi.org/10.1109/globalsip45357.2019.8969160
Publikováno v:
SPAWC
We show that compact fully connected (FC) deep learning networks trained to classify wireless protocols using a hierarchy of multiple denoising autoencoders (AEs) outperform reference FC networks trained in a typical way, i.e., with a stochastic grad
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::00d1ad56cff8eeb2f448ecbc274addef
https://doi.org/10.1109/spawc.2019.8815550
https://doi.org/10.1109/spawc.2019.8815550
Publikováno v:
WiseML@WiSec
Adversarial examples (AdExs) in machine learning for classification of radio frequency (RF) signals can be created in a targeted manner such that they go beyond general misclassification and result in the detection of a specific targeted class. Moreo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d1ed6aeb8d9121d5223062a4f92febf9
https://doi.org/10.1145/3324921.3328792
https://doi.org/10.1145/3324921.3328792
Autor:
Heechang Kim, Brian Wilson, Nicholas B. Chang, Joshua Morman, Sarry Habiby, Tom Banwell, Richard Lau
Publikováno v:
ACSSC
This paper describes a systematic approach towards incorporating prediction theory into a Mission-aware Predictive Network (MaPN) framework. Although prior examples indicate prediction has successfully improved performance, there has been limited qua
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4ef40b27e41515b0191e16715e8480fb
https://doi.org/10.1109/acssc.2018.8645126
https://doi.org/10.1109/acssc.2018.8645126
Autor:
James M. Dailey, Anjali Agarwal, Paul Toliver, Robert Miller, Joshua Morman, Joseph C. Liberti
Publikováno v:
2016 IEEE International Topical Meeting on Microwave Photonics (MWP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b5167f18850abb4dfda6fa81361cc166
https://doi.org/10.1109/mwp.2016.7791310
https://doi.org/10.1109/mwp.2016.7791310