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We introduce a powerful recurrent neural network based method for novelty detection to the application of detecting radio anomalies.
“Constant false alarm rate processing in search radars(receiver output noise control)”
V˜Gregers Hansen · 1973
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“Long short-term memory”
Sepp Hochreiter and J“”urgen Schmidhuber · 1997
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“Intrusion detection in wireless ad-hoc networks”
Yongguang Zhang and Wenke Lee · 2000
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“Novelty detection in learning systems”
Stephen Marsland · 2003
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“GNU radio: tools for exploring the radio frequency spectrum”
Eric Blossom · 2004
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“USRP user’s and developer’s guide”
Matt Ettus · 2005
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“Cognitive radio architecture”
Joseph Mitola˜III · 2006
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“An overview of anomaly detection techniques: Existing solutions and latest technological trends”
Animesh Patcha and Jung-Min Park · 2007
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“Anomaly detection in wireless sensor networks”
Sutharshan Rajasegarar, Christopher Leckie and Marimuthu Palaniswami · 2008
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“A novel connectionist system for unconstrained handwriting recognition”
Alex Graves et al · 2009
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“Anomaly detection in wireless sensor networks: A survey”
Miao Xie, Song Han, Biming Tian and Sazia Parvin · 2011
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“Second-order Kalman filters using multi-complex step derivatives”
Vivek Vittaldev, Ryan˜P Russell, Nitin Arora and David Gaylor · 2012
Cited alongside, same era.
“A review of novelty detection”
Marco˜AF Pimentel, David˜A Clifton, Lei Clifton and Lionel Tarassenko · 2014
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“Recurrent neural network regularization”
Wojciech Zaremba, Ilya Sutskever and Oriol Vinyals · 2014
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“Deep residual learning for image recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2015
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“Long short term memory networks for anomaly detection in time series”
Pankaj Malhotra, Lovekesh Vig, Gautam Shroff and Puneet Agarwal · 2015
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“Kalman and Bayesian Filters in Python”, 2016
Roger˜R. Labbe · 2016
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Aaron van˜den Oord et al · 2016
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