Results 51 to 60 of about 7,963 (189)
CAAI Transactions on Intelligence Technology, EarlyView.Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma +4 more
wiley +1 more source
Vulnerability of LTE to Hostile Interference
, 2014 LTE is well on its way to becoming the primary cellular standard, due to its performance and low cost. Over the next decade we will become dependent on LTE, which is why we must ensure it is secure and available when we need it.
Clancy, T. Charles +3 more
core +1 more source
Building centaur responders: is emergency management ready for artificial intelligence?
Disasters, Volume 50, Issue 3, July 2026.Abstract This article examines the preparedness of emergency management (EM) for addressing questions pertaining to artificial intelligence (AI), encompassing its benefits to EM missions, the potential biases, the societal impacts, and more. We pinpoint two key shortcomings in early EM research on AI: (i) insufficient discussion of both AI's history ...
Christopher Whyte +1 more
wiley +1 more source
Spoofer-to-Target Association in Multi-Spoofer Multi-Target Scenario for Stealthy GPS Spoofing
IEEE Access, 2021 Global navigation satellite system (GNSS) based navigation is omnipresent in today’s world, providing position, velocity, and time (PVT) information with inexpensive GPS receivers.
Bethi Pardhasaradhi +2 more
doaj +1 more source
Authentication of Satellite Navigation Signals by Wiretap Coding and Artificial Noise
, 2018 In order to combat the spoofing of global navigation satellite system (GNSS) signals we propose a novel approach for satellite signal authentication based on information-theoretic security.
Formaggio, Francesco, Tomasin, Stefano
core +1 more source
A Survey for Deep Reinforcement Learning Based Network Intrusion Detection
Applied AI Letters, Volume 7, Issue 2, June 2026.This paper surveys deep reinforcement learning (DRL) for network intrusion detection, evaluating model efficiency, minority attack detection, and dataset imbalance. Findings show DRL achieves state‐of‐the‐art results on public datasets, sometimes surpassing traditional deep learning.
Wanrong Yang +3 more
wiley +1 more source
Improve the Security of GNSS Receivers Through Spoofing Mitigation
IEEE Access, 2017 Spoofing attacks are one of the most dangerous threats for the application of the global navigation satellite system (GNSS), especially for autonomous driving and unmanned aerial vehicles.
Shuai Han +3 more
doaj +1 more source
Engineering Proceedings, 2023 NovAtel has recently leveraged its expertise in both receiver design and anti-jam technology to develop solutions for space- and weight-constrained applications in challenged GNSS environments.
Ali Broumandan, Sandy Kennedy
doaj +1 more source
Mass-Market Receiver for Static Positioning: Tests and Statistical Analyses [PDF]
, 2011 Nowadays, there are several low cost GPS receivers able to provide both pseudorange and carrier phase measurements in the L1band, that allow to have good realtime performances in outdoor condition.
Cina, Alberto, Marucco, G., Piras, Marco
core
Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks
, 2018 Intrusion detection has become one of the most critical tasks in a wireless network to prevent service outages that can take long to fix. The sheer variety of anomalous events necessitates adopting cognitive anomaly detection methods instead of the ...
Jauhar, Ahmad +3 more
core +1 more source