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Regularization and Feedforward artificial neural network training with noise
Proceedings of the International Joint Conference on Neural Networks, 2003., 2004Regularization is a method used for controlling the complexity of models. Explicit regularization uses a modifier term, incorporating a-priori knowledge about the function to be approximated by Feedforward Artificial Networks, that is added to the risk functional and implicit regularization where noise is added to the system variables during training ...
Pravin Chandra, Yogesh Singh
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Noise Optimization in Artificial Neural Networks
2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), 2022Li Xiao 0005 +3 more
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Fingerprint Embedding Authentication with Artificial Noise: MISO Regime
2019 IEEE Conference on Communications and Network Security (CNS), 2019We apply artificial noise to the fingerprint embedding authentication framework to improve information-theoretic authentication for the MISO channel. Instead of optimizing for secrecy capacity, we examine the trade-off between message rate, authentication, and key security.
Jake Bailey Perazzone +3 more
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Artificial noise-aided secure beamforming for multigroup multicast
2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC), 2018We investigate the physical layer security for multiple-input single-output (MISO) multigroup multicast system in the presence of multiple eavesdroppers (Eves). To elaborate, we aim to tackle the secrecy rate maximization (SRM) problem of the multicast system by proposing an algorithm for secure beam-forming.
Wanjik Kim +3 more
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Secrecy signal and artificial noise designs in cellular network
2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP), 2015In this paper, we propose a new method of secrecy signal and artificial noise (AN) transmission for a MISO wiretap channel in wireless cellular networks. We first design the secrecy signal beamformer such that its power leakage to the unintended directions is minimized, then we concentrate the AN on the directions with higher risk of information ...
Huiming Wang 0001, Feng Liu 0010
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Preventing Eavesdropper by Artificial Noise in MIMO-OFDM Systems
Wireless Personal Communications, 2018The PHY security has been the focus of the researchers since the day it was proposed. The researchers have mentioned many kinds of methods to prevent the eavesdropper from overhearing the confidential signal in different types of networks and environments. However, none of them have proposed a method which can be used in MIMO-OFDM.
Tao Jing +3 more
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Artificial Noise Elimination: From the Perspective of Eavesdroppers
IEEE Transactions on Communications, 2022Hong Niu 0001 +3 more
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Secrecy Degradation for Inevitable Phase Noise in Artificial Noise Shielded FH Systems
2021 IEEE Globecom Workshops (GC Wkshps), 2021Changqing Song +4 more
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