The application of the equal energy hypothesis (EEH) to interrupted, intermittent and time‐varying Gaussian noise exposures

2006 ◽  
Vol 120 (5) ◽  
pp. 3129-3129
Author(s):  
Wei Qiu ◽  
Roger P. Hamernik ◽  
Robert I. Davis
Keyword(s):  
Gaussian Noise ◽  
Time Varying ◽  
Equal Energy

scholarly journals Gaussian Noise Time-Varying Power Spectrum Estimation With Minimal Statistics

2014 ◽  
Vol 62 (22) ◽  
pp. 5892-5906 ◽  
Author(s):  
Julien Huillery ◽  
Fabien Millioz ◽  
Nadine Martin
Keyword(s):  
Power Spectrum ◽  
Gaussian Noise ◽  
Spectrum Estimation ◽  
Time Varying ◽  
Power Spectrum Estimation

scholarly journals Generalized Automatic Modulation Classification Under Non-Gaussian Noise with Varying SNR Conditions: A CNN Enable Method

2020 ◽  
Author(s):  
Yu Wang ◽  
Guan Gui ◽  
Tomoaki Ohtsuki ◽  
Fumiyuki Adachi
Keyword(s):  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Modulation Classification ◽  
Time Varying ◽  
Signal Modulation ◽  
Generalization Capability ◽  
Common Features ◽  
Automatic Modulation Classification ◽  
Simulation Results ◽  
Non Gaussian

Automatic modulation classification (AMC) is an critical step to identify signal modulation types so as to enable more accurate demodulation in the non-cooperative scenarios. Convolutional neural network (CNN)-based AMC is believed as one of the most promising methods with great classification accuracy. However, the conventional CNN-based methods are lack of generality capabilities under time-varying signal-to-noise ratio (SNR) conditions, because these methods are merely trained on specific datasets and can only work at the corresponding condition. In this paper, a novel CNN-based generalized AMC method is proposed, and a more realistic scenario is considered, including white non-Gaussian noise and synchronization error. Its generalization capability stems from the mixed datasets under varying noise scenarios, and the CNN can extract common features from these datasets. Simulation results show that our proposed architecture can achieve higher robustness and generalization than the conventional ones.

Gearbox Fault Identification Under Non-Gaussian Noise and Time-Varying Operating Conditions

2021 ◽  
pp. 1-9
Author(s):  
Stephan Schmidt ◽  
Fakher Chaari ◽  
Radoslaw Zimroz ◽  
P. Stephan Heyns ◽  
Mohamed Haddar
Keyword(s):  
Gaussian Noise ◽  
Operating Conditions ◽  
Fault Identification ◽  
Time Varying ◽  
Non Gaussian

scholarly journals Generalized Automatic Modulation Classification Under Non-Gaussian Noise with Varying SNR Conditions: A CNN Enable Method

2020 ◽  
Author(s):  
Yu Wang ◽  
Guan Gui ◽  
Tomoaki Ohtsuki ◽  
Fumiyuki Adachi
Keyword(s):  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Modulation Classification ◽  
Time Varying ◽  
Signal Modulation ◽  
Generalization Capability ◽  
Common Features ◽  
Automatic Modulation Classification ◽  
Simulation Results ◽  
Non Gaussian

Automatic modulation classification (AMC) is an critical step to identify signal modulation types so as to enable more accurate demodulation in the non-cooperative scenarios. Convolutional neural network (CNN)-based AMC is believed as one of the most promising methods with great classification accuracy. However, the conventional CNN-based methods are lack of generality capabilities under time-varying signal-to-noise ratio (SNR) conditions, because these methods are merely trained on specific datasets and can only work at the corresponding condition. In this paper, a novel CNN-based generalized AMC method is proposed, and a more realistic scenario is considered, including white non-Gaussian noise and synchronization error. Its generalization capability stems from the mixed datasets under varying noise scenarios, and the CNN can extract common features from these datasets. Simulation results show that our proposed architecture can achieve higher robustness and generalization than the conventional ones.

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