Fiber Nonlinear Noise-to-Signal Ratio Monitoring Using Artificial Neural Networks.
Aazar S. KashiQunbi ZhugeJohn C. CartledgeAndrzej BorowiecDouglas CharltonCharles LaperleMaurice O'SullivanPublished in: ECOC (2017)
Keyphrases
- using artificial neural networks
- random noise
- additive noise
- signal detection
- nonlinear filters
- artificial neural networks
- myelinated nerve
- low signal to noise ratio
- condition monitoring
- received signal
- white noise
- vibration signal
- wide band
- monitoring system
- low snr
- noisy environments
- noise variance
- impulse response
- signal to noise ratio
- stochastic resonance
- real time
- image processing
- signal processing
- noisy images
- non stationary
- linear transform
- noise model
- median filter
- noise reduction
- weak signal
- high frequency
- power spectrum
- wiener filter
- finite element analysis
- low frequency
- noise level
- single channel
- frequency domain
- acoustic emission
- noise ratio
- denoising methods
- source separation
- noisy data
- gaussian noise
- short time fourier transform
- direct sequence spread spectrum
- linear filters