Fiber Nonlinear Noise-to-Signal Ratio Estimation by Machine Learning.
Ke ZhangYangyang FanTong YeZhenning TaoShoichiro OdaTakahito TanimuraYuichi AkiyamaTakeshi HoshidaPublished in: OFC (2019)
Keyphrases
- machine learning
- random noise
- signal subspace
- myelinated nerve
- additive noise
- nonlinear filters
- low signal to noise ratio
- white noise
- received signal
- wide band
- signal detection
- signal to noise ratio
- signal processing
- median filter
- stochastic resonance
- impulse response
- noisy environments
- machine learning methods
- additive gaussian noise
- machine learning algorithms
- decision trees
- multiplicative noise
- frequency domain
- wiener filter
- low snr
- noise variance
- noise ratio
- direct sequence spread spectrum
- learning algorithm
- estimation algorithm
- noise reduction
- noisy data
- feature selection
- image processing
- linear transform
- denoising
- model selection
- original signal
- power spectrum
- noisy images
- source separation
- standard deviation
- noise model
- image denoising
- high frequency
- single channel
- non stationary
- low frequency
- medical images
- noise removal
- biomedical signals
- impulse noise
- support vector machine
- linear filters