Wavelet Transfer (WT) is a powerful technique in signal separation. It is very easy for WT to improve the signal-to-noise (SNR) by separating the random noise and useful signal.
小波变换技术具有很强的信号分离能力容易把随机噪声从信号中分离出来，从而提高信号的信噪比。
Blind separation of sources based on higher order cumulants is applied to the separation between vibration signal and noise, which is important to diagnosi.
本文利用基于高阶累积量的盲信号分离方法，设计了相应算法，成功地从被噪声污染的信号中恢复源振动信号，从而可确保碰摩故障的检测和诊断能顺利进。
Blind Sources Separation (BSS) provides a new alterative for extraction of certain signal components in the signal corrupted by noise, and presents a new method for mechanical fault diagnosis.
盲源分离应用于机械振动信号的预处理中，提供了一个新的处理机制，在机械状态监测和故障诊断中具有一定的价值。
Some methods of SEMG signal pretreatment based on blind source separation using second order statistics were proposed for noise separation and the elementary decomposition of multi-channel SEMG.
采用基于二阶统计量的盲源分离算法对多导表面肌电信号进行处理，实现噪声的分离和表面肌电信号的初步分解。
Seismic data of dense spatial sample is more suitable to conventional 2-D methods for separation of signal and noise.
高密度数据的无假频特征更加适合于常规二维信噪分离方法。
Seismic data of dense spatial sample is more suitable to conventional 2-D methods for separation of signal and noise.
高密度数据的无假频特征更加适合于常规二维信噪分离方法。
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