Extraction of the Characteristics of the Acoustic Emission Signals of the Pitting of Low Carbon Steel Based on Independent Component Analysis and Wevelet Transform;

Signal Processing of Acoustic Emission of Metal Corrosion Based on the Independent Component Analysis;

Noise removal of pulse wave signal based on independent component analysis;

Using simulated SEMG signals,the performance of proposed decomposition algorithm was analyzed and compared with that of the decomposition technique adopting independent component analysis(ICA),the decomposition experimen.

Independent component analysis(ICA)can separate the sources from their mixtures and make the output statistically as independent as possible;it can remove noises effectively.

In order to blind estimation the sequence of asynchronous direct spread signals, this work used independent component analysis(ICA) combining with principle component analysis(PCA) to blindly separate the data bits of different users without the knowledge of the channel.

To solve the problem that the number of available training samples is great less than that of the training vector in traditional independent component analysis(ICA) and to improve the efficiency of face recognition,an independent component analysis feature extraction technique based on two dimension image matrixes is proposed.

An image fusion method based on contourlet transform,independent component analysis(ICA) and support vector machine(SVM) is proposed.