Extracting a fetal electrocardiogram based on independent component analysis

Based on the basic generation model for the independent component analysis (ICA) and the fixed point FastICA algorithm using negentropy, three observed signals containing maternal electrocardiogram (MECG) and fetal electrocardiogram (FECG) components obtained from abdomen of a pregnant woman are successfully separated by using the FastICA algorithm with progressive orthogonalization, and the FECG is extracted. By invoking the wavelet de noising program in Matlab, the extracted FECG is decomposed to 8 levels with db2 wavelet to obtain the default soft threshold for de noising. The results show that the FastICA algorithm with progressive orthogonalization performed fast convergence. The three source components were extracted with only seven, three, and two iterations, respectively. The noise in the extracted FECG can be eliminated using wavelet de noising.     

The on line monitoring method of power transmission line fault basednon linear frequency response function

A novel method for power transmission line monitoring and fault diagnosis is proposed based on non linear frequency response analysis. The power line carrier signal has been used for on line monitoring of power transmission line. As the non linear frequency response function describes the system inherent characterization, different frequency response patterns corresponding to different operation states of transmission line can be established. Based on the analysis of transmission line characteristics of fault modes, various fault features can be extracted, thereby achieve online monitoring and fault diagnosis on transmission line. Simulation experiments show the effectiveness of the proposed method.