In this paper, the effect of the coupling strength to the complex network synchronizability is investigated. For a given network with identical node dynamics, it is shown that the coupling strength among the nodes is one of key factors influencing the network synchronizability besides the network inner linking matrix and the eigenvalues of the network topological matrix. It is point that if the synchronized region is an unbounded sector, for achieving synchronizability, the coupling strength must be greater than or equal to the minimum coupling strength, and with the increasing of the coupling strength, network synchronizability is improved; if is a bounded sector, for achieving network synchronizability, the coupling strength must be in a certain range, and the larger coupling strength does not necessarily indicate better synchronizability.

In this paper, a novel wavelet based contourlet transform for texture extraction is presented. In the texture analysis section, we propose a novel wavelet based contourlet transform, which can be considered as a simplified but more sufficient for texture analysis for nonwoven image compared with version of the one introduced by Eslami in theory view. In experiment, nonwoven images of five different visual quality grades, 125 of each grade, are decomposed using wavelet based contourlet transform with ‘PKVA’ filter as the default filter of Laplacian Pyramid (LP) and Directional Filter Bank (DFB) at 3 levels and two energy-based features, norm-1 L1 and norm-2 L2, are calculated from the wavelet coefficients at the first level and contourlet coefficients of each high frequency subband at different levels and directions to train and test SVM. When the nonwoven images are decomposed at 3 levels and 16 L1s are extracted, with 500 samples to train the SVM, the average recognition accuracy of test set is 98.4%, which is superior to the comparative method using wavelet texture analysis.