While the significance of oscillator dynamics and coupling structure to synchronization behaviors has been well addressed in the literature, little attention has been paid to the possible influence of coupling functions. In the present paper, adopting the scheme of dual-channel time-delayed couplings, we investigate how the synchronization behaviors of networked chaotic oscillators are influenced by parameters in the coupling functions. It is found that, with the introduction of the second coupling channel, the synchronization region, as calculated according to the method of master stability function(MSF), can be largely modified. In particular, by a slight change of the time delay, it is found that the synchronization region can be significantly adjusted, or even switched from non-existing to existing. We demonstrate this interesting phenomenon for both situations of processing and propagation induced time delays, as well as for different coupling functions. Our studies shed new light on the mechanism of chaos synchronization, and may potentially be used for the control of complex network dynamics.
Due to the demand of data processing for polar ice radar in our laboratory, a Curvelet Thresholding Neural Network (TNN) noise reduction method is proposed, and a new threshold function with infinite-order continuous derivative is constructed. The method is based on TNN model. In the learning process of TNN, the gradient descent method is adopted to solve the adaptive optimal thresholds of different scales and directions in Curvelet domain, and to achieve an optimal mean square error performance. In this paper, the specific implementation steps are presented, and the superiority of this method is verified by simulation. Finally, the proposed method is used to process the ice radar data obtained during the 28th Chinese National Antarctic Research Expedition in the region of Zhongshan Station, Antarctica. Experimental results show that the proposed method can reduce the noise effectively, while preserving the edge of the ice layers.
Discriminating internal layers by radio echo sounding is important in analyzing the thickness and ice deposits in the Antarctic ice sheet.The signal processing method of synthesis aperture radar(SAR)has been widely used for improving the signal to noise ratio(SNR)and discriminating internal layers by radio echo sounding data of ice sheets.This method is not efficient when we use edge detection operators to obtain accurate information of the layers,especially the ice-bed interface.This paper presents a new image processing method via a combined robust principal component analysis-total variation(RPCA-TV)approach for discriminating internal layers of ice sheets by radio echo sounding data.The RPCA-based method is adopted to project the high-dimensional observations to low-dimensional subspace structure to accelerate the operation of the TV-based method,which is used to discriminate the internal layers.The efficiency of the presented method has been tested on simulation data and the dataset of the Institute of Electronics,Chinese Academy of Sciences,collected during CHINARE 28.The results show that the new method is more efficient than the previous method in discriminating internal layers of ice sheets by radio echo sounding data.
