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A new method to perform blind separation of chaotic signals is articulated in this paper, which takes advantage of the underlying features in the phase space for identifying various chaotic sources. Without incorporating any prior information about the source equations, the proposed algorithm can not only separate the mixed signals in just a few iterations, but also outperforms the fast independent component analysis (FastICA) method when noise contamination is considerable.

SUMMARYChaotic signals are widely exploited for the spread spectrum communication technique. Synchronization of a chaotic communication systems between a single point and multiple points is recognized as an essential issue. In this paper, a chaotic network synchronization scheme is proposed to tackle the problem of multi-access synchronization. The proposed synchronization scheme enables the realization of a fast synchronization of multiple chaotic systems. In this paper, the proposed system is validated by application to direct-sequence (DS) spread spectrum communication (SSC) with code division multiple access (CDMA). Promising results were obtained on the applications of speech, characters and image communications. The obtained results indicate that the proposed SSC is e!ective and reliable even under the situations of a noisy channel, and multi-path interference.

This paper presents a variant of the iterative shrinkage-thresholding (IST) algorithm, called backtracking-based adaptive IST (BAIST), for image compressive sensing (CS) reconstruction. For increasing iterations, IST usually yields a smoothing of the solution and runs into prematurity. To add back more details, the BAIST method backtracks to the previous noisy image using L2 norm minimization, i.e., minimizing the Euclidean distance between the current solution and the previous ones. Through this modification, the BAIST method achieves superior performance while maintaining the low complexity of IST-type methods. Also, BAIST takes a nonlocal regularization with an adaptive regularizor to automatically detect the sparsity level of an image. Experimental results show that our algorithm outperforms the original IST method and several excellent CS techniques.

Abstract-This brief addresses the channel-distortion problem and proposes a technique for channel equalization in chaos-based communication systems. The proposed equalization is realized by a modified recurrent neural network incorporating a specific training (equalizing) algorithm.Index Terms-Channel equalization, chaos-based communications, recurrent neural networks (RNNs).