Learn More
Recently, a RGB image encryption algorithm based on DNA encoding and chaos map has been proposed. It was reported that the encryption algorithm can be broken with four pairs of chosen plain-images and the corresponding cipher-images. This paper re-evaluates the security of the encryption algorithm, and finds that the encryption algorithm can be broken(More)
We present a novel image encryption algorithm based on DNA subsequence operation. Different from the traditional DNA encryption methods, our algorithm does not use complex biological operation but just uses the idea of DNA subsequence operations (such as elongation operation, truncation operation, deletion operation, etc.) combining with the logistic(More)
The existence of equilibrium point and global exponential stability (GES) for cellular neural networks with time-varying delay are explored in this paper by applying the extended Halanay's delay differential inequality, the theory of homotopy invariance, Dini's derivative, and several functional analysis techniques. Some simple and new sufficient conditions(More)
Global asymptotic stability of the equilibrium point of neural networks with time-varying delays is considered in this paper. By utilizing the Lyapunov--Razumikhin technique, some new sufficient conditions are given. The new criteria do not require the delay function to be differentiable and the activation functions to be bounded or monotone nondecreasing.(More)
Global exponential stability of a class of discrete-time Hopfield neural networks with variable delays is considered. By making use of a difference inequality, a new global exponential stability result is provided. The result only requires the delay to be bounded. For this reason, the result is milder than those presented in the earlier references.(More)
Global exponential stability is considered for a class of discrete-time cellular neural networks with variable delays. By employing a discrete Halanay inequality, a new result is presented ensuring global exponential stability of the unique equilibrium point of the networks. The result extends and improves the earlier publications due to the fact that it(More)