In this paper, a novel 3D image encryption based on the memristive chaotic system and RNA crossover and mutation is proposed. Firstly, the dynamic characteristics of the nonlinear system with two memristors are analyzed, including phase diagrams, Lyapunov exponential spectrums, and bifurcation diagrams. According to the merged image of three 3D images, the initial values of the memristive chaotic system are generated by SHA-256. Then the vertex coordinates are scrambled and diffused by 3D Arnold matrix and chaotic sequences. Finally, according to the dynamical encoding and decoding rules, crossover and RNA mutation are designed to confuse and diffuse the vertex coordinates. Throughout the encryption process, the Arnold matrix, RNA encoding and decoding rules, and crossover and mutation algorithms are determined by the memristive chaotic system. The experimental results verify that the proposed cryptosystem could encrypt three 3D images at the same time and resist various attacks effectively, and has good security performance.
Mobile Ad hoc Network (MANET) is a wireless network composed of multiple wireless nodes without fixed infrastructure support, which is expected to play an important role in future commerce and military, especially in marine and aerospace communications systems. In this paper, for link failure caused by node mobility in MANET, the prediction of link availability is given according to the dynamic characteristics of the link, and transmission modes and relay nodes are selected to optimize the link capacity and reduce the interference. Simulation results show that the proposed routing metric method can select stable routing paths with less interference, and reduce routing overhead caused by node movement based on link availability analysis.
In this paper, a visual security encryption scheme for multi-color images based on BP neural network and fractional chaotic map is proposed, which disguises secret images as a meaningful visual image. Firstly, three color images are compressed based on BP neural network. Then, according to the pseudo-random sequence generated by fractional chaotic map, the merged compressed images are scrambled by spiral transformation and diffused by XOR, in which the direction and degree of spiral transformation can be adjusted. In order to ensure the visual effect of the camouflage image, the lifting wavelet transform (LWT) is used to decompose the carrier image to obtain the coefficient matrix, and the cipher images are adjusted to a narrow range and embedded into the coefficient matrixes based on the pseudo-random sequence. Finally, visually secure image can be generated by inverse lifting wavelet transform. The reverse algorithm can restore the images by extraction, decryption and decompression. Experimental results verify that the proposed scheme has feasibility, robustness, anti-noise and clipping capability, and the PSNR value is no less than 31.4 under various attacks.
A color image encryption based on the chaotic system, PSO-BP neural network and DNA mutation is proposed in this paper. Firstly, chaotic characteristics of the non-autonomous laser system are analyzed by phase diagram, Lyapunov exponent, and bifurcation diagram. Secondly, the hash value calculated by SHA-256 algorithm is used to change the initial conditions of chaotic system and generate chaotic sequence. Then, the color image is compressed by the back-propagation neural network based on particle swarm optimization. Based on dynamic encoding and decoding rules, image confusion and diffusion and DNA mutation are designed. Finally, experiments verify that the scheme can compress and encrypt color images, save transmission cost and improve the security performance, which is beneficial to the efficient transmission.
Dynamical analysis of the incommensurate fractional-order neural network is a novel topic in the field of chaos research. This article investigates a Hopfield neural network (HNN) system in view of incommensurate fractional orders. Using the Adomian decomposition method (ADM) algorithm, the solution of the incommensurate fractional-order Hopfield neural network (FOHNN) system is solved. The equilibrium point of the system is discussed, and the dissipative characteristics are verified and discussed. By varying the order values of the proposed system, different dynamical behaviors of the incommensurate FOHNN system are explored and discussed via bifurcation diagrams, the Lyapunov exponent spectrum, complexity, etc. Finally, using the DSP platform to implement the system, the results are in good agreement with those of the simulation. The actual results indicate that the system shows many complex and interesting phenomena, such as attractor coexistence and an inversion property, with dynamic changes of the order of q0, q1, and q2. These phenomena provide important insights for simulating complex neural system states in pathological conditions and provide the theoretical basis for the later study of incommensurate fractional-order neural network systems.
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