At present, most image encryption algorithms directly encrypt the plaintext image into a ciphertext image without visual meaning, which is easy to be found by hackers during the transmission process and therefore subjected to various attacks. In order to solve the problem, combining Hopfield chaotic neural network and compressive sensing technology, a visually meaningful image encryption algorithm was proposed. Firstly, the two-dimensional discrete wavelet transform was used to sparse the plaintext image. Secondly, the sparse matrix after threshold processing was encrypted and measured by compressive sensing. Thirdly, the quantized intermediate ciphertext image was filled with random numbers, and Hilbert scrambling and diffusion operations were performed to the image. Finally, the generated noise-like ciphertext image was embedded into the Alpha channel of the carrier image though the Least Significant Bit (LSB) replacement to obtain the visually meaningful steganographic image. Compared with the existing visual image encryption algorithms, the proposed algorithm demonstrates very good visual security, decryption quality and robustness, showing that it has widely application scenarios.

In order to solve the optimization problem of dynamic task allocation in multi-robot system, a new quadratic task allocation algorithm based on Pareto improvement was proposed based on the initial task allocation of contract net. When the fire fighting task was performed by the multi-robot system in parallel, firstly, the multiple robots were divided into several sub-group through the initialization of task allocation. Then, a fire fighting task was undertook by a subgroup, and the robots needed to be migrated were determined by the Pareto improvement of the subgroup and its nearest subgroup while the subgroup performing the task to achieve the Pareto optimality between the two subgroups. Finally, the global Pareto optimality was achieved by the Pareto improvement of all subgroups with posterior binary tree traversal. The theoretical analysis and simulation results show that, compared with reinforcement learning algorithm and ant colony algorithm, the fire fighting time of the proposed algorithm is reduced by 26.18% and 37.04% respectively. And compared with the traditional contract net method, the proposed algorithm can perform the fire fighting task efficiently in time, and also has the obvious advantage in system revenue.

A regularized image zooming model based on nonlocal total variation was proposed, with regard to that the Chambolle image zooming model has blocky effects. It consisted of regular term and fidelity term. The zoomed image was obtained by minimizing the variational function which used the nonlocal total variation norm to measure the regularity of the image. Unlike the traditional image zooming by interpolation, the variational model was incorporated in the new zooming algorithm and the use of nonlocal operator made the algorithm not just use a single pixel of the image, or gray and gradient information in a neighborhood to amplify, but use the information of image content itself widely that will avoid blocky effects of Chambolle's model. The experimental results show that the new algorithm can preserve better the border and details. It achieves better effect than Chambolle's method and the interpolation by using spline.