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In this paper, we propose an image encryption algorithm based on random walk and two hyperchaotic systems. The random walk method is adopted to scramble the position of pixels within a block. Furthermore, the permutation operation between blocks is presented to enhance the scramble effect. Thus, high correlation among pixels of original image is broken by permutation. Moreover, the chosen plaintext attack is used to test the anti-attack ability of the proposed algorithm. By analyzing experimental results and comparing with other image encryption algorithms, we show that the proposed algorithm has better performance and higher security.

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Memristive neurons and memristive neural networks constructed based on memristors have important research significance for revealing the mystery of the brain. This paper proposes a compound hyperbolic tangent cubic nonlinear memristor, which has nonvolatile memory characteristics and local activity characteristics. In particular, the memristor also has three stable pinched hysteresis loops under different initial values. The memristor is applied to Fitzhugh-Nagumo neuron and Hindmarsh-Rose neuron to establish five different memristive neural models, and a series of firing dynamics analysis are carried out on them. At the same time, we not only discuss multiple firing patterns on a single memristive neuron and double memristive coupled neurons, but also compare which neuron and which coupled neural network the proposed memristor is more suitable for, which is a lack of comprehensive investigation in the published research. Furthermore, digital circuit experiment is performed on the FPGA development board to verify the firing mechanism of these memristive neural models, which has potential application value for some practical projects.

Accurate and reliable traffic flow prediction is critical to the safe and stable deployment of intelligent transportation systems. However, it is very challenging since the complex spatial and temporal dependence of traffic flows. Most existing works require the information of the traffic network structure and human intervention to model the spatial-temporal association of traffic data, resulting in low generality of the model and unsatisfactory prediction performance. In this paper, we propose a general spatial-temporal graph attention based dynamic graph convolutional network (GAGCN) model to predict traffic flow. GAGCN uses the graph attention networks to extract the spatial associations among nodes hidden in the traffic feature data automatically which can be dynamically adjusted over time. And then the graph convolution network is adjusted based on the spatial associations to extract the spatial features of the road network. Notably, the information of rode network structure and human intervention are not required in GAGCN. The forecasting accuracy and the generality are evaluated with two real-world traffic datasets. Experimental results indicate that our GAGCN surpasses the state-of-the-art baselines on one of two datasets.

Mixed-dimensional (PEA)2(MA)4Pb5Br16 perovskite microcrystals are synthesized by an anti-solvent vapor-assisted method. The photodetector based on the individual perovskite microcrystal exhibits improved performances and humidity resistance.