A new lattice model of traffic flow based on Nagatani's model is proposed by taking the effect of driver's memory into account. The linear stability condition of the extended model is obtained by using the linear stability theory. The analytical results show that the stabile area of the new model is larger than that of the original lattice hydrodynamic model by adjusting the driver's memory intensity parameter p of the past information in the system. The modified KdV equation near the critical point is derived to describe the traffic jam by nonlinear analysis, and the phase space could be divided into three regions: the stability region, the metastable region, and the unstable region, respectively. Numerical simulation also shows that our model can stabilize the traffic flow by considering the information of driver's memory.

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Various techniques have previously been proposed for thresholding of images to separate objects from the background. Although these thresholding techniques have been proven effective on particular types of images, none of them is able to produce consistently good results on a wide range of existing images. The nonextensive cross-entropy (also known as Tsallis cross-entropy) is introduced to determine the optimal threshold value. The new thresholding scheme aims to minimize the Tsallis cross-entropy between the original image and the thresholded image. The effectiveness of the proposed scheme is demonstrated by using examples from the synthetic images, natural scene images, and an image dataset that includes nondestructive testing images and document images, on the basis of comparison with the traditional cross-entropy, Otsu's, minimum error thresholding, and two state-of-the-art methods. Furthermore, a tunable parameter q of Tsallis cross-entropy in the presented scheme gives the proposed methods the potential to handle the different image segmentation tasks.

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In the flotation process, the flotation froth texture is an indicator of the flotation state. To recognize the flotation state based on texture features accurately and to provide guidance for production operations, this paper proposes a method for flotation froth image texture extraction based on the deterministic tourist walks algorithm. First, a weighted graph model of a froth image is built using deterministic tourist walks. Next, the degree distribution and the unit intensity distribution of the weighted graph are extracted. The contrast of the node degree and the contrast of the node unit intensity are calculated as the texture feature indexes. The texture feature indexes are used for flotation production state classification and recognition. The experimental results demonstrate that the proposed method can extract froth image texture features accurately and provide effective guidance for flotation production.