To study the impact of traffic sign on pedestrian walking behavior, the paper applies cellular automaton to simulate one-way pedestrian flow. The channel is defined as a rectangle with one open entrance and two exits of equal width. Traffic sign showing that exit is placed with some distance in the middle front of the two exits. In the simulation, walking environment is set with various input density, width of exit, width and length of the channel, and distance of the traffic sign to exit. Simulation results indicate that there exists a critical distance from the traffic sign to exit for a given channel layout. At the critical distance, pedestrian flow fluctuates. Below such critical distance, flow is getting larger with the increase of input density. However, the flow drops sharply when the input density is over a critical level. If the distance is a little bit further than the critical distance, the largest flow occurs and the flow can remain steady no matter what input density will be.

No abstract

The structural information of blood vessels in ultra-wide-angle fundus images has important guiding significance for diagnosing ophthalmic diseases. Besides, efficient and correct segmentation of ultra-wide-angle fundus vascular images has become an urgent clinical need. However, manual diagnosis is a time-consuming and laborious work. Because of the limitation of primary medical resources, the situation of missed diagnosis and misdiagnosis would probably appear in n the diagnosis of artificial experience. In this paper, we proposed an automatic vascular segmentation algorithm based on attention gate based atrous residual (AAR) Unet combined with Top-Hat transform vascular enhancement. In AAR-Unet, we integrated atrous convolution into Res-Unet to expand the receptive field and improve the correlation between objects. To improve the segmentation effect of small blood vessels under low contrast, the attention gate module is introduced. The experimental results show the overall accuracy, sensitivity, specificity and DSC of the proposed algorithm are 0.988, 0.890, 0.993 and 0.875 respectively. The results indicate the algorithm can segment the blood vessels of ultra-wide-angle fundus images accurately.

The automatic segmentation of retinal images obtained by optical coherence tomography is increasingly important for ophthalmologists to diagnose and monitor many kinds of ophthalmic diseases. U-Net is the most widely used deep learning network in retinal segmentation, but the limited number of data-flow paths made it hard to capture complex features. We proposed here an optimized Mobile Ladder U-Net (MLU-Net), which consists of a Ladder Connection for increasing the network’s data-flow paths and a depthwise separable convolution for reducing the model’s parameters. Experiments on 100 B-scans from 10 human eyes demonstrated that the 9 retinal layer boundaries can be segmented accurately with the MLU-Net. In addition, compared with the original U-Net and LadderNet, the segmentation result of our method is closest to the expert label.

No abstract