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Currently, visual perceptions generated by visual prosthesis are low resolution with unruly color and restricted grayscale. This severely restricts the ability of prosthetic implant to complete visual tasks in daily scenes. Some studies explore existing image processing techniques to improve the percepts of objects in prosthetic vision. However, most of them extract the moving objects and optimize the visual percepts in general dynamic scenes. The application of visual prosthesis in daily life scenes with high dynamic is greatly limited. Hence, in this study, a novel unsupervised moving object segmentation model is proposed to automatically extract the moving objects in high dynamic scene. In this model, foreground cues with spatiotemporal edge features and background cues with boundary-prior are exploited, the moving object proximity map are generated in dynamic scene according to the manifold ranking function. Moreover, the foreground and background cues are ranked simultaneously, and the moving objects are extracted by the two ranking maps integration. The evaluation experiment indicates that the proposed method can uniformly highlight the moving object and keep good boundaries in high dynamic scene with other methods. Based on this model, two optimization strategies are proposed to improve the perception of moving objects under simulated prosthetic vision. Experimental results demonstrate that the introduction of optimization strategies based on the moving object segmentation model can efficiently segment and enhance moving objects in high dynamic scene, and significantly improve the recognition performance of moving objects for the blind.

Visual prosthesis applying electrical stimulation to restore visual function for the blind has promising prospects. However, due to the low resolution, limited visual field, and the low dynamic range of the visual perception, huge loss of information occurred when presenting daily scenes. The ability of object recognition in real-life scenarios is severely restricted for prosthetic users. To overcome the limitations, optimizing the visual information in the simulated prosthetic vision has been the focus of research. This paper proposes two image processing strategies based on a salient object detection technique. The two processing strategies enable the prosthetic implants to focus on the object of interest and suppress the background clutter. Psychophysical experiments show that techniques such as foreground zooming with background clutter removal and foreground edge detection with background reduction have positive impacts on the task of object recognition in simulated prosthetic vision. By using edge detection and zooming technique, the two processing strategies significantly improve the recognition accuracy of objects. We can conclude that the visual prosthesis using our proposed strategy can assist the blind to improve their ability to recognize objects. The results will provide effective solutions for the further development of visual prosthesis.

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Intelligent power module (IPM) short-circuit protection is a key factor in improving the reliability of power electronics systems. The conventional short-circuit detection method based on monitoring the collector-emitter voltage (V CE) desaturation has a blanking time and is slow to respond to any type of shortcircuits. Furthermore, the di C /dt method cannot be used in the IPM due to the absence of Kelvin emitter. A slow short-circuit protection process can have an irrecoverable and destructive impact on the reliability of the IPM. In this paper, a new high-power IPM topology with an internally integrated shunt is designed to realize real-time current detection, which can achieve fast short-circuit detection without any blanking time. A prototype 1700 V/150 A IPM is manufactured, and a corresponding fast short-circuit protection circuit is designed. Experimental results show the effectiveness of the integrated shunt method as its performance is significantly better than that of the V CE desaturation method. The proposed IPM needs 380 ns and 1.4 µs to detect short-circuits of types I and II, respectively. The short-circuit withstand times for short-circuits of types I and II are 2.06 µs and 0.62 µs, respectively. In addition, the short-circuit energy losses for shortcircuits of types I and II are reduced by 66% and 64.3%, respectively, compared to the V CE desaturation method. The proposed method can also be used as a reference for other IPM designs.

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