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The fifth-generation (5G) wireless communication has an urgent need for target tracking. Digital programmable metasurface (DPM) may offer an intelligent and efficient solution owing to its powerful and flexible controls of electromagnetic waves and advantages of lower cost, less complexity and smaller size than the traditional antenna array. Here, we report an intelligent metasurface system to perform target tracking and wireless communications, in which computer vision integrated with a convolutional neural network (CNN) is used to automatically detect the locations of moving targets, and the dual-polarized DPM integrated with a pre-trained artificial neural network (ANN) serves to realize smart beam tracking and wireless communications. Three groups of experiments are conducted for demonstrating the intelligent system: detection of moving targets, detection of radio-frequency signals, and real-time wireless communications. The proposed method sets the stage for an integrated implementation of target identification, radio environment tracking, and wireless communications. This strategy opens up a new avenue for intelligent wireless networks and self-adaptive systems.

In this paper, silt sediment is considered to be Bingham body, which is made up of coarse and fine particles in front of a hydraulic gate. The coarse and fine particles provide friction and shear stress in the course of opening the gate. They constitute together the adhesion force of the sediment. Based on this viewpoint, this paper puts forward a formula for the effect of silt sediment on the lifting force. The formula includes gate weight, down-suction force, sealing rubber friction, plus-weight, water-column pressure, plus-silted-sediment weight and rolling (or sliding)-bearing friction. Finally, the verification results show that the formula has certain reliability and the calculation accuracy can meet the need of practical engineering.