With the rapid development of deep learning, neural network models have become increasingly complicated, leading to larger storage space requirements and slower reasoning speed. These factors make it difficult to be deployed on resourcelimited platforms. To alleviate this problem, network pruning, an effective model compression method, is commonly performed in a deep neural network. However, traditional pruning methods simply set redundant weights to zero, thus failing to achieve the acceleration effect. In this paper, a channel-wise model scaling method is proposed to reduce the model size and speed up reasoning by structurally removing the redundant filters in convolutional layers. To make the residual block more sparse, we develop a pruning method for residual cells. Experimental results on the YOLOv3 detector show that our proposed approach achieves a 70.6% parameter compression ratio without compromising accuracy.
Terahertz (THz) technology is increasingly being used in a wide range of applications, and terahertz radar systems have also been developed in radar applications. In this paper, the terahertz radar system is used for 2 dimensional (2D) realtime imaging in near-field scenario within 20m. A real-time imaging system of 170GHz Synthetic Aperture Radar (SAR) is designed, and the system is simulated and verified by Doppler Beam Sharpening (DBS) algorithm. The simulation results show that the system can utilize uniform linear motion to synthesize a short aperture in the near-field range and form 2D image of scattering points in the scene. The imaging effect is good.
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