KEYWORDS: Physical coherence, Covariance matrices, Education and training, Convolutional neural networks, Computer simulations, Sensors, Monte Carlo methods, Deep learning, Neural networks, Data modeling
A direction of arrival (DOA) estimation method for coherent signals is proposed based on an improved convolutional neural network (CNN) with attention mechanism. The real, imaginary and phase components of the covariance matrix are employed as CNN multi-channel inputs. A regression model is adopted as CNN output which avoids the grid mismatch problem caused by multi-classification output. Moreover, to enhance the accuracy of the estimation, attention mechanism is added to the CNN, which could give more weight to the main features effectively. The simulation results demonstrate that the proposed method surpasses the performance of other approaches, which is suitable to resolve coherent signals in multipath environments.
An X-ray nondestructive detector for high-speed running conveyor belt with steel wire ropes is researched in the paper.
The principle of X-ray nondestructive testing (NDT) is analyzed, the general scheme of the X-ray nondestructive testing
system is proposed, and the nondestructive detector for high-speed running conveyor belt with steel wire ropes is
developed. The hardware of system is designed with Xilinx's VIRTEX-4 FPGA that embeds PowerPC and MAC IP
core, and its network communication software based on TCP/IP protocol is programmed by loading LwIP to PowerPC.
The nondestructive testing of high-speed conveyor belt with steel wire ropes and network transfer function are
implemented. It is a strong real-time system with rapid scanning speed, high reliability and remotely nondestructive
testing function. The nondestructive detector can be applied to the detection of product line in industry.
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