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In the harsh electromagnetic environment with strong interference, the prior information of the received signal can not be fully obtained, and considering the complex and variable modulation modes, the modulation recognition of radio signals has brought great trouble. In this paper, we propose a method for automatic modulation recognition based on deep convolutional neural network for channel and spatial self-attention mechanism by combining the architecture of feature autonomous learning of deep learning. The correlation of input vectors in channel and spatial dimensions is enhanced by a self-attentive mechanism, and the number of layers of network structure, connection method, pooling method, and hyper-parameters are optimized, to enhance the overall fitting ability of the network and improve the accuracy and robustness of modulation recognition. On the RadioML2016.10A dataset, the proposed method is compared with the previous baseline method at different signal-to-noise ratios. The experimental results show that the performance of this paper’s method is better in the identification of 16QAM versus 64QAM.

Continuous terahertz detection imaging technology is widely used in polymer detection. However, it is very difficult to quantitatively analyze defects due to the characteristics of focusing lenses. On the one hand, a lens with a short focal length means the achievement of a better spatial resolution and a poor depth resolution. On the other hand, a lens with a long focal length results in a better depth resolution and a worse spatial resolution. In addition, in order to ensure the positioning accuracy, the upper surface of a sample is often used as the focal plane for detection, which causes the energy outside the range of the focal depth to drastically decrease and seriously affect the imaging quality. In this paper, a method is proposed for debonding detection and quantitative analysis in layered structures. Firstly, by using multi-directional structural elements for measuring the difference between the debonding region and local surroundings, we extract defective edges and suppress clutters by establishing a multi-directional morphological filter. Then, the improved Otsu method is used for threshold segmentation and the population mean entropy is used to calculate the optimal threshold. Finally, the superiority of the proposed method is confirmed on synthetic datasets with low SNR images, and it is successfully applied to terahertz image processing. Moreover, the results of the proposed algorithm show that the debonding region detection error of the terahertz image is controlled within 10.17%-11.27%. It provides a new method and idea for quantitative analysis of terahertz detection in layered structures.

In order to meet the increasing demand of non-destructive testing (NDT) in engineering practice, a continuous terahertz NDT platform based on linear scanning has been developed, with a center frequency of 154 GHz and a bandwidth of 56 GHz. This system combines frequency modulation continuous wave (FMCW) radar technology with a continuous scanning structure, as well as a data acquisition platform to provide a non-contact detection method; this is highly efficient and compensates for the shortcomings of traditional methods such as microwave, X-ray, ultrasonic, and others in safety inspection and special detection. In addition, a signal processing method of spectral refinement and correction is proposed in this paper for accurate thickness measurement. The results show that the method has a high accuracy for ABS, PVC, and ceramic matrix composites. By extracting the characteristic parameters, the detection and imaging of prefabricated defects, such as debonding and bubbles in composite materials, have been successfully achieved. This helps to evaluate the internal state of the inspected object more intuitively and further meets the requirements of industrial NDT.