The surface defect detection of industrial products has become a crucial link in industrial manufacturing. It has a series of chain effects on the control of product quality, the safety of the subsequent use of products, the reputation of products, and production efficiency. However, in actual production, it is often difficult to collect defect image samples. Without a sufficient number of defect image samples, training defect detection models is difficult to achieve. In this paper, a defect image generation method DG-GAN is proposed for defect detection. Based on the idea of the progressive generative adversarial, $D2$ adversarial loss function, cyclic consistency loss function, a data augmentation module, and a self-attention mechanism are introduced to improve the training stability and generative ability of the network. The DG-GAN method can generate high-quality and high-diversity surface defect images. The surface defect image generated by the model can be used to train the defect detection model and improve the convergence stability and detection accuracy of the defect detection model. Validation was performed on two data sets. Compared to the previous methods, the FID score of the generated defect images was significantly reduced (mean reductions of 16.17 and 20.06, respectively). The YOLOX detection accuracy was significantly improved with the increase in generated defect images (the highest increases were 6.1% and 20.4%, respectively). Experimental results showed that the DG-GAN model is effective in surface defect detection tasks.

In non-cooperative communication systems such as radio spectrum resource regulation and modern electronic warfare, automatic modulation recognition is a key technology. Traditional modulation recognition methods mainly rely on manual feature extraction, decision theory, and recognition selection. The Deep Leaning (DL) algorithm automatically obtains signal features directly from massive data, and realizes feature extraction and recognition at the same time. However, most of the research on DL-AMR methods focuses on single input single output (SISO) systems, while there are few studies on DL-AMR methods in multiple-input, multiple-output (MIMO) systems, so the integration of deep learning models into modulation recognition of MIMO systems has attracted the attention of many researchers. The purpose of this paper is to provide a comprehensive review of modulation recognition methods for MIMOsystems based on DL. Firstly, the basic theory of MIMO and its derivative systems and modulation recognition is introduced in detail, then the traditional modulation recognition algorithms and deep learning-based modulation recognition algorithms of MIMO systems are introduced, and finally, on the basis of discussion and summary, the problems to be solved, the challenges and potential research directions are proposed.

In this paper, a computationally efficient optimization algorithm for independent vector analysis (IVA) is proposed to accelerate iterative convergence speed and enhance the overdetermined convolutive blind speech separation performance. An iterative projection with adjustment (IPA) is investigated to estimate the unmixing matrix for OverIVA. The IPA algorithm jointly executes the iterative projection (IP) algorithm and the iterative source steering (ISS) algorithm to jointly update one row and one column of the mixing matrix, which can perform computationally-efficient blind source separation. It is achieved by updating one demixing filter and jointly adjusting all the other sources along its current direction. Motivated by its technology superiorities, this paper proposes a modified algorithm for the OverIVA, fully exploiting the computational efficiency of IPA optimization scheme. Experimental results corroborate the proposed OverIVA-IPA algorithm converges faster and performs better than the existing state-of-the-arts algorithms.

The following aspects are contributed here: (1) we improve the efficiency of original elastic registration method; (2) we combine this registration method with our modified IBP method to reconstruct better SR images.