Imbalance in data distribution between static background information and moving foreground objects often leads to insufficient learning of abnormal foreground region information， thereby affecting the accuracy of Video Anomaly Detection （VAD）. To address this issue， a Nested U-shaped Frame Predictive Generative Adversarial Network （NUFP-GAN） was proposed for VAD. In the proposed method， a nested U-shaped frame prediction network architecture， which had the capability to highlight significant targets in video frames， was utilized as the frame prediction module. In the discrimination phase， a self-attention patch discriminator was designed to extract more important appearance and motion features from video frames using receptive fields of different sizes， thereby enhancing the accuracy of anomaly detection. Additionally， to ensure the consistency of multi-scale features of predicted frames and real frames in high-level semantic information， a multi-scale consistency loss was introduced to further improve the method’s anomaly detection performance. Experimental results show that the proposed method achieves the Area Under Curve （AUC） values of 87.6%， 85.2%， 96.0%， and 73.3%， respectively， on CUHK Avenue， UCSD Ped1， UCSD Ped2， and ShanghaiTech datasets； on ShanghaiTech dataset， the AUC value of the proposed method is 1.8 percentage points higher than that of MAMC （Memory-enhanced Appearance-Motion Consistency） method. It can be seen that the proposed method can meet the challenges brought by data distribution imbalance in VAD effectively.

Due to the inherent characteristics of sonar imaging principles and the interference of complex underwater environments， underwater sonar images generally suffer from insufficient resolution and missing target details. To address these issues， a high-quality sonar image generation method based on multi-scale feature fusion was proposed. Firstly， the Residual Dense Blocks （RDBs） were used to extract image features at shallow level， thereby capturing basic texture and contour information， and establishing spatial layout of the image. Secondly， a Multi-Scale Attention feature extraction module （MSA） was designed to focus on key features at different scales adaptively and further enhance the expression of key features while suppressing redundant information expression through the attention mechanism. Finally， a discriminator network was constructed using a pixel-by-pixel discrimination strategy based on spectral normalization， which improved the reconstruction ability of complex object contours and details. Experimental results on an underwater sonar image dataset show that the proposed method achieves relative improvements of 6.7% and 5.4%， respectively， in Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity （SSIM） metrics compared to the existing representative method ESRGAN （Enhanced Super-Resolution Generative Adversarial Network）. It can be seen that the proposed method improves the generation performance on underwater sonar image dataset effectively.

Otsu algorithm is widely used in classic image segmentation, while the application of the two-dimensional Otsu thresholding algorithm based on the Otsu algorithm has been restricted for the complex computation. Concerning this, this paper proposed an improved two-dimensional Otsu thresholding algorithm. The authors first divided the two-dimensional histogram into regions, and took each region as a point to form a new two-dimensional histogram, to which 2D Otsu thresholding algorithm and the fast recursive algorithm were applied, getting the region number of the threshold. Then the two algorithms were applied again on the region and finally the threshold for the original image was obtained. The experimental results show that the proposed algorithm greatly reduces the running time and the storage space, and gets basically the same results as the original algorithm.

This paper introduced UML to software product line. With analyzing variability of product line, we propose an approach of variability modeling for software product line with UML. It not only provides specification of variability type such as option and alternative, but also provides constrains modeling of software product line variability. Finally a case study of variability modeling in mobile software product line was presented to demonstrate our method.