Focused on the challenges of edge information loss and incomplete segmentation of large lesions in endoscopic semantic segmentation networks， a Boundary-Cross Supervised semantic Segmentation Network （BCS-SegNet） with Decoupled Residual Self-Attention （DRA） was proposed. Firstly， DRA was introduced to enhance the network’s ability to learn distantly related lesions. Secondly， a Cross Level Fusion （CLF） module was constructed to combine multi-level feature maps within the encoding structure in a pairwise way， so as to realize the fusion of image details and semantic information at low computational cost. Finally， multi-directional and multi-scale 2D Gabor transform was utilized to extract edge information， and spatial attention was used to weight edge features in the feature maps， so as to supervise decoding process of the segmentation network， thereby providing more accurate intra-class segmentation consistency at pixel level. Experimental results demonstrate that on ISIC2018 dermoscopy and Kvasir-SEG/CVC-ClinicDB colonoscopy datasets， BCS-SegNet achieves the mIoU （mean Intersection over Union） and Dice coefficient of 84.27%， 90.68% and 79.24%， 87.91%， respectively； on the self-built esophageal endoscopy dataset， BCS-SegNet achieves the mIoU of 82.73% and Dice coefficient of 90.84%， while the above mIoU is increased by 3.30% over that of U-net and 4.97% over that of UCTransNet. It can be seen that the proposed network can realize visual effects such as more complete segmentation regions and clearer edge details.

For the purpose of improving the recognition accuracy of bird sounds in various kinds of noisy environments in real world, a new bird sounds recognition approach based on energy detection was proposed. First of all, the useful bird sound signals were detected and selected by the method of energy detection from the bird sounds with noises. Secondly, according to the distribution of Mel scale, the feature of Wavelet Packet decomposition Subband Cepstral Coefficient (WPSCC) was extracted from the useful signals. Finally, the classifier of Support Vector Machine (SVM) was applied to model on the WPSCC and Mel-Frequency Cepstral Coefficient (MFCC) respectively for classification and identification. Meanwhile, the comparisons of recognition performance difference were implemented on 15 kinds of bird sounds at different Signal-to-Noise Ratio (SNR) in different noises, before or after energy detection. The experimental results show that WPSCC has better noise immunity function, and the recognition performance after energy detection can be greatly improved, which means it is more suitable for the bird sounds recognition in complex environments.

In this paper, a new image encryption algorithm was presented by employing Cellular Neural Network (CNN). The main objective was to solve the problem of traditional stream ciphers insensitivity to the change of plain text. By using a hyper chaotic system of 6-D CNN as the key source, selecting the secret key based on the results of logical operations of pixel values in the plain image, and introducing simultaneously both position permutation and value transformation, the new algorithm was presented. It is shown that both NPCR value and the sensitivity to key (>0.996) can meet the security requirements of image encryption. The simulation process also indicates that the algorithm is relatively easy to realize with low computation complexity, and ensures, accordingly, the secure transmission of digital images.