At present, super-resolution reconstruction of a single image achieves a good effect, but most models achieve the good effect by increasing the number of network layers rather than exploring the correlation between channels. In order to solve this problem, an image super-resolution reconstruction method based on Channel Attention mechanism (CA) and Depthwise Separable Convolution (DSC) was proposed. The multi-path global and local residual learning were adopted by the entire model. Firstly, the shallow feature extraction block was used to extract the features of the input image. Then, the channel attention mechanism was introduced in the deep feature extraction block, and the correlation of the channels was increased by adjusting the weights of the feature graphs of different channels to extract the high-frequency feature information. Finally, a high-resolution image was reconstructed. In order to reduce the huge parameter influence brought by the attention mechanism, the depthwise separable convolution technology was used in the local residual block to greatly reduce the training parameters. Meanwhile, the Adaptive moment estimation (Adam) optimizer was used to accelerate the convergence of the model, so as to improve the algorithm performance. The image reconstruction by the proposed method was carried out on Set5 and Set14 datasets. Experimental results show that the images reconstructed by the proposed method have higher Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity index (SSIM), and the parameters of the proposed model are reduced to 1/26 of that of the depth Residual Channel Attention Network (RCAN) model.

Source code comments is an important part of the software, so researchers need to use manual or automated methods to generate comments. In the past, the quality assessment of this kind of comments is done manually, which is inefficient and not objective. In order to solve this problem, an assessment criterion was built in which four aspects of the comments including comment format, language form, content and code-related degree were considered. Then a code comments quality assessment method based on an aggregation of classification algorithms was proposed, in which machine learning and natural language processing technology were introduced into comments quality assessment, by using classification algorithms the comments were classified into four levels, including unqualified, qualified, good and excellent ones. The evaluation results were improved by the aggregation of the basic classification algorithms. The precision and F1 measure of the aggregated classification algorithm were improved about 20 percentage points compared with using a single classification algorithm, and all the indexes have reached more than 70% except the macro average F1 measure. The experimental results show that this method can be applied to assess the quality of comments effectively.

To improve the perception ability of indoor mobile robots, the classification method for the commonly structured corridor-scenes, Spiking Neural Network (SNN) and NeuCube, which is a novel computing model based on SNN, were studied. SNN can convey spatio-temporal information by spikes. Besides, SNN is more suitable for analyzing dynamic and time-series data, and for recognizing data of various patterns than traditional Neural Network (NN). It is easy to be implemented by hardware. The principle, learning methods and calculation steps of NeuCube were discussed. Then seven common corridor scenes were recognized by the classification method based on multi-sonar-sensor information and NeuCube. The experimental results show that the proposed method is effective. Additionally, it is helpful for improving autonomy and intelligence of mobile robots.