Concerning the problem of streak artifacts generated in the sparse reconstruction of analytic method， a Channel Attention U-shaped Transformer （CA-Uformer） was proposed to achieve high-precision Computed Tomography （CT） sparse reconstruction. In CA-Uformer， channel attention and spatial attention in Transformer were fused， and with the dual-attention mechanism， image detail information was easier learnt by the network； an excellent U-shaped architecture was adopted to fuse multi-scale image information； a forward feedback network design was implemented by using convolutional operations， which further coupled the local information association ability of Convolutional Neural Network （CNN） and the global information capturing ability of Transformer. Experimental results show that CA-Uformer has the Peak Signal-to-Noise Ratio （PSNR）， Structural Similarity （SSIM） 3.27 dB and 3.14% higher， and Root Mean Square Error （RMSE） 35.29% lower than the classical U-Net， which is a significant improvement. It can be seen that CA-Uformer has sparse reconstruction with higher precision and better ability to suppress artifacts.

Representing Point-Of-Interest （POI） categories （e.g.， universities， restaurants） accurately is the key to understand urban space and assist urban computing. Existing models for POI category representation usually only mine users’ mobility behaviors among POIs and learn sequential features， while ignoring spatial and textual semantic features of POI data. In order to solve the above problems， a POI category representation learning model incorporating spatial and textual information — Cat2Vec was proposed. Firstly， a POI category co-occurrence Point-wise Mutual Information （PMI） matrix was constructed by using the spatial co-occurrence relationships of POIs. Then， the text semantic features of POIs were learnt by a pre-trained text representation model. Finally， a new mapping matrix was introduced， and based on the matrix factorization technology， the PMI matrix was decomposed into an inner product of a POI category representation matrix， a text semantic feature matrix and a mapping matrix. In the evaluation of semantic overlapping of POIs on two real-world datasets Yelp and AMap， compared to Doc2Vec， the best model among baselines， the proposed model has the performance improved by 5.53% and 8.17% averagely and respectively. Experimental results show that the proposed model can embed the semantics of POIs more effectively.

For improving recovery performance of signals sampled by sub-Nyquist sampling system with Compressed Sensing (CS), the block Simultaneous Iterative Hard Thresholding (SIHT) recovery algorithm for joint sparse model based on ε-closure was proposed. Firstly, The CS synthesis model for Multiple Measurement Vector (MMV) of sampling system was analyzed and the concepts of ε-coherence and Restricted Isometry Property (RIP) were proposed. Then, according to the block coherence of redundant dictionaries, the SIHT algorithm was improved by optimizing the support sets in iterations. In addition, the iterative convergence constant was given and the algorithm convergence property was analyzed. At last, the simulation experiments show that, compared with traditional method, the new algorithm can achieve recovery success rate of 100% with enough sampling channels, while the noise suppressing ability was increased by 7 dB to 9 dB and the total execution time was brought down by at least 37.9%, with higher convergence speed.

To solve the redundancy and high-dimensional problem of the electronic system condition monitoring data, a monitoring data optimization algorithm that combined the sample optimization and features optimization was put forward. Firstly, monitoring data samples were optimized by feature space sample selection algorithm, and the most representative samples were found; then monitoring data characteristics were optimized by KPCA-EDA algorithm after the sample optimization. More recognition information was retained on guarantee that the feature information was enough. Finally, a filter circuit was taken as an example to simulate, and the result shows that this method is effective.