Blockchain stores transaction data in the form of distributed ledger, and its nodes hold copies of current data by storing hash chain. Due to the particularity of the blockchain structure, the number of blocks increases over time and the storage pressure of nodes also increases with the increasing of blocks, so that the storage scalability has become one of the bottlenecks in blockchain development. To address this problem, a blockchain storage expansion model based on Chinese Remainder Theorem (CRT) was proposed. In the model, the blockchain was divided into high-security blocks and low-security blocks, which were stored by different storage strategies. Among them, low-security blocks were stored in the form of network-wide preservation (all nodes need to preserve the data), while the high-security blocks were stored in a distributed form after being sliced by the CRT-based partitioning algorithm. In addition, the error detection and correction of Redundant Residual Number System (RRNS) was used to restore data to prevent malicious node attacking, so as to improve the stability and integrity of data. Experimental results and security analysis show that the proposed model not only has security and fault tolerance ability, but also ensures the integrity of data, as well as effectively reduces the storage consumption of nodes and increases the storage scalability of the blockchain system.

For the problem that the existing deep-learning based super-resolution reconstruction methods mainly study on the reconstruction problem of amplifying integer times, not on the cases of amplifying arbitrary times (e.g. non-integer times), a super-resolution reconstruction method with arbitrary magnification based on spatial meta-learning was proposed. Firstly, the coordinate projection was used to find the correspondence between the coordinates of high-resolution image and low-resolution image. Secondly, based on the meta-learning network, considering the spatial information of feature map, the extracted spatial features and coordinate positions were combined as the input of weighted prediction network. Finally, the convolution kernels predicted by the weighted prediction network were combined with the feature map in order to amplify the size of feature map effectively and obtain the high-resolution image with arbitrary magnification. The proposed spatial meta-learning module was able to be combined with other deep networks to obtain super-resolution reconstruction methods with arbitrary magnification. The provided super-resolution reconstruction method with arbitrary magnification (non-integer magnification) was able to solve the reconstruction problem with a fixed size but non-integer scale in the real life. Experimental results show that, when the space complexity (network parameters) is equivalent, the time complexity (computational cost) of the proposed method is 25%-50% of that of the other reconstruction methods, the Peak Signal-to-Noise Ratio (PSNR) of the proposed method is 0.01-5 dB higher than that of the others, and the Structural Similarity (SSIM) of the proposed method is 0.03-0.11 higher than that of the others.

For analyzing the effect of the call distance on the detecting probability in call magnetic anomaly searching submarine, the model for calculating the submarine distribution probability was deduced, and then the relation between the call distance and the call magnetic anomaly searching submarine probability was established. At last, some calculation results were given out for some typical cases. The results show the call magnetic anomaly searching submarine probability descends rapidly with the call distance increasing, just only in near call distance, small initial distribution radius and low velocity, the call magnetic anomaly searching submarine has a high detecting probability. This shows that the call distance has a serious effect on the detecting probability in call magnetic anomaly searching submarine, and the magnetic anomaly detecting is not fit for searching submarine for far call distance.

Considering massive scale of cloud storage solutions, the traditional data distribution strategy confronts challenges to improve scalability and flexibility. This paper proposed an efficient data distribution strategy. Based on consistent hashing algorithm, the strategy introduced the virtualization technology, and employed virtual node to improve load balance. Moreover, the strategy used a new capacity-aware method to improve the performance of the cloud storage system. The evaluation experiments demonstrate that the proposed data distribution strategy improves system performance in both homogeneous and heterogeneous distributed storage architectures.