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.

In order to solve the problems of low accuracy and poor generalization of traffic sign recognition caused by factors such as fog, light, occlusion and large inclination, a lightweight traffic sign recognition method based on neural network was proposed. First, in order to improve image quality, the methods of image normalization, affine transformation and Contrast Limited Adaptive Histogram Equalization (CLAHE) were used for image preprocessing. Second, based on Convolutional Neural Network (CNN), spatial pyramid structure and Batch Normalization (BN) were fused to construct an improved CNN with Spatial Pyramid Pooling (SPP) and BN (SPPN-CNN), and Softmax classifier was used to perform the traffic sign recognition. Finally, the German Traffic Sign Recognition Benchmark (GTSRB) was used to compare the training effects of different image preprocessing methods, model parameters and model structures, and to verify and test the proposed model. Experimental results show that for SPPN-CNN model, the recognition accuracy reaches 98.04% and the loss is less than 0.1, and the recognition rate is greater than 3 000 frame/s under the condition of GPU with low configuration,verifying that the SPPN-CNN model has high accuracy, strong generalization and good real-time performance.

Internet of Things (IoT) communication system has the characteristics of small active user number and short data frame, while the pilot and user identification code required by channel estimation and multi-user detection will greatly reduce the communication efficiency and response speed of IoT system. To solve these problems, a blind channel estimation and multi-user detection algorithm based on Non-Orthogonal Multiple Access (NOMA) was proposed. Firstly, the spread spectrum matrix in Code Division Multiple Access (CDMA) system was used to allocate the carrier to each user, and the constellation rotation problem caused by blind estimation was solved by differential coding. Secondly, according to the sparsity of carriers allocated to users, the Bernoulli-Gaussian (B-G) distribution was introduced as a prior distribution, and the hidden Markov characteristic between the variables was used to perform the factor decomposition and modeling, and the multi-user identification was carried out based on sparse features of user data. Finally, the above model was deduced by message passing algorithm to solve multi-user interference caused by NOMA, and the joint channel estimation and detection receiver algorithm for IoT environment was obtained. The simulation results show that, compared with Block Sparse Single Measurement Vector (BS-SMV) algorithm and Block Sparse Adaptive Space Pursuit (BSASP) algorithm, the proposed algorithm can achieve a performance gain of about 1 dB without increasing the complexity.

The stable operation of virtual machine is an important support of cloud service. Because of the tremendous amount of virtual machine and their changing status, it is hard for management system to train classifier for each virtual machine individually. In order to improve the performance of real-time performance and detection ability, a new dividing mechanism based on modified k-medoids clustering algorithm for dividing virtual machine detection region was proposed, the iterate process of clustering was optimized to improve the speed of dividing detection region, and the efficiency and accuracy of anomaly detection were enhanced consequently by using this proposed detecting region strategy. Experiments and analysis show that the modified clustering algorithm has lower time complixity, the detection method with dividing detection region performs better than the original algorithm in efficiency and accuracy.

In order to reduce the noise of coronary angiography image sequence, enhance the diagnostic accuracy for coronary heart disease, and eventually acquire superior image quality under low X-ray dose, a method of spatial-temporal filtering for coronary angiography images was proposed. By introducing the idea of threshold noising in wavelet denoising into the Fast Discrete Orthonormal Stockwell Transform (FDOST), a soft-threshold denoising algorithm based on FDOST was proposed for the spatial denoising of coronary angiography images. The conventional wavelet denoising was used for temporal denoising of coronary angiography images, taking advantage of its time smoothing feature. Hessian matrix was used in pre-processing to track the line-like structure of coronary angiography images. The simulation and experimental results show that the signal-to-noise ratio and contrast-to-noise ratio of the denoised images are improved significantly compared with the original image, and the proposed method is suitable for the denoising of low-dose coronary angiography image sequence.

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.

Name-Your-Own-Price (NYOP), a new sales mode, has emerged in recent years and is different from traditional pricing mode. To solve the problem of optimal pricing strategy of a name-your-own-price retailer when marketing period is limited and the retailers' inventory is limited, the online retailers' maximum expected revenue model was put forward based on optimization method. The relationship between the optimal threshold price and limited inventory and the selling time were obtained by numerical analysis of the model. The conclusion shows that the retailer should set the optimal threshold price based on sales period and inventory.