To solve the problem that information hiding algorithms based on neural style transfer do not solve the embedding problem of color images， a color image information hiding algorithm based on style transfer process was proposed. Firstly， the advantages of feature extraction of Convolutional Neural Network （CNN） were utilized to extract the semantic information of the carrier image， the style information of the style image and the feature information of the color image， respectively. Then， the semantic content of images and different styles were fused together. Finally the embedding of color image was completed while performing the style transfer of the carrier image through the decoder. Experimental results show that the proposed algorithm can integrate the secret image into the generated stylized image effectively， making the secret information embedding behavior indistinguishable from the style change behavior. Under the premise of maintaining the security of the algorithm， the proposed algorithm has the hiding capacity increased to 24 bpp， and the average values of Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity （SSIM） reached 25.29 dB and 0.85 respectively， thereby solving the color image embedding problem effectively.

For addressing real root isolation problem of transcendental function polynomials， an interval isolation algorithm for exponential function polynomials named exRoot was proposed. In the algorithm， the real root isolation problem of non-polynomial real functions was transformed into sign determination problem of polynomial， then was solved. Firstly， the Taylor substitution method was used to construct the polynomial nested interval of the objective function. Then， the problem of finding the root of the exponential function was transformed into the problem of determining the positivity and negativity of the polynomial in the intervals. Finally， a comprehensive algorithm was given and applied to determine the reachability of rational eigenvalue linear system tentatively. The proposed algorithm was implemented in Maple efficiently and easily with readable output results. Different from HSOLVER and numerical calculation method fsolve， exRoot avoids discussing the existence of roots directly， and theoretically has termination and completeness. It can reach any precision and can avoid the systematic error brought by numerical solution when being applied into the optimization problem.

The mixed noise formed by a large number of spikes， speckles and multi-directional stripe errors in Shuttle Radar Terrain Mission （SRTM） will cause serious interference to the subsequent applications. In order to solve the problem， a Low-Rank Group Sparsity_Total Variation （LRGS_TV） algorithm was proposed. Firstly， the uniqueness of the data in the local range low-rank direction was used to regularize the global multi-directional stripe error structure， and the variational idea was used to perform unidirectional constraints. Secondly， the non-local self-similarity of the weighted kernel norm was used to eliminate the random noise， and the Total Variation （TV） regularity was combined to constrain the data gradient， so as to reduce the difference of local range changes. Finally， the low-rank group sparse model was solved by the alternating direction multiplier optimization to ensure the convergence of model. Quantitative evaluation shows that， compared with four algorithms such as TV， Unidirectional Total Variation （UTV）， Low-Rank-based Single-Image Decomposition （LRSID） and Low-Rank Group Sparsity （LRGS） model， the proposed LRGS_TV has the Peak Signal-to-Noise Ratio （PSNR） of 38.53 dB and the Structural SIMilarity （SSIM） of 0.97， which are both better than the comparison algorithms. At the same time， the slope and aspect results show that after LRGS_TV processing， the subsequent applications of the data can be significantly improved. The experimental results show that， the proposed LRGS_TV can repair the original data better while ensuring that the terrain contour features are basically unchanged， and can provide important support to the reliability improvement and subsequent applications of SRTM.

With the development of sharing economy, there is an urgent need for highly trusted distributed transaction management; however, traditional centralized information systems are difficult to meet it. Blockchain technology provides a shared ledger mechanism, which laid foundation for building credible distributed transaction management service. As blockchain 2.0 platform supporting smart contract, Ethereum platform was used as the basic framework to deeply study the operation mechanism and implementation technology of the decentralized shared goods transaction service system based on blockchain technology. Decentralized item sharing transaction service system framework based on Ethereum was designed, and a transaction management process based on intelligent contract mechanism was proposed. The system implementation technology including user interface was described in detail, and the performance of the system in transaction processing was tested. The experimental results indicate that the Ethereum-based transaction management system can ensure the creditability of the data and has a high operational efficiency, with average transaction processing speed of 21.7 items/s, and indexed average query speed of 117.6 items/s.

Concerning the application problem of the existing scheduling algorithms for guaranteeing the temporal consistency of real-time data objects in the soft real-time database system environment, a Statistical Deferrable Scheduling-OPTimization (SDS-OPT)algorithm was proposed. At first, the characteristics and shortcomings of the existed algorithms were analyzed and compared in terms of scheduling, Quality of Service (QoS) and workload, then the necessity of optimizing the existing algorithms was pointed out. Secondly, in order to maximize QoS of temporal consistency for real-time data objects by advancing the schedulable job quantity of real-time updating transactions, the steepest descend method was used to increase the reference value of the screening benchmark for job execution time. Finally, the proposed algorithm was compared with the existing algorithms in terms of workload and QoS. The experimental results show that, compared with the Deferrable Scheduling algorithm for Fixed Priority transactions (DS-FP) and Deferring Scheduling-Probability Statistic algorithm (DS-PS), the proposed optimization algorithm can guarantee temporal consistency of real-time data objects effectively and reduce the workload, while the QoS is improved significantly.

The problem of NoC (Network on Chip) mapping for complex SoC (System on Chip) chip is urgently needed to be solved while most of the existing mapping schemes do not considered testing requirements. This paper proposed a novel NoC mapping algorithm optimized for testing, which considered the improvement of testability and the minimization of mapping cost together. Firstly, the partition algorithm was adopted to arrange all the IP cores into parallel testing groups, combined with the optimized test structure, so that the testing time was minimized. Then, based on traffic information between IP cores, genetic algorithm was applied to accomplish the NoC mapping, which was aimed to the minimum mapping cost. The experimental results on ITC02 benchmark circuits show that the testing time can be reduced by 12.67% on average and the mapping costs decreased by 24.5% on average compared with the random mapping.

In order to reduce the node localization error of DV-Hop algorithm in Wireless Sensor Network (WSN), a calculation method of average distance per hop was adjusted by using the shuffled frog leaping algorithm. The improved DV-Hop algorithm makes the average distance per hop closer to the actual value, thereby reducing the localization error. The simulation results indicate that the improved DV-Hop algorithm reduces localization error effectively and has good stability without additional devices; therefore, it is a practical localization solution for WSN.

With the development of digital library technology, many digital literatures have come into being. It is very important to mine these sources for providing convenience for the user, and it is paramount to analyze these texts to help user understand topic evolution and development. Dynamic Bayesian network model was used to investigate the research field diffusion, and experiments on real data sets indicate that the proposed method can help researchers get a better insight into research field diffusion for various research fields.

The issue of Bit-Error-Rate control and the throughput capacity of UWB(Ultra-Wide Band) Wireless Mobile Ad Hoc Networks were studied in this paper. Based on the theory of conflict graph of information sent and events received, the paper mainly addressed the impact of interference among nodes on networks throughput. The numerical value of expectant achievable throughput capacity of the system was reached by solving the linear constraints problem with the throughput graph. A model simulation was conducted to testify the results.

An information transmission routing algorithm and a node mobile tracking model based on semi-Markov process for hierarchical mobile ad hoc networks were investigated. This algorithm can be applied to the communications and information exchange between the hierarchical sub-networks efficiently. By the mobile tracking model of relay nodes deduced from the routing algorithm and computer simulation, we analyzed the transmission capacity and routing cost of hierarchical mobile ad hoc networks and arrived at the conclusion that, the transmission capacity is influenced by the mobile calling ratio ρ when 0≤ρ≤1 and, in the other hand, influenced by node’s totality, mobile velocity, and acceleration when ρ>1.