Considering the data sharing difficulties and the risk of privacy disclosure in grid cloud server based on blockchain, a Data Sharing model based on Double Consortium Blockchains in smart grid(DSDCB) was proposed. Firstly, the data of electricity was stored under-chain by Inter Planetary File System(IPFS), the IPFS file fingerprints were stored on-chain, and the electricity data was shared to other consortium blockchain based on the multi-signature notary technology. Secondly, with ensuring privacy from leakage, proxy re-encryption and secure multi-party computing were combined to share single-node or multi-node security data. Finally, fully homomorphic encryption algorithm was used to integrate ciphertext data reasonably without decrypting the electricity data. The 51% attack, sybil attack, replay attack and man-in-the-middle attacks were resisted by the single-node cross-chain data sharing model of DSDCB. It was verified that the security and privacy of data were guaranteed by the secure multi-party cross-chain data sharing model of DSDCB when the number of malicious participants was less than k and the number of honest participants was more than 1. The simulation comparison shows that the computational cost of the DSDCB model is lower than those of Proxy Broadcast Re-Encryption(PBRE) and Data Sharing scheme based on Conditional PBRE(CPBRE-DS), and the model is more feasible than the Fully Homomorphic Non-interactive Verifiable Secret Sharing(FHNVSS) scheme.

With the arrival of big data, network public opinion data reveals the features of massive information and wide coverage. For the complicated network public opinion data, traditional single models may not efficiently predict the trend of network public opinion. To address this question, the improved combination model based on the Empirical Mode Decomposition-AutoRegression (EMD-AR) model was proposed, called EMD-ARXG (Empirical Mode Decomposition-AutoRegression based on eXtreme Gradient boosting)model. EMD-ARXG model was applied to the prediction of the trend of complex network public opinion. In this model, the Empirical Mode Decomposition (EMD) algorithm was employed to decompose the time series, and then AutoRegression (AR) model was applied to fit the decomposed time series and establish sub-models. Finally, the sub-models were reconstructed and then the modelling process was completed. In addition, in the fitting process AR model, in order to reduce the fitting error, the residual error was learned by eXtreme Gradient Boosting (XGBoost), and each sub-model was iteratively updated to improve its prediction accuracy. In order to verify the prediction performance of EMD-ARXG model, the proposed model was compared with wavelet neural network model and back propagation neural network based on EMD model. The experimental results show that the EMD-ARXG model is superior to two other models in terms of the statistical indicators including Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Theil Inequality Coefficient (TIC).

Concerning the drawbacks of the Ant Colony Optimization (ACO) algorithm such as low convergence rate and being easy to fall into local optimum solutions, an ACO algorithm based on Improved Pheromones Double Updating and Local Optimization (IPDULACO) was proposed. Double updating was performed on the pheromones of subpaths whose path contribution degrees to the current global optimal solution obtained by colony were bigger than the prescribed path contribution threshold. The selected probability of the subpaths which were used to constitute the potential optimal solution was increased and the convergence rate of the proposed algorithm was accelerated. Then, when the ant colony fell into the local optimal solution in the search process, the random insertion method was utilized to change the city sequences of the current local optimal solution in order to enhance the algorithm's ability of jumping out of local optimal solution. The improved algorithm was applied to several classical Traveling Salesman Problem (TSP) instances in the simulation experiments. The experimental results show that, for small-size TSP instances, the IPDULACO can obtain the known optimal solution in less number of iterations. For relatively large-size TSP instances, the IPDULACO can obtain the optimal solution with higher accuracy in less number of iterations. Therefore, the IPDULACO has the stronger ability of searching for the global optimal solution and faster convergence rate, and it can be used for solving TSP effectively.

To solve the problem that basic discrete Particle Swarm Optimization (PSO) algorithm often leads the computation process into local optimum and premature convergence when applied to Traveling Salesman Problem (TSP), a PSO based on Self-adaptive Excellence Coefficients (SECPSO) algorithm was proposed. To improve the global search ability, heuristic information was further utilized to modify the static excellence coefficients of paths based on previous work, so that these coefficients could be adjusted adaptively and dynamically according to the process of searching for the solutions. Furthermore, a 3-opt search mechanism was added to improve the accuracy of the solution and the convergence rate of the algorithm. Through simulation experiments with Matlab, the performance of the proposed algorithm was evaluated using several classical examples in the international general TSP database (TSPLIB). The experimental results indicate that the proposed SECPSO algorithm performs better in terms of global search ability and convergence rate compared with several other algorithms, and thus is a potential intelligent algorithm for solving TSP.

To avoid the failure of data forwarding, brought by some characteristics of Vehicular Ad Hoc Networks (VANET), uniform distribution of vehicles, frequent network partition and mergence, etc., a new data delivery method based on Weak State Routing (WSR) from Traffic Control Center (TCC) to driving vehicles, called Weak State Forwarding (WSFD), was introduced in VANET. Firstly, a data packet collected by TCC was delivered to an Access Point (AP) along the direction of the destination vehicle. Secondly, the data packet was forwarded to the destination vehicle by AP within its communication range, at the same time, the location information of destination vehicle was carried by the data packet. Then, after comparing all the mapping information owned by the vehicle which received the data packet, the most deterministic map information was chosen by the vehicle and compared to the location information carried by the data packet so as to ensure the next forwarding direction. If the confidential level was quite high, the data packet was revised to move towards the mapping's corresponding central area, meanwhile, the information of destination vehicle carried by the data packet was updated. Otherwise, the original direction would be kept. Lastly, through several times' forwarding and revising, the data packet would be gradually approached to the area where the destination vehicle located, and the whole data delivery would be finally completed. Compared with Trajectory-based Statistical Forwarding for multihop infrastructure-to-vehicle data delivery (TSF) and Greedy Perimeter Stateless Routing (GPSR) algorithm, the WSFD algorithm could reduce the delivery delay to 5 seconds or less and elevate the delivery rate to 0.92 or more generally in the experiment of data transmission in 30 km*30 km square area. The experimental results show that the WSFD algorithm can improve safety of drivers and alleviate the traffic jam effectively.

Joint blind estimation of multiple frequency offsets and multiple channels is difficult in distributed Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system under the multipath fading channel. In order to solve the problem, an effective algorithm was proposed. The proposed algorithm made use of blind deconvolution separation method to receive signal and got the multiple channels embedded with frequency offsets meanwhile. After estimating frequency offsets of the separated signals, the real channels estimation could be obtained by removing channel ambiguity and compensating the whole channels. The simulation results show that, the proposed algorithm is able to get 1e-6 average Mean Square Error (MSE) of frequency offsets estimation at 5 dB and 1e-2 average MSE of channels estimation at 15 dB compared with existing frequency offset channel estimation method based on pilot, the joint blind estimation of multiple frequency offsets and multiple channels for distributed MIMO-OFDM signal is realized

Concerning the problem that the least square method in the third stage of DV-Hop algorithm has low positioning accuracy, a localization algorithm was proposed which is the fusion of hybrid bat-quasi-Newton algorithm and DV-Hop algorithm. First of all, the Bat Algorithm (BA) was improved from two aspects: firstly, the random vector β was adjusted adaptively according to bats' fitness so that the pulse frequency had the adaptive ability. Secondly, bats were guided to move by the average position of all the best individuals before the current iteration so that the speed had variable performance; Then in the third stage of DV-Hop algorithm the improved bat algorithm was used to estimate node location and then quasi-Newton algorithm was used to continue searching for the node location from the estimated location as the initial searching point. The simulation results show that, compared with the traditional DV-Hop algorithm and the improved algorithm of DV-Hop based on bat algorithm(BADV-Hop), positioning precision of the proposed algorithm increases about 16.5% and 5.18%, and the algorithm has better stability, it is suitable for high positioning precision and stability situation.

Aiming at the problem of single image degradation and high time complexity of exiting defogging methods under foggy weather, a fast visual optimization defogging algorithm based on atmospheric physical model was proposed. The proposed method firstly used threshold segmentation to find the sky region, and combined with binary tree algorithm to locate global atmospheric light precisely, and then adopted improved constrained least squares filter which can keep the edge detail and reduce noise to optimize original transmittance map. Finally, the fog image could be restored by atmospheric physical model, and the average gradient, information entropy and the visual information fidelity index were adopted to evaluate the image. The experimental results show that compared with the adaptive image enhancement method based on multi-scale Retinex algorithm, the image restoration based on independent component analysis, a quick visual image restoration method and the dark-channel prior de-hazing algorithm, the proposed method has good visual evaluation indexes and strong real-time processing capability.

In order to remove the image blurring caused by reconstructing video frames independently frame by frame using traditional compressed video sensing method, this paper proposed a new approach to video compressed sensing based on motion estimation and motion compensation by combining the compressed sensing theory with related technology of MPEG standard video coding, so as to remove the spatial and temporal redundancy of video signal. This method took full account of the temporal correlations of video sequences and firstly compensated video frames using forward, backward and bidirectional prediction, then adopted the Backtracking-based Adaptive Orthogonal Matching Pursuit (BAOMP) algorithm to reconstruct the motion prediction residuals and finally reconstructed current frames. The experimental results indicate that the proposed method can gain a better video image quality compared with frame-by-frame reconstruction method and achieve a higher Peak Signal-to-Noise Ratio (PSNR).

Reciprocal Velocity Obstacles (RVO) can process collision avoiding between large-scale agents, and be used in many crowd simulation engines. However, due to the lack of optimized path planning, it is difficult for RVO to simulate crowd evacuation in complicated environment. In this paper, based on RVO mechanism, a new global optimal path planning method, comprising path preprocessing and dynamical computation, was proposed for crowd evacuation simulation in complicated environment. SPFA (Shortest Path Faster Algorithm) algorithm was firstly used for pre-calculating SSP (Scene Shortest Path), and then the SSP was utilized to compute optimized evacuation path for each Agent in complicated scenes in real-time. KD tree (K-Dimension tree) was also used to further improve processing performance. Some examples demonstrate that the method can do well in global path planning for large-scale crowd evacuation in complicated scenes, especially in multi-floor, multi-obstacle, multi-stair, and multi-outlet scenes.

A satellite antenna characterized by wide-beam, wide-bandwidth and microwave right-hand-circular polarization was designed. First, the Dynamic Dominant Evolution Algorithm (DDEA) was used to search globally on a parallel computing platform. Then orthogonal design and HFSS software based on finite element method were used to search within local scope evenly and elaborately, so that the antenna gain was further improved. The improved antenna meets the requirements on beam and saves the feeding power of satellite.

An improved approach: median-length included angle chain, combined with the included angle chain was presented to extract the structural features of the palm vein. The way is to model a curve segment of palm vein textures by a number of variable-length line segments through media value iteration and let distance criterion control the fitting error, under permitted error, using the included angles sequence between a pair of neighboring line segments to represent the curve segment. Experimental results show that while the computation precision is ensured, the proposed algorithm still can reduce the computation and acquire the structural features of the palm vein.

MVC model is an important method in developing Web application program. First of all, the common MVC model and its running procession were introduced briefly. Then a kind of improved MVC model based on double Servlet controller was put forward. With great emphasis put on the design and implementation of this improved MVC model, its feature and performance were analyzed.