To solve the problems of insufficient utilization of residual features and loss of details in existing residual networks， a deep neural network model combining the two-layer structure of residual aggregation and dual-attention mechanism with receptive field expansion， was proposed for Single Image Super-Resolution （SISR） reconstruction. In this model， a two-layer nested network structure of residual aggregation was constructed through skip connections， to agglomerate and fuse hierarchically the residual information extracted by each layer of the network， thereby reducing the loss of residual information containing image details. Meanwhile， a multi-scale receptive field expansion module was designed to capture a larger range of context-dependent information at different scales for the effective extraction of deep residual features； and a space-channel dual attention mechanism was introduced to enhance the discriminative learning ability of the residual network， thus improving the quality of reconstructed images. Quantitative and qualitative assessments were performed on benchmark datasets Set5， Set14， B100 and Urban100 for comparison with the mainstream methods. The objective evaluation results indicate that the proposed method outperforms the comparative methods on all four datasets； compared with the classical SRCNN （Super-Resolution using Convolutional Neural Network） model and second best performing comparison model ISRN （Iterative Super-Resolution Network）， the proposed model improves the average values of Peak Signal-to-Noise Ratio （PSNR） by 1.91， 1.71， 1.61 dB and 0.06， 0.04， 0.04 dB， respectively， at the magnification of 2， 3 and 4. Visual effects show that the proposed model reconstructs clearer image details and textures.

Conventional Particle Swarm Optimization (PSO) algorithm has disadvantage of premature convergence and is easily trapped in local optima. An improved PSO algorithm with adaptive task allocation was proposed to avoid those disadvantages. Adaptive task allocation was applied to particles according to their distribution status and fitness. All the particles were divided into exploration particles and exploitation particles, and carried out different tasks with global model and dynamic local model respectively. This strategy can make better trade-off between exploration and exploitation and enhance the diversity of particle. Dynamic neighborhood strategy broadened the search space and effectively inhibited the premature stagnation. Gaussian disturbance learning was applied to the stagnant elite particles to help them jump out from local optima region. The superior performance of the proposed algorithm in global search ability and solution accuracy was validated by optimizing six complicated composition test functions.

This paper focused on the application of cloud computing technology to purchase link to form a cloud procurement platform, and to explore how to allocate the virtual machine with the virtual suppliers resources, so as to improve the satisfaction of customers. Firstly, this paper proposed the concept of cloud purchase platform, assuming that the virtual machine containing the suppliers of resources; secondly proposed the allocation processes of virtual machines which contained the virtual suppliers resources and modeling; then the Best Fit Decreasing (BFD) and Finder-tracker multi-swarm Particle Swarm Optimization (FTMPSO) were adopted to get the solution; finally the results of computing were analyzed. In the BFD algorithm, the priority of each of the three attributes met different preferences of the customer's requirements. Using FTMPSO algorithm to allocate virtual suppliers resources got higher satisfaction of customer than using BFD to allocated virtual suppliers resources.

Concerning that conventional Particle Swarm Optimization (PSO) is easy trapped in local optima and with low search efficiency in later stage, an improved PSO based on mean information and elitist mutation, named MEPSO, was proposed. Average information of swarm was introduced into MEPSO to improve the global search ability, and Time-Varying Acceleration Coefficient (TVAC) strategy was adopted to balance the local search and global search ability. In the latter stage of the iteration, the Cauchy mutation operation was applied to the global best particle to improve the global search ability and to further reduce the risk of trapping into local optimum. Contrast experiments on six benchmark functions were given. Compared with Basic PSO (BPSO), PSO with TVAC (PSO-TVAC), PSO with Time-Varying Inertia Weight factor (PSO-TVIW) and Hybrid PSO with Wavelet Mutation (HPSOWM), MEPSO achieved better mean value and standard variance with shorter optimization time and better reliability. The results show that MEPSO can better balance the ability of local search and global search, and can converge faster with higher accuracy and efficiency.

Concerning the impact on network performance of the mobile nodes and the constantly changing data transmission rate, the authors proposed a new backoff scheme for IEEE802.15.4, which used Probability Judgment based on Network Load and Exponentially Weighted Moving Average (PJNL_EWMA) method. According to a realtime monitoring of current network status by probability judgment of network load, this method dynamically adjusted backoff exponent by EWMA when Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) began. Compared with the IEEE802.15.4 standard protocol and MBS (Memorized Backoff Scheme)+EWMA algorithm, the simulation experiments on NS2 platform show that the PJNL_EWMA algorithm not only improves the throughput of the network, but also reduces the packet loss rate and the collision ratio, significantly improving the network performance.

Current works on the Approximate Pattern Matching with Wildcards and Length constraints (APMWL) problem can only cope with replacement operation. This paper proposed an Edit Distance Matrix (EDM) method based on dynamic programming and the Approximate Pattern Matching with EDM (APM) algorithm. APM can handle all approximate operations including insertion, replacement and deletion. Moreover, this paper extended APM to the APM-OF algorithm with a strict constraint condition that each character can be used at most once for pattern matching in a sequence. The experiments verify that both APM and APM-OF have significant advantages on matching solutions against other peers. The average improvement rates of matching compared to SAIL-Approx are up to 8.34% and 12.37% respectively. It also demonstrates an advantage on approximate pattern mining that the number of approximate patterns mined by APM-OF is 2.07 times of that mined by OneoffMining.