Device-to-Device（D2D） communication leverages the local computing and caching capabilities of the edge network to meet the demand for low-latency， energy-efficient content sharing among future mobile network users. The performance improvement of content sharing efficiency in edge networks not only depends on user social relationships， but also heavily relies on the characteristics of end devices， such as computation， storage， and residual energy resources. Therefore， a D2D content sharing mechanism was proposed to maximize energy efficiency with multidimensional association features of user-device-content， which took into account device heterogeneity， user sociality， and interest difference. Firstly， the multi-objective constraint problem about the user cost-benefit maximization was transformed into the optimal node selection and power control problem. And the multi-dimensional knowledge association features and the graph model for user-device-content were constructed by processing structurally multi-dimensional features related to devices， such as computing resources and storage resources. Then， the willingness measurement methods of users on device attributes and social attributes were studied， and a sharing willingness measurement method was proposed based on user socialization and device graphs. Finally， according to user sharing willingness， a D2D collaboration cluster oriented to content sharing was constructed， and a power control algorithm based on shared willingness for energy efficiency was designed to maximize the performance of network sharing. The experimental results on a real user device dataset and infocom06 dataset show that， compared to nearest selection algorithm and a selection algorithm without considering device willingness， the proposed power control algorithm based on shared willingness improves the system sum rate by about 97.2% and 11.1%， increases the user satisfaction by about 72.7% and 4.3%， and improves the energy efficiency by about 57.8% and 9.7%， respectively. This verifies the effectiveness of the proposed algorithm in terms of transmission rate， energy efficiency and user satisfaction.

To improve the recognition rate and reduce the false-recognition rate in real-time detection of flame in video surveillance, a fast flame recognition algorithm based on multi-feature logarithm regression model was proposed. Firstly, the image was segmented according to the chromaticity of the flame, and the Candidate Fire Region (CFR) was obtained by subtracting the moving target image with reference image. Secondly the features of the CRF such as area change rate, circularity, number of sharp corners and centroid displacement were extracted to establish the logarithmic regression model. Then, a total of 300 images including flame and non-flame images, which were got from National Institute of Standards and Technology (NIST), Computer Vision laboratory of Inha University (ICV), Fire detection based on computer Vision (VisiFire) and the experimental library consisting of the candle and paper combustion were used to parametric learning. Finally, 8 video clips including 11071 images were used to validate the proposed algorithm. The experimental results show that the True Positive Rate (TPR) and True Negative Rate (TNR) of the proposed algorithm are 93% and 98% respectively. The average time of identification is 0.058 s/frame. Because of its fast identification and high recognition rate, the proposed algorithm can be applied in embedded real-time flame image recognition.

In order to improve the accuracy and universality of computer-assisted classification algorithm, a Electrocardiography (ECG) beat classification algorithm based on cluster analysis was presented in this paper. The algorithm considered that one patients' ECG beats repeated periodically, and used the method of two-stage cluster analysis, and selecting representative ECG beats, combined with the diagnosis of cardiac physicians to achieve accurate ECG beat classification rate. In order to verify the accuracy of the algorithm, using the internationally standard database MIT-BIH arrhythmia database, the ECG beat classification method and the accuracy evaluation method specified by AAMI/ANSI standard were used to perform simulation experiments, the final overall classification accuracy rate is 99.07%. Compared with Kiranyaz' method(KIRANYAZ S, INCE T,PULKKINEN J, et al. Personalized long-term ECG classification: A systematic approach［J］. Expert Systems with Applications, 2011, 38(4): 3220-3226.), this method does not require specific training step, and the sensitivity of the ECG beats which labeled as S raise to 89.82% from 40.15%, significantly improving classification algorithm's generalization capability.

Model Driven Architecture (MDA) refers to the method and standard system for the application of model technology in software development proposed by Object Management Group (OMG), the core idea of which includes the modeling of platform-independent model and the transformation of platform-specific model. The framework was realized by programming based on Meta Object Facility 2.0 (MOF2.0) Query/View/Transformation (QVT) standard definition, and it could achieve transformation of meta model to specific N-layer application class and realize main functions of the program, thus greatly improving the development efficiency. The flexibility of model-driven transformation and diversity of function realization achieved by programming were verified, including the description of model specification by XML documents and the integrity of code generation

In order to improve the convergence performance of Quantum-behaved Particle Swarm Optimization (QPSO) algorithm, this paper proposed an improved QPSO algorithm which was called RE-QPSO based on the random evaluation strategy. The new algorithm evaluated the innovation of particles by using a random factor and improved the ability of the particles to get rid of the local optima. Fixed value strategy and linear decreasing strategy were proposed for controling the theunique parameter of QPSO algorithm and they were tested on six benchmark functions. According to the test results, some conclusions concerning the selection of the parameter were drawn.

Concerning the problem of system reliability assessment for complicated electronic equipment under the zero-failure data, this paper proposed a method of simulation assessment based on circuit units performance. Firstly all circuit function units in the system were fixed on, and system performance reliability model was set up according to the logistic relation between performance parameter of units and system output performance. Secondly the performance distributive parameters of units were estimated through statistical analysis for performance data, the values of random sample for performance data were gained according to these distributive parameters, and the system performance parameters were simulated using sample results. Finally the failure ratio of system parameters was got by using simulation results, and then the system reliability assessment can be achieved. The validity and practicality of the method were validated through an example.

A way for solving the important and complicated problem of the inventory/transportation integrated optimization in the modern logistics tactics plan was discussed,and a heuristic algorithm based on the method for soling the capacitated concentrator location problem and the tabu search algorithm was discussed. And then the main points of programming were emphasized. Finally, the algorithm was tested by some simulated cases and the result was perfect.