In the reliability evaluation of many high-reliability and high-value products， product reliability often cannot be accurately evaluated due to the lack of objective test data. Aiming at this problem， a reliability evaluation method for high-reliability products based on improved evidence fusion was proposed in order to make full use of reliability information from different sources. Firstly， combining the characteristics of reliability engineering， the modified weight of each evidence was determined by the consistency of the evidence at credal level， pignistic level and the uncertainty of the evidence itself. Secondly， the optimal comprehensive weight was obtained by linear combination of each weight vector based on game theory. Finally， the Dempster’s combination rule was used to fuse the modified evidence， and the probability distribution of the product reliability index was obtained through the Pignistic probability transformation formula to complete the product reliability evaluation. The reliability evaluation results of one electronic device show that compared with the results of Jiang’s combination method and Yang’s combination method， which also consider multi-dimensional weight modification， the credibility of the conflict interval given by the proposed method is reduced by 69.6% and 54.6% respectively， and the credibility of the overall frame of discrimination given by the proposed method is reduced by 5.6% and 3.7% respectively. Therefore， in the application of reliability engineering， the performance of the proposed method in solving evidence conflict and reducing the uncertainty of fusion results is better than that of the comparison methods， and this method can fuse multi-source reliability information effectively and improve the credibility of the results of product reliability evaluation.

With the continuous development of network applications， network resources are growing exponentially and information overload is becoming increasingly serious， so how to efficiently obtain the resources that meet the user needs has become one of the problems that bothering people. Recommendation system can effectively filter mass information and recommend the resources that meet the users needs. The research status of the recommendation system was introduced in detail， including three traditional recommendation methods of content-based recommendation， collaborative filtering recommendation and hybrid recommendation， and the research progress of four common deep learning recommendation models based on Convolutional Neural Network （CNN）， Deep Neural Network （DNN）， Recurrent Neural Network （RNN） and Graph Neural Network （GNN） were analyzed in focus. The commonly used datasets in recommendation field were summarized， and the differences between the traditional recommendation algorithms and the deep learning-based recommendation algorithms were analyzed and compared. Finally， the representative recommendation models in practical applications were summarized， and the challenges and the future research directions of recommendation system were discussed.

Due to the increasingly high performance requirements on the Advanced Encryption Standard (AES) algorithm which was widely used, software-based cryptographic algorithms have been increasingly difficult to meet the demands of high-throughput ciper cracking. As a result, more and more encryption algorithms have been accelerated by using Field-Programmable Gate Array (FPGA) platform. Focused on the issue that the development of AES algorithm based on FPGA has high complexity and long development cycle, with High-Level Synthesis (HLS) design methodologies, AES hardware acceleration algorithm was designed by using high-level programming language. Firstly, loop unrolling, etc were used to improve operation parallelism. Secondly, to make full use of on-chip memory and circuit resources, the resource balance optimization technology was used. Finally, the full pipeline structure was added to improve the clock frequency and throughput of the overall design. The detailed analysis and comparison of the benchmark design and different optimized designs with structural expansion, resource balance and pipeline were decribed. The experimental results show that the clock frequency of AES algorithm is up to 127.06 MHz and the throughput eventually achieves 16.26 Gb/s on Xilinx xc7z020clg484 platform, compared with the benchmark AES design, performance increases by three orders of magnitude.

Aiming at the problem of connectivity in Vehicular Ad Hoc Network (VANET), the evolution characteristics of connectivity characteristics for VANET were analyzed. Firstly, the number of connected components, connectivity probability and connectivity length were proposed to be used for the evaluation connectivity metrics for VANET. Then, based on Intelligent Driver Model with Lane Changes (IDM-LC), the VANET was set up through VanetMobiSim software. Finally, the relation of node communication radius and the average number of connected components, average connectivity probability and average connectivity length were given. At the same time, the statistical distribution of the number of connected components was also analyzed. The results show that number of connected components follows a normal distribution by using Q-Q plot and T-test. Moreover, the results also show that the statistical distribution of the number of connected components is independent of the node communication radius.

When using Linear Deconvolution (LD) algorithm in the selection process, endmembers subset has similar endmembers and similar endmembers have an impact on the accuracy of spectral unmixing,a hyperspectral unmixing optimization algorithm based on per-pixel optimal endmember selection named Spectral Information Divergence (SID) and Spectral Angle Mapping (SAM) was proposed. At the end of the second choice, the method adopted Spectral Information Divergence mixed with Spectral Angle (SID-SA) rule as the most similar endmember selection criteria, removed the similar endmembers and reduced the effect of the accuracy by spectral unmixing. The experiment results show that hyperspectral unmixing optimization algorithm based on SID and SAM makes Root Mean Square Error (RMSE) of reconstruction images be reduced to 0.0104. This method improves the accuracy of endmember selection in comparison with traditional method, reduces abundance estimation error and error distributes more evenly.

There is a long time detection problem caused by too large surveyed area in the classical Tracking-Learning-Detection (TLD) target tracking algorithm. Moreover, the TLD algorithm could not do the similar targets processing well. So in this paper, an efficient approach called TLD-DO was proposed for tracking targets in which the surveyed areas could be automatically adjusted according to the target's velocity of movement. In order to accelerate the process speed of TLD algorithm without reducing tracking precision, a novel algorithm named Double Kalman Filter (DKF) with optimal surveyed area which could reduce the detection range of TLD detector was constructed based on twice Kalman filtering operation for acceleration correction. Meanwhile, the improved method could also increase the accuracy of target tracking through eliminating the interference of the similar targets in complex background. The experimental results show that tracking effect of improved method is better than that of the original TLD algorithm under the circumstance of similar target disturbance. Furthermore, the detection speed has been improved 1.31-3.19 times for different videos and scenes. In addition, the improved method is robust to target vibration or distortion.

A block resource scheduling strategy for remote sensing images in multi-line server environment was proposed with the problems of huge amount of remote sensing data, heavy server load caused by multi-user concurrent requests which decreased the transmission efficiency of remote sensing images. To improve the transmission efficiency, an Improved Ant Colony Optimization (IACO) algorithm was used, which introduced a line waiting factor γ to dynamically select the optimal transmission lines. Intercomparison experiments among IACO, Ant Colony Optimization (ACO), Max-min, Min-min, and Random algorithm were conducted and IACO algorithm finished the tasks in the client and executed in the server with the shortest time, and the larger the amount of tasks, the more obvious the effect. Besides, the line resource utilization of IACO was the highest. The simulation results show that: combining multi-line server block scheduling strategy with IACO algorithm can raise the speed of remote sensing image transmission and the utilization of line resource to some degree.

Through the analysis on the characteristics of the moving objects in video sequence, a method to acquire background was proposed. Moving point accumulation was applied to extract the background, and then background subtraction was employed to detect moving objects. Because of visual comparability, the result included shadow. Shadow filter function was used to remove the shadow to get the completely moving objects. Experimental results show that the algorithm is efficient, adaptive, and establishes a foundation for advanced image processing.

The problems and defections of the existing methods of SVM multi-class classification were analyzed.A multi-class classification based on binary tree was put forward.A modified self-organization map(SOM),KSOM(kernel-based SOM),was introduced to convert the multi-class problem into binary tress,in which the binary decisions were made by SVMs.The results show that it has higher multiclass classification accuracy than the multi-class SVM approaches with "one-versus-one" and "one-versus-the rest".