Camouflaged Object Detection （COD） aims to detect objects hidden in complex environments. The existing COD algorithms ignore the influence of feature expression and fusion methods on detection performance when combining multi-level features. Therefore， a COD algorithm based on progressive feature enhancement aggregation was proposed. Firstly， multi-level features were extracted through the backbone network. Then， in order to improve the expression ability of features， an enhancement network composed of Feature Enhancement Module （FEM） was used to enhance the multi-level features. Finally， Adjacency Aggregation Module （AAM） was designed in the aggregation network to achieve information fusion between adjacent features to highlight the features of the camouflaged object area， and a new Progressive Aggregation Strategy （PAS） was proposed to aggregate adjacent features in a progressive way to achieve effective multi-level feature fusion while suppressing noise. Experimental results on 3 public datasets show that the proposed algorithm achieves the best performance on 4 objective evaluation indexes compared with 12 state-of-the-art algorithms， especially on COD10K dataset， the weighted F-measure and the Mean Absolute Error （MAE） of the proposed algorithm reach 0.809 and 0.037 respectively. It can be seen that the proposed algorithm achieves better performance on COD tasks.

In order to solve the problem that classical Laplacian Pyramid (LP) transformin image fusion losses high frequency information of the fused image seriously and has no translation invariance, with the use of non-separable wavelet which has translation invariance and accurate description of image details, a new construction method of non-sampling non-separable LP was proposed and applied to the multi-spectral image fusion. Firstly, a six-channel non-separable low-pass filter was constructed and used to construct non-sampling non-separable wavelet pyramid for multi-spectral image and panchromatic image, and then the image was processed by non-sampling non-separable LP decomposition. Then, different fusion rules were used for the fusion of different decomposition layers. Finally, the fused image was obtained by using non-separable LP reconstruction algorithm. The experimental results show that compared with the algorithms based on Discrete Wavelet Transformation (DWT), Contourlet Transformation (CT), and Midway Histogram Equalization (MHE), the spatial correlation coefficient of the proposed method was increased by 1.84%, 1.56%, and 11.06% respectively, and the relative global dimensional synthesis error of the proposed method was reduced by 49.26%, 48.15%, and 89.19% respectively. The proposed method can effectively improve the spatial resolution while obtaining good spectral information of image, well preserve the edge information and structure information of the image.

A hybrid algorithm of Simulated Annealing (SA) and flower pollination algorithm was presented to overcome the problems of low-accuracy computation, slow-speed convergence and being easily relapsed into local extremum. The sudden jump strategy in SA was utilized to avoid falling into local optimum, and the global searching performance of SA was exploited to enhance the global searching ability of the hybrid algorithm. The hybrid algorithm was tested through six standard functions and compared to basic Flower Pollination Algorithm (FPA), Bat Algorithm (BA), Particle Swarm Optimization (PSO) algorithm and improved PSO algorithm. The simulation results show that the optimal value of 4 functions were found by the hybrid algorithm with better convergence precision, convergence rate and robustness. At the same time, the experimental results of solving nonlinear equation group verify the validity of the hybrid algorithm.

In order to solve the problems of Cuckoo Search (CS) algorithm including low optimizing accuracy and weak local search ability, an improved CS algorithm with differential evolution strategy was presented. The individual variation was completed in the algorithm before population with two weighted differences increased on its individuals entering the next iteration, then crossover operation and select operation were performed to obtain optimal individual, which making the CS algorithm lack of mutation mechanism have the variation mechanism, so as to increase the diversity of the CS algorithm, avoid individual species into local optimum and enhance the global optimization ability. The algorithm was put through several classical test functions and a typical application example. The simulation results show that the new algorithm has better global searching ability, and the convergence precision, convergence speed and optimization success rate are significantly better than those of the basic CS algorithm.

It began with the requirement of Sudoku to establish the system of equations. The solution of equation system was equivalent to the original Sudoku. And many mathematical properties were derived from equation system, including candidates elimination techniques, unique determination property, contradiction property and invariance property. It also illustrated that the artificial deductive rules included these properties. Finally this paper proposed a algorithm which calculated the solution of equation system from these properties. The algorithm used a three-dimensional matrix to express the candidate matrix of an unsolved Sudoku, and according to the above properties the candidate matrix could be deleted until Sudoku was solved. The algorithm can resolve many difficult Sudoku puzzles. Two difficult Sudoku puzzles were demonstrated and solved easily with the method in this paper, which shows the method is very effective.

Data can not be communicated so well according to the unified data standards among the health information systems in our country that information sharing is impeded. On the basis of the HL7 V2.x (health level seven Version2.x) protocol of health information communication standard and the theory of HL7V2.x message parsing, the method in which data in the database fields is extracted to generate HL7V2.x messages and parsed message information is stored in the specific database is introduced in detail. Then an optimized model of the message store and the message generation is brought forward. In this model, mapping files are designed to save much time in redundant manual configurations and the hash table is used as a data structure during the process of message generation to make it more efficient. Simulation experiment results show the feasibility of the model.