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Optimization of architecture design has recently drawn research interest. System deployment optimization (SDO) refers to the process of optimizing systems that are being deployed to activities. This paper first formulates a mathematical model to theorize and operationalize the SDO problem and then identifies optimal solutions to solve the SDO problem. In the solutions, the success rate of the combat task is maximized, whereas the execution time of the task and the cost of changes in the system structure are minimized. The presented optimized algorithm generates an optimal solution without the need to check the entire search space. A novel method is finally proposed based on the combination of heuristic method and genetic algorithm (HGA), as well as the combination of heuristic method and particle swarm optimization (HPSO). Experiment results show that the HPSO method generates solutions faster than particle swarm optimization (PSO) and genetic algorithm (GA) in terms of execution time and performs more efficiently than the heuristic method in terms of determining the best solution.

Today’s electromagnetic environment is still not optimistic enough, and spectrum resources are relatively few. In the actual allocation, unevenness will inevitably occur. In addition, the current level of intelligent monitoring is limited, and it is impossible to fully grasp the dynamics of frequency use. Spectrum monitoring emergency maneuverability still needs to be further improved, and there is a lack of refined spectrum resource management measures. In order to solve this problem as soon as possible, the article proposed a solution to the electromagnetic spectrum monitoring problem based on Cloud Computing, Big Data and Artificial Intelligence technology, proposed construction plans for various main applications and corresponding monitoring services, mainly strengthening the construction of handheld monitoring systems, electromagnetic spectrum monitoring and control systems, cloud monitoring systems, Big Data analysis systems, and intelligent monitoring and dispatching systems.

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Abstract. In this paper, we proposed a novel image feature descriptor, namely texture structure histogram (TSH) for content-based image retrieval. This method using the color and edge orientation information to describe the image texture structure information. Considering the HSV color space conforms to humans' visual perception mechanism, the feature extraction is conducted in the HSV color space. This paper puts forward the non-equal interval quantization scheme that makes the expression of the image information to be more reasonable. In feature representation phase, we use the feature fusion mechanism that makes the color and shape information merge together and get a better results. The experiment results demonstrate that the proposed method more efficient and have a high retrieval performance.