Hyperspectral images have been widely used in earth observation. However, there are some problems such as huge amount of data and high correlation between bands. An application of particle swarm optimization algorithm based on B distance was proposed to band selection of hyperspectral images. First of all, bands are grouping by the correlation coefficient of the band and adjacent bands. B distance was used as separability criterion between classes and the fitness function comes into being. Finally, the classification results illustrate that the total classification accuracy of the proposed method is higher than the traditional method.

Urban drainage system involves urban surface runoff, drainage pipeline system and rivers and its dynamic behavior is driven both by natural and artificial forces. There is a lack of appropriate and progressive hydraulic dynamic models for whole urban drainage system, together with much difficulty in collecting operation data, and backwardness of operation control techniques, thereby causing the frequent occurrence of urban flooding, sewage overflow and high energy-consumption of the pump stations. Therefore, it is hard to guarantee the security, reliability and high-efficiency of the operation of the urban drainage networks. To solve these problems, this paper proposed a large closed-loop control system model to achieve multi-objective and comprehensive operation optimization of urban drainage networks, based on the design of a new control model of a progressive system of city runoffs, drainage pipeline network and river tunnels.

Aiming at the difficulties in the segmentation for high-resolution remote multispectral sensing images, this paper proposed a segmentation approach for remote sensing images based on texture features. The algorithm implemented precipitation watershed transform respectively on the texture images obtained by the different characteristics of GLCM, and then superimposed the two segmentation results, finally completing the image segmentation by using a novel regional consolidation method that combined the texture features. The experiments were implemented on the high-resolution ALOS and SPOT 5 remote sensing images respectively. Compared with the traditional watershed segmentation approach based on gradient information, the experimental results showed that the proposed algorithm can accurately locate the edges of objects, effectively overcome the phenomenon of over-segmentation and under-segmentation, with a higher segmentation accuracy and stability.

Relay technology is an important technical means to achieve a smooth transition from Long Term Evolution (LTE) to LTE-Advanced (LTE-A). Theoretically, the addition of nodes will improve the performance of the system effectively. While the relay is active nodes, addition of nodes become a new source of interference. Based on the LTE-A simulation platform, the effect of relay node on the system is analyzed.

In this paper, the principal component analysis method is applied in the underwater image data for detecting the image objects. The system is designed to assist the underwater monitor system survey operations, specialized to the task of object identification. Firstly, the nature of the underwater is analyzed according to the image formation model and the appearance. Then, the discipline of the principal component analysis is theoretically analysis. Third, the principal component analysis method is applied in the underwater image for dimension reduction, extracting the image feather for recognition. Experimental results, which have been performed on a set of real underwater images, demonstrate the robustness and the accuracy of the principal component analysis in the task of underwater object recognition.