Fan Xinnan

A fast super-resolution algorithm with despeckling for multi-frame sonar images

To obtain a high-resolution sonar image from a set of shifted and decimated low-resolution sonar images, a fast super-resolution algorithm with despeckling is proposed in this paper. According to the sonar imaging model, low-resolution images can be viewed as speckled and downsampled versions of the high-resolution image. Based on L1 norm minimization, original high-resolution sonar image can be obtained by non-iterative data fusion. Then the high-resolution sonar image is despeckled in Curvelet domain to get the final high-resolution image. Without iterative operation, the proposed algorithm is fast. Final simulation results indicate that the proposed algorithm is superior to other super-resolution methods.

Research on Agent Coordination Controller Based On Fuzzy Petri Nets

Agent is one of the representative subjects for research in the field of artificial intelligent. And the architecture of agent is a problem under hot discussion. The classes of agent architecture are introduced in detail and the main approaches to improve the performance of the hybrid agent is discussed in this paper, based on the analysis on the architecture of agent. A coordination controller model of agent based on fuzzy Petri nets is proposed, aim at the problem exist in the coordination controller in the hybrid agent model. And some experiments are given out to illustrate the working process of agent coordination controller based on fuzzy Petri nets.

Algorithm of Classifying Classes of Social Networks

Social Networks have become embedded in our daily life so much that we no longer realize it. But researches on the structures of networks are correspondingly fewer than their statistical properties, however, the studies of structural features are necessary and significant. Here we present an approach to study the structures of social networks based on Role-to-role Connectivity Profiles (RCP). We conduct experiments in some classical social networks, we find that the social networks with different functions exhibit obvious different profiles. Different from the classification of complex networks, social networks are divided into two distinct classes with strict judgement basis rather than vague judgement. In addition, according to the RCP, we can find important nodes in social networks, which is more significant than those with high degree. Through the use of the algorithm in social networks, we can thoroughly understand a social network by the comprehensive network classifications rather than only relying on community detection and other global properties.