The community structure in complex networks can help people recognize basic structure and functions of network. Aiming at the problems of low accuracy and high complexity of most community division algorithms, a community division algorithm based on similarity of common neighbor nodes was proposed. Firstly, a similarity model was proposed in order to calculate the similarity between nodes. In the model, the accuracy of similarity measurement was improved by calculating the tested node pairs and their neighbor nodes together. Secondly, local influence values of nodes were calculated, objectively showing the importances of nodes in the network. Thirdly, the nodes were hierarchically clustered according to the similarity and local influence values of nodes, and preliminary division of network community structure was completed. Finally, the preliminary divided sub-communities were clustered until the optimal modularity value was obtained. The simulation results show that compared with the new Community Detection Algorithm based on Local Similarity (CDALS), the proposed algorithm has the accuuracy improved by 14%, which proves that the proposed algorithm can divide the community structure of complex networks accurately and effectively.
Based on open source softwares of Computer Haptics, visualizAtion and Interactive in 3D (CHAI 3D) and Open Graphic Library (OpenGL), a virtual surgical system was designed for reduction of maxillary fracture. The virtual simulation scenario was constructed with real patients' CT data. A geomagic force feedback device was used to manipulate the virtual 3D models and output haptic feedback. On the basis of the original single finger-proxy algorithm, a multi-proxy collision algorithm was proposed to solve the problem that the tools might stab into the virtual organs during the simulation. In the virtual surgical system, the operator could use the force feedback device to choose, move and rotate the virtual skull model to simulate the movement and placement in real operation. The proposed system can be used to train medical students and for preoperative planning of complicated surgeries.
To overcome the salient extraction results cannot preserve edge and enrich the inner details when extracting image salient region, a new multi-scale extraction approach based on frequency domain was proposed. In order to remove redundant information and get the innovation, the image was Fourier-transformed to get the spectral residual on multiple resolutions. Then normalization processing was applied to obtain the final saliency image. The simulation results show that the proposed method has good visual effect, which can keep the edges of salient region and highlight the whole significant target uniformly at the same time. The area under Receiver Operating Characteristic (ROC) curve of these results also has satisfied performance.
To reduce the calculation complexity of the Joint Probabilistic Data Association (JPDA) joint-association events, due to multiple targets' tracks aggregation, an improved JPDA algorithm, clustering by Meanshift algorithm and optimizing confirmation matrix by Bhattacharya coefficients,was proposed.The clustering center was created by Meanshift algorithm. Then the tracking gate was obtained by calculating Mahalanobis distance between the clustering center and targets' prediction observation. The Bhattacharya likelihood matrix which was as a basis for low probability events was created, consequently the computing complexity of JPDA joint-association events which was related to low probability events was reduced. The experimental results show that the new method is superior to the conventional JPDA both in computational complexity and precision of estimation for multiple targets' tracks aggregation.