To solve problems of population diversity and convergence speed when an Ant Colony System (ACS) is used to robot path planning, a dynamic chaos operator was introduced in the ACS. The dynamic chaotic ACS can balance population diversity and convergence speed. The core of dynamic chaotic ACS is that a Logistic chaotic operator was added to the traditional ACS to increase population diversity and improve the quality of the solutions. First, the chaotic operator was added to the pre-iteration to adjust the global pheromone value in the path to increase the population diversity of the algorithm, so as to avoid the algorithm to fall into the local optimal solution. Then, in the later stage, the ACS was used to ensure convergence speed of the dynamic chaotic ACS. The experimental results show that the dynamic chaotic ACS has better population diversity compared with the ACS for the robot path planning problem. The solution quality is higher and the convergence speed is faster. Compared with the Elitist Ant colony System (EAS) and the rank-based Ant System (ASrank), the dynamic chaotic ACS can balance the relationship between the quality of the solutions and the convergence speed. The dynamic chaotic ACS can find better optimal solutions even in the complex obstacle environment. The dynamic chaotic ACS can improve the efficiency of mobile robot path planning.

The Host Identity Protocol (HIP) macro mobility management was introduced into Distributed Mobility Management (DMM) architecture, and Rendezvous Server (RVS) was co-located with the DMM mobility access routing functionality in Distributed Access Gateway (D-GW). By extending the HIP protocol package header parameters, the HIP BEX messages carried host identifier tuple (HIT, IP address) to the D-GW new registered, and the new D-GW forwarded the IP address using the binding massage. Through the established tunnel, data cached in the front D-GW would be later loaded to the new D-GW. This paper proposed a handover mechanism to effectively ensure data integrity, and the simulation results show that this method can effectively reduce the total signaling overhead. Furthermore, the security of HIP-based mobility management can be guaranteed.

Images captured in hazy weather suffer from poor contrast and low visibility. This paper proposed a single image defogging algorithm to remove haze by combining with the characteristics of HSI color space. Firstly, the method converted original image from RGB color space to HSI color space. Then, based on the different affect to hue, saturation and intensity, a defogged model was established. Finally, the range of weight in saturation model was obtained by analyzing original images saturation, then the range of weight in intensity model was also estimated, and the original image was defogged. In comparison with other algorithms, the experimental results show that the running efficiency of the proposed method is doubled. And the proposed method effectively enhances clarity, so it is appropriate for single image defogging.

UWB (UltraWideBand) technology is considered to be the most suitable for indoor wireless location and IEEE802.15.4a is the first radio ranging and positioning physical layer IEEE standard. In order to let the sender know the quality of the ranging, FoM (Figure of Merit) is added in this protocol, but how to produce FoM is not given. On the basis of analyzing the statistical characteristics of the received signal energy block, a method based on the joint parameters of skewness and the maximum slope was proposed to estimate the FoM in UWB radio channel. The simulation finds that this method can provide reference for accurate ranging and positioning, and can improve the ranging accuracy about 30% in the CM1(Channel Model) channel.

For resolving the problem of curvature calculation for scattered point cloud data with strong noise, a robust statistics approach to curvature estimation was presented. Firstly the local shape at a sample point in 3D space was fitted by a quadratic surface. In addition,the fitting was performed at multiple times with randomly sampled subsets of points, and the best fitting result evaluated by variable-bandwidth maximum kernel density estimator was obtained. At last, the sample point was projected onto the best fitted surface and the curvatures of the projected point was estimated. The experimental results demonstrate that the proposed method is robust to noise and outliers. Especially with increasing noise variance, the proposed method is significantly better than the traditional parabolic fitting method.

Concerning to the low efficiency and less availability for re-use in automation marking method applied in webpage making operational questions, this paper built up a marking model based on formal language. It analyzed and solved two key issues.Marking information was designed by descriptive language and formal syntax analyses and matching were carried out. Furthermore, some case study illustrates that this particular method improves assessment efficiency and availability for code re-use.

This paper proposed an improved method for traditional D1 shape distribution function with complex computation which obtains large amount of samples and can not fully describe the model content and also has low search rate. There are key points of this method as follows: first, it normalizes the model by using translation and zoom so that it can reduce the difference between the triangular patches and make the sampling points uniformly fall on the surface of the model; secondly, it uses the index number of the triangular patches to generate the random numbers, and uses the gravity of model and centric of triangular patch for effective computation, to reduce the processing time and improve the efficiency of three-dimensional model retrieval. In addition, this paper validated the practical value of improved D1 shape distribution method by using the idea of semi-automatic model classification. Using part models of three-dimensional model database in Princeton University, the results show that: both improved methods will not decrease the precision and recall of model retrieval, but effectively reduce the processing time.

Web software complexity metrics does play a very important role in the software development. The traditional software complexity metrics method mainly targets on the non-Web applications which use language like C/C++ and Ada. This paper took object-oriented Web software based on Struts framework as research subject and put forward three complexity metrics suitable for the Web-Struts software. Besides, this paper also proposed a method for computing Web software complexity metrics based on Artificial Fish Swarm Algorithm (AFSA) with cross operator and Projection Pursuit (PP) algorithm. After integrating multiple complexity metrics into one-dimension comprehensive projection value, the optimized projection direction could be acquired through sample data. Then the comprehensive projection value of evaluation grades could also be determined. According to the comparison between the comprehensive projection values of the testing samples and the interval of level, the comprehensive metrics result could be finally obtained. The example evaluation results prove the feasibility and effectiveness of the proposed method.

Document clustering had been employed in information filtering,web page classification and so on.K-means is one of the widely used clustering techniques because of its simplicity and high scalability.Owing to its random selection of initial centers,unstable results were often got when using traditional K-means and its variants.Here a technique of optimization initial centers of clustering was proposed.Combined with incremental iteration,it can produce clustering results with high purity,low entropy as well as good stableness.