For redundancy chaos, and the lack of representation of group observers' scanpath data in natural scenes, by mining the potential characteristics of individual scanpaths, a method for group scanpath generation based on fixation Regions of Interest (ROI) spatial temporal clustering and transferring was proposed. Firstly, multiple observers' scanpaths under the same stimulus sample were analyzed, and multiple fixation regions of interest were generated by utilizing affinity propagation clustering algorithm to cluster the fixation points. Then, the statistics and analysis of the information related to fixation intensity such as the number of observers, fixation frequency and lasting time were carried out and the regions of interest were filtered. Afterwards, the subregions of interest with different types were extracted via defining fixation behaviors in the regions of interest. Finally, the transformation mode of regions and subregions of interest was proposed on the basis of fixation priority, so as to generate the group scanpath in natural scenes. The group scanpath generation experiments were conducted on two public datasets MIT1003 and OSIE. The results show that compared with the state-of-the-art methods, such as eMine, Scanpath Trend Analysis (STA), Sequential Pattern Mining Algorithm (SPAM), Candidate-constrained Dynamic time warping Barycenter Averaging method (CDBA) and Heuristic, the proposed method has the group scanpath generated of higher entirety similarity indexes with ScanMatch (w/o duration) reached 0.426 and 0.467 respectively, and ScanMatch (w/duration) reached 0.404 and 0.439 respectively. It can be seen that the scanpath generated by the proposed method has high overall similarity to the real scanpath, and has a certain function of representation.

Considering the antenna oscillation phenomenon, the performance of spotbeam selection in Geostationary Earth Orbit Mobile Radio Interface (GMR-1) system under antenna oscillation was analyzed and an improved spotbeam selection algorithm was proposed. The improved algorithm could dynamically set hysteresis using the distance between the Mobile Earth Station (MES) and its Gateway Station (GS). A simulation model was implemented using OPNET. The simulation results show that the MES at different location is influenced by antenna oscillation to different extent. Besides, the wrong times of spotbeam selection increases with the increase of hysteresis and the maximum amplitude in the traditional algorithm. Finally, the improved algorithm can reduce the wrong times of spotbeam selection and restrain the influence on spotbeam selection under antenna oscillation efficiently.

The original New Edge-Directed Interpolation (NEDI) algorithm is of high complexity, difficult for hardware implementation, and the interpolated images may suffer from blurring edges around edge area. To achieve a better subjective quality, an improved NEDI algorithm was proposed in this paper. In the new algorithm, a circular window was adopted, and the interpolation coefficient calculation was calculated only once, which could be reused in interpolating the center-pixels, thus the errors introduced by iterative computation were avoided and the interpolation time was saved. As to non-center pixels, six original neighbors were involved to estimate local covariance characteristics at high resolution. In comparison with the results of bi-cubic interpolation and the traditional NEDI, the experimental results indicate that proposed algorithm can eliminate the sawtooth of the interpolated picture in large-scale, and decrease the computational complexity.

The functionality of a framework that supported location-based services on moving objects in road networks was extended and Snapshot K Nearest Neighbor (SKNN) queries based on Mobile Network Distance Range (MNDR) queries was proposed using an on-disk R-tree to store the network connectivity and an in-memory grid structure to maintain the moving object position updates. The minimum and maximum number of grid cells of a given arbitrary edge in the space that were possibly affected were analyzed. The maximum bound that could be used in snapshot range query processing to prune the search space was shown. SKNN estimated the subspace containing the query results and used the subspace as range to efficiently compute the KNN POI from the query points to reduce I/O cost and time of query. Analysis shows that the maximum bound can be used in snapshot range query processing to prune the search space. The contrast experiments show that SKNN has better system throughput than S-GRID while scaling to hundreds of thousands of moving objects.

Because of the user's mobility and the location dependency of data, new challenge has been brought to cache replacement strategy for Location Dependent Query (LDQ). Based on the detailed analysis of the space location characteristics of Location Dependent Data (LDD) and several typical replacement strategies of location dependent cache, the authors proposed a prioritized approach cache replacement based on the lowest access cost (PLAC), the PLAC took some important factors into account such as access probabilities, update rates, data distance, valid scope. To ensure the maximum utilization of limited cache, the PLAC cache replacement strategy decided which data would be replaced according to the value of the lowest cost function. The contrast experiments show that the PLAC increases cache hit rate and shortens query average response time more effectively than other location dependent cache replacement strategies.

Mining association rules is an important research field in data mining.The traditional algorithm mining association rules,or slowly produces association rules,or produces too many redundant rules,or it is probable to find an association rule,which posses high support and confidence,but is uninteresting,and even is false.Furthermore,a rule with negative-item can’t be produced.This paper put forwards a new algorithm MVNR(Mining Valid and non-Redundant Association Rules Algorithm),which primely solved above problems by using the minimal subset of frequent itemset.

Mining association rule is an important matter in data mining, in which mining maximum frequent itemsets is a key problem in mining association rule. Many of the previous algorithms mine maximum frequent itemsets by producing candidate itemsets firstly, then pruning. But the cost of producing candidate itemsets is very high, especially when there exist long patterns. In this paper, the structure of a FP-tree was improved, a fast algorithm DMFIA-1 based on FP-tree for mining maximum frequent itemsets was proposed, which did not produce maximum frequent candidate itemsets and was more effective than DMFIA. The new FP-tree is a one-way tree and there is no pointer pointing its children in each node, so at least one third of memory is saved.

Regression testing is an important part in Software Testing. When software has some changes, Regression Testing needs to check if the changes have affected the software quality. These changes include bugs fixing, functionality changes, build process changes, configurations added etc. Changes to one part of software often impact of other working parts and impair the whole system’s quality and functionality. Regression Test can monitor these changes and report the test results to the developer in time. Automation Regression Testing can improve efficiency and quality of Regression Testing. This paper firstly introduces the conceptions of Software Testing and Regression Testing. Secondly it gives an example of a Java project CADS (Cross Architecture Development Suite) to introduce how to design Automation Regression Testing in Java project. At last it summarizes the advantages of the Automation Regression Testing and its scope.