Concerning the problem that existing data discrimination methods can not adapt to changeable test environment and realize continuous real-time discriminating process with low error rate when applied in ground integrated test of payload, a Multi-Agent-based Real-time self-Adaptive Discrimination (MARAD) method was proposed. Firstly, based on the design principle of "sensing-decision-execution", four Agents which had own tasks but also interact and cooperate with each other were adopted in order to adapt the changeable test situation. Secondly, an activity-oriented model was constructed, and the C Language Integrated Production System (CLIPS) was used as an inference engine to make the discrimination rules independent of test sequences and assure the continuity of discrimination. Finally, fault-tolerant mechanism was introduced to the discrimination rules to decrease fault positive rate without changing the correctness. With the same test data, compared with the state modeling method with the average result of three times after discriminating, MARAD method has the same parameter missing rate 0% but decreases the activity false-positive rate by 10.54 percentage points; compared with the manual method, MARAD method decreases the parameter missing rate by 5.97 percentage points and activity false-positive rate by 3.02 percentage points, and no person is needed to participate in the discrimination. The proposed method can effectively improve the environment self-adaptability, real-time discriminating continuity and correctness of the system.

For implementing dynamic aggregation of dispersive Geography Markup Language (GML) geospatial data, a aggregation mapping model based on GeoRSS standards was proposed considering the openness, self-description and dispersion characteristics of GML. A four-tier integrating framework of prototype architechture and its workflow were generated to utilize the above model to construct the related program. Then a aggregation prototype system was designed and developed for aggregating dispersive GML spatial data according to the system architechture. Based on this prototype system, the related experiments were performed to verify the feasibility of GML geospatial data aggregation, and confirm the correctness and availablity of the model. This prototype can help users to quikly find the GML geospatial data from the massive Geographic Information System (GIS) data, to achieve selection, analysis and classification of the existing data, as well as the instant updating and aggregating integration.

In distributed wireless communication and Coordinated Multi-Point (CoMP) system, the deterioration of channel quality seriously affect the system receptivity of edge users, and therefore the coordinated coding processing is used to improve the quality of reception. A coordinated transmission and joint pre-coding method was proposed in this paper. Coordinated Remote Antenna Units (RAU) were processed jointly. According to the state information of channel, different pre-coding rules were adopted by each RAU to transmit data to the same user. A Maximal-Ratio Combining (MRC) algorithm was used to calculate the received signals of terminals. The simulation results show that the proposed method can effectively decrease the Bit Error Rate (BER) and improve the transmission reliability.

A new algorithm was proposed based on the geometric symmetry to detect grid corners. The method can be utilized in camera calibration where chessboard-like patterns were often used. Based on the observation of the geometric symmetry of such patterns, the new algorithm could be regarded as a coarse-to-fine process. Coarse detection defined the corner candidates and then precise extraction was used based on symmetry analysis. Experimental results show that the algorithm assures an efficient and accurate detection and the procedure can be carried out automatically.

To solve the problem of choosing a best reduction from several reductions of one decision table, after thinking about the average significance of attributes in reduction and the number of attributes, a new definition named attribute significance was proposed and proved in detail. Then the algorithm of finding the best reduction was presented based on this new definition. Finally, examples demonstrate the usefulness of the algorithm.