Concerning the problem that single UML model can not test the software sufficiently, this paper proposed a new method of automatically generating software test clues by combining the characteristics of UML2.0 interaction overview diagram. First, this paper gave the formal definition of UML class diagrams, sequence diagrams and Interaction Overview Diagrams (IOD) . Second, the Node Control Flow Graph (NCFG) was constructed by extracting the process information of the interaction overview diagram while the Message Sequence Diagrams (MSD) were constructed by extracting the object interaction information. The testable model of IOD was constructed by embedding the MSD's message path into NCFG. At last, the paper adopted two-two coverage criterion to generate test clues. The experiment verifies that this method which automatically generates test clues avoids the combinatorial explosion while guaranteeing the test adequacy.

Maximizing the user's signal interference noise ratio and minimizing interference leakage is a contradiction in preprocessing for network Multiple-Input Multiple-Output (MIMO) system. Therefore, an equilibrium precoding algorithm based on equilibrium strategy was proposed. Mathematic model for precoding algorithm was constructed and interference pricing function was also introduced to obtain the precoding vector by Lagrange equation. Implementation procedure of the proposed algorithm was also summarized. Finally the proposed algorithm was compared with conventional algorithms and convergence of utility function was discussed by simulation. The results show that the proposed algorithm performs better than 〖BP(〗other schemes.〖BP)〗those existing schemes in convergence and throughput.

In order to avoid the blend choice of the initial control input in Iterative Learning Control (ILC) when the control system faced a new desired trajectory-tracked task or a new environment, an improved algorithm was proposed to obtain the initial value of the iterative learning control based on Back Propagation (BP) neural network. Desired control input of iterative learning control was estimated by BP neural network incorporated experience database. The simulation results show that the algorithm is feasible and effective.