Aiming at the performance degradation of transmission performance of Transmission Control Protocol (TCP) in wireless network caused by the loss packet retransmission mechanism triggered by packet loss, an Adaptive transmission mechanism based on Forward Error Correction (AdaptiveFEC) was proposed. In the mechanism, the transmission performance of TCP was improved by the avoidance of triggering TCP loss packet retransmission mechanism, which realized by reducing data segment loss with forward error correction. Firstly, the optimal redundant segment ratio in current time was selected according to the current network status and the data transmission characteristics of the current connection. Then, the network status was estimated by analyzing the data segment sequence number in the TCP data segment, so that the redundant segment ratio was dynamically updated according to the network. Large number of experiment results show that, in the transmission environment with a round-trip delay of 20 ms and a packet loss rate of 5%, AdaptiveFEC can increase the transmission rate of TCP connection by 42% averagely compared to static forward error correction mechanism, and the download speed can be twice as much as the original speed with the proposed mechanism applied to file download applications.

The diversity of population, the searching capability and the robustness are three key points to the multi-objective optimization problem, which directly affect the convergence of algorithm and the spread of solutions set. To better deal with above problems, a Scatter Search hybrid Multi-Objective Evolutionary optimization Algorithm (SSMOEA) was proposed. The SSMOEA followed the scatter search structure but designed a new selection strategy of diversity and integrated the method of co-evolution in the process of subset generation. Additionally, a novel adaptive multi-crossover operation was employed to improve the self-adaptability and robustness of the algorithm. The experimental results on twelve standard benchmark problems show that, compared with three state-of-the-art multi-objective optimizers, SPEA2, NSGA-Ⅱ and AbYSS, SSMOEA outperforms the other three algorithms as regards the coverage, uniformity and approximation. Meanwhile, its robustness is also significantly improved.

A kind of technique for detection of multiple moving objects based on fussy clustering and Kalman filtering was brought forward, and has been applied to vehicle detection and tracking. An improved fussy C mean clustering algorithm was used, in which the matrix of grade of membership was modified in order to speed up convergent velocity. Kalman filtering was used to track moving target. Corresponding state equation and plus matrix were constructed based on video sequence to track multiple moving objects at the same time. It can achieve fine object searching with more reliability and efficiency.