It the rate control algorithms of the new generation video coding standard H.266/VVC （Versatile Video Coding）， the rate-distortion optimization technique with independent coding parameters is adopted. However， the Coding Tree Units （CTUs） within the same frame affect others in the spatial domain， and there are global coding parameters. At the same time， in the CTU-level bit allocation formulas， approximated coding parameters for bit allocation are used， resulting in the reduction of rate control accuracy and coding performance. To address these issues， a spatial-domain global optimization algorithm for CTU-level bit allocation called RTE_RC （Rate Control with Recursive Taylor Expansion） was proposed， and the global coding parameters were approximated by using a recursive algorithm. Firstly， a globally optimized bit allocation model in spatial-domain was established. Secondly， a recursive algorithm was used to calculate the global Lagrange multiplier in the CTU-level bit allocation formula. Finally， the bit allocation of coding units was optimized and the coding units were coded. Experimental results show that under the Low-Delay Prediction frame （LDP） configuration， compared with the rate control algorithm VTM_RC （Rate Control algorithm Versatile Test Model）， the proposed algorithm has the rate control error decreased from 0.46% to 0.02%， the bit-rate saved by 2.48 percentage points， and the coding time reduced by 3.52%. Therefore， the rate control accuracy and rate distortion performance are significantly improved by the proposed algorithm.

Aiming at the problem of neglecting some narrow roads due to the formation constraints in the multi-UAV （Unmanned Aerial Vehicle） cooperative trajectory planning, a Fast Particle Swarm Optimization method based on Adaptive Distributed Model Predictive Control (ADMPC-FPSO) was proposed. In the method, the formation strategy combining leader-follower method and virtual structure method was used to construct adaptive virtual formation guidance points to complete the cooperative formation control task. According to the idea of model predictive control, combined with the distributed control method, the cooperative trajectory planning was transformed into a rolling online optimization problem, and the minimum distance and other performance indicators were used as cost functions. By designing the evaluation function criterion, the variable weight fast particle swarm optimization algorithm was used to solve the problem. The simulation results show that the proposed algorithm can effectively realize the multi-UAV cooperative trajectory planning, can quickly complete the adaptive formation transformation according to the environmental changes, and has lower cost than the traditional formation strategy.

By the combination of fuzzy particle swarm optimization algorithm based on receding horizon control and improved artificial potential field, an on-line path planning method for achieving fixed-wing Unmanned Aerial Vehicle (UAV) path planning in uncertain environment was proposed. Firstly, the minimum circumscribed circle fitting was performed on the convex polygonal obstacles. Then, aiming at the static obstacles, the path planning problem was transformed into a series of on-line sub-problems in the time domain window, and the fuzzy particle swarm algorithm was applied to optimize and solve the sub-problems in real time, realizing the static obstacle avoidance. When there were dynamic obstacles in the environment, the improved artificial potential field was used to accomplish the dynamic obstacle avoidance by adjusting the path. In order to meet the dynamic constraints of fixed-wing UAV, a collision detection method for fixed-wing UAV was proposed to judge whether the obstacles were real threat sources or not in advance and reduce the flight cost by decreasing the turning frequency and range. The simulation results show that, the proposed method can effectively improve the planning speed, stability and real-time obstacle avoidance ability of fixed-wing UAV path planning, and it overcomes the shortcoming of easy to falling into local optimum in traditional artificial potential field method.

To improve the calculation speed and robustness of the Speaker Recognition (SR) system, the authors proposed a speaker recognition algorithm method based on utterance level Principal Component Analysis (PCA), which was derived from the frame level features. Instead of frame level features, this algorithm used the utterance level features in both training and recognition. What's more, the PCA method was also used for dimension reduction and redundancy removing. The experimental results show that this algorithm not only gets a little higher recognition rate, but also suppresses the effect of the noise on speaker recognition system. It verifies that the algorithm based on utterance level features PCA can get faster recognition speed and higher system recognition rate, and it enhances system recognition rate in different noise environments under different Signal-to-Noise Ratio (SNR) conditions.

In order to provide a logical basis and mathematical model for directly processing natural language, the Lukasiewicz implication algebra based on the graded linguistic values chain and the linguistic truth-valued concept lattice were established. The natural language used to depict certain values in practical problems was analyzed and equivalently expressed as the graded linguistic values set, on which the definitions of partial order relations and binary operators were given. The bijective relation was established between the graded linguistic values chain and the natural language set, and the specific linguistic truth-valued concept lattice was constructed based on the linguistic truth-valued lattice implication algebra. And then, the linguistic truth-valued concept lattice was applied into the analytical system with natural language for vehicle transport safety performance, which verified the feasibility of the model to directly deal with the natural language and the readability of the structure graph.

Making use of the flexible programmability of SOPC (System On a Programmable Chip) and strong parallel processing ability of FPGA (Field Programmable Gate Array), the speaker recognition algorithm was implemented on FPGA, and the system was optimized in terms of identification speed and accuracy. The principle of speaker recognition algorithm got researched, and according to the characteristics, the SOPC was constructed. It used ping-pong operation to implement voice collection and processing, and used the hardware of FPGA to deal with some time-consuming modules in algorithm so as to quicken the recognition. It also used Genetic Algorithm （GA） to generate template codebook to improve the identification accuracy. Finally, the system realized the function of identity recognition with high real-time quality and accuracy.

Many different communication radius of the communication nodes that may cause an unstable network performance can be easily found in Epidemic Routing (ER) network. A network model that combines network coding and epidemic routing can solve this problem. Compared with the traditional epidemic routing, the Network Coding Based Epidemic Routing (NCER) can transmit packets with network coding. In order to compare the performances of the ER and NCER, a probability model of the transmission delay of the network was built. The comparative results between the two protocols with the probability model above show that NCER can be more efficient and stable than ER. The correctness of this probability model has been proved in the simulation. Finally, according to the model evaluation results, a scheme has been given to reduce the network transmission delay.