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.

The existing attribute value reduction models are complex to implement， and the key information extracted by the models is often too concise， which affects the representation ability of the decision system. To resolve above problems， a heuristic attribute value reduction model based on certainty factor was proposed. Firstly， several attribute set tools with different properties were constructed， and the relevant theorems and proofs were shown； at the same time， a reduced information function was developed to assign values to the reduced attributes. Secondly， the certainty factor was taken as heuristic information and the strategy of bottom-up hierarchical search was adopted to construct a heuristic attribute value reduction model， and the layout path and operation process of the model were visually displayed in the form of the pseudo-codes of the program. Finally， the application and verification of the model were performed on simulation data from the existing research， the advantages， applicability， and scalability of the model were summarized and discussed. The results show that the new model is feasible and effective， easy to implement by programming； it has low requirements of data characteristics and is suitable for general expert systems；moreover， the value information extracted by the new model is diverse and concise with strong generalization， and does not lose the key information of the decision system.

The Fuzzy C-Means clustering (FCM) algorithm has the defects of being sensitive to initial clustering center,being susceptible to noise point interference and poor robustness in solving the problem of speech underdetermined mixing matrix estimation. An improved WEighted FCM algorithm based on evolutionary programming (WE-FCM) was proposed to eliminate the defects. Firstly, the powerful search ability of Evolutionary Programming (EP) algorithm was used to optimize FCM for obtaining FCM algorithm based on EP (EP-FCM), in order to obtain a better initial clustering center. Then, the Local Outlier Factor (LOF) algorithm was used to perform weighting to reduce the effects of noise points. The simulation experiment results show that, the normalized mean square error value and the deviation angle value of the proposed algorithm were both much smaller than those of the classical K -means clustering, K -Hough, FCM algorithm based on Genetic Algorithm (GAFCM) and FCM algorithm based on Find Density Peaks (FDP-FCM) when the number of source signals were 3 and 4. The experimental results show that, the proposed algorithm significantly improves the robustness of FCM algorithm and the accuracy of mixing matrix estimation.

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.

Focused on the potential safety hazard of leakage of fingerprint and other biometrics in the cloud environment, as well as the lack of security or convenience of the existing biometric authentication schemes, a terminal APP of trusted fingerprint authentication based on orthogonal decomposition and TrustZone was designed and implemented. The sensitive operations such as fingerprint feature extraction, fingerprint template generation were executed in the trusted execution environment provided by the hardware isolation mechanism of TrustZone, making these operations isolated from the applications in the general execution environment to resist the attacks of malicious programs and ensure the security of the authentication process. The fingerprint template generated on the basis of orthogonal decomposition algorithm integrate the random noise while remaining the matching ability, so that it was able to resist the attack against the feature template to a certain extent. As a result, the fingerprint template was able to be stored and transmitted in the cloud environment, so that the user and the device were unbound, which improved the convenience of biometric authentication. Experiments and theoretical analysis show that the correlation and randomness of the fingerprint template of the proposed algorithm is higher than those of original feature and random projection algorithms, so that the algorithm has stronger security. In addition, the experimental results of time and storage overheads as well as recognition accuracy show that, both convenience and security are considered in this APP, meeting the requirements of security authentication in mobile cloud environment.

To solve the problem that the existing order preserving encryption schemes are difficult to give consideration to security, efficiency and ease of use at the same time, an order preserving encryption scheme of non-linear mapping based on random function was proposed. In the scheme, the plaintext space was considered as an increasing arithmetic sequence, and each element of the sequence was mapped to a separate ciphertext space based on the key. The key was generated by a random number generating function with non-uniform distribution, and the ciphertext space was constructed by a computer program. During encrypting, the value randomly selected from the corresponding ciphertext space was able to be used as the ciphertext. Analysis and experimental results show that the proposed scheme achieves INDistinguishability under Ordered Chosen Plaintext Attack (IND-OCPA) safety and can effectively prevent statistical attacks; it has the average encryption time per 100 000 data of from 30 ms to 50 ms, resulting in high encryption efficiency; the complex parameter presets are not required in the scheme, and the scheme can be implemented in any computer language, so that it is easy to use.

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.

Spiking neuron is a novel artificial neuron model. The purpose of its supervised learning is to stimulate the neuron by learning to generate a series of spike sequences for expressing specific information through precise time coding, so it is called spike sequence learning. Because the spike sequence learning for single neuron has the characteristics of significant application value, various theoretical foundations and many influential factors, the existing spike sequence learning methods were reviewed and contrasted. Firstly, the basic concepts of spiking neuron models and spike sequence learning were introduced. Then, the typical learning methods of spike sequence learning were introduced in detail, the theoretical basis and synaptic weight adjustment way of each method were pointed out. Finally, the performance of these learning methods was compared through experiments, the characteristics of each method was systematically summarized, the current research situation of spike sequence learning was discussed, and the future direction of development was pointed out. The research results are helpful for the comprehensive application of spike sequence learning methods.

Focusing on the problem that the privacy-preserving of identity authentication in Vehicular Ad Hoc NETworks (VANET), a conditional privacy-preserving authentication scheme was proposed. Firstly, this paper introduced the short signature technology, and then constructed a new identity-based short signature scheme. Compared with the well-known Conditional Privacy-Preserving Authentication Scheme (CPAS), the proposed scheme could reduce the computation costs required for both signature and verification processes and improve the communication efficiency. Secondly, the scheme divided the private signature key into two correlative sub-segments, so that it could effectively solve the issue of key escrow. Therefore, the scheme was especially suitable for the environment of VANET. Based on the proposed signature scheme, a conditional privacy-preserving authentication scheme was presented, which can achieve identity authentication with conditional privacy preservation. The theoretical and efficiency analysis shows that the scheme needs only three dot multiplication in the signature process and takes one dot multiplication, two pairing operation in the verification process. Especially, the proposed scheme use batch verification by adding the small coefficient test to accelerate the authentication speed and reduce the error rate.

Accurate background model is the paramount base for object extracting and tracing. In response to swing objects which part quasi-periodically changed in intricate scene, based on multi-Gaussian background model, a new Quasi-Periodic Background Algorithm (QPBA) was proposed to suppress the swing objects and establish an accurate and stable background model. The specific process included: According to multi-Gaussian background model, the object classification in scene was set up, and the effect on Gaussian model's parameters caused by swing objects was analyzed. By using color distribution values as samples to establish Gaussian model to keep swing pixels, the swing model in swing pixels was integrated into background model with weight factors of occurrence frequency and time interval. Comparison among QPBA and the classical background modeling algorithms such as GMM (Gaussian Mixture Model), ViBe (Visual Background extractor) and CodeBook was put forward, and the results were assessed in aspects of quality, quantity and efficiency. It shows that QPBA has a more obvious suppression on swing objects, and its fall-out ratio is less than 1%, so that it can handle the scene with swing objects. At the same time, its correct detection number is consistent with other algorithms, thus the moving objects can be reserved perfectly. In addition, the efficiency of QPBA is high, and its resolving time is approximate to CodeBook, which can satisfy the requirements of real-time computation.

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.

In this paper, a new efficient algorithm for ellipse detection was proposed, which was based on edge grouping, different from standard Hough transform. Firstly, It separated edge boundary into different arcs at the intersections, divided those arcs into two categories: the long and the short and sorted the two categories at non-increasing sequence, then estimated the parameters of the ellipses using least square fitting method with arcs which may belong to the same ellipse; at last testified whether ellipses coming from the front steps are real ones. The method has been tested on synthetic and real-world images containing both complete and incomplete ellipses. The outcome demonstrates that the algorithm is robust, accurate and effective.