Existing multi-hop clustered routing protocols for Intelligent Road Cone Ad-hoc Network （IRCAN） suffer from redundancy in network control overhead and the average number of hops for data packet transmission is not guaranteed to be minimal. To solve the above problems， combined with the link characteristics of the network topology， an efficient clustered routing protocol based on non-random retroverted clustering， called Retroverted-Clustering-based Hierarchy Routing （RCHR）， was proposed. Firstly， the retroverted clustering mechanism based on central extension and the cluster head selection algorithm based on overhearing， cross-layer sharing， and extending the adjacency matrix was proposed. Then， the proposed mechanism and the proposed algorithm were used to generate clusters with retroverted characteristics around sink nodes in sequence， and to select the optimal cluster heads for sink nodes at different directions without additional conditions. Thus， networking control overhead and time were decreased， and the formed network topology was profit for diminishing the average number of hops for data packet transmission. Theoretic analysis validated the effectiveness of the proposed protocol. The simulation experiment results show that compared with Ring-Based Multi-hop Clustering （RBMC） routing protocol and MODified Low Energy Adaptive Clustering Hierarchy （MOD-LEACH） protocol， the networking control overhead and the average number of hops for data packet transmission of the proposed protocol are reduced by 32.7% and 2.6% at least， respectively.

Considering the degradation and problem that the weight distribution of training targets is wider than average in the traditional AdaBoost algorithm in the process of human face image training, an improved AdaBoost algorithm was proposed based on adjusting margin of error and setting the threshold value. First, the weight values of the samples were updated according to the comparative result between the threshold value and the weight value of the matching errors of the current samples. Then, the emphasis of the training samples was controlled by adjusting the emphasis relation between positive error and negative error. The experimental results showed that different human face image databases and different ratios of positive and negative errors had little effects on the validness of the improved AdaBoost algorithm. Under the positive and negative error ratio of 1:1 in unrestricted face database LFW, the detection rate was 86.7%, which was higher than that of the traditional AdaBoost algorithm; the number of weak classifiers was 116, which was 15 more than that of the traditional AdaBoost algorithm. The results prove that the proposed algorithm suppresses the degradation and the problem that the weight distribution of training targets is wider than average, and effectively improves the detection rate of human face images.

The cuckoo search (CS) algorithm has a few disadvantages in the global searching, including slow convergence and high possibility of being trapped in local optimum. In overcome these disadvantages, a effective hybrid CS algorithm based on Rosenbrock local search and Cauchy mutation is proposed to solve constrained numerical and chemical engineering optimization problems. Firstly, good point set method was used to initiate bird nests position, which strengthened the diversity of global searching. Secondly, for the current best position, Rosenbrock local search technique is introduced to improve the convergence speed of CSA. Thirdly, a Gaussian mutation operator would be given on the global optimum of each generation, thus, the algorithm could effectively jump out of local minima. Experimental results are examined with several constrained numerical functions and chemical engineering optimization problems and the results show a promising performance of the proposed algorithm.

The technology of Digital Time-Stamping (DTS) is widely used in digital signature, electronic commerce and patents and property right protection of various software and hardware. For some unreliable networks, of which the network situation is poor, the net speed changes greatly and the net links are usually intermittent, there is no necessary technological means to guarantee the normal and effective operation of DTS service. According to the characteristics of the unreliable networks, a new time-stamping scheme was proposed. In the scheme, it did not need to communicate with Time Stamp Authority (TSA) each time when a time-stamping service was required. The local trusted platform would offer the time-stamping service itself. A new DTS service protocol based on Trusted Platform Module (TPM) was also proposed under the circumstances of unreliable networks. The results of the security analysis of the protocol show that the protocol is secure and the time error in the protocol can be kept under control. The adaptability of the protocol for the unreliable network is excellent.

Concerning the contradiction of the limited computer memory and the huge terrain data in terrain rendering, the tile-pyramid model was built up and the optimum size of the tile was analyzed. An efficient method of quadtree tile-index according to the relation of quardtree tiles and a thought of data compress based on "tile" were also put forward. The method not only analyzed but also optimized the interactive strategy between the massive terrain data and the three-dimensional display from the data pre-loaded, double-caching mechanism, multi-thread and memory. In combination with classic Geometry Clipmaps algorithm, the terrain rendering was accomplished. The experimental results show excellent rendering effect and good real-time performance, and prove the feasibility and efficiency of the optimized strategy.

Traditional level set method based on gradient model has the leaking problem during the curve evolution. A level set method based on region competition model was proposed to solve this problem. Firstly, the probabilities of the level set curve that belonged to the object region and background region were calculated respectively using probability distribution equation; Secondly, the difference of the foreground and background probabilities and weights were added to the level set function to minimize the energy function during the curve evolution; Finally, the images regional information could be used to identify the object boundary. Experimental results show that the proposed method can segment the medical image well.