When using the in-network nodes with cache ability for collaborative caching, the packets need to be forward quickly according to the surrounding caching status. A new data-plane-fast-forwarding method was proposed for this problem. Two bloom filters were kept for each port in the switch to maintain the surrounding caching status at the data plane. Meanwhile, the action of protocol oblivious forwarding was also extended. The extended action searched the bloom filters directly, and the optimized forwarding process was used to forward packets according to the searching results, then the packets were forwarded quickly based on the surrounding caching status. The evaluation results show that the caching status maintained by the controller reaches the forwarding performance bottleneck when the input rate is 80 Kb/s. The packets can be forwarded at line speed when the input rate is 111 Mb/s by using the data-plane-fast-forwarding method, which efficiency of forwarding is superior to the output action of protocol oblivious forwarding. The memory overhead of maintaining caching status by using the bloom filter is up to 20% of that by using the flow table. In Software Defined Networking (SDN) with cache ability, the proposed method can maintain the surrounding caching status at the data plane and promote the efficiency of forwarding packets by the surrounding caching status for collaborative caching.

In order to solve the problem of updating distance information frequently in the traditional method of setting uplink synchronization based on ranging information, a uplink clock synchronization method based on location information was proposed. Firstly, by measuring the pseudoranges to form a nonlinear system of equations, the location information of the terrestrial unit was located by using the solution method based on the principle of least squares. Then, due to the known location information of satellite movement, the change relationship of the distance between the satellite and the ground with time could be further obtained. The distance was converted into time delay to obtain the time advance of the uplink signal transmission of the terrestrial unit. Finally, the transmitter of the terrestrial unit was adjusted to make that the uplink signal could just arrive at the satellite in the assigned time slot with high accuracy, and the purpose of uplink clock synchronization was achieved. The simulation results show that, the proposed method can realize the synchronization of uplink clock in the satellite constellation communication system with high accuracy for the static units in the earth surface all over the world, and avoid the frequent ranging updates with high accuracy.

Aiming at the shortages in similarity calculation and rating prediction in traditional recommendation system, in order to further improve the accuracy and stability of the algorithm, a new recommendation algorithm was proposed. Firstly, according to the number of important labels for an item, the M ² similarity between the item and other items was calculated, which was used to constitute the nearest item set of the item. Then, according to the Slope One weighting theory, a new rating prediction method was designed to predict users' ratings based on the nearest item set. To validate the accuracy and stability of the proposed algorithm, comparison experiments with the traditional recommendation algorithms including K-Nearest Neighbor (KNN) algorithm based on Manhattan distance were conducted on MovieLens dataset. The experimental results showed that compared with the KNN algorithm, the mean absolute error and the root mean square error of the new algorithm were decreased by 7.6% and 7.1% respectively. Besides, the proposed algorithm performs better in stability, which can provide more accurate and personalized recommendation.

To solve the problem of how to measure diversity among weak classifiers created by AdaBoost as well as the over-adaptation problem of AdaBoost, an improved AdaBoost method based on double-fault measure was proposed, which was based on the analysis and study of the relationship between four diversity measures and the classification accuracy of AdaBoost. Firstly, Q statistics, correlation coefficient, disagreement measure and double-fault measure were selected for experiment on the data sets from the UCI (University of CaliforniaIrvine Irvine) machine learning repository. Then, the relationship between diversity and ensemble classifier's accuracy was evaluated with Pearson correlation coefficient. The results show that each measure tends to a stable value in the later stage of iteration; especially double-fault measure changes similarly on different data sets, increasing in the early stage and tending to be stable in the later stage of iteration. Finally, a selection strategy of weak classifier based on double-fault measure was put forward. The experimental results show that compared with the other commonly used ensemble methods, the test error of the improved AdaBoost algorithm is reduced by 1.5 percentage points in average, and 4.8 percentage points maximally. Therefore, the proposed algorithm can improve classification performance.

Aiming at the problem that there is redundancy when using the greedy algorithm to solve the minimum MultiPoint Relay (MPR) set in the traditional Optimized Link State Routing (OLSR) protocol, a Global_OP_MPR algorithm based on the improvement of overall situation was proposed. First, an improved OP_MPR algorithm based on the greedy algorithm was introduced, and this algorithm removed the redundancy by gradually optimizing MPR set, which could simply and efficiently obtain the minimum MPR set; then on the basis of OP_MPR algorithm, the algorithm of Global_OP_MPR added the overall factors into MPR selection criteria to introduce "overall optimization" instead of "local optimization", which could eventually obtain the minimum MPR set in the entire network. Simulations were conducted on the OPNET using Random Waypoint motion model. In the simulation, compared with the traditional OLSR protocol, the OLSR protocol combined with OP_MPR algorithm and Global_OP_MPR algorithm effectively reduced the number of MPR nodes in the entire network, and had less network load to bear Topology Control (TC) grouping number and lower network delay. The simulation results show that the proposed algorithms including OP_MPR and Global_OP_MPR can optimize the size of the MPR set and improve the network performance of the protocol. In addition, due to taking the overall factors into consideration, Global_OP_MPR algorithm achieves a better network performance.

Aiming at the problem that thermal comfort prediction, which is a complicated nonlinear process, can not be applied to real-time control of air conditioning directly, this paper proposed a thermal comfort prediction model based on the improved Particle Swarm Optimization-Back Propagation (PSO-BP) neural network algorithm. By using PSO algorithm to optimize initial weights and thresholds of BP neural network, the problem that traditional BP algorithm converges slowly and is sensitive to the initial value of the network was improved in this prediction model. Meanwhile, for the standard PSO algorithm prone to premature convergence, weak local search capabilities and other shortcomings, this paper put forward some improvement strategies to further enhance the PSO-BP neural network capabilities. The experimental results show that, the thermal comfort prediction model based on the improved PSO-BP neural network algorithm has faster algorithm converges and higher prediction accuracy than the traditional BP model and standard PSO-BP model.

The Host Identity Protocol (HIP) macro mobility management was introduced into Distributed Mobility Management (DMM) architecture, and Rendezvous Server (RVS) was co-located with the DMM mobility access routing functionality in Distributed Access Gateway (D-GW). By extending the HIP protocol package header parameters, the HIP BEX messages carried host identifier tuple (HIT, IP address) to the D-GW new registered, and the new D-GW forwarded the IP address using the binding massage. Through the established tunnel, data cached in the front D-GW would be later loaded to the new D-GW. This paper proposed a handover mechanism to effectively ensure data integrity, and the simulation results show that this method can effectively reduce the total signaling overhead. Furthermore, the security of HIP-based mobility management can be guaranteed.

In order to improve the robustness of visual tracking algorithm when the target appearance changes rapidly, a particle filter tracking algorithm based on adaptive subspace learning was presented in this paper. In the particle filter framework, this paper established a state decision mechanism, chose the appropriate learning method by combining the verdict and the characteristics of the Principal Component Analysis (PCA) subspace and orthogonal subspace. It not only can accurately, stably learn target in low dimensional subspace, but also can quickly learn the change trend of the target appearance. For the occlusion problem, robust estimation techniques were added to avoid the impact of the target state estimation. The experimental results show that the algorithm has strong robustness in the case of illumination change, posture change, and occlusion.

Concerning to the problem that Wireless Sensor Network (WSN) data collection has large data redundancy, large cumulative error and low data accuracy, according to the temporal correlation between collection data, a data compression and optimization algorithm for WSN was proposed. It established segmented one-dimensional linear regression model by analyzing linear relationship of collection data in temporal series. According to the error between collection data and predicted value of regression model, it adaptively adjusted next collection time, and dynamically adjusted the regression model. The simulation results show that the proposed algorithm can reduce the data redundancy and network traffic, and improve the reconfiguration precision of the collection data under different conditions of data changes. The test results in a real scenario show the feasibility of the proposed algorithm.

In order to improve the performance of face recognition under non-uniform illumination conditions, a face recognition method based on Patterns of Monogenic Oriented Magnitudes (PMOM) was proposed. Firstly, multi-scale monogenic filter was used to get monogenic magnitude maps and orientation maps of a face image. Secondly, a new operator named PMOM was proposed to decompose the monogenic orientation and magnitude into several PMOM maps by accumulating local energy along several orientations, then Local Binary Pattern (LBP) was used to get LBP feature map from each PMOM map. Finally, LBP feature maps were divided into several blocks, and the concatenated histogram calculated over each block was used as the face feature. The experimental results on the CAS-PEAL and the YALE-B face databases show that the proposed approach improves the performance significantly for the image face with illumination variations. Other advantages of our approach include its simplicity and generality. Its parameter setting is simple and does not require any training steps or lighting assumption and can be implemented easily.

Concerning the disadvantages of traditional face recognition methods, such as high dimension of extracted feature, higher computational complexity, a new method of face recognition combining monogenic filtering with Local Quantiztative Pattern (LQP) was proposed. Firstly, the multi-modal monogenic features of local amplitude, local orientation and local phase were extracted by a multi-scale monogenic filter; secondly, the LQP operator was used to get LQP feature maps by encoding the three kinds of monogenic features in each pixel; finally, the LQP feature maps were divided into several blocks, from which spatial histograms were extracted and connected as the face feature. ORL and CAS-PEAL face databases were used to test the proposed algorithm and the recognition rates were higher than all the other methods used in the experiments. The results validate that the proposed method has higher recognition accuracy and can reduce the computational complexity significantly.

In order to use the magnitude and phase information of filtered image for face recognition, a new method fusing Monogenic local phase and local magnitude was proposed. Firstly, the authors encoded the phase using the exclusive or (XOR) operator, and combined the orientation and scale information. Then the authors divided the phase pattern maps and binary pattern maps based on magnitude into blocks. After that, they extracted the histograms from blocks. Secondly, they used the block-based Fisher principle to reduce the feature dimension and improve the discrimination ability. At last, the authors fused the cosine similarity of magnitude and phase at score level. The phase method Monogenic Local XOR Pattern (MLXP) reached the recognition rate of 0.97 and 0.94, and the fusing method recognition rate was 0.99 and 0.979 on the ORL and CAS-PEAL face databases respectively and the fusing method outperformed all the other methods used in the experiment. The results verify that the MLXP method is effective. And the method fusing the Monogenic magnitude and phase not only avoids the Small Sample Size (3S) problem in conventional Fisher discrimination methods, but also improves the recognition performance significantly with smaller time and space complexity.

In Ecommerce, in order to know more about the inherent characteristics of user access and make better marketing strategies, a Web clustering algorithm based on Hybrid Probabilistic Latent Semantic Analysis (HPLSA) model was proposed in this paper. The Probabilistic Latent Semantic Analysis (PLSA) models were established respectively on user browsing data, page information and enhanced user transaction data by using PLSA technology. Using loglikelihood function, three PLSA models were merged to get the user clustering HPLSA model and the page clustering HPLSA model. Similarity calculation was based on the conditional probability among latent themes and user, page as well as site in the clustering analysis. The kmedoids algorithm based on distance was adopted in this clustering algorithm. The HPLSA model was designed and constructed in this article, and the Web clustering algorithm was verified on this HPLSA model. Then it is proved that the algorithm is effective.

Concerning the Maximum Transmission Unit (MTU) problems of nested mobile networks, based on the analysis of the existing mechanisms and the characteristics of the network structure, a tunnel MTU model and a tunnel MTU discovery mechanism based on location update were proposed. By storing the path MTU values between the home Agents at them, and adding the MTU information into the signaling messages, such as router advertisement and binding update, the mechanism can track the tunnel MTU fast and securely with the location update process, and adapt to the multihoming configuration and a variety of route optimization schemes. The simulation and analysis show that this mechanism can reduce packet delay and transmission overhead, and improve bandwidth utilization compared to the existing mechanisms.

Semiconductor manufacturing process's complexity and randomness make it difficult to build determinate prediction model. A new method was presented which used RBF neural network to model this process. Manufacturing lines with various release control and scheduling policies were simulated by software simul8, and the sampling data got from the simulation model were used in the training and test of the prediction model. Results demonstrate that the model's output and the real samples output are basically identical and the model has great generalization ability. So the well-trained network can be used to forecast the state of the process rapidly and accurately, which lays foundation to prediction control and real time scheduling.

Auther proposed a brand new encryption method for video sequence, based on Chaos theory. This method adopted multilayer chaotic transformation,therefore,the key can be changed during theencryption process, and the security performance is greatly improved.Besides, this algorithm inherited the selective encrytion idea, it only processed the key messages in the video sequence, so encryption efficiency is enhanced remarkably to satisfy the requirements of realtime communication. Furthermore, through computer simulations, the security performance and encryption efficiency of this method was demonstrated.

Chaos is widely used in image encryption because of its high sensitivity to initial conditions and parameters and its stochastic series. A kind of chaos map whose parameters were randomly changed was brought forward. A chaos key stream with good randomcity and long cycle was made out, whose statistic character was proved strictly. The key stream has been used to encrypt image, and method was proved to work well.