Aiming at the problem of spectrum allocation based on maximizing network benefit in cognitive radio and the fact that Manta Ray Foraging Optimization （MRFO） algorithm is difficult to solve the problem of spectrum allocation， a Discrete Manta Ray Foraging Optimization （DMRFO） algorithm was proposed.Considering the pro-1 characteristic of spectrum allocation problem in engineering， firstly， MRFO algorithm was discretely binarized based on the Sigmoid Function （SF） discrete method. Secondly， the XOR operator and velocity adjustment factor were used to guide the manta rays to adaptively adjust the position of next time to the optimal solution according to the current velocity. Then， the binary spiral foraging was carried out near the global optimal solution to avoid the algorithm from falling into the local optimum. Finally， the proposed DMRFO algorithm was applied to solve the spectrum allocation problem. Simulation results show that the convergence mean and standard deviation of the network benefit when using DMRFO algorithm to allocate spectrum are 362.60 and 4.14 respectively， which are significantly better than those of Discrete Artificial Bee Colony （DABC） algorithm， Binary Particle Swarm Optimization （BPSO） algorithm and Improved Binary Particle Swarm Optimization （IBPSO） algorithm.

Aiming at the low efficiency of linear combination of base classifiers and over-adaptation of the traditional AdaBoost algorithm, an improved algorithm based on coefficients and diversity of base classifiers - WD AdaBoost (AdaBoost based on Weight and Double-fault measure) was proposed. Firstly, according to the error rates of the base classifiers and the distribution status of the sample weights, a new method to solve the base classifier coefficients was given to improve the combination efficiency of the base classifiers. Secondly, the double-fault measure was introduced into WD AdaBoost algorithm in the selection strategy of base classifiers for increasing the diversity among base classifiers. On five datasets of different actual application fields, compared with the traditional AdaBoost algorithm, CeffAda algorithm uses the new base classifier coefficient solution method to make the test error reduced by 1.2 percentage points on average; meanwhile, WD AdaBoost algorithm has the lower error rate compared with WLDF_Ada, AD_Ada (Adaptive to Detection AdaBoost), sk_AdaBoost and other algorithms. Experimental results show that WD AdaBoost algorithm can integrate base classifiers more efficiently, resist overfitting, and improve the classification performance.

In view of the problem of subjectivity and easy misdiagnosis in the artificial identification of Alzheimer's Disease (AD) through brain imaging, a method of automatic identification of AD by constructing brain network based on Magnetic Resonance Imaging (MRI) image was proposed. Firstly, MRI images were superimposed and were divided into structural blocks, and the Structural SIMilarity (SSIM) between any two structural blocks was calculated to construct the network. Then, the complex network theory was used to extract structural parameters, which were used as the input of machine learning algorithm to realize the AD automatic identification. The analysis found that the classification effect was optimal with two parameters, especially the node betweenness and edge betweenness were taken as the input. Further study found that the classification effect was optimal when MRI image was divided into 27 structural blocks, and the accuracy of weighted network and unweighted network was up to 91.04% and 94.51% respectively. The experimental results show that the complex network of structural similarity based on MRI block division can identify AD with higher accuracy.

As the turbofan engine is one of the core equipment in the field of aerospace, its health condition determines whether the aircraft could work stably and reliably. And the prediction of the Remaining Useful Life (RUL) of turbofan engine is an important part of equipment monitoring and maintenance. In view of the characteristics such as complicated operating conditions, diverse monitoring data, and long time span existing in the turbofan engine monitoring process, a remaining useful life prediction model for turbofan engines integrating Genetic Algorithm-based Selective ENsembling (GASEN) and Temporal Convolutional Network (TCN) (GASEN-TCN) was proposed. Firstly, TCN was used to capture the inner relationship between data under long span, so as to predict the RUL. Then, GASEN was applied to ensemble multiple independent TCNs for enhancing the generalization performance of the model. Finally, the proposed model was compared with the popular machine learning methods and other deep neural networks on the general Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset. Experimental results show that, the proposed model has higher prediction accuracy and lower prediction error than the state-of-the-art Bidirectional Long-Short Term Memory (Bi-LSTM) network under many different operating modes and fault conditions. Taking FD001 dataset as an example:on this dataset, the Root Mean Square Error (RMSE) of the proposed model is 17.08% lower than that of Bi-LSTM, and the relative accuracy (Accuracy) of the proposed model is 12.16% higher than that of Bi-LSTM. It can be seen that the proposed model has considerable application prospect in intelligent overhaul and maintenance of equipment.

PICO proposed in recent years is a bit-based ultra lightweight block cipher algorithm. The security of this algorithm to resist integral cryptanalysis was evaluated. Firstly, by analyzing the structure of PICO cipher algorithm, a Mixed-Integer Linear Programming (MILP) model of the algorithm was established based on division property. Then, according to the set constraints, the linear inequalities were generated to describe the propagation rules of division property, and the MILP problem was solved with the help of the mathematical software, the success of constructing the integral distinguisher was judged based on the objective function value. Finally, the automatic search of integral distinguisher of PICO algorithm was realized. Experimental results showed that, the 10-round integral distinguisher of PICO algorithm was searched, which is the longest one so far. However, the small number of plaintexts available is not conducive to key recovery. In order to obtain better attack performance, the searched 9-round distinguisher was used to perform 11-round key recovery attack on PICO algorithm. It is shown that the proposed attack can recover 128-bit round key, the data complexity of the attack is 2 ^63.46, the time complexity is 2 ⁷⁶ 11-round encryptions, and the storage complexity is 2 ²⁰.

Analysis and research on interbank market based on multi-layer network structure is conducive to avoiding or weakening the risk impact on financial market. Based on test data simulated by credit lending business scenario, combined with the multi-layer network structure and complex network analysis method of interbank market, the important nodes in interbank market were judged and identified from different angles, meanwhile Jaccard similarity coefficient between the layers and inter-institution Pearson similarity coefficient were calculated and the infectousness of risk contagion of interbank market was measured from macroscopic and microscopic perspectives. The experimental results show that large-scale state-owned financial institutions such as Bank of China and China Development Bank are of high importance in the system, and the greater the similarity between institutions, the greater the infectiousness of risk contagion. Therefore, by calculating the important node measure index in the network layer, comprehensive and complete analysis of the risk contagion of the entire system can help the regulators to achieve accurate monitoring of important institutions in the system. At the same time, from the perspectives of inter-layer analysis and intra-layer analysis, comprehensive measurement of the infectious degree of risk contagion between institutions after financial shock provides policy advice to regulators.

Artificial identification of ischemic stroke lesion is time-consuming, laborious and easy be added subjective differences. To solve this problem, an automatic segmentation algorithm based on 3D deep residual network and cascade U-Net was proposed. Firstly, in order to efficiently utilize 3D contextual information of the image and the solve class imbalance issue, the patches were extracted from the stroke Magnetic Resonance Image (MRI) and put into network. Then, a segmentation model based on 3D deep residual network and cascade U-Net was used to extract features of the image patches, and the coarse segmentation result was obtained. Finally, the fine segmentation process was used to optimize the coarse segmentation result. The experiment results show that, on the dataset of Ischemic Stroke LEsion Segmentation (ISLES), for the proposed algorithm, the Dice similarity coefficient reached 0.81, the recall reached 0.81 and the precision reached 0.81, the distance coefficient Average Symmetric Surface Distance (ASSD) reached 1.32 and Hausdorff Distance (HD) reached 22.67. Compared with 3D U-Net algorithm, level set algorithm, Fuzzy C-Means (FCM) algorithm and Convolutional Neural Network (CNN) algorithm, the proposed algorithm has better segmentation performance.

Aiming at the problem of low computational performance of distributed database in analysis applications, taking MongoDB database as an example, a method was put forward to improve the performance of database based on chip and index. Firstly, the characteristics of the business was analyzed to guide the choice of shard key field, and the selected key field needed to ensure that the data is evenly distributed on the cluster nodes. Secondly, by studying the index efficiency of the distributed database, the method of deleting the query field index was used to further improve the computing performance, which could make full use of hardware resources to improve the performance of aggregation computing. The analysis and experimental results show that the shard key field with high cordinality can distribute data evenly on each data node in the cluster, and the use of full table query can effectively improve the convergence speed, thus the optimization method can effectively improve the performance of aggregation computing.

Duing to the complicated pore structure of low porosity and low permeability reservoirs, the prediction accuracy of the existing Nuclear Magnetic Resonance (NMR) logging permeability model for low porosity and low permeability reservoirs is not high. In order to solve the problem, a permeability prediction method based on Deep Belief Network (DBN) algorithm and Kernel Extreme Learning Machine (KELM) algorithm was proposed. The pre-training of DBN model was first carried out, and then the KELM model was placed as a predictor in the trained DBN model. Finally, the Deep Belief Kernel Extreme Learning Machine Network (DBKELMN) model was formed with supervised training by using the training data. Considering that the proposed model should make full use of the information of the transverse relaxation time spectrum which reflected the pore structure, the transverse relaxation time spectrum of NMR logging after discretization was taken as the input, and the permeability was taken as the output. The functional relationship between the transverse relaxation time spectrum of NMR logging and permeability was determined, and the reservoir permeability was predicted based on the functional relationship. The applications of the example show that the permeability prediction method based on DBN algorithm and KELM algorithm is effective and the Mean Absolute Error (MAE) of the prediction sample is 0.34 lower than that of Schlumberger Doll Researchcenter (SDR) model. The experimental results show that the combination of DBN algorithm and KELM algorithm can improve the prediction accuracy of low porosity and low permeability reservoir, and can be used to the exploration and development of oil and gas fields.

To optimize the balance issue of local area accuracy and efficiency in the progressive mesh generation of the 3D model simplification, a new simplification algorithm for the half-edge collapse progressive mesh based on vector angle change between the local area ring was proposed. Firstly, the normal vector was obtained which restricted by center of gravity measurement distance in the local neighborhood area and consisted of points near the first ring of 3D data points. Secondly, the triangle set was selected as the second ring neighborhood area which intersected with the triangle assembly points of the first ring neighborhood area. Then the value multiplied by the two local normal vectors was made as the edge collapse cost. The smaller the value was, the plainer the region was inclined to be and had the priority of simplification, otherwise it would be retained. Finally, the method of angles judgment of a triangle was adopted as the restriction of half-edge collapse to ensure the regular degree of the triangle in simplification mesh and reduce the error caused by the deformation. The experimental results show that the proposed algorithm can better balance the preserving of local detail features and efficiency in the simplification of progressive mesh of 3D model and can meet the needs of practical applications.

A new realistic rendering method for translucent material jade was put forward by superposition of high light, diffuse and transmission. Firstly, subsurface scattering of translucent material jade was simulated by combination of scattering layer and diffuse profile, then a flexible diffuse profile method was proposed to simulate the diffuse profile characteristic of different types of jade. Secondly, by combining pre-computed local thickness maps and Gaussian linear sum, light transmission effect of transmission layer was realized based on the surface thickness, which was superimposed with specular reflection items based on micro-plane using energy conservation, thus a realistic translucent material representation based on three layers of lighting model was achieved. Experimental results show that the proposed method can achieve photorealistic rendering of different kinds of translucent jade, and ensure the real-time efficiency of 30 frames per second when the triangular patch number reaches 1.6 million.

Prevailing cloud storage systems normally use master/slave structure, which may cause performance bottlenecks and scalability problems in some extreme cases. So, fully distributed cloud storage system based on Distributed Hash Table (DHT) technology is becoming a new choice. How to solve load balancing problem for nodes, is the key for this technology to be applicable. The Kademlia algorithm was used to locate storage target in cloud storage system and its load balancing performance was investigated. Considering the load balancing performance of the algorithm significantly decreased in heterogeneous environment, an improved algorithm was proposed, which considered heterogeneous nodes and their storage capacities and distributed loads according to the storage capacity of each node. The simulation results show that the proposed algorithm can effectively improve load balance performance of the system. Compared with the original algorithm, after running a long period (more than 1500 hours in simulation), the number of overloaded nodes in system dropped at an average percentage 7.0%(light load) to 33.7%(heavy load), file saving success rate increased at an average percentage 27.2%(light load) to 35.1%(heavy load), and also its communication overhead is acceptable.

To comprehensively consider the factors influencing the moving objects in uncertain environment, a k-anonymity privacy-preserving method for the trajectory recorded by automatic identification system was presented. Firstly, an uncertain spatial index model was established which was stored in grid quadtree. Then the continuous k-Nearest Neighbor ( KNN) query method was used to find the trajectory which had the similar area to the current trajectory, and the trajectory was added to the anonymous candidate set. By considering the network scale influence on the effectiveness of the anonymous information and the probability of attacker's attack on trajectory, the optimal exploit chain of trajectory was generated by using the heuristic algorithm to strengthen the trajectory privacy-preserving. Finally, the experimental results show that, compared with the traditional method, the proposed method can decrease the information loss by 20% to 50%,while the information distortion can maintain below 50% with the enlarge of query range and the cost loss is cut down by 10% to 30%.The proposed method can effectively prevent malicious attackers from the information access of trajectory,and can be applied for the official boat to law enforcement at sea.

The existing Time Division Multiple Access (TDMA) scheduling methods for industrial emergency data under the conditions of asynchronous and multi-channel medium have the problems of high delay, saturated Control Channel (CC), and large energy consumption. To solve these problems, an Emergency data scheduling algorithm Oriented Asynchronous Multi-channel industrial wireless sensor networks, called EOAM, was proposed. First, the receiver-based strategy was adopted to solve the problem of saturated control channel during asynchronous multi-channel scheduling. Then a well-designed Special Channel (SC) together with the priority indication method was proposed to provide fast channel switch and real-time transmission of emergency data; additionally, the non-urgent data was allowed to occupy channel by a backoff-based mechanism indicated by the priority indication method, which could ensure the utilization of special channel. EOAM was suitable for both unicast and broadcast communications. The simulation results show that, compared with the Distributed Control Algorithm (DCA), the transmission delay of EOAM can reach 8 ms, the reliability is above 95%, and the energy consumption is reduced by 12.8%, which can meet the transmission requirements of industrial emergency data.

Concerning the delay of related task scheduling in cloud computing, a Related Task Scheduling algorithm based on Task Hierarchy and Time Constraint (RTS-THTC) was proposed. The related tasks and task execution order were represented by Directed Acyclic Graph (DAG), and the task execution concurrency was improved by using the proposed hierarchical task model. Through the calculation of the total time constraint in each task layer, the tasks were dispatched to the resource with the minimum execution time. The experimental results demonstrate that the proposed RTS-THTC algorithm can achieve better performance than Heterogeneous Earliest-Finish-Time (HEFT) algorithm in the terms of the total execution time and task delay.

An fast image stitching algorithm based on improved Speeded Up Robust Feature (SURF) was proposed to overcome the real-time and robustness problems of the original SURF based stitching algorithms. The machine learning method was adopted to build a binary classifier, which identified the critical feature points obtained by SURF and removed the non-critical feature points. In addition, the Relief-F algorithm was used to reduce the dimension of the improved SURF descriptor to accomplish image registration. The weighted threshold fusion algorithm was adopted to achieve seamless image stitching. Several experiments were conducted to verify the real-time performance and robustness of the improved algorithm. Furthermore, the efficiency of image registration and the speed of image stitching were improved.

The existing blind recoginition methods of cyclic code have poor effects in the high or only Bit Error Ratio (BER) better in low code rate conditions, or the method is only for a subclass of the cyclic codes. In order to solve the blind identification for cyclic code with high BER or high code rate effectively, a method based on code weight distribution and matrix transformation was proposed. First of all the article structured the receiving sequence to matrix according to the estimated code length, and then realized the blind recognition using the improved weight distribution distance formula. The simulation results show that the method can realize the blind recognition for cyclic code with high BER and high code rate, and the results are better.

To ensure good dynamic performance, a dynamic simulation analysis is needed for Computer Numerical Control (CNC) floor type boring and milling machine in the design stage because of its complex structure. According to the request, a dynamic simulation idea with a practical effect was presented. The proposed scheme firstly obtained modal parameters of joint surface such as bed-slide, slide-column, column-spindle box via the modal test of CNC floor boring and milling machine tool prototype; then joint parameters obtained by finite element optimization identification of modal parameters and furthermore simulated by COMBIN14 element were used to establish the finite element model of CNC. With the dynamic simulation on the dyna971 software platform, a good dynamic characteristic was displayed due to reposeful waveform of stress and strain, while machine tool was subject to external force effect. At present, the research result had been integrated into batch production of this kind of machine.

To solve the problem that Low Density Lattice Codes (LDLC) cannot be used on the constrained power communication Additive White Gaussian Noise (AWGN) channel, the shaping methods were studied. In this paper, a lower triangular Hmatrix with a special structure was constructed first, together with the hypercube and systematic shaping method, and then the average power was fixed, the position change of lattice point before and after the shaping process, and its corresponding shaping gain were analyzed. The simulation results show that the codeword is uniformly distributed within the Voronoi regions of the lattice after shaping, and these shaping methods can achieve a shaping gain of 1.31dB when Symbol Error Rate (SER) 10 -5 and code length 10000 which improves 0.31dB compared with the traditional shaping technique. Power limited lattice points were generated efficiently after shaping.

An object-oriented moving object data model supporting full-time data storage and query was put forward, which added dynamic attributes to the object-oriented model. The influence of Global Positioning System (GPS) positioning accuracy and direction on the moving object location updating was discussed, and a new location updating strategy of dynamic threshold based on the positioning accuracy, speed and direction was constructed. Therefore, the storage and query of full time domain of the mobile object were solved. Finally, an experiment about this new moving object data model was carried out. And the result show that the dynamic threshold location updating strategy of the model can effectively reduce the frequency of location update, save the data transmission flow, and reduce the amount of data storage without affecting the moving objects trajectory precision.

The available network management approaches are not able to monitor Multi-protocol Label Switch (MPLS) Traffic Engineering (TE) tunnels dynamically in real-time. To address this problem, a real-time monitoring approach for MPLS TE tunnels, called MPLS TE Tunnels Real-time Monitoring (MTRM), was proposed in this paper. A probe was placed in the network to collect Open Shortest Path First-TE (OSPF-TE) signaling messages passively. Based on the collected information, a MPLS network model was built and tunnel paths were dynamically computed using the tunnel paths real-time monitoring algorithm. The MPLS TE tunnel paths could thus be monitored in real-time. The simulation experiments were carried out on a MPLS network with 15 nodes. The results show that the proposed approach can monitor the changes of MPLS TE tunnels within five seconds, with precision over 90%. This real-time monitoring approach can greatly reduce the difficulties of MPLS network management and TE implementation, and it has a wide application prospect.

After qualitative discussion of the synchronization performance in complex network models, simulation analysis of the networks with relatively larger size was presented. Through data analysis, network topology visualization, and topology evolution with simulated annealing algorithm, some rules of synchronization optimization were found, that is, making the degree distribution and average distance uniform and centralized, and proper clustering coefficient can reduce network connection without influencing synchronization. Considering the situation of future power grid, optimization strategies for the stability of synchronization were developed and tested on the data of the actual power grid, exploring the application value of optimizing practical networks from the angle of topology and satisfying the requirement of real-time quality, stability and distribution. The optimization is proved to be effective.

In order to solve the problem that channel resource cannot be fully utilized by single-path routing protocol in multi-channel wireless mesh networks, a Parallel Multi-path Routing Protocol (PMRP) based on congestion control was proposed. This protocol spread a data flow over multiple paths and only re-found new route after all routes have broken. It prevented the congested node to transmit new data flow by utilizing congestion control mechanism. The simulation results demonstrate that, compared with Ad hoc On-demand Distance Vector Routing (AODV) routing protocol, PMRP can reduce the average end to end delay, and improve the data packet delivery ratio and the network throughput effectively.

The principle of content switching was introduced,and the way to guarantee the Web QoS of the end to end through content switching was analyzed.An instance was given out to show how to realize Web QoS in host.

The SIMD（Single-Instruction Multiple-Data） instruction system was introduced,and further Integer DCT（Discrete Cosine Transform）,quant,interpolation and motion estimation of H.264 were optimized with the SIMD technology.The experiment indicates that the encoding speed of program after optimization reaches about 30fps and the speed has been improved by 68 times.