With the features of low Signal-to-Noise Ratio （SNR） and blurred edges， ultrasound images of the brachial plexus nerve are hard to be segmented manually. Although some results have been gained by existing segmentation models， the segmentation effect is not satisfied due to the small target area and irregular shape of the brachial plexus nerve structure. Aiming at the above problems， a multi-scale feature fusion-based brachial plexus nerve segmentation model was proposed， namely Nerve-segmentation Feature Pyramid Network （Ner-FPN）. In the feature extraction stage， an Xception-like structure was designed for multi-scale feature extraction. In the prediction segmentation stage， a bidirectional FPN structure was used for feature fusion prediction. The BP （Brachial Plexus） dataset from the Kaggle brachial plexus nerve ultrasound image segmentation competition was used as the experimental data. The experimental results show that compared with the mainstream deep learning segmentation models U-Net and SegNet （Segmentation Network），the Dice Similar Coefficient （DSC） of Ner-FPN model for brachial plexus nerve segmentation can reach 0.703， which is 10.7 percentage points and 14.5 percentage points higher than those of U-Net and SegNet， and 5.5 percentage points and 3.4 percentage points higher than those of improved models QU-Net and Efficient+U-Net in the same dataset， verifying that the proposed model can be an aid for diagnosis.

Camouflaged Object Detection （COD） aims to detect objects hidden in complex environments. The existing COD algorithms ignore the influence of feature expression and fusion methods on detection performance when combining multi-level features. Therefore， a COD algorithm based on progressive feature enhancement aggregation was proposed. Firstly， multi-level features were extracted through the backbone network. Then， in order to improve the expression ability of features， an enhancement network composed of Feature Enhancement Module （FEM） was used to enhance the multi-level features. Finally， Adjacency Aggregation Module （AAM） was designed in the aggregation network to achieve information fusion between adjacent features to highlight the features of the camouflaged object area， and a new Progressive Aggregation Strategy （PAS） was proposed to aggregate adjacent features in a progressive way to achieve effective multi-level feature fusion while suppressing noise. Experimental results on 3 public datasets show that the proposed algorithm achieves the best performance on 4 objective evaluation indexes compared with 12 state-of-the-art algorithms， especially on COD10K dataset， the weighted F-measure and the Mean Absolute Error （MAE） of the proposed algorithm reach 0.809 and 0.037 respectively. It can be seen that the proposed algorithm achieves better performance on COD tasks.

Forged and tampered data frames should be identified and filtered out to ensure network security and efficiency. However， the existing schemes usually fail to work when verification devices are attacked or maliciously controlled in the Software Defined Network （SDN）. To solve the above problem， a blockchain-based data frame security verification mechanism was proposed. Firstly， a Proof of Frame Forwarding （PoFF） consensus algorithm was designed and used to build a lightweight blockchain system. Then， an efficient data frame security verifying scheme for SDN data frame was proposed on the basis of this blockchain system. Finally， a flexible semi-random verifying scheme was presented to balance the verification efficiency and the resource cost. Simulation results show that compared with the hash chain based verifying scheme， the proposed scheme decreases the missed detection rate significantly when an equal proportion of switches are maliciously controlled. Specifically， when the proportion is 40%， the decrease effect is very obvious， the missed detection rate can still be kept no more than 32% in the basic verification mode， and can be further reduced to 7% with the assistance of the semi-random verifying scheme. Both are much lower than the missed detection rate of 72% in the hash chain based verifying scheme， and the resource overhead and communication cost introduced by the proposed mechanism are within a reasonable range. Additionally， the proposed scheme can still maintain good verification performance and efficiency even when the SDN controller is completely unable to work.

The formation of carotid artery plaque is closely related to complex hemodynamic factors. The accurate simulation of complex flow conditions is of great significance for the clinical diagnosis of carotid artery plaque. In order to simulate the pulsating flow accurately， Large Eddy Simulation （LES） model was combined with Lattice Boltzmann Method （LBM） to construct a LBM-LES carotid artery simulation algorithm， and a real geometric model of carotid artery stenosis was established through medical image reconstruction software， thus the high-resolution numerical simulation of carotid artery stenosis flows was conducted. By calculating blood flow velocity and Wall Shear Stress （WSS）， some meaningful flow results were obtained， proving the effectiveness of LBM-LES in the study of blood flow in the carotid artery narrow posterior. Based on the OpenMP programming environment， the parallel computation of the grid of ten million magnitude was carried out on the fully interconnected fat node of high-performance cluster machine. The results show that the LBM-LES carotid artery simulation algorithm has good parallel performance.

Graphlet Degree Vector （GDV） is an important method for studying biological networks， and can reveal the correlation between nodes in biological networks and their local network structures. However， with the increasing number of automorphic orbits that need to be researched and the expanding biological network scale， the time complexity of the GDV method will increase exponentially. To resolve this problem， based on the existing serial GDV method， the parallelization of GDV method based on Message Passing Interface （MPI） was realized. Besides， the GDV method was improved and the parallel optimization of the optimized method was realized. The calculation process was optimized to solve the problem of double counting when searching for automorphic orbits of different nodes by the improved method， at the same time， the tasks were allocated reasonably combining with the load balancing strategy. Experimental results of simulated network data and real biological network data indicate that parallel GDV method and the improved parallel GDV method both obtain better parallel performance， they can be widely applied to different types of networks with different scales， and have good scalability. As a result， they can effectively maintain the high efficiency of searching for automorphic orbits in the network.

In the study of model parameter based transfer learning, both the sample distribution discrepancy between two domains and the co-adaptation between convolutional layers of the source model impact performance of model. In response to these problems, a Multi-Convolution Adaptation (MCA) deep transfer framework was proposed and applied to the grade classification of typhoons in satellite cloud images, and a CE-MMD loss function was defined by adding the improved L-MMD (Maximum Mean Discrepancy) algorithm as a regular term to the cross-entropy function and applying the linear unbiased estimation to the distribution of the samples in Reproducing Kernel Hilbert Space (RKHS). In the back propagation process, the residual error and the distribution discrepancy between the samples in two domains were used as common indexes to update the network parameters, making model converge faster and have higher accuracy. Comparison experimental results of L-MMD and two measurement algorithms-Bregman difference and KL (Kullback-Leibler) divergence on the self-built typhoon dataset show that the precision of the proposed algorithm is improved by 11.76 percentage points and 8.05 percentage points respectively compared to those of the other two algorithms. It verifies that L-MMD is superior to other measurement algorithms and the MCA deep transfer framework is feasible.

In view of the problem that conventional incremental learning models may go through a way of performance degradation during the update stage, a kernelized incremental learning method was proposed based on objective equilibrium measurement. By setting the optimization term of "empirical risk minimization", an optimization objective function fulfilling the equilibrium measurement with respect to training data size was designed. The optimal solution was given under the condition of incremental learning training, and a lightweight incremental learning classification model was finally constructed based on the effective selection strategy of new data. Experimental results on a publicly available fall detection dataset show that, when the recognition accuracy of representative methods falls below 60%, the proposed method can still maintain the recognition accuracy more than 95%, while the computational consumption of the model update is only 3 milliseconds. In conclusion, the proposed method contributes to achieving a stable growth of recognition performance as well as efficiently decreasing the time consumptions, which can effectively realize wearable devices based intellectual applications in the cloud service platform.

The network replication technology has become the cornerstone of the evaluation platform for network security experiments and the system for network emulation. Facing the requirements of fidelity and scalability of network replication, a multi-scale network replication technology based on cloud platform for the fusion of lightweight virtualization, full virtualization and digital simulation was proposed. The architecture of the seamless fusion of these three scales was introduced at the beginning; And then the network construction technology based on the architecture was studied. The emulation experimental results show that the emulation network which is built with the construction technology has the characteristics of flexibility, transparency and concurrency; in addition, the construction technology is capable of emulating networks with high extensibility. At last, communication tests for a variety of protocols and simple network security experiments on the large-scale emulation network were conducted to verify the availability of this large-scale emulation network. The extensive experimental results show that the multi-scale network replication technology for the fusion of virtualization and digital simulation can be used as the powerful support for creating large-scale emulation networks.

Aiming at the problem of advanced persistent threats in power information networks, a hybrid Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) intrusion detection model was proposed, by which current network states were classified according to various statistical characteristics of network traffic. Firstly, pre-processing works such as feature encoding and normalization were performed on the network traffic obtained from log files. Secondly, spatial correlation features between different hosts' intrusion traffic were extracted by using deformable convolution kernels in CNN. Finally, the processed data containing spatial correlation features were staggered in time, and the temporal correlation features of the intrusion traffic were mined by RNN. The experimental results showed that the Area Under Curve (AUC) of the model was increased by 7.5% to 14.0% compared to traditional machine learning models, and the false positive rate was reduced by 83.7% to 52.7%. It indicates that the proposed model can accurately identify the type of network traffic and significantly reduce the false positive rate.

Bernoulli-Gaussian (BG) model in Expectation-Maximization Bernoulli-Gaussian Approximate Message Passing (EM-BG-AMP) algorithm is constrained by its symmetry and restricted in the approximation of the actual signal prior distribution. Gaussian-Mixture (GM) model in Expectation-Maximization Gaussian-Mixture Approximate Message Passing (EM-GM-AMP) algorithm is a high-order model of BG model and has quite high complexity. In order to solve these problems, the Bernoulli-Asymmetric-Gaussian (BAG) model was proposed. Based on the new model, by further derivation, the Expectation-Maximization Bernoulli-Asymmetric-Gaussian Approximate Message Passing (EM-BAG-AMP) algorithm was obtained. The main idea of the proposed algorithm was based on the assumption that the input signal obeyed the BAG model. Then the proposed algorithm used Generalized Approximate Message Passing (GAMP) to reconstruct signal and update the model parameters in iteration. The experimental results show that, when processing different images, compared to EM-BG-AMP,the time and the Peak Signal-to-Noise Ratio (PSNR) values of EM-BAG-AMP are increased respectively by 1.2% and 0.1-0.5 dB, especially in processing images with simple texture and obvious color difference changing, the PSNR values are increased by 0.4-0.5 dB. EM-BAG-AMP is the expansion and extension of EM-BG-AMP and can better adapt to the actual signal.

Because existing passenger-finding algorithms do not consider taxi's spatio-temporal context, a collaborative filtering recommendation algorithm of taxi passenger-finding based on spatio-temporal context was proposed. The proposed algorithm mapped potential passenger locations to space network, and introduced time delay factor to similarity measure to get the neighbor set which was similar to a target taxi's driving behavior. Based on location context, the proposed algorithm chose the target taxi's most interest potential passenger location from similar neighbor set. The experimental results on Fuzhou taxi trajectory data show that the proposed algorithm can get the best recommendation result when the time delay factor is 0.7. Meanwhile, compared to the traditional collaborative filtering recommendation algorithms, the proposed algorithm obtains better recommendation result under the neighbor sets with different size, which means the proposed algorithm is more accurate than the traditional collaborative filtering algorithms.

The existed trust models have two shortages in searching the trust path:firstly, factors affecting the trust value were not considered fully in the searching, or considered the same. Meanwhile, many algorithms ignored the importance of the interaction number when searching the trust path. In view of these problems, a searching algorithm of trust path based on graph theory was proposed. The concept of probability of honesty was put forward to weigh the credibility of the node further, and as the searching priority basis, it is more reasonable in the priority searching. Meanwhile it searched by filtering and used probability of multi-factors which affect the credibility of the node. The analyses of algorithm show that the complexity of the proposed algorithm is (n-m)² magnitude, much lower than the original fine-grained algorithm which complexity is n² magnitude. The experimental results show that the proposed algorithm can better filter out malicious nodes, improve the accuracy of the trust path search algorithms, and resist the attacks of malicious nodes.

The model parameters of Video Compressed Sensing of Linear Dynamic System (CS-LDS) can be estimated directly from random sampling data. If all video frames are sampled in the same way, the sampling data will be redundant. To solve this problem, an adaptive improvement algorithm based on adaptive compression sampling technology was proposed in this paper. Firstly, a Linear Dynamic System (LDS) model of the video signal was established. And then the sampling data of video signal was obtained by using the adaptive compression sampling method. Finally, the model parameters were estimated and the video signal was reconstructed by the sampling data. Without affecting the video reconstruction quality, the experimental results show that the proposed algorithm is better than the CS-LDS algorithm, it can not only reduce 20%-40% sampling data in the uniform measurement process, but also save the average running time of 0.1-0.3 s per frame. The improved algorithm reduces the number of samples and the algorithm's running time.

The traditional testing process does not specifically consider the system performance. With the wide application of parallel testing method, more attention was paid to the system performance and data throughput capacity. Optimizing the software design with multithreading technology becomes an effective way to improve the performance of automatic test system. By modeling testing pipeline process, using asynchronous pipeline design patterns and combining task-oriented concepts, an available test system programming model was proposed. The experiment results prove that the model can significantly shorten the average test time in the ideal case of random input of test tasks. Applying this model to an instance of measuring characteristic parameters of Alternating Current (AC) contactor, the results further indicate that this model can significantly increase the flexibility of test configuration and avoid the complexity of multi-threaded programming.

Abstract: To overcome the shortcoming that random measurement matrix is hard for hardware implementation due to its randomly generated elements, a new structural and sparse deterministic measurement matrix was proposed by studying the theory of measurement matrix in Compressed Sensing (CS). The new matrix was based on parity check matrix in Quasi-Cyclic Low Density Parity Check (QC-LDPC) code over finite field. Due to the good channel decoding performance of QC-LDPC code, the CS measurement matrix based on it was expected to have good performance. To verify the performance of the new matrix, CS reconstruction experiments aiming at one-dimensional signals and two-dimensional signals were conducted. The experimental results show that, compared with the commonly used matrices, the proposed matrix has lower reconstruction error under the same reconstruction algorithm and compression ratio. The proposed method achieves certain improvement (about 0.5-1dB) in Peak Signal-to-Noise Ratio (PSNR). Especially, if the new matrix is applied to hardware implementation, the need for physical storage space and the complexity of the hardware implementation should be greatly reduced due to the quasi-cyclic and symmetric properties in the structure.

In the prediction of moving object trajectory, concerning the low accuracy rate of low order Markov model and the expansion of state space in high order model, a dynamic adaptive Probabilistic Suffix Tree (PST) prediction method based on variable length Markov model was proposed. Firstly, moving objects trajectory path was serialized according to the time; then the probability characteristic of sequence context was trained and calculated from the historical trajectory data of moving objects, the probabilistic suffix tree model based path sequence was constructed, combined with the actual trajectory data, thus the future trajectory information could be predicted dynamically and adaptively. The experimental results show that the highest prediction accuracy was obtained in second order model, with the order of the model increasing, the prediction accuracy was maintained at about 82% and better prediction results were achieved. In the meantime, space complexity was decreased exponentially and storage space was reduced greatly. The proposed method made full use of historical data and current trajectory information to predict the future trajectory, and provided a more flexible and efficient location-based services.

Concerning the key problems of selecting threshold function in wavelet threshold denoising, in order to address the discontinuity of conventional threshold function and large deviation existing in the estimated wavelet coefficients, a continuous adaptive threshold function in the whole wavelet domain was proposed. It fully considered the characteristics of different sub-band coefficients in different scales, and set the energy of sub-band coefficients in different scales as threshold function's initial weights. Optimal weights were iteratively solved by using interval advanced-retreat method and golden section method, so as to adaptively improve approximation level between estimated and decomposed wavelet coefficients. The experimental results show that the proposed method can both efficiently reduce noise and simultaneously preserve the edges and details of image, also achieve higher Peak Signal-to-Noise Ratio (PSNR) under different noise standard deviations.

The identification of one-way road state can provide relevant information of road network to the public timely and accurately, improve the efficiency of public travel, and enhance the service level of dynamic traffic information. This paper presented a dynamic identification algorithm of one-way road state based on Floating Car Data (FCD). Firstly the line feature information of maps was got, and the matching of spatial information grid with the traffic roads was pretreated to achieve fast matching for massive FCD; Then statistical characteristics of FCD direction information was analyzed to filter dual-threshold information and direction information; Finally one-way road state information was got dynamically. The actual road network tests show the algorithm can identify one-way road state information effectively.

Dynamic tree rendering plays an important role in the natural scenery simulation. In this paper, by using Cook-Torrance lighting model and pre-computed translucency texture, rendering scattering and translucency of the leaf were implemented. Using the polynomial fitted from tapered circular beam model expression and length correct method, the speed of calculation deform was boosted. By introducing the hierarchical branches texture with index, branches deform could be calculated in Graphic Processing Unit (GPU). Using pre-compaction and GPU helps to balance the reality and real-time in the simulation. The experiments show that the proposed method can render the dynamic tree scene vividly and rapidly.

For the requirements of building Project Information Portal (PIP) data center based on a unified data model, a manageable entity object-oriented data model was proposed. The project data were treated as a series of managerial entity based on management workflows which were decomposed according to the whole life cycle. The conceptual layer data model was designed. The project data could be naturally represented and recorded by using this model. The data organization method was presented based on MongoDB (document-oriented database technology). The cluster storage architecture for PIP was also addressed. The experiments show that it has efficient performance in data writing and querying. It also has high availability and storage capacity scalability.

In order to achieve dynamic update of digital map data to support the geographic information services in traffic network with rapid development, a new-road automatic detection algorithm was proposed based on the Floating Car Data (FCD) technology. In this method, the moving trajectories of massive floating cars were calculated in real-time, then the suspected new road sets were extracted with the image matching between the existing map layers and the trajectories. After applying a filtering algorithm to the data sets for cleaning, the new road detection reports covering the new roads' location and length were generated automatically and saved as temporary map layers. The field test results show that this algorithm can detect the new roads quickly, so far as to detect new road within five minutes. It is a cost-effective solution for the real-time road map layer update.

Considering the problems for nonlinearnoise processing and taking account of that higher-order statistics of the signal and unitary space can be a good deal with non-Gaussian noise，the noise processing algorithm based on Max Correntropy Criteria (MCC) in the time domain and the unitary space was proposed. Combining the MCC and gradient descent algorithm, a nonlinearnoise filtering algorithm in the time domain was designed. At the same time, extending the algorithm to the noise processing in the unitary space, the unitary space filtering algorithm based on the MCC was put forward. The simulation study shows that the algorithm based on the MCC algorithm has significant advantages compared with the traditional Least Mean Square (LMS) based filtering algorithm, which means it can achieve more complete signal characteristics by faster convergence.

Based on the research of two-dimensional code and recognition of mobile phone, the authors have developed a two-dimensional code recognition system on Android platform. In the system architecture design, the two-dimensional code recognition system platform support layer, client application layer, cloud handle layer were hierarchically designed to ensure the system security. According to the requirements, the system function models and cases analyses were given. Finally, the two-dimensional code core encode and decode functions of mobile phone were achieved.

The multi-hop transmission of multimedia emergency communication system based on Wireless Mesh Network (WMN) in underground mine have two problems: low basis bandwidth and high multi-hop transmission attenuation. This paper aimed to improve the multi-hop transmission performance for the system. In this paper, a trunk line network structure of multimedia emergency communication system based on WMN in under-ground mine was proposed. The authors established its transmission model, and then had a research on the main factors that affected the transmission performance. The multi-radio node structure of multi-hop mesh backbone network based on 802.11n was proposed and solved the two problems of multi-hop transmission. The experimental results show that it has more than 165Mbps basis bandwidth, and under the limited 60Mbps environment, the bandwidth attenuation of per hop is less than 1%, basically satisfying the application requirements of multimedia transmission in underground mine.

To protect the security of social network, ensure the availability of social network after perturbation, the paper proposed perturbation method of social network based on the signless Laplacian matrix and the social network non-randomness. In the perturbation process, this method controlled the social network spectral radius and the social network non-randomness by certain constraints, thus ensuring the usability and improving the privacy protection degree of the social network. The paper analyzed the security of this method in theory, and provided corresponding algorithm. At last, the experimental results on comparison of harmonic mean of the shortest distance of the social network, subgraph centrality and the social network non-randomness of change, show that the proposed method effectively protects the structural feature of social network and improving the availability of the social network.

The multicast routing method has been applied into the Network on Chip (NoC) since traditional unicast communication cannot meet the increasingly rich application requirements of NoC. Three kinds of path-based multicast routing algorithms including XY routing, UpDown routing and SubPartition routing algorithms were applied to 2D Mesh or Torus NoC. The congestion control strategy was proposed. The simulation results show multicast routing algorithms have shorter average latency and higher throughput and balanced applied load compared with unicast routing algorithms. SubPartition routing algorithm was confirmed to have a more stable and better performance as the network size increases. Finally, multicast congestion control techniques for NoC were employed to make multicast communications more efficient and enhance the NoC performance.

The major procedures of text classification such as uniform text format expression, training, testing and classifying based on Naive Bayesian text classification algorithm were implemented using MapReduce programming mode. The experiments were given in Hadoop cloud computing environment. The experimental results indicate basically linear speedup with an increasing number of node computers. A recall rate of 86% was achieved when classifying Chinese Web pages.

Argumentation model is the theoretical basis to establish group argumentation environment. Based on Issue-Based Information System (IBIS) model and Toulmin' argument schema, a group argumentation model was proposed, which was able to evaluate the argumentative utterance. With this model, the group argumentative information could be structured as a graph which consisted of utterance nodes and semantic links. A method of evaluating utterance nodes based on Language Weighted Aggregation (LWA) operator and node reduction was proposed. A group argumentation on the issue of system architecture design was illustrated as an example to show the usability and effectiveness of the proposed model.

An automatically-controlled power transformation system was studied and a distributed control scheme for the system was proposed and implemented in order to improve the safety and efficiency of power systems. Advanced software and hardware techniques, system architectures and safety requirements of power transformation systems were all taken into account. Real-time event and disturbance logs and control commands were communicated over a local power transformation system based on the embedded operating system of VxWorks and the hardware platform of PowerPC860 CPU. The distributed control scheme for power transformation systems realized supervisory control, data acquisition and logic functions, and has been validated by industrial experiments.