Aiming at the problems of low efficiency， low accuracy， excessive occupancy of human resources and intelligent classification algorithm miniaturization deployment requirements in China Customs risk control methods at this stage， a customs risk control method based on an improved Butterfly Feedback neural Network Version 2 （BFNet-V2） was proposed. Firstly， the Filling in Code （FC） algorithm was used to realize the semantic replacement of the customs tabular data to the analog image. Then， the analog image data was trained by using the BFNet-V2. The regular neural network structure was composed of left and right links， different convolution kernels and blocks， and small block design， and the residual short path was added to improve the overfitting and gradient disappearance. Finally， a Historical momentum Adaptive moment estimation algorithm （H-Adam） was proposed to optimize the gradient descent process and achieve a better adaptive learning rate adjustment， and classify customs data. Xception （eXtreme inception）， Mobile Network （MobileNet）， Residual Network （ResNet）， and Butterfly Feedback neural Network （BF-Net） were selected as the baseline network structures for comparison. The Receiver Operating Characteristic curve （ROC） and the Precision-Recall curve （PR） of the BFNet-V2 contain the curves of the baseline network structures. Taking Transfer Learning （TL） as an example， compared with the four baseline network structures， the classification accuracy of BFNet-V2 increases by 4.30%，4.34%，4.10% and 0.37% respectively. In the process of classifying real-label data， the misjudgment rate of BFNet-V2 reduces by 70.09%，57.98%，58.36% and 10.70%， respectively. The proposed method was compared with eight classification methods including shallow and deep learning methods， and the accuracies on three datasets increase by more than 1.33%. The proposed method can realize automatic classification of tabular data and improve the efficiency and accuracy of customs risk control.

Focusing on the problems of disturbed car-following behavior and instability of traffic flow caused by the uncertainty of the driver’s acquisition of road velocity limit and time delay information， a car-following model TD-VDVL （Time-Delayed Velocity Difference and Velocity limit） was proposed with the consideration of the time-delayed velocity difference and the velocity limit information in the Internet of Vehicles （IoV） environment. Firstly， the speed change caused by time delay and road velocity limit information were introduced to improve the Full Velocity Difference （FVD） model. Then， the linear spectrum wave perturbation method was used to derive the traffic flow stability judgment basis of TD-VDVL model， and the influence of each parameter in the model on the stability of the system was analyzed separately. Finally， the numerical simulation experiments and comparative analysis were carried out using Matlab. In the simulation experiments， straight roads and circular roads were selected， and slight disturbance was imposed on the fleet during driving. When conditions were the same， TD-VDVL model had the smallest velocity fluctuation rate and the fluctuation of fleet headway compared to the Optimal Velocity （OV） and FVD models. Especially when the sensitivity coefficient of the velocity limit information was 0.3， and the sensitivity coefficient of the time-delayed speed difference was 0.3， the proposed model had the average fluctuation rate of the fleet velocity reached 2.35% at time of 500 s， and the peak and valley difference of fleet headway of only 0.019 4 m. Experimental results show that TD-VDVL model has a better stable area after introducing time-delayed velocity difference and velocity limit information， and can significantly enhance the ability of car-following fleet to absorb disturbance.

Cryptographic S-boxes （or black boxes） are nonlinear components in symmetric encryption algorithms， and their algebraic properties usually determine the security performance of these encryption algorithms. Differential uniformity， nonlinearity and revised transparency order are three basic indicators to evaluate the security properties of cryptographic S-boxes. They describe the S-box’s ability against differential cryptanalysis， linear cryptanalysis and differential power attack respectively. When the input size of the cryptographic S-box is large （for example， the input length of the S-box is larger than 15 bits）， the needed solving time in Central Processing Unit （CPU） is still too long， or even the solution is impracticable. How to evaluate the algebraic properties of the large-size S-box quickly is currently a research hot point in the field. Therefore， a method to evaluate the algebraic properties of cryptographic S-boxes quickly was proposed on the basis of Graphics Processing Unit （GPU）. In this method， the kernel functions were split into multiple threads by slicing technique， and an optimization scheme was proposed by combining the characteristics of solving differential uniformity， nonlinearity and revised transparency order to realize parallel computing. Experimental results show that compared with CPU-based implementation environment， single GPU based environment has the implementation efficiency significantly improved. Specifically， the time spent on calculating differential uniformity， nonlinearity， and revised transparency order is saved by 90.28%， 80%， and 66.67% respectively， which verifies the effectiveness of this method.

Aiming at the problems of slow detection and low recognition accuracy of road traffic signs in Chinese intelligent driving assistance system， an improved road traffic sign detection algorithm based on YOLOv3 （You Only Look Once version 3） was proposed. Firstly， MobileNetv2 was introduced into YOLOv3 as the basic feature extraction network to construct an object detection network module MN-YOLOv3 （MobileNetv2-YOLOv3）. And two Down-up links were added to the backbone network of MN-YOLOv3 for feature fusion， thereby reducing the model parameters， and improving the running speed of the detection module as well as information fusion performance of the multi-scale feature maps. Then， according to the shape characteristics of traffic sign objects， K-Means++ algorithm was used to generate the initial cluster center of the anchor， and the DIOU （Distance Intersection Over Union） loss function was introduced to combine DIOU and Non-Maximum Suppression （NMS） for the bounding box regression. Finally， the Region Of Interest （ROI） and the context information were unified by ROI Align and merged to enhance the object feature expression. Experimental results show that the proposed algorithm has better performance， and the mean Average Precision （mAP） of the algorithm on the dataset CSUST （ChangSha University of Science and Technology） Chinese Traffic Sign Detection Benchmark （CCTSDB） can reach 96.20%. Compared with Faster R-CNN （Region Convolutional Neural Network）， YOLOv3 and Cascaded R-CNN detection algorithms， the proposed algorithm has better real-time performance， higher detection accuracy， and is more robustness to various environmental changes.

For addressing real root isolation problem of transcendental function polynomials， an interval isolation algorithm for exponential function polynomials named exRoot was proposed. In the algorithm， the real root isolation problem of non-polynomial real functions was transformed into sign determination problem of polynomial， then was solved. Firstly， the Taylor substitution method was used to construct the polynomial nested interval of the objective function. Then， the problem of finding the root of the exponential function was transformed into the problem of determining the positivity and negativity of the polynomial in the intervals. Finally， a comprehensive algorithm was given and applied to determine the reachability of rational eigenvalue linear system tentatively. The proposed algorithm was implemented in Maple efficiently and easily with readable output results. Different from HSOLVER and numerical calculation method fsolve， exRoot avoids discussing the existence of roots directly， and theoretically has termination and completeness. It can reach any precision and can avoid the systematic error brought by numerical solution when being applied into the optimization problem.

A Feature Fusion Network （FFN） was proposed to judge the quality of 3D face point cloud acquired by binocular structured light scanner. Firstly， the 3D point cloud was preprocessed to cut out the face area， and the image obtained from the point cloud and the corresponding 2D plane projection was used as the input. Secondly， Dynamic Graph Convolutional Neural Network （DGCNN） and ShuffleNet were trained for point cloud learning. Then， the middle layer features of the two network modules were extracted and fused to fine-tune the whole network. Finally， three full connected layers were used to realize the five-class classification of 3D face point cloud （excellent， ordinary， stripe， burr， deformation）. The proposed FFN achieved the classification accuracy of 83.7%， which was 5.8% higher than that of ShufflNet and 2.2% higher than that of DGCNN. The experimental results show that the weighted fusion of two-dimensional image features and point cloud features can achieve the complementary effect between different features.

With the development of communication technology， communication terminals gradually adopt software to be compatible with multiple communication modes and protocols. As in the traditional software radio architecture with a Central Processing Unit （CPU） of computer as an arithmetic unit， the wideband data throughput of high-speed wireless communication systems such as Multiple-Input Multiple-Output （MIMO） is not be satisfied， an acceleration method of Low Density Parity Check （LDPC） code decoder based on Graphics Processing Unit （GPU） was proposed. Firstly， according to the theoretical analysis of the acceleration performance of GPU parallelly accelerated heterogeneous computing in GNU Radio 4G/5G physical layer signal processing module， a more parallelly efficient Layered Normalized Min-Sum （LNMS） algorithm was adopted. Then， the decoding delay of the decoder was reduced by using the methods such as global synchronization strategy， reasonably allocation of GPU memory space and stream parallelism mechanism. At the same time， the LDPC code decoding process was optimized in parallel with the multi-threaded parallel technology in GPU. Finally， the GPU accelerated decoder was implemented and verified on the software radio platform， and the bit error rate performance and acceleration performance bottlenecks of the parallel decoder were analyzed. Experimental results show that compared with the traditional CPU serial code processing method， CPU+GPU heterogeneous platform has the decoding rate for LDPC codes increased to about 200 times， and the throughput of decoder can reach more than 1 Gb/s， especially in the case of large-scale data， the decoding performance is greatly improved compared with traditional decoder.

The key of cross-modal image-text retrieval is how to capture the semantic correlation between images and text effectively. Most of the existing methods learn the global semantic correlation between image region features and text features or local semantic correlation between inter-modality objects， and ignore the correlation between the intra-modality object relationships and inter-modality object relationships. To solve this problem， a method of Cross-Modal Tensor Fusion Network based on Semantic Relation Graph （CMTFN-SRG） for image-text retrieval was proposed. Firstly， the relationships of image regions and text words were generated by Graph Convolutional Network （GCN） and Bidirectional Gated Recurrent Unit （Bi-GRU） respectively. Then， the fine-grained semantic correlation between the data of two modals was learned by using the tensor fusion network to match the learned semantic relation graph of image regions and the graph of text words. At the same time， Gated Recurrent Unit （GRU） was used to learn global features of the image， and the global features of the image and the text were matched to capture the inter-modality global semantic correlation. The proposed method was compared with the Multi-Modality Cross Attention （MMCA） method on the benchmark datasets Flickr30K and MS-COCO. Experimental results show that the proposed method improves the Recall@1 of text-to-image retrieval task by 2.6%， 9.0% and 4.1% respectively on the test datasets Flickr30K， MS-COCO1K and MS-COCO5K.And mean Recall （mR） improves by 0.4， 1.3 and 0.1 percentage points respectively. It can be seen that the proposed method can effectively improve the precision of image-text retrieval.

In view of the disadvantages of the standard Artificial Bee Colony （ABC） algorithm such as weak development ability and slow convergence， a new ABC algorithm based on multi-population combination strategy was proposed. Firstly， the different-dimensional coordination and multi-dimensional matching update mechanisms were introduced into the search equation. Then， two combination strategies were designed for the hire bee and the follow bee respectively. The combination strategy was composed of two sub-strategies focusing on breadth exploration and depth development respectively. In the follow bee stage， the population was divided into free subset and non-free subset， and different sub-strategies were adopted by the individuals belonging to different subsets to balance the exploration and development ability of algorithm. The 15 benchmark functions were used to compare the proposed improved ABC algorithm with the standard ABC algorithm and other three improved ABC algorithms. The results show that the proposed algorithm has better optimization performance in both low-dimensional and high-dimensional problems.

Focusing on the issue that the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm may introduce some false targets in moving target detection with the presence of multipath reflections, a descending dimension method for designed matrix based on LASSO was proposed. Firstly, the multipath propagation increases the spatial diversity and provides different Doppler shifts over different paths. In addition, the application of broadband OFDM signal provides frequency diversity. The introduction of spatial diversity and frequency diversity to the system causes target space sparseness. Sparseness of multiple paths and environment knowledge were applied to estimate paths along the receiving target responses. Simulation results show that the improved LASSO algorithm based on the descending dimension method for designed matrix has better detection performance than the traditional algorithms such as Basis Pursuit (BP), Dantzig Selector (DS) and LASSO at the Signal-to-Noise Ratio (SNR) of -5 dB, and the target detection probability of the improved LASSO algorithm was 30% higher than that of LASSO at the false alarm rate of 0.1. The proposed algorithm can effectively filter the false targets and improve the radar target detection probability.

In Positron Emission Tomography (PET) computed imaging, traditional iterative algorithms have the problem of details loss and fuzzy object edges. A high quality Median Prior (MP) reconstruction algorithm based on correlation coefficient and Forward-And-Backward (FAB) diffusion was proposed to solve the problem in this paper. Firstly, a characteristic factor called correlation coefficient was introduced to represent the image local gray information. Then through combining the correlation coefficient and forward-and-backward diffusion model, a new model was made up. Secondly, considering that the forward-and-backward diffusion model has the advantages of dealing with background and edge separately, the proposed model was applied to Maximum A Posterior (MAP) reconstruction algorithm of the median prior distribution, thus a median prior reconstruction algorithm based on forward-and-backward diffusion was obtained. The simulation results show that, the new algorithm can remove the image noise while preserving object edges well. The Signal-to-Noise Ratio (SNR) and Root Mean Squared Error (RMSE) also show visually the improvement of the reconstructed image quality.

In visual detection of subminiature accessory, the extracted target contour will be affected by the existence of foreign matter in the field like dust and hair crumbs. In order to avoid the impact for measurement brought by foreign matter, a method of culling foreign matter fake information based on prior knowledge was put forward. Firstly, the corners of component image with foreign matter were detected. Secondly, the corner-distribution features of standard component were obtained by statistics. Finally, the judgment condition of foreign matter fake imformation was derived from the corner-distribution features of standard component to cull the foreign matter fake information. Through successful application in an actual engineering project, the processing experiments on three typical images with foreign matter prove that the proposed algorithm ensures the accuracy of the measurement, while effectively culling the foreign matter fake information in the images.

To correctly obtain the high-level language control structures of embedded executables and assembly code, and resolve the problem that the existing recovery methods for high-level language control structures cannot handle the unstructured region, the classical control analysis method, structural analysis algorithm, was introduced to study the recovery method for high-level control structures of embedded assembly code. The structural analysis algorithm was improved according to the characteristics of embedded executables, and the high-level language code was generated by using the program control tree, which can be obtained from the results of structural analysis algorithm. Compared with the open source decompiler named DCC, the results show that the improved algorithm is feasible and efficient.

To improve the performance of the co-training algorithm and expand the range of applications, a multi-view semi-supervised collaboration classification algorithm with the combination of consistent and inconsistent label rules was proposed, which aimed at providing a more effective method for the classification of the bridge structured health data. The proposed algorithm used combination of agreement and disagreement label rules for the unlabeled data by judging whether the two classifiers were consistent. Put the sample to the label set, if the label results were consistent. If the label results were inconsistent and the confidence was beyond the threshold, it put the label result of the high confidence to the label set, took full use of the unlabeled data to improve the performance of the classifier, and updated the classification model by the difference of the classifiers. The experimental results of the proposed algorithm on the bridge structured health datasets and standard UCI datasets verify the effectiveness and feasibility of the proposed model on the multi-view classification problems.

A fuzzy diffusion Positron Emission Tomography (PET) reconstruction algorithm based on anatomical non-local means prior was proposed to solve the problem in traditional Maximum A Posteriori (MAP) algorithm, that the details at low gradient value of reconstruction image cannot be maintained effectively and the appeared ladder artifacts. Firstly, the median prior distribution MAP reconstruction algorithm was improved, namely an anisotropic diffusion filter combined with fuzzy function was introduced before each median filtering. Secondly, the fuzzy membership function was used as diffusion coefficient in the anisotropic diffusion process, and the details of the image were considered by anatomical non-local prior information. The simulation results show that, compared with the traditional algorithms, the new algorithm improves the Signal-to-Noise Ratio (SNR) and anti-noise capability, and has good visual effects and clear edges. Thus the algorithm achieves a good balance between noise reduction and edge maintenance.

Since the traditional wavelet threshold functions have some drawbacks such as the non-continuity on the points of threshold, large deviation of estimated wavelet coefficients, Gibbs phenomenon and distortion are generated and Signal-to-Noise Ratio (SNR) can be hardly improved for the denoised signal. To overcome these drawbacks, an improved wavelet threshold function was proposed. Compared with the soft, hard, semi-soft threshold function and others, this function was not only continuous on the points of threshold and more convenient to be processed, but also was compatible with the performances of traditional functions and the practical flexibility was greatly improved via adjusting dual threshold parameters and dual variable factors. To verify this improved function, a series of simulation experiments were performed, the SNR and Root-Mean-Square Error (RMSE) values were compared between different denoising methods. The experimental results demonstrate that the smoothness and distortion are greatly enhanced. Compared with soft function, its SNR increases by 22.2% and its RMSE decreases by 42.6%.

Concerning the thresholds often being needed in the image edge detection and it is difficult to set good threshold values for the variant illumination image, a new edge detection method was proposed to solve these problems. Firstly, according to the logarithm, an image was decomposed into high frequency and low frequency, and the high frequency image was extracted by the logarithmic image minus the image by the maximum value filter. Then based on the Stevens theorem from cognitive psychology, the high frequency information was transformed into visual psychological quantity. After the edges were thinned by non-minimum suppression, they were extracted by Pillar K-means algorithm. The proposed method has good effect on the variant illumination image and does not need to set threshold value. The experimental results prove the effectiveness of the proposed method, and also show that the edge value in variant intensity may be agreed by converting the intensity to the psychological value.

The Firefly Algorithm (FA) has a few disadvantages in the global searching, including slow convergence speed, low solving precision and high possibility of being trapped in local optimum. A FA based on chaotic dynamic population was proposed. Firstly, chaotic sequence generated by cube map was used to initiate individual position, which strengthened the diversity of global searching; secondly, through dynamic monitoring of population, whenever the algorithm meets the preset condition, the new population individuals were generated using chaotic sequences, thus effectively improving convergence speed; thirdly, a Gaussian disturbance would be given on the global optimum of each generation, thus the algorithm could effectively jump out of local minima. Based on six complex test functions, the test results show that chaos-based dynamic population FA improves the capacity of global searching optimal solution, convergence speed and computational precision of solution.

An efficient Morphological Un-Decimated Wavelet (MUDW) transform with more delicate and accurate multi-scale decomposition performance that suites real-time image fusion was proposed. It took the average of dilation and erosion as the analysis operator, and the difference of adjacent scale images as the detail image. Size-increasing structure elements were adopted to get better fusion result. Due to the simplicity of dilation and erosion operator, computation time is shorter than other real-time algorithms. Furthermore, a factor was added during reconstruction, to obtain an obvious enhancement effect. The experimental results show that the new method outperforms other real-time algorithms.

Concerning the Virtual Machine (VM) migration problems in Wide Area Network (WAN), a live migration model was proposed. The link state between nodes was continuously detected, and the migration time of disk, memory, CPU status and network were optimized. The disk cycle synchronization, unidirectional tunnel and virtual machine localization were implemented. The experimental results show that the model reduces amount of migration data and shortens redirection path in WAN. The total time and pause time are close to the manner of shared storage under simulated conditions.

The authors studied the clustering mechanism in Mobile Ad Hoc Network (MANET) and proposed a maximal weighted clustering algorithm based on connected dominating set, including clustering algorithm and clustering maintenance strategy. The comprehensive performance of nodes was quantized by the weighted amount of node mobility, minimum average emissive power, and the energy consumption rate. The improved algorithm for solving connected dominating set was used for clustering the nodes, which made the higher performance nodes be the cluster heads and reduced the number of clusters. The simulation results show that the proposed algorithm is beneficial to improving the load balancing ability and enhancing the robustness and stability of the network.

Attribute-Based Encryption (ABE) scheme has been drawing attention for having a broad application in the area of fine-grained access control, directed broadcast, and so on. Combined with NAXOS technique, this paper proposed a fully secure Attribute-Based Authenticated Key Exchange (ABAKE) protocol based on an ABE scheme, and gave a detailed security proof in the Attribute-Based eCK (ABeCK) model by provable security theory. Compared with other similar protocols, the proposed protocol obtains stronger security and flexible attribute authentication policy, while decreasing communications cost.

Concerning the collision and access failure which are very likely to occur when the intensity of the users’ access increases, a random access packetbased strategy was proposed. The users were divided into several groups according to the strategy; certain subframes formed a superframe to enable different users to send Uplink Synchronization Code (SYNCUL) to the corresponding subframe of superframe when the users needed random access information, and based on it, priority was set for the users from different groups. Compared with the general strategy, packetbased strategy has greatly improved the success rate of the users’ access, and grouping based on priority was adopted to ensure a higher success rate for advanced users. The theoretical analysis and simulation results show that the proposed packetbased strategy can significantly improve the system Quality of Service (QoS) and could be an effective measurement for decreasing the probability of collision in random access.

Most of the existing detection algorithms for image copy-move forgery cannot effectively detect the sequential mixed image forgeries with regional duplication, so a new detection method based on fractal and statistics was proposed. The presented method first divided an image into overlapping blocks, and the each block would respectively be extracted to an eigenvector, which was composed by fractal dimension and three statistical data. Then, all the eigenvectors were lexicographically sorted. Finally, the forgery part was localized by means of the location information of the blocks and the Euclidean distance. The proposed method can not only detect the traditional copy-move forgery, but also detect the multi-region forgery for images subjected to rotation, flipping, and a mixture of these processing operations. The method is also robust to tampered images undergoing some attacks like Gaussian blurring, contrast adjustment, brightness adjustment, etc. The experimental results show the validity of the method.

In trust computing, recommendation trust has very strong subjectivity and some aggressive behaviors like deception and slander. Those factors will conceal the authenticity of behaviors of user who is recommended and threat the system security. To address the problem, this paper proposed a weighted trust computing method based on user’s behaviors. The time attribute of feedback information was identified by using time attenuation. And the trustworthiness of users was computed based on directness trust and recommendation trust with different weights. Also, this paper introduced Feedback credibility to evaluate the authenticity of recommendation trust. Simulation experiments show that this method has better adaptability to the dynamics of trust. It can reduce effectively the impact of malicious recommended trust, and compute accurately the trust of users according to user’s behaviors, which provide reliable information to correctly make security decision for the system.

Since the real node number in cash transport network changes dynamically, a route planning strategy for dynamic cash transport routing was proposed. The strategy did partitioning and optimizing in sequence. Firstly, Dijkstra algorithm was adopted to compute the shortest route between two nodes, and then vehicle number and node on each route were gotten by nearest neighbor algorithm and workload balancing factor. Secondly, the pre-cross genetic algorithm was adopted to optimize each route and get node sequence on the route, which could get the route with shortest distance and minimum time consumption. The experimental results show that the proposed strategy can meet the requirements of dynamic vehicle number and route, and achieve the purpose of saving resources.

The reliability of the large-scale cluster storage system is one important research aspect in the related domain. A high-availability data objects placement algorithm was proposed. It groups objected into redundancy sets using RAID at the algorithm level. The redundancy allowed us to reconstruct any corrupted data objects and storage nodes when it failed and assured efficiently the high availability of storage system. The availability of storage system was quantitatively analyzed by using Markov reward model, and the computing results indicate the algorithm is efficient.

An approach to implement creative conception design system by using fractal was presented to improve the initial conception design process that used math function in the past. An example of architectural modeling creative design was given: swarms were initialized by fractal operations such as mergers and so on, and then calculated by evolution algorithm. Experiments indicate that the fractal approach is promising to develop creative conception design system.