The Minimum Cut Problem （MCP） is a classic NP （Nondeterministic Polynomial） -hard problem with broad applications in image segmentation and network reliability analysis. Warning Propagation （WP） algorithm， a factor graph-based inference method， has shown good scalability and structural adaptability when solving combinatorial optimization problems. However， its effectiveness on MCP is limited by path selection， factor graph construction， and convergence. Therefore， an Entropy-based Warning Propagation （EWP） algorithm was proposed to solve MCP. First， a Hidden Markov Model （HMM） -driven jump-point mechanism was introduced to select core propagation regions. At the same time， a batch-mapping factor graph transformation way was employed to reduce graph structural dimensionality effectively. Finally， a propagation freezing strategy that combined marginal probability and variable entropy was adopted to accelerate convergence. Experimental results demonstrate that EWP achieves good performance on graphs with different sizes， and especially has significant potential in large-scale graph optimization problems.

A design and implementation method of Field Programmable Gate Array （FPGA） hardware structure optimization based on Radix 2² Fast Fourier Transformation （R2²FFT） algorithm was proposed to solve the problem that Fast Fourier Transformation （FFT） algorithm requires a lot of resources and time to process large-scale data and thus leading to a low operation speed. Firstly， by using R2²FFT algorithm， a Y-shaped dual parallel array structure combining a sequence conversion function and a pipeline structure was constructed， which reduced the usage number of hardware multipliers and increases throughput of hardware structure， so as to improve operation speed of FFT algorithm on FPGA. Secondly， correlation characteristics of the twiddle factors were adopted in single-stage operation of the R2²FFT pipeline to optimize the on-chip storage resource consumption and reduce storage space by about 50.00%. Finally， scalability of hardware structure was further improved in realizing expansion operations such as \mathrm{2} N points and \mathrm{4} N points based on optimization of N-point R2²FFT algorithm structure. Verilog HDL language and Modelsim were utilized to implement hardware design and simulation， respectively. Then， the proposed method was synthesized and placed-and-routed by using Vivado2018.3 software， and performance analysis was carried out. Experimental results show that compared with four improved FFT hardware implementation methods， the proposed method reduces the operation time by 75.10%， 95.34%， 38.49%， and 49.20%， respectively， which shows significant improvement of the method in operation speed. At the same time， the resource consumption of the proposed method is reasonable and the consumption proportion of the method is low， and the method has characteristics of low operation power consumption and strong scalability.

Visual object tracking is one of the important tasks in computer vision， in order to achieve high-performance object tracking， a large number of object tracking methods have been proposed in recent years. Among them， Transformer-based object tracking methods become a hot topic in the field of visual object tracking due to their ability to perform global modeling and capture contextual information. Firstly， existing Transformer-based visual object tracking methods were classified based on their network structures， an overview of the underlying principles and key techniques for model improvement were expounded， and the advantages and disadvantages of different network structures were also summarized. Then， the experimental results of the Transformer-based visual object tracking methods on public datasets were compared to analyze the impact of network structure on performance. in which MixViT-L （ConvMAE） achieved tracking success rates of 73.3% and 86.1% on LaSOT and TrackingNet， respectively， proving that the object tracking methods based on pure Transformer two-stage architecture have better performance and broader development prospects. Finally， the limitations of these methods， such as complex network structure， large number of parameters， high training requirements， and difficulty in deploying on edge devices， were summarized， and the future research focus was outlooked， by combining model compression， self-supervised learning， and Transformer interpretability analysis， more kinds of feasible solutions for Transformer-based visual target tracking could be presented.

In the field of open social text， the generated text content lacks personalized features. In order to solve the problem， a user-level fine-grained control generation model was proposed， namely PTG-GPT2-Chinese （Personalized Text Generation Generative Pre-trained Transformer 2-Chinese）. In the proposed model， on the basis of the GPT2 （Generative Pre-trained Transformer 2.0） structure， an Encoder-Decoder model framework was designed. First， the static personalized information of a user was modeled and encoded on the Encoder side， a bidirectional independent attention module was added on the Decoder side to receive the static personalized feature vector， and the attention module in the original GPT2 structure was used for capturing the dynamic personalized features in the user’s text. Then， the scores of different attention modules were weighted and fused dynamically， and were participated in the subsequent decoding， thereby automatically generating social text constrained by the user’s personalized feature attributes. However， the semantic sparsity of the user’s basic information may cause conflicts between the generated text and some personalized features. Aiming at this problem， the BERT （Bidirectional Encoder Representations from Transformers） model was used to perform the secondary enhanced generation of consistent understanding between the output data of the Decoder side and the user’s personalized features， and finally the personalized social text generation was realized. Experimental results show that compared with the GPT2 model， the proposed model has the fluency improved by 0.36% to 0.72%， and on the basis of no loss of language fluency， the secondary generation makes the two evaluation indicators： personalization and consistency increase by 10.27% and 13.24% respectively. It is proved that the proposed model can assist user’s creation effectively and generate social text that is fluent and personalized for the user.

The existing similarity-based moving target trajectory prediction algorithms are generally classified according to the spatial-temporal characteristics of the data， and the characteristics of the algorithms themselves cannot be reflected. Therefore， a classification method based on algorithm characteristics was proposed. The calculation of the distances between two points is required for the trajectory similarity algorithms to carry out the subsequent calculations， however， the commonly used Euclidean Distance （ED） is only applicable to the problem of moving targets in a small region. A method of similarity calculation using geodetic distance instead of ED was proposed for the trajectory prediction of sea targets moving in a large region. Firstly， the trajectory data were preprocessed and segmented. Then， the discrete Fréchet Distance （FD） was adopted as similarity measure. Finally， synthetic and real data were used to test. Experimental results indicate that when sea targets move in a large region， the ED-based algorithm may gain incorrect prediction results， while the geodetic distance-based algorithm can output correct trajectory prediction.

Brain tumor Magnetic Resonance Imaging(MRI) has problems such as multi-modality, lacking of training data, class imbalance, and large differences between private databases, which lead to difficulties in segmentation. In order to solve these problems, the few-shot segmentation method was introduced, and a Prototype network based on U-net(PU-net) was proposed to segment brain tumor Magnetic Resonance(MR) images. First, the U-net structure was modified to extract the features of various tumors, which was used to calculate the prototypes. Then, on the basis of the prototype network, the prototypes were used to classify the spatial locations pixel by pixel, so as to obtain the probability maps and segmentation results of various tumor regions. Aiming at the problem of class imbalance, the adaptive weighted cross-entropy loss function was used to reduce the influence of the background class on loss calculation. Finally, the prototype verification mechanism was added, which means the probability maps obtained by segmentation were fused with the query image to verify the prototypes. The proposed method was tested on the public dataset BraTS2018, and the obtained results were as following:the average Dice coefficient of 0.654, the positive prediction rate of 0.662, the sensitivity of 0.687, the Hausdorff distance of 3.858, and the mean Intersection Over Union(mIOU) reached 61.4%. Compared with Prototype Alignment Network(PANet) and Attention-based Multi-Context Guiding Network(A-MCG), all indicators of the proposed method were improved. The results show that the introduction of the few-shot segmentation method has a good effect on brain tumor MR image segmentation, and the adaptive weighted cross-entropy loss function is also helpful, which can play an effective auxiliary role in the diagnosis and treatment of brain tumors.

Concerning the low classification accuracy of Motor Imagery ElectroEncephaloGram(MI-EEG), a new Convolutional Neural Network(CNN) model based on deep framework was introduced. Firstly, the time-frequency information under two resolutions was obtained by using Short-Time Fourier Transform(STFT) and Continuous Wavelet Transform(CWT). Then, it was combined with the channel position information and used as the inputs of the CNN in the form of three-dimensional tensor. Secondly, two network models based on different convolution strategies, namely MixedCNN and StepByStepCNN, were designed to perform feature extraction and classification recognition of the two types of inputs. Finally, in order to solve the problem of overfitting due to insufficient training samples, the mixup data augmentation strategy was introduced. Experimental results on BCI Competition Ⅱ dataset Ⅲ showed that the model performed highest accuracy by training the CWT samples reconstructed by mixup data augmentation on MixedCNN(93.57%), which was 19.1%, 20.2%, 11.7% and 2.3% higher than those of the other four analysis methods including Common Spatial Pattern(CSP) + Support Vector Machine(SVM), Adaptive Autoregressive Model(AAR) + Linear Discriminant Analysis(LDA), Discrete Wavelet Transform(DWT) + Long Short-Term Memory(LSTM), STFT + Stacked AutoEncoder(SAE). The proposed method can provide a reference for MI-EEG classification tasks.

For the given multiple sequences, a certain threshold and the gap constraints, the study objective is to discover frequent patterns whose supports in multiple sequences are no less than the given threshold value, where any two successive elements of pattern fulfill the user-specified gap constraints, and any two occurrences of a pattern in a given sequence meet the one-off condition. To solve this problem, the existing algorithms only consider the first occurrence of each character of a pattern when they compute the support of a pattern in a given sequence, so that many frequent patterns are not mined. An efficient mining algorithm of multiple sequential patterns with gap constraints, named MMSP, was proposed. Firstly, it stored the candidate positions of a pattern using two-dimensional table, then it selected the position from the candidate positions according to the left-most strategy. The experiments were conducted on DNA sequences. The number of frequent patterns mined by MMSP was 3.23 times of that mined by the related algorithm named M-OneOffMine when the number of multiple sequence elements is constant and the sequence length changes, and the average number of mining patterns by MMSP was 4.11 times of that mined by M-OneOffMine when the number of multiple sequence elements changes. The average number of mined patterns by MMSP was 2.21 and 5.24 times of that mined by M-OneOffMine and MPP respectively when the number of multiple sequence elements changes, and the frequent patterns mined by M-OneOffMine was a subset of MMSP. The experimental results show that MMSP can mine more frequent patterns with shorter time, and it is more suitable for practical applications.

In order to achieve fast collision detection in robot assisted virtual surgery, an algorithm based on changeable direction hull was proposed. It combined fixed direction hull algorithm with the two characteristics in virtual scene, complex motion of surgical instrument end and continuous deformation of soft tissues. Action mode between instruments and soft tissue was analyzed and deformation of soft tissue was predicted. Then the set of box directions was changed to improve the tightness of bounding volume trees. Accordingly, collision detection was accelerated with decreased interaction tests. The simulation results show that collision information can be obtained through the proposed algorithm and the new method implements faster compared with fixed direction hull algorithm.

There is a great demand for publicly verifiable encryption in key escrow, optimistic fair exchange, publicly verifiable secret sharing and secure multiparty computation, but the current schemes are either chosen plaintext secure or chosen ciphertext secure in the random oracle model, which obviously are not secure enough to be applied in the complicated circumstances. Based on the analysis of the current schemes and application of the reality, this paper proposed a new publicly verifiable encryption scheme by combining the CS encryption scheme with the non-interactive zero knowledge proof protocol. The new scheme enabled any third party other than the sender and receiver to verify the validity of the ciphertext, but leaked no information about the message. Finally, without using the random oracle, the adaptively chosen ciphertext security of the scheme is proved in the standard model.

It is essential for software development and maintenance to predict which modules are change-prone in an Object-Oriented (OO) software system. In this paper, a light-weight approach was developed to compute the change probability metric by leveraging the dependence relationships between classes in a system. Then, based on Logistic regression model, an experimental analysis was conducted using Eclipse 2.0. The experimental results indicate that, on one hand, the proposed change probability metric captures different information from traditional OO metrics. On the other hand, when being used with traditional OO metrics together, the proposed change probability metric can significantly improve the accuracy for predicting the change-prone classes.

Smart Rail Transportation (SRT) is a new concept in rail transportation industry and poses technical challenges for the academic circle as well as the industry. It involves "in-depth sensibility and perceptibility", "extensive interconnectivity and interoperability among human beings, computers, and physical objects", and "premier intelligent data processing" to aim at SRT. Having analyzed the development trend pertinent to in-depth sensing and perceiving in SRT, this paper provided a comprehensive discussion on the requirements of relevant techniques, characterized by "sharpness", "swiftness", "reliability", "high-efficiency", "completeness", and "intelligence" in SRT.

Contrast mining is one of fairly new hot data mining topics. Contrast mining focuses on knowledge that describes differences between classes and conditions, or describes changes over time. Contrast mining aims at developing techniques to discover patterns or models that contrast, and characterize multiple datasets associated with different classes or conditions. Contrast mining has wide applications in reality, due to its ability of simplifying problems and classifying accurately. Research on the mining and application of emerging patterns represents a major direction of contrast mining. This paper provided a survey of such issue. More specifically, after introducing the background, basic concepts and principles of emerging patterns, the paper analyzed the mining methods of emerging patterns, discussed extended definitions of emerging patterns and their mining, stated methods for constructing emerging pattern based classifiers, and illustrated applications of emerging pattern in several real-world fields. Finally, this paper gave out some topics for future research on emerging pattern based contrast mining.

Based on Shao et al’s ID-based proxy re-signature, an ID-based bidirectional threshold proxy re-signature scheme in the standard model is presented in this paper. Our scheme eliminates the cost of restoring and managing certificates, and solves the difficult problem of excessive rights of the proxy in the proxy re-signature shceme. The scheme can tolerate t

Based on the study of RGB (red-green-blue) color space and the comparison and analysis of several color difference formulas in RGB color space, this paper summarized three rules of color difference in RGB color space and proposed the conception of the importance of the color component. A new color difference formula in RGB color space was achieved by dynamically adjusting the vector distance and angular between a pair of RGB colors. The dynamic coefficient was weighted by the importance of color component. Color quantization experiment of highly complex color images demonstrates that this new color difference formula's performance is better than those widely used color difference formulas in RGB color space.

The existing certificateless public key signature schemes are based on elliptic curve or Tate pairing. The proposed scheme was certificateless public signature scheme without pairing. New scheme was proved to be unforgery under random oracle model. New scheme avoids the using of certificate in certificate-based public key signature scheme, removes key escrow in ID-based signature scheme, needs simpler algorithm, and is convenient to practical application.

It is very hard to develop encryption technology used in cheap Radio Frequency Identification (RFID) tags. In this paper, intrusion detection, as a new methodology, was adopted to create security model for RFID system. By analyzing typical security attacks on RFID systems, and based on artificial immune network, a solution to extract intrusion characteristics and to identify intrusion was proposed. A self-adaptive intrusion detection model for RFID system was designed. The model can enhance the defense capabilities of RFID systems by cooperating with encryption technology, but has no need to amend the technical standards of RFID. Stimulation results prove that the mistake rate and miss rate of the model are fairly low.

The difficulties of availability management design in supercomputer system were discussed.An availability management mode based on message bus was put forward. The design and realization of message bus was described in detail and the capability test of message bus was offered.The availablity management on a kind of supercomputer with message bus architecture was designed and implemented.