To address the problems of high memory access overhead， redundant computation， and limited efficient deployment caused by the large parameter size of Convolutional Neural Networks （CNNs）， a CNN pruning and quantization compression method for reconfigurable structures was proposed to integrate network structure characteristics and hardware deployment requirements， and to perform co-optimization from both pruning and quantization dimensions. First， a convolutional layer pruning strategy based on feature similarity was introduced， in which feature evaluation， cluster and grouping， similarity calculation， and redundancy removal were performed in turn to filter out low-contribution and redundant filters. Second， progressive threshold pruning was applied at the fully connected layers to compress redundant weights. Third， in the quantization part， layer sensitivity indices were constructed using Hessian traces， and the precision of each layer was assigned adaptively under a bit-width budget. Finally， combining the characteristics of reconfigurable structures， an optimized deployment scheme was designed. Experimental results on the CIFAR-10 dataset show that the proposed method achieves a compression ratio of 16.2x for VGG16， surpassing Automated deep neural network Pruning and Quantization framework （APQ） （13.9x）. Compared to the model using fixed 16-bit precision， the pruned VGG16 using the proposed deployment scheme on a self-reconfigurable and self-evolvable Artificial Intelligence （AI） chip has the inference latency reduced from 23.3 ms to 9.1 ms， achieving a 2.56x speedup. It can be seen that the proposed method reduces the storage and transmission costs while maintaining the classification accuracy， improving the deployment efficiency and computational performance on edge devices.

Due to the high complexity， weak individual differences， and short inter-class distances of fundus image features， pure Convolutional Neural Networks （CNNs） and attention based networks cannot achieve satisfactory accuracy in fundus disease image classification tasks. To this end， Attention Bilinear Involution Neural Network （ABINN） model was implemented for fundus disease image classification by using the involution operator. The parameter amount of ABINN model was only 11% of that of the traditional Bilinear Convolutional Neural Network （BCNN） model. In ABINN model， the underlying semantic information and spatial structure information of the fundus image were extracted and the second-order features of them were fused. It is an effective parallel connection between CNN and attention method. In addition， two instantiation methods for attention calculation based on involution operator， Attention Subnetwork based on PaTch （AST） and Attention Subnetwork based on PiXel （ASX）， were proposed. These two methods were able to calculate attention within the CNN basic structure， thereby enabling bilinear sub-networks to be trained and fused in the same architecture. Experimental results on public fundus image dataset OIA-ODIR show that ABINN model has the accuracy of 85%， which is 15.8 percentage points higher than that of the common BCNN model and 0.9 percentage points higher than that of TransEye （Transformer Eye） model.

Virtual digital currency provides a breeding ground for terrorist financing， money laundering， drug trafficking and other criminal activities. As a representative emerging digital currency， Monero has a universally acknowledged high anonymity. Aiming at the problem of using Monroe anonymity to commit crimes， Monero anonymity technology and tracking technology were explored as well as the research progresses were reviewed in recent years， so as to provide technical supports for effectively tackling the crimes based on blockchain technology. In specific， the evolution of Monero anonymity technology was summarized， and the tracking strategies of Monero anonymity technology in academic circles were sorted out. Firstly， in the anonymity technologies， ring signature， guaranteed unlinkability （one-off public key）， guaranteed untraceability， and the important version upgrading for improving anonymity were introduced. Then， in tracking technologies， the attacks such as zero mixin attack， output merging attack， guess-newest attack， closed set attack， transaction flooding attack， tracing attacks from remote nodes and Monero ring attack were introduced. Finally， based on the analysis of anonymity technologies and tracking strategies， four conclusions were obtained： the development of anonymity technology and the development of tracking technology of Monero promote each other； the application of Ring Confidential Transactions （RingCT） is a two-edged sword， which makes the passive attack methods based on currency value ineffective， and also makes the active attack methods easier to succeed； output merging attack and zero mixin attack complement each other； Monero’s system security chain still needs to be sorted out.

For the consensus problem of time-varying multi-agent systems under time-varying topology connection, an event-triggered impulsive control protocol was proposed. In this protocol, for each agent, the controller would be updated only when the related state error exceeded a threshold, and the control inputs would be carried out only at the event triggering instants, and continuous communication between agents was avoided. This protocol would greatly reduce the cost of communication and control for network consensus. The sufficient conditions for the multi-agent systems with time-varying characteristics to achieve consensus under event-triggered impulsive control were analyzed based on the algebraic graph theory, Lyapunov stability and impulsive differential equation. At the same time, it was proved theoretically that there was no Zeno behavior in the event-triggered time sequences. Finally, the effectiveness of the obtained theoretical conclusion was verified through several numerical simulations.

In order to improve the efficiency of dam safety operation and maintenance, the dam defect object detection models can help to assist inspectors in defect detection. There is variability of the geometric shapes of dam defects, and the Single Shot MultiBox Detector (SSD) model using traditional convolution methods for feature extraction cannot adapt to the geometric transformation of defects. Focusing on the above problem, a DeFormable convolution Single Shot multi-box Detector (DFSSD) was proposed. Firstly, in the backbone network of the original SSD:Visual Geometry Group (VGG16), the standard convolution was replaced by the deformable convolution, which was used to deal with the geometric transformation of defects, and the model's spatial information modeling ability was increased by learning the convolution offset. Secondly, according to the sizes of different features, the ratio of the prior bounding box was improved to prompt the detection accuracy of the model to the bar feature and the model's generalization ability. Finally, in order to solve the problem of unbalanced positive and negative samples in the training set, an improved Non-Maximum Suppression (NMS) algorithm was adopted to optimize the learning effect. Experimental results show that the average detection accuracy of DFSSD is improved by 5.98% compared to the benchmark model SSD on dam defect images. By comparing with Faster Region-based Convolutional Neural Network (Faster R-CNN) and SSD models, it can be seen that DFSSD model has a better effect in improving the detection accuracy of dam defect objects.

In order to solve the problems of poor denoising effect and low computational efficiency in image denoising, a double blind denoising algorithm based on Denoising Convolutional Neural Network (DnCNN) and Conditional Generative Adversarial Net (CGAN) was proposed. Firstly, the improved DnCNN model was used as the CGAN generator to capture the noise distribution of the noisy image. Secondly, the noisy image after eliminating the noise distribution and the tag were sent to the discriminator to distinguish the noise reduction image. Thirdly, the results of discrimination were used to optimize the hidden layer parameters of the whole model. Finally, a balance between the generator and the discriminator was achieved in the game, and the generator's residual capture ability was optimal. Experimental results show that on Set12 dataset, when the noise levels are 15, 25, 50 respectively:compared with the DnCNN algorithm, the proposed algorithm has the Peak Signal-to-Noise Ratio (PSNR) increased by 1.388 dB, 1.725 dB and 1.639 dB respectively based on the error evaluation index between pixel points. Compared with the existing algorithms such as Block Matching 3D (BM3D), Weighted Nuclear Norm Minimization (WNNM), DnCNN, Cascade of Shrinkage Fields (CSF) and ConSensus neural NETwork (CSNET), the proposed algorithm has the index value of Structural SIMilarity (SSIM) improved by 0.000 2 to 0.104 1 on average based on the evaluation index of structural similarity. The above experimental results verify the superiority of the proposed algorithm.

Focusing on the large decryption overhead of the resource limited edge devices and the lack of effective user tracking and revocation in attribute-based encryption, a traceable and revocable Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme supporting cloud-fog computing was proposed. Firstly, through the introduction of fog nodes, the ciphertext storage and outsourcing decryption were able to be carried out on fog nodes near the users, which not only effectively protected users' private data, but also reduced users' computing overhead. Then, in response to the behaviors such as user permission changes, users intentionally or unintentionally leaking their own keys in the attribute-based encryption system, user tracking and revocation functions were added. Finally, after the identity of malicious user with the above behaviors was tracked through the algorithm, the user would be added to the revocation list, so that user's access right was cancelled. The performance analysis shows that the decryption overhead at the user end is reduced to one multiplication and one exponential operation, which can save large bandwidth and decryption time for users; at the same time, the proposed scheme supports the tracking and revocation of malicious users. Therefore, the proposed scheme is suitable for data sharing of devices with limited computing resources in cloud-fog environment.

To solve the problems of secondary injury, muscle dysfunction, stiffness of the affected limb, damage to the blood supply, and poor dynamic performance of postoperative correction in traditional reduction methods (such as manual reduction, traction reduction, and surgical reduction) of fracture treatment, a Cyber-Physical and Human System (CPHS) was proposed to guide the reduction movement of the robot. First of all, the composition of the cyber-physical system of the parallel robot was illustrated from the aspects of CPHS system such as digital twin, information perception, system integration, surgical procedure, and simulated reduction. The high positioning accuracy and repeatability of the robot were combined with minimally invasive methods effectively to guide doctors to complete a series of operations such as simulation planning and intraoperative monitoring. Secondly, according to the clinical fracture reduction process, the reduction experiments were performed on simulated fracture cases of 5 groups of different fracture postures under robot operation. Finally, the remaining placements and angle errors in each experimental group were calculated after reduction operation and were compared with the corresponding data of the traditional reduction methods. Experimental results show that the CPHS fracture reduction robot has obvious advantages in fracture reduction and patient postoperative rehabilitation compared with the traditional reduction methods.

To overcome low pathfinding efficiency and slow convergence speed of Rapid-exploring Random Tree star(RRT ^*) in high-dimensional and complex environment, an Unmanned Aerial Vehicle(UAV) path planning algorithm with self-adjusting sampling space based on RRT ^* named Adjust Sampling space-RRT ^*(AS-RRT ^*) was proposed. In this algorithm, by adjusting the sampling space adaptively, the tree was guided to grow more efficiently, which was realized through three strategies including:biased sampling, node selection and node learning. Firstly, the light and dark areas in the sampling space were defined to performing biased sampling, and the probability weights of the light and dark areas were determined by the current expansion failure rate, so as to ensure that the algorithm was both exploratory and directional when searching for the initial path. Then, once the initial path was found,the nodes were periodically filter,and the high-quality nodes were used as learning samples to generate the new sampling distribution, the lowest-quality nodes were replaced by new nodes after the algorithm reaching the maximum number of nodes. Simulation experiments for comparison were conducted in multiple types of environments. The results show that the proposed algorithm improves the inherent randomness of the sampling algorithm to a certain extent, and compared with the traditional RRT ^* algorithms, it has less pathfinding time used in the same environment, lower cost path generated in the same time, and the improvements are more obvious in three-dimensional space.

It is of great significance to apply the blockchain technology to the field of digital music copyright management in view of the difficulties in copyright confirmation, infringement monitoring, right protection and evidence collection, royalty settlement and other problems in the traditional music copyright industry. A digital music copyright management system was designed and constructed by using the VNT Chain blockchain platform. In the system, the blockchain technology was used to provide the proof of music copyright and realize the evidence solidification, the Shazam algorithm was used to provide the proof of originality for music copyright, and the smart contract was used to guarantee the security and reliability of transactions. This system included six function modules:user management, copyright registration, copyright trading, infringement monitoring, evidence solidification and music ecology, covering the main parts of copyright management. According to different needs of business data, blockchain, InterPlanetary File System(IPFS) and MySQL were adopted as storage engines respectively for the system. Experimental results show that the copyright registration time of each music increases by about 1.9 s, and the storage of music feature fingerprint data of one song on IPFS costs about 8 MB averagely, which meets the expected system performance requirements.

Aiming at the problems of information transmission efficiency and user safety and privacy in the Internet of Vehicles (IoV), an IoV system based on improved Proof of Vote (PoV) consensus protocol was proposed. First, according to the actual needs of IoV, the blockchain technology was used to ensure basic information transmission efficiency and user safety. Second, the structure and algorithm were optimized to improve the traditional PoV consensus protocol in order to further improve the transmission efficiency of entire IoV system. Finally, a supervision and punishment mechanism was designed to ensure the reliability of the system in order to protect the safety and privacy of IoV users. The protocol does not rely on third-party intermediaries, so that it is able to protect the privacy of vehicles and owners while ensuring the consensus efficiency, and is closer to the actual needs of IoV. Theoretical analysis and simulation experiments showed that compared with those of the traditional PoV consensus protocol, the transaction confirmation time and block interval time of the improved PoV consensus protocol were both reduced from 0.25 minutes to 0.2 minutes; and in the reliability comparison with the improved consensus protocol without supervision and punishment mechanism, the improved consensus protocol with supervision and punishment mechanism had the accuracy improved by 29.4%. Experimental results prove that the improved consensus protocol has higher consensus efficiency and safety in IoV.

Aspect-based sentiment analysis tries to estimate different emotional tendencies expressed in different aspects of a sentence. Aiming at the problem that the existing network model based on Recurrent Neural Network (RNN) combined with attention mechanism has too many training parameters and lacks explanation of related syntax constraints and long distance word dependence mechanism, a self-attention gated graph convolutional network was proposed, namely MSAGCN. First, the multi-headed self-attention mechanism was used to encode context words and targets, thus capturing semantic associations within the sentence. Then, a graph convolutional network was established on the sentence's dependency tree to obtain syntactic information and word dependencies. Finally, the sentiment of the specific target was obtained through the GTRU (Gated Tanh-ReLU Unit). Compared with the baseline model, the proposed model has the accuracy and F1 improved by 1%-3.3% and 1.4%-6.3% respectively. At the same time, the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model was also applied to the current task to further improve the model effect. Experimental results verify that the proposed model can better grasp the emotional tendencies of user reviews.

In order to solve the problems of too many parameters, long training time and slow detection speed of the existing human skeleton key point detection models, a detection method combining the human skeleton key point detection model called Convolutional Pose Machines (CPMs) and the lightweight convolutional neural network model called SqueezeNet was proposed. Firstly, the CPMs with 4 stages (CPMs-Stage4) was used to detect the key points of the human images. Then, the Fire Module network structure of SqueezeNet was introduced into CPMs-Stage4 to reduce the model parameters greatly, and thus to obtain a new lightweight human skeleton key point detection model called SqueezeNet15-CPMs-Stage4. The verification results on the extended Leeds Sports Pose (LSP) dataset show that, compared with CPMs, SqueezeNet15-CPMs-Stage4 model has the training time reduced by 86.68%, the detection time of single image reduced by 44.27%, and the detection accuracy of 90.4%; and the proposed model performs the best in training time, detection speed and accuracy compared with three reference models improved VGG-16, DeepCut and DeeperCut. The experimental results show that the proposed model achieves high detection accuracy with short training time and fast detection speed, and can effectively reduce the training cost of the human skeleton key point detection model.

Outlier detection algorithms are widely used in various fields such as network intrusion detection, and medical aided diagnosis. Local Distance-Based Outlier Factor (LDOF), Cohesiveness-Based Outlier Factor (CBOF) and Local Outlier Factor (LOF) algorithms are classic algorithms for outlier detection with long execution time and low detection rate on large-scale datasets and high dimensional datasets. Aiming at these problems, an outlier detection algorithm Based on Graph Random Walk (BGRW) was proposed. Firstly, the iterations, damping factor and outlier degree for every object in the dataset were initialized. Then, the transition probability of the rambler between objects was deduced based on the Euclidean distance between the objects. And the outlier degree of every object in the dataset was calculated by iteration. Finally, the objects with highest outlier degree were output as outliers. On UCI （University of California， Irvine） real datasets and synthetic datasets with complex distribution, comparison between BGRW and LDOF, CBOF, LOF algorithms about detection rate, execution time and false positive rate were carried out. The experimental results show that BGRW is able to decrease execution time and false positive rate, and has higher detection rate.

Aiming at the low efficiency and the complex simulation process of the traditional reconstruction methods for porous media such as Multi-Point Statistics(MPS)which require scanning the training image many times and to obtain simulation results by complex probability calculations,a method to reconstruct porous media using adaptive deep transfer learning was presented. Firstly,deep neural network was used to extract the complex features from the training image of porous media. Secondly,the adaptive layer was added in deep transfer learning to reduce the difference in data distribution between training data and prediction data. Finally,through copying features by transfer learning,the simulation result consistent with the real training data was obtained. The performance of the proposed method was evaluated by comparing with the classical porous media reconstruction method MPS in multiple-point connectivity curve,variogram curve and porosity. The results indicate that the proposed method has high reconstruction quality. Meanwhile,the method has the average running time reduced from 840 s to 166 s,the average CPU usage dropped from 98% to 20%,and the average memory utilization decreased by 69%. The proposed method significantly improves the efficiency of porous media reconstruction under the premise of ensuring better quality of reconstruction results.

Item-based collaborative filtering learns user preferences from the user's historical interaction items and recommends similar new items based on the user's preferences. The existing collaborative filtering methods assume that a set of historical items that user has interacted with have the same impact on user, and all historical interaction items are considered to have the same contribution to the prediction of target item, which limits the accuracy of these recommendation methods. In order to solve the problems, a new collaborative filtering recommendation algorithm based on dual most relevant attention network was proposed, which contained two attention network layers. Firstly, the item-level attention network was used to assign different weights to different historical items in order to capture the most relevant items in the user historical interaction items. Then, the item-interaction-level attention network was used to perceive the correlation degrees of the interactions between the different historical items and the target item. Finally, the fine-grained preferences of users on the historical interaction items and the target item were simultaneously captured through the two attention network layers, so as to make the better recommendations for the next step. The experiments were conducted on two real datasets of MovieLens and Pinterest. Experimental results show that, the proposed algorithm improves the recommendation hit rate by 2.3 percentage points and 1.5 percentage points respectively compared with the benchmark model Deep Item-based Collaborative Filtering (DeepICF) algorithm, which verifies the effectiveness of the proposed algorithm on making personalized recommendations for users.

Concerning the problems that the traditional Convolutional Neural Network (CNN) ignores the contextual semantics of words when performing sentiment analysis tasks and CNN loses a lot of feature information during max pooling operation at the pooling layer, which limit the text classification performance of model, a parallel hybrid neural network model, namely CA-BGA (Convolutional Neural Network Attention and Bidirectional Gated Recurrent Unit Attention), was proposed. Firstly, a feature fusion method was adopted to integrate Bidirectional Gated Recurrent Unit (BiGRU) into the output of CNN, thus semantic learning was enhanced by integrating the global semantic features of sentences. Then, the attention mechanism was introduced between the convolutional layer and the pooling layer of CNN and at the output of BiGRU to reduce noise interference while retaining more feature information. Finally, a parallel hybrid neural network model was constructed based on the above two improvement strategies. Experimental results show that the proposed hybrid neural network model has the characteristic of fast convergence, and effectively improves the F1 value of text classification. The proposed model has excellent performance in Chinese short text sentiment analysis tasks.

Morphological reconstruction is a fundamental and critical operation in medical image processing, in which dilation operations are repeatedly carried out on the marker image based on the characteristics of mask image, until no change occurs on the pixels of the marker image. Concerning the problem that traditional CPU-based morphological reconstruction system has low computational efficiency, using Graphics Processing Unit (GPU) to quicken the morphological reconstruction was proposed. Firstly, a GPU-friendly data structure:parallel heap cluster was proposed. Then, based on the parallel heap cluster, a GPU-based morphological reconstruction system was designed and implemented. The experimental results show that compared with traditional CPU-based morphological reconstruction system, the proposed GPU-based morphological reconstruction system can achieve speedup ratio over 20 times. The proposed system demonstrates how to efficiently port complex data structure-based software system onto GPU.

Existing entity and relation extraction methods that rely on distant supervision suffer from noisy labeling problem. A model for joint entity and relation extraction from noisy data based on reinforcement learning was proposed to reduce the impact of noise data. There were two modules in the model:an sentence selector module and a sequence labeling module. Firstly, high-quality sentences without labeling noise were selected by instance selector module and the selected sentences were input into sequence labeling module. Secondly, predictions were made by sequence labeling module and the rewards were provided to sentence selector module to help the module select high-quality sentences. Finally, two modules were trained jointly to optimize instance selection and sequence labeling processes. The experimental results show that the F1 value of the proposed model is 47.3% in the joint entity and relation extraction, which is 1% higher than those of joint extraction models represented by CoType and 14% higher than those of serial models represented by LINE(Large-scale Information Network Embedding). The results show that the joint entity and relation extraction model in combination with reinforcement learning can effectively improve F1 value of sequential labeling model, in which the sentence selector can effectively deal with the noise of data.

Aiming at the problem that the characteristic and evaluation indexes for reasearch of traffic behavior spectrum are incompleted both at home and abroad and quantitative analysis cannot be performed in the research, the corresponding characteristic and evaluation indexes were defined to establish a complete traffic behavior spectrum system with quantitative analysis of regional traffic behavior data. Firstly, based on the characteristics of traffic behavior, an improved Analytic Hierarchy Process (AHP) was used to classify the traffic order types. Secondly, Real-Time System Integration (RTSI) algorithm with multi-data fusion was used to comprehensively evaluate the traffic safety of a certain road. Finally, a traffic behavior spectrum analysis tool was developed, calculating traffic safety index of a road section according to the traffic live data, and analyzing traffic behavior in the section more completely.

To ensure the security and reliability of communication in the smart grid, more and more authentication protocols have been applied in the communication process. For the authentication protocol proposed by Mahmood et al. (MAHMOOD K, CHAUDHRY S A, NAQVI H, et al. An elliptic curve cryptography based lightweight authentication scheme for smart grid communication. Future Generation Computer Systems. 2018,81:557-565), some defects were pointed out. For example, this protocol can be easily attacked by internal privileged personnel, is lack of password replacement phase and unfriendly to users, in which unique username cannot be guaranteed, even a formula error exists. To improve this protocol, an authentication protocol based on elliptic curve was proposed. Firstly, a login phase between the user and the device was added in the improved protocol. Secondly, elliptic curve cryptography puzzle was used to realize information exchange. Finally, the password replacement phase was added. Through the formal analysis by BAN (Burrows-Abadi-Needha) logic, the improved protocol is safe and feasible, which can resist internal personnel attacks, has password replacement and unique username, and is more friendly to users.

The popular Radio Frequency IDentification (RFID) tags are some passive ones and they only have very limited computing and memory resources, which makes it difficult to solve the security, privacy and scalability problems of RFID authentication protocols. Based on Hash function, a security-provable lightweight authentication protocol was proposed. The protocol ensures the confidentiality and privacy of the sessions during the authentication process by Hashing and randomizing. Firstly, the identity of a tag was confirmed by its pseudonym and was preserved from leaking to any untrusted entity such as a reader. Secondly, only one Hashing computation was needed to confirm a tag's identity in the backend server, and the searching time to the tag's identity was limited to a constant by using the identifier to construct a Hash table. Finally, after each authentication, the secrecy and pseudonym of the tag were updated to ensure forward security of the protocol. It is proved that the proposed protocol satisfies scalability, forward security and anonymity demands and can prevent eavesdropping, tracing attack, replay attack and de-synchronization attack. The protocol only needs Hash function and pseudorandom generating operation for the tag, therefore it is very suitable to low-cost RFID systems.

Focusing on the issue that our country's driver behavior spectrum research is still not perfect, and there is no corresponding behavioral spectrum analysis tool in the professional field, a set of complete driver behavior spectrum system for commercial motor vehicle of passenger transport was proposed and an analyzing tool was designed. Firstly, the characteristic indexes and the evaluation indexes of driver behavior spectrum were designed and defined. Secondly, the characteristic indexes analysis method and algorithm of driver behavior spectrum were given, the improved K-means algorithm based on Markov chain Monte Carlo sampling and outlier removing was used to analyze driving styles of drivers, and regression learning was used to analyze driving skills of drivers. Then, the basic data acquisition scheme and preprocessing methods of driver behavior spectrum based on car networking and big data were designed and proposed. Finally, Java language and the Spring MVC (Model View Controller) architecture were used to develop the profiling tool of driver behavior spectrum. Data mining and data analysis methods in machine learning were combined with traffic safety, which has theoretical significance for perfecting the driver behavior spectrum framework. It provides a scientific and quantitative analysis tool for our country's driver behavior spectrum analysis work. It also provides guiding significance for traffic management department to standardize the driving behaviors of drivers, improves the road safety index and makes reasonable traffic safety management strategies.

The physical layer secure transmission in two-way multi-relay system can not obtain the accurate Channel State Information (CSI) of eavesdroppers. In order to solve the problem, a robust joint physical layer secure transmission scheme of multi-relay cooperative beamforming and artificial noise was proposed to maximize the secrecy sum rate in the worst case of channel state under the total power constraint of system. In the proposed scheme, the problem to be solved was a complex non-convex optimization problem. The alternating iteration and Successive Convex Approximation (SCA) methods were used for the alternating optimization iteration of beamforming vector, artificial noise covariance matrix and source node transmit power, and the optimal solution of the above problem was obtained. The simulation results verify the effectiveness of the proposed scheme and show that the proposed scheme has better security performance.

In the open network environment, identity authentication is an important means to ensure information security. Aiming at the authentication protocol proposed by Li, et al (LI X, WU F, KHAN M K, et al. A secure chaotic map-based remote authentication scheme for telecare medicine information systems. Future Generation Computer Systems, 2017, 84:149-159.), some security defects were pointed out, such as user impersonation attacks and denial service attacks. In order to overcome those vulnerabilities, a new protocol scheme with multi-factor was proposed. In this protocol, extended chaotic mapping was adopted, dynamic identity was used to protect user anonymity, and three-way handshake was used to achieve asynchronous authentication. Security analysis result shows that the new protocol can resist impersonation attacks and denial service attacks and protect user anonymity and unique identity.

A group decision making model based on optimization model and consistency adjustment algorithm was established for the group decision problems with incomplete occurrence probability information of hesitant fuzzy elements. First of all, some new concepts were introduced, including Probability Incomplete Hesitant Fuzzy Preference Relations (PIHFPRs), the expected consistency of PIHFPRs and the acceptable additive expected consistency of PIHFPRs. Secondly, the minimization of deviations between PIHFPRs and the weight vectors was regarded as the objective function, a linear optimization model was constructed to calculate the probability information of the PIHFPRs. Then, by using the integrated operator for weighted probability incomplete hesitant fuzzy preference relations, the comprehensive PIHFPR was determined. A group consistency adjustment algorithm was further designed, which not only makes the adjusted PIHFPRs are acceptable expected consistent, but also can obtain the weight vectors for alternatives. Finally, the proposed group decision-making model was applied to a numerical example about the selection of block chain. Experimental results show that the decision-making results are reasonable and reliable, and the actual situation can be reflected.

To solve problems of population diversity and convergence speed when an Ant Colony System (ACS) is used to robot path planning, a dynamic chaos operator was introduced in the ACS. The dynamic chaotic ACS can balance population diversity and convergence speed. The core of dynamic chaotic ACS is that a Logistic chaotic operator was added to the traditional ACS to increase population diversity and improve the quality of the solutions. First, the chaotic operator was added to the pre-iteration to adjust the global pheromone value in the path to increase the population diversity of the algorithm, so as to avoid the algorithm to fall into the local optimal solution. Then, in the later stage, the ACS was used to ensure convergence speed of the dynamic chaotic ACS. The experimental results show that the dynamic chaotic ACS has better population diversity compared with the ACS for the robot path planning problem. The solution quality is higher and the convergence speed is faster. Compared with the Elitist Ant colony System (EAS) and the rank-based Ant System (ASrank), the dynamic chaotic ACS can balance the relationship between the quality of the solutions and the convergence speed. The dynamic chaotic ACS can find better optimal solutions even in the complex obstacle environment. The dynamic chaotic ACS can improve the efficiency of mobile robot path planning.

In the transportation field, analyzing passengers' travel destinations brings a lot of commercial value. However, research on the passengers' travel destinations is difficult because of its uncertainty. In order to solve this problem, in existing studies, entropy is used to measure the uncertainty of human mobility to describe individuals' travel features, and the spatiotemporal correlation of individual trajectories is taken into account simultaneously, which can not achieve the desired accuracy. Therefore, an algorithm for airline prediction based on improved Markov chain was proposed to predict passengers' travel destinations. First, the distance distribution, site distribution and temporal regularity on history records of passengers' travels were analyzed. Then, the dependence of human mobility on historical behavior and current location was analyzed. Finally, the characteristics of passengers' permanent residence and the exploration probability of new airlines were added into the calculation transition matrix, and an algorithm based on improved Markov chain was proposed and realized to predict passengers' next travels. The experimental results show that the average prediction accuracy of the proposed model can reach 66.4%. Applying in the field of customer travel analysis, airline company can benefit from the research to predict passenger travel better and provide personalized travel services.

Focusing on the low accuracy problem of regular fingerprint matching indoor localization algorithm for Wi-Fi network confronted with signal fluctuation or jamming, the fingerprint matching indoor localization algorithm based on Dynamic Time Warping (DTW) similarity for Wi-Fi network was proposed. Firstly, the Wi-Fi signal characteristics in localization area were converted to the time-series fingerprints according to the sequence of sampling. The similarity between the locating data and sampling data was obtained by computing the fingerprint DTW distance of Wi-Fi signal. Then, according to the structural characteristics of the sampling area, the fingerprint sampling problem of Wi-Fi signal was divided into three kinds of basic sampling methods based on dynamic path. Finally, the accuracy and completeness of the fingerprint feature information were increased by the combination of multiple dynamic path sampling methods, which improved the accuracy and location precision of fingerprint matching. The extensive experimental results show that, compared with the instantaneous fingerprint matching indoor localization algorithm, within the location error of 3 m, the cumulative error frequency of the proposed localization algorithm, was 10% higher for uniform motion and 13% higher for variable motion within routing area, and 9% higher for crossed curvilinear motion and 3% higher for S-type curvilinear motion within open area. The proposed localization algorithm can improve accuracy and location precision of fingerprint matching effectively in real indoor localization applications.

To reduce the impact of Programmed I/O (PIO) write latency in PCI express (PCIe) transmission process, too many times of interaction between the host and the embedded processing system and other issues on transmission bandwidth, a Direct Memory Access (DMA) controller based on command buffering mechanism was designed to improve the transmission bandwidth utilization. Using the internal command buffer of the Field-Programmable Gate Array (FPGA), the DMA controller could cache the data transfer request of the PC. The FPGA could dynamically access the storage space of the PC according to its own requirements and enhance the transmission flexibility. At the same time, a dynamic mosaic DMA scheduling method was proposed to reduce the times of host-to-hardware interaction and interrupt generation by merging the access requests of adjacent storage areas. In the system transmission rate test, the maximum write speed of DMA was 1631 MB/s, the maximum rate of DMA read was up to 1582 MB/s, the maximum of bandwidth was up to 85.4% of the theoretical bandwidth of PCIe bus. Compared with the traditional PIO mode DMA transfer method, DMA read bandwidth increased by 58%, DMA write bandwidth increased by 36%. The experimental results show that the proposed design can effectively improve the DMA transfer efficiency, and is significantly better than PIO method.