最新刊期
卷 47 ， 期 10 ， 2019
- 摘要：In the industrial environment, the influence of the complex wireless communication on the localization network leads to poor real-time reliability and low accuracy of localization. This paper proposes the deterministic localization network model for TDOA (time difference-of-arrival) measurement in the case of asynchronous anchor nodes called ASync-DetNET. After that, we propose the sensing model and localization model, which can adapt to ASync-DetNET. These models can simplify the way of obtaining data information, comply with various requirements of localization accuracy and guarantee the real-time reliability with reasonably communication resources scheduling. Subsequently, we implement a localization system with ultra-wideband signals for complicated environment and finally we estimate the influence of the ASync-DetNET on the localization. The experiments show that the proposed models can improve the accuracy of localization and guarantee the deterministic of localization.关键词：industrial;indoor localization;deterministic network;real-time;reliability82|245|1更新时间：2025-07-16
- 摘要：Up to now, the Dynamic Heterogeneous Redundancy (DHR) structure is a kind of important engineering model about the Mimic Defense (MD) technique. However, there is still a lack of way of formal analysis for DHR structures as there is no formal model available for a DHR structure. To address this problem, we use a Finite State Automaton (FSA) and its Parallel Automaton (PA) to establish a computing model for some attacks and mimic defenses. First, each FSA is employed to model each execution body, while there are a number of execution bodies in a DHR structure. Second, these FSAs are combined in parallel to model the combination of execution bodies. Third, one can get a model of MD automaton which can describe the attacks and MD actions, by modifying the state transition rules. Finally, one can analyse the attacks and MD actions on a DHR structure, according to the conditions of the PA states. Furthermore, we use an Alternating Finite Automaton (AFA) to model some attacks and MD actions. As a result, the automatic MD analysis problem is reduced to the solved AFA model checking problem.关键词：dynamic heterogeneous redundancy;mimic defense;automata64|205|5更新时间：2025-07-16
- 摘要：The detection of the vulnerability of the target host from the intranet is the main way to initiate the network attack. The static characteristics of the current network are beneficial to the implementation of attacker reconnaissance, and the L2/L3 address inside the network is the main information that the attacker expects to scout. In order to change the network attack and defense situation in the reconnaissance stage, based on the idea of mimicry camouflage, a collaborative dynamic technology of L2 and L3 addresses is proposed to strategically hide the real network host without affecting normal business conditions. Firstly, the cyber reconnaissance game (CRG) is established.Based on the NASH equilibrium solution,the mimetic camouflage strategy of L2/L3 address is solved,and the optimal mutation period calculation formula is given. Secondly, based on the software-defined network architecture, the cooperative mutation intranet defense system (CMID) is designed and implemented, and the SDN controller cooperatively controls the camouflage transformation of the L2/L3 address. Finally, the theoretical analysis and experimental results show that the above method can effectively cut off the correlation between L2/L3 address and real network identity and upper-layer services, maximally hide the internal hosts of the network, delay the reconnaissance speed, and block the continuity of network attacks.关键词：reconnaissance;software defined network;mimicry;NASH equilibrium;cyber defense;address mutation128|193|3更新时间：2025-07-16
- 摘要：As an innovative network architecture decoupling data forwarding and control logic, and opening underlying programming interfaces, software-defined networking (SDN) provides a novel solution to reduce deployment and operation costs and improve business application performance in core networks. However, logically centralized control plane under the SDN architecture is prone to performance bottlenecks, and increases packet forwarding delay. Thus it is necessary to understand the characteristics of its packet forwarding performance. To this end, we first introduce typical deployment scenarios of software-defined core networks,and analyze Packet-in message arrival process in its control plane and packet arrival process in its data plane.Then the M/M/n/m and M/M/1/m queueing models are respectively applied to depict Packet-in message processing process of its controller clusters and packet processing process of its OpenFlow switches. On this basis, we establish a prioritized queueing model of OpenFlow packet forwarding, and derive packet forwarding delays of different priorities and its cumulative distribution function. Finally, experimental evaluation in virtue of the controller performance measurement tool OFsuite_Performance shows that our proposed M/M/n/m model can accurately estimate actual performance of controller clusters compared with existing models. Meanwhile, we contrast packet forwarding delays of different priorities in various cases and their CDF curves by numerical analysis, which provides effective references for practical deployments of software-defined core networks.关键词：software-defined core networks;packet forwarding performance;prioritized queueing models;SDN controller clusters;OpenFlow switches105|218|1更新时间：2025-07-16
- 摘要：The internal community structure is evolving with the change of network structure. These changes in different time slices can be defined as four different evolutionary events: survive, split, fusion and disappearance. In this paper, the network representation learning method is used to map the graph embedding of the network into the low-dimensional vector space to study the prediction of dynamic community evolution events. In the features, based on the features of community internal attributes, the change of temporal attributes,and the previous community evolution events, the potential structure characteristics of the four evolutionary events are introduced and obtained by using random walk and Softmax. In the model,the strategy of deep random forest is proposed. Feature fusion and feature training are carried out by using the attention mechanism and Monte Carlo feature sampling strategy, which overcomes the shortcomings of the existing algorithms that only acquire local structural features. Finally, by comparing SVM, XGBOOST, RIDGE model training in the DBLP, FACEBOOK and Bitcoin datasets, it is confirmed that the embedding feature of community structure and the attention deep random forest model improvement have greatly improved the accuracy of final prediction.关键词：community evolution;graph embedding;network representation learning;deep random forests;attention mechanism160|236|5更新时间：2025-07-16
- 摘要：Applications deployed in data center networks generate a mix of flows with and without deadlines.To reduce the average flow complete time (AFCT) while maintain a low deadline miss rate (DMR),a slack time and accumulation-based mix-flow scheduling mechanism (STAM) is proposed in this paper.Firstly,the delay tolerance of deadline flows on non-deadline flows is measured with the introduction of Slack Time.Then the cost of complete time of non-deadline flows is reduced by completing deadline flows barely before their deadlines.Lastly,non-deadline flow is scheduled according to the number of bytes it has sent to reduce the average flow completion time.Experiment results show that the proposed mechanism can effectively reduce the average flow completion time of non-deadline flows while maintaining low deadline miss rate.关键词：data center network;flow scheduling;deadline;slack time93|199|3更新时间：2025-07-16
- 摘要：Wi-Fi backscatter communication has lower power consumption and lower cost than active Wi-Fi. However, most of the current researches have the problems of large power consumption of field programmable gate array (FPGA) and high manufacturing cost of application specific integrated circuit (IC). Therefore, a passive Wi-Fi backscatter communication method based on microcontroller (MCU) is proposed. The low-frequency MCU is used to generate the Wi-Fi backscatter modulation waveform with nanosecond timing accuracy. After the low power design and optimization, the MCU power management based on energy harvesting and passive system design are carried out. And the passive Wi-Fi prototype is demonstrated for the first time. Tests show that the instantaneous power consumption of communication is 1.8mW, which is 2~3 orders of magnitude lower than that of active Wi-Fi. The system has a standby power consumption of 2.5μW and an effective communication distance of 15 meters. It is compatible with existing commercial Wi-Fi devices and has low cost and no battery.关键词：passive Wi-Fi;backscatter;Internet of Things (IoT);low power consumption53|158|1更新时间：2025-07-16
- 摘要：Visual target tracking is to find samples that have the same semantic information as the tracking target and pinpoint the position of the sample in the video. Recently, the deep classification model is used to extract the deep embedded features of the tracking target. However, since the deep classification model gives the same class of sample labels, it can easily lead to tracking and even failure. In order to solve this problem, we add the spatial location information of the sample to the deep classification model and propose a location-sensitive loss function. The proposed loss function not only inherits the characteristics of classification loss, but also sorts samples of the same label according to the spatial location information of samples. In other words, the loss function in this paper can also encourage the classification between classes and classes. Compared with the classification loss function, the loss function in this paper is more suitable for the task of target tracking. In this paper, OTB100 and VOT2016 were tested, the results show that this algorithm can achieve better tracking performance.关键词：target tracking;deep learning;location-sensitive model;intra-class ordering203|202|1更新时间：2025-07-16
- 摘要：Aiming at the problem of energy consumption and co-channel interference caused by dense deployment of access points (APs) in wireless local area networks (WLANs), we propose a Bayesian game-based energy saving scheme (BYG-ES). Firstly, we measure energy consumption on the different real AP devices, and model the relationship among AP transmission power, forwarding load and energy consumption. Then, based on the mathematical relationship model and network status information collected by controller of software-defined network in real time, a Bayesian game based energy optimization design is proposed. Finally, the social selection function is used to solve the energy optimization model, and then the sets of sleeping APs and transmit power configuration rules under the interference constraints are obtained to complete user traffic offloading and AP transmit power configuration. Meanwhile, the BYG-ES can guarantee honesty of all participants during the Bayesian game. Simulation results demonstrate that the BYG-ES can minimize system energy consumption and improve network performances simultaneously.关键词：software defined wireless networks;software defined network;Bayesian game;co-channel interference;traffic offloading;transmission power configuration89|162|2更新时间：2025-07-16
- 摘要：The Gauss elimination method to recognize Turbo code frame structure has the shortcomings of poor fault tolerance and high complex computation, which directly leads to the inapplicability of subsequent algorithms to recognize the component coder and interleaver. For this reason, a novel method based on correlation between columns of the matrix is proposed. Firstly, analysis matrix is constructed under the undetermined frame length, with the neighboring columns being differentiated. Then statistics about column weight is established, and frame length is confirmed by the statistical values, which can pass the decision threshold when the column is frame length and its multiple. Finally, frame staring point is synchronized according to the relative position of statistical values which pass the threshold. The simulation results show that the correct recognition rate of parameters of Turbo frame structure can reach 100%, when the bit error rate is as high as 0.1. Compared with the existing methods, the proposed algorithm has lower computational complexity and better recognition performance, and is very suitable for non-cooperative communication fields such as cognitive radio.关键词：error-correcting codes;Turbo codes on trellis termination;the structure of frame;differential processing;cognitive radio32|104|3更新时间：2025-07-16
- 摘要：Sub-Nyquist radar is an analog-to-information based radar system with the assumption of sparse echo signals. We study the reconstruction of moving target echoes obtained from the sub-Nyquist radar. Different from the reconstruction of echo signals from a single pulse, the moving targets may be across range cells in a coherent processing interval and thus the echo signals have the property of the time-varying sparsity. However, it has been found that the sparse positions obey a probabilistic distribution and then a random model is adopted to describe the sparsity. The reconstruction of the multiple echoes is transformed into a weighted sparse reconstruction problem. An orthogonal-projection-based weighted sparse segment-sliding reconstruction (OP-WSegSR) scheme is proposed in this paper. Utilizing the estimation of sparse positions in the previous pulse, the scheme projects the measurements of the current segment onto the orthogonal complement subspace spanned by the interference generated in the segmentation process and then the adjacent interference is greatly rejected. Simulations show the effectiveness of the OP-WSegSR scheme.关键词：compressive sampling;time-varying sparsity;weighted sparse reconstruction;segment-sliding reconstruction;orthogonal projection41|101|2更新时间：2025-07-16
- 摘要：We proposed two new methods for visualization analysis based on N-gram features of malware. Method 1 uses space filling curves to solve the problem that the existing grayscale method cannot locate character information for interactive analysis. Method 2 visualizes the bi-gram features of malware to solve the problem that the attackers may relocate code sections or add redundant data to change the global image features of the visualized results. We designed the deep fusion networks to validate the detection and classification performances of the proposed methods,and the experimental results are very promising.关键词：malware;visualization analysis;space filling curves;convolution neural networks;transfer learning109|157|5更新时间：2025-07-16
- 摘要：A reliable physical unclonable function (PUF) for chip fingerprint using current starved delay element (CSDE) is described in this paper. The proposed PUF is composed of CSDE-based sensors, time difference amplifier, time difference comparator, voting mechanism and diffusion algorithm circuit. By capturing manufacturing process variations, each sensor produces two slightly different delay-time values that can be compared in order to create a digital identification (ID) for the chip. A new diffusion algorithm is designed to further improve the uniqueness of PUFs. Time difference amplifier and voting mechanism are introduced to simultaneously improve the reliability of PUFs across temperature and supply voltage variations. The proposed PUF is designed in 0.18μm CMOS technology. Simulated results show that the proposed PUF has a good output statistical characteristic of uniform distribution, and a high stability of 97.5% with respect to temperature variation from -40℃ to 100℃, and supply voltage variation from 1.7V to 1.9V.关键词：physical unclonable functions;current starved delay element;time difference amplifier;voting mechanism;diffusion algorithm46|117|1更新时间：2025-07-16
- 摘要：In order to improve the efficiency of high-resolution image segmentation and solve the problem of incomplete segmentation caused by small discrimination of foreground and background in the complex pattern near the edge of the target to be segmented, we propose a fast image segmentation algorithm based on superpixel multi-feature fusion (SMFF). Firstly, the most effective superpixel algorithm is used for superpixel processing, and then the superpixel-based HOG feature, laboratory color feature and spatial position feature are extracted. Lastly, by designing a multi-feature measurement algorithm, the fast image segmentation algorithm based on superpixel multi-feature fusion is implemented. Experimental results verify the effectiveness of the proposed algorithm, which is close to the most classical image segmentation algorithm, and the time performance of the proposed algorithm is significantly better than other comparison algorithms.关键词：image segmentation;multi-feature fusion;HOG features;superpixel42|222|0更新时间：2025-07-16
- 摘要：The bilinear convolutional neural network(B-CNN) has been widely used in computer vision. B-CNN can capture the linear correlation between different channels by performing the outer product operation on the features of the convolutional layer output, thus enhancing the representative ability of the convolutional network. Since the non-linear relationship between the channels in the feature map is not taken account of, this method cannot make full use of the richer information contained between the channels. In order to solve this problem, this paper proposes a kernelized bilinear convolutional neural network employing the kernel function to effectively capture the non-linear relationship between the channels in the feature map, and further enhancing the representative ability of the convolutional network. In this paper, the method is evaluated on three common fine-grained benchmarks CUB-200-2011, FGVC-Aircraft and Cars. Experiments show that our method is superior to its counterparts on all three benchmarks.关键词：kernelized bilinear pooling;bilinear convolution neural network;end to end learning;fine-grained visual recognition177|265|7更新时间：2025-07-16
- 摘要：Considering the disadvantage of traditional dehazing algorithm, a single image dehazing algorithm based on haze layers learning is proposed. According to the atmospheric scattering model, the end-to-end network is designed which directly learn the mapping between the haze images and their corresponding haze layers. The network takes the haze image as the input. Then the recovered haze-free image can be gotten by removing the residual image from the hazy image. Residual learning allows the network to estimate the initial haze layer with relatively high learning rates, which can reduce computational complexity and speed up the convergence process. Otherwise, we use guided filter to refine images avoiding halos and block artifacts, which make the recovered image more similar to the real scene. Finally, the experimental results are analyzed and contrasted carefully. In this paper, the effect on fog images with different fog density is tested, and many comparisons are listed with other classical algorithms. Experiments demonstrate that the proposed algorithm has better results than state-of-the-art methods on both synthetic and real-world images qualitatively and quantitatively.关键词：image dehaze;deep learning;convolutional neural network;residual learning;end-to-end system44|149|7更新时间：2025-07-16
- 摘要：Breast MR image segmentation is difficult because of complex organization and intensity inhomogeneity. This paper proposes a segmentation method based on dual-tree complex wavelet transform and density clustering. Firstly, the image is denoised by using complex wavelet domain bivariate model combined with anisotropic diffusion function; Then simple linear iterative clustering (SLIC) algorithm is used to obtain the neighbors of each superpixel, thereby reducing the time of searching for the nearest neighbor of each sample in KNN-DPC algorithm. Finally, nearest neighbor sample density information of superpixel region is introduced,and distribution strategies from KNN-DPC algorithm are used for adaptive clustering. The segmentation results of simulation and clinical data show that the proposed algorithm can segment breast MR images effectively.关键词：breast MR image segmentation;dual tree complex wavelet transform;bivariate model;hyper pixel classification;density peak fast search clustering140|183|7更新时间：2025-07-16
- 摘要：A sensor selection optimization algorithm is proposed for decentralized large-scale multi-target tracking network. In this method, the lower bound of mean square optimal sub-pattern assignment error between multi-target state set and its estimation is taken as optimized objective function while the rule of weighted Kullback-Leibler average (KLA) is used to fuse local multi-target densities. The coordinate descent method is proposed to compromise the computation cost and tracking accuracy. Simulations verify the effectiveness of our method under different signal-to-noise ratio scenarios.关键词：sensor selection;multi-target tracking;labeled random finite set;decentralized sensor network30|105|1更新时间：2025-07-16
- 摘要：For the characteristics of being unknown and time-varying of dynamic environment, a new method for mobile robot path planning is proposed in this paper. Firstly, in this method the environment model established by the grid method is convexized in order for the robot to avoid falling into U-shaped traps when moving along the planned path and thus to speed up the path planning. Secondly,a double-layer ant colony optimization (DACO) algorithm is proposed.In each iteration of DACO algorithm, a path is found firstly by the outer ACO algorithm, then on basis of which a small environment is constructed,and then the robot re-plans path by the inner ACO algorithm in the small environment; if the newly obtained path is better,then the global optimal path is updated and a new pheromone secondary update strategy proposed in this paper is executed. At last, three kinds of obstacle avoidance strategies are put forward according to the volume and speed of different dynamic obstacles in the environment. In the dynamic environment, the robot plans a global optimal path from the starting point to the destination with respect to the static environment via the DACO algorithm, then from the current starting point, the robot acquires the dynamic environment's information by its embedded sensor and implements waiting-for-obstacle avoidance strategy, collision avoidance strategy or rear-end collision avoidance strategy when necessary, and then the robot moves to next position as a new starting point. Simulation results show that the proposed can plan a safe and shortest path for mobile robot in real time under dynamic environment and is a practical and effective method for mobile robot path planning.关键词：dynamic obstacles;mobile robot;path planning;ant colony optimization;grid method191|244|24更新时间：2025-07-16
- 摘要：In order to improve the slow convergence rate and low stability of WOA, elite opposition-based golden-sine whale optimization algorithm is proposed. Elite opposition-based learning strategy is used to improve the diversity and quality of the population so that the convergence rate can be promoted. At the same time,golden ratio is introduced to improve the optimal method of WOA, so as to ocoordinate the global exploration and local exploitation. Twenty unimodal and multimodal benchmark functions are tested and compared with that of other algorithms,and the experimental results show that EGolden-SWOA has a better performance in convergence rate and stability. The high dimensional function test shows that EGolden-SWOA perform well in solving large scale optimization problem. Finally, EGolden-SWOA is applied to the optimization design of the pressure vessel and tension/compression spring, the result shows that its performance in project optimization is better than RCSA and CPSO, it can be effectively applied to project optimization.关键词：whale optimization algorithm(WOA);elite opposition-based learning;golden sine algorithm(Golden-SA);large scale optimization problem;project optimization52|132|40更新时间：2025-07-16
- 摘要：Superconducting RSFQ (rapid single-flux-quantum) circuit was proposed for high performance processors, which is mainly composed of Josephson junctions and superconducting passive transmission lines. Its modeling and analysis are the basis of design of superconducting RSFQ circuits. In this paper, a method based on FDTD (finite-difference time-domain) to co-analyze superconducting transmission line combined with Josephson junctions is proposed. The FDTD numerical method is used to solve the telegraph equations of superconducting transmission line and the Newton-Raphson iterative algorithm is used to solve the nonlinear boundary conditions at the interfaces between the superconducting transmission line and Josephson junctions. The numerical results show that the proposed method has the same accuracy and higher efficiency compared with WRspice simulation software.关键词：Josephson junction;superconducting transmission line;finite-difference time-domain47|108|1更新时间：2025-07-16
- 摘要：With the introduction of network function virtualization (NFV), operators can provide more flexible network service. However, most existing orchestration schemes of service function chain (SFC) are limited to the static or reactive policy where the virtual network functions (VNFs) need to be deployed or migrated frequently, and will easily lead to the mismatch of service supplement and high operational expenditure under time-varying workload. This paper proposes an online elastic orchestration policy (OEOP) based on the evolution perception of flow rate to solve the above mentioned problem. OEOP introduces online learning into the evolution perception of flow rate, which helps to predetermine the fine-grained VNF scaling demands. In addition, the online elastic deployment is achieved according to the real-time update information of SFC paths and the load of nodes. The newly deployed VNF instances can respond to the time-varying workload by taking place of the mission of VNF migration. The simulation results demonstrate that OEOP can significantly enhance the matching between virtual resource supply and workload demand. The throughput of VNF is improved by 10.2%~24.8%, and the operational expenditure can be reduced by 26.7% on average compared with other solutions.关键词：network function virtualization;virtual network functions;operational expenditure;flow evolution perception;online convex optimization113|13|1更新时间：2025-07-16
- 摘要：Image semantic segmentation not only predicts different categories in an image, but also orientates different semantic categories locations, which has important research significance and application value. This paper expounds the latest research results and methods of image semantic segmentation,and from three perspectives: based candidate region models、based full convolutional network models and based weakly-supervised learning models. The image semantic segmentation model methods and structure based on deep convolutional neural network are deeply overviewed in this paper. This paper compares the performance of some representative semantic segmentation algorithms on the PASCAL VOC 2012 dataset.关键词：image semantic segmentation;deep convolutional neural network;candidate region;full convolution network;weakly-supervised learning;PASCAL VOC 2012 dataset143|507|20更新时间：2025-07-16
- 摘要：Twin support vector machine improves the performance of the classifier by solving the smaller quadratic programming problem. However, this method mainly utilizes the separability between classes and constructs the corresponding model using the hinge loss function. Not considering the structural information of the intra-class data and the influence of different samples on the classification, the method has strong sensitivity to noise and instability of resampling. In order to further improve the performance of the twin support vector machine, the structural information of different classes in the data and the effects of different samples are introduced into the twin support vector machine based on the pinball loss function, and the structure fuzzy support vector machine model based on pinball loss is obtained. The structural fuzzy twin support vector machine algorithm pin-sftsvm based on the pinball loss is derived theoretically. The presented algorithm pin-sftsvm is tested by selecting the artificially generated data set and the UCI standard data set, and compared with the tbsvm, s-tsvm and pin-tsvm algorithms. Experimental results show the effectiveness of the proposed algorithm.关键词：structural information;pinball loss;membership degree of fuzzy;twin support vector machine42|134|1更新时间：2025-07-16
- 摘要：Toxic comment detection is an important work to prevent the negative impact of social media platform on users, and it is also one of the important fields of natural language processing. In order to solve the problems of unstable model accuracy and low accuracy of boosting ensemble model when an individual classifier detects toxic comments, a stack generalization with heterogeneous classifiers is proposed. In this method, the classification problem of multi-label toxic comments is transformed into binary categories by using deep recurrent neural network, which prevents the model accuracy from being unstable. Individual classifiers called GRU (Gated Recurrent Unit) and NB-SVM (Naïve Bayes-Support Vector Machine) are used during stacked generalization in order to embody the differences on model structure and classification deviation of individual classifiers, the goal is to improve the model accuracy. Experimental results on Wikipedia toxic comments show that the proposed method has better than boosting ensemble, which reports that stacked generalization of heterogeneous classifiers is feasible and effective for toxic comments detection.39|127|4更新时间：2025-07-16
- 摘要：In order to diminish the deviation of far-field time-difference-of-arrival (TDOA) measurements from the ideal line-of-sight (LOS) model due to some microphones shadowed by the system shell of an embedded planar microphone array, which is not considered and cannot be resolved by existing TDOA correction studies, a novel convex optimization method was proposed for correcting the measured TDOA matrix. A closed-form solution was obtained by integrating the rank-2 algebraic constraint with a set of linear geometric constraints corresponding to the equilong parallel lines (EPLs) that are easily achieved in practical acoustic detection systems using approximately symmetric convex polygons as preferable array shapes.As long as the array shape contains at least one pair of linearly independent EPLs, the proposed method gains the capability of effectively suppressing the diffraction induced deviations in measured TDOAs relevant to non-line-of-sight (NLOS) array elements. Meanwhile, the adverse effects of measurement noise and TDOA outliers can be also mitigated. Considering both the above capabilities and the low computational complexity, the proposed method is more suitable for ever-growing applications of small-scale, real-time acoustic detection systems. Numerical simulations verified its effectiveness.关键词：microphone array;time-difference-of-arrival correction;diffraction propagation effect;embedded planar array;linear geometric constraint;time-difference-of-arrival matrix;time-difference-of-arrival outlier59|172|1更新时间：2025-07-16
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