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Virtual Network Function Migration Algorithm Based on Reinforcement Learning for 5G Network Slicing
Lun TANG, Yu ZHOU, Qi TAN, Yannan WEI, Qianbin CHEN
, doi: 10.11999/JEIT190290
[Abstract](0) [FullText HTML](0) [PDF 1975KB](0)
Abstract:
In order to solve the Virtual Network Function (VNF) migration optimization problem caused by the dynamicity of service requests on the 5G network slicing architecture, firstly, a stochastic optimization model based on Constrained Markov Decision Process (CMDP) is established to realize the dynamic deployment of multi-type Service Function Chaining (SFC). This model aims to minimize the average sum operating energy consumption of general servers, and is subject to the average delay constraint for each slicing as well as the average cache, bandwidth resource consumption constraints. Secondly, in order to overcome the issue of having difficulties in acquiring the accurate transition probabilities of the system states and the excessive state space in the optimization model, a VNF intelligent migration learning algorithm based on reinforcement learning framework is proposed. The algorithm approximates the behavior value function by Convolutional Neural Network (CNN), so as to formulate a suitable VNF migration strategy and CPU resource allocation scheme for each network slicing according to the current system state in each discrete time slot. The simulation results show that the proposed algorithm can effectively meet the QoS requirements of each slice while reducing the average energy consumption of the infrastructure.
Range Spread Target Detection Based on OnlineEstimation of Strong Scattering Points
Pengcheng GUO, Zheng LIU, Dingli LUO, Jianpu LI
, doi: 10.11999/JEIT190417
[Abstract](0) [FullText HTML](0) [PDF 1496KB](0)
Abstract:
The traditional range-extended target detection is usually completed under the condition of scattering point density or scattering point number priori. The detection performance will be greatly reduced when the scattering point information of the target is completely unknown. To solve this problem, a Range Spread Target Detection method based on Online Estimation of Strong Scattering(OESS-RSTD) points is proposed. Firstly, the unsupervised clustering algorithm in machine learning is used to estimate the number of strong scattering points and the first detection threshold adaptively. Then, the second detection threshold is determined according to false alarm rate. Finally, the existence of the target is determined through two detection thresholds. In this paper, the simulation data and the measured data are used to verify and compare with other algorithms. By comparing the Signal-to-Noise Ratio (SNR) -detection probability curves of various methods with a given false alarm probability, it is verified that the proposed method in this paper has higher robustness than the traditional algorithm, and the method does not need any priori information of target scattering points.
A New Link Prediction Method for Complex Networks Based onTopological Effectiveness of Resource Transmission Paths
Kai WANG, Xing LI, Julong LAN, Hongquan WEI, Shuxin LIU
, doi: 10.11999/JEIT190333
[Abstract](0) [FullText HTML](0) [PDF 1550KB](0)
Abstract:
Link prediction considers to discover the unknown or missing links of complex networks by using the existing topology or other information. Resource Allocation index can achieve a good performance with low complexity. However, it ignores the path effectiveness of resource transmission process. The resource transmission process is an important internal driving force for the evolution of the network. By analyzing the effectiveness of the topology around the resource transmission path between nodes, a link prediction method based on topological effectiveness of resource transmission paths is proposed. Firstly, the influence of potential resource transmission paths between nodes on resource transmission is analyzed, and a quantitative method for resource transmission path effectiveness is proposed. Then, based on the effectiveness of the resource transmission path, after studying the two-way resource transmission amount between two nodes, the transmission path effectiveness index is proposed. The experimental results of 12 real networks show that compared with other link prediction methods, the proposed method can achieve higher prediction accuracy under the AUC and Precision metrics.
Quasi-periodic, Chaotic-torus Bursting Oscillations and SlowPassage Effect in Memristive High-pass Filter Circuit
Fangyuan LI, Mo CHEN, Huagan WU
, doi: 10.11999/JEIT190317
[Abstract](0) [FullText HTML](0) [PDF 3686KB](0)
Abstract:
A memristive high-pass filter circuit is presented, which is composed of an active high-pass RC filter parallelly coupling with a memristor emulator of diode-bridge cascaded by LC oscillator. The circuit equations and system model are established. Based on bifurcation diagram, phase plane plot, and Poincaré mapping, bifurcation analysis with the feedback gain as adjustable parameter is performed, from which bursting oscillating behaviors including quasi-period, chaotic-torus, chaos, and multiple period that exist in such a memristive high-pass filter circuit are disclosed. Furthermore, through fast-slow analysis method, Hopf bifurcation set of the fast sub-system is derived, with which the formation mechanism of slow passage effect in the memristive high-pass filter circuit is expounded. Finally, the numerical simulation results are validated based on Multisim circuit simulations.
Speaker Recognition Based on Multimodal GenerativeAdversarial Nets with Triplet-loss
Ying CHEN, Huangkang CHEN
, doi: 10.11999/JEIT190154
[Abstract](0) [FullText HTML](0) [PDF 1388KB](0)
Abstract:
In order to explore the correlation between face and audio in the field of speaker recognition, a novel multimodal Generative Adversarial Network (GAN) is designed to map face features and audio features to a more closely connected common space. Then the Triplet-Loss is used to constrain further the relationship between the two modals, with which the intra-class distance of the two modals is narrowed, and the inter-class distance of the two modals is extended. Finally, the cosine distance of the common space features of the two modals is calculated to judge whether the face and the voice are matched, and Softmax is used to recognize the speaker identity. Experimental results show that this method can effectively improve the accuracy of speaker recognition.
An Offloading Mechanism Based on Software Defined Network and Mobile Edge Computing in Vehicular Networks
Haibo ZHANG, Kunlun JING, Kaijian LIU, Xiaofan HE
, doi: 10.11999/JEIT190304
[Abstract](39) [FullText HTML](22) [PDF 1614KB](3)
Abstract:
In the emerging vehicular networks, the task of the car terminal requesting offloading has more stringent requirements for network bandwidth and offload delay, and the proposed Mobile Edge Computing (MEC) in the new communication network research solves better this challenge. This paper focuses on matching the offloaded objects when the car terminal performs the task offloading. By introducing the Software-Defined in-Vehicle Network (SDN-V) to schedule uniformly global variables, which realizes resource control management, device information collection and task information analysis. Based on the differentiated nature of user tasks, a model of importance is defined. On this basis, task priority is divided by designing the task to offload the priority mechanism. For the multi-objective optimization model, the non-convex optimization model is solved by the multiplier method. The simulation results show that compared with other offloading strategies, the proposed offloading mechanism has obvious effects on delay and energy consumption optimization, which can guarantee the benefit of users to the greatest extent.
A Mobile Crowdsensing Data Security Delivery Model Based on Tangle Network
Guosheng ZHAO, Hui ZHANG, Jian WANG
, doi: 10.11999/JEIT190370
[Abstract](65) [FullText HTML](45) [PDF 1997KB](4)
Abstract:
Considering at the security risks and privacy leaks in the process of data and reward in the Mobile CrowdSensing (MCS), a distributed security delivery model based on Tangle network is proposed. Firstly, in the data perception stage, the local outlier factor detection algorithm is used to eliminate the anomaly data, cluster the perception data and determine the trusted participant. Then, in the transaction writing stage, Markov Monte Carlo algorithm is used to select the transaction and verify its legitimacy. The anonymous identity data is uploaded by registering with the authentication center, and the transaction is synchronously written to the distributed account book. Finally, combined with Tangle network cumulative weight consensus mechanism, when the security of transaction reaches its threshold, task publishers can safely deliver data and rewards. The simulation results show that the model not only protects user privacy, but also enhances the ability of secure delivery of data and reward. Compared with the existing sensing platform, the model reduces the time complexity and task publishing cost.
A New Coding Scheduling Strategy Based on Virtual Queuein Wireless Multicast Network
Rui ZHANG, You ZHAN, Quan QIAN
, doi: 10.11999/JEIT190059
[Abstract](24) [FullText HTML](15) [PDF 2249KB](0)
Abstract:
Network coding is widely used in wireless multicast networks in recent years due to its high transmission efficiency. To address the low efficiency of automatic retransmission caused by packet loss in wireless multicast network, a new Coding Scheduling strategy based on Arriving Time (CSAT) in virtual queue is proposed. For improving encoding efficiency, virtual queues are used to store packets that are initially generated and not received by all receivers. Considering the stability of the queue, CSAT strategy chooses to send packet from the primary and secondary queue at a certain ratio. Both encoding and non-encoding methods are combined to send in the secondary queue. According to the arrival sequence of packets in the queue, the sending method that makes more packets participate in encoding is selected. Simulation results show that the proposed CSAT not only effectively improves packet transmission efficiency, but also improves network throughput and reduces average wait delay.
A Dual Satellite Interferometric Precise Localization Method Based on Short Synthetic Aperture
Guangcai SUN, Yuqi WANG, Zhaozhao GAO, Fan JIANG, Mengdao XING, Zheng BAO
, doi: 10.11999/JEIT180940
[Abstract](23) [FullText HTML](13) [PDF 1448KB](1)
Abstract:
Dual satellite TDOA/FDOA localization is achieved by the TDOA hyperboloid and FDOA hyperboloid. The accuracy of localization is affected by TDOA/FDOA accuracy. In order to measure accurately the TDOA/FDOA, a method of TDOA/FDOA measurement based on short synthetic aperture is presented in this paper. This method improves the measurement accuracy by using a certain length of synthetic aperture. For narrowband signals, the method has the ability to estimate a single satellite Doppler, and the frequency difference can be obtained from the results estimated by the two satellites. For wideband signals, high-precision estimates of frequency differences can be obtained by dual satellite data interference. For short-term stable radar signals, the processing results of STK simulation data confirm the effectiveness of the proposed method.
Multi-Level Attention Feature Network for Few-shot Learning
Ronggui WANG, Mengya HAN, Juan YANG, Lixia XUE, Min HU
, doi: 10.11999/JEIT190242
[Abstract](18) [FullText HTML](14) [PDF 1651KB](1)
Abstract:
Existing few-shot methods have problems that feature extraction scale is single, the learned class representations are inaccurate, the similarity calculation still rely on standard metrics. In order to solve the above problems, multi-level attention feature network is proposed. Firstly, the multiple scale images are obtained by scale processing, the features of multiple scale images are extracted and the image-level attention features are obtained by the image-level attention mechanism to fusion them. Then, class-level attention features are learned by using the class-level attention mechanism. Finally, the classification is performed by using the network to compute the similarity scores between features. The proposed method is evaluated on the Omniglot dataset and the MiniImagenet dataset. The experimental results show that multi-level attention feature network can further improve the classification accuracy under small sample conditions compared to the single-scale image features and average prototypes.
An Anti-Dense False Target Jamming Algorithm Based on Agile Frequency Joint Hough Transform
Yinghui QUAN, Xiada CHEN, Feng RUAN, Xia GAO, Yachao LI, Mengdao XING
, doi: 10.11999/JEIT190010
[Abstract](412) [FullText HTML](255) [PDF 1646KB](32)
Abstract:
Forwarding dense false target jamming disturbs the detection and recognition of real targets by generating multiple false targets in the range dimension. Because the false echo signal is highly correlated with the real signal, it is difficult for radar to recognize and suppress it effectively. Frequency agile radar improves greatly the low interception and anti-jamming ability of radar by randomly changing the carrier frequency of transmitting adjacent pulses. However, agile radar can not completely eliminate the interference, some target echo pulses may be submerged by the interference, agile radar can not complete coherent accumulation and target detection well either. To solve the above problems, an anti-jamming method of frequency agility combined with Hough transform is proposed. Firstly, the inter-pulse frequency agility technology is used to avoid most narrowband aiming and deceptive jamming. Then, according to the time discontinuity of the jamming signal, Hough transform and peak extraction are used to identify and suppress the jamming. Frequency agility is incompatible with the traditional Moving Target Detection(MTD). Target detection is accomplished by sparse reconstruction. The simulation and actual radar and jammer countermeasure experiments show that the proposed method can achieve good anti-jamming performance and target detection performance.
Target Tracking with Underwater Sensor Networks Based on Grubbs Criterion and Improved Particle Filter Algorithm
Ying ZHANG, Lingjun GAO
, doi: 10.11999/JEIT190079
[Abstract](75) [FullText HTML](53) [PDF 2236KB](7)
Abstract:
When the Underwater Wireless Sensor Network (UWSN) performs target tracking, the contributions of the measured values of the nodes are different, and the battery energy carried by the sensor node is limited. Therefore, a good node fusion weight method and node planning mechanism can obtain better tracking performance. A distributed particle filter target tracking algorithm based on Grubbs criterion and Mutual Information Entropy Weighted (GMIEW) fusion is proposed to solve the above problem in this paper. Firstly, the Grubbs criterion is used to analyze and verify the information obtained by the sensor nodes before the information fusion, and the interference information and error information are removed. Secondly, in the process of calculating the importance weight of particle filter, the dynamic weighting factor is introduced. The mutual information entropy between the measured value of the sensor node and the target state is used to reflect the amount of target information provided by the sensor node, so as to obtain the corresponding weighting factor of each node. Finally, the improved cluster-tree network topology is used to track the target in three-dimensional space. Simulation results show that the proposed algorithm improves greatly the accuracy of underwater sensor measurement data for target tracking prediction and reduces the tracking error.
The Combination and Pooling Based on High-level Feature Map for High-resolution Remote Sensing Image Retrieval
Yun GE, Lin MA, Shunliang JIANG, Famao YE
, doi: 10.11999/JEIT190017
[Abstract](362) [FullText HTML](222) [PDF 1374KB](17)
Abstract:
High-resolution remote sensing images have complex visual contents, and extracting feature to represent image content accurately is the key to improving image retrieval performance. Convolutional Neural Networks (CNN) have strong transfer learning ability, and the high-level features of CNN can be efficiently transferred to high-resolution remote sensing images. In order to make full use of the advantages of high-level features, a combination and pooling method based on high-level feature maps is proposed to fuse high-level features from different CNNs. Firstly, the high-level features are adopted as special convolutional features to preserve the feature maps of the high-level outputs under different input sizes, and then the feature maps are combined into a larger feature map to integrate the features learned by different CNNs. The combined feature map is compressed by max-pooling method to extract salient features. Finally, the Principal Component Analysis (PCA) is utilized to reduce the redundancy of the salient features. The experimental results show that compared with the existing retrieval methods, the features extracted by this method have advantages in retrieval efficiency and precision.
Depth Map Error Concealment for 3D High Efficiency Video Coding
Yang ZHOU, Jiayi WU, Yu LU, Haibing YIN
, doi: 10.11999/JEIT180926
[Abstract](271) [FullText HTML](177) [PDF 1575KB](6)
Abstract:
By using the intra-view and inter-view correlations and the motion vector-sharing, a depth map error concealment approach is proposed for 3D video coding based on the High Efficiency Video Coding (3D-HEVC) to combat the packet loss of the depth video transmission. Based on the Hierarchical B-frame Prediction (HBP) structure in 3D-HEVC and textured features of the depth map, all the lost coding units are firstly categorized into two classes, i.e., motion blocks and static blocks. Then, according to the outer boundary matching criterion combining the texture structure, the optimal motion/disparity vector is chosen for the damaged motion blocks to conduct the motion/disparity compensation based error concealment. Whereas, the direct copy is applied to concealling the damaged static blocks quickly. Finally, for the concealed blocks whose qualities are not ideal, the new motion/disparity compensation blocks reconstructed by the reference frames recombination are applied to improning the qualities of those blocks. The experimental results show that the repaired depth map concealed by the proposed approach can achieve 0.25~2.03 dB gain in term of the Peak-Signal-to-Noise Ratio (PSNR) and 0.001~0.006 gain in term of Structural Similarity Index Measure(SSIM). Moreover, the subjective visual quality of the repaired area is better in lines with the original depth maps.
Correntropy-based Fusion Extreme Learning Machine forRepresentation Level Feature Fusion and Classification
Chao WU, Yaqian LI, Yaru ZHANG, Bing LIU
, doi: 10.11999/JEIT190186
[Abstract](62) [FullText HTML](43) [PDF 2754KB](6)
Abstract:
Based on the network structure and training methods of the Extreme Learning Machine (ELM), Correntropy-based Fusion Extreme Learning Machine (CF-ELM) is proposed. Considering the problem that the fusion of representation level features is insufficient in most classification methods, the kernel mapping and coefficient weighting are combined to propose a Fusion Extreme Learning Machine (F-ELM), which can effectively fuse the representation level features. On this basis, the Mean Square Error (MSE) loss function is replaced by the correntropy-based loss function. A correntropy-based cycle update formula for training the weight matrices of the F-ELM is derived to enhance classification ability and robustness. Extensive experiments are performed on Caltech 101, MSRC and 15 Scene datasets respectively. The experimental results show that CF-ELM can further fuse the representation level features to improve the classification accuracy.
RGB-D Image Saliency Detection Based on Multi-modal Feature-fused Supervision
Zhengyi LIU, Quntao DUAN, Song SHI, Peng ZHAO
, doi: 10.11999/JEIT190297
[Abstract](109) [FullText HTML](55) [PDF 2710KB](9)
Abstract:
RGB-D saliency detection identifies the most visually attentive target areas in a pair of RGB and Depth images. Existing two-stream networks, which treat RGB and Depth data equally, are almost identical in feature extraction. As the lower layers Depth features with a lot noise, it causes image features not be well characterized. Therefore, a multi-modal feature-fused supervision of RGB-D saliency detection network is proposed, through two-stream study RGB and Depth data independently, double-side supervision module respectively to obtain saliency maps of each layer, and then the multi-modal feature-fused module is used to later three layers of the fused RGB and Depth of higher dimensional information to generate saliency predicted results. Finally, the information of lower layers are fused to generate the ultimate saliency maps. Experiments on three open data sets show that the proposed network has better performance than the current RGB-D saliency detection models and stronger robustness.
Imaging Technology of Forward-looking SAR Imaging for Unmanned Aerial Vehicle
Zhichao MENG, Jinyue LU, Pengfei XIE, Lei ZHANG, Hongxian WANG
, doi: 10.11999/JEIT190096
[Abstract](62) [FullText HTML](45) [PDF 2576KB](7)
Abstract:
Forward-looking Synthetic Aperture Radar (SAR) imaging has the problem of left-right Doppler ambiguity, so it is necessary to use spatial resources for ambiguity resolution. Due to the weight and size of Unmanned Aerial Vehicle (UAV), the receiving array is usually small, and the ability of spatial beam-forming for Doppler ambiguity resolution is insufficient. In addition, the small Doppler gradient and narrow bandwidth of forward-looking SAR echo make the receiving bandwidth underutilized. Based on the above problems, a Doppler diversity Multiple Input Multiple Output (MIMO) forward-looking SAR imaging method is proposed in this paper. Based on the forward-looking SAR imaging technology, the narrow-band forward-looking Doppler echo is modulated to different Doppler centers by using Doppler diversity MIMO technology to make full use of the Doppler receiving bandwidth. Furthermore, a virtual receiving array with several times the aperture of the real receiving array can be obtained, which expands greatly the receiving channel and improves effectively the performance of forward-looking SAR imaging in de-Doppler left-right ambiguity.
Analysis for Effect of Radar Intermittent Radiation on the Performance of Cross Location
Yatao WANG, Xiaodong ZENG, Longjian ZHOU
, doi: 10.11999/JEIT190110
[Abstract](60) [FullText HTML](39) [PDF 794KB](2)
Abstract:
For the radio frequency stealth control measure of radar intermittent radiation, the relationship between radiation time ratio and positioning performance is studied which takes cross location with two stations as an example. Firstly, the control method of radar intermittent radiation is analyzed. Then, under the assumption of uniform linear motion of the carrier aircraft, the influence model of radiation time ratio on positioning accuracy is established by using the Cramer-Rao Lower Bound (CRLB). Finally, the solution steps of the model are given and verified by simulation. The simulation results show that different radiation time ratios have different effects on the location performance. When the initial distance is 100 km and the radiation time ratio is less than 0.5, the location convergence time exceeds 10 s, which can effectively reduce the performance of cross location with two stations.
Design of Ka-band Linear Tapered Slot Antennas Based on Substrate Integrated Waveguide Feed
Honggang HAO, Jiang LI, Ting ZHANG, Wei RUAN
, doi: 10.11999/JEIT190218
[Abstract](83) [FullText HTML](49) [PDF 4404KB](5)
Abstract:
Linear tapered slot antennas have significant advantages over traditional horn antennas, dielectric rod antenna when used as feed elements in Focal Plane Arrays (FPA) of Passive MilliMeter Wave(PMMW) imaging. In this paper, a novel Antipodal Linear Tapered Slot Antenna(ALTSA) is designed and optimized. The proposed antenna, the gain of which is improved by loading metamaterial structure, is fed by the Substrate Integrated Waveguide(SIW). Simulation and measure analysis show that the good impedance characteristics, low sidelobe levels, high and smooth gain are all achieved in a wide frequency band. Meanwhile, the designed antenna has a smaller aperture width and is easier to form a denser feed array in the focal plane to improve the spatial resolution of passive millimeter wave imaging.
Research on the Adaptive Synchrosqueezing Algorithm
Lin LI, Lin WANG, Hongxia HAN, Hongbing JI, Li JIANG
, doi: 10.11999/JEIT190146
[Abstract](133) [FullText HTML](112) [PDF 1986KB](20)
Abstract:
The improvement of time-frequency resolution plays a crucial role in the analysis and reconstruction of multi-component non-stationary signals. For traditional time-frequency analysis methods with fixed window, the time-frequency concentration is low and hardly to distinguish the multi-component signals with fast-varying frequencies. In this paper, by adopting the local information of the signal, an adaptive synchrosqueezing transform is proposed for the signals with fast-varying frequencies. The proposed method is with high time-frequency resolution, superior to existing synchrosqueezing methods, and particularly suitable for multi-component signals with close and fast-varying frequencies. Meanwhile, by using the separability condition, the adaptive window parameters are estimated by local Rényi entropy. Finally, experiments on synthetic and real signals demonstrate the correctness of the proposed method, which is suitable to analyze and recover complex non-stationary signals.
Performance of Rank Sum Nonparametric Detector at Clutter Edge
Xiangwei MENG
, doi: 10.11999/JEIT190136
[Abstract](105) [FullText HTML](78) [PDF 812KB](20)
Abstract:
The performance of a Constant False Alarm Rate (CFAR) detector is often evaluated in three typical backgrounds - homogeneous environment, multiple targets situation and clutter edges described by Prof. Rohling. However, there is a lack of the analytic expression of the false alarm rate for the Rank Sum (RS) nonparametric detector at clutter boundaries, and lack of a comparison of the ability for the RS detector to control the rise of the false alarm rate at clutter edges to that of the conventional parametric CFAR schemes, which is incomplete and imperfect for the detection theory of the nonparametric detectors. The analytic expression of the false alarm rate Pfa for the RS nonparametric detector at clutter edges is given in this paper, and the ability of the RS nonparametric detector to control the rise of the false alarm rate at clutter edges is compared to that of the Cell Averaing (CA) CFAR, the Greatest Of (GO) CFAR and the Ordered Statistic (OS) CFAR with incoherent integration. When both of the heavy and the weak clutters follow a Rayleigh distribution, it is shown that the rise of the false alarm rate for the RS detector at clutter edges lies between that of the CA-CFAR and that of the OS-CFAR with incoherent integration. If a non-Gaussian distributed clutter with a long tail moves into the reference window, the rise of the CA-CFAR, the GO-CFAR and the OS-CFAR with incoherent integration reaches a peak of more than 3 orders of magnitude, and can not return to the original pre-designed Pfa. However, the RS nonparametric detector exhibits its inherent advantage in such situation, it can maintain the constant false alarm rate even the distribution form of clutter becomes a different one.
An Optimal Number of Indices Aided gOMP Algorithm for Multi-user Detection in NOMA System
Bin SHEN, Hebiao WU, Taiping CUI, Qianbin CHEN
, doi: 10.11999/JEIT190270
[Abstract](137) [FullText HTML](75) [PDF 1735KB](8)
Abstract:
As one of the Key 5G technologies, Non-Orthogonal Multiple Access (NOMA) can improve spectrum efficiency and increase the number of user connections by utilizing the resources in a non-orthogonal manner. In the uplink grant-free NOMA system, the Compressive Sensing (CS) and generalized Orthogonal Matching Pursuit (gOMP) algorithm are introduced in active user and data detection, to enhance the system performance. The gOMP algorithm is literally generalized version of the Orthogonal Matching Pursuit (OMP) algorithm, in the sense that multiple indices are identified per iteration. Meanwhile, the optimal number of indices selected per iteration in the gOMP algorithm is addressed to obtain the optimal performance. Simulations verify that the gOMP algorithm with optimal number of indices has better recovery performance, compared with the greedy pursuit algorithms and the Gradient Projection Sparse Reconstruction (GPSR) algorithm. In addition, given different system configurations in terms of the number of active users and subcarriers, the proposed gOMP with optimal number of indices also exhibits better performance than that of the other algorithms mentioned in this paper.
Design and Implementation of Robust Particle Filter Algorithms under Student-t Measurement Distribution
Zongyuan WANG, Weidong ZHOU
, doi: 10.11999/JEIT190144
[Abstract](231) [FullText HTML](107) [PDF 1722KB](8)
Abstract:
Outliers are non-Gaussian measurement values far from the bulk of data. In practical transmission, the signals added with outlier often have the heavy-tailed property. Particle filter is based on the Bayesian framework and applicable to the non-linear and non-Gaussian system. However, measurement noise with outlier degrades the performance of particle filter. In this paper, student-t distribution is used to model the measurement noise, combined with Variational Bayes (VB), a novel particle filter Marginalized Particle Filter with VB Mean(MPF-VBM) is designed, which can estimate all parameters of t-distributed measurement distribution including mean parameter as well as state. Further, particle filter with noise correlation (MPF-VBM-COR-1) at the same epoch which is applicable to time variant measurement noise is developed. For verifying the performances of the proposed algorithms, the simulations on the typical univariate non-stationary growth model are performed under the different noise conditions in detail. The outcomes show that the proposed two algorithms of MPF-VBM and MPF-VBM-COR-1 (MPF-VBM-Corrlation-1) have the superior performances to the compared ones.
Security Analysis and Improvements of Hybrid Group Signcryption Scheme Based on Heterogeneous Cryptosystem
Yulei ZHANG, Xiangzhen LIU, Xiaoli LANG, Yongjie ZHANG, Caifen WANG
, doi: 10.11999/JEIT190129
[Abstract](255) [FullText HTML](134) [PDF 650KB](22)
Abstract:
Heterogeneous hybrid group signcryption can not only solve the confidentiality and unforgeability of data transmission under different cryptosystems, but also encrypt data of any length. Firstly, the security of a hybrid group signcryption scheme under heterogeneous cryptosystem is analyzed, and it is pointed out that the scheme does not satisfy the correctness, confidentiality and unforgeability. And a new efficient heterogeneous hybrid group signcryption scheme is proposed. Secondly, it is proved that the proposed scheme is safe under the random oracle model. Finally, the efficiency analysis shows that the proposed scheme reduces the computational cost while realizing all the functions of the original scheme.
A Spatial and Temporal Optimal Method of Service Function Chain Orchestration Based on Overlay Network Structure
Yunjie GU, Yuxiang HU, Jichao XIE
, doi: 10.11999/JEIT190145
[Abstract](334) [FullText HTML](207) [PDF 2128KB](15)
Abstract:
With the introduction of Network Function Virtualization (NFV), the operating costs of operators can be greatly reduced. However, most existing Service Function Chain (SFC) orchestration researches can not optimize the resources utilization while guaranteeing the performance of service delay. A spatial and temporal optimal method of Service Function Chain (SFC) orchestration based on an overlay network structure is proposed. Based on the consideration of the restrictions such as computing resource, network resource and fine-grained end to end delay, this method separates the computing resource and network resource. The resources cost and related delay of SFC can be abstracted into the links weight of overlay network, which can help to convert the SFC orchestration problem into the shortest path problem that can be easily solved. As for the SFC requests set requiring batch processing, an Overlay Network based Simulated Annealing iterative optimal orchestration algorithm(ONSA) is designed. The simulation results demonstrate that the proposed orchestration scheme can reduce the end-to-end delay, the utilization ratio of link bandwidth resource and the operational expenditure by 29.5%, 12.4% and 15.2%, and the acceptance ratio of requests set can be improved by 22.3%. The performance of Virtual Network Function (VNF) load balancing can be significantly improved.
Research on D2D Multi-multiplex Communication Resource Blocks Allocation Algorithm Based on Unbalanced Solution
Zhihong QIAN, Liangshuai HU, Chunsheng TIAN, Xue WANG
, doi: 10.11999/JEIT190171
[Abstract](392) [FullText HTML](236) [PDF 1705KB](35)
Abstract:
In order to solve the problem of the D2D multi-multiplex communication resource blocks allocation in a cell, the resource blocks allocation scheme about D2D multi-multiplex mode based on non-equilibrium solution is proposed after analyzing a D2D user to multiplex two and three cells respectively. The problem of resource blocks partitioning is transformed into the problem of solving the joint revenue maximum value of the multiplexed cellular user by using game theory. When the Nash equilibrium solution does not exist, the objective function is analyzed, the "optimal solution" is solved in the feasible domain and the optimality of unbalanced solution processing is guaranteed. When the equilibrium solution exists, it is rounded up and used as the basis of the resource allocation scheme to maintain its optimality. The theoretical analysis and simulation results show that the proposed algorithm enhances significantly the system performance and sum rate.
A Doppler Resampling Based Imaging Algorithm for High Squint SAR with Constant Acceleration
Ning LI, Bowen BIE, Mengdao XING, Guangcai SUN
, doi: 10.11999/JEIT180953
[Abstract](286) [FullText HTML](225) [PDF 3319KB](17)
Abstract:
A modified SPECtral ANalysis (SPECAN) algorithm based on Doppler resampling is proposed to deal with the azimuth Space-Variant (SV) phase coefficients of the High Squint (HS) SAR data acquired from maneuvering platform. Firstly, for HS SAR with constant acceleration, an orthogonal coordinate slant range model is presented, which can handle the coordinate rotation caused by the traditional method of Range Walk Correction (RWC), and solve the mismatch between the range model and the signal after RWC. Then azimuth Doppler resampling is used to correct the SV phase coefficients. The focused image is achieved by SPECAN technique. Finally, the proposed algorithm is validated by processing of simulated SAR data, and has significant improvement on focusing quality over the reference one.
BP Neural Network Fuzzy Image Restoration Basedon Brainstorm Optimization Algorithm
Xiaoping LIANG, Zhenjun GUO, Changhong ZHU
, doi: 10.11999/JEIT190261
[Abstract](73) [FullText HTML](34) [PDF 2242KB](2)
Abstract:
A kind of restoration method of BP neural network fuzzy image based on Optimized Brain Storming intelligent Optimized(OBSO-BP) algorithm is proposed in this paper. With the method of brain storming intelligent optimized algorithm which is optimized in both clustering and variation, issues of multi-peak high-dimensional function is easily solved. This method optimizes brain storming intelligence algorithm from two aspects of clustering and mutation. This method makes use of the characteristics of brain storming optimization algorithm, which is easy to solve multi-peak and high-dimensional function problems, to automatically search for better initial weights and thresholds of BP neural network, thus reducing the sensitivity of BP network to its initial weights and thresholds, avoiding the network falling into local optimal solution, increasing the convergence speed of the network and reducing the network error and improving the quality of image restoration. Twenty different images are adopted to the image restoration experiment of their fuzzy images with wiener filtering restoration(Wiener), Wiener filtering restoration based on optimized Brain Storming intelligent Optimized algorithm(Wiener-BSO), BP neural network restoration and BP neural network restoration based on optimized Brain Storming intelligent Optimized algorithm(BSO-BP). Results show that a better effect of image restoration can be achieved with this method.
Research on the Novel Ultra-wideband Power Divider Based on Beetle Antennae Search Algorithm
Jie LI, Yuepeng YAN, Xiaoxin LIANG, Jing WAN, Kuisong WANG
, doi: 10.11999/JEIT181003
[Abstract](282) [FullText HTML](239) [PDF 1263KB](9)
Abstract:
Based on the study of the spur-line, a novel spurs-line structure is proposed. The design of a novel Ultra-WideBand (UWB) power divider is described based on the novel spur line structure for the 2.5~13.2 GHz frequency range. The designed device is compact and has a simple structure and good frequency response in the band. Its return loss insertion is less than –12 dB and its insertion loss is less than 3.5 dB. The equations used for the design are based on the concept of odd-even modes and transmission line analysis. The Beetle Antennae Search (BAS) algorithm is used to improve the efficiency and accuracy of the power divider design. In order to verify the accuracy of the design, a UWB power divider is designed by using material RO4003C as substrate. The results validate the feasibility of the spur line-based design and demonstrat that the BAS algorithm has a shortened running time and improved precision compared to other optimization methods. It can be widely used in UWB power divider design.
Design of Convolutional Neural Networks Accelerator Based on Fast Filter Algorithm
Wei WANG, Kaili ZHOU, Yichang WANG, Guang WANG, Jun YUAN
, doi: 10.11999/JEIT190037
[Abstract](368) [FullText HTML](290) [PDF 2545KB](27)
Abstract:
In order to reduce the computational complexity of Convolutional Neural Network(CNN), the two-dimensional fast filtering algorithm is introduced into the CNN, and a hardware architecture for implementing CNN layer-by-layer acceleration on FPGA is proposed. Firstly, the line buffer loop control unit is designed by using the cyclic transformation method to manage effectively different convolution windows and the input feature map data between different layers, and starts the convolution calculation acceleration unit by the flag signal to realize layer-by-layer acceleration. Secondly, a convolution calculation accelerating unit based on 4 parallel fast filtering algorithm is designed. The unit is realized by a less complex parallel filtering structure composed of several small filters. Using the handwritten digit set MNIST to test the designed CNN accelerator circuit, the results show that on the xilinx kintex7 platform, when the input clock is 100 MHz, the computational performance of the circuit reaches 20.49 GOPS, and the recognition rate is 98.68%. It can be seen that the computational performance of the circuit can be improved by reducing the amount of calculation of the CNN.
Research on Shift Generation of Foreign Airlines Service Personnel Based on Tabu Search Algorithm
Xia FENG, Ling TANG, Min LU
, doi: 10.11999/JEIT181196
[Abstract](292) [FullText HTML](176) [PDF 948KB](6)
Abstract:
To solve the problem for the large amount of tasks, complex constraint conditions and manual which is hard to generation shifts of airport foreign airline service personnel. A shift generation model is studied and constructed for multi-task hierarchical qualification which including employees have hierarchical qualifications for tasks and shift needs to meet all kinds of labor laws and regulations and others constraints to minimize the total working time of shifts for optimum. Tabu search algorithm is designed to solve the model. Experiments, based on the actual scheduling data set of the foreign airlines service department of capital airport, verify the practicability and effectiveness of the model and the algorithm. The results show that compared to the existing manual shifts schemes, shifts obtained by using the model can fulfill all constraint conditions, shorten the total working time, reduce the number of employees and improve the utilization rate of airport resources.
Power Control Algorithm Based on Q-Learning in Femtocell
Yun LI, Ying TANG, Hanxiao LIU
, doi: 10.11999/JEIT181191
[Abstract](299) [FullText HTML](179) [PDF 2124KB](16)
Abstract:
The power control problem of mobile users in macro-femto heterogeneous cellular networks is studied. Firstly, an optimization model that maximizes the total energy efficiency of femtocells with the minimum received signal-to-noise ratio as the constraint is established. Then, a femtocell centralized Power Control algorithm based on Q-Learning (PCQL) is proposed. Based on reinforcement learning, the algorithm can adjust the transmit power of the user terminal without accurate channel state information simultaneously. The simulation results show that the algorithm can effectively control the power of the user terminal and improve system energy efficient.
A Reliability-guarantee Method for Service Function Chain Deployment Based on Joint Backup
Hongbo TANG, Hang QIU, Wei YOU, Xinsheng JI
, doi: 10.11999/JEIT190013
[Abstract](285) [FullText HTML](212) [PDF 1550KB](12)
Abstract:
In the Network Function Virtualization (NFV) environment, for the reliability problem of Service Function Chain (SFC) deployment, a joint optimization method is proposed for backup Virtual Network Function (VNF) selection, backup instance placement and service function chain deployment. Firstly, the method defines a virtual network function measurement standard named the unit cost reliability improvement value to improve the backup virtual network function selection method. Secondly, the joint backup mode is used to adjust the placement strategy between adjacent backup instances to reduce bandwidth resources overhead. Finally, the reliability-guarantee problem of the whole service function chain deployment is modeled as integer linear programming, and a heuristic algorithm based on the shortest path is proposed to overcome the complexity of integer linear programming. The simulation results show that the method optimizes resource allocation while prioritizing the network service reliability requirements, and improves the request acceptance rate.
Signal Detection Based on Sigmoid Function in Non-Gaussian Noise
Zhen DAI, Pingbo WANG, Hongkai Wei
, doi: 10.11999/JEIT190012
[Abstract](285) [FullText HTML](192) [PDF 1158KB](19)
Abstract:
To solve the problem of weak signals detection in non-Gaussian background, a method based on sigmoid function is proposed which is named Sigmoid Function Detector (SFD). Firstly, the non-Gaussian background is modeled as a mixed Gaussian model. Based on this, the relationship between parameter k and SFD's performance and characteristics are systematically analyzed. It is pointed out that SFD will be a constant false alarm detector when its detection performance is optimal. Secondly, a new non-parametric detector is proposed via fixing the parameter k, which has significant improvement over matched filter. Finally, simulation analysis is carried out to verify the effectiveness and superiority of SFD.
Scheduling Algorithm Based on Value Optimization for Phased Array Radar
Shanchao YANG, Kangsheng TIAN, Renzheng LIU, Yujun ZHENG
, doi: 10.11999/JEIT190147
[Abstract](116) [FullText HTML](68) [PDF 1622KB](10)
Abstract:
A task scheduling algorithm based on value optimization is proposed for phased array radar. Firstly, the schedulability of tracking tasks is obtained through feasibility analysis and selecting operation on the task queue, using the proposed schedulability parameters. Then, a dynamic task value function about the actual execution time is established according to the peak value and value changing slope of tasks. A value optimization model for tracking task scheduling is constructed based on the task value function. Timeliness can be better achieved while adopting this model to assign execution time for tasks. Finally, searching tasks are scheduled using the idle time intervals between tracking tasks which are going to be executed. Simulation results show that proposed algorithm reduces the average time shift ratio, and improves the value achieving ratio compared with the traditional scheduling algorithms.
A Time-frequency Analysis Method for Linear Frequency Modulation Signal with Low Sidelobe and Nonaliasing Property
Huijie LIU, Xinhai GAO, Rujiang GUO
, doi: 10.11999/JEIT181190
[Abstract](28) [FullText HTML](18) [PDF 3571KB](3)
Abstract:
Chirp signals are widely used in communication and exploration. The parameter analysis of the chirp signals often uses a Wigner-Ville Distribution (WVD) based time-frequency analysis method, which achieves high time-frequency resolution. However, this method has defects in cross terms, high sidelobes, and spectral aliasing problems. To solve these problems, a time-frequency analysis method called Spatially Variant Apodiztion-rearrange Wigner Ville Distribution (SVA-rWVD) is proposed, which achieves low sidelobes by exploiting the Spatially Variant Apodization (SVA) techniques, and avoids the cross terms and the spectral aliasing problems by applying the Short Time Fourier Transform (STFT). Furthermore, a new time-frequency distribution is obtained from the proposed method. Extensive simulations show that the time-frequency distribution obtained by the proposed method not only reduces the sidelobe level to –40 dB but also eliminates cross terms and spectral aliasing for both single-component and multi-component chirp signals.
Robust and Efficient Sparse-feature Enhancementfor Generalized SAR Imagery
Lei YANG, Pucheng LI, Huijuan LI, Cheng FANG
, doi: 10.11999/JEIT190173
[Abstract](45) [FullText HTML](34) [PDF 5070KB](1)
Abstract:
For the problem of sparse feature enhancement in Synthetic Aperture Radar (SAR) imagery, conventional methods are difficult to achieve a preferable balance between accuracy and efficiency. In this paper, a robust and efficient SAR imaging algorithm based on Complex Alternating Direction Method of Multipliers(C-ADMM) is proposed for general SAR imaging feature enhancement within complex raw data domain. The problem is firstly imposed by an augmented Lagrange function, and the complex ${\ell _1}$-norm of the intended SAR image is jointly formulated within the C-ADMM framework. Then, the proximal mapping of the sparse feature is derived as a soft-thresholding operator. Further, an iterative processing procedure is designed according to Gaussian-Deidel principle, and the convergence of the proposed algorithm is analyzed. In the experiment, the performance of the proposed algorithm is firstly examined by the simulated data in terms of Phase Transition Diagram (PTD) under different under-sampling rate and degree of sparsity. Then, various raw SAR and Inverse SAR(ISAR) data, for both stationary ground scene and moving targets, are applied to further verify the proposed C-ADMM, and comparisons with classical Convex(CVX) and Bayesian Compress Sensing(BCS) algorithms are performed, so that both the effectiveness and superiority of the C-ADMM algorithm can be verified.
Applying Chernoff Weighted Classification Frame Method to MotorImagery Brain Computer Interface
Ping TAN, Limei LIU, Fan GUO, Kaijun ZHOU
, doi: 10.11999/JEIT181132
[Abstract](49) [FullText HTML](37) [PDF 1527KB](4)
Abstract:
For the problem that the classifier is less considered to be combined with the brain's cognitive process in the Brain-Computer Interface (BCI) system, a Chernoff-weighted based classifier integrated frame method is proposed and used in synchronous motor imagery BCI. In the method, the statistic characteristics of EEG (ElectroEncephaloGraphy) are obtained by analysing in each time point of synchronous BCI, and then the probability model is established to compute the Chernoff error bound, which is adopted as the weight of common classifier to take the discriminant process. The test experiments are based on the datasets from BCI competitions, and the proposed frame method is employed to compose with LDA, SVM, ELM respectively. The experimental results demonstrate that the proposed frame method shows competitive performance compared with other methods.
A Hardware Trojan Detection Method Based on Compression Marginal Fisher Analysis
Xiaohan WANG, Tao WANG, Xiongwei LI, Yang ZHANG, Changyang HUANG
, doi: 10.11999/JEIT190004
[Abstract](352) [FullText HTML](203) [PDF 2817KB](7)
Abstract:
Against the problem of low detection rate to detect small hardware Trojan by side-channel in physical environment, the Marginal Fisher Analysis (MFA) is introduced. On the basis, a hardware Trojan detection method based on Compression Marginal Fisher Analysis (CMFA) is proposed. The projection space is constructed by reducing the distance between the sample and its same neighbor samples, and the distance between the same neighbor samples and the center of the same kind, and increasing the distance between the same neighbor samples of the center and the sample in different kind. Thus, the difference in the original data is found without any assumptions about data distribution, and the detection of hardware Trojan is achieved. The hardware Trojan detection experiment in AES encryption circuit shows that this method can effectively distinguish the statistical difference in side-channel signal between reference chip and Trojan chip and detect the hardware Trojan whose scale is 0.04% of the original circuit.
An Error Bound of Signal Recovery for Penalized Programs in Linear Inverse Problems
Huan ZHANG, Hong LEI
, doi: 10.11999/JEIT181125
[Abstract](279) [FullText HTML](189) [PDF 794KB](5)
Abstract:
Penalized programs are widely used to solve linear inverse problems in the presence of noise. For now, the study of the performance of panelized programs has two disadvantages: First, the results have some limitations on the tradeoff parameters; Second, the effect of the direction of the noise is not clear. This paper studies the performance of penalized programs when bounded noise is presented. A geometry condition which has been used to study the noise-free problems and constrained problems is provided. Under this condition, an explicit error bound which guarantees stable recovery (i.e., the recovery error is bounded by the observation noise up to some constant factor) is proposed. The results are different from many previous studies in two folds. First, the results provide an explicit bound for all positive tradeoff parameters, while many previous studies require that the tradeoff parameter is sufficiently large. Second, the results clear the role of the direction of the observation noise plays in the recovery error, and reveal the relationship between the optimal tradeoff parameters and the noise direction. Furthermore, if the sensing matrix has independent standard normal entries, the above geometry condition can be studied using Gaussian process theory, and the measurement number needed to guarantee stable recovery with high probability is obtained. Simulations are provided to verify the theoretical results.
A Distributed Node Localization Algorithm for Large Scale Sensor Networks
Junzheng JIANG, Yangjian LI, Haibing ZHAO, Shan OUYANG
, doi: 10.11999/JEIT181101
[Abstract](334) [FullText HTML](266) [PDF 783KB](33)
Abstract:
A distributed algorithm based on modified Newton method is proposed to solve the nodes localization problem in large scale Wireless Sensor Network(WSN). The algorithm includes network partitioning and distributed algorithm. Firstly, the network is divided into several overlapping subregions according to the nodes positions and the distance information between the sensors. The localization problem of subregions is formulated into an unconstrained optimization problem and each subregion can be calculated independently. Then distributed algorithm is used to determine nodes positions in subregions and merge the subregions. Simulation results indicate that the proposed algorithm is superior to the existing algorithms in terms of accuracy in large scale network, which can meet the needs of nodes localization in large scale network.
A Fast and Stable Design Method for Dense Focal Plane Array Feed
Shanhong HE, Mengqian JI, Liangyu XIE, Jin FAN, Chong FAN
, doi: 10.11999/JEIT190026
[Abstract](519) [FullText HTML](214) [PDF 3427KB](15)
Abstract:
The Dense Focal Plane Array Feed (DFPAF), which integrates the characters of multi-beam feed with multiple independent horns and Phased Array Feed (PAF), can simultaneously provide more fixed shaped beams and wider field of view than multi-beam feed with multiple independent horns and PAF. It attracts more attention in radio telescope, radar, electronic reconnaissance, satellite communication and so on. Its unique structure promotes the studies on special design method recently. Combing the theory of array antenna and inherent characteristic of parabolic reflector antenna, a fast design method with robust processing procedure is proposed in this paper. The design principle, calculated results, and comparison between DFPAF and the most representative multi-beam feed with multiple independent horns are presented. All these provide a theoretical basis and reference data for the design of giant reflector with DFPAF.
Semi-Markov Decision Process-based Resource Allocation Strategy for Virtual Sensor Network
Ruyan WANG, Hongjuan LI, Dapeng WU, Hongxia LI
, doi: 10.11999/JEIT190016
[Abstract](322) [FullText HTML](203) [PDF 1042KB](22)
Abstract:
The close relationship between resource deployment and specific tasks in traditional Wireless Sensor Network(WSN) leads to low resource utilization and revenue. According to the dynamic changes of Virtual Sensor Network Request(VSNR), the resource allocation strategy based on Semi-Markov Decision Process(SMDP) is proposed in Virtual Sensor Network(VSN). Then, difining the state, action, and transition probability of the VSN, the expected reward is given by considering the energy and time to complete the VSNR, and the model-free reinforcement learning approach is used to maximize the long-term reward of the network resource provider. The numerical results show that the resource allocation strategy of this paper can effectively improve the revenue of the sensor network resource providers.
An New High Order Finite Difference Time Domain Method
Jie XU, Ke XU, Zhixiang HUANG
, doi: 10.11999/JEIT190050
[Abstract](62) [FullText HTML](45) [PDF 1116KB](5)
Abstract:
Compared with the traditional high-order Finite Difference Time Domain(FDTD) Method, an improved high-order FDTD optimization method is proposed.This algorithm is based on Ampere’s law of circuits and finds a set of optimal coefficients through computer technology to minimize the global dispersion error of the FDTD method.The simulation of point source radiation with different resolutions shows that this method still has very low phase error in the case of lower resolution. It provides an effective solution to the problem of numerical dispersion in the modeling of large size structures.
Super-Resolution Reconstruction of Deep Residual Network with Multi-Level Skip Connections
Xiaoqiang ZHAO, Zhaoyang SONG
, doi: 10.11999/JEIT190036
[Abstract](259) [FullText HTML](149) [PDF 1320KB](14)
Abstract:
The Fast Super-Resolution Convolutional Neural Network algorithm (FSRCNN) is difficult to extract deep image information due to the small number of convolution layers and the correlation lack between the feature information of adjacent convolutional layers. To solve this problem, a deep residual network super-resolution reconstruction method with multi-level skip connections is proposed. Firstly, a residual block with multi-level skip connections is designed to solve the problem that the characteristic information of adjacent convolutional layers lacks relevance. A deep residual network with multi-level skip connections is constructed on the basis of the residual block. Then, the deep residual network connected to the multi-level skip is trained by using the adaptive gradient rate strategy of Stochastic Gradient Descent (SGD) method and the network super-resolution reconstruction model is obtained. Finally, the low-resolution image is input into the deep residual network super-resolution reconstruction model with the multi-level skip connections, and the residual eigenvalue is obtained by the residual block connected the multi-level skip connections. The residual eigenvalue and the low resolution image are combined and converted into a high resolution image. The proposed method is compared with the bicubic, A+, SRCNN, FSRCNN and ESPCN algorithms in the Set5 and Set14 test sets. The proposed method is superior to other comparison algorithms in terms of visual effects and evaluation index values.
A Method for Constructing GC Constant Weight DNA Codes
Jing LIANG, Hongju LI, Feng ZHAO, Jian DING
, doi: 10.11999/JEIT190070
[Abstract](73) [FullText HTML](55) [PDF 457KB](1)
Abstract:
GC weight is an important parameter of DNA code, and how to meet GC constant weight constraint DNA code is an interesting problem. In this paper, by establishing a bijection between DNA code and quaternion code, the DNA code that satisfies the GC constant weight constraint is converted into a GC constant weight quaternary code. Through the algebraic method, three types of DNA codes that meet the constant weight constraints of GC are constructed.
Joint Design of Quasi-cyclic Low Density Parity Check Codes and Performance Analysis of Multi-source Multi-relay Coded Cooperative System
Shunwai ZHANG, Qi WEI
, doi: 10.11999/JEIT190069
[Abstract](460) [FullText HTML](223) [PDF 1403KB](23)
Abstract:
To solve the problems of high encoding complexity and long encoding delay in the multi-source multi-relay Low Density Parity Check (LDPC) coded cooperative system, a special kind of structured LDPC codes—Quasi-Cyclic LDPC (QC-LDPC) codes based on generator matrix is proposed, which combines the characteristics of QC-LDPC codes and Generator-matrix-based LDPC (G-LDPC) codes. It can perform completely parallel encoding, which greatly reduces the encoding complexity and delay at the relays. Based on this, a joint parity check matrix corresponding to the QC-LDPC codes adopted by the sources and relays is deduced, and the matrix is further jointly designed based on the Greatest Common Divisor (GCD) theorem to eliminate all cycles of girth-4 and girth-6. Theoretical analysis and simulation results show that under the same conditions, the Bit Error Rate (BER) performance of the proposed system is better than that of the corresponding point-to-point system. The simulation results also show that the cooperative system with jointly designed QC-LDPC codes can obtain a higher coding gain than the system with explicitly constructed QC-LDPC codes or generally constructed QC-LDPC codes.
A Small Moving Object Detection Algorithm Based on Track in Video Surveillance
Yifeng SUN, Jiang WU, Yanyan HUANG, Guangming TANG
, doi: 10.11999/JEIT181110
[Abstract](407) [FullText HTML](319) [PDF 4057KB](35)
Abstract:
To solve the problem that small moving object is difficult to be detected in video surveillance, a track-based detection algorithm is proposed. Firstly, in order to reduce missing alarm, an adaptive foreground extraction method combining regional texture features and difference probability is presented. Then, for reducing false alarm, the probability computing model of track correlation is designed to establish the correlation of suspected objects between frames, and double-threshold are set to distinguish between true and false positive. Experimental results show that compared with many classical algorithms, this algorithm can accurately detect small moving object within the quantitative range with lower missing and false alarm.
Facial Expression Recognition Method Based on Multi-scale Detail Enhancement
Xiaohui TAN, Zhaowei LI, Yachun FAN
, doi: 10.11999/JEIT181088
[Abstract](289) [FullText HTML](204) [PDF 2475KB](15)
Abstract:
Facial expression is the most intuitive description of changes in psychological emotions, and different people have great differences in facial expressions. The existing facial expression recognition methods use facial statistical features to distinguish among different expressions, but these methods are short of deep exploration for facial detail information. According to the definition of facial behavior coding by psychologists, it can be seen that the local detail information of the face determines the meaning of facial expression. Therefore, a facial expression recognition method based on multi-scale detail enhancement is proposed, because facial expression is much more affected by the image details than other information, the method proposed in this paper extracts the image detail information with the Gaussian pyramid firstly, thus the image is enhanced in detail to enrich the facial expression information. Secondly, for the local characteristics of facial expressions, a local gradient feature calculation method is proposed based on hierarchical structure to describe the local shape features of facial feature points. Finally, facial expressions are classified using a Support Vector Machine (SVM). The experimental results in the CK+ expression database show that the method not only proves the important role of image detail in facial expression recognition, but also obtains very good recognition results under small-scale training data. The average recognition rate of expressions reaches 98.19%.
Automatic Rank Estimation Based Riemannian Optimization Matrix Completion Algorithm and Application to Image Completion
Jing LIU, Han LIU, Kaiyu HUANG, Liyu SU
, doi: 10.11999/JEIT181076
[Abstract](319) [FullText HTML](232) [PDF 1924KB](10)
Abstract:
As an extension of Compressed Sensing(CS), Matrix Completion(MC) is widely applied to different fields. Recently, the Riemannian optimization based MC algorithm attracts a lot of attention from researchers due to its high accuracy in reconstruction and computational efficiency. Considering that the Riemannian optimization based MC algorithm assumes a fixed rank of the original matrix, and selects a random initial point for iteration, a novel algorithm is proposed, namely automatic rank estimation based Riemannian optimization matrix completion algorithm. In the proposed algorithm, the estimate of rank is obtained minimizing the objective function that involving the rank regulation, in addition, the iterative starting point is optimized based on Riemannian manifold. The Riemannian manifold based conjugate gradient method is then used to complete the matrix, thereby improving the reconstruction precision. The experimental results demonstrate that the image completion performance is significantly improved using the proposed algorithm, compared with several classical image completion methods.
Candidate Label-Aware Partial Label Learning Algorithm
Hongchang CHEN, Tian XIE, Chao GAO, Shaomei LI, Ruiyang HUANG
, doi: 10.11999/JEIT181059
[Abstract](297) [FullText HTML](212) [PDF 3576KB](15)
Abstract:
In partial label learning, the true label of an instance is hidden in a label-set consisting of a group of candidate labels. The existing partial label learning algorithm only measures the similarity between instances based on feature vectors and lacks the utilization of the candidate labelset information. In this paper, a Candidate Label-Aware Partial Label Learning (CLAPLL) method is proposed, which combines effectively candidate label information to measure the similarity between instances during the graph construction phase. First, based on the jaccard distance and linear reconstruction, the similarity between the candidate labelsets of instances is calculated. Then, the similarity graph is constructed by combining the similarity of the instances and the label-sets, and then the existing graph-based partial label learning algorithm is presented for learning and prediction. The experimental results on 3 synthetic datasets and 6 real datasets show that disambiguation accuracy of the proposed method is 0.3%~16.5% higher than baseline algorithm, and the classification accuracy is increased by 0.2%~2.8%.
Inverse Synthetic Aperture Radar Imaging with Non-Coherent Short Pulse Radar and Its Sparse Recovery
Haibo WANG, Wenhua HUANG, Tao BA, Yue JIANG
, doi: 10.11999/JEIT180912
[Abstract](278) [FullText HTML](159) [PDF 1733KB](17)
Abstract:
The microwave source of Non-Coherent Short Pulse (NCSP) radar transmits short pulse. Thus, for high velocity targets, the motion effect in the pulse duration can be neglected, and the echo signal does not need special motion compensation. In order to use the NCSP radar signal for Inverse Synthetic Aperture Radar (ISAR) imaging, the compensation coherent processing method is applied to removing the uncertainty of the envelope time and the initial phase uncertainty. Assuming that the echo is envelope-aligned and initially compensated by conventional methods, ISAR radar imaging can be performed using the Range-Doppler (RD) method, subsequently. The simulation verifies the feasibility of the compensation signal ISAR imaging. However, the carrier-frequency random jitter factor of NCSP radar causes random-modulated sidelobes in the Doppler dimension, which affect imaging quality. In this paper, the sparse recovery technique is used to perform sparse reconstruction of the target scattering center in the imaging space. The Orthogonal Matching Pursuit (OMP) algorithm and the Sparse Bayesian Learning (SBL) algorithm are used as the recovery algorithm for imaging simulation experiments. The simulation results show that the sparse recovery technique can suppress the imaging sidelobes caused by non-coherence and improve the imaging quality.
Research on Radar Waveform Design Strategy under Game Condition
Wei LI, Honglin WANG, Jiayi ZHENG, Jianye XU, Junlong ZHAO, Kun ZOU
, doi: 10.11999/JEIT190114
[Abstract](94) [FullText HTML](60) [PDF 1649KB](7)
Abstract:
In order to improve missile-borne radar detection performance in modern electronic warfare, a radar waveform design method based on Nash equilibrium is proposed. Firstly, the radar and jammer game signal models are established in electronic warfare. Based on maximum Signal-to-Interference-plus-Noise Ratio (SINR), waveform strategies of radar and jammer are designed respectively. Secondly, the existence of Nash equilibrium solution is demonstrated by mathematical derivation and verified in experimental simulation. A multiple iterative water-filling method which repeatedly eliminates strict disadvantages is designed to achieve Nash equilibrium. The maxmin scheme of disequilibrium game is deduced by two-step water-filling method. Finally, the radar detection performance of optimization strategies is tested by simulation experiments. Simulation results reveal that the radar waveform design based on Nash equilibrium is beneficial to improve the radar detection performance under game conditions. Compared with no-game and maxmin strategies, the radar detection probability of Nash equilibrium strategy can be increased by 12.02% and 3.82%, respectively. It is proved that the Nash equilibrium strategy of this paper is closer to the Pareto optimality.
Iterative Multiple Signal Classification Algorithm with Small Sample Size
Juan WANG, Tong WANG, Jianxin WU
, doi: 10.11999/JEIT190160
[Abstract](78) [FullText HTML](53) [PDF 1465KB](4)
Abstract:
For cases with small samples, the estimated noise subspace obtained from sample covariance matrix deviates from the true one, which results in MUltiple SIgnal Classification (MUSIC) Direction-Of-Arrival (DOA) estimation performance breakdown. To deal with this problem, an iterative algorithm is proposed to improve the MUSIC performance by modifying the signal subspace in this paper. Firstly, the DOAs are roughly estimated based on the noise subspace obtained from sample covariance matrix. Then, considering the sparsity of signals and the low-rank property of steering matrix, a new signal subspace is got from the steering matrix consisting of estimated DOAs and their adjacent angles. Finally, the signal subspace is modified by solving an optimization problem. Simulation results demonstrate the proposed algorithm can improve the subspace accuracy and furtherly improve the MUSIC DOA estimation performance, especially in small sample cases.
Error Analysis of Dynamic Sea Surface Height Measurement by Near-nadir Interferometric SAR
Yao CHEN, Mo HUANG, Xiaoqing WANG, Haifeng HUANG
, doi: 10.11999/JEIT190191
[Abstract](62) [FullText HTML](43) [PDF 2536KB](12)
Abstract:
The wide-swath interferometric altimeter which working at near-nadir is a newly developed ocean surface topography measurement technology in recent years. Different from land elevation measurement, for the dynamic ocean surface waves, they move randomly all the time and this will bring bias in Synthetic Aperture Radar (SAR) imaging and interferometric processes and leads to the final height measurement errors. For the requirement of centimeter-level precision, this error is the main source of measurement errors. The errors due to the characteristics of ocean surface and their impact on near-nadir InSAR’s precision are investigated. The motion error theoretical model is established combining the characteristics of the ocean surface and InSAR working mechanism, and the electromagnetic bias and layover bias are also taken into consideration. The error models in different SAR modes under various sea states are simulated. The error model is validated by the interferometric SAR full-link experimental simulation and the simulation results are consistent with the theoretical values. The results show that the errors are approximately linear changing with the Doppler centroid frequency and is proportional to the radial velocity of targets modulated by scattering. The error is not only related to the characteristics of the waves, but also related to system parameters. This work can provide the feasible suggestions for future system design, error budget and data processing.
Multi-context Autoencoders for Multivariate Medical Signals Based on Deep Convolutional Neural Networks
Ye YUAN, Kebin JIA, Pengyu LIU
, doi: 10.11999/JEIT190135
[Abstract](203) [FullText HTML](98) [PDF 4783KB](9)
Abstract:
Learning unsupervised representations from multivariate medical signals, such as multi-modality polysomnography and multi-channel electroencephalogram, has gained increasing attention in health informatics. In order to solve the problem that the existing models do not fully incorporate the characteristics of the multivariate-temporal structure of medical signals, an unsupervised multi-context deep convolutional autoencoder is proposed in this paper. Firstly, by modifying traditional convolutional neural networks, a multivariate convolutional autoencoder is proposed to extract multivariate context features within signal segments. Secondly, semantic learning is adopted to auto-encode temporal information among signal segments, to further extract temporal context features. Finally, an end-to-end multi-context autoencoder is trained by designing objective function based on shared feature representation. Experimental results conducted on two public benchmark datasets (UCD and CHB-MIT) show that the proposed model outperforms the state-of-the-art unsupervised feature learning methods in different medical tasks, demonstrating the effectiveness of the learned fusional features in clinical settings.
Channel Estimation Algorithm of Maritime Sparse Channel Based on Fast Bayesian Matching Pursuit Optimization
Ying ZHANG, Yufeng YAO
, doi: 10.11999/JEIT190102
[Abstract](66) [FullText HTML](63) [PDF 1212KB](5)
Abstract:
In the Orthogonal Frequency Division Multiplexing (OFDM) system, the receiver often needs to know the channel state information because the frequency selective fading channel will generate inter-symbol interference in the data transmission. In the case of maritime communication, the method of channel estimation is often needed to detect the channel subjected to the interference of various external factors. In order to improve the estimation performance, the Fast Bayesian Matching Pursuit based on singular-value-decomposition for Optimizing observation matrix (FBMPO) is proposed, which fully considers not only the sparse channel of maritime communication, but also reduces the influence of uncertainty of the unpredictable channel. Computer simulation shows, compared with traditional channel estimation algorithms, the proposed algorithm can effectively improve the accuracy of channel estimation.
A Privacy-aware Computation Offloading Method Based on Lyapunov Optimization
Xing ZHAO, Jianhua PENG, Wei YOU
, doi: 10.11999/JEIT190170
[Abstract](110) [FullText HTML](85) [PDF 2189KB](10)
Abstract:
The decision on computation offloading to Mobile Edge Computing (MEC) may expose user’s characteristics and cause the user to be locked. A privacy-aware computation offloading method based on Lyapunov optimization is proposed in this paper. Firstly, the privacy of task is defined, and privacy restrictions are introduced to minimize the cumulative privacy of each MEC node; then, the fake task mechanism is proposed to balance the terminal energy consumption and privacy protection, reducing the cumulative privacy of MEC node by generating a fake task non-feature task when offloading is not performed due to privacy restrictions; finally, the privacy-aware computing offloading decision is modeled and solved based on the Lyapunov optimization. Simulation results validate that the Lyapunov optimization-based Privacy-aware Offloading Algorithm (LPOA) can stabilize user’s privacy near zero, and the total offloading frequency is consistent with the decision that don’t consider privacy, effectively protecting user’s privacy while maintaining a low average energy consumption.
Research on Non-local Multi-scale Fractional Differential Image Enhancement Algorithm
Guo HUANG, Li XU, Qingli CHEN, Yifei Pu
, doi: 10.11999/JEIT19032
[Abstract](104) [FullText HTML](71) [PDF 5633KB](11)
Abstract:
In order to enhance the useful information in the image and improve the visual effect of the image, a Non-local Multi-scale Fractional Differential(NMFD) image enhancement operator is proposed. The operator divides the image into several sub-images and calculates the edge intensity coefficient, entropy value and roughness of each sub-image, and the obtained feature data are normalized in a unified scale in the global image range. Then, the normalized data are weighted to be the non-local eigenvalues of the image. Finally, an exponential function is used to establish the non-linear quantization relationship between image detail features and the value of fractional order. Thus, the fractional order of different scales can be determined in different image sub-block regions, so that the non-local multi-scale image enhancement model is realized.
A Moving Target Imaging Approach for the Multichannel in Azimuth High Resolution Wide Swath SAR System
Yuying WANG, Zhimin ZHANG, Ning LI, Huaitao FAN, Qingchao ZHAO
, doi: 10.11999/JEIT190211
[Abstract](87) [FullText HTML](86) [PDF 1698KB](7)
Abstract:
Since the echo characteristics of moving targets are different from that of stationary targets, the traditional reconstruction filter bank algorithm, i.e., the reconstruction filter algorithm, is not applicable. In this paper, a novel reconstruction approach of the moving target for a multichannel in azimuth High-Resolution Wide-Swath (HRWS) Synthetic Aperture Radar (SAR) system is proposed. The approach firstly analyzes the echo characteristics of the moving target for the multi-channel in azimuth SAR system and gives the main reason for the failure of the traditional reconstruction method in contrast to the form of the stationary target echo. By introducing the radial velocity of the moving target, the spectrum reconstruction of the uniform moving target is effectively realized, and the azimuth ambiguities of the uniform moving target for the multi-channel in azimuth SAR system is well suppressed. Space-borne simulated results confirm the effectiveness of the proposed reconstruction approach.
A SDN Routing Optimization Mechanism Based on Deep Reinforcement Learning
Julong LAN, Changhe YU, Yuxiang HU, Ziyong LI
, doi: 10.11999/JEIT180870
[Abstract](447) [FullText HTML](310) [PDF 1504KB](33)
Abstract:
In order to achieve routing optimization in the Software Defined Network (SDN) environment, deep reinforcement learning is imposed to the SDN routing process and a mechanism based on deep reinforcement learning is proposed to optimize routing. This mechanism can improve network performance such as delay, throughput, and realize black-box optimization in continuous time, which surely reduces network operation and maintenance costs. Besides, the proposed routing optimization mechanism is evaluated through a series of experiments. The experimental results show that the proposed SDN routing optimization mechanism has good convergence and effectiveness, and can provide better routing configurations and performance stability than traditional routing protocols.
A Novel Shaping Design Technique of the Elliptical Beam Antenna
Xinglong LIU, Biao Du, Jianzhai ZHOU
, doi: 10.11999/JEIT190142
[Abstract](121) [FullText HTML](65) [PDF 3660KB](6)
Abstract:
In order to satisfy the requirement of elliptical beam antenna with low profile, a novel design technique of the hybrid-structural antenna with elliptical beam is proposed. The hybrid-structural antenna consists of the ring-focus elliptical antenna in inner-ring region and the cassegrain elliptical antenna in outer-ring region. The design method, procedure and shaping formula are presented in detail. A 600 mm×1200 mm reflector antenna is designed and its tolerance analysis is also given. The results show that the novel structural antenna can operate in Ku/Ka dual bands, antenna efficiency is greater than 58% and Voltage Standing Wave Ratio (VSWR) is better than 1.27, and its side lobe levels in the EL and AZ planes are below –12.2 dB and –14.6 dB respectively. The simulated results of Grasp and CST software agree well, which verified the effectiveness of the design method.
A Service Function Chain Deployment Method Against Side Channel Attack
Peng YI, Jichao XIE, Zhen ZHANG, Yunjie GU, Dan ZHAO
, doi: 10.11999/JEIT190127
[Abstract](316) [FullText HTML](237) [PDF 2196KB](14)
Abstract:
Side channel attack is the primary way to leak information between tenants in current cloud computing environment. However, existing Service Function Chain (SFC) deployment methods do not fully consider the side channel attack problem faced by the Virtual Network Function (VNF) in the multi-tenant environment. A SFC deployment method is proposed against side channel attack. A tenant classification strategy based on average time and a deployment strategy considering historical information are introduced. Under the resource constraints of the SFC, the optimization model is established with the goal of minimizing the number of servers that the tenant can cover. And a deployment algorithm is designed based on the greedy choice. The experimental results show that, compared with other deployment methods, this method can significantly improve the difficulty and cost of malicious tenant to realize co-residence, and reduces the risk of side channel attack faced by tenants.
Energy-efficient Scheduling Algorithm for All Optical IP Multicast Based on Colorless, Directionless and Contentionless-Flexible Reconfigurable Optical Add/Drop Multiplexer Node
Huanlin LIU, Fei FANG, Yong CHEN, Min XIANG, Yue MA
, doi: 10.11999/JEIT180937
[Abstract](309) [FullText HTML](198) [PDF 1168KB](10)
Abstract:
In order to improve multicast’s spectrum energy-efficient of elastic optical network configured with Colorless, Directionless and Contentionless-Flexible Reconfigurable Optical Add/Drop Multiplexer (CDC-F ROADM) nodes, an All-optical Multicast Energy Efficiency Scheduling Algorithm (AMEESA) is proposed. In the routing phase, considering both energy consumption and link spectrum resource utilization, the link cost function is designed to establish the multicast tree with the least cost. In the spectrum allocation phase, a spectrum conversion method based on High Spectral Resolution (HSR) is designed by changing the spectrum slot index of adjacent links according to links availability of spectrum blocks. And an energy-saving spectrum conversion scheme is selected to allocate spectrum block resources for the multicast tree. Simulation analysis shows that the proposed algorithm can effectively improve the network energy efficiency and reduce the bandwidth blocking probability of IP multicast.
The Application of the G-matrix Modification Methods in the Imaging of the 1-D Synthetic Aperture Microwave Radiometer
Aili ZHANG, Hao LIU, Lin WU, Lijie NIU, Cheng ZHANG, Xue CHEN, Ji WU
, doi: 10.11999/JEIT181067
[Abstract](441) [FullText HTML](161) [PDF 1669KB](7)
Abstract:
The G-matrix model method is usually used to achieve the brightness temperature reconstruction for the one-Dimensional (1-D) synthetic aperture microwave radiometer system. For the 1-D radiometer system, the imaging process mainly includes: the radiometer instrument observes the full field of view of the 2-D target scene maps, and obtains the 1-D samples of the visibility, and then inverts the system parameter matrix G to realize the reconstruction of the 1-D image of the target scene. Since the system sampling baselines are only distributed in the 1-D of the spatial frequency domain, in the process of the brightness temperature image reconstruction, the matrix G needs to realize 2-D to 1-D conversion. Therefore, two G-matrix modification methods are proposed to improve the imaging quality for the 1-D synthetic aperture microwave radiometer. For the 8-element ground radiometer prototype system and the 10-element salinity radiometer system, theoretical analysis and simulation experiments have verified that the G-matrix modification methods proposed in this paper can effectively improve the imaging results, and can effectively suppress the imaging error caused by the side-lobed degradation of the antenna patterns.
Performance Analysis of Massive MIMO-OFDM System with Hybrid-precision Analog-to-digital Converter
Kai LIU, Guichao CHEN, Cheng TAO, Tao ZHOU
, doi: 10.11999/JEIT181136
[Abstract](472) [FullText HTML](208) [PDF 1160KB](17)
Abstract:
The spectral efficiency and energy efficiency of the uplink of massive MIMO-OFDM system is studied using mixed-precision Analog-Digital Converter (ADC) and Zero-Forcing (ZF) reception algorithm at the receiver. By using the additive quantization noise model to analyze the performance of the system, the approximate closed expression of the spectral efficiency and energy efficiency of the whole system is derived, and the correctness of the expression is proved by simulation. The research results show that the spectral efficiency of the system is related to the transmission power of each user, the number of antennas at the receiver and the quantization accuracy of the receiver. Numerical and simulation results also show that the performance loss caused by the low-precision ADC can be compensated by increasing the number of antennas at the base station.
Research and Development on Applications of Convolutional Neural Networks of Radar Automatic Target Recognition
Fengshou HE, You HE, Zhunga LIU, Cong’an XU
, doi: 10.11999/JEIT180899
[Abstract](613) [FullText HTML](347) [PDF 900KB](97)
Abstract:
Automatic Target Recognition(ATR) is an important research area in the field of radar information processing. Because the deep Convolution Neural Network(CNN) does not need to carry out feature engineering and the performance of image classification is superior, it attracts more and more attention in the field of radar automatic target recognition. The application of CNN to radar image processing is reviewed in this paper. Firstly, the related knowledges including the characteristics of the radar image is introduced, and the limitations of traditional radar automatic target recognition methods are pointed out. The principle, composition and development of CNN the field of computer vision are introduced. Then, the research status of CNN in radar automatic target recognition is provided. The detection and recognition method of SAR image are presented in detail. Then the challenge of radar automatic target recognition is analyzed. Finally, the new theory and model of convolution neural network, the new imaging technology of radar and the application to complex environments in the future are prospected.
Two Dimensional DOA Estimation Based on Polarization Sensitive Array and Uniform Linear Array
Lutao LIU, Chuanyu WANG
, doi: 10.11999/JEIT180832
[Abstract](430) [FullText HTML](236) [PDF 1434KB](28)
Abstract:
To solve the problem that polarization sensitive array of defective electromagnetic vector sensor estimate multi parameter, a two-dimensional DOA estimation algorithm based on orthogonal dipole is proposed in this paper. First, eigendecomposition of the covariance matrix is produced by the received data vectors of the polarization sensitive array. Then the signal subspace is divided into four subarrays, and the phase difference between one of the subarray and the others is obtained according to the ESPRIT algorithm. Then the phase difference between different subarrays is paired. Finally, the DOA estimation and polarization parameters of the signal are calculated according to the phase difference. The uniform linear array composed by orthogonal dipoles can not be two-dimensional DOA estimated by using the MUSIC algorithm and the traditional ESPRIT algorithm. The algorithm proposed in this paper solves this problem, and compared with the polarization MUISC algorithm greatly reduces the complexity of the algorithm. The simulation results verify the effectiveness of the proposed algorithm.
Combined Bipercentile Parameter Estimation of Generalized Pareto Distributed Sea Clutter Model
Han YU, Penglang SHUI, Sainan SHI, Chunjiao YANG
, doi: 10.11999/JEIT190148
[Abstract](61) [FullText HTML](37) [PDF 2494KB](5)
Abstract:
The generalized Pareto distributed sea clutter model, known as one of the compound-Gaussian models, is able to describe heavy-tailed characteristic of sea clutter under high-resolution and low grazing angle detection scene efficiently, and the accuracy of parameter estimation under this condition heavily impacts radar’s detection property. In this paper, Combined BiPercentile (CBiP) estimator is proposed to estimate the parameters. The CBiP estimator is realized based on the explicit roots of low-order polynomial equations and full application of sample information in returns, which provides a highly-accurate parameter estimation process. Besides, the CBiP estimator can maintain the robustness of estimation performance when outliers with extremely large power are existing in samples, while other estimators, including moment-based and maximum likelihood (ML) estimators, degrade extremely in estimation accuracy. Without outliers in samples, the combined bipercentile estimator shows similar accuracy with the ML estimator. With outliers, the combined percentile estimator is the only method with robustness in performance, compared with other estimators aforementioned. Moreover, the ability of the new estimator is verified by measured clutter data.
Fully Digital Feedforward Background Calibration of Time Skew for Sub-Sampling Time-Interleaved Analog-to-Digital Converter
Honghui DENG, Hui YAN, Rui XIAO, Hongmei CHEN
, doi: 10.11999/JEIT190052
[Abstract](109) [FullText HTML](64) [PDF 2072KB](5)
Abstract:
A full digital feedforward Time-Interleaved Analog-to-Digital Converter (TIADC) time skew calibration algorithm is presented, the time skew estimation adopts the feedforward extraction method of the improved derivative module of time skew function, which can greatly improve the accuracy of skew estimation when the input signal frequency is high. At the same time, the time skew function is based on subtraction, in order to reduce the complexity of skew estimation unit. Finally, the time skew is corrected by using first-order Taylor compensation. The simulation results show that when the input signal is a multi-frequency signal, the Spurious-Free Dynamic Range (SFDR) increases from 48.6 dB to 80.7 dB, after adopting the proposed time skew correction for a 4-channal 14-bit TIADC system. Compared with the traditional feedforward calibration structure based on correlation operation, the effective calibration input signal bandwidth can be increased from 0.19 to 0.39, which greatly increases the application range of the calibration algorithm.
Control Resource Optimization Mechanism of SDN Based on Traffic Engineering
Yuxiang HU, Ziyong LI, Zongkui HU, Tao HU
, doi: 10.11999/JEIT190276
[Abstract](225) [PDF 1945KB](13)
Abstract:
In Software-Defined Networking (SDN) with distributed control plane, network expansion problems arise due to network domain management. To address this issue, a Traffic Engineering-based control Resource Optimization (TERO) mechanism of SDN is proposed. It analyzes the control resource consumption of flow requests processing with different path characteristics, and points out that the control resource consumption can be reduced by changing the association relationship between controllers and switches. The controller association mechanism is divided into two phases: firstly, a minimum set cover algorithm is designed to solve the controller association problem efficiently in large-scale network. Then, a coalitional game strategy is introduced to optimize the controller association relationship to reduce both control resource consumption and control traffic overhead. The simulation results demonstrate that while keeping control traffic overhead low, mechanism which in this paper can reduce control resource consumption by about 28% in comparison with the controller proximity mechanism.
Traceable Lightweight and Fine-grained Access Control in Named Data Networking
Jiangtao LUO, Chen HE, Junxia WANG
, doi: 10.11999/JEIT181160
[Abstract](297) [FullText HTML](218) [PDF 1134KB](9)
Abstract:
Due to the feature of in-network caching in Named Data Networking (NDN), any consumer might fetch the cached contents from NDN routers, but the content producers have no idea about details of certain contents being accessed. Considering these problems, a fine-grained Traceable and Lightweight Access Control (TLAC) scheme is presented. In the TLAC scheme, an anonymous and secure " three-way handshake” authentication protocol is presented by collaboratively leveraging the combined public key and the Schnorr signature, and an improved secret sharing method is used to distribute the key efficiently. Finally, the experimental results prove the efficiency of TLAC scheme.
Deep Convolution Blind Separation of Acoustic Signals Based on Joint Diagonalization
Yang LI, Weitao ZHANG, Shuntian LOU
, doi: 10.11999/JEIT190067
[Abstract](351) [FullText HTML](252) [PDF 1120KB](9)
Abstract:
The propagation of acoustic signal in space has a strong multipath effect, and the receiver often overlaps in the form of convolution. Especially in strong reverberation conditions such as ocean and theatre, where the length of impulse response of hybrid filter increases significantly. In order to eliminate the problem that long impulse response leads to the failure of the frequency domain convolution blind separation algorithm, two Short-Time Fourier Transforms (STFT) are applied to the observed signal. The first STFT shortens the length of the hybrid filter. The second STFT converts the signal model into instantaneous blind separation. Finally, the separation matrix is estimated by Joint Diagonalization (JD) technique. Compared with the existing methods, this method solves the problem of model failure under deep convolution mixing, and can obtain better separation performance when the number of source signals is large or additive noise exists. The simulation results verify the effectiveness and performance advantages of the proposed method.
Image Saliency Detection Based on Object Compactness and Regional Homogeneity Strategy
Hongmei TANG, Biying WANG, Liying HAN, Yatong ZHOU
, doi: 10.11999/JEIT190101
[Abstract](309) [FullText HTML](222) [PDF 5665KB](13)
Abstract:
Considering the inaccurate description of feature differences between nodes in the graph-based saliency detection algorithm, an image saliency detection algorithm combining object compactness and regional homogeneity strategy is proposed. Different from the commonly used graph-based model, a sparse graph-based structure closer to the human visual system and a novel regional homogeneity graph-based structure are established. They are used to describe the correlation within the foreground and the difference between foreground and background. Therefore, many redundant connections of nodes are eliminated and the local spatial relationship of nodes is strengthened. Then the clusters are combined to form a saliency map by means of manifold ranking. Finally, the background confidence is introduced for saliency optimization by the similarity of the background region clusters and the final detection result is obtained. Compared with 4 popular graph-based algorithms on the four benchmark datasets, the proposed algorithm can highlight the salient regions clearly and has better performance in the evaluation of multiple comprehensive indicators.
Integrated Programmable Microwave Photonic Filter with High Shape-factor
Shasha LIAO, Ke LIAO, Xi LIAO, Li LIU
, doi: 10.11999/JEIT181156
[Abstract](214) [FullText HTML](97) [PDF 2604KB](8)
Abstract:
In order to accommodate the development of new communication technology, an integrated programmable microwave photonic filter with high shape-factor is proposed in this paper. This filter is based on Silicon-On-Insulator (SOI) and an eight-tap finite impulse response. By controlling the thermal heaters on the amplitude modulator and phase modulator of each tap, A rectangular filter with tunable bandwidth and high shape-factor greater than 0.55 is obtained. Furthermore, the tunability of central frequency, bandwidth and variable pass-band shape can be also realized. Small size, light weight and flexibility are advantages of our filters, moreover, it can be applied in large bandwidth signal processing and an alternative method to part the channels. So it can be widely used in defense field and 5G networks.
Linear Complexity of Binary Sequences Derived from Euler Quotients Modulo 2pm
Xiaoni DU, Li LI, Fujun ZHANG
, doi: 10.11999/JEIT190071
[Abstract](293) [FullText HTML](161) [PDF 360KB](16)
Abstract:
Families of pseudorandom sequences derived from Euler quotients modulo an odd prime power possess sound cryptographic properties. In this paper, according to the theory of residue class ring, a new classes of binary sequences with period $2{p^{m + 1}}$ is constructed using Euler quotients modulo $2{p^m},$ where $p$ is an odd prime and integer $m \ge 1.$ Under the condition of ${2^{p - 1}}\not \equiv 1 ({od}\; \;{p^2})$, the linear complexity of the sequence is examined with the method of determining the roots of polynomial over finite field ${F_2}$. The results show that the linear complexity of the sequence takes the value $2({p^{m + 1}} - p)$ or $2({p^{m + 1}} - 1)$, which is larger than half of its period and can resist the attack of Berlekamp-Massey (B-M) algorithm. It is a good sequence from the viewpoint of cryptography.
Optimal Scheme of Resource Allocation for Ultra-reliable and Low-latency in Machine Type Communications Based on Non-orthogonal Multiple Access with Short Block Transmission
Xianzhong XIE, Jia LI, Qian HUANG, Jie CHEN
, doi: 10.11999/JEIT190128
[Abstract](313) [FullText HTML](158) [PDF 1434KB](12)
Abstract:
For the service characteristics and Quality of Service (QoS) requirements of Machine Type Communications (MTC), short-packet/short-coded block transmission in MTC based on Non-Orthogonal Multiple Access (NOMA) is considered in this paper, and the resource optimization problem of the Ultra-Reliable and Low-Latency (URLL) in MTC based on NOMA is discussed. Currently, uplink transmission is a bottleneck of MTC based on NOMA. Firstly, considering the performance requirements supporting NOMA and high reliability and low latency in wireless cellular networks, a system model for uplink wireless resource optimization is established. Then, the uplink transmission delay is analyzed and the link reliability function based on distance is derived. Further, with the constraints of delay, reliability and bandwidth, a wireless resource allocation algorithm for maximizing the sum rates of central users is proposed, and also the convergence proof and complexity analysis of the algorithm are given. Finally, the simulation results show the performance advantages of the proposed optimal scheme.
Adaptive Knowledge Transfer Based on Classification-error Consensus Regularization
Shuang LIANG, Wenlong HANG, Wei FENG, Xuejun LIU
, doi: 10.11999/JEIT181054
[Abstract](300) [FullText HTML](205) [PDF 1010KB](14)
Abstract:
Most current transfer learning methods are modeled by utilizing the source data with the assumption that all data in the source domain are equally related to the target domain. In many practical applications, however, this assumption may induce negative learning effect when it becomes invalid. To tackle this issue, by minimizing the integrated squared error of the probability distribution of the source and target domain classification errors, the Classification-error Consensus Regularization (CCR) is proposed. Furthermore, CCR-based Adaptive knowledge Transfer Learning (CATL) method is developed to quickly determine the correlative source data and the corresponding weights. The proposed method can alleviate the negative transfer learning effect while improving the efficiency of knowledge transfer. The experimental results on the real image and text datasets validate the advantages of the CATL method.
A Capacitor-less Low Dropout Regulator with Fast Response
Xingyuan TONG, Mao LI, Siwan DONG
, doi: 10.11999/JEIT181060
[Abstract](189) [FullText HTML](109) [PDF 2070KB](7)
Abstract:
A novel technique for increasing the load response speed of Capacitor-Less Low-DropOut linear regulator (CL-LDO) is proposed to improve the transient response of CL-LDO when its load current changes. With an additional fast signal feedback path, the CL-LDO can achieve fast transient response so that the overshoot and undershoot of its output voltage can be dramatically reduced. A CL-LDO with fast response is realized in 0.18 μm CMOS and occupies an active area of 0.00529 mm2. The CL-LDO has an output voltage of 1.194 V when the input supply voltage ranges from 1.5 V to 2.5 V. When the load current changes from 100 μA to 10 mA with the rise and fall time of 1 μs, the output of LDO can be recovered from its overshoot and undershoot to a stable voltage within 489.537 ns and 960.918 ns, respectively. Compared with a traditional CL-LDO without this proposed technique, the transient response speed of this CL-LDO is increased by 7.41 times. The overshoot and undershoot of the output voltage is decreased by 35.3% and 78.1%, respectively.
Energy Efficiency and System Capacity Based Multi-Objective Radio Resource Management in M2M Communications
Shaoyi XU, Shuai GAO
, doi: 10.11999/JEIT181168
[Abstract](101) [FullText HTML](70) [PDF 1643KB](4)
Abstract:
Machine-to-Machine (M2M) and Device-to-Device (D2D) communications are both key technologies in the fifth Generation (5G) mobile communication systems. In M2M communications, the Energy Efficiency (EE) especially needs to be improved to extend the life cycle of the M2M equipment. In this paper, the M2M and D2D technologies are combined and the D2D technology is used to realize M2M transmission. At the same time, M2M users are allowed to reuse spectrum resources with Human-to-Human (H2H) devices in the cellular networks. To guarantee the Quality of Service (QoS) of these two systems simultaneously, a Multi-Objective Optimization Problem (MOOP) is then formulated to maximize the sum throughput of H2H systems, and the sum EE of M2M systems and to minimize the interference from M2M communications to H2H networks. To solve this MOOP, the penalty function method is firstly adopted to relax the original binary variables, and then the ConCave-Convex Procedure (CCCP) method is used to convert the non-convex single-objective problems into convex problems. Finally, the weighted Tchebyshev algorithm is utilized to obtain the Pareto solution of the original MOOP. By comparing with the traditional weighted sum method, the effectiveness of the proposed method is proved by simulation results.
Ciphertext Sorting Search Scheme Based on B+ Tree Index Structure on Blockchain
Shufen NIU, Jinfeng WANG, Bobin WANG, Xiangdong JIA, Xiaoni DU
, doi: 10.11999/JEIT190038
[Abstract](380) [FullText HTML](256) [PDF 773KB](31)
Abstract:
In order to overcome the problem that cloud storage is not trusted and the low efficiency of ciphertext retrieval in cloud storage, a searchable ciphertext sorting encryption scheme based on B+ tree on the block chain is proposed.Combined with the blockchain technology, the problem of establishing reliable trust in multiple parties that do not understand each other is solved. A vector space model is used to reduces the complexity of the text and an efficient text retrieval system is implemented. The index structure of the B+ tree is used to improve the retrieval of ciphertext transactions on the blockchain.The ranking of multi-keyword query results is realized by the Term Frequency–Inverse Document Frequency (TF-IDF) algorithm. Under the random oracle model, it is proved that the scheme is adaptive and indistinguishable. Through the comparative analysis of efficiency, it is shown that the scheme achieves efficient ciphertext retrieval on the blockchain.
Constructions of Maximal Distance Separable Matrices with Minimum XOR-counts
Shaozhen CHEN, Yifan ZHANG, Jiongjiong REN
, doi: 10.11999/JEIT181113
[Abstract](308) [FullText HTML](204) [PDF 447KB](12)
Abstract:
With the development of the internet of things, small-scale communication devices such as wireless sensors and the Radio Frequency IDentification(RFID) tags are widely used, these micro-devices have limited computing power, such that traditional cryptographic algorithms are difficult to implement on these devices. How to construct a high-efficiency diffusion layer became an urgent problem. With the best diffusion property, the Maximal Distance Separable (MDS) matrix is often used to construct the diffusion layer of block ciphers. The number of XOR operations (XORs) is an indicator of the efficiency of hardware applications. Combined with the XORs calculation method which can evaluate hardware efficiency more accurately and a matrix with special structure——Toeplitz matrix, efficient MDS matrices with less XORs can be constructed. Using the structural characteristics of the Toeplitz matrix, the constraints of matrix elements are improved, and the complexity while search matrices is reduced. The 4×4 MDS matrices and the 6×6 MDS matrices with the least XORs in the finite field ${\mathbb{F}_{{2^8}}}$ are obtained, and the 5×5 MDS matrices with the XORs which is equal to the known optimal results are obtained too. The proposed method of constructing MDS Toeplitz matrices with the least XORs has significance on the design of lightweight cryptographic algorithms.
A Lossy Frame Memory Compression Algorithm Using Directional Interpolation Prediction Variable Length Coding
Yu LUO, Zhenzhen ZHANG
, doi: 10.11999/JEIT181195
[Abstract](478) [FullText HTML](215) [PDF 541KB](9)
Abstract:
A lossy frame memory compression algorithm using Direction Interpolation Prediction Variable Length Coding (DIPVLC) is proposed to improve frame memory compression performance. Firstly, the prediction residual is obtained by adaptive texture directional interpolation. Then, a new rate-distortion is optimized to quantize prediction residual. Finally, the run length Golomb method is used to entropy coding for quantized residual. Simulation results show that compared with parallel Content Aware Adaptive Quantization (CAAQ) oriented lossy frame memory recompression for HEVC, the proposed algorithm improves the compression rate by 10.05% and reduces the encoding time by 10.62% with less PSNR reduction.
Coherent Integration Algorithm Based on Adjacent Cross Correlation Function-Parameterized Centroid Frequency-Chirp Rate Distribution -Keystone Transform for Maneuvering Target in Passive Radar
Yongsheng ZHAO, Dexiu HU, Zhixin LIU, Yongjun ZHAO, Chuang ZHAO
, doi: 10.11999/JEIT180858
[Abstract](117) [FullText HTML](95) [PDF 2350KB](6)
Abstract:
Increasing the integration time can effectively improve the detection performance of passive radar. However, for maneuvering targets, the complex motions, such as high velocity, acceleration and jerk, will cause existing detection methods to suffer the Range Migration (RM) and Doppler Frequency Migration (DFM) during the integration time, which will deteriorate the detection performance. This paper addresses the long time coherent integration for a maneuvering target with high-order motion (e.g., jerk motion) in passive radar systems. A method based on Adjacent Cross Correlation Function (ACCF), Parameterized Centroid Frequency-Chirp Rate Distribution (PCFCRD) and Keystone Transform (KT)(ACCF-PCFCRD-KT), is proposed. Firstly, the signal model for the maneuvering targets is given, and the influence of the target velocity, acceleration and jerk on the coherent integration is analyzed. For the Doppler curvature induced by the jerk motion, the ACCF is firstly applied to reducing the order of RM and DFM. Then the PCFCRD operation is employed to estimate the acceleration and jerk parameters. After compensating the RM and DFM caused by the acceleration and jerk, the RM arising from the velocity is corrected via the KT operation and the target echo energy is coherently integrated. Simulation results demonstrate that the proposed method can effectively compensate the RM and DFM caused by the target motion parameters in passive radar, and for a maneuvering target with jerk motion, the proposed method achieves better integration performance over the existing methods.
The Method of Virtual Machine Live Migration Based on HashGraph
Junfeng TIAN, Xueqing QU, Xinfeng HE, Zhen LI
, doi: 10.11999/JEIT190200
[Abstract](103) [FullText HTML](65) [PDF 2430KB](5)
Abstract:
Live migration of Virtual Machines(VMs) across WANs is an important support for multi-datacenter cloud computing environments. The current live migration of VMs across WANs faces many technical challenges due to the limitations of small bandwidth and no shared storage, such as ensuring the security and consistency of image data migration. Therefore, a method for VM live migration across datacenters based on HashGraph is proposed in this paper, decentralized ideas are used to achieve reliable and efficient image distribution between datacenters. The Merkle DAG of HashGraph improves the deficiencies of deduplication when migrating images across datacenters. Compared with existing methods, it can reduce total migration time.
Salient Object Detection Using Weighted K-nearest Neighbor Linear Blending
Wei LI, Quanlong LI, Zhengyi LIU
, doi: 10.11999/JEIT190093
[Abstract](363) [FullText HTML](230) [PDF 2230KB](27)
Abstract:
Salient object detection which aims at automatically detecting what attracts human’s attention most in a scene, bootstrap learning based on Support Vector Machine(SVM) has achieved excellent performance in bottom-up methods. However, it is time-consuming for each image must be trained once based on multiple kernel SVM ensemble. So a salient object detection model via Weighted K-Nearest Neighbor Linear Blending (WKNNLB) is proposed in the paper. First of all, existing saliency detection methods are employed to generate weak saliency maps and obtain training samples. Then, Weighted K-Nearest Neighbor (WKNN) is introduced to learning salient score of samples. WKNN model need no pre-training process, only need selecting K value and computing saliency value by the K-nearest neighbors labels of training sample and the distances between the K-nearest neighbors training samples and the testing sample. In order to reduce the influence of selecting K value, linear blending of multi-WKNNs is applied to generate strong saliency maps. Last, multi-scale saliency maps of weak and strong model are integrated together to further improve the detection performance. The experimental results on common ASD and complex DUT-OMRON datasets show that the algorithm is effective and superior in running time and performance. It can even perform favorable against the state-of-the-art methods when adopts better weak saliency map.
Energy-saving Virtual Network Embedding Algorithm Based on Sliding Region Particle Swarm
Lei ZHUANG, Shuaikui TIAN, Mengyang HE, Yu SONG, Guoqing WANG, Wentan LIU, Ling MA
, doi: 10.11999/JEIT190168
[Abstract](260) [FullText HTML](232) [PDF 2050KB](21)
Abstract:
Considering the problem of scattered node mapping and more hops of link mapping in the traditional virtual network energy-saving embedding, the node and link are mapped simultaneously by using the minimum spanning tree topology of the virtual network request, and Energy-saving Virtual Network Embedding algorithm based on Sliding Region Particle Swarm (EVNE_SRPS) is proposed. When a virtual network request arrives, the minimum spanning tree topology is generated, the root node is the node with the shortest path length; Multiple regions are randomly selected as the particle object in the substrate network, and the minimum spanning tree topology of the virtual network request is mapped in the regional center; The fitness of the particles is calculated. The optimal solution of the group and the individual is finded, and the sliding direction and the location of the update region under the guidance of the optimal solution are determined. After the iteration, the mapping scheme of the virtual network is obtained. The experimental results show that compared with the existing algorithms, the network energy consumption is reduced, and the internet service providers revenue to cost ratio is improved.
A Novel Fuzzy Clustering Algorithm Based on Similarity of Attribute Space
Weifeng SHI, Jinbao ZHUO, Ying LAN
, doi: 10.11999/JEIT180974
[Abstract](339) [FullText HTML](250) [PDF 386KB](22)
Abstract:
With the attribute feature information of the fuzzy membership matrix and cluster centers after the iteration not fully utilized, the results of Fuzzy C-Means (FCM) Clustering and related modified algorithms are determined based on the principle of maximum fuzzy membership, causing bad influence on the clustering accuracy. To solve this problem, the improvement ideas are proposed: to improve classification principle of FCM. The formula definition of attribute similarity in binary topological subspaces is given. Then, the improved FCM algorithm based on the similarity of attribute space is proposed: First, samples with fuzzy membership degree lower than the clustering reliability are selected as suspicious samples. Next, the attribute similarity between the suspicious samples and the cluster centers after clustering are calculated. Finally, cluster labels of suspicious samples based on the principle of maximum attribute similarity are updated. The validity and superiority of the proposed algorithm is verified by the UCI sample set experiments and comparisons with other modified algorithms based on the principle of maximum fuzzy membership.
A Computation Offloading and Resource Allocation Mechanism Based on Minimizing Devices Energy Consumption and System Delay
Meiling DAI, Zhoubin LIU, Shaoyong GUO, Sujie SHAO, Xuesong QIU
, doi: 10.11999/JEIT180970
[Abstract](405) [FullText HTML](263) [PDF 1351KB](27)
Abstract:
To support the execution of computation-intensive, delay-sensitive computing task by moving down the computing and processing capability in mobile edge computing becomes the current trend. However, when serving a large number of mobile users, how to effectively use the edge nodes with limited computing resources to ensure Quality of service (QoS) of end-user has become a key issue. To solve this problem, the edge cloud and remote cloud are combined to build a layered edge cloud computing architecture. Based on this architecture, with the goal of minimizing mobile device energy consumption and task execution time, the problem which is proved to be convex is formulated to minimize the weight sum of energy and delay. A computation offloading and resource allocation mechanism based on multiplier method is proposed. Simulations are conducted to evaluate the proposed mechanism. Compared with local computing and computation offloading mechanism, the proposed mechanism can effectively reduce the energy consumption of mobile device and the delay of system by up to 60% and 10%, respectively.
Channel Estimation Algorithm Using Temporal-spatial Structure for Up-link of Massive MIMO Systems
Xinhua LU, Carles Navarro MANCHÓN, Zhongyong WANG, Chuanzong ZHANG
, doi: 10.11999/JEIT180676
[Abstract](395) [FullText HTML](289) [PDF 1065KB](23)
Abstract:
To deal with the estimation problem of non-stationary channel in massive Multiple-Input Multiple-Output (MIMO) up-link, the 2D channels’ sparse structure information in temporal-spatial domain is used, to design an iterative channel estimation algorithm based on Dirichlet Process (DP) and Variational Bayesian Inference (VBI), which can improve the accuracy under a lower pilot overhead and computation complexity. On account of that the stationary channel models is not suitable for massive MIMO systems anymore, a non-stationary channel prior model utilizing Dirichlet Process is constructed, which can map the physical spatial correlation channels to a probabilistic channel with the same sparse temporal vector. By applying VBI technology, a channel estimation iteration algorithm with low pilot overhead and complexity is designed. Experiment results show the proposed channel method has a better performance on the estimation accuracy than the state-of-art method, meanwhile it works robustly against the dynamic system key parameters.
Design of Convolutional Neural Networks Hardware Acceleration Based on FPGA
Huabiao QIN, Qinping CAO
, doi: 10.11999/JEIT190058
[Abstract](467) [FullText HTML](349) [PDF 1911KB](41)
Abstract:
Considering the large computational complexity and the long-time calculation of Convolutional Neural Networks (CNN), an Field-Programmable Gate Array(FPGA)-based CNN hardware accelerator is proposed. Firstly, by deeply analyzing the forward computing principle and exploring the parallelism of convolutional layer, a hardware architecture in which parallel for the input channel and output channel, deep pipeline for the convolution window is presented. Then, a full parallel multi-addition tree is designed to accelerate convolution and efficient window buffer to implement deep pipelining operation of convolution window. The experimental results show that the energy efficiency ratio of proposed accelerator reaches 32.73 GOPS/W, which is 34% higher than the existing solutions, as the performance reaches 317.86 GOPS.
Depth Estimation of Monocular Road Images Based on Pyramid Scene Analysis Network
Wujie ZHOU, Ting PAN, Pengli GU, Zhinian ZHAI
, doi: 10.11999/JEIT180957
[Abstract](498) [FullText HTML](424) [PDF 1200KB](18)
Abstract:
Considering the problem that the prediction accuracy is not accurate enough when the depth information is recovered from the monocular vision image, a method of depth estimation of road scenes based on pyramid pooling network is proposed. Firstly, using a combination of four residual network block, the road scene image features are extracted, and then through the sampling, the features are gradually restored to the original image size, and the depth of the residual block is increased. Considering the diversity of information in different scales, the features with same sizes extracted from the sampling process and the feature extraction process are merged. In addition, pyramid pooling network blocks are added to the advanced features extracted by four residual network blocks for scene analysis, and the feature graph output of pyramid pooling network blocks is finally restored to the original image size and input prediction layer together with the output of the upper sampling module. Through experiments on KITTI data set, the results show that the proposed method is superior to the existing method.
Online Blind Equalization Algorithm for Satellite Channel Based on Echo State Network
Ling YANG, Bin ZHAO, Liang CHEN, Yuan LI, Guolong ZHANG
, doi: 10.11999/JEIT190034
[Abstract](332) [FullText HTML](271) [PDF 2002KB](16)
Abstract:
Two online blind equalization algorithms based on Echo State Network (ESN) in this paper are proposed for the nonlinear satellite channel. These two algorithms take advantage of the good nonlinear approximation of ESN to bring the High-Order Statistics (HOS) of the transmitted signal into the ESN, and constructing cost function of blind equalization by combining Constant Modulus Algorithm (CMA) and Multi-Modulus Algorithm (MMA). Then, the Recursive Least Squares (RLS) algorithm is used to iteratively optimize the network output weights, and the online blind equalization of the constant modulus signals and the multi-modulus signals over the channel of Volterra satellite are realized. Experiments show that the proposed algorithms can effectively reduce the distortion of the transmitted signal by the nonlinear channel. Compared with the traditional Volterra filtering method, they have faster convergence speed and lower mean square error.
Calculation of Forced Vital Capacity Based on Turbine Air Flow Sensor
Chenshuo WANG, Guangqiang HE, Yueqi LI, Rongjian ZHAO, Xianxiang CHEN, Lidong DU, Zhan ZHAO, Zhen FANG
, doi: 10.11999/JEIT190051
[Abstract](327) [FullText HTML](221) [PDF 1335KB](2)
Abstract:
Currently, the turbine air flow sensors are widely used to record the human exhalation signals in spirometry, but test results would vary due to different expiratory flow for the same Forced Vital Capacity(FVC) measurements, and the differences are usually not in an acceptable range. To address this issue, the present study proposes a FVC velocity penalty model by introducing speed penalty items to the traditional mathematical model of turbine. Moreover, the authors propose to use an over-amplitude drop sampling approach to calculate the rotations of the turbine due to the needs for the velocity penalty model to be able to accurately obtain the number of turbine rotations. The performance of the proposed approach are evaluated by using a syringe dispenser of 3L capacity and results demonstrated that it could reduce the differences and meet the acceptable and accuracy criteria of the American Thoracic Society(ATS) and the European Respiratory Society(ERS) to some extent.
JPEG Compression Artifacts Reduction Algorithm Based on Multi-scale Dense Residual Network
Shuzhen CHEN, Yijun ZHANG, Qiusheng LIAN
, doi: 10.11999/JEIT180963
[Abstract](528) [FullText HTML](270) [PDF 1923KB](22)
Abstract:
In the case of high compression rates, the JPEG decompressed image can produce blocking artifacts, ringing effects and blurring, which affect seriously the visual effect of the image. In order to remove JPEG compression artifacts, a multi-scale dense residual network is proposed. Firstly, the proposed network introduces the dilate convolution into a dense block and uses different dilation factors to form multi-scale dense blocks. Then, the proposed network uses four multi-scale dense blocks to design the network into a structure with two branches, and the latter branch is used to supplement the features that are not extracted by the previous branch. Finally, the proposed network uses residual learning to improve network performance. In order to improve the versatility of the network, the network is trained by a joint training method with different compression quality factors, and a general model is trained for different compression quality factors. Experiments demonstrate that the proposed algorithm not only has high JPEG compression artifacts reduction performance, but also has strong generalization ability.
An Incremental Feature Extraction Method without Estimating Image Covariance Matrix
Xiaofeng WANG, Mingyue SUN, Weimin GE
, doi: 10.11999/JEIT181138
[Abstract](313) [FullText HTML](198) [PDF 1451KB](14)
Abstract:
To solve the problems that Two-Dimensional Principal Component Analysis (2DPCA) can not implement the on-line feature extraction and can not represent the complete structure information, an Incremental 2DPCA (I2DPCA) without estimating covariance matrices is presented by an iterative estimation method, not to deal with the image covariance matrices by the eigenvalue decomposition or the singular value decomposition. The complexity will be greatly reduced and the on-line feature extraction speed can be improved. The proposed I2DPCA can only extract the horizontal features, and thus another Incremental Row-Column 2DPCA (IRC2DPCA) is proposed to incrementally extract the longitudinal ones from the projected subspaces of the I2DPCA. The IRC2DPCA can preserve the horizontal and longitudinal features and implement the dimensionality reduction in both row and column directions. Finally, a series of experiments are carried out with the self-built block dataset, ORL and Yale face datasets, respectively. The results show that the proposed algorithms have significantly improved the performances of the convergence rate, the classification rate and the complexity. The convergence rate is over 99%, the classification rate can reach 97.6% and the average processing speed is about 29 frames per second, and it can meet the on-line feature extraction requirements for incremental learning.
Image Forgery Detection Algorithm Based on Cascaded Convolutional Neural Network
Xiuli BI, Yang WEI, Bin XIAO, Weisheng LI, Jianfeng MA
, doi: 10.11999/JEIT190043
[Abstract](391) [FullText HTML](244) [PDF 3318KB](24)
Abstract:
The image forgery detection algorithm based on convolutional neural network can implement the image forgery detection that does not depend on a single image attribute by using the learning ability of convolutional neural network, and make up for the defect that the previous image forgery detection algorithm relies on a single image attribute and has low applicability. Although the image forgery detection algorithm using a single network structure of deep layers and multiple neurons can learn more advanced semantic information, the result of detecting and locating forgery regions is not ideal. In this paper, an image forgery detection algorithm based on cascaded convolutional neural network is proposed. Based on the general characteristics exhibited by convolutional neural network, and then the deeper characteristics are further explored. The cascaded network structure of shallow layers and thin neurons figures out the defect of the single network structure of deep layers and multiple neurons in image forgery detection. The proposed detection algorithm in this paper consists of two parts: the cascade convolutional neural network and the adaptive filtering post-processing. The cascaded convolutional neural network realizes hierarchical forgery regions localization, and then the adaptive filtering post-processing further optimizes the detection result of the cascaded convolutional neural network. Through experimental comparison, the proposed detection algorithm shows better detection results and has higher robustness.
Doppler Frequency Estimation Method Based on Chinese Remainder Theorem with Spectrum Correction
Chenghu CAO, Yongbo ZHAO, Zhiling SUO, Xiaojiao PANG, Baoqing XU
, doi: 10.11999/JEIT181102
[Abstract](303) [FullText HTML](230) [PDF 1993KB](25)
Abstract:
It makes the Pulse Doppler (PD) radar widely applied that the PD radar has the obvious advantages of detecting the Doppler frequency of the target and suppressing the clutter effectively. However, it is difficult for the PD radar to detect the target due to velocity ambiguity. Combining with the characteristic and stagger-period model of the PD radar, a Doppler frequency estimation method based on all phase DFT Closed-Form Robust Chinese Remainder Theorem (CFRCRT) with spectrum correction is proposed in this paper. Both theoretical analysis and simulation experiment demonstrate that the proposed method can satisfy the engineering demand in measure accuracy and real-time performance.
Analysis of Beam Wave Interaction in a Planar Metallic Grating Based on Cyclotron Resonance Enhancement Effect
Jing WANG, Yu FAN, Ding ZHAO, Chen YANG, Gang WANG, Jirun LUO
, doi: 10.11999/JEIT181145
[Abstract](284) [FullText HTML](172) [PDF 1025KB](10)
Abstract:
Based on the beam wave synchronization interaction in transverse and longitudinal directions at the same time and derived from Maxwell’s equation and linear Vlasov equation, the planar metallic grating beam-wave interaction " hot” dispersion equation considering both cyclotron resonance and Cherenkov resonance is deduced. Through the reasonable selection for geometric and electrical parameters, the numerical calculation and analysis of the " hot” dispersion equation show that the beam-wave interaction gain and frequency band with the cyclotron resonance enhancement effect are higher than those with only Cherenkov resonance radiation.
A Novel Micro-motion Multi-target Wideband Resolution Algorithm Based on Curve Overlap Extrapolation
Jiaqi WEI, Lei ZHANG, Hongwei LIU, Jialian SHENG
, doi: 10.11999/JEIT190033
[Abstract](303) [FullText HTML](176) [PDF 2040KB](12)
Abstract:
To solve the problem that the traditional micro-Doppler feature extraction technologies are generally hard to achieve resolution and parameter estimation of multi-target, a novel curve overlap extrapolation algorithm for wide-band resolution of micro-motion multi-target is proposed. According to the relative distance between filtering data points and the historical slope information of each curve, the point trace behind the overlapping location can be extrapolated to realize data association of micor-motion curve for each signal component. On this basis, the multi-target resolution can be realized by analyzing the difference of micor-motion characteristics between each curve. Extensive simulation experiments are provided to illustrate the effectiveness and robustnees of the proposed algorithm.
A Method of Establishing Mine Target Fingerprint Database Based on Distributed Compressed Sensing
Zijian TIAN, Fangyuan HE
, doi: 10.11999/JEIT180857
[Abstract](350) [FullText HTML](243) [PDF 1528KB](26)
Abstract:
A method of establishing a fingerprint database, which is based on distributed compressed sensing, is proposed to improve the low positioning accuracy and poor real-time positioning that exist in the current mine target positioning in China. Using the method, the fingerprint information of mine target fingerprint database can be reconstructed with high probability by collecting only a few fingerprint information (reference node IDs, Time Of Arrival (TOA) measurements based on electromagnetic wave and actual distance values) in the roadway in the off-line stage. Therefore, the data collection workload can be reduced and the work efficiency can be improved as well. In the subsequent on-line stage, according to the pattern matching method, the estimated distance between the target node and the reference nodes at the certain time can be obtained only by getting the reference node IDs and the real-time TOA measurements measured by the reference nodes at a certain moment, which guarantees the positioning accuracy and positioning real-time performance. Based on this method, an improved Compressive Sampling Modifying Matching Pursuit (CoSaMMP) algorithm is proposed to reconstruct the fingerprint information. The algorithm can effectively shorten the reconstruction time by using the folding method to increase the cutting force. The simulation results show that the proposed algorithm is feasible and effective.
Frequency-hopping Transmitter Classification Based on Chaotic Attractor Reconstruction and Low-rank Clustering
Ping SUI, Ying GUO, Hongguang LI, Yuzhou WANG
, doi: 10.11999/JEIT180947
[Abstract](153) [PDF 0KB](0)
Abstract:
The transient signal without modulation information of the radiation source can characterize the unintentional modulation characteristics of the radiation source. The analysis of the transient signal can realize the radiation source identification. In the switching on and frequency conversion process of the frequency-hopping signal, there is a transient adjustment time without information transmission. In the transient adjustment moment, the signal transmitted by the transmitter is a non-linear, non-stationary and non-Gaussian signal without modulation information. This transient time series can reflect the device characteristics of the frequency-hopping transmitter, and the sequence often exhibits complex chaotic characteristics. Therefore, from the idea of chaotic time series analysis and Low-rank characteristics of transient signal, a frequency-hopping transmitter classification algorithm is proposed based on chaotic attractor reconstruction and Low-rank clustering. The experimental tests show that the transient signal of the frequency-hopping transmitter belongs to the chaotic time series. At the same time, the classification results of the frequency-hopping signals demonstrate the feasibility of the Low-rank clustering algorithm in frequency-hopping transmitter classification.
Design of a Novel Broadband Low RCS Array Based on Three Types of Reflective Cell Shared Aperture
Guowen ZHANG, Jun GAO, Xiangyu CAO, Huanhuan YANG, Sijia LI
, doi: 10.11999/JEIT181049
[Abstract](323) [FullText HTML](184) [PDF 3704KB](7)
Abstract:
A novel wideband low RCS new super-surface array based on three reflective cell shared aperture is designed, which is composed of three kinds of Artificial Magnetic Conductor (AMC). Compared with the traditional AMC array, the new array uses one of AMC as phasor interference unit. A new phase cancellation relation is presented, the new phase cancellation relation is used to extend the traditional array phase cancellation band. Then, the parameters of the cell structure are further optimized to realize the reduction of RCS and the improvement of bandwidth. The physical sample is processed and tested. The results of simulation and field test show that: the backward reduction of RCS in the range of 5.2~13.9 GHz reaches more than 10 dB, and the relative bandwidth reaches 91%. It is shown that the new array can overcome the defect of the discontinuous operating band of the traditional array and has broadband low scattering characteristics.
Construction of a Class of Linear Codes with Four-weight and Six-weight
Xiaoni DU, HongXia LÜ, Rong WANG
, doi: 10.11999/JEIT180939
[Abstract](238) [FullText HTML](169) [PDF 338KB](8)
Abstract:
Due to the wide applications in association schemes, authentication codes and secret sharing schemes etc., construction of the linear codes with a few weights is an important research topic. A class of linear codes with four-weight and six-weight over finite field ${F_p}$ (p is an odd prime) is constructed by a proper selection of the defining set. The explicit weight distribution is obtained using Gauss sums, and some examples from Magma program to illustrate the validity of the conclusions are provided. The results show that these codes include almost optimal codes with respect to Singleton bound.
Target Assignment Method for Phased Array Radar Network Based on Quality of Service
Shanchao YANG, Kangsheng TIAN, Changfei WU
, doi: 10.11999/JEIT181133
[Abstract](234) [FullText HTML](179) [PDF 1816KB](15)
Abstract:
The constraint conditions of target assignment model for phased array radar network are unreasonable and the performance of model solving algorithms are not good enough. To solve these problems, a target assignment model for radar network based on Quality of Service (QoS) is constructed in this paper, and a model solving algorithm based on strong majorant function approximation is proposed. Through the establishment of resource space and environment space in QoS model, radar resource constraints as well as the visibility constraints between radars and targets are described accurately. Then, sufficient conditions for the optimal solution of QoS model are derived by Karush-Kuhn-Tucker(KKT) condition, and a two-dimensional fast traversal method is used to approximate the strong concave function curve. Finally, the optimal assignment scheme is obtained by the stepwise iteration of operation setting points on the strong concave curve of each target. The simulation results show that the model proposed in this paper can effectively accomplish the target assignment of radar network, and model solving algorithm has better performance than the typical intelligent search algorithms.
LoRa Backscatter Communication Method Based on Direct Digital Frequency Synthesis
Xiaoqing TANG, Guihui XIE, Yajun SHE, Shuai ZHANG
, doi: 10.11999/JEIT190001
[Abstract](289) [FullText HTML](267) [PDF 2400KB](23)
Abstract:
LoRa (Long Range) Backscattering Communication (BC) not only has the advantages of low cost and low power consumption, but also has a long communication distance. However, the existing LoRa BC scheme is complex and cannot be applied to actual engineering. For this purpose, a new LoRa BC method is proposed. A Direct Digital frequency Synthesis (DDS) technique is used to generate a square wave with a linear frequency variation in this paper as a LoRa scattering modulation signal. And for the first time, the prototype of LoRa BC system based on MCU is demonstrated. Experimental results show that design can successfully backscatter at any position between the station and the receiver which are 208 meters apart, while being compatible with commodity LoRa chipset. In addition, the method is also applicable to an Application Specific Integrated Circuit (ASIC) design, which enables the LoRa backscattering IC to have higher robustness, lower cost, and lower power consumption.
Bistatic Radar Coincidence Imaging Based on Sparse Bayesian Learning
Rui LI, Qun ZHANG, Linghua SU, Jia LIANG, Ying LUO
, doi: 10.11999/JEIT180933
[Abstract](512) [FullText HTML](282) [PDF 1088KB](30)
Abstract:
Bistatic radar has the advantages of high concealment and strong anti-interference performance, and plays an important role in modern electronic warfare. Based on the principle of radar coincidence imaging, the problem of bistatic radar coincidence imaging of moving targets is studied. Firstly, based on the bistatic radar system that uses uniform linear array as the transmitting and receiving antenna, the characteristics of the moving target radar echo signal are analyzed under the condition of transmitting random frequency modulation signal, and a bistatic radar coincidence imaging parametric sparse representation model is established. Secondly, an iterative coincidence imaging algorithm based on sparse Bayesian learning is proposed for the parametric sparse representation model established. Based on the Bayesian model, the sparse reconstructed signal is obtained by Bayesian inference, so that the moving target imaging and accurate estimation of motion parameters can be achieved. Finally, the effectiveness of the proposed method is verified by simulation experiments.
A Novel Radiometric Signature of Time-Division Multiple Access Signals and Its Application to Specific Emitter Identification
Yiwei PAN, Hua PENG, Tianyun LI, Wenya WANG
, doi: 10.11999/JEIT190163
[Abstract](349) [FullText HTML](257) [PDF 1743KB](17)
Abstract:
For Time-Division Multiple Access (TDMA) signals, the performance of Specific Emitter Identification (SEI) is primarily limited by burst duration. To remedy this shortcoming, a novel radiometric signature is presented, which reveals whether the users of the adjacent time slots are the same from a perspective of carrier phase, thereby providing the basis for data accumulation of the same user. First, the feature mechanism is introduced, as well as the extraction method. Thereafter, user identity detection of the adjacent slots is implemented with an adaptive threshold, which is derived from the distribution of the signature. Finally, a new SEI processing procedure is designed with data accumulation, which breaks the routine of identifying only one slot at a time. Simulation results demonstrate that the proposed signature is resilient against the noise, and can accurately detect the user identity of the adjacent slots. Compared with the traditional processing procedure, the proposed one can effectively improve the SEI performance of TDMA signals.
Research on Efficient FPGA Bitstream Generation System Based on Mode Matching and Hierarchical Mapping
Kaihui TU, Zhihong HUANG, Zhengrong HOU, Haigang YANG
, doi: 10.11999/JEIT190143
[Abstract](232) [FullText HTML](177) [PDF 2352KB](7)
Abstract:
Bitstream generator in FPGA Electronic Design Automation(EDA) offers precise configuration information, which enables the application circuits to be implemented on the target device. On one hand, modern FPGAs tend to have larger device scale and more configuration bits, on the other hand, embedded applications (like eFPGAs) require better configuration efficiency and smaller, more adaptive database. In order to meet these new requirements, a bit-stream generation method is proposed which firstly models the configurable resources by configuration modes and matches the netlist with these models, then hierarchical mapping strategy is used to search every bit on a dynamically generated database determined by the array floorplan. This method well meets the challenges that embedded applications may bring-the surge of configuration bit count and the changeable size of the array. Compared to flattened modelling and mapping method, its time complexity is reduced from O(n) to O(lgn).
Cognitive Emotion Interaction Model of Robot Based on Game Theory
Hongcheng HUANG, Ning LIU, Min HU, Yang TAO, Lan KOU
, doi: 10.11999/JEIT180867
[Abstract](381) [FullText HTML](237) [PDF 993KB](19)
Abstract:
To solve the problems of the existing in the process of human-computer interaction system, such as lack of emotion and low participation, a cognitive emotion interaction model based on game theory in PAD emotion space is proposed. Firstly, the interactive input emotion of participant is evaluated and some influence factors such as friendship and resonance are extracted to analyze the current human-computer interaction relationship. Secondly, modeling the emotional generation process of participants and robots by simulating the psychological game process in interpersonal communication, and the optimal emotional strategy of the robot is obtained by using the sub-game perfection equilibrium of the embedded game. Finally, the emotional state transition probability of the robot is updated according the optimal emotional strategy. The spatial coordinates of the six basic emotional states are used as labels to obtain the PAD spatial coordinate of the robot emotional state after emotional stimulate, The results of experiment show that compared with the others emotional interaction model, the proposed model can reduce the dependence of robots on external emotional stimuli and effective guide participants to participate in human-computer interaction, which provides some ideas for the emotion cognition model of robot in human-computer interaction.
Joint Clustering and Content Deployment Algorithm for Cellular D2D Communication Based on Delay Optimization
Rong CHAI, Ling WANG, Minglong CHEN, Qianbin CHEN
, doi: 10.11999/JEIT180408
[Abstract](237) [FullText HTML](174) [PDF 1236KB](18)
Abstract:
Due to the limited transmission performance of cellular network and the buffering capabilities of the Base Station (BS), it is very difficult to achieve the Quality of Service (QoS) requirements of multi-user content requests. In this paper, a joint user association and content deployment algorithm is proposed for cellular Device-to-Device (D2D) communication network. Assuming that multiple users located in a specific area may have content requests for the same content, a clustering and content deployment mechanism is presented in order to achieve efficient content acquisition. A joint clustering and content deployment optimization model is formulated to minimize total user service delay, which can be solved by Lagrange partial relaxation, iterative algorithm and Kuhn-Munkres algorithm, and the joint clustering and content deployment optimization strategies can be obtained. Finally, the effectiveness of the proposed algorithm is verified by MATLAB simulation.
Transfer Weight Based Conditional Adversarial Domain Adaptation
Jin WANG, Ke WANG, Zijian MIN, Kaiwei SUN, Xin DENG
, doi: 10.11999/JEIT190115
[Abstract](220) [FullText HTML](156) [PDF 1080KB](9)
Abstract:
Considering the failure of the Conditional adversarial Domain AdaptatioN(CDAN) to fully utilize the sample transferability, which still struggle with some hard-to-transfer source samples disturbed the distribution of the target domain samples, a Transfer Weight based Conditional adversarial Domain AdaptatioN(TW-CDAN) is proposed. Firstly, the discriminant results in the domain discriminant model as the main factor are employed to measure the transfer performance. Then the weight is applied to class loss and minimum entropy loss. It is for eliminating the influence of hard-to-transfer samples of the model. Finally, experiments are carried out using the six domain adaptation tasks of the Office-31 dataset and the 12 domain adaptation tasks of the Office-Home dataset. The proposed method improves the 14 domain adaptation tasks and increases the average accuracy by 1.4% and 3.1% respectively.
Adaptive Strategy Fusion Target Tracking Based on Multi-layer Convolutional Features
Yanjing SUN, Yunkai SHI, Xiao YUN, Xuran ZHU, Sainan WANG
, doi: 10.11999/JEIT180971
[Abstract](473) [FullText HTML](298) [PDF 2570KB](47)
Abstract:
To solve the problems of low robustness and tracking accuracy in target tracking when interference factors occur such as target fast motion and occlusion in complex video scenes, an Adaptive Strategy Fusion Target Tracking algorithm (ASFTT) is proposed based on multi-layer convolutional features. Firstly, the multi-layer convolutional features of frame images in Convolutional Neural Network(CNN) are extracted, which avoids the defect that the target information of the network is not comprehensive enough, so as to increase the generalization ability of the algorithm. Secondly, in order to improve the tracking accuracy of the algorithm, the multi-layer features are performed to calculate the correlation responses, which improves the tracking accuracy. Finally, the target position strategy in all responses are dynamically merged to locate the target through the adaptive strategy fusion algorithm in this paper. It comprehensively considers the historical strategy information and current strategy information of each responsive tracker to ensure the robustness. Experiments performed on the OTB2013 evaluation benchmark show that that the performance of the proposed algorithm are better than those of the other six state-of-the-art methods.
Neighbor Information Constrained Node Scheduling in Stochastic Heterogeneous Wireless Sensor Networks
Ningning QIN, Lei JIN, Jian XU, Fan XU, Le YANG
, doi: 10.11999/JEIT190094
[Abstract](288) [FullText HTML](186) [PDF 1999KB](14)
Abstract:
Considering coverage redundancy problem existed in random heterogeneous sensor networks with high density deployment, a Node Scheduling algorithm for Stochastic Heterogeneous wireless sensor networks(NSSH) is proposed. The Delaunary triangulation is constructed based on the network prototype topology to work out a local subset of nodes for localization scheduling. Independent configuration of the perceived radius is achieved by discounting the radius of the circumcircle with the adjacent node. The concept of geometric line and plane is introduced, and the overlapping area and the effective constrained arcs are used to classify and identify the grey and black nodes. So the node only relies on local and neighbor information for radius adjustment and redundant node sleep. The simulation results show that NSSH can approximately match the dropping redundancy of greedy algorithm at the cost of low complexity, and exhibit low sensitivity to network size, heterogeneous span and parameter configuration.
Meteorological Radar Noise Image Semantic Segmentation Method Based on Deep Convolutional Neural Network
Hongyun YANG, Fengyan WANG
, doi: 10.11999/JEIT190098
[Abstract](453) [FullText HTML](349) [PDF 3060KB](51)
Abstract:
Considering the problem that the scattering echo image of the new generation Doppler meteorological radar is reduced by the noise echoes such as non-rainfall, the accuracy of the refined short-term weather forecast is reduced. A method for semantic segmentation of meteorological radar noise image based on Deep Convolutional Neural Network(DCNN) is proposed. Firstly, a Deep Convolutional Neural Network Model (DCNNM) was designed. The training set data of the MJDATA data set are used for training, and the feature is extracted by the forward propagation process, and the high-dimensional global semantic information of the image is merged with the local feature details. Then, the network parameters are updated by using the training error value back propagation iteration to optimize the convergence effect of the model. Finally, the meteorological radar image data are segmented by the model. The experimental results show that the proposed method has better denoising effect on meteorological radar images, and compared with the optical flow method and the Fully Convolutional Networks (FCN), the method has high recognition accuracy for meteorological radar image real echo and noise echo, and the image pixel precision is high.
Research on Channel Selection and Power Control Strategy for D2D Networks
Zhihong QIAN, Chunsheng TIAN, Xin WANG, Xue WANG
, doi: 10.11999/JEIT190149
[Abstract](291) [FullText HTML](224) [PDF 2077KB](22)
Abstract:
Considering the resource allocation problem for Device-to-Device (D2D) communications, a channel selection and power control strategy for D2D communications is investigated. On the premise of guaranteeing the Quality of Service (QoS) of cellular users, a heuristic based D2D channel selection algorithm is proposed to find the suitable channel reusing resources for D2D users in the system. At the same time, the optimal transmission power of D2D users is obtained by using the Lagrange dual method. Simulation results demonstrate that when the cellular user shares channel resources with multiple pairs of D2D users, the system throughput can be dramatically improved. The performance of this algorithm outperforms the exiting algorithms under the same conditions.
Student’s t Mixture Cardinality Balanced Multi-target Multi-Bernoulli Filter
Shuxin CHEN, Lei HONG, Hao WU, Zhuowei LIU, Longhua YUE
, doi: 10.11999/JEIT181121
[Abstract](330) [FullText HTML](163) [PDF 2355KB](17)
Abstract:
The filtering performance of Gaussian Mixture Cardinality Balanced Multi-target Multi-Bernoulli (GM-CBMeMBer) filter can be effected by the heavy-tailed process noise and measurement noise. To solve this problem, a new STudent’s t Mixture Cardinality Balanced Multi-target Multi-Bernoulli (STM-CBMeMBer) filter is proposed. The process noise and measurement noise approximately obey the Student’s t distribution in the filter, where the Student’s t mixture model is used to describe approximately the posterior intensity of the multi-target. The predictive intensity and posterior intensity of Student’s t mixture form are deduced theoretically, and the closed recursive framework of cardinality balanced multi-target multi-Bernoulli filter is established. The simulation results show that, in the presence of the heavy-tailed process noise and the measurement noise, the filter can effectively suppress its interference, its tracking accuracy is superior over the traditional methods.
Stochastic Resonance Detection Method for the Dual-Sequence Frequency Hopping Signal under Extremely Low Signal-to-Noise Radio
Guangkai LIU, Houde QUAN, Huixian SUN, Peizhang CUI, Kuo CHI, Shaolin YAO
, doi: 10.11999/JEIT190157
[Abstract](660) [FullText HTML](191) [PDF 1945KB](10)
Abstract:
Considering the problem that the Dual-Sequence Frequency Hopping (DSFH) can not communicate at extremely low Signal-to-Noise Ratio (SNR), a Stochastic Resonance (SR) detection method is proposed. The SR takes full advantage of the physical characteristics of DSFH signal to improve the detection performance. Firstly, the SR is constructed by analyzing signals of transmission, reception and the Intermediate Frequency (IF). The scale transaction is used to adjust the IF signal to fit the SR. Secondly, the non-autonomous Fokker-Plank Equation (FPE) is transformed into an autonomous equation by introducing the decision time. Therefore, the analytical solution of the probability density function with the parameter of decision time is obtained. Finally, the detection probability, false alarm probability and Receiver Operating Characteristics (ROC) curve are obtained, when the criterion is the Maximum A Posterior probability (MAP) Simulation analysis results show three conclusions: (1) The SNR of DSFH signal can be as low as –18 dB, which uses the matched SR detection. (2) Method for combining DSFH with the matched SR is suitable to detect the signals with SNR of –18 ~–14 dB. (3) In the case of –14 dB SNR, the DFSH signal detection performance increases by 25.47%, when using SR. The proposed method effectiveness is proved with simulation results.
A Satellite Calibration Method for the Baseline Coordinate and Phase Difference of Distributed Radar Array
Lu LU, Meiguo GAO
, doi: 10.11999/JEIT181152
[Abstract](378) [FullText HTML](210) [PDF 2111KB](23)
Abstract:
In the system of distributed radar array system using phase interference angle measurement, the phase center coordinate error of arrays and the phase difference error have relatively large influence on the angle measurement. And the phase center position is often inconsistent with physical center position. Thus it is necessary to compensate these errors precisely. Far field radiation sources are often used to calibrate radar in traditional calibration methods. However, it is usually hard to achieve far field radiation sources for distributed radar array with large space between units surveilling space targets. In this paper, a calibration method based on the precise ephemeris of refined orbit satellites without measuring with special instruments is proposed. The phase error caused by coordinate error can be whitened by the precise ephemeris of multiple arcs, and the coordinate and phase difference will be searched out by matching the minimum variance. This method can get the errors easily. The simulation results and actual data verify that angle measurement accuracy gets large improvement by the method.
Semi-supervised Indoor Fingerprint Database Construction Method Based on the Nonhomogeneous Distribution Characteristic of Received Signal Strength
Shibao LI, Shengzhi WANG, Jianhang LIU, Tingpei HUANG, Xin ZHANG
, doi: 10.11999/JEIT180599
[Abstract](468) [FullText HTML](227) [PDF 1933KB](10)
Abstract:
The radio map construction is time consuming and labor intensive, and the conventional semi-supervised based methods usually ignore the influence of the uneven distribution of high-dimensional Received Signal Strength (RSS). In order to solve that problem, a semi-supervised radio map construction approach which is based on the nonhomogeneous distribution characteristic of RSS is proposed. The approach utilizes the RSS local scale and common neighbors similarities to calculate the weighted matrix. Thus, the weighted graph that reflects accurately the structure of RSS data manifold is presented. In addition, the weighted graph is used to find the optimal solution of the objective function to calibrate the locations of plenty of unlabeled data by a small number of labeled RSS. The extensive experiments demonstrate that the proposed method is capable of not only construct an accurate radio map at a low manual cost, but also achieve a high localization accuracy.
Multi-party Contract Signing Protocol Based on Certificateless
Suzhen CAO, Fei WANG, Xiaoli LANG, Rui WANG, Xueyan LIU
, doi: 10.11999/JEIT190166
[Abstract](240) [FullText HTML](166) [PDF 1018KB](20)
Abstract:
Online contract signing is becoming more and more popular in e-commerce. It is not easy to sign a contract between two parties who do not trust each other. Many of these protocols involve the participation of third parties, but they are not advantageous in efficiency and prone to security problems. Currently, contract signing agreements with third-party participation are replaced by block chain technology, but the public verification of block chain challenges the sensitive information of both the signer and the contract to be signed. And most of the agreements are for the signing of contracts between the two parties. With the increase of the number of signatories, the communication cost and complexity of the agreements increase sharply. Combined with the existing protocols, this paper proposes an efficient multi-party contract signing protocol. In the protocol, an efficient aggregation signature scheme based on no certificate is used to improve the signature verification efficiency of the signer under the block chain, and only the temporary key of the signer is disclosed on the block chain to reduce the system overhead. The protocol satisfies the requirements of correctness, security, fairness, privacy and high efficiency.
Matrix Metric Learning for Person Re-identification Based on Bidirectional Reference Set
Ying CHEN, Xiaoyue XU
, doi: 10.11999/JEIT190159
[Abstract](273) [PDF 0KB](3)
Abstract:
Targeting the problem of inaccurate feature representation caused by indistinctive appearance difference in person re-identification domain, a new matrix metric learning based on bidirectional reference set is proposed. Firstly, reciprocal-neighbor reference sets in different camera views are respectively constructed by the reciprocal-neighbor scheme. To ensure the robustness of reference sets, the reference sets in different camera views are jointly considered to generate the Bidirectional Reference Set (BRS). With hard samples which are mined by the BRS to represent feature descriptors, accurate appearance difference representations could be obtained. Finally, these representations are utilized to conduct more effective matrix metric learning. Experimental results on several public datasets demonstrate the superiority of the proposed method.
A Hierarchical Vertical Handover Algorithm Based on Fuzzy Logic in Heterogeneous Wireless Networks
Bin MA, Shangru LI, Xianzhong XIE
, doi: 10.11999/JEIT190190
[Abstract](220) [FullText HTML](157) [PDF 2031KB](2)
Abstract:
In the heterogeneous wireless networks, the parameter weight is difficult to determine for the vertical handover algorithm considering the parameters of the network and the user, at the same time, the vertical handover algorithm based on fuzzy logic has high complexity. Considering this problem, a hierarchical vertical handover algorithm based on fuzzy logic is proposed. Firstly, the Received Signal Strength (RSS), bandwidth and delay are input into the first-level fuzzy logic system. Combining with the rule adaptive matching, the QoS fuzzy value is inferred, and the network is initially filtered by the QoS fuzzy value to obtain the candidate network set; Then, the second-level fuzzy logic system is triggered by the trigger mechanism, and the QoS fuzzy value, network load rate and user access cost of the candidate network are input into the second-level fuzzy logic system. At the same time, the output decision value is obtained by combining the adaptive rule matching, so as to select the best access network. Finally, the experimental results show that the algorithm can guarantee the network performance while reducing the time cost of the system.
Binaural Target Sound Source Localization Based on Time-frequency Units Selection
Ruwei LI, Tao LI, Xiaoyue SUN, Dengcai YANG, Qi WANG
, doi: 10.11999/JEIT181127
[Abstract](252) [FullText HTML](198) [PDF 569KB](9)
Abstract:
The performance of the existing target localization algorithms is not ideal in complex acoustic environment. In order to improve this problem, a novel target binaural sound localization algorithm is presented. First, the algorithm uses binaural spectral features as input of a time-frequency units selector based on deep learning. Then, to reduce the negative impact of the time-frequency unit belonging to noise on the localization accuracy, we employ the selector to select the reliable time-frequency units from binaural input sound signal. At the same time, a Deep Neural Network (DNN)-based localization system map the binaural cues of each time-frequency unit to the azimuth posterior probability. Finally, the target localization is completed according to the azimuth posterior probability belonging to the reliable time-frequency units. Experimental results show that the performance of the proposed algorithm is better than comparison algorithms and achieves a significant improvement in target localization accuracy in low Signal-to-Noise Ratio(SNR) and various reverberation environments, especially when there is noise similar to the target sound source.
Performances Analysis in Uplink Non-orthogonal Multiple Access System with Imperfect Successive Interference Cancellation
Xiyu WANG, Xiaoming Xu, Yajun CHEN
, doi: 10.11999/JEIT181165
[Abstract](297) [FullText HTML](236) [PDF 1171KB](12)
Abstract:
Non-Orthogonal Multiple Access (NOMA) serves multiple transmitters using the same resource block, and the receiver decodes the information from different transmitters through Successive Interference Cancellation (SIC). However, most of the researches on NOMA systems are based on perfect SIC assumption, in which the impact of imperfect SIC on NOMA system is not considered. Focusing on this problem, a framework is provided to analyze the performance of single-cell uplink NOMA system under the assumption of imperfect SIC. Firstly, the Binomial Point Process (BPP) is used to model the spatial distribution of base station and user equipment in uplink NOMA system. Based on this model, the interference cancellation order which is based on large-scale fading is adopted, and then the error of interference cancellation is analyzed. Then, based on stochastic geometry theory and order statistics theory, the expression of coverage probability of user equipment which is at rank k in terms of the distance from the base station is derived, besides, the average coverage probability is adopted to reflect the reliability of NOMA transmission system. The analytical and simulation results show the influence of system parameters such as distance order and base station radius on transmission reliability. Also, the validity of theoretical deduction is verified.
Quality of Service-aware Elastic Flow Aggregation Based on Enhanced Rough k-means
Zheng WU, Yuning DONG, Wei TIAN, Pingping TANG
, doi: 10.11999/JEIT181169
[Abstract](238) [FullText HTML](205) [PDF 1860KB](8)
Abstract:
Facing changeable network environment, current Quality of Service (QoS)-aware flow aggregation scheme is lack of flexibility. A dynamic flow aggregation method to overcome present problems is proposed. An Enhanced Rough k-Means (ERKM) algorithm is used to aggregate network flows properly. Importantly, it is able to adjust degree of membership to face ever-changing internet environment to make algorithm more flexible. Internet scheduler experiment is carried out and a comparison is made with existing methods. Experimental results suggest that proposed method has advantages not only on flexibility of aggregation but on assurance of QoS of Internet flows. In addition, the consistency of QoS allocation under different network environment is investigated.
Real-time Estimation of Tropospheric Scattering Slant Delay of Low-elevation Obtained by Improved Ray Tracing
Wenyi WU, Fangping ZHONG, Wanpeng WANG, Xihong CHEN, Dan ZHU
, doi: 10.11999/JEIT190014
[Abstract](245) [FullText HTML](174) [PDF 2058KB](14)
Abstract:
Considering the disadvantage of oblique delay estimation of tropospheric scattering at arbitrary stations, which is difficult to obtain real-time sounding meteorological data, an oblique delay estimation algorithm of tropospheric scattering based on improved ray tracing method with ground meteorological parameters is proposed. In order to get rid of the method’s dependence on radiosonde data, the algorithm infers the relationship between refractive index and altitude through the formula of meteorological parameters in the model of medium latitude atmosphere. The interpolation of meteorological parameters in the model of UNB3m is used to gain the coefficient of temperature and water vapor pressure. Meteorological data for 2012 from 6 International GNSS Service (IGS) stations in Asia are selected to test the applicability of new method, the results suggest that precision is less than 1 cm. Then, the tropospheric slant delays of three parts observation stations under different angles of incidence (0°~5°) are calculated by the modified algorithm. The results suggest that the maximum delay is 17.03~33.10 m in a single way time transfer. In two way time transfer, when the delay can counteract 95%, time delay is 2.88~5.52 ns.
An Improved Four-component Decomposition Method Based on the Characteristic of Polarization and the Optimal Parameters of PolInSAR
Yu WANG, Weidong YU, Xiuqing LIU
, doi: 10.11999/JEIT190108
[Abstract](305) [FullText HTML](214) [PDF 5051KB](7)
Abstract:
The backscattering of the radar targets is sensitive to the relative geometry between orientations of the targets and the radar line of sight. When the orientations of the same target are different from the radar line of sight, the scattering characteristics are quite different. Targets such as inclined ground and inclined buildings may reverse the polarization base of the backscattered echo, which causes the cross-polarization component to be too high and the volume scattering component of the image is overestimated. In this paper, a polarimetric interferometric decomposition method based on polarimetric parameters ($ H/{\alpha} $) and Polarimetric Interferometric Similarity Parameters (PISP) is proposed to solve the overestimation problem. The method makes full use of the scattering diversity of the scatterer in the radar line of sight. The cross-polarization components generated by targets such as inclined grounds and inclined buildings with different orientations are better adapted to obtain better decomposition results. Finally, the effectiveness of the proposed method in polarimetric interferometric decomposition is verified by the airborne C-band PolInSAR data obtained by the Institute of Electronics, Chinese Academy of Sciences. The experimental results show that the proposed improved algorithm can distinguish the scattering characteristics of terrain types effectively and correctly.
A Cluster Algorithm Based on Interference Increment Reduction in Ultra-Dense Network
Yanxia LIANG, Jing JIANG, Changyin SUN, Xin LIU, Yongbin XIE
, doi: 10.11999/JEIT181144
[Abstract](305) [FullText HTML](209) [PDF 1199KB](12)
Abstract:
Ultra-Dense Networks (UDNs) shorten the distance between terminals and nodes, which improve greatly the spectral efficiency and expand the system capacity. But the performance of cell edge users is seriously degraded. Reasonable planning of Virtual Cell (VC) can only reduce the interference of moderate scale UDNs, while the interference of users under overlapped base stations in a virtual cell needs to be solved by cooperative user clusters. A user clustering algorithm with Interference Increment Reduction (IIR) is proposed, which minimizes the sum of intra-cluster interference and ultimately maximizes system sum rate by continuously switching users with maximum interference between clusters. Compared with K-means algorithm, this algorithm, no need of specifying cluster heads, avoids local optimum without increasement of the computation complexity. The simulation results show that the system sum rate, especially the throughput of edge users, can be effectively improved when the network is densely deployed.
Design and Implementation of High Speed PCIe Cipher Card Supporting GM Algorithms
Jun ZHAO, Xuewen ZENG, Zhichuan GUO
, doi: 10.11999/JEIT190003
[Abstract](273) [FullText HTML](314) [PDF 744KB](14)
Abstract:
Cipher cards play an important role in the field of information security. However, the performance of cipher cards are insufficient, and it is difficult to meet the needs of high-speed network security services. A design and system implementation method of high-speed PCIe cipher card based on MIPS64 multi-core processor is proposed, which supports the GM algorithm SM2/3/4 and international cryptographic algorithms, such as RSA, SHA and AES. The implemented system includes module of hardware, cryptographic algorithm, host driver and interface calling. An optimization scheme for the implementation of SM3 is proposed, the performance is improved by 19%. And the host to send requests in Non-Blocking mode is supported, so a single-process application can get the cipher card’s full load performance. Under 10-core CPU, the speed of SM2 signature and verification are 18000 and 4200 times/s, SM3 hash speed is 2200 Mbps, SM4 encryption/decryption speed is 8/10 Gbps, multiple indicators achieve higher level; When using 16-core CPU @1300 MHz, SM2/3 performance can be improved by more than 100%, and the speed of SM2 signature could achieve 105 times/s with 48-core CPU.
A Circuit Optimization Method of Improved Lookup Table for Highly Efficient Resource Utilization
Lijiang GAO, Haigang YANG, Wei LI, Yanan HAO, Changlong LIU, Caixia SHI
, doi: 10.11999/JEIT190095
[Abstract](313) [FullText HTML](185) [PDF 2331KB](8)
Abstract:
The circuit structure optimization method for Basic programmable Logic Element (BLE) of FPGA is studied. Considering finding the solution to the bottleneck problem of low resource utilization efficiency in logic and arithmetic operations with 4-input Look Up Table (LUT), some efforts to improve BLE design based on 4-input LUT are explored. A high area-efficient LUT structure is proposed, and the possible benefits of such a new structure are analyzed theoretically and simulated. Further, a statistical method for evaluation of the post synthesis and mapping netlist is also proposed. Finally, a number of experiments are carried out to assess the proposed structure based on the MCNC and VTR benchmarks. The results show that, compared with Intel Stratix series FPGAs, the optimized structure proposed in this paper improves respectively the area efficiency of the FPGA by 10.428% and 10.433% in average under the MCNC and VTR benchmark circuits.
Sparse Reconstruction OFDM Delay Estimation Algorithm Based on Bayesian Automatic Relevance Determination
Weijia CUI, Peng ZHANG, Bin BA
, doi: 10.11999/JEIT181181
[Abstract](253) [FullText HTML](185) [PDF 1978KB](14)
Abstract:
Considering the problem of Orthogonal Frequency Division Multiplexing (OFDM) signal delay estimation with only a Single Measurement Vector (SMV) in a complex environment, a sparse reconstruction time delay estimation algorithm based on Bayesian Automatic Relevance Determination (BARD) is proposed. The Bayesian framework is used to start from the perspective of further mining useful information, and asymmetric Automatic Relevance Determination(ARD) priori is introduced to integrate into the parameter estimation process, which improves the accuracy of time delay estimation under SMV and low Signal-to-Noise Ratio (SNR) conditions. Firstly, a sparse real-domain representation model is constructed based on the estimated frequency domain response of the OFDM signal physical layer protocol data unit. Then, probability hypothesis for the noise and sparse coefficient vectors are made in the model, and Automatic Relevance Determination (ARD) prior is introduced. Finally, according to the Bayesian framework, the Expectation Maximization (EM) algorithm is used to solve the hyperparameters to estimate the delay. The simulation experiments show that the proposed algorithm has better estimation performance and is closer to the Cramér–Rao Bound (CRB). At the same time, based on the Universal Software Radio Peripheral (USRP), the effectiveness of the proposed algorithm is verified by the actual signal.
Multi-feature Map Pyramid Fusion Deep Network for Semantic Segmentation on Remote Sensing Data
Fei ZHAO, Wenkai ZHANG, Zhiyuan YAN, Hongfeng YU, Wenhui DIAO
, doi: 10.11999/JEIT190047
[Abstract](416) [FullText HTML](560) [PDF 1192KB](37)
Abstract:
Utilizing multiple data (elevation information) to assist remote sensing image segmentation is an important research topic in recent years. However, the existing methods usually directly use multivariate data as the input of the model, which fails to make full use of the multi-level features. In addition, the target size varies in remote sensing images, for some small targets, such as vehicles, houses, etc., it is difficult to achieve detailed segmentation. Considering these problems, a Multi-Feature map Pyramid fusion deep Network (MFPNet) is proposed, which utilizes optical remote sensing images and elevation data as input to extract multi-level features from images. Then the pyramid pooling structure is introduced to extract the multi-scale features from different levels. Finally, a multi-level and multi-scale feature fusion strategy is designed, which utilizes comprehensively the feature information of multivariate data to achieve detailed segmentation of remote sensing images. Experiment results on the Vaihingen dataset demonstrate the effectiveness of the proposed method.
Image Semantic Segmentation Based on Region and Deep Residual Network
Huilan LUO, Fei LU, Fansheng KONG
, doi: 10.11999/JEIT190056
[Abstract](426) [FullText HTML](292) [PDF 3206KB](23)
Abstract:
An image semantic segmentation model based on region and deep residual network is proposed. Region based methods use multi-scale to create overlapping regions, which can identify multi-scale objects and obtain fine object segmentation boundary. Fully convolutional methods learn features automatically by using Convolutional Neural Network (CNN) to perform end-to-end training for pixel classification tasks, but typically produce coarse segmentation boundaries. The advantages of these two methods are combined: firstly, candidate regions are generated by region generation network, and then the image is fed through the deep residual network with dilated convolution to obtain the feature map. Then the candidate regions and the feature maps are combined to get the features of the regions, and the features are mapped to each pixel in the regions. Finally, the global average pooling layer is used to classify pixels. Multiple different models are obtained by training with different sizes of candidate region inputs. When testing, the final segmentation are obtained by fusing the classification results of these models. The experimental results on SIFT FLOW and PASCAL Context datasets show that the proposed method has higher average accuracy than some state-of-the-art algorithms.
A Security-oriented Dynamic and Heterogeneous Scheduling Method for Virtual Network Function
Xinsheng JI, Shuiling XU, Wenyan LIU, Qing TONG, Lingshu LI
, doi: 10.11999/JEIT181130
[Abstract](254) [FullText HTML](163) [PDF 1961KB](12)
Abstract:
Network Function Virtualization (NFV) brings flexibility and dynamics to the construction of service chain. However, the software and virtualization may cause security risks such as vulnerabilities and backdoors, which may have impact on Service Chain (SC) security. Thus, a Virtual Network Function (VNF) scheduling method is proposed. Firstly, heterogeneous images are built for every virtual network function in service chain, avoiding widespread attacks using common vulnerabilities. Then, one network function is selected dynamically and periodically. The executor of this network function is replaced by loading heterogeneous images. Finally, considering the impact of scheduling on the performance of network functions, Stackelberg game is used to model the attack and defense process, and the scheduling probability of each network function in the service chain is solved with the goal of optimizing the defender’s benefit. Experiments show that this method can reduce the rate of attacker’s success while controlling the overhead generated by the scheduling within an acceptable range.