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## Latest Articles

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, Available online  , doi: 10.11999/JEIT181053 doi: 10.11999/JEIT181053
[Abstract](6) [FullText HTML] (6)
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In Software Defined Networks (SDN), latency and load are important factors for Controller Placement Problem (CPP). To reduce the transmission latency between controllers, the propagation latency and queuing latency of flow requests, and balance the controller load, a strategy on how to place and adjust the controller is proposed. It mainly includes Genetic Algorithm (GA) and Balanced Control Region Algorithm (BCRA) which are used to place the initial controller and one Algorithm of Dynamic Online Adjustment (ADOA), that is an online adjusting algorithm in term dynamic controlling. The above algorithms all base on the network connectivity. The simulation results show that in initial controller placement situation, under the premise of guaranteeing the lower propagation latency, queue latency and controller transmission latency of flow request, when BCRA is deployed in small and medium-sized networks, its load balancing performance is similar to that of GA and superior to k-center and k-means algorithm; when GA is deployed in large networks, compared with BCRA, k-center and k-means, the load balancing rate increases averagely 49.7%. In the dynamic situation, ADOA can guarantee lower queuing delay and running time, and still make the load balance parameter less than 1.54.
, Available online  , doi: 10.11999/JEIT180936 doi: 10.11999/JEIT180936
[Abstract](5) [FullText HTML] (5) [PDF 2284KB](5)
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Generally speaking, Three Dimension (3D) imaging of spinning space target is obtained by performing matrix factorization method on the scattering trajectories obtained from sequential radar images. Because of the errors of scattering center extraction and association, the 3D reconstruction accurate will be reduced or even fail. In addition, the scattering center trajectory from turntable target consists with circle nature, which is inconsistent with the elliptic property of the scattering center trajectory obtained by optical geometry projection. To tackle these problems, this paper proposes a short time 3D reconstruction method of space target. Firstly, the retrieved trajectory is fitted with 2D circular nature to make the trajectory smooth and closer to the theoretical curve. Then the radar line of sight (LOS) is estimated by multiple views and the circular curve is converted into elliptical curve by multiplying the coefficients calculated by the LOS. The 3D reconstruction can be obtained by performing matrix factorization method on elliptical curves. Finally, the simulations verify the effectiveness of the proposed method.
, Available online  , doi: 10.11999/JEIT180891 doi: 10.11999/JEIT180891
[Abstract](5) [FullText HTML] (4)
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In order to solve the problem of classification and recognition of unknown interference types under large samples, an adaptive recognition method for unknown interference based on signal feature space is proposed. Firstly, the interference signal is processed and the interference signal feature space is established with the Hilbert signal space theory. Then the projection theorem is used to approximate the unknown interference. The classification algorithm based on signal feature space with Probabilistic Neural Network (PNN) is proposed, and the processing flow of unknown interference classifier is designed. The simulation results show that compared with two kinds of traditional methods, the proposed method improves the classification accuracy of the known interference by 12.2% and 2.8% respectively. The optimal approximation effect of the unknown interference varies linearly with the power intensity in the condition, and the overall recognition rate of the designed classifier reaches 91.27% in the various types of interference satisfying the optimal approximation, and the speed of processing interference recognition is improved significantly. When the signal-to-noise ratio reaches 4 dB, the accuracy of unknown interference recognition is more than 92%.
, Available online  , doi: 10.11999/JEIT181130 doi: 10.11999/JEIT181130
[Abstract](5) [FullText HTML] (3) [PDF 1962KB](1)
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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.
, Available online  , doi: 10.11999/JEIT180946 doi: 10.11999/JEIT180946
[Abstract](19) [FullText HTML] (13) [PDF 2706KB](2)
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With the development of light and small Unmanned Aerial Vehicles (UAVs), the detection method of Mini SAR based on UAV platform will bring a revolutionary impact on information acquisition mode. In this paper, a W-band Mini SAR system for UAV is proposed, including the system design proposal and composition, high linearity analog phase-locked frequency modulation, Millimeter Wave (MMW) substrate integrated waveguide antenna, 3D integration and motion compensation methods to solve the key problems of Mini SAR. A W-band Mini SAR prototype is developed and the imaging test based on Multi-rotor UAV is proceeded. The results show the resolution, volume and the weight of Mini SAR prototype is at the industry-leading level. A high SNR imaging with perfect focusing effect is obtained from flight test.
, Available online  , doi: 10.11999/JEIT180247 doi: 10.11999/JEIT180247
[Abstract](10) [FullText HTML] (8) [PDF 1872KB](2)
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Transformation Optics (TO) is a hot topic in the research area of electrical-magnetic fields. Considering providing further theoretical support to the design of stealth carpet based on TO, three basic mathematic problems of TO are discussed in this paper. Firstly, the uniqueness of transformation form in three-dimensional transformation of Maxwell’s equations is analyzed. A new transformation model is proposed, which is different from the classical one shown in reference. The new model also leads to a new transformation method that can generate flexible characteristic impedance in transformation space. Based on this, a design method of stealth cloak or carpet that can be used to hide the target in an area surrounded by medium with given permittivity is discussed. During this process, only the field distribution in free space is required as the original field during mapping. Secondly, the two-dimensional transformation of the wave equation is studied. The transformation of the magnetic field component in the two-dimensional transformation based on the wave equation of the electric field component is analyzed. The boundary matching during transformation is also discussed. The two dimension design method of stealth cloak or carpet that can be used to hide a target in an area surrounded by medium with specified permittivity is also discussed. Finally, the sufficiency and necessity of conformal transformation for designing a two dimension stealth cloak with non-uniform and anisotropic medium are proved strictly. The simulation results of a stealth carpet embedded in material are given to verify the proposed method. The analysis and the related conclusion presented in the paper provide theoretical support to the related application based on TO.
, Available online  , doi: 10.11999/JEIT180886 doi: 10.11999/JEIT180886
[Abstract](25) [FullText HTML] (17) [PDF 1302KB](3)
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The structure of Tree-Augmented Naïve Bayes (TAN) forces each attribute node to have a class node and a attribute node as parent, which results in poor classification accuracy without considering in correlation between each attribute node and the class node. In order to improve the classification accuracy of TAN, firstly, the TAN structure is proposed that allows each attribute node to have no parent or only one attribute node as parent. Then, a learning method of building the tree-like Bayesian classifier using a decomposable scoring function is proposed. Finally, the low-order Conditional Independency (CI) test is applied to eliminate the useless attribute, and then based on improved Bayesian Information Criterion (BIC) function, the classification model with acquired the parent node of each attribute node is established using the greedy algorithm. Through comprehensive experiments, the proposed classifier performs better than Naïve Bayes (NB) and TAN on multiple classification, and the results prove that this learning method has certain advantages.
, Available online  , doi: 10.11999/JEIT190047 doi: 10.11999/JEIT190047
[Abstract](19) [FullText HTML] (12) [PDF 1196KB](4)
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Utilizing multiple data (elevation information) to assist remote sensing image segmentation is an important research topic in recent years. However, the existing methods use usually directly 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.
, Available online  , doi: 10.11999/JEIT181097 doi: 10.11999/JEIT181097
[Abstract](25) [FullText HTML] (23) [PDF 3175KB](10)
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In order to improve the recognition rate of banknotes, the improved banknote recognition algorithm based on Deep Convolutional Neural Network(DCNN) is proposed. Firstly, the algorithm constructs a deep convolution layer by integrating transfer learning, Leaky-Rectified Liner Unit (Leaky ReLU) function, Batch Normalization(BN) and multi-level residual unit that perform stable and fast feature extraction and learning on input different size banknotes. Secondly, a fixed-size output representation of the extracted banknote features is obtained by using the improved multi-level spatial pyramid pooling algorithm. Finally, the banknote classification is implemented by the full connection layer and the softmax layer of the network. The experimental results show that the proposed algorithm can effectively improve the recognition rate of banknotes, and has better generalization ability and robustness than the traditional banknote classification method. At the same time, the algorithm can meet the real-time requirements of the banknote sorting system.
, Available online  , doi: 10.11999/JEIT181090 doi: 10.11999/JEIT181090
[Abstract](19) [FullText HTML] (14) [PDF 1327KB](9)
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The MUltiple SIgnal Classification (MUSIC) algorithm is a classical spatial spectrum estimation algorithm. Taking L-shaped array as an example, an improved 2D-MUSIC algorithm is proposed for the problem that 2D-MUSIC algorithm often fails to estimate accurately targets in close proximity among multiple targets when the signal-to-noise ratio is low.The algorithm identifies the target location through spectrum peak search by first performing conjugate recombination on the covariance matrix generated by the classical 2D-MUSIC algorithm, then calculating the mean of sum of square of the recombined one and the original one as the new matrix, whose corresponding noise subspace then weighted by applying appropriate coefficients to obtain a new noise subspace. The computer simulation results show that compared with the 2D-MUSIC algorithm, the improved algorithm performs well on DOA estimation for the targets in close proximity among multiple targets when the received signal has low signal-to-noise ratio, which improves the resolution of 2D-DOA estimation with L-shaped array, with better engineering application value.
, Available online  , doi: 10.11999/JEIT181014 doi: 10.11999/JEIT181014
[Abstract](15) [FullText HTML] (10)
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The basis of the identification of network security situation element is to perform the feature extraction of situation data effectively. Considering the problem that the Back Propagation(BP) neural networks have an excessive dependence on data labels when it has a learning of massive security situation information data, a network security situation element identification method which combines deep stack encoder and BP algorithm is proposed. It trains the network layer by layer through unsupervised learning algorithm. And on this basis we can get the deep track encoder by stacking. The unsupervised training of the network is realized when using the encoder to extract the characteristic of the data sets. The method is verified by simulation experiments that: it can improve the performance and accuracy of situational awareness effectively.
, Available online  , doi: 10.11999/JEIT190056 doi: 10.11999/JEIT190056
[Abstract](11) [FullText HTML] (10) [PDF 2945KB](3)
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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 in this paper: 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.
, Available online  , doi: 10.11999/JEIT181025 doi: 10.11999/JEIT181025
[Abstract](13) [FullText HTML] (9)
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Firstly, a network Dynamic Threat Attribute Attack Graph(DT-AAG) analysis model is constructed by using Attribute Attack Graph theory. On the base of the comprehensive description of system vulnerability and network service-induced threat transfer relationship, a threat transfer probability measurement algorithm is designed in combination with Common Vulerability Scoring System(CVSS) vulnerability evaluation criteria and Bayesian probability transfer method. Secondly, based on the model, a Dynamic Threat Attribute Attack Graph generation Algorithm(DT-AAG-A) is designed by using the relationship between the threat and the vulnerability as well as the service. What’s more, to solve the problem that threat transfer loop existing in the generated attribute attack graph, the loop digestion mechanism is designed. Finally, the effectiveness of the proposed model and algorithm is verified by experiments.
, Available online  , doi: 10.11999/JEIT181117 doi: 10.11999/JEIT181117
[Abstract](15) [FullText HTML] (11) [PDF 1823KB](1)
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Network multi-alarm information fusion processing is one of the most important methods to implement effectively network dynamic threat analysis. Focusing on this, a mechanism for dynamic threat tracking and quantitative analysis by using network system multi-alarm information is proposed. Firstly, the attack graph theory is used to construct the system dynamic threat attribute attack graph. Secondly, based on the privilege escalation principle, Antecedent Predictive Algorithm(APA), the Consequent Predictive Algorithm(CPA) and the Comprehensive Alarm Information Inference Algorithm(CAIIA) are designed integrate the multi-alarm information and do threat analysis. Then, the network dynamic threat tracking graph is generated to visualize the threat change situation. Finally, the effectiveness of the mechanism and algorithm is validates through experiments.
, Available online  , doi: 10.11999/JEIT181169 doi: 10.11999/JEIT181169
[Abstract](10) [FullText HTML] (10)
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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 algorithm (ERKM) 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.
, Available online  , doi: 10.11999/JEIT181134 doi: 10.11999/JEIT181134
[Abstract](17) [FullText HTML] (12)
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To solve the problem of heat dissipation in Three Dimensional Field Programmable Gate Array Technology (3D FPGA), an interconnect channel architectural design method with low thermal gradient feature is proposed. A thermal resistance network model is established for the 3D FPGA, and theoretical studies and thermal simulation experiments are carried out on the influence of different types of channels on the thermal performance of 3D FPGA. Further, non-uniform vertical direction channel structures of 3D FPGA are proposed. Experiments indicate that 3D FPGA designed using the method proposed can reduce the maximum temperature gradient between different layers by 76.8% and the temperature gradient within the same layer by 10.4% compared with the traditional channel structure of 3D FPGA.
, Available online  , doi: 10.11999/JEIT181041 doi: 10.11999/JEIT181041
[Abstract](14) [FullText HTML] (10)
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In order to improve the utilization of non-contiguous virtual array elements in the underdetermined DOA estimation of the coprime array, a DOA estimation method based on Toeplitz covariance matrix reconstruction is proposed. First, the virtual array element distribution characteristics of the matrix are analyzed from the perspective of the difference coarray. Additionally, according to the correspondence between the difference coarray and the wave path difference, the covariance matrix is extended to a Toeplitz array covariance matrix, of which some elements are zero. Then, the Toeplitz matrix is recovered to the full covariance matrix according to the low rank matrix completion theory. Finally, the root-MUSIC method is employed for the DOA estimation. Theoretical analysis and simulation results show that this method can increase the number of the resolvable signals by increasing the number of virtual array elements, eliminate the effect of the off-grid effect without discretization of the angle domain, and avoid regularization parameter selection. Therefore, the estimation accuracy and resolution are improved.
, Available online  , doi: 10.11999/JEIT180261 doi: 10.11999/JEIT180261
[Abstract](61) [FullText HTML] (37) [PDF 2225KB](11)
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In broadband Power-Line Communications (PLC), the background noise commonly assumed as Gaussian may not truly depict the effect of the human activities on noise characteristics. Symmetric Alpha-Stable (S\begin{document}$\alpha$\end{document}S) process is used to model the PLC background noise, obtaining analytical expressions, and the analytical Symbol Error Rate (SER) performance is investigated for Orthogonal Frequency Division Multiplexing (OFDM)-based PLC systems. The analysis shows that the real or the imaginary component after the Fast Fourier Transform (FFT) of the received complex baseband S\begin{document}$\alpha$\end{document}S noise samples follows univariate S\begin{document}$\alpha$\end{document}S distribution. Due to the fact that the S\begin{document}$\alpha$\end{document}S background noise occupies the whole OFDM system bandwidth, the SER performance of the system decreases as the employed FFT size grows.
, Available online  , doi: 10.11999/JEIT180982 doi: 10.11999/JEIT180982
[Abstract](9) [FullText HTML] (8)
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Heterogeneous signcryption can ensure the confidentiality and unforgeability of information data between different cryptosystems systems. Security for traditional Public Key Infrastructure(PKI) and Identity-Based Cryptosystem(IBC) two-way and anonymous heterogeneous signcryption scheme between PKI→IBC and IBC→PKI is analyzed. It is pointed out that PKI→IBC scheme and IBC→PKI scheme can not resist adversary attacks. The ciphertext can be decrypted under the adversary obtaining the ciphertext. To enhance security, a new PKI→IBC and IBC→PKI scheme is proposed, and then confidentiality and unforgeability of the scheme in the random oracle model on the basis of the assumptions of Computational Diffie-Hellman problem and Bilinear Diffie-Hellman problem is proved. The efficiency analysis shows that the new scheme has higher communication efficiency.
, Available online  , doi: 10.11999/JEIT180588 doi: 10.11999/JEIT180588
[Abstract](53) [FullText HTML] (32) [PDF 3996KB](1)
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For the deficiency of traditional Continuously Adaptive Mean-shift (CAMshift) tracking algorithm can easily contain a large number of color information which belongs to the background in the process of establishing the target color model, an improved algorithm is proposed. The original image is divided into foreground and background based on the Gaussian Mixture Model(GMM). In the original image and the background image, the histogram of the hue component is established. Hue histograms of the background image are used to calculate the weight of the hue component in the original image. The hues belong to the background are suppressed and the color differences between foreground and background were expanded. Experiment shows that by suppressing the hue components belong to the background, the saliency of the target color model is expanded. The accuracy and stability of the target recognition are improved. The ratio of the max deviation to the target is less than 20%, which ensures the target not to be lost.
, Available online  , doi: 10.11999/JEIT180949 doi: 10.11999/JEIT180949
[Abstract](90) [FullText HTML] (80) [PDF 1941KB](13)
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, Available online  , doi: 10.11999/JEIT180762 doi: 10.11999/JEIT180762
[Abstract](70) [FullText HTML] (42) [PDF 1602KB](4)
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Simultaneous Wireless Information and Power Transfer (SWIPT) is an effective technique to solve the energy limitation problem of wireless networks. A multi-carrier SWIPT communication system that includes one Base Station (BS) and multiple users is investigated. Both the uplink and downlink of the system apply the OFDM transmission. In the downlink, the BS transmits information and power over different subcarriers to the users simultaneously. In the uplink, the user transmits information to the BS by using the power harvested from the BS in the downlink. This paper aims to maximize the weighted sum of the downlink and uplink achievable rates by jointly optimizing subcarrier allocation and power allocation of the uplink and downlink. An optimal algorithm is proposed to solve this resulted optimization problem, which is based on the Lagrange duality method and the ellipsoid method. The performances of the proposed algorithm are verified by computer simulations.
, Available online  , doi: 10.11999/JEIT181107 doi: 10.11999/JEIT181107
[Abstract](50) [FullText HTML] (31) [PDF 2068KB](4)
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For the fact that exsiting MIMO transmit beampattern design methods suffer from huge computational burden, a novel MIMO transmit beampattern design method based on atomic norm is proposed. According to the signal model of atomic norm, firstly a multi-rank transmit beamformer and a set of orthogonal signals are selected. Then the transmit beampattern matching design problem is formulated into an atomic norm minimization problem. And the multi-rank transmit beamformer is achieved by Vandermonde decomposition method of positive semidefinite Toeplitz matrix, which is attained by the solution of the atomic norm minimization problem with Semi-Definite Programming (SDP). Finally, the transmit waveforms can be acquired from the resulting multi-rank transmit beamformer and existing orthogonal waveforms. The theoretical analysis and simulation results verify that the proposed method satisfies the uniform element power constraint and low Peak to Average Power Ratio (PAPR). Simultaneously, compared with current methods, the proposed method has lower computational burden and comparable matching performance.
, Available online  , doi: 10.11999/JEIT181103 doi: 10.11999/JEIT181103
[Abstract](67) [FullText HTML] (51) [PDF 719KB](7)
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Most existing searchable encryption schemes only support the search for keyword sets, and the data users can not quickly identify the file keyword information returned by the server. Meanwhile, considering the server has strong computing power, it may judge keyword information from single keywords and the identity of the data consumer is not verified. In this paper, the data user and data owner are delegated server to verify whether the data ueer is a legitimate user; if legal, the delegated server can detect the validity of the return ciphertext with data user. The data user uses the server public key, keywords and pseudo-keywords to generate trapdoor, in order to ensure the indistinguishable of the keywords, a delegated multi-keyword searchable encryption scheme is designed, which is resistant to keyword guessing of data user authentication. Meanwhile, when the data owner encrypts, the public key of the cloud server, the delegated server, and the data user can be used to prevent collusion attacks. In the random oracle model the security of the proposed scheme is proved. The experiment results show that the scheme is efficient under the multi-keyword environment.
, Available online  , doi: 10.11999/JEIT180897 doi: 10.11999/JEIT180897
[Abstract](68) [FullText HTML] (32) [PDF 1512KB](16)
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Recently, the mobile charging and data collecting by using Mobile Equipment (ME) in Wireless Sensor Networks (WSNs) is a hot topic. Existing studies determine usually the traveling path of ME according to the charging requirements of sensor nodes firstly, and then handle the data collecting. In this paper, charging requirement and data collecting are taken into consideration simultaneously. A one-to-many charging and data collecting model for ME are established with two optimization objectives, maximizing the total energy utilization and minimizing the average delay of data collecting. Due to the limited energy of the ME, the path planning strategy and the equalization charging strategy are designed. An improved multi-objective ant colony algorithm is proposed to solve the problem. Experiments show that the objective values, the number of Pareto solutions, the homogeneity of Pareto solutions and the distribution of Pareto solutions obtained by the proposed algorithm are all superior to NSGA-II algorithm.
, Available online  , doi: 10.11999/JEIT180595 doi: 10.11999/JEIT180595
[Abstract](96) [FullText HTML] (51) [PDF 1554KB](16)
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Semi-honest data collectors may cause privacy leaks during the collection and use of Sensitive Attribute (SA) data. In view of the problem, real-time data leaders are added in the traditional model and a privacy-protected data collection protocol based on the improved model is proposed. Without the assumption of trusted third party, the protocol ensures that data collectors maximize data utility can only be established on the basis of K-anonymized data. Data owners participates in the protocol flow in a distributed and collaborative manner to achieve the transmission of SA after the Quasi-Identifier (QI) is anonymized. This reduces the probability that the data collector uses the QI to associate SA values and weakens the risk of privacy leakage caused by internal identity disclosure. It divides the coded value of the SA into two shares of a random anchor point and a compensation distance through the tree coding structure and the members of the equivalent class formed by K-anonymity elect two data leaders to aggregate and forward the two shares respectively, which releases the association between unique network identification and SA values and prevents leakage of privacy caused by external identification effectively. Formal rules are established that meet the characteristics of the protocol and analyze the protocol to prove that the protocol meets privacy protection requirements.
, Available online  , doi: 10.11999/JEIT180357 doi: 10.11999/JEIT180357
[Abstract](109) [FullText HTML] (50) [PDF 1445KB](11)
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To maximize the long-term spectral efficiency and energy efficiency of a full duplex wireless access and backhaul integrated small base station scene, approximate dynamic programming based joint admission control and resource allocation optimization algorithm is proposed. The algorithm firstly considers the resource usage and power configuration of the current base station, the dynamic demand of user, the constraints of average delay as well as backhaul rate and transmission power. The corresponding multi-objective optimization model of maximum spectrum efficiency and minimizes power consumption is established by using the Constrained Markov Decision Process(CMDP). Then, the Chebyshev theory is used to transform the multi-objective into a single-objective optimization, and the Lagrange dual decomposition method is then used to convert the single-objective problem into unrestricted Markov decision process problem. Finally, To solve the " dimension disaster” explosion that generated when solving this unrestricted Markov Decision Process(MDP) problem, a dynamic resource allocation algorithms based on approximate dynamic programming is presented, and the access and resource allocation strategy is obtained during this process. The simulation results show that the algorithm can maximize the long-term average spectrum efficiency and energy efficiency, within the constraints of the average delay, backhaul rate and transmission power, under the scenario of integrated access and backhaul small base station.
, Available online  , doi: 10.11999/JEIT180157 doi: 10.11999/JEIT180157
[Abstract](112) [FullText HTML] (62) [PDF 2648KB](20)
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To deal with the problem of joint estimation of spreading codes and information sequences for asynchronous long code DS-CDMA signals in multipath channels, an algorithm is introduced based on Sequential Monte Carlo(SMC) for blind estimation. The proposed algorithm emploies hybrid important sampling density to draw the samples from joint posterior distribution iteratively, and computes the importance weight to complete the estimation of the state variable. During the realization of the algorithm, in order to reduce the computational complexity. Firstly, the modified algorithm estimates the spreading code of each user, then processes the observation data, thereby modifies the original iteration step. Simulation results verify the adaptability of the proposed algorithms for multiple conditions. Moreover, it can obtain good estimation performance in time varying multipath channels.
, Available online  , doi: 10.11999/JEIT180898 doi: 10.11999/JEIT180898
[Abstract](14) [FullText HTML] (12)
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In order to solve the problem of member information leakage in multi-party cooperative design of integrated circuits, a orthogonal obfuscation scheme of multi-hardware IPs core security protection is proposed. Firstly, the orthogonal obfuscation matrix generates orthogonal key data, and the obfuscated key of the hardware IP core is designed with the physical feature of the Physical Unclonable Function (PUF) circuit. Then the security of multiple hardware IP cores are realized by the orthogonal obfuscation state machine. Finally, the validity of orthogonal aliasing is verified using the ISCAS-85 circuit and cryptographic algorithm. The multi-hardware IP core orthogonal obfuscation scheme is tested under Taiwan Semiconductor Manufacturing Company (TSMC) 65nm process, the difference of Toggle flip rate between the correct key and the wrong key is less than 5%, and the area and power consumption of the larger test circuit are less than 2%. The experimental results show that orthogonal obfuscation can improve the security of multi-hardware IP cores, and can effectively defend against member information leakage and state flip rate analysis attacks.
, Available online  , doi: 10.11999/JEIT180392 doi: 10.11999/JEIT180392
[Abstract](69) [FullText HTML] (36) [PDF 2791KB](6)
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The Mann-Whitney rank sum test based Wireless Local Area Network (WLAN) indoor mapping and localization approach is proposed. Firstly, according to the localization accuracy requirement, this approach performs the motion paths segmentation in target area, and meanwhile merges the similar motion path segments based on the Mann-Whitney rank sum test. Then, a signal clustering algorithm based on the similar Received Signal Strength (RSS) sequence segments is adopted to guarantee the physical adjacency of the RSS samples in the same cluster. Finally, the backbone nodes based diffusion mapping is used to construct the mapping relations between the physical and signal spaces, and the motion user localization is consequently achieved. The experimental results indicate that compared with the existing WLAN indoor mapping and localization approaches, the proposed one is able to achieve higher mapping and localization accuracy without motion sensor assistance or location fingerprint database construction.
, Available online  , doi: 10.11999/JEIT180228 doi: 10.11999/JEIT180228
[Abstract](26) [FullText HTML] (21)
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This paper considers a massive MIMO full-duplex relaying system, in which multiple single-antenna sources simultaneously communicate with multiple single-antenna destinations using a single relay that is equipped with \begin{document}${N_{{\mathop{\rm rx}\nolimits} }}$\end{document} receive antennas and \begin{document}${N_{{\mathop{\rm tx}\nolimits} }}$\end{document} transmit antennas. Under imperfect Channel State Information (CSI) and hardware impairment, the relay processes the received and transmitted signals by means of Zero-Forcing (ZF) and uses Decode-and-Forward (DF) scheme. The closed-form expressions of achievable rate are deduced. Based on these expressions, the various power scaling laws can be obtained. It is shown that when the two numbers of the relay receive and transmit antennas go to infinity but with a fixed ratio, the system can maintain a desirable quality of service in the case of scaling the transmit powers of the sources, relay and pilots.
, Available online  , doi: 10.11999/JEIT180805 doi: 10.11999/JEIT180805
[Abstract](98) [FullText HTML] (51) [PDF 836KB](14)
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In a cloud database environment, data is usually encrypted and stored to ensure the security of cloud storage data. To encrypt the data query overhead is big, does not support the cipher text sorting, query and other shortcomings, this paper puts forward a kind of f - mOPE cryptograph database retrieval scheme. Based on mOPE sequential encryption algorithm, this scheme uses the idea of binary sort tree data structure to generate plaintext one-to-one corresponding sequential coding. Data is converted plaintext into ciphertext storage based on AES encryption scheme. The improved partial homomorphic encryption algorithm is used to improve the security of sequential encryption scheme. The security analysis and experimental results show that this scheme can not only resist statistical attack, but also reduce effectively server computing cost and improve database processing efficiency on the basis of guaranteeing data privacy.
, Available online  , doi: 10.11999/JEIT180914 doi: 10.11999/JEIT180914
[Abstract](98) [FullText HTML] (48) [PDF 2872KB](9)
Abstract:
Because of restricted earth-based tracking network, Tracking, Telemetry and Command (TT&C) for lunar orbit micro-satellite is depended on Unified S/X Band (USB) antennas in China Chang’E-4 lunar exploration. Based on analysis of the geometry between relay satellite, micro-satellite and earth-based antennas during earth-moon transfer orbit, an applicable method to acquire delay observable through Same-Beam Interferometry (SBI) tracking by China deep space network is discussed. Benefited from more kinds of tracking resources and high accuracy orbit of relay satellite, delay observable for angular position measurement of micro-satellite in the order of 1 ns is obtained, which improves the micro-satellite orbit determination accuracy from 2 km to less than 1 km and improves orbit prediction accuracy from 6 km to 2 km. SBI tracking plays an important role in short arc orbit determination of micro-satellite.
, Available online  , doi: 10.11999/JEIT180587 doi: 10.11999/JEIT180587
[Abstract](214) [FullText HTML] (107) [PDF 1937KB](22)
Abstract:
Auction based resource allocation can make resource provider get more profit, which is a major challenging problem for cloud computing. However, the resource allocation problem is NP-hard and can not be solved in polynomial time. Existing studies mainly use approximate algorithms or heuristic algorithms to implement resource allocation in auction, but these algorithms have the disadvantages of low computational efficiency or low allocate accuracy. In this paper, the classification and regression of supervised learning is used to model and analyze multi-dimensional cloud resource allocation, for the different scale of problem, three resource allocation predict algorithms based on linear regression, logistic regression and Support Vector Machine (SVM) are proposed. Through the learning of the small-scale training set, the predict model can guarantee that the social welfare, allocation accuracy, and resource utilization in the feasible solution are very close to the optimal allocation solution. The payment price algorithm based on the critical value theory is proposed which ensure the truthful property of the auction mechanism design. Final experimental results show that the proposed scheme has good effect for resource allocation in cloud computing.
, Available online  , doi: 10.11999/JEIT180589 doi: 10.11999/JEIT180589
[Abstract](189) [FullText HTML] (94) [PDF 1330KB](13)
Abstract:
The existing IP location technology determines the location of IP by querying IP to register information databases or using time-delay information. In fact, due to the influence of various factors, most of the IP in the network can not get accurate and reasonable positioning results. For this reason, a region recognition method of IP is proposed based on network structure features. This method obtains the network topology information between the two nodes by sending the Traceroute detection packet from the detection nodes to the IPs that need to be located Comparing the network structure features between the nodes to be located and the known geographical nodes determines where the nodes located. The actual test shows that this method can achieve better results.
, Available online  , doi: 10.11999/JEIT180554 doi: 10.11999/JEIT180554
[Abstract](210) [FullText HTML] (85) [PDF 860KB](10)
Abstract:
Group signcryption is a cryptosystem which can realize group signature and group encryption. However, the message sender and receiver of existing group signcryption schemes are basically in the same cryptosystem, which does not meet the needs of the real environment and the public key encryption technology is basically used, public key encryption technology in encrypted long message efficiency is too low. Therefore, this paper proposes a hybrid group signcryption scheme based on heterogeneous cryptosystem from Identity-Based Cryptosystem (IBC) to CertificateLess Cryptosystem (CLC). In the scheme, The Private Key Generator (PKG) in the IBC cryptosystem and Key Generation Center (KGC) in the CLC cryptosystem generate their own system master keys, and group members can only solve signcryption through collaboration, which improves the security of the scheme. Meanwhile, the user can dynamically join the group without changing the group public key and other members’ private key. The scheme uses hybrid signcryption and has the ability to encrypt any long message. It is proved that the scheme satisfies confidentiality and unforgeability in computing the Diffie-hellman hard problem in the random oracle model. Theoretical and numerical analysis shows that the scheme is more efficient and feasible.
, Available online  , doi: 10.11999/JEIT180818 doi: 10.11999/JEIT180818
[Abstract](120) [FullText HTML] (52) [PDF 756KB](13)
Abstract:
A two-user single cell network employing Non-Orthogonal Multiple Access (NOMA) technique is studied. Accounting for time-varying channel fading and dynamic traffic arrival, a stochastic optimization issue is formulated, which aims to balance the queue delay of users and maximize the network total throughput. Based on Lyapunov optimization method, the closed-form optimal solution of the stochastic optimization issue is derived, and a low-complexity optimal delay equilibrium and power control method is proposed. The method is compared with a NOMA scheme with a non-optimal resource management method and a Time Division Multiple Access (TDMA) scheme with an optimal resource management method. Simulation results show that the proposed method can significantly improve network performance.
, Available online  , doi: 10.11999/JEIT180748 doi: 10.11999/JEIT180748
[Abstract](11) [FullText HTML] (10) [PDF 2017KB](0)
Abstract:
In order to overcome the accumulation error in Micro-Electro-Mechanical System-Inertial Navigation System (MEMS-INS) and the jump error in iBeacon fingerprint positioning, an iBencon/INS data fusion location algorithm based on Unscented Kalman Filter (UKF) is proposed. The new algorithm solves the distance between the iBeacon anchor and the locating target. The solution of attitude matrix and position are obtained respectively by using accelerometer and gyroscope data. Bluetooth anchor position vector, the carrier speed error and other information constitute state variables. Inertial navigation location and bluetooth system distance information constitute measure variables. Based on state variables and measure variables, the UKF is designed to realize iBencon/INS data fusion indoor positioning. The experimental results show that the proposed algorithm can effectively solve the problem of the large accumulation error of INS and the jump error of iBeacon fingerprint positioning, and this algorithm can realize 1.5 m positioning accuracy.
, Available online  , doi: 10.11999/JEIT180922 doi: 10.11999/JEIT180922
[Abstract](80) [FullText HTML] (35) [PDF 4662KB](1)
Abstract:
A broadband metasurface antenna array with wideband low Radar Cross Section (RCS) is proposed. Two kind metasurface antennas with nearly the same radiation performance are designed and fabricated in a chessboard configuration. For x-polarized incidence, the reflected energy from the two elements is dissipated based on destructive phase difference. For y-polarized incidence, the incident energy is absorbed by matching load. By this means, inherent wideband low RCS is achieved for both polarizations without redundant structures. The proposed antenna array is fabricated and measured. Simulated and measured results show that the working frequency band is 6.0～8.5 GHz. Meanwhile under X polarization the antenna monostatic RCS is reduced significantly 6 dB RCS reduction is achieved over the range of 6.2～10.5 GHz and the peak reduction is up to 21.07 dB. Under Y polarization the antenna monostatic RCS is reduced over 3 dB RCS reduction in bandwidth. Both measured and simulated results verify the proposed antenna array is characterized with wideband low-RCS without degrading the radiation performance.
, Available online  , doi: 10.11999/JEIT180905 doi: 10.11999/JEIT180905
[Abstract](90) [FullText HTML] (41) [PDF 1679KB](15)
Abstract:
In order to defend against side-channel attacks in network slicing, the existing defense methods based on dynamic migration have the problem that the conditions for sharing of physical resources between different virtual nodes are not strict enough, a virtual node migration method is proposed for sensing side-channel risk. According to the characteristics of side-channel attacks, the entropy method is used to evaluate the side-channel risks and migrate the virtual node from a server with large deviation from average risk. The Markov decision process is used to describe the migration of virtual nodes for network slicing, and the Sarsa learning algorithm is used to solve the optimal migration scheme. The simulation results show that this method can separates malicious network slice instances from other target network slice instances to achieve the purpose of defense side channel attacks.
, Available online  , doi: 10.11999/JEIT180832 doi: 10.11999/JEIT180832
[Abstract](82) [FullText HTML] (42) [PDF 1439KB](7)
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.
, Available online  , doi: 10.11999/JEIT180836 doi: 10.11999/JEIT180836
[Abstract](105) [FullText HTML] (58) [PDF 1219KB](14)
Abstract:
Large integer multiplication is the most important part in public key encryption, which often consumes most of the computing time in RSA, ElGamal, Fully Homomorphic Encryption (FHE) and other cryptosystems. Based on Schönhage-Strassen Algorithm (SSA), a design of high-speed 768 kbit multiplier is proposed in this paper. As the key component, an 64K-point Number Theoretical Transform (NTT) is optimized by adopting parallel architecture, in which only addition and shift operations are employed and thus the processing speed is improved effectively. The large integer multiplier design is validated on Stratix-V FPGA. By comparing its results with CPU using Number Theory Library(NTL) and GMP library, the correctness of this design is proved. The results also show that the FPGA implementation is about eight times faster than the same algorithm executed on the CPU.
, Available online  , doi: 10.11999/JEIT180850 doi: 10.11999/JEIT180850
[Abstract](100) [FullText HTML] (54) [PDF 634KB](13)
Abstract:
The definition and security models of partial blind signcryption scheme in heterogeneous environment between CertificateLess Public Key Cryptography (CLPKC)and Traditional Public Key Infrastructure (TPKI) are propsed, and a construction by using the bilinear pairing is propsed. Under the random oracle model, based on the assumptions of Computational Diffie-Hellman Problem(CDHP) and Modifying Inverse Computational Diffie-Hellman(MICDHP), the scheme is proved to meeting the requirment of the unforgeability, confidentiality, partial blindness, and untraceability, undeniability. Finally, compared with the related scheme, the scheme increases the blindness and does not significantly increase the computational cost.
, Available online  , doi: 10.11999/JEIT181040 doi: 10.11999/JEIT181040
[Abstract](129) [FullText HTML] (114) [PDF 1668KB](30)
Abstract:
The secret key generation method based on random signal may leak part of the common randomness information and reduce the achievable secret key rate when legal transmitter transmits random signal. In response to this problem, the secret key generation method based on multi-stream random signal is proposed. Firstly, the transmitter uses the channel reciprocity and uplink pilot to estimate the downlink channel, then the transmitter transmits mutually independent signal on every antenna. The eavesdropper is difficult to estimate all the random signal. It is difficult to estimate all the random signal for the eavesdropper, so the overlapping signal received by every antenna is difficult to be obtained by the eavesdropper. However, the legal transmitter is able to calculate the signal received by legal receiver by using the downlink channel estimated and the signal transmitted. So, the overlapping signal on every legal antenna can be used to extract secret key as common randomness. Also, the achievable secret key rate expression and the mutual information expression of common randomness are derived, and the relationship between them and the secret key security is analyzed. At last, the effectiveness of this method is verified by the simulation. The simulation results show that this method can reduce the common randomness observed by the eavesdropper to raise the achievable secret key rate and secret key security.
, Available online  , doi: 10.11999/JEIT180793 doi: 10.11999/JEIT180793
[Abstract](108) [FullText HTML] (54) [PDF 1985KB](19)
Abstract:
Ontology, as the superstructure of knowledge graph, has great significance in knowledge graph domain. In general, structural redundancy may arise in ontology evolution. Most of existing redundancy elimination algorithms focus on transitive redundancies while ignore equivalent relations. Focusing on this problem, a redundancy elimination algorithm based on super-node theory is proposed. Firstly, the nodes equivalent to each other are considered as a super-node to transfer the ontology into a directed acyclic graph. Thus the redundancies relating to transitive relations can be eliminated by existing methods. Then equivalent relations are restored, and the redundancies between equivalent and transitive relations are eliminated. Experiments on both synthetic dynamic networks and real networks indicate that the proposed algorithm can detect redundant relations precisely, with better performance and stability compared with the benchmarks.
, Available online  , doi: 10.11999/JEIT180804 doi: 10.11999/JEIT180804
[Abstract](100) [FullText HTML] (48) [PDF 2650KB](6)
Abstract:
Present researches on Distributed Luby’s Transmission codes(DLT) are restricted on several-sources and one-layer-relay networks, thus the Multiple Layers Distributed LT Code (MLDLT) for multiple-layers-relays networks is proposed. In MLDLT, sources are grouped and realys are layered in order that scores of sources could be connected to the only destination through the layered relays. By this scheme, the distributed communication between scores of sources and the destination could be performed. Through the and-or tree analysis, the linear procedures for the optimization of the relays' degree distributions are derived. On both lossless and lossy links, asymptotic performances of MLDLT are analized and the numberical simulations are experimented. The results demonstrate that MLDLT could achieve satisfying erasure floors on both lossless and lossy links. MLDLT is a feasible solution for the scores-sources and multiple-layers-realys networks.
, Available online  , doi: 10.11999/JEIT180752 doi: 10.11999/JEIT180752
[Abstract](111) [FullText HTML] (53) [PDF 2397KB](13)
Abstract:
A 10 bit fully differential dual slope Analog-to-Digital Converter (ADC) for Time Delay Integration (TDI) CMOS image sensors is realized based on column-parallel single-slope ADC. Top plates of the two capacitors are used for sampling differential inputs, and the bottom plates are connected to ramp generator for conversion. Current steering is used to generate the rising and falling ramp with same step voltage simultaneously. The proposed ADC is fabricated in SMIC 0.18 μm CMOS process. Simulated spurious free dynamic range and effective number of bits are 87.92 dB and 9.84 bit with the input frequency of 1.32 kHz at 19.49 kS/s sampling rate, respectively. Measured results show that the ADC has a differential nonlinearity of –0.7/+0.6 LSB and integral nonlinearity of –2.6/+2.1 LSB.
, Available online  , doi: 10.11999/JEIT181086 doi: 10.11999/JEIT181086
[Abstract](117) [FullText HTML] (62) [PDF 1925KB](17)
Abstract:
The equivalent rotation center should be estimated accurately in the Inverse Synthetic Aperture Radar (ISAR) for the issue of image defocusing induced by the Migration Through Resolution Cells (MTRC). In this paper, an equivalent rotation center estimation algorithm based on image rotation and correlation is proposed for the space target. First, the instantaneous imaging mechanism of ISAR is analyzed. Second, two images with different observation angles are obtained by using the echo data with the same motion compensation algorithm. Finally, the equivalent rotation center is estimated based on the scaled image pixel rotation and image correlation. Consequently, the estimated position of the rotation center is obtained, when the assumed rotation center is in accordance with the real one and the maximum correlation coefficient of two images is achieved. The results demonstrate the effectiveness and robustness of the proposed algorithm.
, Available online  , doi: 10.11999/JEIT180670 doi: 10.11999/JEIT180670
[Abstract](132) [FullText HTML] (67) [PDF 1375KB](16)
Abstract:
The diversity of the population and the crossover operator algorithm play an important role in solving global optimization problems in Differential Evolution (DE). The Multi-poplutions Covariance learning Differential Evolution (MCDE) algorithm is proposed. Firstly, the population structure is a multi-poplutions mechanism, and each subpopulation combines the corresponding mutation strategy to ensure the individual diversity in the evolutionary process. Then, the covariance learning establishes a proper rotation coordinate system for the crossover operation in the population. At the same time, the adaptive control parameters are used to balance the ability of population survey and convergence. Finally, the proposed algorithm is conducted on 25 benchmark functions including unimodal, multimodal, shifted and high-dimensional test functions and compared with the state-of-the-art evolutionary algorithms. The experimental results show that the proposed algorithm compared with other algorithms has the best effect on solving the global optimization problem.
, Available online  , doi: 10.11999/JEIT180722 doi: 10.11999/JEIT180722
[Abstract](137) [FullText HTML] (63) [PDF 1394KB](10)
Abstract:
In Cloud Radio Access Network (Cloud-RAN), most of the existing work assumes Remote Radio Heads (RRHs) could not cache content. To better adapt the content-centric feature of next-generation communication networks, it is necessary to consider caching function for RRHs in Cloud-RAN. Motivated by this, this paper intends to design the suitable caching schemes and reduce the burden of fronthaul link burden through resource allocation. It is assumed the system utilizes Orthogonal Frequency Division Multiple Access (OFDMA) technique. A jointly optimization scheme of SubCarrier (SC) allocation, RRH selection, and transmission power is proposed to minimize the total downlink power consumption. To transform the original non-convex problem, a Lagrange dual decomposition is utilized to design the optimal allocation scheme. The experimental results show that the proposed algorithms can effectively improve the energy efficiency of the system, which meets the requirements of green communication in the future.
, Available online  , doi: 10.11999/TEIT180875 doi: 10.11999/TEIT180875
[Abstract](102) [FullText HTML] (39) [PDF 2154KB](7)
Abstract:
Surface Acoustic Wave (SAW) resonator measuring technology can be used in high temperature and high pressure, strong electromagnetic radiation and strong electromagnetic interference to realize wireless passive parameter detection. Based on the the non-stationary characteristics of the SAW signal, a kind of echo signal frequency estimation method, Digital Frequency Significant Place Tracking method (DFSPT) is put forward. Compared with the existing methods based on Fast Fourier Transform (FFT) and Singular Value Decomposition (SVD), the simulation results show that the method can determine the number of significant digits of digital frequency according to the difference of signal-to-noise ratio. Thus, it can increase stability and accuracy. The experiment of wireless SAW temperature sensor shows that the frequency estimation standard deviation of this method is small and the robustness is high.
, Available online  , doi: 10.11999/JEIT180828 doi: 10.11999/JEIT180828
[Abstract](120) [FullText HTML] (62) [PDF 2158KB](12)
Abstract:
In order to reconstruct natural image from Compressed Sensing(CS) measurements accurately and effectively, a CS image reconstruction algorithm based on Non-local Low Rank(NLR) and Weighted Total Variation(WTV) is proposed. The proposed algorithm considers the Non-local Self-Similarity(NSS) and local smoothness in the image and improves the traditional TV model, in which only the weights of image’s high-frequency components are set and constructed with a differential curvature edge detection operator. Besides, the optimization model of the proposed algorithm is built with constraints of the improved TV and the non-local low rank model, and a non-convex smooth function and a soft thresholding function are utilized to solve low rank and TV optimization problems respectively. By taking advantage of them, the proposed method makes full use of the property of image, and therefore conserves the details of image and is more robust and adaptable. Experimental results show that, compared with the CS reconstruction algorithm via non-local low rank, at the same sampling rate, the Peak Signal to Noise Ratio(PSNR) of the proposed method increases 2.49 dB at most and the proposed method is more robust, which proves the effectiveness of the proposed algorithm.
, Available online  , doi: 10.11999/JEIT180666 doi: 10.11999/JEIT180666
[Abstract](141) [FullText HTML] (75) [PDF 2750KB](20)
Abstract:
To solve the problem of real-time migration of Virtual Network Function (VNF) caused by lacking effective prediction in 5G network, a VNF migration algorithm based on deep belief network prediction of resource requirements is proposed. The algorithm builds firstly a system cost evaluation model integrating bandwidth cost and migration cost,and then designs a deep belief network prediction algorithm based on online learning which adopts adaptive learning rate and introduces multi-task learning mode to predict future resource requirements. Finally, based on the prediction result as well as the perception of network topology and resources, the VNFs are migrated to the physical nodes that meet the resource threshold constraints through greedy selection algorithm with the goal to optimize system cost,and then a migration mechanism based on tabu search is proposed to further optimize the migration strategy.The simulation results show that the prediction model can obtain good prediction results and adaptive learning rate accelerates the convergence speed of the training network.Moreover, the combination with the migration algorithm reduces effectively system cost and the number of Service Level Agreements (SLA) violations during the migration process, and improves the performance of network services.
, Available online  , doi: 10.11999/JEIT180904 doi: 10.11999/JEIT180904
[Abstract](82) [FullText HTML] (41) [PDF 2422KB](12)
Abstract:
For the passive radar based on LTE signal, the received signal contains direct-path and multipath clutters interference of multiple co-channel base station, and the traditional passive radar signal processing flow is improved, and the processing steps of co-channel base station interference are added. A blind source separation algorithm based on convolutive mixtures is proposed. The algorithm can suppress the clutters interference of co-channel base station. It is assumed that the mixing matrix is a vector linear time-invariant filter matrix. The mutual information is used as a cost function. By finding the gradient of mutual information, it is iterated by the steepest descent method. The separation criterion is to minimize the mutual information between the separated signals. The simulation results show that the proposed algorithm can effectively suppress the clutters interference of the LTE signal co-channel base station, and provide a basis for the subsequent clutters cancellation processing of the main base station.
, Available online  , doi: 10.11999/JEIT180874 doi: 10.11999/JEIT180874
[Abstract](165) [FullText HTML] (79) [PDF 1132KB](12)
Abstract:
In order to solve the problem that users can not request to exit during the bitcoin confusion process, an anonymous revocation scheme for Bitcoin confusion is proposed. The commitment is used to bind the user with its destination address. When the user requests to quit the shuffle service, a zero-knowledge proof of the commitment is made using the accumulator and the signatures of knowledge. Finally, the shuffled output address of the user who quits the service is modified to its destination address. Security analysis shows that the scheme satisfies the anonymity of the user who quits the service based on the double discrete logarithm problem and the strong RSA assumption, and can be implemented without modifying the current bitcoin system. The scheme allows at most n–2 users to exit in the confusion process of n (n≥10) honest users participation.
, Available online  , doi: 10.11999/JEIT180885 doi: 10.11999/JEIT180885
[Abstract](102) [FullText HTML] (50) [PDF 1287KB](13)
Abstract:
A Priority Scheduling scheme based on Adaptive Random Linear Network Coding (PSARLNC) is proposed, to avoid the high computation complexity of the scheduling scheme based on Random Linear Network Coding (RLNC) and the high feedback dependence of the network performance. The characteristics of the video stream and RLNC adapted to multicast are combined in this scheme. Compared with the traditional RLNC, the computation complexity of this scheme is reduced. After the initial transmission, the transmission slots left of the data packet are comprehensively considered in the subsequent data recovery phase, and the maximum transmission node of the destination node gain is selected to maximize data transmission. At the same time, the decoding probability is available according to the different receiving situations in each relay node. According to the decoding probability value, the scheduling priority is determined, and the forwarding is completed. The transmission of each node is adaptively adjusted, and the feedback information is effectively reduced. The simulation results show that the performance of this scheme is approached to the full-feedback scheme, with the better advantages in the reducing computational complexity and the decreasing feedback dependence.
, Available online  , doi: 10.11999/JEIT180807 doi: 10.11999/JEIT180807
[Abstract](117) [FullText HTML] (48) [PDF 2141KB](6)
Abstract:
To satisfy the demand of the high precision time and frequency synchronization for engineering application, to reduce system complexity and ensure the construction of large-scale optical fiber network for time and frequency transmission, a method of high precision time and frequency integration transfer via optical fiber based on pseudo-code modulation is developed. The optical fiber time and frequency transfer system is designed and built. The unidirectional and bidirectional time and frequency transfer test via optical fiber are completed. In the unidirectional time-frequency transfer test, the influence of temperature change on the transmission delay of the system is analyzed. In the bidirectional time-frequency transfer test, the system additional time transfer jitter is 0.28 ps/s, 0.82 ps/1000 s, the additional frequency transfer instability is 4.94×10–13/s, and 6.39×10–17/40000 s. The results show that the proposed method achieves high precision time and frequency integration synchronization, and the system additional time transfer jitter is better than the current optical fiber time synchronization schemes.
, Available online  , doi: 10.11999/JEIT180851 doi: 10.11999/JEIT180851
[Abstract](100) [FullText HTML] (58) [PDF 3290KB](12)
Abstract:
Benefit from the rapid development of digital frequency storage technology, the Intermittent Sampling Repeater Jamming(ISRJ) is widely used. Existing radar anti-jamming means are hard to against this jamming effectively. Based on the analysis of the principles of the ISRJ, for the discontinuities of ISRJ in time domain, an anti ISRJ method based on LFM segmented pulse compression is proposed. This method utilizes the orthogonality between LFM segmented signals, combines with cover waveform concept, distinguishes jamming and target through narrow band filter group, then suppresses jamming, finally accumulates signals in intra-pulse and inter-pulse. Theoretical analysis and experimental results show the anti ISRJ method can effectively resist the intermittent sampling interference which combine with different styles of multiple jammers.
, Available online  , doi: 10.11999/JEIT180948 doi: 10.11999/JEIT180948
[Abstract](107) [FullText HTML] (46) [PDF 2508KB](9)
Abstract:
Based on the analysis on the difference between vector tracking loop and scalar tracking loop on fault detection, it is pointed out that in vector receiver of Global Navigation Satellite System (GNSS), the detection statistic of Receiver Autonomous Integrity Monitoring (RAIM) algorithm is inaccurate because of the influence of noise, and the propagation of fault information in the loop makes it difficult to identify the fault source. To solve the problems, a double loop tracking structure based on pre-filter is proposed after modifying the structure of vector receiver. In the new receiver, the influence of noise is reduced by pre-filter based on cubature Kalman filtering algorithm, and the fault information is prevented from propagating to each other by switching the loop. Finally, the method is verified by simulation. Simulation results show that the improved vector receiver not only greatly reduces the mean and variance of RAIM detection statistics, but also improves the accuracy of fault identification. Thus, the performance of RAIM is significantly improved.
, Available online  , doi: 10.11999/JEIT180703 doi: 10.11999/JEIT180703
[Abstract](101) [FullText HTML] (48) [PDF 474KB](9)
Abstract:
Two constructions of Gaussian integer Zero Correlation Zone (ZCZ) sequence set are researched. In Construction I, the method of zero padding is implemented on the ZCZ sequence set, and then the Gaussian integer ZCZ can be obtained by the filtering operation. Furthermore, the degree of the Gaussian integer ZCZ sequence set is calculated in this paper. In Construction II, two constructions of Gaussian integer orthogonal matrix are proposed. In addition, the optimal Gaussian integer ZCZ sequence sets are constructed based on the orthogonal matrix. The two classes of Gaussian integer ZCZ sequence sets presented in this paper can be applied to many communication systems such as Quasi-Synchronous Code Division Multiple Access (QS-CDMA)、Orthogonal Frequency Division Multiplexing (OFDM) and Mutiple-Input Multiple-Output (MIMO) system to suppress the interference and improve the spectrum efficiency.
, Available online  , doi: 10.11999/JEIT180831 doi: 10.11999/JEIT180831
[Abstract](101) [FullText HTML] (51) [PDF 2700KB](7)
Abstract:
Long configuration time is a significant factor which restricts the performance improvement of the reconfigurable system, and a reasonable task scheduling technology can effectively reduce the system configuration time. A three-dimensional task scheduling model for Coarse-Grain Dynamic Reconfigurable System (CGDRS) and flow applications with data dependencies is proposed. Firstly, based on this model, a Configuration Prefetching Schedule Algorithm (CPSA) applying pre-configured strategy is designed. Then, the interval and continuous configuration reuse strategy are proposed according to the configuration reusability between tasks, and the CPSA algorithm is improved accordingly. The experimental results show this algorithm can avoid scheduling deadlock, reduce the execution time of flow applications and improve scheduling success rate. The optimization ratio of total execution time of flow applications achieves 6.13%～19.53% averagely compared with other scheduling algorithms.
, Available online  , doi: 10.11999/JEIT180747 doi: 10.11999/JEIT180747
[Abstract](110) [FullText HTML] (53) [PDF 821KB](14)
Abstract:
In view of the imaging quality of sparse ISAR imaging methods is limited by the inaccurate sparse representation of the scene to be imaged, the Dictionary Learning (DL) technique is introduced into ISAR sparse imaging to get better sparse representation of the scene. An off-line DL based imaging method and an on-line DL based imaging method are proposed. The off-line DL imaging method can obtain a better sparse representation via a dictionary learned from the available ISAR images. The on-line DL imaging method can obtain the sparse representation from the data currently considered by jointly optimizing the imaging and DL processes. The results of both simulated and real ISAR data show that the on-line DL imaging method and the off-line dictionary imaging method are both able to better sparsely represent the target scene leading to better imaging results. The off-line DL based imaging method works better than the on-line DL based imaging method with respect to both imaging quality and computational efficiency.
, Available online  , doi: 10.11999/JEIT180837 doi: 10.11999/JEIT180837
[Abstract](140) [FullText HTML] (69) [PDF 1690KB](11)
Abstract:
Considering lacking of centralized and synergistic scheduling for time-slots and wavelength resources of the inter-TWDM-PONs, a novel Resource Allocation based on Bandwidth Prediction (RABP) strategy with software-defined centralized schedule is proposed. For intra-Optical Line Terminal (OLT), the BP neural network model based on Particle Swarm Optimization (PSO) algorithm is designed to predict the required bandwidth of each OLT in order to avoid the impact of delay between controller and OLT on real-time resource allocation. For inter-OLT, the slide cycle is dynamically set, and then the shared bandwidth of resource pool is counted in real-time according to the authorized information of optical network unit. In the process of wavelength scheduling, a wavelength scheduling mechanism with load balancing to achieve efficiently utilize of wavelength resource is designed. The simulation results show that the proposed strategy not only effectively improves the utilization of channel resources, but also reduce the average packet delay.
, Available online  , doi: 10.11999/JEIT180824 doi: 10.11999/JEIT180824
[Abstract](129) [FullText HTML] (56) [PDF 500KB](6)
Abstract:
In order to comprehensively study the security of the Attribute-Based Encryption (ABE) scheme based on Learning With Errors (LWE) and test its ability to resist existing attacks, an analysis method for concrete security of ABE based on LWE is proposed. After consideration of the parameter restrictions caused by algorithms on lattices and noise expansion, this method applies the existing algorithms for solving LWE and the available program modules and it can quickly provide the specific parameters that satisfy the scheme and estimate the corresponding security level. In addition, it can output the specific parameters that satisfy the pre-given security level. Finally, four existing typical schemes are analyzed by this method. Experiments show that the parameters are too large to be applied to practical applications.
, Available online  , doi: 10.11999/JEIT180942 doi: 10.11999/JEIT180942
[Abstract](109) [FullText HTML] (52) [PDF 1249KB](6)
Abstract:
Data compression and decompression are widely used in modern communication and data transmission. However, how to decompress the damaged lossless compressed files is still a challenge. For the lossless data compression algorithm widely used in the general coding field, an effective method is proposed to repair the error and decompress and restore the corrupted LZSS files, and the theoretical basis is given. By using the residual redundancy left by the encoder to carry the check information, the method can repair the errors in LZSS compressed data without loss of any compression performance. The proposed method does not require additional bits or changes in coding rules and data formats, thus it is fully compatible with standard algorithms. That is, the data compressed by LZSS with error repair capability can still be decompressed by standard LZSS decoder. The experimental results verify the validity and practicability of the proposed algorithm.
, Available online  , doi: 10.11999/JEIT180751 doi: 10.11999/JEIT180751
[Abstract](121) [FullText HTML] (51) [PDF 812KB](13)
Abstract:
To overcome the mechanism shortcomings of wormhole and white hole selection in the base Multi-Verse Optimizer (MVO), an Improved Multi-Universes Optimization Algorithm is proposed. To speed up global exploration ability and quick iteration ability, this thesis designs the existence mechanism of wormhole with fixed probability and the Travel Distance Rate (TDR) that its convergence from early stage's smoothly to later stage's fast. The random white hole selection mechanism is proposed; Black holes can revolve around selected white hole stars and is modelled to solve the problem of information communication of the Inter-generational Universes. The performance of IMVO is verified by comparison experiments in low-middle dimensions. Three benchmarks test functions are selected for comparison in large scale which are difficult to be optimized, the experimental results show that IMVO has good applicability and robustness with higher solving accuracy and success rate in large scale optimization problem.
, Available online  , doi: 10.11999/JEIT180702 doi: 10.11999/JEIT180702
[Abstract](110) [FullText HTML] (59) [PDF 1951KB](11)
Abstract:
Because of the classic Faster RCNN training proccess with too many difficult training samples and low recall rate problem, a method which combines the techniques of Online Hard Example Mining (OHEM) and Hard Negative Example Mining (HNEM) is adopted, which carries out the error transfer for the difficult samples using its corresponding maximum loss value from real-time filtering. It solves the problem of low detection of hard example and improves the efficiency of the model training. To improve the recall rate and generalization of the model, an improved Non-Maximum Suppression (NMS) algorithm is proposed by setting confidence thresholds penalty function; in addition, multi-scale training and data augmentation are also introduced. Finally, the results before and after improvement are compared: Sensibility experiments show that the algorithm achieves good results in VOC2007 data set and VOC2012 data set, with the mean Average Percision (mAP) increasing from 69.9% to 74.40%, and 70.4% to 79.3% respectively, which demonstrates strongly the superiority of the algorithm.
, Available online  , doi: 10.11999/JEIT180677 doi: 10.11999/JEIT180677
[Abstract](219) [FullText HTML] (108) [PDF 1645KB](53)
Abstract:
In order to meet the demand for high real-time and high generalization performance of radar recognition, a radar High Resolution Range Profile (HRRP) recognition method based on deep multi-scale one dimension convolutional neural network is proposed. The multi-scale convolutional layer that can represent the complex features of HRRP is designed based on two features of the convolution kernels which are weight sharing and extraction of different fineness features from different scales, respectively. At last, the center loss function is used to improve the separability of features. Experimental results show that the model can greatly improve the accuracy of the target recognition under non-ideal conditions and solve the problem of the target aspect sensitivity, which also has good robustness and generalization performance.
, Available online  , doi: 10.11999/JEIT180707 doi: 10.11999/JEIT180707
[Abstract](150) [FullText HTML] (70) [PDF 2133KB](16)
Abstract:
The detection performance of ship targets by skywave Over-The-Horizon Radar (OTHR) is affected by the sea clutter seriously. Accurate and adaptive suppression of sea clutter is significant for improving the detection performance of ship target. To solve the non-adaptive shortness of the sea clutter suppression algorithm based on High-Order Singular Value Decomposition (HOSVD), a modified adaptive algorithm based on Peak Signal-to-Noise Ratio (PSNR)-HOSVD is proposed by introducing the PSNR. The modified algorithm has a smaller computational complexity than the one based on HOSVD, since only one projection matrix is established from the left singular vectors of the third-mode unfolding matrix. Meanwhile, the modified algorithm has a better performance than the HOSVD based one, because the components of sea clutter are only aggregated in the column space of the third-mode unfolding matrix. Experimental results based on two sets of measured data received in ideal and non-ideal situations in respective show that, the modified adaptive algorithm based on PSNR-HOSVD has a better performance than the peer algorithms.
, Available online  , doi: 10.11999/JEIT180944 doi: 10.11999/JEIT180944
[Abstract](141) [FullText HTML] (86) [PDF 809KB](18)
Abstract:
Considering the existing attribute-based searchable encryption scheme lacks the authorization service to the cloud server, a multi-server Ciphertext Polity Attribute Base Encryption (CP-ABE) scheme with searchable is proposed based on authorization. The scheme implements search services through a cloud filter server, cloud search server and cloud storage server cooperation mechanism. The users send the authorization information to the cloud filter server at once, then the server creates the authorization information; the cloud search server creates the trapdoor information based on the trapdoor information sent by the users. Without decrypting the cipher text, the cloud filter server can detect all the cipher text. Multiple attribute authorities can be used to ensure efficient and fine-grained access under the premise of ensuring data confidentiality, solve the problem of leakage of data user keys. It can improve the data retrieval efficiency when people user the cloud server. Through security analysis, it is proved that the scheme can not steal sensitive information of data users while providing data retrieval services, can effectively prevent the leakage of data privacy.
, Available online  , doi: 10.11999/JEIT180663 doi: 10.11999/JEIT180663
[Abstract](158) [FullText HTML] (78) [PDF 2058KB](9)
Abstract:
The moving target component is often defocused in spaceborne SAR images. Therefore, the moving target detection performance is affected depending on the degree of defocusing. Combined with the RD algorithm, a moving-targets detection algorithm for spaceborne SAR based on a two-dimensional velocity search is proposed. Through velocity search on the distance direction and the azimuth direction, the Doppler parameters of possible moving targets can be matched. Then the strongest value among all the searching velocity results for each pixel is used for Constant False Alarm Rate(CFAR) detector. This core process can improve the detection performance of moving target component. Simulation results validate the effectiveness of the proposed method.
, Available online  , doi: 10.11999/JEIT180661 doi: 10.11999/JEIT180661
[Abstract](104) [FullText HTML] (58) [PDF 3447KB](13)
Abstract:
Microwave photonics radar generates signals with large bandwidth and small wavelength. It has capability of ultra-high resolution of Inverse Synthetic Aperture Radar(ISAR) image. Because the approximation of rotational components is not tenable, traditional ISAR imaging algorithm is not suitable to microwave photonics radar. In the microwave photonics radar imaging, the rotational components result in range curvature and quadratic phase error changing with distance. To solve this problem, an effective ISAR imaging algorithm is put forward which considers the influence of the target’s rotational component to echo envelope and phase. The value of envelope correlation is take as objective function and the target’s rotate speed is estimated by iteration; The range curvature is corrected by time resampling; The quadratic phase error is compensated by azimuth compensation function. Both simulated and real-measured data experimental results confirm the effectiveness of the proposed algorithm.
, Available online  , doi: 10.11999/JEIT180780 doi: 10.11999/JEIT180780
[Abstract](132) [FullText HTML] (53) [PDF 1417KB](11)
Abstract:
Because of the problem that the target is prone to drift in complex background, a robust tracking algorithm based on spatial reliability constraint is proposed. Firstly, the pre-trained Convolutional Neural Network (CNN) model is used to extract the multi-layer deep features of the target, and the correlation filters are respectively trained on each layer to perform weighted fusion of the obtained response maps. Then, the reliability region information of the target is extracted through the high-level feature map, a binary matrix is obtained. Finally, the obtained binary matrix is used to constrain the search area of the response map, and the maximum response value in the area is the target position. In addition, in order to deal with the long-term occlusion problem, a random selection model update strategy with the first frame template information is proposed. The experimental results show that the proposed algorithm has good performance in dealing with similar background interference, occlusion, and other scenes.
, Available online  , doi: 10.11999/JEIT180758 doi: 10.11999/JEIT180758
[Abstract](119) [FullText HTML] (44) [PDF 2258KB](5)
Abstract:
This paper presents a method of low-altitude wind-shear speed estimation based on Generalized adjacent Multi-Beam (GMB) Adaptive Processing under aircraft yawing. The clutter range-dependence compensation method based on echo data is first used to correct the range dependence of clutter for estimating the clutter covariance matrix. Then the dimension-reduced transform matrix is calculated by combining adjacent multiple beams in the airspace and adjacent multiple Doppler channels in time domain simultaneously, and the radar echo data of the measured range bin is reduced in dimension, and then the optimal weight vector of the GMB adaptive processor is constructed to filter adaptively the dimension-reduced data. Finally, the accurate estimation of the wind speed under the aircraft yawing is got. The simulation results show that the proposed method can obtain an effective estimation of wind speed under aircraft yawing.
, Available online  , doi: 10.11999/JEIT180723 doi: 10.11999/JEIT180723
[Abstract](185) [FullText HTML] (87) [PDF 1680KB](19)
Abstract:
In order to realize underdetermined wideband Direction Of Arrival(DOA) estimation based on sparse array, an algorithm on account of Distributed Compressive Sensing(DCS) is proposed. Firstly, wideband signal processing model based on sparse array is deduced and the underdetermined wideband DOA estimation is formulated as a DCS problem. Then, the DCS-Simultaneous Orthogonal Matching Pursuit(DCS-SOMP) algorithm is utilized to solve this problem. Finally, the off-grid problem is considered and a joint DCS model containing off-grid parameters is established. Estimations of DOAs and off-grid parameters are achieved through iterative solution. Simulation results show that the proposed algorithm is effective and have advantages in resolution and computational complexity.
, Available online  , doi: 10.11999/JEIT180651 doi: 10.11999/JEIT180651
[Abstract](199) [FullText HTML] (95) [PDF 2455KB](25)
Abstract:
In order to deal with these issues of the traditional Fuzzy C-Means (FCM) algorithm, such as without consideration of the spatial neighborhood information of pixels, noise sensitivity and low convergence speed, a suppressed non-local spatial intuitionistic fuzzy c-means image segmentation algorithm is proposed. Firstly, in order to improve the accuracy of segmentation image, the non-local spatial information of pixel is used to improve anti-noise ability, and to overcome the shortcomings of the traditional FCM algorithm in which only considers the gray characteristic information of single pixel. Secondly, by using the ‘voting model’ based on the intuitionistic fuzzy set theory, the hesitation degrees are adaptively generated as inhibitory factors to modify the membership degrees, and then the operating efficiency is increased. Experimental results show that the new algorithm is robust to noise and has better segmentation performance.
, Available online  , doi: 10.11999/JEIT180777 doi: 10.11999/JEIT180777
[Abstract](150) [FullText HTML] (69) [PDF 1513KB](16)
Abstract:
As machine learning is widely applied to various domains, its security vulnerability is also highlighted. A PSO (Particle Swarm Optimization) based on adversarial example generation algorithm is proposed to reveal the potential security risks of Support Vector Machine (SVM). The adversarial examples, generated by slightly crafting the legitimate samples, can mislead SVM classifier to give wrong classification results. Using the linear separable property of SVM in high-dimensional feature space, PSO is used to find the salient features, and then the average method is used to map back to the original input space to construct the adversarial example. This method makes full use of the easily finding salient features of linear models in the feature space, and the interpretable advantages of the original input space. Experimental results show that the proposed method can fool SVM classifier by using the adversarial example generated by less than 7 % small perturbation, thus proving that SVM has obvious security vulnerability.
, Available online  , doi: 10.11999/JEIT180766 doi: 10.11999/JEIT180766
[Abstract](186) [FullText HTML] (88) [PDF 1577KB](22)
Abstract:
A joint real-valued beamspace-based method for angle estimation in bistatic Multiple-Input Multiple-Output (MIMO) radar is proposed. Instead of using the traditional Discrete Fourier Transform (DFT) beamspace filter, the proposed beamspace filter is designed through convex optimization, which can flexibly control the bandwidth and limit the sidelobe level. Based on this property, the mainlobe-to-sidelobe ratio of the proposed beamspace filter can be greatly improved, which results in the improved estimation performance. More importantly, the structure of the proposed beamspace matrix can be properly designed, which is indispensable in constructing the real-valued signal model. Finally, the mapping relationship to compensate the interpolation error is established. Simulation results verify the effectiveness of the proposed method.
, Available online  , doi: 10.11999/JEIT180637 doi: 10.11999/JEIT180637
[Abstract](218) [FullText HTML] (115) [PDF 1198KB](17)
Abstract:
The traditional fingerprinting localization algorithm has high construct time overhead and low positioning accuracy. Because of this problem, an adaptive fading memory based bluetooth sequence matching localization algorithm is proposed. Firsly, Pedestrian Dead Reckoning(PDR) and Nearest Neighbor Algorithm(NNA) are applied to perform position calibration and Received Signal Strength(RSS) mapping of Motion Sequences. Secoudly, according to the relevance of neighboring locations, a sequence recursive search method is used to construct fingerprint sequence database. Finally, an adaptive fading memory algorithm and initial sequence matching degree are considered to realize the position estimation of target. The experimental results show that this algorithm is able to consume low construct time overhead and achieve high indoor localization precision.
, Available online  , doi: 10.11999/JEIT180764 doi: 10.11999/JEIT180764
[Abstract](220) [FullText HTML] (101) [PDF 1219KB](11)
Abstract:
The Dissimilar Redundancy Structure (DRS) based cyberspace security technology is an active defense technology, which uses features such as dissimilarity and redundancy to block or disrupt network attacks to improve system reliability and security. By analyzing how heterogeneity can improve the security of the system, the importance of quantification of heterogeneity is pointed out and the heterogeneity of DRS is defined as the complexity and disparity of its execution set. A new method which is suitable for quantitative heterogeneity is also proposed. The experimental results show this method can divide 10 execution sets into 9 categories, while the Shannon-Wiener index, Simpson index and Pielou index can only divide into 4 categories. This paper provides a new method to quantify the heterogeneity of DRS in theory, and provides guidance for engineering DRS systems.
, Available online  , doi: 10.11999/JEIT180735 doi: 10.11999/JEIT180735
[Abstract](118) [FullText HTML] (50) [PDF 1232KB](14)
Abstract:
A sufficient condition for general quadratic polynomial systems to be topologically conjugate with Tent map is proposed. Base on this condition, the probability density function of a class of quadratic polynomial systems is provided and transformations function which can homogenize this class of chaotic systems is further obtained. The performances of both the original system and the homogenized system are evaluated. Numerical simulations show that the information entropy of the uniformly distributed sequences is closer to the theoretical limit and its discrete entropy remains unchanged. In conclusion, with such homogenization method all the chaotic characteristics of the original system is inherited and better uniformity is performed.
, Available online  , doi: 10.11999/JEIT180680 doi: 10.11999/JEIT180680
[Abstract](177) [FullText HTML] (103) [PDF 2236KB](15)
Abstract:
Cooperative MIMO technology can transform interference signals into useful signals by means of cooperative transmission or reception. It can solve the echo channel effect and improve the system capacity to be introduced into high-speed railway wireless communication. To master the channel characteristics of cooperative MIMO technology in high-speed railway scenarios, based on the geometric stochastic scattering theories, a new channel model for cooperative MIMO channel in high-speed railway scenarios is proposed, which can be applied to multiple high-speed railway scenarios by simply adjusting its several key parameters. Based on this model, the channel impulse response is calculated, the multi-link spatial correlation function is derived, the numerical calculation, simulation analysis and verification of measured data are carried out. Simulation results show that the multi-link spatial correlation is stronger when the LOS component is stronger and the angle spread of scattered components is smaller. The components which are scattered less times have a stronger spatial correlation. The theoretical model is verified by the measured data of the LTE special network of the Beijing-Tianjin high-speed railway section. These conclusions contribute to understanding the cooperative MIMO channels and conducting effective measurement activities.
, Available online  , doi: 10.11999/JEIT180655 doi: 10.11999/JEIT180655
[Abstract](153) [FullText HTML] (67) [PDF 1322KB](8)
Abstract:
The system biases degrade seriously the location precision for the multi-static passive radar system. A joint registration and passive localization algorithm based on Constrained Total Least Squares (CTLS) using Direction Of Arrival (DOA) and Time Difference Of Arrival (TDOA) measurements is developed to address the multi-static radar localization problem under the influence of system biases. Firstly, the nonlinear DOA and TDOA measurement equations are linearized by introducing auxiliary variables. Considering the statistical correlation properties of the noise matrix in the pseudo-linear equations, a joint biases registration and passive localization problem is formulated as a CTLS problem and the Newton’s method is applied to solving the CTLS problem. Moreover, a dependent least squares algorithm is designed to improve the target position estimation using the relationship between auxiliary variables and target position. An iterative post-estimate procedure is exploited to enhance further the estimation accuracy of the system biases. Finally, the theoretical error of the proposed algorithm is derived. Simulations demonstrate that the proposed algorithm can effectively estimate the system biases and target position.
, Available online  , doi: 10.11999/JEIT180719 doi: 10.11999/JEIT180719
[Abstract](118) [FullText HTML] (49) [PDF 2701KB](9)
Abstract:
A grating lobe suppression method of wideband real time delay pattern based on Particle Swarm Optimization(PSO) algorithm is proposed to solve the problem of grating lobe arise from inter-element is larger than wavelength. Firstly, the array energy pattern based on wideband real time delay is defined. Then, a fitness function is constructed with maximum sidelobe level of the array energy pattern. Finally, the grating lobe is further suppressed by optimizing the elements position distribution using particle swarm optimization algorithm. The simulation results show that the proposed grating lobes suppression method is more effectively than individually using the particle swarm optimization method or the wideband real time delay method. Furthermore, the influence of the element space, the element number, the time width and the center frequency of signal on the performance of grating lobe suppression are studied.
, Available online  , doi: 10.11999/JEIT180631 doi: 10.11999/JEIT180631
[Abstract](182) [FullText HTML] (89) [PDF 1705KB](17)
Abstract:
For problems of not meeting the demand of sampling both large flows and small flows at the same time, and not distinguishing flash crowd from traffic attacks in building network traffic anomaly detection datasets based on probabilistic sampling methods, a probabilistic flow sampling method for traffic anomaly detection is proposed. On the basis of the classification of network data flows according to their destination and source IP addresses, the sampling probability for each class of data flows is set as the maximum of its destination and source IP address’s sampling probability, and the number of sampled data flows is ceiled to ensure that each class of data flows is sampled at least once, so that the sampled dataset can reflect the distributions of large, small flows and source, destination IP addresses in original traffics. Then, the source IP address entropy is used to characterize the source IP dispersion of anomaly flows, and the attack flow sampling algorithm is designed based on the threshold of the source IP address entropy, which reduces the sampling probability of non-attack anomaly flows caused by flash crowd. The simulation results show that the proposed method can satisfy the sampling requirements of both large flows and small flows, it has a high anomaly flows sampling ability, can sample all the suspicious source and destination IP addresses related to anomaly flows, and can effectively filter the non-attack anomaly flows.
, Available online  , doi: 10.11999/JEIT180761 doi: 10.11999/JEIT180761
[Abstract](109) [FullText HTML] (47) [PDF 283KB](4)
Abstract:
Discriminant Neighborhood Embedding (DNE) algorithm is introduced into the speaker recognition system. DNE is a manifold learning approach and aims at preserving the local neighborhood structure on the data manifold. As well, DNE has much more power in discrimination by sufficiently using the between-class discriminant information. The experimental results on the telephone-telephone core condition of the NIST 2010 Speaker Recognition Evaluation (SRE) dataset indicate the effectiveness of DNE algorithm.
, Available online  , doi: 10.11999/JEIT180861 doi: 10.11999/JEIT180861
[Abstract](150) [FullText HTML] (75) [PDF 2166KB](19)
Abstract:
In order to deal with the limited capacity of Virtualized Network Function (VNF), hardware acceleration resources are adopted in Software-Defined Networking and Network Function Virtualization (SDN/NFV) architecture. The deployment of hardware acceleration resources enables VNF to provide service guarantees for increasing data traffic. To overcome the ignorance of the requirements for VNF with high processing throughput in service chain in existing researches, a model for VNF placement with hardware acceleration support is proposed. Based on the bearing characteristics of hardware acceleration resources, the model prioritizes the reuse of acceleration resources in the switch under the optimal placement of VNF without acceleration to commercial servers. And the mapping correlation between hardware acceleration resources and VNF is flexibly adjusted according to the requirements of network services. Simulation results show that the proposed model can bear more service flows and meet the high processing throughput needs of service chains than typical policies in the case of the same amount of resources, which improves effectively the resource utilization of the acceleration hardware deployed in the network.
, Available online  , doi: 10.11999/JEIT180716 doi: 10.11999/JEIT180716
[Abstract](135) [FullText HTML] (74) [PDF 1832KB](4)
Abstract:
Considering the problem that using a large number of reserved paths which causing higher complexity in order to obtain better performance for polar code Successive Cancellation List (SCL) decoding, the adaptive SCL decoding algorithm at a high Signal to Noise Ratio (SNR) reduces a certain amount of calculations, however, brings a higher decoding delay. According to the order of polar code decoding, an SCL decoding algorithm combining segmentationCyclic Redundancy Check (CRC) with adaptively selecting the number of reserved paths is proposed. The simulation results show that compared with the traditional CRC-assisted SCL decoding algorithm and adaptive-SCL algorithm, when the code rate is R=0.5, the complexity under low SNR (–1 dB) is reduced by about 21.6%, and the complexity at high SNR (3 dB) is reduced by about 64%, at the same time, better decoding performance is obtained.
, Available online  , doi: 10.11999/JEIT180720 doi: 10.11999/JEIT180720
[Abstract](350) [FullText HTML] (142) [PDF 925KB](33)
Abstract:
In order to solve the incomplete semantic structure problem that occurs in the process of using the Abstract Meaning Representation (AMR) graph to predict the summary subgraph, a semantic summarization algorithm is proposed based on Integer Linear Programming (ILP) reconstructed AMR graph structure. Firstly, the text data are preprocessed to generate an AMR total graph. Then the important node information of the summary subgraph is extracted from the AMR total graph based on the statistical features. Finally, the ILP method is applied to reconstructing the node relationships in the summary subgraph, which is further utilized to generate a semantic summarization. The experimental results show that compared with other semantic summarization methods, the ROUGE index and Smatch index of the proposed are significantly improved, up to 9% and 14% respectively. This method improves significantly the quality of semantic summarization.
, Available online  , doi: 10.11999/JEIT180653 doi: 10.11999/JEIT180653
[Abstract](164) [FullText HTML] (81) [PDF 1487KB](9)
Abstract:
A Bayesian network structure learning algorithm based on improved whale optimization strategy is proposed to solve the problem that the current Bayesian network structure learning algorithm is easily trapped in local optimal and is of low optimization efficiency. The improved algorithm proposes first a new method to establish a better initial population, and then it uses the cross mutation operator that does not produce the illegal structure to construct an improved predation behavior suitable for Bayesian network structure learning. At the same time, it adopts the dynamic parameter tuning strategy to enhance the individual search ability. The population is updated followed by the fitness order so that the optimal Bayesian network structure is obtained. Simulation results demonstrate that the algorithm has global convergence, high efficiency and higher accuracy than other similar optimization algorithms.
, Available online  , doi: 10.11999/JEIT180714 doi: 10.11999/JEIT180714
[Abstract](234) [FullText HTML] (98) [PDF 2171KB](15)
Abstract:
To address track-to-track association problem of radar and Electronic Support Measurements (ESM) in the presence of sensor biases and different targets reported by different sensors, an anti-bias track-to-track association algorithm based on track vectors hierarchical clustering is proposed. Firstly, the equivalent measurement is derived in the Modified Polar Coordinates (MPC). Linear relationship between state estimates and real states, sensor biases, measurement errors are established based on the approximate expansion of the equivalent measurement. The track vectors are obtained by the real state cancellation method. The homologous tracks are extracted by the method of track vectors hierarchical clustering, according to the statistical characteristics of Gaussian random vectors. The effectiveness of the proposed algorithm are verified by Monte Carlo simulation experiments in the presence of sensor biases, targets densities and detection probabilities.
, Available online  , doi: 10.11999/JEIT180648 doi: 10.11999/JEIT180648
[Abstract](140) [FullText HTML] (48) [PDF 1859KB](7)
Abstract:
A metameterial absorber is designed, fabricated and experimentally demonstrated to realized ultra-wideband absorption based on loading lumped resistances to raise the efficiency of absorber. The proposed structure comprises of an upper absorber and an under absorber by longitudinal cascade to expand bandwidth. The analysis of equivalent circuit show that the absorber has good impedance matching in a wide frequency band and the mechanism of wave absorption is verified by current analysis. The size of the unit is only about 0.089\begin{document}$\lambda_ {\rm{L}}$\end{document}×0.089\begin{document}$\lambda_ {\rm{L}}$\end{document}, where \begin{document}$\lambda_ {\rm{L}}$\end{document} is the wavelength of the lowest frequency, and the total thickness of the absorber is only 0.078\begin{document}$\lambda_ {\rm{L}}$\end{document}. Simulated and experimental results show that the absorber exhibits absorptivity above 90% from 2.24 GHz to 16.14 GHz, and the relative absorption bandwidth is about 151%. Measurement results show good agreement with the numerically simulated results.
, Available online  , doi: 10.11999/JEIT180672 doi: 10.11999/JEIT180672
[Abstract](183) [FullText HTML] (77) [PDF 1785KB](21)
Abstract:
As for super resolution Direction-of-Arrival (DoA) estimation with random arrays, it is still challenged to obtain efficient and statistically unbiased estimates. Based on Augmented Estimation of Signal Parameters via Rotational Invariance Techniques (AESPRIT), an efficient DoA estimation algorithm is proposed for random arrays. AESPRIT is modified with a closed loop structure; Array Interpolation Technique (AIT) is utilized to provide the initial phase compensation angles to the loop. Therefore, the final DoA estimates can be calculated efficiently and accurately through iteration. The proposed algorithm gives algebraic solution directly and has low computational complexity. At the same time, the results are statistically unbiased because no manifold mapping or mode truncation error is introduced. Simulations verify the effectiveness of the proposed algorithm.
, Available online  , doi: 10.11999/JEIT180644 doi: 10.11999/JEIT180644
[Abstract](270) [FullText HTML] (117) [PDF 2294KB](21)
Abstract:
Nowadays, the civil aviation industry has a high-precision prediction demand of flight delays, thus a flight delay prediction model based on the deep SE-DenseNet is proposed. Firstly, flight data, associated airport delay information and meteorological data are fused in the model. Then, the improved SE-DenseNet algorithm is used to extract feature automatically based on the fused flight data set. Finally, the softmax classifier is used to predict the delay level of flight. The proposed SE-DenseNet, combing the advantages of DenseNet and SENet, can not only enhance the transmission of deep information, avoid the problem of vanishing gradients, but also achieve feature recalibration by the feature extraction process. The results indicate that after data fusion, the accuracy of the model is improved 1.8% than only considering the characteristics of the flight itself. The improved algorithm can effectively improve the network performance. The final accuracy of the model reaches 93.19%.
, Available online  , doi: 10.11999/JEIT180691 doi: 10.11999/JEIT180691
[Abstract](245) [FullText HTML] (123) [PDF 1326KB](38)
Abstract:
In order to improve the comprehensive performance of the wireless network intrusion detection model, Recurrent Neural Network (RNN) algorithm is used to build a wireless network intrusion detection classification model. For the over-fitting problem of the classification model caused by the imbalance of training data samples distribution in wireless network intrusion detection, based on the pre-treatment of raw data cleaning, transformation, feature selection, etc., an instance selection algorithm based on window is proposed to refine the train data-set. The network structure, activation function and re-usability of the attack classification model are optimized experimentally, so the optimization model is obtained finally. The classification accuracy of the optimization model is 98.6699%, and the running time after the model reuse optimization is 9.13 s. Compared to other machine learning algorithm, the proposed approach achieves good results in classification accuracy and execution efficiency. The comprehensive performances of our model are better than that of traditional intrusion detection model.
, Available online  , doi: 10.11999/JEIT180705 doi: 10.11999/JEIT180705
[Abstract](17) [FullText HTML] (3) [PDF 2449KB](0)
Abstract:
Considering the problem that the traditional circularly polarized microstrip antenna has narrow Axial Ratio (AR) bandwidth and small system capacity, a new type of broadband dual circularly polarized printed antenna is proposed. The antenna structure is simple with a dual port microstrip feed mode, consisting of only two radiating patches and an improved ground plane, and the entire size of antenna is 48 mm× 48 mm× 1 mm. By optimizing the shape of the radiating patch and adding a circular structure on the ground plane, the impedance bandwidth and the axial ratio bandwidth of the antenna can be effectively increased, achieving the dual circular polarization characteristics. The design process of antenna is given, and the circular polarization mechanism of the antenna is analyzed from the surface current distributions. The simulated and measured results show that the antenna has a very wide impedance bandwidth and axial ratio bandwidth. The working frequency band of the antenna is 1.9～9.6 GHz (the relative bandwidth is 133.9%), and the 3 dB AR bandwidth is 1.9～6.6 GHz (the relative bandwidth is 110.6%). The radiation performance and gain characteristics of the antenna are measured. The measured results agree well with the simulated results, which proves the effectiveness of the antenna. The antenna can be applied to Ultra WideBand (UWB) wireless communication systems and satellite communication systems.
, Available online  , doi: 10.11999/JEIT180640 doi: 10.11999/JEIT180640
[Abstract](219) [FullText HTML] (86) [PDF 1458KB](8)
Abstract:
To solve the problem of insufficient number of participants and poor data quality in the sensing mission, a mobile crowd sensing incentive model for mission cost difference is proposed. First of all, the fuzzy reasoning method is used to analyze the impact of data quantity, environmental conditions and equipment consumption on mission cost, and the sensing mission is divided into different levels on the basis of cost difference. Meanwhile, the method is used to prepare a budget for the requester and give the participant an appropriate reward. Then, the sensing mission is assigned to more appropriate participants to complete the sensing mission and upload the sensing data through credibility assessment and participants’ preference. Finally, the sensing data uploaded by participants is evaluated, and the credibility of participants is updated. Besides, the participants are paid according to the cost level of perceived missions. The simulation experiments based on the real data set show that the model can recruit more users to participate in the sensing mission effectively and promote participants to upload high-quality sensing data by using the mutual influence between different modules.
, Available online  , doi: 10.11999/JEIT180636 doi: 10.11999/JEIT180636
[Abstract](262) [FullText HTML] (120) [PDF 1460KB](23)
Abstract:
In order to meet the diversified service requirements in future mobile communication networks and provide users with customized services while improving network economic efficiency, a resource allocation algorithm of network slicing based on online auction is proposed. The algorithm transforms the service requests of users into the corresponding bidding information according to the service types. For maximizing the social welfare of the auction participants, the slicing resource allocation problem is modeled as a multi-service based online winner determination problem. Combined with the resource allocation and price updating strategy, the optimal resources allocation based on online auction is achieved. The simulation results show that the proposed algorithm can improve the network economic efficiency and satisfy the service requirements of users.
, Available online  , doi: 10.11999/JEIT180580 doi: 10.11999/JEIT180580
[Abstract](236) [FullText HTML] (101) [PDF 2409KB](17)
Abstract:
To address the problems of low spectrum utilization and high energy consumption caused by physical impairment in elastic optical networks, a service differentiated energy efficiency routing strategy with Link Impairment-Aware Spectrum Partition (LI-ASP) is proposed. For reducing the nonlinear impairment between different channels, a path weight formula jointly considering the link spectrum state and transmission impairment is designed to balance the load. A modulation level-layered auxiliary graph is constructed according to traffic’s spectrum efficiency and maximum transmission distance. Starting from the highest modulation in the auxiliary graph, the K link-disjoined maximum weight paths are selected for high quality requests, and the K link-disjoined shortest energy efficiency paths are selected for low quality requests. Then, LI-ASP strategy divides spectrum partition according to requests rate ratio. The First-Fit (FF) and Last-Fit (LF) spectrum allocation policies are used to reduce cross-phase modulation between the requests with different rates. The simulation results show that the proposed LI-ASP strategy can reduce the bandwidth blocking probability and energy consumption effectively.
, Available online  , doi: 10.11999/JEIT180535 doi: 10.11999/JEIT180535
[Abstract](112) [FullText HTML] (54) [PDF 2029KB](12)
Abstract:
With the increasing complexity of radar signals, it is more and more difficult to extract features of the real sequences, but when they are transformed to a symbol sequence, it is usually easier to mine the effective feature parameters. Therefore, a radar signal recognition method based on Multi-Scale Information Entropy (MSIE) is proposed. Firstly, the radar signal is transformed into symbolic sequence by Symbolic Aggregate approXimation (SAX) algorithm under different character number scales. Then, the information entropy of each symbol sequence is combined to form the MSIE feature vector. Finally, the k-Nearest Neighbor (k-NN) is used as a classifier to realize the classification and identification of radar signals. The simulation results of 6 typical radar signals show that using the proposed method the correct recognition rate of different radar signals is greater than 90% when Signal to Noise Ratio (SNR) is 5 dB, and better performance can be obtaned conpared with the traditional identification method based on complexity characteristics (box-dimension and sparseness).
, Available online  , doi: 10.11999/JEIT180592 doi: 10.11999/JEIT180592
[Abstract](212) [FullText HTML] (108) [PDF 702KB](32)
Abstract:
Mobile Edge Computing (MEC) improves the quality of users experience by providing users with computing capabilities at the edge of the wireless network. However, computing offloading in MEC still faces some problems. In this paper, a joint optimization problem of offloading decision and resource allocation is proposed for the computation offloading problem in Ultra-Dense Networks (UDN) with MEC. To solve this problem, firstly, the coordinate descent method is used to formulate the optimization scheme for the offloading decision. Meanwhile, the improved Hungarian algorithm and greedy algorithm are used to allocate the channels to meet the user’s delay requirements. Finally, the problem of minimizing energy consumption is converted into a problem of minimizing power. Then it is converted into a convex optimization problem to get the user’s optimal transmission power. Simulation results show that the proposed scheme can minimize the energy consumption of the system while satisfying the users’ different delay requirements, and improve effectively the performance of the system.
, Available online  , doi: 10.11999/JEIT180576 doi: 10.11999/JEIT180576
[Abstract](283) [FullText HTML] (129) [PDF 3487KB](19)
Abstract:
Eight-Sided Fortress (ESF) is a lightweight block cipher with a generalized Feistel structure, which can be used in resource-constrained environments such as protecting Radio Frequency IDentification (RFID) tags in the internet of things. At present, the research on the security of ESF mainly adopts the impossible differential cryptanalysis. The ability of ESF to resist the related-key impossible differential cryptanalysis is studied based on the characteristics of its S-boxes and key schedule. By constructing an 11-round related-key impossible differential distinguisher, an attack on 15-round ESF is proposed by adding 2-round at the top and 2-round at the bottom. This attack has a time complexity of 240.5 15-round encryptions and a data complexity of 261.5 chosen plaintexts with 40 recovered key-bit. Compared with published results, the time complexity is decreased and the data complexity is ideal with the number of attack rounds increased.
, Available online  , doi: 10.11999/JEIT180600 doi: 10.11999/JEIT180600
[Abstract](146) [FullText HTML] (62) [PDF 653KB](7)
Abstract:
In the standard application mapping problem, it is assumed that the communicating traffic of a task is a fixed value. In the real applications, the communication traffic is uncertain due to the time-varying and bursty characters. Therefore, it has the practical significance modeling the task with communicating traffic uncertainty. Given the interval flow and a conservation factor, the robust application mapping problem is formulated by a min-max model, and then solved by a revised Tabu-based algorithm (Tabu-RAM) in this paper. The algorithm is verified under five benchmark instances. As the experimental results show, under the standard application scenarios, the Tabu-RAM performs better than other methods proposed in the literature. In addition, under the application scenarios with uncertain tasks, experimental results show that the Tabu-RAM performs better and more stable than the traditional tabu algorithm.
, Available online  , doi: 10.11999/JEIT180646 doi: 10.11999/JEIT180646
[Abstract](168) [FullText HTML] (73) [PDF 373KB](17)
Abstract:
Constructions of Gaussian integer periodic complementary sequences are presented in this paper. Based on the relationship between periodic complementary sequences and difference families, the sufficient condition of the existence of Gaussian integer periodic complementary sequences is proposed at first, then Gaussian integer periodic complementary sequences with degree 2 are constructed directly. To extend the number of Gaussian integer complementary sequences, Gaussian integer complementary sequences with degree 4 are constructed based on mappings. Compared with binary complementary sequences, there are more Gaussian integer complementary sequences, as a result, the presented methods will propose an abundance of complementary sequences for communication systems.
, Available online  , doi: 10.11999/JEIT180429 doi: 10.11999/JEIT180429
[Abstract](125) [FullText HTML] (72) [PDF 1796KB](13)
Abstract:
Focusing on the problem that convolutional auto-encoder network based anomaly detection ignores time information, a novel anomaly detection model based on Bayesian fusion of spatial-temporal stream is proposed. A convolution auto-encoder network is used in spatial stream model to reconstructs video frames, and a convolutional Long Short-Term Memory (LSTM) encoder-decoder network is used to reconstruct short-term optical sequence in the temporal stream model. Then, the reconstruction errors under spatial and temporal stream are calculated separately. Meanwhile, an adaptive thresholds is designed to obtain the reconstruction binary error maps. Finally, the Bayesian fusion strategy is developed to combine the reconstruction error of spatial and temporal stream to obtain the final fusion reconstruction error map based on which the abnormal behavior can be determined. Experimental results show that the proposed algorithm is superior to the existing anomaly detection algorithms in UCSD and Avenue datasets.
, Available online  , doi: 10.11999/JEIT180581 doi: 10.11999/JEIT180581
[Abstract](209) [FullText HTML] (89) [PDF 1270KB](14)
Abstract:
In order to improve the performance of the large queue backlogs and low convergence rate in back pressure routing algorithm, the cross-layer optimization of joint congestion control, multi-path routing and power allocation in wireless multi-hop networks is investigated. The system is modeled as a network utility maximization problem under the constraints of flow balancing condition and power. Based on the Newton’s method, the problem is solved and an algorithm with superlinear convergence speed is proposed. With matrix splitting technology, the algorithm can be implemented distributedly further. The simulation results show that the algorithm can effectively increase the energy utility while achieving the maximum network utility, and can keep the queue length at a very low level to decrease the packet transmission delay.
, Available online  , doi: 10.11999/JEIT180604 doi: 10.11999/JEIT180604
[Abstract](195) [FullText HTML] (89) [PDF 1894KB](19)
Abstract:
Comparing with K-means, Fuzzy logic is introduced in Fuzzy C-Means to handle the information between clusters. It can obtain better cluster results. However, fuzzy logic makes observations could belong to more than just one cluster, which results FCM is especially sensitivity to the noisy and outlier and has poor generalization performance. So a Rrobust Fuzzy C-Means clustering integrated Between-cluster Information algorithm (RBI-FCM) is proposed. Taking advantage of the sparsity of K-means, RBI-FCM helps to reduce the interactions among different clusters and improve the separability of sample points which locate in the adjacent domains of different clusters. Beside minimizing the inner-cluster scattering condition, RBI-FCM considers the between-cluster information. The generalization performance of RBI-FCM can be improved. An effective iterative algorithm for solving the model is designed in this paper. The experimental results show that the RBI-FCM improves the robustness of FCM and reduce effectively its sensitivity to size-imbalance and differences on the distribution of clusters of FCM. The great clustering result is obtained.
, Available online  , doi: 10.11999/JEIT180593 doi: 10.11999/JEIT180593
[Abstract](192) [FullText HTML] (62) [PDF 2361KB](5)
Abstract:
As the bandwidth of the traditional TEM cell can not satisfy the growing demand for broadband, a broadband electromagnetic radiation device working from DC to 6 GHz is designed based on the coaxial structure. According to circuit principle and impedance matching of the transmission line, the device adopts the taper transition structure between the N connector and circular coaxial connected, which achieves the advantages of good impedance matching. The device is simulated by the CST software, and has been fabricated and measured. The simulated results show that S11 is better than –10 dB in the frequency range of DC-6 GHz. Due to the machining error, test results are slightly biased at individual frequencies, which have good consistency with the simulated results and demonstrate the desirable transmission performance of the radiation device. The design has great application value in electromagnetic radiation system.
, Available online  , doi: 10.11999/JEIT180609 doi: 10.11999/JEIT180609
[Abstract](219) [FullText HTML] (90) [PDF 1668KB](16)
Abstract:
An adaptive method of limiter design is proposed to suppress impulsive noise. With a purpose of maximizing the efficacy function, the proposed method searches for optimal thresholds of clipper and blanker, via adaptive line search. Firstly, based on analysis on the relationship between the efficacy and the nonlinearity, the key problem of optimization is proposed. Then, since the calculation of efficacy is hard, an adaptive algorithm based on linear search approach is developed based on linear search to optimize the efficacy. Considering the noise distribution is unknown, the proposed method employs the nonparametric kernel density estimation and works robustly in the presence of estimation error. Finally, numeric simulations demonstrate that the proposed method can obtain the optimal performance of clippers and blankers successfully. In the processing of real atmospheric noise from unknown distribution, the proposed method achieves the best detection performance when combining nonparametric kernel density estimation approach.
, Available online  , doi: 10.11999/JEIT180558 doi: 10.11999/JEIT180558
[Abstract](295) [FullText HTML] (128) [PDF 3168KB](42)
Abstract:
To improve the detection accuracy and execution efficiency of the existing Random-Valued Impulse Noise (RVIN) detectors, a fast training-based RVIN detection algorithm is implemented by constructing a more descriptive feature vector and training a detection model with more accurate nonlinear mapping. On the one hand, multiple Rank-Ordered Logarithmic absolute Deviation (ROLD) statistics are extracted and combined with a statistical value reflecting the edge characteristics in the form of feature vector to describe how RVIN-like the center pixel of a patch is. The description ability of the feature vector is improved significantly while the computational complexity is just increased in small amount. On the other hand, an RVIN prediction model (RVIN detector) is obtained by training a Deep Belief Network (DBN) to map the feature vectors to noise labels, which is more accurate than the shallow prediction model. Extensive experimental results show that, compared with the existing RVIN detectors, the proposed one has better performance in terms of detection accuracy and execution efficiency.
, Available online  , doi: 10.11999/JEIT180564 doi: 10.11999/JEIT180564
[Abstract](210) [FullText HTML] (109) [PDF 3052KB](13)
Abstract:
The target location accuracy is an important technical parameter of airborne SAR system, thus the target location of airborne SAR image is important for application. The precision of the moving parameters directly influences the precision of the SAR image target location algorithm based on the Range-Doppler (RD) model. The locating accuracy is greatly affected if the navigation accuracy of the airborne platform is limited. To solve this problem, an airborne SAR image target location algorithm based on RD model parameter refining is proposed. Using the matching points of airborne SAR image matching with the reference image, the moving parameters are refined with better accuracy, and the locating accuracy is improved. The experiments show that the proposed algorithm is effective.
, Available online  , doi: 10.11999/JEIT180578 doi: 10.11999/JEIT180578
[Abstract](189) [FullText HTML] (83) [PDF 815KB](14)
Abstract:
In the light of the disadvantages that Jousselme’s evidential distance function can not describe the local conflicting information of evidence well and can not measure the conflict of high conflicting evidence accurately, an improved Jousselme’s evidential distance function is proposed. In the new function, Jousselme’s evidence distance function is improved by using the non-coincidence degree, which can better describe the local conflict of evidence, so that the conflict measure result of evidence varies proportionally with the value of the non-coincidence degree and the scope of its change. Secondly, an improved fusion conflict measure function is constructed based on the conflict coefficient and the new improved Jousselme’s evidential distance function. On this basis, the weight coefficient formula of focal element is improved, and the local multi-dimensional conflicting information is assigned proportionately. Theoretical and application analysis results show that the new algorithm is a kind of evidence fusion algorithm with wide applicability and good anti-jamming performance.
, Available online  , doi: 10.11999/JEIT180597 doi: 10.11999/JEIT180597
[Abstract](266) [FullText HTML] (112) [PDF 1407KB](31)
Abstract:
The goal of Image Quality Assessment (IQA) research is to simulate the Human Visual System’s (HVS) perception process of assessing image quality and construct an objective evaluation algorithm that is as consistent as the subjective evaluation result. Many existing algorithms are designed based on local structural similarity, but human subjective perception of images is a high-level, semantic process, and semantic information is essentially non-local, so image quality assessment should take the non-local information of the image into consideration. This paper breaks through the classical framework based on local information, and proposes a framework based on non-local information. Under the proposed framework, an image quality assessment algorithm based on non-local gradient is also presented. This algorithm predicts image quality by measuring the similarity between the non-local gradients of reference image and the distorted image. The experimental results on the public test database TID2008, LIVE, and CSIQ show that the proposed algorithm can obtain better evaluation results.
, Available online  , doi: 10.11999/JEIT180695 doi: 10.11999/JEIT180695
[Abstract](203) [FullText HTML] (116) [PDF 1303KB](30)
Abstract:
Velocity estimation of moving targets is a key part of ground moving target imaging and positioning in airborne single-antenna high-resolution SAR system. In order to solute the defects of traditional algorithms, such as high computation brought by searching and interpolation and low reliability caused by range cell migration, a novel method based on least square fitting of echo sequence is proposed. Range changes between adjacent echo sequences are extracted using envelope correlation, and coefficients of range change equation are obtained by least square linear fitting, from which radial velocity and along-track velocity can be derived. Compared with the traditional algorithms, the new method has less computation and can work without RCMC. The mathematical model is presented and the principle of parameter selection is provided, and accuracy, computation and applicable conditions of the algorithm are analyzed. The effectiveness of the proposed algorithm is validated by simulation and real data.
, Available online  , doi: 10.12000/JRIT180605 doi: 10.12000/JRIT180605
[Abstract](264) [FullText HTML] (143) [PDF 2184KB](21)
Abstract:
When multi-objective evolutionary clustering algorithms are applied to image segmentation, the image pixels are always utilized to be clustered. It results in a long running time. In addition, due to not considering the image region information, the image segmentation effect is not ideal. In order to improve the segmentation effect and time efficiency of the multi-objective evolutionary clustering algorithm, the image region information and some supervised information are introduced into multi-objective evolutionary clustering. Then a multi-objective evolutionary semi-supervised fuzzy clustering image segmentation algorithm driven by image region information is presented. First, the region information of the image is obtained through the super-pixel strategy. Second, two novel fitness functions are designed by introducing the supervised information and region information. Third, the multi-objective evolutionary strategy is used to optimize these two fitness functions to obtain an optimal solution set. Finally, an optimal solution evaluation index with region information and supervision information is constructed and utilized to select an optimal solution from the optimal solution set. Experimental results show the proposed algorithm outperforms comparison methods in segmentation performance and running efficiency.
, Available online  , doi: 10.11999/JEIT180569 doi: 10.11999/JEIT180569
[Abstract](209) [FullText HTML] (84) [PDF 2824KB](16)
Abstract:
SMeared SPectrum (SMSP) jamming has lots of coupling in time and frequency domain with Linear Frequency Modulated (LFM) radar signals, which has good jamming performance. This paper proposes a signal processing method for countering SMSP jamming in information domain. According to the formulation and characteristics of SMSP signal, the jamming dictionary is changed automatically, the frequency modulation rate of LFM and SMSP signal is matcheal at the same time, the compressed sampling model is consructed and reconstruction of signal is carried out based on convex optimization. Finally, the recognition of jamming signal and extraction of radar signal are achieved. Pei type fractional Fourier decomposition method is used in construction of redundant dictionary. Modulation and demodulation between time and frequency domain are avoided in this method, which leads to improvement in fewer iteration times and higher arithmetic speed.
, Available online  , doi: 10.11999/JEIT180628 doi: 10.11999/JEIT180628
[Abstract](221) [FullText HTML] (106) [PDF 8286KB](39)
Abstract:
To solve the problems of complex feature extraction process and low characteristic expressiveness of traditional remote sensing image classification methods, a high resolution remote sensing image classification method based on deep convolution neural network and multi-kernel learning is proposed. Firstly, the deep convolution neural network is constructed to train the remote sensing image data set to learn the outputs of two fully connected layers, which are taken as two high-level features of remote sensing images. Then, the multi-kernel learning is used to train the kernel functions for these two high-level features, so that they can be mapped to the high dimensional space, where these two features are fused adaptively. Finally, with the combined features, a remote sensing image classifier based on Multi-Kernel Learning-Support Vector Machine (MKL-SVM) is designed for remote sensing image classification. Experimental results show that compared with the existing deep learning based remote sensing classification methods, the proposed algorithm achieves improved results in terms of classification accuracy, error, and Kappa coefficient. On the experimental test set, the above three indicators reach 96.43%, 3.57%, and 96.25% respectively, and satisfactory results are obtained.
, Available online  , doi: 10.11999/JEIT180590 doi: 10.11999/JEIT180590
[Abstract](185) [FullText HTML] (81) [PDF 2172KB](10)
Abstract:
In order to improve the measurement accuracy of Micro Electro Mechanical Systems (MEMS) gyroscopes, the influence of measurement noise on them is suppressed. The error characteristics of a certain type of MEMS gyroscope are analyzed. A strong tracking self-feedback model based on Recursive Least Square (RLS) multiple wavelet decomposition reconstruction is proposed to establish a new soft threshold function. Since the model processed data has partial singular values, an improved median filtering algorithm is proposed. For the problem of gyro zero-bias noise, a zero-bias stability suppression algorithm is proposed. In this paper, the algorithm model is described in detail, and the experimental data of the train attitude measurement system in a project research are applied to the algorithm model. The test experiments are divided into static and dynamic groups. The results show that the algorithm reduces the noise in the signal, suppresses effectively the random drift of the MEMS gyroscope and improves the accuracy of the attitude calculation. The feasibility and effectiveness of this method are affirmed to remove the signal noise of the gyroscope output and improve the accuracy of the use.
, Available online  , doi: 10.11999/JEIT180563 doi: 10.11999/JEIT180563
[Abstract](204) [FullText HTML] (78) [PDF 5349KB](15)
Abstract:
Due to the 2-D vacancies with serious motion errors when processing 10 GHz ultra-wideband microwave photonic-based SAR, current motion error estimation methods directly estimating with phase error can not obtain correct estimation result in this paper. An ultra-high resolution SAR motion error estimation method jointing envelope and phase is proposed, which can realize accurate estimation of motion error without inertial information. Firstly, the approximate 3-D motion error is obtained by applying the Least Squares Algorithm (LSA) and the Gradient Descent Algorithm (GDA) to the envelope information extracted by the Range Alignment Algorithm (RAA) before Range Curve Migration Correction (RCMC). Then, phase-based motion error estimation is performed on the data after rough compensation and RCMC. After eliminating the azimuth variant phase error, the 2-D space-variant phase error estimation method is used to obtain accurate estimation of residual motion error. Processing of simulated data and real data acquired from vehicle-borne microwave photonic-based radar validates the effectiveness of the proposed method.
, Available online  , doi: 10.11999/JEIT180488 doi: 10.11999/JEIT180488
[Abstract](200) [FullText HTML] (74) [PDF 1296KB](24)
Abstract:
For the target DOA estimation under active deception jamming environment with limited samples, a novel DOA estimation method based on the combination of Adaptive Polarization Filter(APF) and Block Sparse Bayesian Learning(BSBL) algorithm is proposed. First, the interference energy is suppressed using APF. Then, the proposed method constructs a sparse Bayesian model under active deception jamming environment. The target DOA is estimated using the BSBL algorithm based on the neighbor time sampling correlation. Simulated and measured data processing results prove that the proposed method reduces the influence of interference on the BSBL algorithm, and has higher spatial resolution and higher angle measurement accuracy, comparing with the method based on the combination of APF and subspace-based DOA algorithms or maximum likelihood DOA algorithm.
, Available online  , doi: 10.11999/JEIT180545 doi: 10.11999/JEIT180545
[Abstract](261) [FullText HTML] (115) [PDF 1134KB](18)
Abstract:
An accurate wideband beampattern synthesis method based on the space-time structure is proposed. Making use of the property that the magnitude response can be translated into linear function under the condition of conjugate symmetric weights, the beampattern synthesis problem is transformed into the convex optimization problem. The weights of space-time structure can be obtained by utilizing the principle of relationship between the two structures, after the weights of space-frequency structure is calculated by the interior point method. The proposed method can realize the wideband beampattern synthesis accurately, meanwhile ensuring the linear phase characteristic of the array response. Simulation results demonstrate the effectiveness of the proposed method.
, Available online  , doi: 10.11999/JEIT180596 doi: 10.11999/JEIT180596
[Abstract](260) [FullText HTML] (79) [PDF 2466KB](12)
Abstract:
The modern spaceborne SAR system requires both high resolution and wide swath, and the conventional single channel spaceborne SAR system has a contradiction between the two important indexes, the azimuth multichannel method is proposed and used to solve the above problem. Based on the analysis of the azimuth multichannel echo model and the characteristics of the Relax algorithm, a spaceborne SAR High Resolution Wide Swath (HRWS) imaging method is proposed, and the iterative process of the new method is described in detail. By the simulation of point target echo, and comparing with the traditional azimuth multichannel HRWS reconstruction methods, the reliability and effectiveness of the proposed method are verified.
, Available online  , doi: 10.11999/JEIT180656 doi: 10.11999/JEIT180656
[Abstract](166) [FullText HTML] (65) [PDF 1720KB](5)
Abstract:
, Available online  , doi: 10.11999/JEIT181073 doi: 10.11999/JEIT181073
[Abstract](84) [FullText HTML] (51) [PDF 1693KB](13)
Abstract:
In view of the nonlinear and stochastic characteristics of short-term traffic flow data, this article propose a prediction model and algorithm based on hybrid Auto-Regressive Integrated Moving Average (ARIMA) and Genetic Particle Swarm Optimization Wavelet Neural Network (GPSOWNN) in order to improve its prediction accuracy and rate of convergence. In terms of model construction, the ARIMA model prediction value and the historical data of the first three moments with strong correlation with gray correlation coefficient greater than 0.6 are used as input of the Wavelet Neural Network, and the structure of the model is simplified considering both the stationary and non-stationary historical data. In terms of algorithm, by using the genetic particle swarm optimization algorithm to select optimally the initial values of the wavelet neural network, the results can speed up the convergence of network training under the condition that it is not easy to fall into local optimum. The experimental results show that the proposed model is superior to hybrid ARIMA and GPSOWNN in terms of prediction accuracy, the genetic particle swarm optimization algorithm is superior to the genetic algorithm optimization model in terms of convergence speed.
, Available online  , doi: 10.11999/JEIT180867 doi: 10.11999/JEIT180867
[Abstract](86) [FullText HTML] (59) [PDF 984KB](9)
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(Pleasure -Arousal-Dominance) 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.
, Available online  , doi: 10.11999/JEIT181021 doi: 10.11999/JEIT181021
[Abstract](92) [FullText HTML] (30) [PDF 1687KB](6)
Abstract:
In complex indoor environment, the measured received signal strength (RSS) values will fluctuate in different degrees, which lead to inaccurate characterization of wireless signal propagation model. To solve this problem, a universal coarse grained localization method is proposed based on the Wi-Fi ranging location model. This method gets the signal propagation model by fitting the measured RSS value. On this basis, the distance between the unknown node and the access point is calculated, then the location of the unknown node is realized by the beetle antennae search algorithm. The performance of the propagation model and the effectiveness of the optimization algorithm are verified by simulation.
, Available online  , doi: 10.11999/JEIT180963 doi: 10.11999/JEIT180963
[Abstract](52) [FullText HTML] (33) [PDF 1824KB](10)
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.
, Available online  , doi: 10.11999/JEIT180903 doi: 10.11999/JEIT180903
[Abstract](42) [FullText HTML] (23) [PDF 1539KB](3)
Abstract:
In order to overcome the shortcomings of low fault-tolerance and high computational complexity in the process of parameter identification such as code length and synchronization of Turbo code, a new algorithm based on Differential Likelihood Difference (DLD) at low Signal-to-Noise Ratio (SNR) is proposed. Firstly, the concept of DLD is defined, and the analysis matrix is constructed to identify the code length by using the characteristic that the DLD between two codes in Turbo frame terminal is positive ("+"); secondly, a method based on the minimum error decision criterion to decide DLD "+" position is proposed to complete frame synchronization. From the engineering practice, the possible values of the number of registers are traversed to realize the recognition of the code rate, the number of registers and the interleaving length. Simulation results show that the proposed algorithm is effective in identifying parameters such as code length and frame synchronization, the position distribution of DLD ‘+’ is consistent with the data structure characteristics of the analysis, and the threshold can effectively determine the position of DLD ‘+’. At the same time, the algorithm has strong fault-tolerant performance. Under the condition of SNR –5 dB, the identification of code length, frame synchronization and other parameters can reach more than 90%, and the complexity of the algorithm is far less than the existing algorithms.
, Available online  , doi: 10.11999/JEIT180970 doi: 10.11999/JEIT180970
[Abstract](68) [FullText HTML] (39) [PDF 1357KB](11)
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 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. Simulation 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.
, Available online  , doi: 10.11999/JEIT180849 doi: 10.11999/JEIT180849
[Abstract](53) [FullText HTML] (27) [PDF 1452KB](5)
Abstract:
The security of lightweight block cipher Simeck against integral attack is evaluated in this paper. First, a 16-round and a 20-round high-order integral distinguisher of Simeck48 and Simeck64 are constructed by decrypting the existed integral distinguisher forward. Then, combined with the meet-in-the-middle strategy and subkey relationship, the integral attacks on 24-round Simeck48 and 29-round Simeck64 are first proposed utilizing the equivalent-subkey and partial-sum technologies based on the new integral distinguishers. The data, time and memory complexity of attacking 24-round Simeck48 are 246, 295 and 282.52 while the data, time and memory complexity of attacking 29-round Simeck64 are 263, 2127.3 and 2109.02. These new attacks greatly improve the results of the previous integral attack on Simeck. Compared with the known results of the integral attack on Simeck, the number of rounds of the integral attacks on Simeck48 and Simeck64 is increased by 3-round and 5-round, respectively.
, Available online  , doi: 10.11999/JEIT180906 doi: 10.11999/JEIT180906
[Abstract](55) [FullText HTML] (28) [PDF 1673KB](10)
Abstract:
After the Virtual Network Function (VNF) in the 5G access network is deployed, the resource requirements are dynamically changed, resulting in the problem that the Physical Machine (PM) resource utilization in the network is too high or too low. To solve the above problem, the resource usage of PM in the network is divided into five different partitions, and a multi-priority VNF migration request queue scheduling model is proposed. Secondly, based on the model, a joint optimization model is established to minimize the VNF migration cost and minimize the network energy consumption. Finally, a multi-priority VNF migration cost and network energy joint optimization algorithm based on 5G access network is presented to solve the above model. The simulation results show that the algorithm can effectively improve the PM resources utilization, ensure the PM performance and balance the PM load while effectively realizing a compromise between VNF migration cost and network energy consumption.
, Available online  , doi: 10.11999/JEIT180919 doi: 10.11999/JEIT180919
[Abstract](56) [FullText HTML] (33) [PDF 1136KB](12)
Abstract:
In Cooperative Jamming (CJ) system, the Power Amplifier (PA) in the jamming transmitter works in nonlinear region, which results in a large number of nonlinear components in the Self-Interference (SI) signal received by the near-end receiver. To solve the problem of nonlinear distortion suppression, a nonlinear model is established at the receiver. Then, the reconstructed nonlinear signal based on the estimated parameters is subtracted from the received signal to suppress the nonlinear interference in CJ. Simulation and experimental results indicate that the nonlinear suppression scheme proposed in this paper can further suppress the nonlinear interference under the residual frequency offset in CJ, and verify the effectiveness and feasibility of the proposed scheme.
, Available online  , doi: 10.11999/JEIT180599 doi: 10.11999/JEIT180599
[Abstract](66) [FullText HTML] (31) [PDF 2059KB](6)
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.
, Available online  , doi: 10.11999/JEIT180145 doi: 10.11999/JEIT180145
[Abstract](105) [FullText HTML] (67) [PDF 1427KB](25)
Abstract:
In Compressive Sensing (CS) imaging algorithms, the true targets usually can not locate on the pre-defined grids exactly. Such Off-grid problems result in mismatch between true echo and measurement matrix, which seriously degrades the performance of radar imaging. An adaptive calibration method is proposed to solve the off-grid problems in MIMO radar Three-Dimensional (3D) imaging. Bayesian probability density functions can be constructed based on the sparse echo model of Off-grid targets, and the Maximum A Posteriori (MAP) method is used to obtain sparse imaging with mismatch errors. Compared with the traditional methods, the proposed method can make full use of mismatch parameters’ priori information and adaptively update the parameters, which can reduce the influence of mismatch errors, and achieve high-precision estimation for sparse targets and noise power. Finally, the simulation results confirm that the proposed method can effectively optimize mismatch errors with accurate and stable imaging performance.
, Available online  , doi: 10.11999/JEIT180484 doi: 10.11999/JEIT180484
[Abstract](329) [FullText HTML] (198) [PDF 2888KB](43)
Abstract:
Hardware Trojan horse detection has become a hot research topic in the field of chip security. Most existing detection algorithms are oriented to ASIC circuits and FPGA circuits, and rely on golden chips that are not infected with hardware Trojan horses, which are difficult to adapt to the coarse-grained reconfigurable array consisting of large-scale reconfigurable cells. Therefore, aiming at the structural characteristics of Coarse-grained reconfigurable cryptographic logical arrays, a hardware Trojan horse detection algorithm based on partitioned and multiple variants logic fingerprints is proposed. The algorithm divides the circuit into multiple regions, adopts the logical fingerprint feature as the identifier of the region, and realizes the hardware Trojan detection and diagnosis without golden chip by comparing the multiple variant logic fingerprints of the regions in both dimensions of space and time. Experimental results show that the proposed detection algorithm has high detection success rate and low misjudgment rate for hardware Trojan detection.