Articles in press have been peer-reviewed and accepted, which are not yet assigned to volumes/issues, but are citable by Digital Object Identifier (DOI).

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, doi: 10.11999/JEIT190074
[Abstract](275) [FullText HTML](243) [PDF 1337KB](19)
Abstract:
In order to reduce the delay of computing tasks and the total cost of the system, Mobile Eedge Computing (MEC) technology is applied to vehicular networks to improve further the service quality. The delay problem of vehicular networks is studied with the consideration of computing resources. In order to improve the performance of the next generation vehicular networks, a multi-platform offloading intelligent resource allocation algorithm is proposed to allocate the computing resources. In the proposed algorithm, the K-Nearest Neighbor (KNN) algorithm is used to select the offloading platform (i.e., cloud computing, mobile edge computing, local computing) for computing tasks. For the computing resource allocation problem and system complexity in non-local computing, reinforcement learning is used to solve the optimization problem of resource allocation in vehicular networks using the mobile edge computing technology. Simulation results demonstrate that compared with the baseline algorithms (i.e., all tasks offload to the local or MEC server), the proposed multi-platform offloading intelligent resource allocation algorithm achieves a significant reduction in latency cost, and the average system cost can be saved by 80%.
, doi: 10.11999/JEIT190646
[Abstract](9) [PDF 0KB](1)
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In radio monitoring and target location applications, the received signals are often affected by complex electromagnetic environment, such as impulse noise and cochannel interference. Traditional signal processing methods based on second-order statistics often fail to work properly. The signal processing method based on fractional lower order statistics also encounters difficulties due to its dependence on prior knowledge of signals and noises. In recent years, the theory and method of correntropy and cyclic correntropy signal processing, which are widely concerned in the field of signal processing, are put forward. They are effective technical means to solve the problems of signal analysis and processing, parameter estimation, target location and other applications in complex electromagnetic environment. They promote greatly the development of the theory and application of non-Gaussian and non-stationary signal processing. This paper reviews systematically the basic theory and methods of correntropy and cyclic correntropy signal processing, including the background, definition, properties and characteristics of correntropy and cyclic correntropy, as well as their mathematical and physical meanings. This paper introduces also the applications of correntropy and cyclic correntropy signal processing in many fields, hoping to benefit the research and application of non-Gaussian and non-stationary statistical signal processing.
, doi: 10.11999/JEIT190559
[Abstract](15) [PDF 4300KB](2)
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Image thresholding methods based on the rough entropy segment the images without prior information except the images. There are two problems to be considered in the rough entropy based thresholding methods, i.e., measuring the incompleteness of knowledge about an image and granulating the image. In this paper, reciprocal rough entropy, a new form of rough entropy, is defined to measure the incompleteness of the image information. In order to granulate the image effectively, a granule size selection method based on the homogeneity histogram is employed. The proposed reciprocal rough entropy is simple in expression and calculation. The experimental results verify the effectiveness of the proposed algorithm.
, doi: 10.11999/JEIT190597
[Abstract](19) [PDF 1866KB](1)
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Quantum walks are raised for teleporting qubit or qudit. Based on quantum walk teleportation, an arbitrated quantum signature scheme with quantum walks on regular graphs is suggested, in which the entanglement source does not need preparing ahead. In the initial phase, the secret keys are generated via quantum key distribution system. In the signing phase, the signature for the transmitted message is created by the signer. Teleportation of quantum walks on regular graphs is applied to teleporting encrypted message copy from the signer to the verifier. Concretely, the sender encodes the cipher of message copy on coin state. Then two-step quantum walks are performed on the initial system state engendering the necessary entangled state for quantum teleportation, which can be the basis of signature generation and verification. In the verifying phase, the verifier verifies the validity of the completed signature under the aid of an arbitrator. Additionally, the applications of random number and public board deter the verifier’s existential forgery and repudiation attacks before the verifier accepts the true message. Analyses show that the suggested arbitrated quantum signature algorithm satisfies the general two requirements, i.e., impossibility of disavowal from the signer and the verifier and impossibility of forgery from anyone. The discussions demonstrate that the scheme may not prevent disavowal attack from the signer and that the corresponding improvements are presented. The scheme may be realizable because quantum walks have experimentally proven to be implementable in different physical systems.
, doi: 10.11999/JEIT190612
[Abstract](1) [PDF 0KB](0)
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The high-speed movement of vehicles inevitably leads to frequent data migration between edge servers and increases communication delay, which brings great challenges to the real-time computing service of edge servers. To solve this problem, a real-time reinforcement learning method based on Deep Q-learning Networks according to vehicle motion Trajectory Process (DQN-TP) is proposed. The proposed algorithm separates the decision-making process from the training process by using two neural networks. The decision neural network obtains the network state in real time according to the vehicle’s movement track and chooses the migration method in the virtual machine migration and task migration. At the same time, the decision neural network uploads the decision records to the memory replay pool in the cloud. The evaluation neural network in the cloud trains with the records in the memory replay pool and periodically updates the parameters to the on-board decision neural network. In this way, training and decision-making can be carried out simultaneously. At last, a large number of simulation experiments show that the proposed algorithm can effectively reduce the latency compared with the existing methods of task migration and virtual machine migration.
, doi: 10.11999/JEIT190595
[Abstract](23) [FullText HTML](20) [PDF 3358KB](1)
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For the problem that the traditional traffic accident risk prediction algorithm can not automatically discriminate data features, and the model expression ability is poor, a traffic accident risk prediction algorithm based on deep learning is proposed. The algorithm firstly extracts multi-dimensional features by using the convolutional neural network established in the edge server for a large amount of traffic data collected in the edge network of vehicles. After normalization, de-equalization and other pre-processing, the new variables are input into the convolutional layer and the pooling layer for training. Finally, based on the output discrimination value of the fully connected layer, the risk of traffic accidents can be predicted by simulation. The simulation results show that the algorithm is validated to predict the risk of traffic accidents, and has lower loss and higher prediction accuracy than the traditional machine learning BP neural network algorithm and Logical Regression algorithm.
, doi: 10.11999/JEIT190301
[Abstract](17) [FullText HTML](14) [PDF 1578KB](0)
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, doi: 10.11999/JEIT190214
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A new ensemble TSK fuzzy classifier (i,e. IK-D-TSK) is proposed. First, all zero-order TSK fuzzy sub-classifiers are organized in a parallel way, then the output of each sub-classifier is augmented to the original (validation) input space, finally, the proposed Iterative Fuzzy C-means Clustering Algorithm (IFCM) generates dictionary data on augmented validation dataset, and then KNN is used to predict the result for test data. IK-D-TSK has the following advantages: the output of each zero-order TSK subclassifier is augmented to the original input space to open the manifold structure in parallel, according to the principle of stack generalization, the classification accuracy can be improved; Compared with traditional TSK fuzzy classifiers which trains sequentially, IK-D-TSK train all the sub-classifiers in parallel, so the running speed can be effectively guaranteed; Because IK-D-TSK works based on dictionary data obtained by IFCM & KNN, it has strong robustness. The theoretical and experimental results show that IK-D-TSK has high classification performance, strong robustness and high interpretability.
, doi: 10.11999/JEIT180940
[Abstract](217) [FullText HTML](103) [PDF 1436KB](3)
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Dual satellite TDOA/FDOA localization is achieved by the TDOA hyperboloid and FDOA hyperboloid. The accuracy of localization is affected by TDOA/FDOA accuracy. In order to measure accurately the TDOA/FDOA, a method of TDOA/FDOA measurement based on short synthetic aperture is presented. This method improves the measurement accuracy by using a certain length of synthetic aperture. For narrowband signals, the method has the ability to estimate a single satellite Doppler, and the frequency difference can be obtained from the results estimated by the two satellites. For wideband signals, high-precision estimates of frequency differences can be obtained by dual satellite data interference. For short-term stable radar signals, the processing results of STK simulation data confirm the effectiveness of the proposed method.
, doi: 10.11999/JEIT190680
[Abstract](10) [FullText HTML](9) [PDF 2074KB](1)
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Ground penetrating radar, as a non-destructive technology, has been widely used to detect, locate, and characterize subsurface objects. Example applications include underground utility mapping and bridge deck deterioration assessment. However, manually interpreting the GPR scans to detect buried objects and estimate their positions is time-consuming and labor-intensive. Hence, the automatic detection of targets is necessary for practical application. To this end, this paper discussed the feasibility of using GPR to estimate target positions, and reviewed the progress made by domestic and international scholars on automatic hyperbolic signature detection in GPR scans. Thereafter, this paper summarizes and compares the processing methods for target detection. It is concluded that future research should focus on developing deep-learning based method to automatically detect and estimate subsurface features for on-site applications.
, doi: 10.11999/JEIT190657
[Abstract](17) [FullText HTML](17) [PDF 3440KB](2)
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Pattern recognition algorithms can discover valuable information from mass data of biomedical images as guide for basic research and clinical application. In recent years, with improvement of the theory and practice of pattern recognition and machine learning, especially the appearance and application of deep learning, the crossing research among artificial intelligence, pattern recognition, and biomedicine become a hotspot, and achieve many breakthrough successes in related fields. This review introduces briefly the common framework and algorithms of image pattern recognition, summarizes the applications of these algorithms to biomedical image analysis including fluorescence microscopic images, histopathological images, and medical radiological images, and finally analyzes and prospect several potential research directions.
, doi: 10.11999/JEIT190004
[Abstract](537) [FullText HTML](332) [PDF 2907KB](10)
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Against the problem of low detection rate to detect small hardware Trojan by side-channel in physical environment, the Marginal Fisher Analysis (MFA) is introduced. On the basis, a hardware Trojan detection method based on Compression Marginal Fisher Analysis (CMFA) is proposed. The projection space is constructed by reducing the distance between the sample and its same neighbor samples, and the distance between the same neighbor samples and the center of the same kind, and increasing the distance between the same neighbor samples of the center and the sample in different kind. Thus, the difference in the original data is found without any assumptions about data distribution, and the detection of hardware Trojan is achieved. The hardware Trojan detection experiment in AES encryption circuit shows that this method can effectively distinguish the statistical difference in side-channel signal between reference chip and Trojan chip and detect the hardware Trojan whose scale is 0.04% of the original circuit.
, doi: 10.11999/JEIT190317
[Abstract](38) [FullText HTML](20) [PDF 2622KB](3)
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In order to completely remove the spurious-peak side effect in the undersampling based wide-band spectral analysis, this paper proposes a high-performance co-prime spectral analysis method based on paralleled all-phase point-pass filtering. On basis of a deep analysis on the mechanism of the classical co-prime spectral analysis, this paper discovers that this spurious-peak side effect arises from those redudant overlapping boundary-bands related to distinct polyphase filtering branches between the up data path and the down data path. Therefore, through replacing the prototype filters in the classical co-prime spectral analysis by the all-phase point-pass filtering banks, this paper derives a novel co-prime analysis dataflow based on paralleled all-phase point-pass filtering. Both theoretic analysis and numerical simulation show that the proposed spectral analysis method achieves remarkable performance improvement: it can not only completely remove the spurious-peak side effect, but also obtains a much higher spectral resolution than the classical co-prime analysis, thereby possessing another merit of distinguishing dense spectral components. The proposed spectral analysis method possesses vast potentials in the software-defined radio, radar detection, passive positioning and marine wireless communication etc.
, doi: 10.11999/JEIT190303
[Abstract](61) [FullText HTML](50) [PDF 1227KB](5)
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Considering the interference problem of overlapping areas of cells, the service continuity of mobile users and the utilization of spectrum resources in the 5G network. In this paper, we proposed an Energy Efficient resource allocation scheme for the Inactive user(EEI). Firstly, a user-centered virtual cell is generated based on the notification area of the inactive users, and the intra-cell next-generation NodeBs (gNBs) could cooperatively provide communication services for users to improve the communication quality, lower the inter-cell interference, and reduce the handover signaling overhead. Secondly, Lyapunov optimization method is used to maximize the energy efficiency of the network, while ensuring the stability of the data queue. To make the optimization problem tractable, the scheme is decomposed into two sub-problems: the optimal transmission resource allocation and optimal transmission power allocation. Notice that, the optimal solutions are local optimal, which are based on the relaxed optimization problem. The simulation results show that the proposed energy efficiency resource allocation scheme based on the inactive users could achieve a better performance than the comparison algorithms,in the price of higher computational complexity.
, doi: 10.11999/JEIT190287
[Abstract](30) [FullText HTML](33) [PDF 1784KB](1)
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Existing Point-Of-Interest (POI) recommendation algorithms lack adaptability for users with different check-in features. To solve this problem, an adaptive POI recommendation method based on User Check-in Activity (UCA) feature and Temporal-Spatial (TS) probabilistic models UCA-TS is proposed. The user check-in activity is extracted using a probabilistic statistical analysis method, and a calculation method of user's inactive and active membership is given. On this basis, one-dimensional power law function and two-dimensional Gaussian kernel density estimation combined with time factor are used to calculate the probability for inactive and active features respectively, and the popularity of POI is incorporated to recommend. This method can adapt to the users' check-in features and reflect the users' check-in temporal-spatial preferences more accurately. The experiments show that the proposed method can effectively improve the recommendation precision and recall.
, doi: 10.11999/JEIT190277
[Abstract](35) [FullText HTML](25) [PDF 1803KB](7)
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It is of great significance to optimize emergency resource schedule for earthquake emergency rescue. The conflicting multiple schedule goals, such as time, fairness, and cost, should be taken into considerations together in an earthquake emergency resource schedule. A three-objective optimization model with constraints is constructed according to earthquake emergency resource schedule problems in this paper. An Adaptive MultiObjective Particle Swarm Optimization (PSO) based on Evolutionary State Evaluation (AMOPSO/ESE) is proposed to optimize this model for obtaining the Pareto optimal set. At the same time, based on the decision behavior pattern of "macro first and micro later", the two-level optimal solution sets consisting of an interest optimal solution set and their neighborhood optimal solution sets are proposed to represent the Pareto front roughly, which can simplify the decision-making process. The simulation results show that the multiobjective resource schedules can be effectively obtained by the AMOPSO/ESE algorithm, and the performance of the proposed algorithm is better than those of the chosen competed algorithms in terms of convergence and diversity.
, doi: 10.11999/JEIT190216
[Abstract](38) [FullText HTML](27) [PDF 3653KB](4)
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To improve performance of denoising and edge preservation of the total variational image denoising model, a curvature difference driven minimal surface filter is proposed. Firstly, the presented filter model is constructed by adding an adaptive edge detection function of curvature difference to the mean curvature filter model. After that, from the perspective of differential geometry theory, the physical meaning of the energy functional model and the method of reducing the average curvature energy are elaborated. Finally, in the discrete image domain, the surface in the neighborhood of each pixel of the image is iteratively evolved to the minimal surface to minimize the average curvature energy of the energy functional, so that the total energy of the energy functional is also minimized. Experiments show that the filter can not only remove Gauss noise and salt and pepper noise, but also remove the mixed noise composed of these two kinds of noise. And its performance of noise reduction and edge preservation is better than the other five total variational algorithms of the same kind.
, doi: 10.11999/JEIT190725
[Abstract](14) [FullText HTML](13) [PDF 587KB](2)
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To improve the efficiency of the triangularization of ideal lattice basis, a fast algorithm for triangularizing an ideal lattice basis is proposed by studying the polynomial structure in this paper, which runs in time O(n3log2B), where n is the dimension if the lattice, B is the infinity norm of lattice basis. Based on the algorithm, a deterministic algorithm for computing the Smith Normal Form of ideal lattice is given, which has the same time complexity and thus is faster than any previously known algorithms. Moreover, for a special class of ideal lattices, a method to transform such triangular bases into Hermite Normal Form faster than previous algorithms will be present.
, doi: 10.11999/JEIT181102
[Abstract](1601) [FullText HTML](370) [PDF 1996KB](40)
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It makes the Pulse Doppler (PD) radar widely applied that the PD radar has the obvious advantages of detecting the Doppler frequency of the target and suppressing the clutter effectively. However, it is difficult for the PD radar to detect the target due to velocity ambiguity. Combining with the characteristic and stagger-period model of the PD radar, a Doppler frequency estimation method based on all phase DFT Closed-Form Robust Chinese Remainder Theorem (CFRCRT) with spectrum correction is proposed in this paper. Both theoretical analysis and simulation experiment demonstrate that the proposed method can satisfy the engineering demand in measure accuracy and real-time performance.
, doi: 10.11999/JEIT180939
[Abstract](393) [FullText HTML](296) [PDF 340KB](10)
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Due to the wide applications in association schemes, authentication codes and secret sharing schemes etc., construction of the linear codes with a few weights is an important research topic. A class of linear codes with four-weight and six-weight over finite field ${F_p}$ (p is an odd prime) is constructed by a proper selection of the defining set. The explicit weight distribution is obtained using Gauss sums, and some examples from Magma program to illustrate the validity of the conclusions are provided. The results show that these codes include almost optimal codes with respect to Singleton bound.
, doi: 10.11999/JEIT190043
[Abstract](1344) [FullText HTML](596) [PDF 2601KB](48)
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The image forgery detection algorithm based on convolutional neural network can implement the image forgery detection that does not depend on a single image attribute by using the learning ability of convolutional neural network, and make up for the defect that the previous image forgery detection algorithm relies on a single image attribute and has low applicability. Although the image forgery detection algorithm using a single network structure of deep layers and multiple neurons can learn more advanced semantic information, the result of detecting and locating forgery regions is not ideal. In this paper, an image forgery detection algorithm based on cascaded convolutional neural network is proposed. Based on the general characteristics exhibited by convolutional neural network, and then the deeper characteristics are further explored. The cascaded network structure of shallow layers and thin neurons figures out the defect of the single network structure of deep layers and multiple neurons in image forgery detection. The proposed detection algorithm in this paper consists of two parts: the cascade convolutional neural network and the adaptive filtering post-processing. The cascaded convolutional neural network realizes hierarchical forgery regions localization, and then the adaptive filtering post-processing further optimizes the detection result of the cascaded convolutional neural network. Through experimental comparison, the proposed detection algorithm shows better detection results and has higher robustness.
, doi: 10.11999/JEIT190297
[Abstract](526) [FullText HTML](335) [PDF 2713KB](25)
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RGB-D saliency detection identifies the most visually attentive target areas in a pair of RGB and Depth images. Existing two-stream networks, which treat RGB and Depth data equally, are almost identical in feature extraction. As the lower layers Depth features with a lot noise, it causes image features not be well characterized. Therefore, a multi-modal feature-fused supervision of RGB-D saliency detection network is proposed, RGB and Depth data are studied independently through two-stream , double-side supervision module is used respectively to obtain saliency maps of each layer, and then the multi-modal feature-fused module is used to later three layers of the fused RGB and Depth of higher dimensional information to generate saliency predicted results. Finally, the information of lower layers is fused to generate the ultimate saliency maps. Experiments on three open data sets show that the proposed network has better performance and stronger robustness than the current RGB-D saliency detection models.
, doi: 10.11999/JEIT181049
[Abstract](503) [FullText HTML](310) [PDF 3678KB](9)
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A novel wideband low RCS new super-surface array based on three reflective cell shared aperture is designed, which is composed of three kinds of Artificial Magnetic Conductor (AMC). Compared with the traditional AMC array, the new array uses one of AMC as phasor interference unit. A new phase cancellation relation is presented, the new phase cancellation relation is used to extend the traditional array phase cancellation band. Then, the parameters of the cell structure are further optimized to realize the reduction of RCS and the improvement of bandwidth. The physical sample is processed and tested. The results of simulation and field test show that: the backward reduction of RCS in the range of 5.2～13.9 GHz reaches more than 10 dB, and the relative bandwidth reaches 91%. It is shown that the new array can overcome the defect of the discontinuous operating band of the traditional array and has broadband low scattering characteristics.
, doi: 10.11999/JEIT181145
[Abstract](465) [FullText HTML](285) [PDF 1015KB](14)
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Based on the beam wave synchronization interaction in transverse and longitudinal directions at the same time and derived from Maxwell’s equation and linear Vlasov equation, the planar metallic grating beam-wave interaction " hot” dispersion equation considering both cyclotron resonance and Cherenkov resonance is deduced. Through the reasonable selection for geometric and electrical parameters, the numerical calculation and analysis of the " hot” dispersion equation show that the beam-wave interaction gain and frequency band with the cyclotron resonance enhancement effect are higher than those with only Cherenkov resonance radiation.
, doi: 10.11999/JEIT180947
[Abstract](321) [PDF 1297KB](0)
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The transient signal without modulation information of the radiation source can characterize the unintentional modulation characteristics of the radiation source. The analysis of the transient signal can realize the radiation source identification. In the switching on and frequency conversion process of the frequency-hopping signal, there is a transient adjustment time without information transmission. In the transient adjustment moment, the signal transmitted by the transmitter is a non-linear, non-stationary and non-Gaussian signal without modulation information. This transient time series can reflect the device characteristics of the frequency-hopping transmitter, and the sequence often exhibits complex chaotic characteristics. Therefore, from the idea of chaotic time series analysis and Low-rank characteristics of transient signal, a frequency-hopping transmitter classification algorithm is proposed based on chaotic attractor reconstruction and Low-rank clustering. The experimental tests show that the transient signal of the frequency-hopping transmitter belongs to the chaotic time series. At the same time, the classification results of the frequency-hopping signals demonstrate the feasibility of the Low-rank clustering algorithm in frequency-hopping transmitter classification.
, doi: 10.11999/JEIT190264
[Abstract](211) [FullText HTML](139) [PDF 1023KB](13)
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The application of Dynamic Voltage Scaling (DVS) technique in real-time system energy management will result in the decrease of system reliability. A dynamic energy management method based on Improved Bird Swarm Algorithm (IoBSA) is proposed in this paper. Firstly, the population is initialized uniformly with the principle of good point set, so as to improve the quality of initial solution and increase the diversity of population effectively. Secondly, in order to better balance the global and local search ability of BSA algorithm, the nonlinear dynamic adjustment factor is proposed. Then, a power consumption model with time and reliability constraints is established for the dynamic adjustment of processor frequency in embedded real-time systems. On the premise of ensuring real-time performance and stability, the proposed IoBSA algorithm is used to find the solution with minimum energy consumption. The experimental results show that compared with the traditional BSA algorithm and other common algorithms, the improved bird swarm algorithm has a strong advantage in solving the minimum energy consumption and a fast processing speed energy management.
, doi: 10.11999/JEIT190266
[Abstract](356) [FullText HTML](258) [PDF 1817KB](36)
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In view of the problems of low attack detection rate and high false positive rate caused by poor accuracy and robustness of the extracted traffic features in network traffic anomaly detection, a network traffic anomaly detection method based on deep features learning is proposed, which is combined with Stacked Denoising Autoencoders (SDA) and softmax. Firstly, a two-stage optimization algorithm is designed based on particle swarm optimization algorithm to optimize the structure of SDA, the number of hidden layers and nodes in each layer is optimized successively based on the traffic detection accuracy, and the optimal structure of SDA in the search space is determined, improving the accuracy of traffic features extracted by SDA. Secondly, the optimized SDA is trained by the mini-batch gradient descent algorithm, and the traffic features with strong robustness are extracted by minimizing the difference between the reconstruction vector of the corrupted data and the original input vector. Finally, softmax is trained by the extracted traffic features to construct an anomaly detection classifier for detecting traffic attacks with high performance. The experimental results show that the proposed method can adjust the structure of SDA based on the experimental data and its classification tasks, extract traffic features with a higher accuracy and robustness, and detect traffic attacks with high detection rate and low false positive rate.
, doi: 10.11999/JEIT190263
[Abstract](239) [FullText HTML](162) [PDF 1148KB](18)
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Focusing on the problem of rather large estimation error in the traditional Direction Of Arrival (DOA) estimation algorithm induced by finite subsampling, a robust DOA estimation method based on low rank recovery is proposed in this paper. Following the low-rank matrix decomposition method, the received signal covariance matrix is firstly modeled as the sum of the low-rank noise-free covariance matrix and sparse noise covariance one. After that, the convex optimization problem associated with the signal and noise covariance matrix is constructed on the basis of the low rank recovery theory. Subsequently, a convex model of the estimation error of the sampling covariance matrix can be formulated, and this convex set is explicitly included into the convex optimization problem to improve the estimation performance of signal covariance matrix such that the estimation accuracy and robustness of DOA can be enhanced. Finally, with the obtained optimal noiseless covariance matrix, the DOA estimation can be implemented by employing the Minimum Variance Distortionless Response (MVDR) method. In addition, exploiting the statistical characteristics of the sampling covariance matrix estimation error subjecting to the asymptotic normal distribution, an error parameter factor selection criterion is deduced to reconstruct the noise-free covariance matrix preferably. Compared with the traditional Conventional BeamForming (CBF), Minimum Variance Distortionless Response(MVDR), MUltiple SIgnal Classification (MUSIC) and Sparse and Low-rank Decomposition based Augmented Lagrange Multiplier(SLD-ALM) algorithms, numerical simulations show that the proposed algorithm has higher DOA estimation accuracy and better robustness performance under finite sampling snapshot.
, doi: 10.11999/JEIT190111
[Abstract](384) [FullText HTML](202) [PDF 4766KB](21)
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Considering the problem that the existing superpixel methods are usually unable to set an appropriate number of generated superpixels automatically and unable to adhere to image boundaries effectively, a new superpixel method is proposed in this paper, which utilizes local information to perform multi-level simple linear iterative clustering to generate superpixels. First, original image is initially segmented by Simple Liner Iterative Clustering based on Local Information (LI-SLIC). Then, each superpixel is segmented iteratively until its color standard deviation is lower than a predefined threshold. Finally, the over-segmented superpixels are merged based on the color differences between adjacent superpixels. Experiments on Berkeley, Pascal VOC and 3Dircadb databases, as well as comparison with other methods indicate that the proposed method can adhere to image boundaries more accurately, and can prevent over- and under- segmentations more effectively.
, doi: 10.11999/JEIT190485
[Abstract](35) [FullText HTML](27) [PDF 3001KB](3)
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Deep learning has shown excellent performance in the field of artificial intelligence. In the supervised identification task, deep learning algorithms can achieve unprecedented recognition accuracy by training massive tagged data. However, owing to the high cost of labeling massive data and the difficulty of obtaining massive data of rare categories, it is still a serious problem how to identify unknown class that is rarely or never seen during training. In view of this problem, the researches of Zero-Shot Learning (ZSL) in recent years was reviewed and illustrated from the aspects of research background, model analysis, data set introduction and performance analysis in this article. Some solutions of mainstream problem and prospects of future research are provided. Meanwhile, the current technical problems of ZSL was analyzed, which can offer some references to beginners and researchers of ZSL.
, doi: 10.11999/JEIT190185
[Abstract](38) [FullText HTML](26) [PDF 1629KB](5)
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Due to the limitation of energy and bandwidth in Wireless Sensor Networks(WSN), the direct transmission of analog signals in the network is greatly restricted. Therefore, quantization of analog signals is an important means to save network energy and ensure effective bandwidth. To this end, based on the principle of minimum absolute mean reconstruction error a network quantization and energy optimization method is designed in this paper. Firstly, for single sensor, the optimal quantization bit number is derived under the condition of fixed energy and the optimal energy distribution is derived under the condition of fixed quantization bit number. Secondly, on the basis of single sensor, the optimal quantization bit number and optimal energy allocation are further deduced in multi-sensor case. In both cases, the sensor measurement noise and channel fading loss are considered. Finally, the numerical simulation results show that the proposed method is correct and better than the equal energy distribution.
, doi: 10.11999/JEIT190179
[Abstract](13) [FullText HTML](13) [PDF 1504KB](2)
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For the problem that mobile robot can not avoid large concave obstacles during navigation, this paper proposes a multi-state integrated navigation algorithm. The algorithm classifies the running state of mobile robot into running state, switching state and obstacle avoidance state according to different moving environment, and defines the state double switching conditions based on the running speed and running time of the mobile robot. The Artificial Potential Field Method (APFM) is used to navigate and observe the geometric configuration of adjacent obstacles in real time. When encountering an obstacle, the switching state is used to determine whether the state switching condition is satisfied, and the obstacle avoidance algorithm is executed to enter the obstacle avoidance state and enter the obstacle avoidance state to implement the obstacle avoidance algorithm. After the obstacle avoidance is completed, the state automatically switches back to the running state to continue the navigation task. The proposal of multi-state can solve the problem of local oscillation of traditional artificial potential field method in the process of avoiding large concave obstacles. Furthermore, the double-switching condition determination algorithm based on running speed and running time can realize smooth switching between states and optimize the path. The experimental results show that the algorithm can not only solve the local oscillation problem, but also reduce the obstacle avoidance time and improve the efficiency of the navigation algorithm.
, doi: 10.11999/JEIT190257
[Abstract](2042) [FullText HTML](147) [PDF 4548KB](19)
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Based on the in-depth research on the S-box constitution arithmetic of composite, An area optimized generic low-entropy higher-order masking scheme is proposed in this paper. The low entropy masking method is introduced on GF(24), and the partial module reusing design is adopted, which effectively reduces the number of multiplications based on the S-box inversion operation of the composite. The algorithm can be applied to any order masking scheme of arbitrary S-box composed of inversion operation, and further applies this scheme to AES, gives detailed simulation results and optimizes the layout area, compared with the traditional masking scheme. Effectively reduce the use of logical resources, in addition, the security is theoretically proved.
, doi: 10.11999/JEIT190213
[Abstract](288) [FullText HTML](160) [PDF 1636KB](30)
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Orthogonal Frequency Division Multiplexing(OFDM) has been widely used in wireless communication systems, and its data transmission security has certain practical significance. A double encryption scheme is proposed which enhances the confidentiality of the OFDM communication system and can prevent brute force attacks significantly. Specifically, the first encryption is achieved by using neural network to generate the scrambling matrix, and the second encryption is implemented by chaotic sequence generating by composite discrete chaotic system based on Logistic mapping and Sine mapping. Moreover, it has larger secret key space compared with the single one-dimensional Logistic mapping chaotic system. The performance of double encryption is measured by verifying its chaotic characteristics and randomness (Lyapunov exponent and NIST) as well as its security performance in simulation. The results show that Lyapunov index is increased to 0.9850, and the maximum P-value in the NIST test is 0.9995 by using the proposed double encryption in this paper. It indicates such double encryption significantly improve the confidentiality of the OFDM communication system without affecting the transmission performance.
, doi: 10.11999/JEIT190032
[Abstract](385) [FullText HTML](258) [PDF 5644KB](30)
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In order to enhance the useful information in the image and improve the visual effect of the image, a Non-local Multi-scale Fractional Differential(NMFD) image enhancement operator is proposed. The operator divides the image into several sub-images and calculates the edge intensity coefficient, entropy value and roughness of each sub-image, and the obtained feature data are normalized in a unified scale in the global image range. Then, the normalized data are weighted to be the non-local eigenvalues of the image. Finally, an exponential function is used to establish the non-linear quantization relationship between image detail features and the value of fractional order. Thus, the fractional order of different scales can be determined in different image sub-block regions, so that the non-local multi-scale image enhancement model is realized.
, doi: 10.11999/JEIT190135
[Abstract](911) [FullText HTML](318) [PDF 2334KB](20)
Abstract:
Learning unsupervised representations from multivariate medical signals, such as multi-modality polysomnography and multi-channel electroencephalogram, has gained increasing attention in health informatics. In order to solve the problem that the existing models do not fully incorporate the characteristics of the multivariate-temporal structure of medical signals, an unsupervised multi-context deep convolutional autoencoder is proposed in this paper. Firstly, by modifying traditional convolutional neural networks, a multivariate convolutional autoencoder is proposed to extract multivariate context features within signal segments. Secondly, semantic learning is adopted to auto-encode temporal information among signal segments, to further extract temporal context features. Finally, an end-to-end multi-context autoencoder is trained by designing objective function based on shared feature representation. Experimental results conducted on two public benchmark datasets (UCD and CHB-MIT) show that the proposed model outperforms the state-of-the-art unsupervised feature learning methods in different medical tasks, demonstrating the effectiveness of the learned fusional features in clinical settings.
, doi: 10.11999/JEIT190290
[Abstract](459) [FullText HTML](312) [PDF 1973KB](50)
Abstract:
In order to solve the Virtual Network Function (VNF) migration optimization problem caused by the dynamicity of service requests on the 5G network slicing architecture, firstly, a stochastic optimization model based on Constrained Markov Decision Process (CMDP) is established to realize the dynamic deployment of multi-type Service Function Chaining (SFC). This model aims to minimize the average sum operating energy consumption of general servers, and is subject to the average delay constraint for each slicing as well as the average cache, bandwidth resource consumption constraints. Secondly, in order to overcome the issue of having difficulties in acquiring the accurate transition probabilities of the system states and the excessive state space in the optimization model, a VNF intelligent migration learning algorithm based on reinforcement learning framework is proposed. The algorithm approximates the behavior value function by Convolutional Neural Network (CNN), so as to formulate a suitable VNF migration strategy and CPU resource allocation scheme for each network slicing according to the current system state in each discrete time slot. The simulation results show that the proposed algorithm can effectively meet the QoS requirements of each slice while reducing the average energy consumption of the infrastructure.
, doi: 10.11999/JEIT190333
[Abstract](280) [FullText HTML](171) [PDF 1526KB](22)
Abstract:
Link prediction considers to discover the unknown or missing links of complex networks by using the existing topology or other information. Resource Allocation index can achieve a good performance with low complexity. However, it ignores the path effectiveness of resource transmission process. The resource transmission process is an important internal driving force for the evolution of the network. By analyzing the effectiveness of the topology around the resource transmission path between nodes, a link prediction method based on topological effectiveness of resource transmission paths is proposed. Firstly, the influence of potential resource transmission paths between nodes on resource transmission is analyzed, and a quantitative method for resource transmission path effectiveness is proposed. Then, based on the effectiveness of the resource transmission path, after studying the two-way resource transmission amount between two nodes, the transmission path effectiveness index is proposed. The experimental results of 12 real networks show that compared with other link prediction methods, the proposed method can achieve higher prediction accuracy under the AUC and Precision metrics.
, doi: 10.11999/JEIT190373
[Abstract](244) [FullText HTML](138) [PDF 3669KB](9)
Abstract:
A memristive high-pass filter circuit is presented, which is composed of an active high-pass RC filter parallelly coupling with a memristor emulator of diode-bridge cascaded by LC oscillator. The circuit equations and system model are established. Based on bifurcation diagram, phase plane plot, and Poincaré mapping, bifurcation analysis with the feedback gain as adjustable parameter is performed, from which bursting oscillating behaviors including quasi-period, chaotic-torus, chaos, and multiple period that exist in such a memristive high-pass filter circuit are disclosed. Furthermore, through fast-slow analysis method, Hopf bifurcation set of the fast sub-system is derived, with which the formation mechanism of slow passage effect in the memristive high-pass filter circuit is expounded. Finally, the numerical simulation results are validated based on Multisim circuit simulations.
, doi: 10.11999/JEIT190417
[Abstract](583) [FullText HTML](146) [PDF 1531KB](10)
Abstract:
The traditional range-extended target detection is usually completed under the condition of scattering point density or scattering point number priori. The detection performance will be greatly reduced when the scattering point information of the target is completely unknown. To solve this problem, a Range Spread Target Detection method based on Online Estimation of Strong Scattering(OESS-RSTD) points is proposed. Firstly, the unsupervised clustering algorithm in machine learning is used to estimate the number of strong scattering points and the first detection threshold adaptively. Then, the second detection threshold is determined according to false alarm rate. Finally, the existence of the target is determined through two detection thresholds. In this paper, the simulation data and the measured data are used to verify and compare with other algorithms. By comparing the Signal-to-Noise Ratio (SNR) -detection probability curves of various methods with a given false alarm probability, it is verified that the proposed method in this paper has higher robustness than the traditional algorithm, and the method does not need any priori information of target scattering points.
, doi: 10.11999/JEIT190154
[Abstract](624) [FullText HTML](187) [PDF 1309KB](12)
Abstract:
In order to explore the correlation between face and audio in the field of speaker recognition, a novel multimodal Generative Adversarial Network (GAN) is designed to map face features and audio features to a more closely connected common space. Then the Triplet-loss is used to constrain further the relationship between the two modals, with which the intra-class distance of the two modals is narrowed, and the inter-class distance of the two modals is extended. Finally, the cosine distance of the common space features of the two modals is calculated to judge whether the face and the voice are matched, and Softmax is used to recognize the speaker identity. Experimental results show that this method can effectively improve the accuracy of speaker recognition.
, doi: 10.11999/JEIT190304
[Abstract](561) [FullText HTML](544) [PDF 1615KB](35)
Abstract:
, doi: 10.11999/JEIT190242
[Abstract](1634) [FullText HTML](351) [PDF 1654KB](35)
Abstract:
Existing few-shot methods have problems that feature extraction scale is single, the learned class representations are inaccurate, the similarity calculation still rely on standard metrics. In order to solve the above problems, multi-level attention feature network is proposed. Firstly, the multiple scale images are obtained by scale processing, the features of multiple scale images are extracted and the image-level attention features are obtained by the image-level attention mechanism to fusion them. Then, class-level attention features are learned by using the class-level attention mechanism. Finally, the classification is performed by using the network to compute the similarity scores between features. The proposed method is evaluated on the Omniglot dataset and the MiniImagenet dataset. The experimental results show that multi-level attention feature network can further improve the classification accuracy under small sample conditions compared to the single-scale image features and average prototypes.
, doi: 10.11999/JEIT190370
[Abstract](393) [FullText HTML](247) [PDF 1942KB](11)
Abstract:
Considering at the security risks and privacy leaks in the process of data and reward in the Mobile CrowdSensing (MCS), a distributed security delivery model based on Tangle network is proposed. Firstly, in the data perception stage, the local outlier factor detection algorithm is used to eliminate the anomaly data, cluster the perception data and determine the trusted participant. Then, in the transaction writing stage, Markov Monte Carlo algorithm is used to select the transaction and verify its legitimacy. The anonymous identity data is uploaded by registering with the authentication center, and the transaction is synchronously written to the distributed account book. Finally, combined with Tangle network cumulative weight consensus mechanism, when the security of transaction reaches its threshold, task publishers can safely deliver data and rewards. The simulation results show that the model not only protects user privacy, but also enhances the ability of secure delivery of data and reward. Compared with the existing sensing platform, the model reduces the time complexity and task publishing cost.
, doi: 10.11999/JEIT190186
[Abstract](306) [FullText HTML](186) [PDF 2756KB](13)
Abstract:
Based on the network structure and training methods of the Extreme Learning Machine (ELM), Correntropy-based Fusion Extreme Learning Machine (CF-ELM) is proposed. Considering the problem that the fusion of representation level features is insufficient in most classification methods, the kernel mapping and coefficient weighting are combined to propose a Fusion Extreme Learning Machine (F-ELM), which can effectively fuse the representation level features. On this basis, the Mean Square Error (MSE) loss function is replaced by the correntropy-based loss function. A correntropy-based cycle update formula for training the weight matrices of the F-ELM is derived to enhance classification ability and robustness. Extensive experiments are performed on Caltech 101, MSRC and 15 Scene datasets respectively. The experimental results show that CF-ELM can further fuse the representation level features to improve the classification accuracy.
, doi: 10.11999/JEIT190503
[Abstract](57) [FullText HTML](42) [PDF 943KB](3)
Abstract:
Cyclic Redundancy Check (CRC) is used in cascade with channel coding to improve the convergence of the decoding. In the new generation of wireless communication systems, such as 5G, both code length and code rate are diverse. To improve the decoding efficiency of cascaded systems, a CRC parallel algorithm with variable computing width is proposed in this paper. Based on the existing fixed bit-width parallel algorithm, this algorithm combines the parallel calculation of feedback data and input data in the formula recursive method, realizing a highly parallel CRC check architecture with variable bit-width CRC calculation. Compared with the existing parallel algorithms, the merged algorithm saves the overhead of circuit resources. When the bit-width is fixed, the resource saving effect is obvious, and at the same time, the feedback delay is also optimized by nearly 50%. When the bit-width is variable, the use of resources is also optimized accordingly.
, doi: 10.11999/JEIT190001
[Abstract](1044) [FullText HTML](482) [PDF 2384KB](36)
Abstract:
Long Range(LoRa) Backscattering Communication (BC) not only has the advantages of low cost and low power consumption, but also has a long communication distance. However, the existing LoRa BC scheme is complex and cannot be applied to actual engineering. For this purpose, a new LoRa BC method is proposed. A Direct Digital frequency Synthesis (DDS) technique is used to generate a square wave with a linear frequency variation in this paper as a LoRa scattering modulation signal. And for the first time, the prototype of LoRa BC system based on MCU is demonstrated. Experimental results show that design can successfully backscatter at any position between the station and the receiver which are 208 meters apart, while being compatible with commodity LoRa chipset. In addition, the method is also applicable to an Application Specific Integrated Circuit (ASIC) design, which enables the LoRa backscattering IC to have higher robustness, lower cost, and lower power consumption.
, doi: 10.11999/JEIT181133
[Abstract](382) [FullText HTML](294) [PDF 1808KB](18)
Abstract:
The constraint conditions of target assignment model for phased array radar network are unreasonable and the performance of model solving algorithms are not good enough. To solve these problems, a target assignment model for radar network based on Quality of Service (QoS) is constructed in this paper, and a model solving algorithm based on strong majorant function approximation is proposed. Through the establishment of resource space and environment space in QoS model, radar resource constraints as well as the visibility constraints between radars and targets are described accurately. Then, sufficient conditions for the optimal solution of QoS model are derived by Karush-Kuhn-Tucker(KKT) condition, and a two-dimensional fast traversal method is used to approximate the strong concave function curve. Finally, the optimal assignment scheme is obtained by the stepwise iteration of operation setting points on the strong concave curve of each target. The simulation results show that the model proposed in this paper can effectively accomplish the target assignment of radar network, and model solving algorithm has better performance than the typical intelligent search algorithms.
, doi: 10.11999/JEIT190102
[Abstract](274) [FullText HTML](194) [PDF 1431KB](25)
Abstract:
In the Orthogonal Frequency Division Multiplexing (OFDM) system, the receiver often needs to know the channel state information because the frequency selective fading channel will generate inter-symbol interference in the data transmission. In the case of maritime communication, the method of channel estimation is often needed to detect the channel subjected to the interference of various external factors. In order to improve the estimation performance, the Fast Bayesian Matching Pursuit based on singular-value-decomposition for Optimizing observation matrix (FBMPO) is proposed, which fully considers not only the sparse channel of maritime communication, but also reduces the influence of uncertainty of the unpredictable channel. Computer simulation shows, compared with traditional channel estimation algorithms, the proposed algorithm can effectively improve the accuracy of channel estimation.
, doi: 10.11999/JEIT190191
[Abstract](291) [FullText HTML](182) [PDF 2538KB](25)
Abstract:
The wide-swath interferometric altimeter working at near-nadir is a newly developed ocean surface topography measurement technology in recent years. Different from land elevation measurement, for the dynamic ocean surface waves, they move randomly all the time and this brings bias in Synthetic Aperture Radar (SAR) imaging and interferometric processes and leads to the final height measurement errors. For the requirement of centimeter-level precision, this error is the main source of measurement errors. The errors due to the characteristics of ocean surface and their impact on near-nadir InSAR’s precision are investigated. The motion error theoretical model is established combining the characteristics of the ocean surface and InSAR working mechanism, and the electromagnetic bias and layover bias are also taken into consideration. The error models in different SAR modes under various sea states are simulated. The error model is validated by the interferometric SAR full-link experimental simulation and the simulation results are consistent with the theoretical values. The results show that the errors are approximately linear changing with the Doppler centroid frequency and are proportional to the radial velocity of targets modulated by scattering. The errors are not only related to the characteristics of the waves, but also related to system parameters. This work can provide the feasible suggestions for future system design, error budget and data processing.
, doi: 10.11999/JEIT190096
[Abstract](344) [FullText HTML](183) [PDF 2567KB](17)
Abstract:
Forward-looking Synthetic Aperture Radar (SAR) imaging has the problem of left-right Doppler ambiguity, so it is necessary to use spatial resources for ambiguity resolution. Due to the weight and size of Unmanned Aerial Vehicle (UAV), the receiving array is usually small, and the ability of spatial beam-forming for Doppler ambiguity resolution is insufficient. In addition, the small Doppler gradient and narrow bandwidth of forward-looking SAR echo make the receiving bandwidth underutilized. Based on the above problems, a Doppler diversity Multiple Input Multiple Output (MIMO) forward-looking SAR imaging method is proposed in this paper. Based on the forward-looking SAR imaging technology, the narrow-band forward-looking Doppler echo is modulated to different Doppler centers by using Doppler diversity MIMO technology to make full use of the Doppler receiving bandwidth. Furthermore, a virtual receiving array with several times the aperture of the real receiving array can be obtained, which expands greatly the receiving channel and improves effectively the performance of forward-looking SAR imaging in de-Doppler left-right ambiguity.
, doi: 10.11999/JEIT190110
[Abstract](297) [FullText HTML](207) [PDF 794KB](10)
Abstract:
For the radio frequency stealth control measure of radar intermittent radiation, the relationship between radiation time ratio and positioning performance is studied which takes cross location with two stations as an example. Firstly, the control method of radar intermittent radiation is analyzed. Then, under the assumption of uniform linear motion of the carrier aircraft, the influence model of radiation time ratio on positioning accuracy is established by using the Cramer-Rao Lower Bound (CRLB). Finally, the solution steps of the model are given and verified by simulation. The simulation results show that different radiation time ratios have different effects on the location performance. When the initial distance is 100 km and the radiation time ratio is less than 0.5, the location convergence time exceeds 10 s, which can effectively reduce the performance of cross location with two stations.
, doi: 10.11999/JEIT190050
[Abstract](257) [FullText HTML](189) [PDF 1326KB](13)
Abstract:
Compared with the traditional high-order Finite Difference Time Domain(FDTD) Method, an improved high-order FDTD optimization method is proposed in this paper. This algorithm is based on Ampere’s law of circuits and finds a set of optimal coefficients through computer technology to minimize the global dispersion error of the FDTD method.The simulation of point source radiation with different resolutions shows that this method still has very low phase error in the case of lower resolution. It provides an effective solution to the problem of numerical dispersion in the modeling of large size structures.
, doi: 10.11999/JEIT190159
[Abstract](798) [PDF 1602KB](9)
Abstract:
To solve the problem of inaccurate feature representation caused by indistinctive appearance difference in person re-identification domain, a new matrix metric learning based on bidirectional reference set is proposed. Firstly, reciprocal-neighbor reference sets in different camera views are respectively constructed by the reciprocal-neighbor scheme. To ensure the robustness of reference sets, the reference sets in different camera views are jointly considered to generate the Bidirectional Reference Set (BRS). With hard samples which are mined by the BRS to represent feature descriptors, accurate appearance difference representations could be obtained. Finally, these representations are utilized to conduct more effective matrix metric learning. Experimental results on several public datasets demonstrate the superiority of the proposed method.
, doi: 10.11999/JEIT190190
[Abstract](493) [FullText HTML](324) [PDF 2018KB](9)
Abstract:
In the heterogeneous wireless networks, the parameter weight is difficult to determine for the vertical handover algorithm considering the parameters of the network and the user, at the same time, the vertical handover algorithm based on fuzzy logic has high complexity. Considering this problem, a hierarchical vertical handover algorithm based on fuzzy logic is proposed. Firstly, the Received Signal Strength (RSS), bandwidth and delay are input into the first-level fuzzy logic system. Combining with the rule adaptive matching, the QoS fuzzy value is inferred, and the network is initially filtered by the QoS fuzzy value to obtain the candidate network set; Then, the second-level fuzzy logic system is triggered by the trigger mechanism, and the QoS fuzzy value, network load rate and user access cost of the candidate network are input into the second-level fuzzy logic system. At the same time, the output decision value is obtained by combining the adaptive rule matching, so as to select the best access network. Finally, the experimental results show that the algorithm can guarantee the network performance while reducing the time cost of the system.
, doi: 10.11999/JEIT190218
[Abstract](283) [FullText HTML](159) [PDF 4404KB](8)
Abstract:
Linear tapered slot antennas have significant advantages over traditional horn antennas, dielectric rod antenna when used as feed elements in Focal Plane Arrays (FPA) of Passive MilliMeter Wave(PMMW) imaging. In this paper, a novel Antipodal Linear Tapered Slot Antenna(ALTSA) is designed and optimized. The proposed antenna, the gain of which is improved by loading metamaterial structure, is fed by the Substrate Integrated Waveguide(SIW). Simulation and measure analysis show that the good impedance characteristics, low sidelobe levels, high and smooth gain are all achieved in a wide frequency band. Meanwhile, the designed antenna has a smaller aperture width and is easier to form a denser feed array in the focal plane to improve the spatial resolution of passive millimeter wave imaging.
, doi: 10.11999/JEIT190469
[Abstract](280) [FullText HTML](59) [PDF 2808KB](6)
Abstract:
A high-resolution controllable magnification method for visual saliency object based on virtual optics is proposed in this paper. The original image is placed on the virtual object plane. Firstly, the diffractive wave of the original image on the virtual diffraction plane is obtained by inverse diffraction calculation, and then the forward diffraction calculation is carried out after the virtual diffraction wave is irradiated by spherical wave. The original images with different magnification can be reconstructed by changing the position of the observation plane. The simulation results show that compared with the general interpolation method, the magnified image shows a good visual perception effect, especially in the saliency region. When the degraded face image is used as the signal to be reconstructed, the significant areas such as eyes and nose are clearer than the general method. The local salient region in the original image is segmented by the level set method combined with salient map, and the magnification and contour extraction are performed. The contours show good smoothness.
, doi: 10.11999/JEIT190173
[Abstract](364) [FullText HTML](227) [PDF 4802KB](10)
Abstract:
For the problem of sparse feature enhancement in Synthetic Aperture Radar (SAR) imagery, conventional methods are difficult to achieve a preferable balance between accuracy and efficiency. In this paper, a robust and efficient SAR imaging algorithm based on Complex Alternating Direction Method of Multipliers(C-ADMM) is proposed for general SAR imaging feature enhancement within complex raw data domain. The problem is firstly imposed by an augmented Lagrange function, and the complex ${\ell _1}$-norm of the intended SAR image is jointly formulated within the C-ADMM framework. Then, the proximal mapping of the sparse feature is derived as a soft-thresholding operator. Further, an iterative processing procedure is designed according to Gaussian-Deidel principle, and the convergence of the proposed algorithm is analyzed. In the experiment, the performance of the proposed algorithm is firstly examined by the simulated data in terms of Phase Transition Diagram (PTD) under different under-sampling rate and degree of sparsity. Then, various raw SAR and Inverse SAR(ISAR) data, for both stationary ground scene and Ground Moving Target Imaging(CMTIm), are applied to further verify the proposed C-ADMM, and comparisons with classical Convex(CVX) and Bayesian Compress Sensing(BCS) algorithms are performed, so that both the effectiveness and superiority of the C-ADMM algorithm can be verified.
, doi: 10.11999/JEIT190142
[Abstract](308) [FullText HTML](179) [PDF 3666KB](14)
Abstract:
In order to satisfy the requirement of elliptical beam antenna with low profile, a novel design technique of the hybrid-structural antenna with elliptical beam is proposed. The hybrid-structural antenna consists of the ring-focus elliptical antenna in inner-ring region and the cassegrain elliptical antenna in outer-ring region. The design method, procedure and shaping formula are presented in detail. A 600 mm×1200 mm reflector antenna is designed and its tolerance analysis is also given. The results show that the novel structural antenna can operate in Ku/Ka dual bands, antenna efficiency is greater than 56% and Voltage Standing Wave Ratio (VSWR) is better than 1.27, and its side lobe levels in the EL and AZ planes are below –12.2 dB and –14.6 dB respectively. The simulated results of Grasp and CST software agree well, which verified the effectiveness of the design method.
, doi: 10.11999/JEIT190148
[Abstract](228) [FullText HTML](108) [PDF 2721KB](8)
Abstract:
The generalized Pareto distributed sea clutter model, known as one of the compound-Gaussian models, is able to describe heavy-tailed characteristic of sea clutter under high-resolution and low grazing angle detection scene efficiently, and the accuracy of parameter estimation under this condition heavily impacts radar’s detection property. In this paper, Combined BiPercentile (CBiP) estimator is proposed to estimate the parameters. The CBiP estimator is realized based on the explicit roots of low-order polynomial equations and full application of sample information in returns, which provides a highly-accurate parameter estimation process. Besides, the CBiP estimator can maintain the robustness of estimation performance when outliers with extremely large power are existing in samples, while other estimators, including moment-based and Maximum Likelihood (ML) estimators, degrade extremely in estimation accuracy. Without outliers in samples, the combined bipercentile estimator shows similar accuracy with the ML estimator. With outliers, the combined percentile estimator is the only method with robustness in performance, compared with other estimators aforementioned. Moreover, the ability of the new estimator is verified by measured clutter data.
, doi: 10.11999/JEIT190170
[Abstract](461) [FullText HTML](331) [PDF 2206KB](31)
Abstract:
, doi: 10.11999/JEIT190256
[Abstract](145) [FullText HTML](123) [PDF 6982KB](23)
Abstract:
To effectively detect sea surface targets by the passive interferometric microwave technology considered as an important complement for the space-based early warning system of China, a detection algorithm is proposed based on the Passive Interferometric Microwave Images (PIMI). Firstly, the mathematical model of PIMI is established for the sea background and sea surface target. Secondly, the detection algorithm is introduced in detail, and numerical simulations are performed to demonstrate the feasibility of the proposed algorithm. Finally, the air-borne experiments are also carried out. Both theoretical and experimental results demonstrate that the proposed algorithm is feasible, can effectively detect sea surface targets, and shows good performance. That also exhibits that the moving metal vessels on the sea surface show a " hot” and " low” characteristic in PIMI, which can be used to improve the detection probability. The proposed detection algorithm can provide a reference for space-based PIMI to detect sea surface targets.
, doi: 10.11999/JEIT190460
[Abstract](114) [FullText HTML](61) [PDF 2339KB](6)
Abstract:
To solve the problem of asynchronous sampling and communication delay of sensor network in space target tracking, an Asynchronous Distributed algorithm based on Information Filtering (ADIF) is proposed. First, local state information and measurement information with sampling time are transmitted between local sensor and adjacent nodes in a certain topology structure. Then, the local sensor sorts the received asynchronous information by time, and uses ADIF algorithm to calculate the target state respectively. This method is simple to implement, the frequency of communication between sensors is small, and it supports the real-time change of network topology, which is suitable for multi-target tracking. In this paper, single target and multi-target tracking are simulated respectively. The results show that the algorithm can effectively solve the problem of asynchronous sensor filtering, and the distributed filtering accuracy converges to the centralized result.
, doi: 10.11999/JEIT190160
[Abstract](572) [FullText HTML](284) [PDF 1592KB](27)
Abstract:
For cases with small samples, the estimated noise subspace obtained from sample covariance matrix deviates from the true one, which results in MUltiple SIgnal Classification (MUSIC) Direction-Of-Arrival (DOA) estimation performance breakdown. To deal with this problem, an iterative algorithm is proposed to improve the MUSIC performance by modifying the signal subspace in this paper. Firstly, the DOAs are roughly estimated based on the noise subspace obtained from sample covariance matrix. Then, considering the sparsity of signals and the low-rank property of steering matrix, a new signal subspace is got from the steering matrix consisting of estimated DOAs and their adjacent angles. Finally, the signal subspace is modified by solving an optimization problem. Simulation results demonstrate the proposed algorithm can improve the subspace accuracy and furtherly improve the MUSIC DOA estimation performance, especially in small sample cases.
, doi: 10.11999/JEIT190276
[Abstract](795) [FullText HTML](337) [PDF 1932KB](57)
Abstract:
In Software-Defined Networking (SDN) with distributed control plane, network expansion problems arise due to network domain management. To address this issue, a Traffic Engineering-based control Resource Optimization (TERO) mechanism of SDN is proposed. It analyzes the control resource consumption of flow requests processing with different path characteristics, and points out that the control resource consumption can be reduced by changing the association relationship between controllers and switches. The controller association mechanism is divided into two phases: firstly, a minimum set cover algorithm is designed to solve the controller association problem efficiently in large-scale network. Then, a coalitional game strategy is introduced to optimize the controller association relationship to reduce both control resource consumption and control traffic overhead. The simulation results demonstrate that while keeping control traffic overhead low, mechanism which in this paper can reduce control resource consumption by about 28% in comparison with the controller proximity mechanism.
, doi: 10.11999/JEIT190071
[Abstract](532) [FullText HTML](317) [PDF 362KB](29)
Abstract:
Families of pseudorandom sequences derived from Euler quotients modulo an odd prime power possess sound cryptographic properties. In this paper, according to the theory of residue class ring, a new classes of binary sequences with period $2{p^{m + 1}}$ is constructed using Euler quotients modulo $2{p^m},$ where $p$ is an odd prime and integer $m \ge 1.$ Under the condition of ${2^{p - 1}}\not \equiv 1 ({od}\; \;{p^2})$, the linear complexity of the sequence is examined with the method of determining the roots of polynomial over finite field ${F_2}$. The results show that the linear complexity of the sequence takes the value $2({p^{m + 1}} - p)$ or $2({p^{m + 1}} - 1)$, which is larger than half of its period and can resist the attack of Berlekamp-Massey (B-M) algorithm. It is a good sequence from the viewpoint of cryptography.
, doi: 10.11999/JEIT190144
[Abstract](551) [FullText HTML](273) [PDF 1773KB](28)
Abstract:
Outliers are non-Gaussian measurement values far from the bulk of data. In practical transmission, the signals added with outlier often have the heavy-tailed property. Particle filter is based on the Bayesian framework and applicable to the non-linear and non-Gaussian system. However, measurement noise with outlier degrades the performance of particle filter. In this paper, student-t distribution is used to model the measurement noise, combined with Variational Bayes (VB), a novel particle filter Marginalized Particle Filter with VB Mean(MPF-VBM) is designed, which can estimate all parameters of t-distributed measurement distribution including mean parameter as well as state. Further, particle filter with noise correlation (MPF-VBM-COR-1) at the same epoch which is applicable to time variant measurement noise is developed. For verifying the performances of the proposed algorithms, the simulations on the typical univariate non-stationary growth model are performed under the different noise conditions in detail. The outcomes show that the proposed two algorithms of MPF-VBM and MPF-VBM-COR-1 (MPF-VBM-Corrlation-1) have the superior performances to the compared ones.
, doi: 10.11999/JEIT190136
[Abstract](339) [FullText HTML](199) [PDF 812KB](37)
Abstract:
The performance of a Constant False Alarm Rate (CFAR) detector is often evaluated in three typical backgrounds - homogeneous environment, multiple targets situation and clutter edges described by Prof. Rohling. However, there is a lack of the analytic expression of the false alarm rate for the Rank Sum (RS) nonparametric detector at clutter boundaries, and lack of a comparison of the ability for the RS detector to control the rise of the false alarm rate at clutter edges to that of the conventional parametric CFAR schemes; which is incomplete and imperfect for the detection theory of nonparametric detectors. The analytic expression of the false alarm rate Pfa for the RS nonparametric detector at clutter edges is given in this paper, and the ability of the RS nonparametric detector to control the rise of the false alarm rate at clutter edges is compared to that of the Cell Averaing (CA) CFAR, the Greatest Of (GO) CFAR and the Ordered Statistic (OS) CFAR with incoherent integration. When both of the heavy and the weak clutters follow a Rayleigh distribution, it is shown that the rise of the false alarm rate for the RS detector at clutter edges lies between that of the CA-CFAR and that of the OS-CFAR with incoherent integration. If a non-Gaussian distributed clutter with a long tail moves into the reference window, the rise of the CA-CFAR, the GO-CFAR and the OS-CFAR with incoherent integration reaches a peak of more than 3 orders of magnitude, and can not return to the original pre-designed Pfa. However, the RS nonparametric detector exhibits its inherent advantage in such situation, it can maintain a constant false alarm rate even the distribution form of clutter becomes a different one.
, doi: 10.11999/JEIT181003
[Abstract](465) [FullText HTML](394) [PDF 1263KB](15)
Abstract:
Based on the study of the spur-line, a novel spurs-line structure is proposed. The design of a novel Ultra-WideBand (UWB) power divider is described based on the novel spur line structure for the 2.5～13.2 GHz frequency range. The designed device is compact and has a simple structure and good frequency response in the band. Its return loss insertion is less than –12 dB and its insertion loss is less than 3.5 dB. The equations used for the design are based on the concept of odd-even modes and transmission line analysis. The Beetle Antennae Search (BAS) algorithm is used to improve the efficiency and accuracy of the power divider design. In order to verify the accuracy of the design, a UWB power divider is designed by using material RO4003C as substrate. The results validate the feasibility of the spur line-based design and demonstrat that the BAS algorithm has a shortened running time and improved precision compared to other optimization methods. It can be widely used in UWB power divider design.
, doi: 10.11999/JEIT190329
[Abstract](466) [FullText HTML](114) [PDF 4001KB](6)
Abstract:
The radio frequency fingerprinting of the emitter is complex, and the performance of Specific Emitter Identification (SEI) is subjected to the present expertise. To remedy this shortcoming, this paper presents a novel SEI algorithm based on signal trajectory image, which realizes joint extraction of multiple complex fingerprints using deep learning architecture. First, this paper analyses the visual characteristics of multiple emitter imperfections in the signal trajectory image. Thereafter, signal trajectory grayscale image is used as the signal representation. Finally, a deep residual network is constructed to learning the visual characteristics reflected in the images. The proposed method overcomes the limitations of existing knowledge, and combines high information integrity with low computational complexity. Simulation results demonstrate that, compared with the existing algorithms, the proposed one can remarkably improve the SEI performance with a gain of about 30%.
, doi: 10.11999/JEIT190344
[Abstract](188) [FullText HTML](100) [PDF 3867KB](13)
Abstract:
A novel image encryption algorithm is proposed based on a chaos set which consists of discrete chaotic systems and continuous chaotic systems. The chosen combination of chaotic system is dependent on the encryption intensity. The pixel mean value and pixel coordinate value of image are exploited to control the generation of key, so that enhances the relationship between chaotic key and plain text. In addition, the octet of cipher text pixel is divided into three parts, and then hided into a processed public image, which can promote the external characteristics of cipher text. The image histogram analysis, correlation analysis, and information entropy analysis method are adopted to identify the security performance, which indicates the effectiveness of the proposed image encryption algorithm and potential application in the image security transmission.
, doi: 10.11999/JEIT190187
[Abstract](287) [FullText HTML](126) [PDF 1032KB](11)
Abstract:
With the integration of information technology such as industrial Internet of Things (IoT), cloud computing and Industrial Control System (ICS), the security of industrial data is at enormous risk. In order to protect the confidentiality and integrity of data in such a complex distributed environment, a communication scheme is proposed based on Attribute-Based Encryption (ABE) algorithm, which integrates data encryption, access control, decryption outsourcing and data verification. In addition, it has the characteristics of constant ciphertext length. Finally, the scheme is analyzed in detail from three aspects: correctness, security and performance overhead. The simulation results show that the algorithm has the advantage of low decryption overhead.
, doi: 10.11999/JEIT180728
[Abstract](248) [FullText HTML](162) [PDF 768KB](9)
Abstract:
Satellite health monitoring is an important concern for satellite security, for which satellite telemetry data is the only source of data. Therefore, accurate prediction of missing data of satellite telemetry is an important forward-looking approach for satellite health diagnosis. For the high-dimensional structure formed by satellite multi-component system, multi-instrument and multi-monitoring index, the tensor factorization based prediction algorithm for missing telemetry data is proposed. The proposed algorithm surpasses most existing methods, which can only be applied to low-dimensional data or specific dimension. The proposed algorithm makes accurate predictions by modeling the telemetry data as a Tensor to integrally utilize its high-dimensional feature; Computing the component matrixes via Tensor Factorization to reconstruct the Tensor which gives the predictions of the missing data; An efficient optimization algorithm is proposed to implement the related tensor calculations, for which the optimal parameter settings are strictly theoretically deduced. Experiments show that the proposed algorithm has better prediction accuracy than the most existing algorithms.
, doi: 10.11999/JEIT190085
[Abstract](216) [FullText HTML](123) [PDF 3394KB](1)
Abstract:
In order to study the subtle feature recognition of Identification Foe or Friend (IFF) radiation source signals, this paper proposes an IFF individual recognition method based on ensemble intrinsic time-scale decomposition to solve the problem of insufficient research on individual identification of IFF radiation source in complex noise environment. In this algorithm, the Ensemble Intrinsic Time-scale Decomposition (EITD) is applied to dividing the sampled signals into several practical signal components and obtaining the energy distribution diagram of the IFF radiation source signals in time-frequency domain. Through the texture analysis of time-frequency energy spectrum, the unintentional modulation feature of the radiation source signals is represented by the texture features of the image, which are sent to the Support Vector Machine (SVM) for classification and recognition. Experiments show that the proposed method is more accurate than the Hilbert-Huang Transform (HHT) and Inherent Time scale Decomposition (ITD) based method.
, doi: 10.11999/JEIT190261
[Abstract](1083) [FullText HTML](957) [PDF 2246KB](14)
Abstract:
A kind of restoration method of BP neural network fuzzy image based on Optimized Brain Storming intelligent Optimized(OBSO-BP) algorithm is proposed in this paper. With the method of brain storming intelligent optimized algorithm which is optimized in both clustering and variation, issues of multi-peak high-dimensional function is easily solved. This method optimizes brain storming intelligence algorithm from two aspects of clustering and mutation. This method makes use of the characteristics of brain storming optimization algorithm, which is easy to solve multi-peak and high-dimensional function problems, to automatically search for better initial weights and thresholds of BP neural network, thus reducing the sensitivity of BP network to its initial weights and thresholds, avoiding the network falling into local optimal solution, increasing the convergence speed of the network and reducing the network error and improving the quality of image restoration. Twenty different images are adopted to the image restoration experiment of their fuzzy images with wiener filtering restoration(Wiener), Wiener filtering restoration based on optimized Brain Storming intelligent Optimized algorithm(Wiener-BSO), BP neural network restoration and BP neural network restoration based on optimized Brain Storming intelligent Optimized algorithm(BSO-BP). Results show that a better effect of image restoration can be achieved with this method.
, doi: 10.11999/JEIT190171
[Abstract](658) [FullText HTML](439) [PDF 1714KB](52)
Abstract:
In order to solve the problem of the D2D multi-multiplex communication resource blocks allocation in a cell, the resource blocks allocation scheme about D2D multi-multiplex mode based on non-equilibrium solution is proposed after analyzing a D2D user to multiplex two and three cells respectively. The problem of resource blocks partitioning is transformed into the problem of solving the joint revenue maximum value of the multiplexed cellular user by using game theory. When the Nash equilibrium solution does not exist, the objective function is analyzed, the "optimal solution" is solved in the feasible domain and the optimality of unbalanced solution processing is guaranteed. When the equilibrium solution exists, it is rounded up and used as the basis of the resource allocation scheme to maintain its optimality. The theoretical analysis and simulation results show that the proposed algorithm enhances significantly the system performance and sum rate.
, doi: 10.11999/JEIT190200
[Abstract](347) [FullText HTML](222) [PDF 2411KB](14)
Abstract:
Live migration of Virtual Machines(VMs) across WANs is an important support for multi-datacenter cloud computing environments. The current live migration of VMs across WANs faces many technical challenges due to the limitations of small bandwidth and no shared storage, such as ensuring the security and consistency of image data migration. Therefore, a method for VM live migration across datacenters based on HashGraph is proposed in this paper, decentralized ideas are used to achieve reliable and efficient image distribution between datacenters. The Merkle DAG of HashGraph improves the deficiencies of deduplication when migrating images across datacenters. Compared with existing methods, it can reduce total migration time.
, doi: 10.11999/JEIT190270
[Abstract](389) [FullText HTML](206) [PDF 1739KB](21)
Abstract:
As one of the Key 5G technologies, Non-Orthogonal Multiple Access (NOMA) can improve spectrum efficiency and increase the number of user connections by utilizing the resources in a non-orthogonal manner. In the uplink grant-free NOMA system, the Compressive Sensing (CS) and generalized Orthogonal Matching Pursuit (gOMP) algorithm are introduced in active user and data detection, to enhance the system performance. The gOMP algorithm is literally generalized version of the Orthogonal Matching Pursuit (OMP) algorithm, in the sense that multiple indices are identified per iteration. Meanwhile, the optimal number of indices selected per iteration in the gOMP algorithm is addressed to obtain the optimal performance. Simulations verify that the gOMP algorithm with optimal number of indices has better recovery performance, compared with the greedy pursuit algorithms and the Gradient Projection Sparse Reconstruction (GPSR) algorithm. In addition, given different system configurations in terms of the number of active users and subcarriers, the proposed gOMP with optimal number of indices also exhibits better performance than that of the other algorithms mentioned in this paper.
, doi: 10.11999/JEIT190147
[Abstract](368) [FullText HTML](210) [PDF 1625KB](16)
Abstract:
, doi: 10.11999/JEIT190146
[Abstract](469) [FullText HTML](345) [PDF 1986KB](30)
Abstract:
The improvement of time-frequency resolution plays a crucial role in the analysis and reconstruction of multi-component non-stationary signals. For traditional time-frequency analysis methods with fixed window, the time-frequency concentration is low and hardly to distinguish the multi-component signals with fast-varying frequencies. In this paper, by adopting the local information of the signal, an adaptive synchrosqueezing transform is proposed for the signals with fast-varying frequencies. The proposed method is with high time-frequency resolution, superior to existing synchrosqueezing methods, and particularly suitable for multi-component signals with close and fast-varying frequencies. Meanwhile, by using the separability condition, the adaptive window parameters are estimated by local Rényi entropy. Finally, experiments on synthetic and real signals demonstrate the correctness of the proposed method, which is suitable to analyze and recover complex non-stationary signals.
, doi: 10.11999/JEIT190013
[Abstract](509) [FullText HTML](340) [PDF 1550KB](16)
Abstract:
In the Network Function Virtualization (NFV) environment, for the reliability problem of Service Function Chain (SFC) deployment, a joint optimization method is proposed for backup Virtual Network Function (VNF) selection, backup instance placement and service function chain deployment. Firstly, the method defines a virtual network function measurement standard named the unit cost reliability improvement value to improve the backup virtual network function selection method. Secondly, the joint backup mode is used to adjust the placement strategy between adjacent backup instances to reduce bandwidth resources overhead. Finally, the reliability-guarantee problem of the whole service function chain deployment is modeled as integer linear programming, and a heuristic algorithm based on the shortest path is proposed to overcome the complexity of integer linear programming. The simulation results show that the method optimizes resource allocation while prioritizing the network service reliability requirements, and improves the request acceptance rate.
, doi: 10.11999/JEIT190042
[Abstract](409) [FullText HTML](113) [PDF 1821KB](16)
Abstract:
For the problem of spectrum allocation in the multiplexing of cellular user spectrum resources by Device-to-Device (D2D) communication in heterogeneous networks, a D2D communication resource allocation mechanism based on improved discrete Pigeon-Inspired Optimization(PIO) algorithm is proposed. The user's Quality of Service (QoS) is guaranteed by setting the Signal-to-Interference plus Noise Ratio (SINR) threshold, the transmitting power is set for users by power control algorithms. To allocate resources for D2D users, the Binary discrete PIO based on Motion Weight (MWBPIO) algorithm is used. To ensure the communication quality of edge users, the D2D communication technology and relay technology are used to establish D2D relay links, so then the performance of system can be maximized. Simulation results show that the proposed scheme can effectively suppress the interference caused by the introduction of D2D users in heterogeneous communication systems. Moreover, the proposed scheme can effectively improve the communication quality of edge users, and improve the utilization of spectrum resources and the performance of the system.
, doi: 10.11999/JEIT181163
[Abstract](305) [FullText HTML](132) [PDF 3089KB](9)
Abstract:
For the low-rate speech encoding problem, an information hidden algorithm based on the G.723.1 coding standard is proposed. In the pitch prediction coding process, by controlling the search range of the closed-loop pitch period (adaptive codebook), combined with the Random Position Selection (RPS) method and the Matrix Coding Method (MES), the secret information is embedded, which is implemented in the speech coding process. The adoption of the RPS method reduces the correlation between the carrier code-words, and the adoption of the MES method reduces the rate of change of the carrier. The experimental results show that the average PESQ (Perceptual evaluation of speech quality) deterioration rate under the algorithm is 1.63%, and the concealment is good.
, doi: 10.11999/JEIT181002
[Abstract](311) [FullText HTML](113) [PDF 2635KB](1)
Abstract:
According to the HyperSonic Vehicle (HSV) borne radar platform system, a multi-channel SAR-GMTI clutter suppression method is presented based on hypersonic platform forward squint mode. First, range walk correction and range compression are completed in the time domain, and the distance envelope is aligned simultaneously with phase error compensation. Then, the Doppler extended signal is compressed by three-order azimuth Chirp Fourier Transform (CFT), and the azimuth envelope of the echo is aligned with phase error compensation simultaneously. Next, the Digital Beam Forming (DBF) technology is applied to the range time-azimuth CFT domain by nulling the clutter and its ambiguous components to achieve Space-Time Adaptive Processing (STAP). The stationary clutter and its ambiguous components can be suppressed effectively and the echo signs of the moving target without blurring can be extracted.
, doi: 10.11999/JEIT190059
[Abstract](323) [FullText HTML](242) [PDF 2249KB](13)
Abstract:
Network coding is widely used in wireless multicast networks in recent years due to its high transmission efficiency. To address the low efficiency of automatic retransmission caused by packet loss in wireless multicast network, a new Coding Scheduling strategy based on Arriving Time (CSAT) in virtual queue is proposed. For improving encoding efficiency, virtual queues are used to store packets that are initially generated and not received by all receivers. Considering the stability of the queue, CSAT strategy chooses to send packet from the primary and secondary queue at a certain ratio. Both encoding and non-encoding methods are combined to send in the secondary queue. According to the arrival sequence of packets in the queue, the sending method that makes more packets participate in encoding is selected. Simulation results show that the proposed CSAT not only effectively improves packet transmission efficiency, but also improves network throughput and reduces average wait delay.
, doi: 10.11999/JEIT180953
[Abstract](468) [FullText HTML](358) [PDF 3319KB](29)
Abstract:
A modified SPECtral ANalysis (SPECAN) algorithm based on Doppler resampling is proposed to deal with the azimuth Space-Variant (SV) phase coefficients of the High Squint (HS) SAR data acquired from maneuvering platform. Firstly, for HS SAR with constant acceleration, an orthogonal coordinate slant range model is presented, which can handle the coordinate rotation caused by the traditional method of Range Walk Correction (RWC), and solve the mismatch between the range model and the signal after RWC. Then azimuth Doppler resampling is used to correct the SV phase coefficients. The focused image is achieved by SPECAN technique. Finally, the proposed algorithm is validated by processing of simulated SAR data, and has significant improvement on focusing quality over the reference one.
, doi: 10.11999/JEIT190012
[Abstract](473) [FullText HTML](301) [PDF 1158KB](26)
Abstract:
To solve the problem of weak signals detection in non-Gaussian background, a method based on sigmoid function is proposed which is named Sigmoid Function Detector (SFD). Firstly, the non-Gaussian background is modeled as a mixed Gaussian model. Based on this, the relationship between parameter k and SFD's performance and characteristics are systematically analyzed. It is pointed out that SFD will be a constant false alarm detector when its detection performance is optimal. Secondly, a new non-parametric detector is proposed via fixing the parameter k, which has significant improvement over matched filter. Finally, simulation analysis is carried out to verify the effectiveness and superiority of SFD.
, doi: 10.11999/JEIT181132
[Abstract](201) [FullText HTML](176) [PDF 1527KB](6)
Abstract:
For the problem that the classifier is less considered to be combined with the brain's cognitive process in the Brain-Computer Interface (BCI) system, a Chernoff-weighted based classifier integrated frame method is proposed and used in synchronous motor imagery BCI. In the method, the statistic characteristics of EEG (ElectroEncephaloGraphy) are obtained by analysing in each time point of synchronous BCI, and then the probability model is established to compute the Chernoff error bound, which is adopted as the weight of common classifier to take the discriminant process. The test experiments are based on the datasets from BCI competitions, and the proposed frame method is employed to compose with LDA, SVM, ELM respectively. The experimental results demonstrate that the proposed frame method shows competitive performance compared with other methods.
, doi: 10.11999/JEIT181125
[Abstract](413) [FullText HTML](298) [PDF 794KB](6)
Abstract:
Penalized programs are widely used to solve linear inverse problems in the presence of noise. For now, the study of the performance of panelized programs has two disadvantages: First, the results have some limitations on the tradeoff parameters; Second, the effect of the direction of the noise is not clear. This paper studies the performance of penalized programs when bounded noise is presented. A geometry condition which has been used to study the noise-free problems and constrained problems is provided. Under this condition, an explicit error bound which guarantees stable recovery (i.e., the recovery error is bounded by the observation noise up to some constant factor) is proposed. The results are different from many previous studies in two folds. First, the results provide an explicit bound for all positive tradeoff parameters, while many previous studies require that the tradeoff parameter is sufficiently large. Second, the results clear the role of the direction of the observation noise plays in the recovery error, and reveal the relationship between the optimal tradeoff parameters and the noise direction. Furthermore, if the sensing matrix has independent standard normal entries, the above geometry condition can be studied using Gaussian process theory, and the measurement number needed to guarantee stable recovery with high probability is obtained. Simulations are provided to verify the theoretical results.
, doi: 10.11999/JEIT181101
[Abstract](490) [FullText HTML](423) [PDF 783KB](42)
Abstract:
A distributed algorithm based on modified Newton method is proposed to solve the nodes localization problem in large scale Wireless Sensor Network(WSN). The algorithm includes network partitioning and distributed algorithm. Firstly, the network is divided into several overlapping subregions according to the nodes positions and the distance information between the sensors. The localization problem of subregions is formulated into an unconstrained optimization problem and each subregion can be calculated independently. Then distributed algorithm is used to determine nodes positions in subregions and merge the subregions. Simulation results indicate that the proposed algorithm is superior to the existing algorithms in terms of accuracy in large scale network, which can meet the needs of nodes localization in large scale network.
, doi: 10.11999/JEIT190016
[Abstract](486) [FullText HTML](339) [PDF 1042KB](33)
Abstract:
The close relationship between resource deployment and specific tasks in traditional Wireless Sensor Network(WSN) leads to low resource utilization and revenue. According to the dynamic changes of Virtual Sensor Network Request(VSNR), the resource allocation strategy based on Semi-Markov Decision Process(SMDP) is proposed in Virtual Sensor Network(VSN). Then, difining the state, action, and transition probability of the VSN, the expected reward is given by considering the energy and time to complete the VSNR, and the model-free reinforcement learning approach is used to maximize the long-term reward of the network resource provider. The numerical results show that the resource allocation strategy of this paper can effectively improve the revenue of the sensor network resource providers.
, doi: 10.11999/JEIT190211
[Abstract](531) [FullText HTML](206) [PDF 1698KB](12)
Abstract:
Since the echo characteristics of moving targets are different from that of stationary targets, the traditional reconstruction filter bank algorithm, i.e., the reconstruction filter algorithm, is not applicable. In this paper, a novel reconstruction approach of the moving target for a multichannel in azimuth High-Resolution Wide-Swath (HRWS) Synthetic Aperture Radar (SAR) system is proposed. The approach firstly analyzes the echo characteristics of the moving target for the multi-channel in azimuth SAR system and gives the main reason for the failure of the traditional reconstruction method in contrast to the form of the stationary target echo. By introducing the radial velocity of the moving target, the spectrum reconstruction of the uniform moving target is effectively realized, and the azimuth ambiguities of the uniform moving target for the multi-channel in azimuth SAR system is well suppressed. Space-borne simulated results confirm the effectiveness of the proposed reconstruction approach.
, doi: 10.11999/JEIT180899
[Abstract](966) [FullText HTML](602) [PDF 900KB](134)
Abstract:
Automatic Target Recognition(ATR) is an important research area in the field of radar information processing. Because the deep Convolution Neural Network(CNN) does not need to carry out feature engineering and the performance of image classification is superior, it attracts more and more attention in the field of radar automatic target recognition. The application of CNN to radar image processing is reviewed in this paper. Firstly, the related knowledges including the characteristics of the radar image is introduced, and the limitations of traditional radar automatic target recognition methods are pointed out. The principle, composition and development of CNN the field of computer vision are introduced. Then, the research status of CNN in radar automatic target recognition is provided. The detection and recognition method of SAR image are presented in detail. Then the challenge of radar automatic target recognition is analyzed. Finally, the new theory and model of convolution neural network, the new imaging technology of radar and the application to complex environments in the future are prospected.
, doi: 10.11999/JEIT190052
[Abstract](357) [FullText HTML](161) [PDF 2072KB](7)
Abstract:
A full digital feedforward Time-Interleaved Analog-to-Digital Converter (TIADC) time skew calibration algorithm is presented, the time skew estimation adopts the feedforward extraction method of the improved derivative module of time skew function, which can greatly improve the accuracy of skew estimation when the input signal frequency is high. At the same time, the time skew function is based on subtraction, in order to reduce the complexity of skew estimation unit. Finally, the time skew is corrected by using first-order Taylor compensation. The simulation results show that when the input signal is a multi-frequency signal, the Spurious-Free Dynamic Range (SFDR) increases from 48.6 dB to 80.7 dB, after adopting the proposed time skew correction for a 4-channal 14-bit TIADC system. Compared with the traditional feedforward calibration structure based on correlation operation, the effective calibration input signal bandwidth can be increased from 0.19 to 0.39, which greatly increases the application range of the calibration algorithm.
, doi: 10.11999/JEIT190067
[Abstract](782) [FullText HTML](397) [PDF 1120KB](10)
Abstract:
The propagation of acoustic signal in space has a strong multipath effect, and the receiver often overlaps in the form of convolution. Especially in strong reverberation conditions such as ocean and theatre, where the length of impulse response of hybrid filter increases significantly. In order to eliminate the problem that long impulse response leads to the failure of the frequency domain convolution blind separation algorithm, two Short-Time Fourier Transforms (STFT) are applied to the observed signal. The first STFT shortens the length of the hybrid filter. The second STFT converts the signal model into instantaneous blind separation. Finally, the separation matrix is estimated by Joint Diagonalization (JD) technique. Compared with the existing methods, this method solves the problem of model failure under deep convolution mixing, and can obtain better separation performance when the number of source signals is large or additive noise exists. The simulation results verify the effectiveness and performance advantages of the proposed method.
, doi: 10.11999/JEIT181168
[Abstract](259) [FullText HTML](197) [PDF 1643KB](11)
Abstract:
Machine-to-Machine (M2M) and Device-to-Device (D2D) communications are both key technologies in the fifth Generation (5G) mobile communication systems. In M2M communications, the Energy Efficiency (EE) especially needs to be improved to extend the life cycle of the M2M equipment. In this paper, the M2M and D2D technologies are combined and the D2D technology is used to realize M2M transmission. At the same time, M2M users are allowed to reuse spectrum resources with Human-to-Human (H2H) devices in the cellular networks. To guarantee the Quality of Service (QoS) of these two systems simultaneously, a Multi-Objective Optimization Problem (MOOP) is then formulated to maximize the sum throughput of H2H systems, and the sum EE of M2M systems and to minimize the interference from M2M communications to H2H networks. To solve this MOOP, the penalty function method is firstly adopted to relax the original binary variables, and then the ConCave-Convex Procedure (CCCP) method is used to convert the non-convex single-objective problems into convex problems. Finally, the weighted Tchebyshev algorithm is utilized to obtain the Pareto solution of the original MOOP. By comparing with the traditional weighted sum method, the effectiveness of the proposed method is proved by simulation results.
, doi: 10.11999/JEIT190168
[Abstract](418) [FullText HTML](383) [PDF 2050KB](26)
Abstract:
Considering the problem of scattered node mapping and more hops of link mapping in the traditional virtual network energy-saving embedding, the node and link are mapped simultaneously by using the minimum spanning tree topology of the virtual network request, and Energy-saving Virtual Network Embedding algorithm based on Sliding Region Particle Swarm (EVNE_SRPS) is proposed. When a virtual network request arrives, the minimum spanning tree topology is generated, the root node is the node with the shortest path length; Multiple regions are randomly selected as the particle object in the substrate network, and the minimum spanning tree topology of the virtual network request is mapped in the regional center; The fitness of the particles is calculated. The optimal solution of the group and the individual is finded, and the sliding direction and the location of the update region under the guidance of the optimal solution are determined. After the iteration, the mapping scheme of the virtual network is obtained. The experimental results show that compared with the existing algorithms, the network energy consumption is reduced, and the internet service providers revenue to cost ratio is improved.
, doi: 10.11999/JEIT180676
[Abstract](627) [FullText HTML](417) [PDF 1065KB](29)
Abstract:
To deal with the estimation problem of non-stationary channel in massive Multiple-Input Multiple-Output (MIMO) up-link, the 2D channels’ sparse structure information in temporal-spatial domain is used, to design an iterative channel estimation algorithm based on Dirichlet Process (DP) and Variational Bayesian Inference (VBI), which can improve the accuracy under a lower pilot overhead and computation complexity. On account of that the stationary channel models is not suitable for massive MIMO systems anymore, a non-stationary channel prior model utilizing Dirichlet Process is constructed, which can map the physical spatial correlation channels to a probabilistic channel with the same sparse temporal vector. By applying VBI technology, a channel estimation iteration algorithm with low pilot overhead and complexity is designed. Experiment results show the proposed channel method has a better performance on the estimation accuracy than the state-of-art method, meanwhile it works robustly against the dynamic system key parameters.
, doi: 10.11999/JEIT190033
[Abstract](475) [FullText HTML](285) [PDF 2040KB](12)
Abstract:
To solve the problem that the traditional micro-Doppler feature extraction technologies are generally hard to achieve resolution and parameter estimation of multi-target, a novel curve overlap extrapolation algorithm for wide-band resolution of micro-motion multi-target is proposed. According to the relative distance between filtering data points and the historical slope information of each curve, the point trace behind the overlapping location can be extrapolated to realize data association of micor-motion curve for each signal component. On this basis, the multi-target resolution can be realized by analyzing the difference of micor-motion characteristics between each curve. Extensive simulation experiments are provided to illustrate the effectiveness and robustnees of the proposed algorithm.
, doi: 10.11999/JEIT180933
[Abstract](769) [FullText HTML](432) [PDF 1088KB](33)
Abstract:
Bistatic radar has the advantages of high concealment and strong anti-interference performance, and plays an important role in modern electronic warfare. Based on the principle of radar coincidence imaging, the problem of bistatic radar coincidence imaging of moving targets is studied. Firstly, based on the bistatic radar system that uses uniform linear array as the transmitting and receiving antenna, the characteristics of the moving target radar echo signal are analyzed under the condition of transmitting random frequency modulation signal, and a bistatic radar coincidence imaging parametric sparse representation model is established. Secondly, an iterative coincidence imaging algorithm based on sparse Bayesian learning is proposed for the parametric sparse representation model established. Based on the Bayesian model, the sparse reconstructed signal is obtained by Bayesian inference, so that the moving target imaging and accurate estimation of motion parameters can be achieved. Finally, the effectiveness of the proposed method is verified by simulation experiments.
, doi: 10.11999/JEIT181152
[Abstract](535) [FullText HTML](358) [PDF 2111KB](24)
Abstract:
In the system of distributed radar array system using phase interference angle measurement, the phase center coordinate error of arrays and the phase difference error have relatively large influence on the angle measurement. And the phase center position is often inconsistent with physical center position. Thus it is necessary to compensate these errors precisely. Far field radiation sources are often used to calibrate radar in traditional calibration methods. However, it is usually hard to achieve far field radiation sources for distributed radar array with large space between units surveilling space targets. In this paper, a calibration method based on the precise ephemeris of refined orbit satellites without measuring with special instruments is proposed. The phase error caused by coordinate error can be whitened by the precise ephemeris of multiple arcs, and the coordinate and phase difference will be searched out by matching the minimum variance. This method can get the errors easily. The simulation results and actual data verify that angle measurement accuracy gets large improvement by the method.
, doi: 10.11999/JEIT181127
[Abstract](412) [FullText HTML](367) [PDF 569KB](12)
Abstract:
The performance of the existing target localization algorithms is not ideal in complex acoustic environment. In order to improve this problem, a novel target binaural sound localization algorithm is presented. First, the algorithm uses binaural spectral features as input of a time-frequency units selector based on deep learning. Then, to reduce the negative impact of the time-frequency unit belonging to noise on the localization accuracy, we employ the selector to select the reliable time-frequency units from binaural input sound signal. At the same time, a Deep Neural Network (DNN)-based localization system map the binaural cues of each time-frequency unit to the azimuth posterior probability. Finally, the target localization is completed according to the azimuth posterior probability belonging to the reliable time-frequency units. Experimental results show that the performance of the proposed algorithm is better than comparison algorithms and achieves a significant improvement in target localization accuracy in low Signal-to-Noise Ratio(SNR) and various reverberation environments, especially when there is noise similar to the target sound source.
, doi: 10.11999/JEIT181165
[Abstract](515) [FullText HTML](378) [PDF 1171KB](17)
Abstract:
Non-Orthogonal Multiple Access (NOMA) serves multiple transmitters using the same resource block, and the receiver decodes the information from different transmitters through Successive Interference Cancellation (SIC). However, most of the researches on NOMA systems are based on perfect SIC assumption, in which the impact of imperfect SIC on NOMA system is not considered. Focusing on this problem, a framework is provided to analyze the performance of single-cell uplink NOMA system under the assumption of imperfect SIC. Firstly, the Binomial Point Process (BPP) is used to model the spatial distribution of base station and user equipment in uplink NOMA system. Based on this model, the interference cancellation order which is based on large-scale fading is adopted, and then the error of interference cancellation is analyzed. Then, based on stochastic geometry theory and order statistics theory, the expression of coverage probability of user equipment which is at rank k in terms of the distance from the base station is derived, besides, the average coverage probability is adopted to reflect the reliability of NOMA transmission system. The analytical and simulation results show the influence of system parameters such as distance order and base station radius on transmission reliability. Also, the validity of theoretical deduction is verified.
, doi: 10.11999/JEIT181169
[Abstract](369) [FullText HTML](338) [PDF 1860KB](8)
Abstract:
Facing changeable network environment, current Quality of Service (QoS)-aware flow aggregation scheme is lack of flexibility. A dynamic flow aggregation method to overcome present problems is proposed. An Enhanced Rough k-Means (ERKM) algorithm is used to aggregate network flows properly. Importantly, it is able to adjust degree of membership to face ever-changing internet environment to make algorithm more flexible. Internet scheduler experiment is carried out and a comparison is made with existing methods. Experimental results suggest that proposed method has advantages not only on flexibility of aggregation but on assurance of QoS of Internet flows. In addition, the consistency of QoS allocation under different network environment is investigated.
, doi: 10.11999/JEIT190108
[Abstract](480) [FullText HTML](330) [PDF 5051KB](7)
Abstract:
The backscattering of the radar targets is sensitive to the relative geometry between orientations of the targets and the radar line of sight. When the orientations of the same target are different from the radar line of sight, the scattering characteristics are quite different. Targets such as inclined ground and inclined buildings may reverse the polarization base of the backscattered echo, which causes the cross-polarization component to be too high and the volume scattering component of the image is overestimated. In this paper, a polarimetric interferometric decomposition method based on polarimetric parameters ($H/{\alpha}$) and Polarimetric Interferometric Similarity Parameters (PISP) is proposed to solve the overestimation problem. The method makes full use of the scattering diversity of the scatterer in the radar line of sight. The cross-polarization components generated by targets such as inclined grounds and inclined buildings with different orientations are better adapted to obtain better decomposition results. Finally, the effectiveness of the proposed method in polarimetric interferometric decomposition is verified by the airborne C-band PolInSAR data obtained by the Institute of Electronics, Chinese Academy of Sciences. The experimental results show that the proposed improved algorithm can distinguish the scattering characteristics of terrain types effectively and correctly.
, doi: 10.11999/JEIT181144
[Abstract](506) [FullText HTML](392) [PDF 1199KB](14)
Abstract:
Ultra-Dense Networks (UDNs) shorten the distance between terminals and nodes, which improve greatly the spectral efficiency and expand the system capacity. But the performance of cell edge users is seriously degraded. Reasonable planning of Virtual Cell (VC) can only reduce the interference of moderate scale UDNs, while the interference of users under overlapped base stations in a virtual cell needs to be solved by cooperative user clusters. A user clustering algorithm with Interference Increment Reduction (IIR) is proposed, which minimizes the sum of intra-cluster interference and ultimately maximizes system sum rate by continuously switching users with maximum interference between clusters. Compared with K-means algorithm, this algorithm, no need of specifying cluster heads, avoids local optimum without increasement of the computation complexity. The simulation results show that the system sum rate, especially the throughput of edge users, can be effectively improved when the network is densely deployed.
, doi: 10.11999/JEIT180705
[Abstract](353) [FullText HTML](250) [PDF 2449KB](12)
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.

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2019, 41(11): 1 -4
[Abstract](11) [PDF 381KB](3)
Abstract:
2019, 41(11): 2541 -2548   doi: 10.11999/JEIT181136
[Abstract](1095) [FullText HTML](375) [PDF 1184KB](24)
Abstract:
The spectral efficiency and energy efficiency of the uplink of massive MIMO-OFDM system is studied using mixed-precision Analog-Digital Converter (ADC) and Zero-Forcing (ZF) reception algorithm at the receiver. By using the additive quantization noise model to analyze the performance of the system, the approximate closed expression of the spectral efficiency and energy efficiency of the whole system is derived, and the correctness of the expression is proved by simulation. The research results show that the spectral efficiency of the system is related to the transmission power of each user, the number of antennas at the receiver and the quantization accuracy of the receiver. Numerical and simulation results also show that the performance loss caused by the low-precision ADC can be compensated by increasing the number of antennas at the base station.
2019, 41(11): 2549 -2556   doi: 10.11999/JEIT190128
[Abstract](889) [FullText HTML](306) [PDF 1439KB](19)
Abstract:
For the service characteristics and Quality of Service (QoS) requirements of Machine Type Communications (MTC), short-packet/short-coded block transmission in MTC based on Non-Orthogonal Multiple Access (NOMA) is considered in this paper, and the resource optimization problem of the Ultra-Reliable and Low-Latency (URLL) in MTC based on NOMA is discussed. Currently, uplink transmission is a bottleneck of MTC based on NOMA. Firstly, considering the performance requirements supporting NOMA and high reliability and low latency in wireless cellular networks, a system model for uplink wireless resource optimization is established. Then, the uplink transmission delay is analyzed and the link reliability function based on distance is derived. Further, with the constraints of delay, reliability and bandwidth, a wireless resource allocation algorithm for maximizing the sum rates of central users is proposed, and also the convergence proof and complexity analysis of the algorithm are given. Finally, the simulation results show the performance advantages of the proposed optimal scheme.
2019, 41(11): 2557 -2564   doi: 10.11999/JEIT181191
[Abstract](482) [FullText HTML](305) [PDF 2182KB](30)
Abstract:
The power control problem of mobile users in macro-femto heterogeneous cellular networks is studied. Firstly, an optimization model that maximizes the total energy efficiency of femtocells with the minimum received signal-to-noise ratio as the constraint is established. Then, a femtocell centralized Power Control algorithm based on Q-Learning (PCQL) is proposed. Based on reinforcement learning, the algorithm can adjust the transmit power of the user terminal without accurate channel state information simultaneously. The simulation results show that the algorithm can effectively control the power of the user terminal and improve system energy efficient.
2019, 41(11): 2565 -2570   doi: 10.11999/JEIT180408
[Abstract](483) [FullText HTML](335) [PDF 1237KB](30)
Abstract:
Due to the limited transmission performance of cellular network and the buffering capabilities of the Base Station (BS), it is very difficult to achieve the Quality of Service (QoS) requirements of multi-user content requests. In this paper, a joint user association and content deployment algorithm is proposed for cellular Device-to-Device (D2D) communication network. Assuming that multiple users located in a specific area may have content requests for the same content, a clustering and content deployment mechanism is presented in order to achieve efficient content acquisition. A joint clustering and content deployment optimization model is formulated to minimize total user service delay, which can be solved by Lagrange partial relaxation, iterative algorithm and Kuhn-Munkres algorithm, and the joint clustering and content deployment optimization strategies can be obtained. Finally, the effectiveness of the proposed algorithm is verified by MATLAB simulation.
2019, 41(11): 2571 -2577   doi: 10.11999/JEIT180937
[Abstract](501) [FullText HTML](326) [PDF 1119KB](13)
Abstract:
In order to improve multicast’s spectrum energy-efficient of elastic optical network configured with Colorless, Directionless and Contentionless-Flexible Reconfigurable Optical Add/Drop Multiplexer (CDC-F ROADM) nodes, an All-optical Multicast Energy Efficiency Scheduling Algorithm (AMEESA) is proposed. In the routing phase, considering both energy consumption and link spectrum resource utilization, the link cost function is designed to establish the multicast tree with the least cost. In the spectrum allocation phase, a spectrum conversion method based on High Spectral Resolution (HSR) is designed by changing the spectrum slot index of adjacent links according to links availability of spectrum blocks. And an energy-saving spectrum conversion scheme is selected to allocate spectrum block resources for the multicast tree. Simulation analysis shows that the proposed algorithm can effectively improve the network energy efficiency and reduce the bandwidth blocking probability of IP multicast.
2019, 41(11): 2578 -2584   doi: 10.11999/JEIT190037
[Abstract](643) [FullText HTML](471) [PDF 2545KB](38)
Abstract:
In order to reduce the computational complexity of Convolutional Neural Network(CNN), the two-dimensional fast filtering algorithm is introduced into the CNN, and a hardware architecture for implementing CNN layer-by-layer acceleration on FPGA is proposed. Firstly, the line buffer loop control unit is designed by using the cyclic transformation method to manage effectively different convolution windows and the input feature map data between different layers, and starts the convolution calculation acceleration unit by the flag signal to realize layer-by-layer acceleration. Secondly, a convolution calculation accelerating unit based on 4 parallel fast filtering algorithm is designed. The unit is realized by a less complex parallel filtering structure composed of several small filters. Using the handwritten digit set MNIST to test the designed CNN accelerator circuit, the results show that on the xilinx kintex7 platform, when the input clock is 100 MHz, the computational performance of the circuit reaches 20.49 GOPS, and the recognition rate is 98.68%. It can be seen that the computational performance of the circuit can be improved by reducing the amount of calculation of the CNN.
2019, 41(11): 2585 -2591   doi: 10.11999/JEIT190143
[Abstract](404) [FullText HTML](315) [PDF 2291KB](9)
Abstract:
Bitstream generator in FPGA Electronic Design Automation(EDA) offers precise configuration information, which enables the application circuits to be implemented on the target device. On one hand, modern FPGAs tend to have larger device scale and more configuration bits, on the other hand, embedded applications (e.g. eFPGAs) require better configuration efficiency and smaller, more adaptive database. In order to meet these new requirements, a bit-stream generation method is proposed which firstly models the configurable resources by configuration modes and matches the netlist with these models, then hierarchical mapping strategy is used to search every bit on a dynamically generated database determined by the array floorplan. This method well meets the challenges that embedded applications may bring-the surge of configuration bit count and the changeable size of the array. Compared to flattened modelling and mapping method, its time complexity is reduced from O(n) to O(lgn).
2019, 41(11): 2592 -2598   doi: 10.11999/JEIT181060
[Abstract](348) [FullText HTML](244) [PDF 2087KB](16)
Abstract:
A novel technique for increasing the load response speed of Capacitor-Less Low-DropOut linear regulator (CL-LDO) is proposed to improve the transient response of CL-LDO when its load current changes. With an additional fast signal feedback path, the CL-LDO can achieve fast transient response so that the overshoot and undershoot of its output voltage can be dramatically reduced. A CL-LDO with fast response is realized in 0.18 μm CMOS and occupies an active area of 0.00529 mm2. The CL-LDO has an output voltage of 1.194 V when the input supply voltage ranges from 1.5 V to 2.5 V. When the load current changes from 100 μA to 10 mA with the rise and fall time of 1 μs, the output of LDO can be recovered from its overshoot and undershoot to a stable voltage within 489.537 ns and 960.918 ns, respectively. Compared with a traditional CL-LDO without this proposed technique, the transient response speed of this CL-LDO is increased by 7.41 times. The overshoot and undershoot of the output voltage is decreased by 35.3% and 78.1%, respectively.
2019, 41(11): 2599 -2605   doi: 10.11999/JEIT190058
[Abstract](953) [FullText HTML](598) [PDF 1892KB](60)
Abstract:
Considering the large computational complexity and the long-time calculation of Convolutional Neural Networks (CNN), an Field-Programmable Gate Array(FPGA)-based CNN hardware accelerator is proposed. Firstly, by deeply analyzing the forward computing principle and exploring the parallelism of convolutional layer, a hardware architecture in which parallel for the input channel and output channel, deep pipeline for the convolution window is presented. Then, a full parallel multi-addition tree is designed to accelerate convolution and efficient window buffer to implement deep pipelining operation of convolution window. The experimental results show that the energy efficiency ratio of proposed accelerator reaches 32.73 GOPS/W, which is 34% higher than the existing solutions, as the performance reaches 317.86 GOPS.
2019, 41(11): 2606 -2613   doi: 10.11999/JEIT181156
[Abstract](1131) [FullText HTML](255) [PDF 2592KB](21)
Abstract:
In order to accommodate the development of new communication technology, an integrated programmable microwave photonic filter with high shape-factor is proposed in this paper. This filter is based on Silicon-On-Insulator (SOI) and an eight-tap finite impulse response. By controlling the thermal heaters on the amplitude modulator and phase modulator of each tap, a rectangular filter with tunable bandwidth and high shape-factor greater than 0.55 is obtained. Furthermore, the tunability of central frequency, bandwidth and variable pass-band shape can be also realized. Small size, light weight and flexibility are advantages of the preposed filters, moreover, it can be applied to large bandwidth signal processing and an alternative method to part the channels. So it can be widely used in defense field and 5G networks.
2019, 41(11): 2614 -2622   doi: 10.11999/JEIT181190
[Abstract](236) [FullText HTML](134) [PDF 3567KB](12)
Abstract:
Chirp signals are widely used in communication and exploration. The parameter analysis of the chirp signals often uses a Wigner-Ville Distribution (WVD) based time-frequency analysis method, which achieves high time-frequency resolution. However, this method has defects in cross terms, high sidelobes, and spectral aliasing problems. To solve these problems, a time-frequency analysis method called Spatially Variant Apodiztion-rearrange Wigner Ville Distribution (SVA-rWVD) is proposed, which achieves low sidelobes by exploiting the Spatially Variant Apodization (SVA) techniques, and avoids the cross terms and the spectral aliasing problems by applying the Short Time Fourier Transform (STFT). Furthermore, a new time-frequency distribution is obtained from the proposed method. Extensive simulations show that the time-frequency distribution obtained by the proposed method not only reduces the sidelobe level to –40 dB but also eliminates cross terms and spectral aliasing for both single-component and multi-component chirp signals.
2019, 41(11): 2623 -2631   doi: 10.11999/JEIT190026
[Abstract](952) [FullText HTML](365) [PDF 3427KB](24)
Abstract:
The Dense Focal Plane Array Feed (DFPAF), which integrates the characters of multi-beam feed with multiple independent horns and Phased Array Feed (PAF), can simultaneously provide more fixed shaped beams and wider field of view than multi-beam feed with multiple independent horns and PAF. It attracts more attention in radio telescope, radar, electronic reconnaissance, satellite communication and so on. Its unique structure promotes the studies on special design method recently. Combing the theory of array antenna and inherent characteristic of parabolic reflector antenna, a fast design method with robust processing procedure is proposed in this paper. The design principle, calculated results, and comparison between DFPAF and the most representative multi-beam feed with multiple independent horns are presented. All these provide a theoretical basis and reference data for the design of giant reflector with DFPAF.
2019, 41(11): 2632 -2638   doi: 10.11999/JEIT181067
[Abstract](647) [FullText HTML](294) [PDF 1706KB](10)
Abstract:
The G-matrix model method is usually used to achieve the brightness temperature reconstruction for the one-Dimensional (1-D) synthetic aperture microwave radiometer system. For the 1-D radiometer system, the imaging process mainly includes: the radiometer instrument observes the full field of view of the 2-D target scene maps, and obtains the 1-D samples of the visibility, and then inverts the system parameter matrix G to realize the reconstruction of the 1-D image of the target scene. Since the system sampling baselines are only distributed in the 1-D of the spatial frequency domain, in the process of the brightness temperature image reconstruction, the matrix G needs to realize 2-D to 1-D conversion. Therefore, two G-matrix modification methods are proposed to improve the imaging quality for the 1-D synthetic aperture microwave radiometer. For the 8-element ground radiometer prototype system and the 10-element salinity radiometer system, theoretical analysis and simulation experiments have verified that the G-matrix modification methods proposed in this paper can effectively improve the imaging results, and can effectively suppress the imaging error caused by the side-lobed degradation of the antenna patterns.
2019, 41(11): 2639 -2645   doi: 10.11999/JEIT190010
[Abstract](605) [FullText HTML](393) [PDF 1646KB](42)
Abstract:
Forwarding dense false target jamming disturbs the detection and recognition of real targets by generating multiple false targets in the range dimension. Because the false echo signal is highly correlated with the real signal, it is difficult for radar to recognize and suppress it effectively. Frequency agile radar improves greatly the low interception and anti-jamming ability of radar by randomly changing the carrier frequency of transmitting adjacent pulses. However, agile radar can not completely eliminate the interference, some target echo pulses may be submerged by the interference, agile radar can not complete coherent accumulation and target detection well either. To solve the above problems, an anti-jamming method of frequency agility combined with Hough transform is proposed. Firstly, the inter-pulse frequency agility technology is used to avoid most narrowband aiming and deceptive jamming. Then, according to the time discontinuity of the jamming signal, Hough transform and peak extraction are used to identify and suppress the jamming. Frequency agility is incompatible with the traditional Moving Target Detection(MTD). Target detection is accomplished by sparse reconstruction. The simulation and actual radar and jammer countermeasure experiments show that the proposed method can achieve good anti-jamming performance and target detection performance.
2019, 41(11): 2646 -2653   doi: 10.11999/JEIT180912
[Abstract](441) [FullText HTML](274) [PDF 1738KB](19)
Abstract:
The microwave source of Non-Coherent Short Pulse (NCSP) radar transmits short pulse. Thus, for high velocity targets, the motion effect in the pulse duration can be neglected, and the echo signal does not need special motion compensation. In order to use the NCSP radar signal for Inverse Synthetic Aperture Radar (ISAR) imaging, the compensation coherent processing method is applied to removing the uncertainty of the envelope time and the initial phase uncertainty. Assuming that the echo is envelope-aligned and initially compensated by conventional methods, ISAR radar imaging can be performed using the Range-Doppler (RD) method, subsequently. The simulation verifies the feasibility of the compensation signal ISAR imaging. However, the carrier-frequency random jitter factor of NCSP radar causes random-modulated sidelobes in the Doppler dimension, which affect imaging quality. In this paper, the sparse recovery technique is used to perform sparse reconstruction of the target scattering center in the imaging space. The Orthogonal Matching Pursuit (OMP) algorithm and the Sparse Bayesian Learning (SBL) algorithm are used as the recovery algorithm for imaging simulation experiments. The simulation results show that the sparse recovery technique can suppress the imaging sidelobes caused by non-coherence and improve the imaging quality.
2019, 41(11): 2654 -2660   doi: 10.11999/JEIT190114
[Abstract](552) [FullText HTML](213) [PDF 1816KB](31)
Abstract:
In order to improve missile-borne radar detection performance in modern electronic warfare, a radar waveform design method based on Nash equilibrium is proposed. Firstly, the radar and jammer game signal models are established in electronic warfare. Based on maximum Signal-to-Interference-plus-Noise Ratio (SINR), waveform strategies of radar and jammer are designed respectively. Secondly, the existence of Nash equilibrium solution is demonstrated by mathematical derivation and verified in experimental simulation. A multiple iterative water-filling method which repeatedly eliminates strict disadvantages is designed to achieve Nash equilibrium. The maxmin scheme of disequilibrium game is deduced by two-step water-filling method. Finally, the radar detection performance of optimization strategies is tested by simulation experiments. Simulation results reveal that the radar waveform design based on Nash equilibrium is beneficial to improve the radar detection performance under game conditions. Compared with no-game and maxmin strategies, the radar detection probability of Nash equilibrium strategy can be increased by 12.02% and 3.82%, respectively. It is proved that the Nash equilibrium strategy of this paper is closer to the Pareto optimality.
2019, 41(11): 2661 -2668   doi: 10.11999/JEIT190163
[Abstract](2110) [FullText HTML](390) [PDF 1743KB](22)
Abstract:
For Time-Division Multiple Access (TDMA) signals, the performance of Specific Emitter Identification (SEI) is primarily limited by burst duration. To remedy this shortcoming, a novel radiometric signature is presented, which reveals whether the users of the adjacent time slots are the same from a perspective of carrier phase, thereby providing the basis for data accumulation of the same user. First, the feature mechanism is introduced, as well as the extraction method. Thereafter, user identity detection of the adjacent slots is implemented with an adaptive threshold, which is derived from the distribution of the signature. Finally, a new SEI processing procedure is designed with data accumulation, which breaks the routine of identifying only one slot at a time. Simulation results demonstrate that the proposed signature is resilient against the noise, and can accurately detect the user identity of the adjacent slots. Compared with the traditional processing procedure, the proposed one can effectively improve the SEI performance of TDMA signals.
2019, 41(11): 2669 -2674   doi: 10.11999/JEIT180870
[Abstract](699) [FullText HTML](514) [PDF 1499KB](47)
Abstract:
In order to achieve routing optimization in the Software Defined Network (SDN) environment, deep reinforcement learning is imposed to the SDN routing process and a mechanism based on deep reinforcement learning is proposed to optimize routing. This mechanism can improve network performance such as delay, throughput, and realize black-box optimization in continuous time, which surely reduces network operation and maintenance costs. Besides, the proposed routing optimization mechanism is evaluated through a series of experiments. The experimental results show that the proposed SDN routing optimization mechanism has good convergence and effectiveness, and can provide better routing configurations and performance stability than traditional routing protocols.
2019, 41(11): 2675 -2683   doi: 10.11999/JEIT190145
[Abstract](601) [FullText HTML](379) [PDF 2117KB](23)
Abstract:
With the introduction of Network Function Virtualization (NFV), the operating costs of operators can be greatly reduced. However, most existing Service Function Chain (SFC) orchestration researches can not optimize the resources utilization while guaranteeing the performance of service delay. A spatial and temporal optimal method of Service Function Chain (SFC) orchestration based on an overlay network structure is proposed. Based on the consideration of the restrictions such as computing resource, network resource and fine-grained end to end delay, this method separates the computing resource and network resource. The resources cost and related delay of SFC can be abstracted into the links weight of overlay network, which can help to convert the SFC orchestration problem into the shortest path problem that can be easily solved. As for the SFC requests set requiring batch processing, an Overlay Network based Simulated Annealing iterative optimal orchestration algorithm(ONSA) is designed. The simulation results demonstrate that the proposed orchestration scheme can reduce the end-to-end delay, the utilization ratio of link bandwidth resource and the operational expenditure by 29.5%, 12.4% and 15.2%, and the acceptance ratio of requests set can be improved by 22.3%. The performance of Virtual Network Function (VNF) load balancing can be significantly improved.
2019, 41(11): 2684 -2690   doi: 10.11999/JEIT180970
[Abstract](795) [FullText HTML](490) [PDF 1336KB](40)
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 use effectively the edge nodes with limited computing resources to ensure Quality of service (QoS) of end-user has become a key issue. To solve this problem, the edge cloud and remote cloud are combined to build a layered edge cloud computing architecture. Based on this architecture, with the goal of minimizing mobile device energy consumption and task execution time, the problem which is proved to be convex is formulated to minimize the weight sum of energy and delay. A computation offloading and resource allocation mechanism based on multiplier method is proposed. Simulations are conducted to evaluate the proposed mechanism. Compared with local computing and computation offloading mechanism, the proposed mechanism can effectively reduce the energy consumption of mobile device and the delay of system by up to 60% and 10%, respectively.
2019, 41(11): 2691 -2698   doi: 10.11999/JEIT190166
[Abstract](449) [FullText HTML](342) [PDF 1019KB](35)
Abstract:
Online contract signing is becoming more and more popular in e-commerce. It is not easy to sign a contract between two parties who do not trust each other. Many of these protocols involve the participation of third parties, but they are not advantageous in efficiency and prone to security problems. Currently, contract signing agreements with third-party participation are replaced by block chain technology, but the public verification of block chain challenges the sensitive information of both the signer and the contract to be signed. And most of the agreements are for the signing of contracts between the two parties. With the increase of the number of signatories, the communication cost and complexity of the agreements increase sharply. Combined with the existing protocols, this paper proposes an efficient multi-party contract signing protocol. In the protocol, an efficient aggregation signature scheme based on no certificate is used to improve the signature verification efficiency of the signer under the block chain, and only the temporary key of the signer is disclosed on the block chain to reduce the system overhead. The protocol satisfies the requirements of correctness, security, fairness, privacy and high efficiency.
2019, 41(11): 2699 -2707   doi: 10.11999/JEIT190127
[Abstract](520) [FullText HTML](381) [PDF 2165KB](18)
Abstract:
Side channel attack is the primary way to leak information between tenants in current cloud computing environment. However, existing Service Function Chain (SFC) deployment methods do not fully consider the side channel attack problem faced by the Virtual Network Function (VNF) in the multi-tenant environment. A SFC deployment method is proposed against side channel attack. A tenant classification strategy based on average time and a deployment strategy considering historical information are introduced. Under the resource constraints of the SFC, the optimization model is established with the goal of minimizing the number of servers that the tenant can cover. And a deployment algorithm is designed based on the greedy choice. The experimental results show that, compared with other deployment methods, this method can significantly improve the difficulty and cost of malicious tenant to realize co-residence, and reduces the risk of side channel attack faced by tenants.
2019, 41(11): 2708 -2714   doi: 10.11999/JEIT190129
[Abstract](440) [FullText HTML](261) [PDF 652KB](27)