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, doi: 10.11999/JEIT181131
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End-fire array antenna is extremely suitable for forward-looking or backward-looking blind compensation of airborne radar due to its low wind resistance and high-gain characteristics, while the forward-looking or backward-looking placement of antenna can not avoid the problem of range-dependent clutter. In this paper, in view of the fact that the conventional Space-Time Interpolation Technique can not be directly applied to end-fire array clutter compensation in range ambiguity situation, a novel method of end-fire array clutter compensation based on space-time interpolation is proposed based on the characteristics of clutter spectrum for end-fire array airborne radar. The method takes full account of the ambiguous clutter of each range gate and takes the arc corresponding to the main lobe of the long-range stationary clutter ridge as the interpolation reference subspace. Furthermore, it also refines the constrained object of moving target constraints, which achieves effective compensation for the non-stationary clutter of end-fire array in range ambiguity situation. Computer simulation results verify the effectiveness of the proposed method.
, doi: 10.11999/JEIT180857
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A method of establishing a fingerprint database, which is based on distributed compressed sensing, is proposed to improve the low positioning accuracy and poor real-time positioning that exist in the current mine target positioning in China. Using the method, the fingerprint information of mine target fingerprint database can be reconstructed with high probability by collecting only a few fingerprint information (reference node IDs, Time Of Arrival (TOA) measurements based on electromagnetic wave and actual distance values) in the roadway in the off-line stage. Therefore, the data collection workload can be reduced and the work efficiency can be improved as well. In the subsequent on-line stage, according to the pattern matching method, the estimated distance between the target node and the reference nodes at the certain time can be obtained only by getting the reference node IDs and the real-time TOA measurements measured by the reference nodes at a certain moment, which guarantees the positioning accuracy and positioning real-time performance. Based on this method, an improved Compressive Sampling Modifying Matching Pursuit (CoSaMMP) algorithm is proposed to reconstruct the fingerprint information. The algorithm can effectively shorten the reconstruction time by using the folding method to increase the cutting force. The simulation results show that the proposed algorithm is feasible and effective.
, doi: 10.11999/JEIT190013
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/JEIT190012
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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 a significant improvement over matched filter. Finally, simulation analysis is carried out to verify the effectiveness and superiority of SFD.
, doi: 10.11999/JEIT190010
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 cannot completely eliminate the interference, some target echo pulses may be submerged by the interference, agile radar cannot 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 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.
, doi: 10.11999/JEIT180651
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In order to deal with these issues of the traditional Fuzzy C-Means (FCM) algorithm, such as without consideration of the spatial neighborhood information of pixels, noise sensitivity and low convergence speed, a suppressed non-local spatial intuitionistic fuzzy c-means image segmentation algorithm is proposed. Firstly, in order to improve the accuracy of segmentation image, the non-local spatial information of pixel is used to improve anti-noise ability, and to overcome the shortcomings of the traditional FCM algorithm, which only considers the gray characteristic information of single pixel. Secondly, by using the ‘voting model’ based on the intuitionistic fuzzy set theory, the hesitation degrees are adaptively generated as inhibitory factors to modify the membership degrees, and then the operating efficiency is increased. Experimental results show that the new algorithm is robust to noise and has better segmentation performance.
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$v.abstractInfoEn , doi: 10.11999/JEIT180752 [Abstract](141) [FullText HTML](62) [PDF 2392KB](22) Abstract: A 10 bit fully differential dual slope Analog-to-Digital Converter (ADC) for Time Delay Integration (TDI) CMOS image sensors is realized based on column-parallel single-slope ADC. Top plates of the two capacitors are used for sampling differential inputs, and the bottom plates are connected to ramp generator for conversion. Current steering is used to generate the rising and falling ramp with the same step voltage simultaneously. The proposed ADC is fabricated in SMIC 0.18 μm CMOS process. Simulated spurious free dynamic range and effective number of bits are 87.92 dB and 9.84 bit with the input frequency of 1.32 kHz at 19.49 kS/s sampling rate, respectively. Measured results show that the ADC has a differential nonlinearity of –0.7/+0.6 LSB and integral nonlinearity of –2.6/+2.1 LSB. , doi: 10.11999/JEIT180588 [Abstract](89) [FullText HTML](43) [PDF 3991KB](4) Abstract: For the deficiency of traditional Continuously Adaptive Mean-shift (CAMshift) tracking algorithm can easily contain a large number of color information which belongs to the background in the process of establishing the target color model, an improved algorithm is proposed. The original image is divided into foreground and background based on the Gaussian Mixture Model(GMM). In the original image and the background image, the histogram of the hue component is established. Hue histograms of the background image are used to calculate the weight of the hue component in the original image. The hues belonging to the background are suppressed and the color differences between foreground and background are expanded. Experiment shows that by suppressing the hue components belonging to the background, the saliency of the target color model is expanded. The accuracy and stability of the target recognition are improved. The ratio of the max deviation to the target is less than 20%, which ensures the target not to be lost. , doi: 10.11999/JEIT180670 [Abstract](175) [FullText HTML](87) [PDF 1371KB](28) Abstract: The diversity of the population and the crossover operator algorithm play an important role in solving global optimization problems in Differential Evolution (DE). The Multi-poplutions Covariance learning Differential Evolution (MCDE) algorithm is proposed. Firstly, the population structure is a multi-poplutions mechanism, and each subpopulation combines the corresponding mutation strategy to ensure the individual diversity in the evolutionary process. Then, the covariance learning establishes a proper rotation coordinate system for the crossover operation in the population. At the same time, the adaptive control parameters are used to balance the ability of population survey and convergence. Finally, the proposed algorithm is conducted on 25 benchmark functions including unimodal, multimodal, shifted and high-dimensional test functions and compared with the state-of-the-art evolutionary algorithms. The experimental results show that the proposed algorithm compared with other algorithms has the best effect on solving the global optimization problem. , doi: 10.11999/JEIT180357 [Abstract](135) [FullText HTML](65) [PDF 1442KB](15) Abstract: To maximize the long-term spectral efficiency and energy efficiency of a full duplex wireless access and backhaul integrated small base station scene, approximate dynamic programming based joint admission control and resource allocation optimization algorithm is proposed. The algorithm firstly considers the resource usage and power configuration of the current base station, the dynamic demand of user, the constraints of average delay as well as backhaul rate and transmission power. The corresponding multi-objective optimization model of maximum spectrum efficiency and minimizes power consumption is established by using the Constrained Markov Decision Process(CMDP). Then, the Chebyshev theory is used to transform the multi-objective into a single-objective optimization, and the Lagrange dual decomposition method is then used to convert the single-objective problem into unrestricted Markov decision process problem. Finally, To solve the " dimension disaster” explosion that generated when solving this unrestricted Markov Decision Process(MDP) problem, a dynamic resource allocation algorithms based on approximate dynamic programming is presented, and the access and resource allocation strategy is obtained during this process. The simulation results show that the algorithm can maximize the long-term average spectrum efficiency and energy efficiency, within the constraints of the average delay, backhaul rate and transmission power, under the scenario of integrated access and backhaul small base station. , doi: 10.11999/JEIT180644 [Abstract](313) [FullText HTML](139) [PDF 2289KB](26) Abstract: Nowadays, the civil aviation industry has a high-precision prediction demand of flight delays, thus a flight delay prediction model based on the deep SE-DenseNet is proposed. Firstly, flight data, associated airport delay information and meteorological data are fused in the model. Then, the improved SE-DenseNet algorithm is used to extract feature automatically based on the fused flight data set. Finally, the softmax classifier is used to predict the delay level of flight. The proposed SE-DenseNet, combing the advantages of DenseNet and SENet, can not only enhance the transmission of deep information, avoid the problem of vanishing gradients, but also achieve feature recalibration by the feature extraction process. The results indicate that after data fusion, the accuracy of the model is improved 1.8% than only considering the characteristics of the flight itself. The improved algorithm can effectively improve the network performance. The final accuracy of the model reaches 93.19%. , doi: 10.11999/JEIT180702 [Abstract](150) [FullText HTML](68) [PDF 1947KB](15) Abstract: Because of the classic Faster RCNN training proccess with too many difficult training samples and low recall rate problem, a method which combines the techniques of Online Hard Example Mining (OHEM) and Hard Negative Example Mining (HNEM) is adopted, which carries out the error transfer for the difficult samples using its corresponding maximum loss value from real-time filtering. It solves the problem of low detection of hard example and improves the efficiency of the model training. To improve the recall rate and generalization of the model, an improved Non-Maximum Suppression (NMS) algorithm is proposed by setting confidence thresholds penalty function; In addition, multi-scale training and data augmentation are also introduced. Finally, the results before and after improvement are compared: Sensibility experiments show that the algorithm achieves good results in VOC2007 data set and VOC2012 data set, with the mean Average Percision (mAP) increasing from 69.9% to 74.40%, and 70.4% to 79.3% respectively, which demonstrates strongly the superiority of the algorithm. , doi: 10.11999/JEIT181040 [Abstract](161) [FullText HTML](135) [PDF 1754KB](33) Abstract: The secret key generation method based on random signal may leak part of the common randomness information and reduce the achievable secret key rate when legal transmitter transmits random signal. In response to this problem, the secret key generation method based on multi-stream random signal is proposed. Firstly, the transmitter uses the channel reciprocity and uplink pilot to estimate the downlink channel, then the transmitter transmits mutually independent signal on every antenna. The eavesdropper is difficult to estimate all the random signals. It is difficult to estimate all the random signals for the eavesdropper, so the overlapping signal received by every antenna is difficult to be obtained by the eavesdropper. However, the legal transmitter is able to calculate the signal received by legal receiver by using the downlink channel estimated and the signal transmitted. So, the overlapping signal on every legal antenna can be used to extract secret key as common randomness. Also, the achievable secret key rate expression and the mutual information expression of common randomness are derived, and the relationship between them and the secret key security is analyzed. At last, the effectiveness of this method is verified by the simulation. The simulation results show that this method can reduce the common randomness observed by the eavesdropper to raise the achievable secret key rate and secret key security. , doi: 10.11999/JEIT180640 [Abstract](255) [FullText HTML](93) [PDF 1456KB](11) Abstract: To solve the problem of insufficient number of participants and poor data quality in the sensing mission, a mobile crowd sensing incentive model for mission cost difference is proposed. First of all, the fuzzy reasoning method is used to analyze the impact of data quantity, environmental conditions and equipment consumption on mission cost, and the sensing mission is divided into different levels on the basis of cost difference. Meanwhile, the method is used to prepare a budget for the requester and give the participant an appropriate reward. Then, the sensing mission is assigned to more appropriate participants to complete the sensing mission and upload the sensing data through credibility assessment and participants’ preference. Finally, the sensing data uploaded by participants is evaluated, and the credibility of participants is updated. Besides, the participants are paid according to the cost level of perceived missions. The simulation experiments based on the real data set show that the model can recruit more users to participate in the sensing mission effectively and promote participants to upload high-quality sensing data by using the mutual influence between different modules. , doi: 10.11999/JEIT180831 [Abstract](150) [FullText HTML](62) [PDF 2694KB](11) Abstract: Long configuration time is a significant factor which restricts the performance improvement of the reconfigurable system, and a reasonable task scheduling technology can effectively reduce the system configuration time. A three-dimensional task scheduling model for Coarse-Grain Dynamic Reconfigurable System (CGDRS) and flow applications with data dependencies is proposed. Firstly, based on this model, a Configuration Prefetching Schedule Algorithm (CPSA) applying pre-configured strategy is designed. Then, the interval and continuous configuration reuse strategy are proposed according to the configuration reusability between tasks, and the CPSA algorithm is improved accordingly. The experimental results show this algorithm can avoid scheduling deadlock, reduce the execution time of flow applications and improve scheduling success rate. The optimization ratio of total execution time of flow applications achieves 6.13%～19.53% averagely compared with other scheduling algorithms. , doi: 10.11999/JEIT180261 [Abstract](105) [FullText HTML](58) [PDF 2110KB](14) Abstract: In broadband Power-Line Communications (PLC), the background noise commonly assumed as Gaussian may not truly depict the effect of the human activities on noise characteristics. Symmetric Alpha-Stable (S\begin{document}$\alpha$\end{document}S) process is used to model the PLC background noise, obtaining analytical expressions, and the analytical Symbol Error Rate (SER) performance is investigated for Orthogonal Frequency Division Multiplexing (OFDM)-based PLC systems. The analysis shows that the real or the imaginary component after the Fast Fourier Transform (FFT) of the received complex baseband S\begin{document}$\alpha$\end{document}S noise samples follows univariate S\begin{document}$\alpha$\end{document}S distribution. Due to the fact that the S\begin{document}$\alpha$\end{document}S background noise occupies the whole OFDM system bandwidth, the SER performance of the system decreases as the employed FFT size grows. , doi: 10.11999/JEIT180637 [Abstract](256) [FullText HTML](137) [PDF 1194KB](21) Abstract: The traditional fingerprinting localization algorithm has high construct time overhead and low positioning accuracy. Because of this problem, an adaptive fading memory based bluetooth sequence matching localization algorithm is proposed. Firsly, Pedestrian Dead Reckoning(PDR) and Nearest Neighbor Algorithm(NNA) are applied to performing position calibration and Received Signal Strength(RSS) mapping of Motion Sequences. Secoudly, according to the relevance of neighboring locations, a sequence recursive search method is used to construct fingerprint sequence database. Finally, an adaptive fading memory algorithm and initial sequence matching degree are considered to realize the position estimation of target. The experimental results show that this algorithm is able to consume low construct time overhead and achieve high indoor localization precision. , doi: 10.11999/JEIT180595 [Abstract](128) [FullText HTML](68) [PDF 1550KB](19) Abstract: Semi-honest data collectors may cause privacy leaks during the collection and use of Sensitive Attribute (SA) data. In view of the problem, real-time data leaders are added in the traditional model and a privacy-protected data collection protocol based on the improved model is proposed. Without the assumption of trusted third party, the protocol ensures that data collectors maximization data utility can only be established on the basis of K-anonymized data. Data owners participates in the protocol flow in a distributed and collaborative manner to achieve the transmission of SA after the Quasi-Identifier (QI) is anonymized. This reduces the probability that the data collector uses the QI to associate SA values and weakens the risk of privacy leakage caused by internal identity disclosure. It divides the coded value of the SA into two shares of a random anchor point and a compensation distance through the tree coding structure and the members of the equivalent class formed by K-anonymity elect two data leaders to aggregate and forward the two shares respectively, which releases the association between unique network identification and SA values and prevents leakage of privacy caused by external identification effectively. Formal rules are established that meet the characteristics of the protocol and analyze the protocol to prove that the protocol meets privacy protection requirements. , doi: 10.11999/JEIT180631 [Abstract](297) [FullText HTML](103) [PDF 1701KB](20) Abstract: For problems of not meeting the demand of sampling both large flows and small flows at the same time, and not distinguishing flash crowd from traffic attacks in building network traffic anomaly detection datasets based on probabilistic sampling methods, a probabilistic flow sampling method for traffic anomaly detection is proposed. On the basis of the classification of network data flows according to their destination and source IP addresses, the sampling probability for each class of data flows is set as the maximum of its destination and source IP address’s sampling probability, and the number of sampled data flows is ceiled to ensure that each class of data flows is sampled at least once, so that the sampled dataset can reflect the distributions of large, small flows and source, destination IP addresses in original traffics. Then, the source IP address entropy is used to characterize the source IP dispersion of anomaly flows, and the attack flow sampling algorithm is designed based on the threshold of the source IP address entropy, which reduces the sampling probability of non-attack anomaly flows caused by flash crowd. The simulation results show that the proposed method can satisfy the sampling requirements of both large flows and small flows, it has a high anomaly flows sampling ability, can sample all the suspicious sources and destination IP addresses related to anomaly flows, and can effectively filter the non-attack anomaly flows. , doi: 10.11999/JEIT180157 [Abstract](144) [FullText HTML](75) [PDF 2810KB](23) Abstract: To deal with the problem of joint estimation of spreading codes and information sequences for asynchronous long code DS-CDMA signals in multipath channels, an algorithm is introduced based on Sequential Monte Carlo(SMC) for blind estimation. The proposed algorithm emploies hybrid important sampling density to draw the samples from joint posterior distribution iteratively, and computes the importance weight to complete the estimation of the state variable. During the realization of the algorithm, in order to reduce the computational complexity, the modified algorithm estimates the spreading code of each user firsthy, then processes the observation data, thereby modifies the original iteration step. Simulation results verify the adaptability of the proposed algorithms for multiple conditions. Moreover, it can obtain good estimation performance in time varying multipath channels. , doi: 10.11999/JEIT180666 [Abstract](175) [FullText HTML](86) [PDF 2754KB](24) Abstract: To solve the problem of real-time migration of Virtual Network Function (VNF) caused by lacking effective prediction in 5G network, a VNF migration algorithm based on deep belief network prediction of resource requirements is proposed. The algorithm builds firstly a system cost evaluation model integrating bandwidth cost and migration cost,and then designs a deep belief network prediction algorithm based on online learning which adopts adaptive learning rate and introduces multi-task learning mode to predict future resource requirements. Finally, based on the prediction result as well as the perception of network topology and resources, the VNFs are migrated to the physical nodes that meet the resource threshold constraints through greedy selection algorithm with the goal to optimize system cost,and then a migration mechanism based on tabu search is proposed to further optimize the migration strategy.The simulation results show that the prediction model can obtain good prediction results and adaptive learning rate accelerates the convergence speed of the training network.Moreover, the combination with the migration algorithm reduces effectively system cost and the number of Service Level Agreements (SLA) violations during the migration process, and improves the performance of network services. , doi: 10.11999/JEIT180691 [Abstract](293) [FullText HTML](144) [PDF 1321KB](44) Abstract: In order to improve the comprehensive performance of the wireless network intrusion detection model, Recurrent Neural Network (RNN) algorithm is used to build a wireless network intrusion detection classification model. For the over-fitting problem of the classification model caused by the imbalance of training data samples distribution in wireless network intrusion detection, based on the pre-treatment of raw data cleaning, transformation, feature selection, etc., an instance selection algorithm based on window is proposed to refine the train data-set. The network structure, activation function and re-usability of the attack classification model are optimized experimentally, so the optimization model is obtained finally. The classification accuracy of the optimization model is 98.6699%, and the running time after the model reuse optimization is 9.13 s. Compared to other machine learning algorithms, the proposed approach achieves good results in classification accuracy and execution efficiency. The comprehensive performances of the proposed model are better than those of traditional intrusion detection model. , doi: 10.11999/JEIT180703 [Abstract](139) [FullText HTML](58) [PDF 470KB](11) Abstract: Two constructions of Gaussian integer Zero Correlation Zone (ZCZ) sequence set are researched. In Construction I, the method of zero padding is implemented on the ZCZ sequence set, and then the Gaussian integer ZCZ can be obtained by the filtering operation. Furthermore, the degree of the Gaussian integer ZCZ sequence set is calculated in this paper. In Construction II, two constructions of Gaussian integer orthogonal matrix are proposed. In addition, the optimal Gaussian integer ZCZ sequence sets are constructed based on the orthogonal matrix. The two classes of Gaussian integer ZCZ sequence sets presented in this paper can be applied to many communication systems such as Quasi-Synchronous Code Division Multiple Access (QS-CDMA), Orthogonal Frequency Division Multiplexing (OFDM) and Mutiple-Input Multiple-Output (MIMO) system to suppress the interference and improve the spectrum efficiency. , doi: 10.11999/JEIT180896 [Abstract](68) [FullText HTML](42) [PDF 2248KB](11) Abstract: Focusing on the problem of information leakage in secret key agreement, combining information reconciliation and privacy amplification, a method based on Secure Polar Code (SPC) is proposed, which builds the bridge from the condition of Quantized Bit Error Rate (QBER) to the requirement of Secret Key Outage Probability (SKOP). Firstly, QBER is modeled as the Transmitted Bit Error Rate (TBER) of Additional White Gaussian Noise (AWGN) channel, so the advantage of QBER is converted to the advantage of AWGN channel; Then, the TBER of each polarized sub-channel is calculated by Gaussian approximation, and the upper and lower bounds of decoded bit error rate are also derived. Finally, the SPC is constructed based on generic algorithm and SKOP threshold. Simulation results show that the proposed method satisfies the requirement of SKOP and achieves higher secret key agreement efficiency, compared with Low Density Parity Check (LDPC)-based method. , doi: 10.11999/JEIT180653 [Abstract](212) [FullText HTML](95) [PDF 1479KB](12) Abstract: A Bayesian network structure learning algorithm based on improved whale optimization strategy is proposed to solve the problem that the current Bayesian network structure learning algorithm is easily trapped in local optimal and is of low optimization efficiency. The improved algorithm proposes first a new method to establish a better initial population, and then it uses the cross mutation operator that does not produce the illegal structure to construct an improved predation behavior suitable for Bayesian network structure learning. At the same time, it adopts the dynamic parameter tuning strategy to enhance the individual search ability. The population is updated followed by the fitness order so that the optimal Bayesian network structure is obtained. Simulation results demonstrate that the algorithm has global convergence, high efficiency and higher accuracy than other similar optimization algorithms. , doi: 10.11999/JEIT180677 [Abstract](251) [FullText HTML](123) [PDF 2866KB](56) Abstract: In order to meet the demand for high real-time and high generalization performance of radar recognition, a radar High Resolution Range Profile (HRRP) recognition method based on deep multi-scale one dimension convolutional neural network is proposed. The multi-scale convolutional layer that can represent the complex features of HRRP is designed based on two features of the convolution kernels which are weight sharing and extraction of different fineness features from different scales, respectively. At last, the center loss function is used to improve the separability of features. Experimental results show that the model can greatly improve the accuracy of the target recognition under non-ideal conditions and solve the problem of the target aspect sensitivity, which also has good robustness and generalization performance. , doi: 10.11999/JEIT180661 [Abstract](134) [FullText HTML](72) [PDF 3862KB](17) Abstract: Microwave photonics radar generates signals with large bandwidth and small wavelength. It has capability of ultra-high resolution of Inverse Synthetic Aperture Radar(ISAR) image. Because the approximation of rotational components is not tenable, traditional ISAR imaging algorithm is not suitable to microwave photonics radar. In the microwave photonics radar imaging, the rotational components result in range curvature and quadratic phase error changing with distance. To solve this problem, an effective ISAR imaging algorithm is put forward which considers the influence of the target’s rotational component to echo envelope and phase. The value of envelope correlation is take as objective function and the target’s rotate speed is estimated by iteration; The range curvature is corrected by time resampling; The quadratic phase error is compensated by azimuth compensation function. Both simulated and real-measured data experimental results confirm the effectiveness of the proposed algorithm. , doi: 10.11999/JEIT180655 [Abstract](180) [FullText HTML](72) [PDF 1324KB](10) Abstract: The system biases degrade seriously the location precision for the multi-static passive radar system. A joint registration and passive localization algorithm based on Constrained Total Least Squares (CTLS) using Direction Of Arrival (DOA) and Time Difference Of Arrival (TDOA) measurements is developed to address the multi-static radar localization problem under the influence of system biases. Firstly, the nonlinear DOA and TDOA measurement equations are linearized by introducing auxiliary variables. Considering the statistical correlation properties of the noise matrix in the pseudo-linear equations, a joint biases registration and passive localization problem is formulated as a CTLS problem and the Newton’s method is applied to solving the CTLS problem. Moreover, a dependent least squares algorithm is designed to improve the target position estimation using the relationship between auxiliary variables and target position. An iterative post-estimate procedure is exploited to enhance further the estimation accuracy of the system biases. Finally, the theoretical error of the proposed algorithm is derived. Simulations demonstrate that the proposed algorithm can effectively estimate the system biases and target position. , doi: 10.11999/JEIT180680 [Abstract](214) [FullText HTML](115) [PDF 2354KB](18) Abstract: Cooperative MIMO technology can transform interference signals into useful signals by means of cooperative transmission or reception. It can solve the echo channel effect and improve the system capacity to be introduced into high-speed railway wireless communication. To master the channel characteristics of cooperative MIMO technology in high-speed railway scenarios, based on the geometric stochastic scattering theories, a new channel model for cooperative MIMO channel in high-speed railway scenarios is proposed, which can be applied to multiple high-speed railway scenarios by simply adjusting its several key parameters. Based on this model, the channel impulse response is calculated, the multi-link spatial correlation function is derived, the numerical calculation, simulation analysis and verification of measured data are carried out. Simulation results show that the multi-link spatial correlation is stronger when the LOS component is stronger and the angle spread of scattered components is smaller. The components which are scattered less times have a stronger spatial correlation. The theoretical model is verified by the measured data of the LTE special network of the Beijing-Tianjin high-speed railway section. These conclusions contribute to understanding the cooperative MIMO channels and conducting effective measurement activities. , doi: 10.11999/JEIT180714 [Abstract](262) [FullText HTML](104) [PDF 2166KB](18) Abstract: To address track-to-track association problem of radar and Electronic Support Measurements (ESM) in the presence of sensor biases and different targets reported by different sensors, an anti-bias track-to-track association algorithm based on track vectors hierarchical clustering is proposed. Firstly, the equivalent measurement is derived in the Modified Polar Coordinates (MPC). Linear relationship between state estimates and real states, sensor biases, measurement errors are established based on the approximate expansion of the equivalent measurement. The track vectors are obtained by the real state cancellation method. The homologous tracks are extracted by the method of track vectors hierarchical clustering, according to the statistical characteristics of Gaussian random vectors. The effectiveness of the proposed algorithm is verified by Monte Carlo simulation experiments in the presence of sensor biases, targets densities and detection probabilities. , doi: 10.11999/JEIT181086 [Abstract](153) [FullText HTML](72) [PDF 2302KB](20) Abstract: The equivalent rotation center should be estimated accurately in the Inverse Synthetic Aperture Radar (ISAR) for the issue of image defocusing induced by the Migration Through Resolution Cells (MTRC). In this paper, an equivalent rotation center estimation algorithm based on image rotation and correlation is proposed for the space target. First, the instantaneous imaging mechanism of ISAR is analyzed. Second, two images with different observation angles are obtained by using the echo data with the same motion compensation algorithm. Finally, the equivalent rotation center is estimated based on the scaled image pixel rotation and image correlation. Consequently, the estimated position of the rotation center is obtained, when the assumed rotation center is in accordance with the real one and the maximum correlation coefficient of two images is achieved. The results demonstrate the effectiveness and robustness of the proposed algorithm. , doi: 10.11999/JEIT180145 [Abstract](139) [FullText HTML](84) [PDF 1422KB](30) Abstract: In Compressive Sensing (CS) imaging algorithms, the true targets usually can not locate on the pre-defined grids exactly. Such Off-grid problems result in mismatch between true echo and measurement matrix, which seriously degrades the performance of radar imaging. An adaptive calibration method is proposed to solve the off-grid problems in MIMO radar Three-Dimensional (3D) imaging. Bayesian probability density functions can be constructed based on the sparse echo model of Off-grid targets, and the Maximum A Posteriori (MAP) method is used to obtain sparse imaging with mismatch errors. Compared with the traditional methods, the proposed method can make full use of mismatch parameters’ priori information and adaptively update the parameters, which can reduce the influence of mismatch errors, and achieve high-precision estimation for sparse targets and noise power. Finally, the simulation results confirm that the proposed method can effectively optimize mismatch errors with accurate and stable imaging performance. , doi: 10.11999/JEIT180663 [Abstract](200) [FullText HTML](91) [PDF 2054KB](12) Abstract: The moving target component is often defocused in spaceborne SAR images. Therefore, the moving target detection performance is affected depending on the degree of defocusing. Combined with the RD algorithm, a moving-targets detection algorithm for spaceborne SAR based on a two-dimensional velocity search is proposed. Through velocity search on the distance direction and the azimuth direction, the Doppler parameters of possible moving targets can be matched. Then the strongest value among all the searching velocity results for each pixel is used for Constant False Alarm Rate(CFAR) detector. This core process can improve the detection performance of moving target component. Simulation results validate the effectiveness of the proposed method. , doi: 10.11999/JEIT180247 [Abstract](50) [FullText HTML](42) [PDF 1868KB](10) Abstract: Transformation Optics (TO) is a hot topic in the research area of electrical-magnetic fields. For providing further theoretical support to the design of stealth carpet based on TO, three basic mathematic problems of TO are discussed in this paper. Firstly, the uniqueness of transformation form in three-dimensional transformation of Maxwell’s equations is analyzed. A new transformation model is proposed, which is different from the classical one shown in reference. The new model also leads to a new transformation method that can generate flexible characteristic impedance in transformation space. Based on this, a design method of stealth cloak or carpet that can be used to hide the target in an area surrounded by medium with given permittivity is discussed. During this process, only the field distribution in free space is required as the original field during mapping. Secondly, the two-dimensional transformation of the wave equation is studied. The transformation of the magnetic field component in the two-dimensional transformation based on the wave equation of the electric field component is analyzed. The boundary matching during transformation is also discussed. The two dimension design method of stealth cloak or carpet that can be used to hide a target in an area surrounded by medium with specified permittivity is also discussed. Finally, the sufficiency and necessity of conformal transformation for designing a two dimension stealth cloak with non-uniform and anisotropic medium are proved strictly. The simulation results of a stealth carpet embedded in material are given to verify the proposed method. The analysis and the related conclusion presented in the paper provide theoretical support to the related application based on TO. , doi: 10.11999/JEIT180648 [Abstract](186) [FullText HTML](60) [PDF 2419KB](10) Abstract: A metameterial absorber is designed, fabricated and experimentally demonstrated to realized ultra-wideband absorption based on loading lumped resistances to raise the efficiency of absorber. The proposed structure comprises of an upper absorber and an under absorber by longitudinal cascade to expand bandwidth. The analysis of equivalent circuit show that the absorber has good impedance matching in a wide frequency band and the mechanism of wave absorption is verified by current analysis. The size of the unit is only about 0.089\begin{document}$\lambda_ {\rm{L}}$\end{document}×0.089\begin{document}$\lambda_ {\rm{L}}$\end{document}, where \begin{document}$\lambda_ {\rm{L}}$\end{document} is the wavelength of the lowest frequency, and the total thickness of the absorber is only 0.078\begin{document}$\lambda_ {\rm{L}}$\end{document}. Simulated and experimental results show that the absorber exhibits absorptivity above 90% from 2.24 GHz to 16.14 GHz, and the relative absorption bandwidth is about 151%. Measurement results show good agreement with the numerically simulated results. , doi: 10.11999/JEIT180672 [Abstract](237) [FullText HTML](88) [PDF 2057KB](25) Abstract: As for super resolution Direction-of-Arrival (DoA) estimation with random arrays, it is still challenged to obtain efficient and statistically unbiased estimates. Based on Augmented Estimation of Signal Parameters via Rotational Invariance Techniques (AESPRIT), an efficient DoA estimation algorithm is proposed for random arrays. AESPRIT is modified with a closed loop structure; Array Interpolation Technique (AIT) is utilized to provide the initial phase compensation angles to the loop. Therefore, the final DoA estimates can be calculated efficiently and accurately through iteration. The proposed algorithm gives algebraic solution directly and has low computational complexity. At the same time, the results are statistically unbiased because no manifold mapping or mode truncation error is introduced. Simulations verify the effectiveness of the proposed algorithm. , doi: 10.11999/JEIT181171 [Abstract](19) [FullText HTML](18) [PDF 2900KB](4) Abstract: In order to solve the problem of speed and position error divergence in the integrated navigation system based on MicroElectro Mechanical Systems (MEMS) inertial device and GPS system combined positioning, an improved Adaptive Unsecnted Kalman Filter (AUKF) enhanced by the Radial Basis Function(RBF) neural network based on Artificial Bee Colony(ABC) algorithm is proposed. When the GPS signal is out of lock, the trained network outputs predictied information to perform error correction on the Strapdown Inertial Navigation System(SINS). Finally, the performance of the method is verified by vehicle-mounted semi-physical simulation experiments. The experimental results show that the proposed method has a significant inhibitory effect on the error divergence of the strapdown inertial navigation system in the case of loss of lock. , doi: 10.11999/JEIT180978 [Abstract](10) [FullText HTML](13) [PDF 0KB](1) Abstract: In the field of computer vision, predicting human motion is very necessary for timely human–computer interaction and personnel tracking. In order to improve the performance of human–computer interaction and personnel tracking, an encoder-decoder model called EBiGRU–D (Bi–directional Gated Recurrent Unit encoder–decoder) based on Gated Recurrent Unit (GRU) is proposed to learn 3D human motion and give a prediction of motion over a period of time. EBiGRU–D is a deep Recurrent Neural Network (RNN) in which the encoder is a BIdirectional GRU (BiGRU) unit and the decoder is a unidirectional GRU unit. BiGRU allows raw data to be simultaneously input from both the forward and reverse directions and then encoded into a state vector, which is then sent to the decoder for decoding. BiGRU associates the current output with the state of the front and rear time, so that the output fully considers the characteristics of the time before and after, so that the prediction is more accurate. Experimental results on the human3.6m dataset demonstrate that EBiGRU–D not only improves greatly the error of 3D human motion prediction but also increases greatly the time for accurate prediction. , doi: 10.11999/JEIT181083 [Abstract](54) [FullText HTML](34) [PDF 1683KB](7) Abstract: Magnetic induction detection technology is a non-contact and non-invasive electrical impedance detection technology. Multi-frequency synchronous detection can simultaneously obtain the impedance information of the tested object at different frequencies. Firstly, the principle of multi-frequency synchronous excitation and detection of magnetic induction signal are studied. Five-frequency excitation signal is synthesized based on Walsh function. Secondly, the performance of synthesized multi-frequency synchronous detection is analyzed, and a synthesized multi-frequency magnetic induction signal synchronous detection system is designed. Finally, the detection experiments of NaCl solution with different conductivities are carried out by synthesizing five-frequency excitation signal and synchronous detection system. The results show that the measurement results of five main harmonics of synthesized five-frequency excitation signal have good linearity. It provides an excitation-detection method for multi-frequency synchronous detection of magnetic induction signal. , doi: 10.11999/JEIT181038 [Abstract](4) [FullText HTML](4) [PDF 0KB](1) Abstract: Considering the shortcomings of Differential Chaos Shift Keying (DCSK) transmission rate and further improving the system error performance, a Short reference Orthogonal Multiuser DCSK(SOM-DCSK) communication system is proposed. The system shortens the reference signal to 1/P of each information bearing signal, and transmits multiple users by different delay times. Then the orthogonality of Hilbert transform is used in each information slot to achieve the purpose of transmitting a two-bit information signal. The Bite Error Rate (BER) formula of SOM-DCSK system in Additive White Gaussian Noise (AWGN) and Rayleigh fading channel is derived and experimentally simulated. The simulation results show that the scheme has obvious improvement compared with the traditional multi-user system under the same conditions, and it has good practical value. , doi: 10.11999/JEIT190026 [Abstract](7) [FullText HTML](7) [PDF 0KB](1) 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. , doi: 10.11999/JEIT180936 [Abstract](53) [FullText HTML](37) [PDF 2278KB](18) Abstract: Generally speaking, Three Dimension (3D) imaging of spinning space target is obtained by performing matrix factorization method on the scattering trajectories obtained from sequential radar images. Because of the errors of scattering center extraction and association, the 3D reconstruction accurate is reduced or even fail. In addition, the scattering center trajectory from turntable target consists with circle nature, which is inconsistent with the elliptic property of the scattering center trajectory obtained by optical geometry projection. To tackle these problems, this paper proposes a short time 3D reconstruction method of space target. Firstly, the retrieved trajectory is fitted with 2D circular nature to make the trajectory smooth and closer to the theoretical curve. Then the radar Line Of Sight (LOS) is estimated by multiple views and the circular curve is converted into elliptical curve by multiplying the coefficients calculated by the LOS. The 3D reconstruction can be obtained by performing matrix factorization method on elliptical curves. Finally, the simulations verify the effectiveness of the proposed method. , doi: 10.11999/JEIT190014 [Abstract](53) [FullText HTML](32) [PDF 2058KB](8) Abstract: Considering the disadvantage of oblique delay estimation of tropospheric scattering at arbitrary stations, which is difficult to obtain real-time sounding meteorological data, an oblique delay estimation algorithm of tropospheric scattering based on improved ray tracing method with ground meteorological parameters is proposed. In order to get rid of the method’s dependence on radiosonde data, the algorithm infers the relationship between refractive index and altitude through the formula of meteorological parameters in the model of medium latitude atmosphere. The interpolation of meteorological parameters in the model of UNB3m is used to gain the coefficient of temperature and water vapor pressure. Meteorological data for 2012 from 6 International GNSS Service (IGS) stations in Asia are selected to test the applicability of new method, the results suggest that precision is less than 1 cm. Then, the tropospheric slant delays of three parts observation stations under different angles of incidence (0°～5°) are calculated by the modified algorithm. The results suggest that the maximum delay is 17.03～33.10 m in a single way time transfer. In two way time transfer, when the delay can counteract 95%, time delay is 2.88～5.52 ns. , doi: 10.11999/JEIT190108 [Abstract](40) [FullText HTML](27) [PDF 5051KB](4) 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 (\begin{document}$ H/{\alpha} \$\end{document}) 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](37) [FullText HTML](24) [PDF 1199KB](9)
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/JEIT181078
[Abstract](52) [FullText HTML](29) [PDF 2199KB](6)
Abstract:
High Frequency Surface Wave Radar (HFSWR) utilizes electromagnetic wave diffracting along the earth to detect targets over the horizon. However, the increase of target distance will decrease the received echo energy, and this will degrade the detection capability. A joint domain matrix Constant False Alarm Rate (CFAR) detector is proposed to improve the detection performance. It employs the multi-dimensional information of signal in azimuth, Doppler velocity and range domain to detect target, and Log-Determinant Divergence (LDD) and Symmetrized Log-Determinant Divergence (SLDD) are used to replace the Riemannian Distance (RD) as the measure of distance. Finally, the experiment results show that the detector presented by the paper can improve the detection performance effectively.
, doi: 10.11999/JEIT190003
[Abstract](38) [FullText HTML](22) [PDF 744KB](3)
Abstract:
Cipher cards play an important role in the field of information security. However, the performance of cipher cards are insufficient, and it is difficult to meet the needs of high-speed network security services. A design and system implementation method of high-speed PCIe cipher card based on MIPS64 multi-core processor is proposed, which supports the GM algorithm SM2/3/4 and international cryptographic algorithms, such as RSA, SHA and AES. The implemented system includes module of hardware, cryptographic algorithm, host driver and interface calling. An optimization scheme for the implementation of SM3 is proposed, the performance is improved by 19%. And the host to send requests in Non-Blocking mode is supported, so a single-process application can get the cipher card’s full load performance. Under 10-core CPU, the speed of SM2 signature and verification are 18000 and 4200 times/s, SM3 hash speed is 2200 Mbps, SM4 encryption/decryption speed is 8/10 Gbps, multiple indicators achieve higher level; When using 16-core CPU @1300 MHz, SM2/3 performance can be improved by more than 100%, and the speed of SM2 signature could achieve 105 times/s with 48-core CPU.
, doi: 10.11999/JEIT190095
[Abstract](68) [FullText HTML](35) [PDF 2331KB](4)
Abstract:
The circuit structure optimization method for Basic programmable Logic Element (BLE) of FPGA is studied. Considering finding the solution to the bottleneck problem of low resource utilization efficiency in logic and arithmetic operations with 4-input Look Up Table (LUT), some efforts to improve BLE design based on 4-input LUT are explored. A high area-efficient LUT structure is proposed, and the possible benefits of such a new structure are analyzed theoretically and simulated. Further, a statistical method for evaluation of the post synthesis and mapping netlist is also proposed. Finally, a number of experiments are carried out to assess the proposed structure based on the MCNC and VTR benchmarks. The results show that, compared with Intel Stratix series FPGAs, the optimized structure proposed in this paper improves respectively the area efficiency of the FPGA by 10.428% and 10.433% in average under the MCNC and VTR benchmark circuits.
, doi: 10.11999/JEIT181073
[Abstract](123) [FullText HTML](63) [PDF 1689KB](16)
Abstract:
In view of the nonlinear and stochastic characteristics of short-term traffic flow data, this article propose a prediction model and algorithm based on hybrid Auto-Regressive Integrated Moving Average (ARIMA) and Genetic Particle Swarm Optimization Wavelet Neural Network (GPSOWNN) in order to improve its prediction accuracy and rate of convergence. In terms of model construction, the ARIMA model prediction value and the historical data of the first three moments with strong correlation with gray correlation coefficient greater than 0.6 are used as input of the Wavelet Neural Network, and the structure of the model is simplified considering both the stationary and non-stationary historical data. In terms of algorithm, by using the genetic particle swarm optimization algorithm to select optimally the initial values of the wavelet neural network, the results can speed up the convergence of network training under the condition that it is not easy to fall into local optimum. The experimental results show that the proposed model is superior to hybrid ARIMA and GPSOWNN in terms of prediction accuracy, the genetic particle swarm optimization algorithm is superior to the genetic algorithm optimization model in terms of convergence speed.
, doi: 10.11999/JEIT180946
[Abstract](81) [FullText HTML](48) [PDF 2701KB](13)
Abstract:
With the development of light and small Unmanned Aerial Vehicles (UAV), the detection method of Mini SAR based on UAV platform will bring a revolutionary impact on information acquisition mode. In this paper, a W-band Mini SAR system for UAV is proposed, including the system design proposal and composition, high linearity analog phase-locked frequency modulation, MilliMeter Wave (MMW) substrate integrated waveguide antenna, 3D integration and motion compensation methods to solve the key problems of Mini SAR. A W-band Mini SAR prototype is developed and the imaging test based on Multi-rotor UAV is proceeded. The results show the resolution, volume and the weight of Mini SAR prototype is at the industry-leading level. A high SNR imaging with perfect focusing effect is obtained from flight test.
, doi: 10.11999/JEIT180748
[Abstract](46) [FullText HTML](38) [PDF 2012KB](7)
Abstract:
In order to overcome the accumulation error in Micro-Electro-Mechanical System-Inertial Navigation System (MEMS-INS) and the jump error in iBeacon fingerprint positioning, an iBencon/INS data fusion location algorithm based on Unscented Kalman Filter (UKF) is proposed. The new algorithm solves the distance between the iBeacon anchor and the locating target. The solution of attitude matrix and position are obtained respectively by using accelerometer and gyroscope data. Bluetooth anchor position vector, the carrier speed error and other information constitute state variables. Inertial navigation location and bluetooth system distance information constitute measure variables. Based on state variables and measure variables, the UKF is designed to realize iBencon/INS data fusion indoor positioning. The experimental results show that the proposed algorithm can effectively solve the problem of the large accumulation error of INS and the jump error of iBeacon fingerprint positioning, and this algorithm can realize 1.5 m positioning accuracy.
, doi: 10.11999/JEIT180740
[Abstract](13) [FullText HTML](11) [PDF 0KB](3)
Abstract:
In order to improve the accuracy rate of person re-identification. A pedestrian attribute hierarchy recognition neural network is proposed in this paper based on attention model. Compared with the existing algorithms, the model has the following three advantages. Firstly, the attention model is used in this paper to identify the pedestrian attributes, and to extract of pedestrian attribute information and degree of significance. Secondly, the attention model in used in this paper to classify the attributes according to the significance of the pedestrian attributes and the amount of informationcontained. Thirdly, this paper analyzes the correlation between attributes, and adjust the next level identification strategy according to the recognition results of the upper level. It can improve the recognition accuracy of small target attributes, and the accuracy of pedestrian recognition is improved. The experimental results show that the proposed model can effectively improve the first accuracy rate (rank-1) of person re-identification compared with the existing methods. On the Market1501 dataset, the first accuracy rate is 93.1%, and the first accuracy rate is 81.7% on the DukeMTMC dataset.
, doi: 10.11999/JEIT180931
[Abstract](107) [FullText HTML](56) [PDF 1583KB](20)
Abstract:
In order to improve the quality of reconstruction image by Block Compressed Sensing (BCS), a Total Variation Iterative Threshold regularization image reconstruction algorithm (BCS-TVIT) is proposed. Combining the properties of local smoothing and bounded variation of the image, BCS-TVIT use the minimization l0 norm and total variation to construct the objective function. To solve the problem that l0 norm term and the block measurement constraint cannot be optimized directly, the iterative threshold method is used to minimize the l0 norm of the reconstructed image, and the convex set projection was employed to guarantee the block measurement constraint condition. Experiments show that BCS-TVIT has better performance than BCS-SPL in PSNR by 2 dB. Meanwhile, BCS-TVIT can eliminate the " bright spot” effect of BCS-SPL, having better visual effect. Comparing with the minimum total variation, the proposed algorithm increases PSNR by 1 dB, and the reconstruction time is reduced by two orders of magnitude.
, doi: 10.11999/JEIT181102
Abstract:
It makes the Pulse Doppler (PD) radar widely applied that the PD radar has the obvious advantages of detecting the Doppler frequency of the target and suppressing the clutter effectively. However, it is difficult for the PD radar to detect the target due to velocity ambiguity. Combining with the characteristic and stagger-period model of the PD radar, a Doppler frequency estimation method based on all phase DFT Closed-Form Robust Chinese Remainder Theorem (CFRCRT) with spectrum correction is proposed in this paper. Both theoretical analysis and simulation experiment demonstrate that the proposed method can satisfy the engineering demand in measure accuracy and real-time performance.
, doi: 10.11999/JEIT181138
[Abstract](5) [FullText HTML](6) [PDF 0KB](0)
Abstract:
To solve the problems that Two-Dimensional Principal Component Analysis (2DPCA) can not implement the on-line feature extraction and can not represent the complete structure information, an Incremental 2DPCA (I2DPCA) without estimating covariance matrices is presented by an iterative estimation method, not to deal with the image covariance matrices by the eigenvalue decomposition or the singular value decomposition. The complexity will be greatly reduced and the on-line feature extraction speed can be improved. The proposed I2DPCA can only extract the horizontal features, and thus another Incremental Row-Column 2DPCA (IRC2DPCA) is proposed to incrementally extract the longitudinal ones from the projected subspaces of the I2DPCA. The IRC2DPCA can preserve the horizontal and longitudinal features and implement the dimensionality reduction in both row and column directions. Finally, a series of experiments are carried out with the self-built block dataset, ORL and Yale face datasets, respectively. The results show that the proposed algorithms have significantly improved the performances of the convergence rate, the classification rate and the complexity. The convergence rate is over 99%, the classification rate can reach 97.6% and the average processing speed is about 29 frames per second, and it can meet the on-line feature extraction requirements for incremental learning.
, doi: 10.11999/JEIT190016
[Abstract](4) [FullText HTML](2) [PDF 0KB](0)
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/JEIT190033
[Abstract](5) [FullText HTML](5) [PDF 0KB](0)
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/JEIT180926
[Abstract](18) [FullText HTML](13) [PDF 1578KB](3)
Abstract:
By using the intra-view and inter-view correlations and the motion vector-sharing, a depth map error concealment approach is proposed for 3D video coding based on the High Efficiency Video Coding (3D-HEVC) to combat the packet loss of the depth video transmission. Based on the Hierarchical B-frame Prediction (HBP) structure in 3D-HEVC and textured features of the depth map, all the lost coding units are firstly categorized into two classes, i.e., motion blocks and static blocks. Then, according to the outer boundary matching criterion combining the texture structure, the optimal motion/disparity vector is chosen for the damaged motion blocks to conduct the motion/disparity compensation based error concealment. Whereas, the direct copy is applied to conceal the damaged static blocks quickly. Finally, for the concealed blocks whose qualities are not ideal, the new motion/disparity compensation blocks reconstructing by the reference frames recombination are applied to improve the qualities of those blocks. The experimental results show that the repaired depth map concealed by the proposed approach can achieve 0.25～2.03 dB gain in term of the Peak-Signal-to-Noise Ratio (PSNR) and 0.001～0.006 gain in term of Structural Similarity Index Measure(SSIM). Moreover, the subjective visual quality of the repaired area is better in lines with the original depth maps.
, doi: 10.11999/JEIT181121
[Abstract](12) [FullText HTML](7) [PDF 2346KB](4)
Abstract:
The filtering performance of Gaussian Mixture Cardinality Balanced Multi-Target Multi-Bernoulli (GM-CBMeMBer) filter can be effected by the heavy-tailed process noise and measurement noise. To solve this problem, a new STudent’s t Mixture Cardinality Balanced Multi-Target Multi-Bernoulli (STM-CBMeMBer) filter is proposed. The process noise and measurement noise approximately obey the Student’s t distribution in the filter, where the Student’s t mixture model is used to describe approximately the posterior intensity of the multi-target. The predictive intensity and posterior intensity of Student’s t mixture form are deduced theoretically, and the closed recursive framework of cardinality balanced multi-target multi-Bernoulli filter is established. The simulation results show that, in the presence of the heavy-tailed process noise and the measurement noise, the filter can effectively suppress its interference, its tracking accuracy is superior over the traditional methods.
, doi: 10.11999/JEIT181196
[Abstract](14) [FullText HTML](5) [PDF 1008KB](2)
Abstract:
To solve the problem for the large amount of tasks, complex constraint conditions and manual which is hard to generation shifts of airport foreign airline service personnel. A shift generation model is studied and constructed for multi-task hierarchical qualification which including employees have hierarchical qualifications for tasks and shift needs to meet all kinds of labor laws and regulations and others constraints to minimize the total working time of shifts for optimum. Tabu search algorithm is designed to solve the model. Experiments, based on the actual scheduling data set of the foreign airlines service department of capital airport, verify the practicability and effectiveness of the model and the algorithm. The results show that compared to the existing manual shifts schemes, shifts obtained by using the model can fulfill all constraint conditions, shorten the total working time, reduce the number of employees and improve the utilization rate of airport resources.
, doi: 10.11999/JEIT181191
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.
, doi: 10.11999/JEIT180676
[Abstract](13) [FullText HTML](9) [PDF 912KB](2)
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/JEIT190043
[Abstract](10) [FullText HTML](8) [PDF 3318KB](3)
Abstract:
The image forgery detection algorithm based on convolutional neural network can implement the image forgery detection that does not depend on a single image attribute by using the learning ability of convolutional neural network, and make up for the defect that the previous image forgery detection algorithm relies on a single image attribute and has low applicability. Although the image forgery detection algorithm using a single network structure of deep layers and multiple neurons can learn more advanced semantic information, the result of detecting and locating forgery regions is not ideal. In this paper, an image forgery detection algorithm based on cascaded convolutional neural network is proposed. Based on the general characteristics exhibited by convolutional neural network, and then the deeper characteristics are further explored. The cascaded network structure of shallow layers and thin neurons figures out the defect of the single network structure of deep layers and multiple neurons in image forgery detection. The proposed detection algorithm in this paper consists of two parts: the cascade convolutional neural network and the adaptive filtering post-processing. The cascaded convolutional neural network realizes hierarchical forgery regions localization, and then the adaptive filtering post-processing further optimizes the detection result of the cascaded convolutional neural network. Through experimental comparison, the proposed detection algorithm shows better detection results and has higher robustness.
, doi: 10.11999/JEIT181049
[Abstract](28) [FullText HTML](19) [PDF 3562KB](3)
Abstract:
A novel wideband low RCS new super-surface array based on three reflective cell shared aperture is designed, which is composed of three kinds of Artificial Magnetic Conductor (AMC). Compared with the traditional AMC array, the new array uses one of AMC as phasor interference unit. A new phase cancellation relation is presented, the new phase cancellation relation is used to extend the traditional array phase cancellation band. Then, the parameters of the cell structure are further optimized to realize the reduction of RCS and the improvement of bandwidth. The physical sample is processed and tested. The results of simulation and field test show that: the backward reduction of RCS in the range of 5.2～13.9 GHz reaches more than 10 dB, and the relative bandwidth reaches 91%. It is shown that the new array can overcome the defect of the discontinuous operating band of the traditional array and has broadband low scattering characteristics.
, doi: 10.11999/JEIT180937
[Abstract](20) [FullText HTML](15) [PDF 1180KB](3)
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.
, doi: 10.11999/JEIT180912
[Abstract](21) [FullText HTML](11) [PDF 1783KB](3)
Abstract:
The microwave source of Non-Coherent Short Pulse (NCSP) radar transmits short pulse. Thus, as to the high velocity target, 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 imaging, the compensation coherent processing method is applied to remove 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 inverse synthetic aperture radar. However, the carrier-frequency random jitter factor of NCSP radar causes random-modulated side lobes in the Doppler dimension, which affects 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 side lobes caused by non-coherence and improve the imaging quality.
, doi: 10.11999/JEIT181076
[Abstract](21) [FullText HTML](14) [PDF 1860KB](2)
Abstract:
As an extension of Compressed Sensing(CS), Matrix Completion(MC) is widely applied to different fields. Recently, the Riemannian optimization based MC algorithm attracts a lot of attention from researchers due to its high accuracy in reconstruction and computational efficiency. Considering that the Riemannian optimization based MC algorithm assumes a fixed rank of the original matrix, and selects a random initial point for iteration, a novel algorithm is proposed, namely automatic rank estimation based Riemannian optimization matrix completion algorithm. In the proposed algorithm, the estimate of rank is obtained minimizing the objective function that involving the rank regulation, in addition, the iterative starting point is optimized based on Riemannian manifold. The Riemannian manifold based conjugate gradient method is then used to complete the matrix, thereby improving the reconstruction precision. The experimental results demonstrate that the image completion performance is significantly improved using the proposed algorithm, compared with several classical image completion methods.
, doi: 10.11999/JEIT181061
[Abstract](18) [FullText HTML](11) [PDF 2309KB](3)
Abstract:
With the development of earth remote sensing technology, SAR system is required to obtain high resolution and wide swath simultaneously, the space borne array SAR combined with Digital Beam Forming(DBF) technology provides a good solution to solve the problem. However, the phase error between channels will degrade the quality of DBF, and the traditional compensation methods suffer from large error or limited application. In this paper, a compensation method based on antenna pattern and Doppler correlation coefficient is proposed, using the antenna pattern and meanwhile utilizing the Doppler correlation coefficient. By minimizing the combined cost function, the phase error between channels are estimated. Simulation results using RADAR-SAT data validate the effectiveness of the proposed method.
, doi: 10.11999/JEIT180842
[Abstract](75) [FullText HTML](41) [PDF 1639KB](14)
Abstract:
A novel scheme termed Hybrid Power Allocation Strategy (H-PAS), which integrated with Statistical Channel State Information (S-CSI) and Instantaneous Channel State Information (I-CSI), is proposed for Non-Orthogonal Multiple Access (NOMA) based cooperative relaying systems to achieve a better performance-complexity tradeoff. Simulation results demonstrate that, with the proposed H-PAS, on the one hand, NOMA shows a distinct advantage on the sum-rate when compared with conventional orthogonal multiple access techniques in which only the knowledge of S-CSI is available; on the other hand, NOMA reduces the signaling overhead and computational complexity at the expense of marginal sum rate degradation when compared with the cases in which only the knowledge of I-CSI is available for it.
, doi: 10.11999/JEIT181088
[Abstract](21) [FullText HTML](16) [PDF 2210KB](5)
Abstract:
Facial expression is the most intuitive description of changes in psychological emotions, and different people have great differences in facial expressions. The existing facial expression recognition methods use facial statistical features to distinguish among different expressions, but these methods are short of deep exploration for facial detail information. According to the definition of facial behavior coding by psychologists, it can be seen that the local detail information of the face determines the meaning of facial expression. Therefore, a facial expression recognition method based on multi-scale detail enhancement is proposed, because facial expression is much more affected by the image details than other information, the method proposed in this paper extracts the image detail information with the Gaussian pyramid firstly, thus the image is enhanced in detail to enrich the facial expression information. Secondly, for the local characteristics of facial expressions, a local gradient feature calculation method is proposed based on hierarchical structure to describe the local shape features of facial feature points. Finally, facial expressions are classified using a Support Vector Machine (SVM). The experimental results in the CK+ expression database show that the method not only proves the important role of image detail in facial expression recognition, but also obtains very good recognition results under small-scale training data. The average recognition rate of expressions reaches 98.19%.
, doi: 10.11999/JEIT180796
[Abstract](89) [FullText HTML](42) [PDF 2365KB](16)
Abstract:
For the passive detection of underwater line-spectrum target, the information such as the azimuth, frequency and number of the line-spectrum signals is usually unknown, and the line-spectrum detection performance is affected by broadband interferences and noise. For this issue, a method of detecting the unknown line-spectrum target by space-time domain processing is proposed. Firstly, a space-time filter that autonomously matches the unknown line-spectrum signals is constructed to filter out the broadband interferences and noise. Secondly, the conventional frequency domain beamforming is performed on the filtered signals, and then a space-time two-dimensional beam output with relatively pure line-spectrum spectral peaks is obtained. The line-spectrum signals are extracted from the space-time two-dimensional beam output, and the spatial spectrum is calculated using the extracted line-spectrum information. Then, the detection of the line-spectrum target is realized. Theoretical derivation and simulation results verify that the proposed method performs the spatiotemporal filtering on the unknown line-spectrum signals in the minimum mean square error sense, and fully utilizes the line-spectrum information for the passive detection of underwater line-spectrum target. Compared with the existing line-spectrum target detection methods utilizing the line-spectrum features, the proposed method requires lower Signal to Noise Ratio (SNR), and has better detection performance under the complex multi-target multi-spectrum-line conditions.
, doi: 10.11999/JEIT180775
[Abstract](91) [FullText HTML](49) [PDF 3346KB](8)
Abstract:
In order to overcome the vulnerability of Physical Unclonable Function (PUF) to modeling attacks, a controlled PUF architecture based on sensitivity confusion mechanism is proposed. According to the Boolean function definition of PUF and Walsh spectrum theory, it is derived that each excitation bit has different sensitivity, and the position selection rules related to the parity of the confound value bit width are analyzed and summarized. This rule guides the design of the Multi-bit Wide Confusion Algorithm (MWCA) and constructs a controlled PUF architecture with high security. The basic PUF structure is evaluated as a protective object of the controlled PUF. It is found that the response generated by the controlled PUF based on the sensitivity confusion mechanism has better randomness. Logistic regression algorithm is used to model different PUF attack. The experimental results show that compared with the basic ROPUF, the arbiter PUF and the OB-PUF based on the random confusion mechanism, the controlled PUF based on the sensitivity confusion mechanism can significantly improve the PUF resistance capabilities for modeling attack.
, doi: 10.11999/JEIT180983
[Abstract](27) [FullText HTML](20) [PDF 1508KB](3)
Abstract:
, doi: 10.11999/JEIT180891
[Abstract](59) [FullText HTML](40) [PDF 1556KB](12)
Abstract:
In order to solve the problem of classification and recognition of unknown interference types under large samples, an adaptive recognition method for unknown interference based on signal feature space is proposed. Firstly, the interference signal is processed and the interference signal feature space is established with the Hilbert signal space theory. Then the projection theorem is used to approximate the unknown interference. The classification algorithm based on signal feature space with Probabilistic Neural Network (PNN) is proposed, and the processing flow of unknown interference classifier is designed. The simulation results show that compared with two kinds of traditional methods, the proposed method improves the classification accuracy of the known interference by 12.2% and 2.8% respectively. The optimal approximation effect of the unknown interference varies linearly with the power intensity in the condition, and the overall recognition rate of the designed classifier reaches 91.27% in the various types of interference satisfying the optimal approximation, and the speed of processing interference recognition is improved significantly. When the signal-to-noise ratio reaches 4 dB, the accuracy of unknown interference recognition is more than 92%.
, doi: 10.11999/JEIT181110
[Abstract](58) [FullText HTML](46) [PDF 3553KB](9)
Abstract:
To solve the problem that small moving object is difficult to be detected in video surveillance, a track-based detection algorithm is proposed. Firstly, in order to reduce missing alarm, an adaptive foreground extraction method combining regional texture features and difference probability is presented. Then, for reducing false alarm, the probability computing model of track correlation is designed to establish the correlation of suspected objects between frames, and double-threshold are set to distinguish between true and false positive. Experimental results show that compared with many classical algorithms, this algorithm can accurately detect small moving object within the quantitative range with lower missing and false alarm.
, doi: 10.11999/JEIT180520
[Abstract](53) [FullText HTML](38) [PDF 1011KB](10)
Abstract:
Appropriate warhead structure modeling is the basis for warhead parameters estimation. In this paper, the warhead is modeled by the blunt-nosed chamfered cone model, which regarding the spherical center and the chamfer scattering centers as the sliding centers and taking the influence of the side of the cone into account, the general form of the position of the scattering centers is given based on the model. Then, the micro-motion of the scattering centers in the blunt-nosed chamfered cone model is derived. Based on this, a nonlinear optimization method is proposed to estimate the target's motion parameters and structural parameters. Finally, simulation results verify the correctness of the model and the effectiveness of the parameter estimation method.
, doi: 10.11999/JEIT181091
[Abstract](41) [FullText HTML](51) [PDF 1289KB](11)
Abstract:
Range sidelobes may lead to weak targets masked by strong targets and false alarm. This paper proposes a sequential optimization method against to the sidelobe suppression of cognitive radar. First, the region to detect is divided according to range cell. Second, the transmit waveform and receive filter are optimized jointly based on the principle of minimum mean square error against one range cell. The optimized transmit and receive systems are used in Radar Cross Section (RCS) estimation for the scatter in the current range cell. The above process is carried out in each range cell in the scene sequentially. The acquired RCS estimate is used in the sidelobe suprresion for the following range cells. The RCS estimation for all the range cells in the scene is obtained in a bootstrapping way successively and updated circularly. The proposed method forms a closed loop detection system. The transmitting and receiving systems are adjusted according to the feedback scene information in real time. The sensing ability about the environment can be enhanced. The detection performance and robustness against noise can be improved. The efficiency and validity are verified by the simulation results.
, doi: 10.11999/JEIT181181
[Abstract](53) [FullText HTML](41) [PDF 1978KB](10)
Abstract:
Considering the problem of Orthogonal Frequency Division Multiplexing (OFDM) signal delay estimation with only a Single Measurement Vector (SMV) in a complex environment, a sparse reconstruction time delay estimation algorithm based on Bayesian Automatic Relevance Determination (BARD) is proposed. The Bayesian framework is used to start from the perspective of further mining useful information, and asymmetric Automatic Relevance Determination(ARD) priori is introduced to integrate into the parameter estimation process, which improves the accuracy of time delay estimation under SMV and low Signal-to-Noise Ratio (SNR) conditions. Firstly, a sparse real-domain representation model is constructed based on the estimated frequency domain response of the OFDM signal physical layer protocol data unit. Then, probability hypothesis for the noise and sparse coefficient vectors are made in the model, and Automatic Relevance Determination (ARD) prior is introduced. Finally, according to the Bayesian framework, the Expectation Maximization (EM) algorithm is used to solve the hyperparameters to estimate the delay. The simulation experiments show that the proposed algorithm has better estimation performance and is closer to the Cramér–Rao Bound (CRB). At the same time, based on the Universal Software Radio Peripheral (USRP), the effectiveness of the proposed algorithm is verified by the actual signal.
, doi: 10.11999/JEIT181165
[Abstract](73) [FullText HTML](47) [PDF 869KB](6)
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/JEIT181059
[Abstract](23) [FullText HTML](18) [PDF 3575KB](2)
Abstract:
In partial label learning, the true label of an instance is hidden in a label-set consisting of a group of candidate labels. The existing partial label learning algorithm only measures the similarity between instances based on feature vectors and lacks the utilization of the candidate labelset information. In this paper, a Candidate Label-Aware Partial Label Learning (CLAPLL) method is proposed, which combines effectively candidate label information to measure the similarity between instances during the graph construction phase. First, based on the jaccard distance and linear reconstruction, the similarity between the candidate labelsets of instances is calculated. Then, the similarity graph is constructed by combining the similarity of the instances and the label-sets, and then the existing graph-based partial label learning algorithm is presented for learning and prediction. The experimental results on 3 synthetic datasets and 6 real datasets show that disambiguation accuracy of the proposed method is 0.3～16.5% higher than baseline algorithm, and the classification accuracy is increased by 0.2～2.8%.
, doi: 10.11999/JEIT181054
[Abstract](19) [FullText HTML](10) [PDF 1014KB](3)
Abstract:
Most current transfer learning methods are modeled by utilizing the source data with the assumption that all data in the source domain are equally related to the target domain. In many practical applications, however, this assumption may induce negative learning effect when it becomes invalid. To tackle this issue, by minimizing the integrated squared error of the probability distribution of the source and target domain classification errors, the Classification-error Consensus Regularization (CCR) is proposed. Furthermore, CCR-based Adaptive knowledge Transfer Learning (CATL) method is developed to quickly determine the correlative source data and the corresponding weights. The proposed method can alleviate the negative transfer learning effect while improving the efficiency of knowledge transfer. The experimental results on the real image and text datasets validate the advantages of the CATL method.
, doi: 10.11999/JEIT190047
[Abstract](51) [FullText HTML](33) [PDF 1192KB](13)
Abstract:
Utilizing multiple data (elevation information) to assist remote sensing image segmentation is an important research topic in recent years. However, the existing methods usually directly use multivariate data as the input of the model, which fails to make full use of the multi-level features. In addition, the target size varies in remote sensing images, for some small targets, such as vehicles, houses, etc., it is difficult to achieve detailed segmentation. Considering these problems, a Multi-Feature map Pyramid fusion deep Network (MFPNet) is proposed, which utilizes optical remote sensing images and elevation data as input to extract multi-level features from images. Then the pyramid pooling structure is introduced to extract the multi-scale features from different levels. Finally, a multi-level and multi-scale feature fusion strategy is designed, which utilizes comprehensively the feature information of multivariate data to achieve detailed segmentation of remote sensing images. Experiment results on the Vaihingen dataset demonstrate the effectiveness of the proposed method.
, doi: 10.11999/JEIT190056
[Abstract](65) [FullText HTML](43) [PDF 3206KB](14)
Abstract:
An image semantic segmentation model based on region and deep residual network is proposed. Region based methods use multi-scale to create overlapping regions, which can identify multi-scale objects and obtain fine object segmentation boundary. Fully convolutional methods learn features automatically by using Convolutional Neural Network (CNN) to perform end-to-end training for pixel classification tasks, but typically produce coarse segmentation boundaries. The advantages of these two methods are combined: firstly, candidate regions are generated by region generation network, and then the image is fed through the deep residual network with dilated convolution to obtain the feature map. Then the candidate regions and the feature maps are combined to get the features of the regions, and the features are mapped to each pixel in the regions. Finally, the global average pooling layer is used to classify pixels. Multiple different models are obtained by training with different sizes of candidate region inputs. When testing, the final segmentation are obtained by fusing the classification results of these models. The experimental results on SIFT FLOW and PASCAL Context datasets show that the proposed method has higher average accuracy than some state-of-the-art algorithms.
, doi: 10.11999/JEIT181130
[Abstract](52) [FullText HTML](35) [PDF 1961KB](9)
Abstract:
Network Function Virtualization (NFV) brings flexibility and dynamics to the construction of service chain. However, the software and virtualization may cause security risks such as vulnerabilities and backdoors, which may have impact on Service Chain (SC) security. Thus, a Virtual Network Function (VNF) scheduling method is proposed. Firstly, heterogeneous images are built for every virtual network function in service chain, avoiding widespread attacks using common vulnerabilities. Then, one network function is selected dynamically and periodically. The executor of this network function is replaced by loading heterogeneous images. Finally, considering the impact of scheduling on the performance of network functions, Stackelberg game is used to model the attack and defense process, and the scheduling probability of each network function in the service chain is solved with the goal of optimizing the defender’s benefit. Experiments show that this method can reduce the rate of attacker’s success while controlling the overhead generated by the scheduling within an acceptable range.
, doi: 10.11999/JEIT181134
[Abstract](50) [FullText HTML](33) [PDF 3929KB](6)
Abstract:
To solve the problem of heat dissipation in Three Dimensional Field Programmable Gate Array Technology (3D FPGA), an interconnect channel architectural design method with low thermal gradient feature is proposed. A thermal resistance network model is established for the 3D FPGA, and theoretical studies and thermal simulation experiments are carried out on the influence of different types of channels on the thermal performance of 3D FPGA. Further, non-uniform vertical direction channel structures of 3D FPGA are proposed. Experiments indicate that 3D FPGA designed using the method proposed can reduce the maximum temperature gradient between different layers by 76.8% and the temperature gradient within the same layer by 10.4% compared with the traditional channel structure of 3D FPGA.
, doi: 10.11999/JEIT181103
[Abstract](114) [FullText HTML](71) [PDF 713KB](15)
Abstract:
Most existing searchable encryption schemes only support the search for keyword sets, and the data users can not quickly identify the file keyword information returned by the server. Meanwhile, considering the server has strong computing power, it may judge keyword information from single keywords and the identity of the data consumer is not verified. In this paper, the data user and data owner are delegated server to verify whether the data ueer is a legitimate user; if legal, the delegated server can detect the validity of the return ciphertext with data user. The data user uses the server public key, keywords and pseudo-keywords to generate trapdoor, in order to ensure the indistinguishable of the keywords, a delegated multi-keyword searchable encryption scheme is designed, which is resistant to keyword guessing of data user authentication. Meanwhile, when the data owner encrypts, the public key of the cloud server, the delegated server, and the data user can be used to prevent collusion attacks. In the random oracle model the security of the proposed scheme is proved. The experiment results show that the scheme is efficient under the multi-keyword environment.
, doi: 10.11999/JEIT181025
[Abstract](86) [FullText HTML](56) [PDF 2410KB](8)
Abstract:
Firstly, a network Dynamic Threat Attribute Attack Graph (DT-AAG) analysis model is constructed by using Attribute Attack Graph theory. On the base of the comprehensive description of system vulnerability and network service-induced threat transfer relationship, a threat transfer probability measurement algorithm is designed in combination with Common Vulerability Scoring System (CVSS) vulnerability evaluation criteria and Bayesian probability transfer method. Secondly, based on the model, a Dynamic Threat Attribute Attack Graph generation Algorithm (DT-AAG-A) is designed by using the relationship between the threat and the vulnerability as well as the service. What’s more, to solve the problem that threat transfer loop existing in the generated attribute attack graph, the loop digestion mechanism is designed. Finally, the effectiveness of the proposed model and algorithm is verified by experiments.
, doi: 10.11999/JEIT180886
[Abstract](87) [FullText HTML](63) [PDF 1303KB](17)
Abstract:
The structure of Tree-Augmented Naïve Bayes (TAN) forces each attribute node to have a class node and a attribute node as parent, which results in poor classification accuracy without considering correlation between each attribute node and the class node. In order to improve the classification accuracy of TAN, firstly, the TAN structure is proposed that allows each attribute node to have no parent or only one attribute node as parent. Then, a learning method of building the tree-like Bayesian classifier using a decomposable scoring function is proposed. Finally, the low-order Conditional Independency (CI) test is applied to eliminating the useless attribute, and then based on improved Bayesian Information Criterion (BIC) function, the classification model with acquired the parent node of each attribute node is established using the greedy algorithm. Through comprehensive experiments, the proposed classifier outperforms Naïve Bayes (NB) and TAN on multiple classification, and the results prove that this learning method has certain advantages.
, doi: 10.11999/JEIT181014
[Abstract](64) [FullText HTML](42) [PDF 1532KB](6)
Abstract:
The basis of the identification of network security situation element is to perform the feature extraction of situation data effectively. Considering the problem that the Back Propagation(BP) neural networks have excessive dependence on data labels when it has a learning of massive security situation information data, a network security situation element identification method, is proposed which combines deep stack encoder and BP algorithm. It trains the network layer by layer through unsupervised learning algorithm. On this basis the deep track encoder by stacking can be obtained. The unsupervised training of the network is realized when using the encoder to extract the characteristic of the data sets. It is verified by simulation experiments that the method can improve the performance and accuracy of situational awareness effectively.
, doi: 10.11999/JEIT180729
[Abstract](89) [FullText HTML](41) [PDF 1831KB](8)
Abstract:
LiCi algorithm is a newly lightweight block cipher. Due to its new design idea adopted by Patil et al, it has the advantages of compact design, low energy consumption and less chip area, which is especially suitable for resource-constrained environments. Currently, its security receives extensively attention, and Patil et al. claimed that the 16-round reduced LiCi can sufficiently resist both differential attack and linear attack. In this paper, a new 10-round impossible differential distinguisher is constructed based on the differential characteristics of the S-box and the meet-in-the-middle technique. Moreover, on the basis of this distinguisher, a 16-round impossible differential attack on LiCi is proposed by respectively extending 3-round forward and backward via the key scheduling scheme. This attack requires a time complexity of about 283.08 16-round encryptions, a data complexity of about 259.76 chosen plaintexts, and a memory complexity of 276.76 data blocks, which illustrates that the 16-round LiCi cipher can not resist impossible differential attack.
, doi: 10.11999/JEIT180771
[Abstract](124) [FullText HTML](65) [PDF 1984KB](12)
Abstract:
To solve the problem of lacking efficient and dynamic resource allocation schemes for 5G Network Slicing (NS) in Cloud Radio Access Network (C-RAN) scenario in the existing researches, a virtual resource allocation algorithm for NS in virtualized C-RAN is proposed. Firstly, a stochastic optimization model in virtualized C-RAN network is established based on the Constrained Markov Decision Process (CMDP) theory, which maximizes the average sum rates of all slices as its objective, and is subject to the average delay constraint for each slice as well as the average network backhaul link bandwidth consumption constraint in the meantime. Secondly, in order to overcome the issue of having difficulties in acquiring the accurate transition probabilities of the system states in the proposed CMDP optimization problem, the concept of Post-Decision State (PDS) as an " intermediate state” is introduced, which is used to describe the state of the system after the known dynamics, but before the unknown dynamics occur, and it incorporates all of the known information about the system state transition. Finally, an online learning based virtual resource allocation algorithm is presented for NS in virtualized C-RAN, where in each discrete resource scheduling slot, it will allocate appropriate Resource Blocks (RBs) and caching resource for each network slice according to the observed current system state. The simulation results reveal that the proposed algorithm can effectively satisfy the Quality of Service (QoS) demand of each individual network slice, reduce the pressure of backhaul link on bandwidth consumption and improve the system throughput.
, doi: 10.11999/JEIT181097
[Abstract](83) [FullText HTML](66) [PDF 3174KB](21)
Abstract:
In order to improve the recognition rate of banknotes, the improved banknote recognition algorithm based on Deep Convolutional Neural Network(DCNN) is proposed. Firstly, the algorithm constructs a deep convolution layer by integrating transfer learning, Leaky-Rectified Liner Unit (Leaky ReLU) function, Batch Normalization(BN) and multi-level residual unit that perform stable and fast feature extraction and learning on input different size banknotes. Secondly, a fixed-size output representation of the extracted banknote features is obtained by using the improved multi-level spatial pyramid pooling algorithm. Finally, the banknote classification is implemented by the full connection layer and the softmax layer of the network. The experimental results show that the proposed algorithm can effectively improve the recognition rate of banknotes, and has better generalization ability and robustness than the traditional banknote classification method. At the same time, the algorithm can meet the real-time requirements of the banknote sorting system.
, doi: 10.11999/JEIT181090
[Abstract](117) [FullText HTML](85) [PDF 1304KB](23)
Abstract:
The MUltiple SIgnal Classification (MUSIC) algorithm is a classical spatial spectrum estimation algorithm. Taking L-shaped array as an example, an improved 2D-MUSIC algorithm is proposed for the problem that 2D-MUSIC algorithm often fails to estimate accurately targets in close proximity among multiple targets when the signal-to-noise ratio is low.The algorithm identifies the target location through spectrum peak search by first performing conjugate recombination on the covariance matrix generated by the classical 2D-MUSIC algorithm, then calculating the mean of sum of square of the recombined one and the original one as the new matrix, whose corresponding noise subspace then weighted by applying appropriate coefficients to obtain a new noise subspace. The computer simulation results show that compared with the 2D-MUSIC algorithm, the improved algorithm performs well on DOA estimation for the targets in close proximity among multiple targets when the received signal has low signal-to-noise ratio, which improves the resolution of 2D-DOA estimation with L-shaped array, with better engineering application value.
, doi: 10.11999/JEIT180392
[Abstract](110) [FullText HTML](56) [PDF 2807KB](14)
Abstract:
The Mann-Whitney rank sum test based Wireless Local Area Network (WLAN) indoor mapping and localization approach is proposed. Firstly, according to the localization accuracy requirement, this approach performs the motion paths segmentation in target area, and meanwhile merges the similar motion path segments based on the Mann-Whitney rank sum test. Then, a signal clustering algorithm based on the similar Received Signal Strength (RSS) sequence segments is adopted to guarantee the physical adjacency of the RSS samples in the same cluster. Finally, the backbone nodes based diffusion mapping is used to construct the mapping relations between the physical and signal spaces, and the motion user localization is consequently achieved. The experimental results indicate that compared with the existing WLAN indoor mapping and localization approaches, the proposed one is able to achieve higher mapping and localization accuracy without motion sensor assistance or location fingerprint database construction.
, doi: 10.11999/TEIT180875
[Abstract](198) [FullText HTML](61) [PDF 2138KB](19)
Abstract:
Surface Acoustic Wave (SAW) resonator measuring technology can be used in high temperature and high pressure, strong electromagnetic radiation and strong electromagnetic interference to realize wireless passive parameter detection. Based on the the non-stationary characteristics of the SAW signal, a kind of echo signal frequency estimation method, Digital Frequency Significant Place Tracking method (DFSPT) is put forward. Compared with the existing methods based on Fast Fourier Transform (FFT) and Singular Value Decomposition (SVD), the simulation results show that the method can determine the number of significant digits of digital frequency according to the difference of signal-to-noise ratio. Thus, it can increase stability and accuracy. The experiment of wireless SAW temperature sensor shows that the frequency estimation standard deviation of this method is small and the robustness is high.
, doi: 10.11999/JEIT180737
[Abstract](89) [FullText HTML](45) [PDF 931KB](17)
Abstract:
, doi: 10.11999/JEIT180914
[Abstract](146) [FullText HTML](72) [PDF 3247KB](18)
Abstract:
Because of restricted earth-based tracking network, Tracking, Telemetry and Command (TT&C) for lunar orbit micro-satellite is depended on Unified S/X Band (USB) antennas in China Chang’E-4 lunar exploration. Based on analysis of the geometry between relay satellite, micro-satellite and earth-based antennas during earth-moon transfer orbit, an applicable method to acquire delay observable through Same-Beam Interferometry (SBI) tracking by China deep space network is discussed. Benefited from more kinds of tracking resources and high accuracy orbit of relay satellite, delay observable for angular position measurement of micro-satellite in the order of 1 ns is obtained, which improves the micro-satellite orbit determination accuracy from 2 km to less than 1 km and improves orbit prediction accuracy from 6 km to 2 km. SBI tracking plays an important role in short arc orbit determination of micro-satellite.
, doi: 10.11999/JEIT180898
[Abstract](40) [FullText HTML](30) [PDF 1288KB](9)
Abstract:
In order to solve the problem of member information leakage in multi-party cooperative design of integrated circuits, a orthogonal obfuscation scheme of multi-hardware IPs core security protection is proposed. Firstly, the orthogonal obfuscation matrix generates orthogonal key data, and the obfuscated key of the hardware IP core is designed with the physical feature of the Physical Unclonable Function (PUF) circuit. Then the security of multiple hardware IP cores are realized by the orthogonal obfuscation state machine. Finally, the validity of orthogonal aliasing is verified using the ISCAS-85 circuit and cryptographic algorithm. The multi-hardware IP core orthogonal obfuscation scheme is tested under Taiwan Semiconductor Manufacturing Company (TSMC) 65 nm process, the difference of Toggle flip rate between the correct key and the wrong key is less than 5%, and the area and power consumption of the larger test circuit are less than 2%. The experimental results show that orthogonal obfuscation can improve the security of multi-hardware IP cores, and can effectively defend against member information leakage and state flip rate analysis attacks.
, doi: 10.11999/JEIT180897
[Abstract](120) [FullText HTML](59) [PDF 1518KB](27)
Abstract:
Recently, the mobile charging and data collecting by using Mobile Equipment (ME) in Wireless Sensor Networks (WSNs) is a hot topic. Existing studies determine usually the traveling path of ME according to the charging requirements of sensor nodes firstly, and then handle the data collecting. In this paper, charging requirement and data collecting are taken into consideration simultaneously. A one-to-many charging and data collecting model for ME are established with two optimization objectives, maximizing the total energy utilization and minimizing the average delay of data collecting. Due to the limited energy of the ME, the path planning strategy and the equalization charging strategy are designed. An improved multi-objective ant colony algorithm is proposed to solve the problem. Experiments show that the objective values, the number of Pareto solutions, the homogeneity of Pareto solutions and the distribution of Pareto solutions obtained by the proposed algorithm are all superior to NSGA-II algorithm.
, doi: 10.11999/JEIT180922
[Abstract](108) [FullText HTML](56) [PDF 4662KB](4)
Abstract:
A broadband metasurface antenna array with wideband low Radar Cross Section (RCS) is proposed. Two kind metasurface antennas with nearly the same radiation performance are designed and fabricated in a chessboard configuration. For x-polarized incidence, the reflected energy from the two elements is dissipated based on destructive phase difference. For y-polarized incidence, the incident energy is absorbed by matching load. By this means, inherent wideband low RCS is achieved for both polarizations without redundant structures. The proposed antenna array is fabricated and measured. Simulated and measured results show that the working frequency band is 6.0～8.5 GHz. Meanwhile under X polarization the antenna monostatic RCS is reduced significantly 6 dB RCS reduction is achieved over the range of 6.2～10.5 GHz and the peak reduction is up to 21.07 dB. Under Y polarization the antenna monostatic RCS is reduced over 3 dB RCS reduction in bandwidth. Both measured and simulated results verify the proposed antenna array is characterized with wideband low-RCS without degrading the radiation performance.
, doi: 10.11999/JEIT180812
[Abstract](90) [FullText HTML](31) [PDF 3633KB](10)
Abstract:
For the problem of Direct Sequence-Code Division Multiple Access (DS-CDMA) signal in traditional asynchronous single-channel, including blind estimation of the Pseudo-Noise (PN) sequence and information sequence, a method using multi-channel synchronous and asynchronous based on PARAllel FACtor (PARAFAC) is proposed. Firstly, the signal is modeled as a multi-channel receiving model, then the observed data matrix is equivalent to a factor model. Finally, the iterative least squares algorithm is applied to decompose the parallel factor, and the information sequence and PN sequences of DS-CDMA signals are further estimated. The simulation results show that the proposed method not only can effectively estimate the PN sequence and information sequence of the short code DS-CDMA signal, but also the estimation of 6 user PN sequences can be realized under the condition of the number of channels is 10 and the Signal-to-Noise Ratio (SNR) is –10 dB.
, doi: 10.11999/JEIT180798
[Abstract](94) [FullText HTML](37) [PDF 3659KB](11)
Abstract:
In order to encode better the depth maps in 3D video, the 3D-High Efficiency Video Coding (3D-HEVC) standard is introduced in Depth Modeling Modes(DMMs), which increase the quality of original algorithm while improving the encoding complexity. The traditional architecture of DMM-1 encoder circuit has a longer coding period and can only meet real-time coding requirements of lower resolution and frame rate. In order to improve the performance of DMM-1 encoder, the structure of DMM-1 algorithm is researched and a five-stage pipeline architecture of DMM-1 encoder is proposed. The pipeline architecture can reduce the coding cycles. The architecture is implemented by Verilog HDL. Experiments show that this architecture can reduce the coding cycle by at least 52.3%, at the cost of 1568 gates compared to previous work by Sanchez G. et al. (2017).
, doi: 10.11999/JEIT181016
[Abstract](88) [FullText HTML](37) [PDF 1224KB](5)
Abstract:
The measurement accuracy for lightning direction finding by the Orthogonal Magnetic Loop Antenna (OMLA) is continuously improved, which results in the Angle Measurement Error (AME) caused by the OMLA machining error increasing. A theoretical model is established for the relationship between the machining error and AME of OMLA. With the compensation coefficient and equivalent non-orthogonal angle error, a AME correction method for OMLA is proposed. The AME of the conventional measurement way and the corrected measurement way are compared through three groups of data experimentally. The experimental results show that the AME by the corrected measurement way is significantly reduced by about 50%. Therefore, this correction method can help the OMLA with the same hardware condition to obtain higher measurement accuracy for lightning direction finding.
, doi: 10.11999/JEIT180933
[Abstract](127) [FullText HTML](53) [PDF 1032KB](13)
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/JEIT180905
[Abstract](144) [FullText HTML](56) [PDF 1679KB](21)
Abstract:
In order to defend against side-channel attacks in network slicing, the existing defense methods based on dynamic migration have the problem that the conditions for sharing of physical resources between different virtual nodes are not strict enough, a virtual node migration method is proposed for sensing side-channel risk. According to the characteristics of side-channel attacks, the entropy method is used to evaluate the side-channel risks and migrate the virtual node from a server with large deviation from average risk. The Markov decision process is used to describe the migration of virtual nodes for network slicing, and the Sarsa learning algorithm is used to solve the optimal migration scheme. The simulation results show that this method can separates malicious network slice instances from other target network slice instances to achieve the purpose of defense side channel attacks.
, doi: 10.11999/JEIT180832
[Abstract](132) [FullText HTML](53) [PDF 1439KB](8)
Abstract:
To solve the problem that polarization sensitive array of defective electromagnetic vector sensor estimate multi parameter, a two-dimensional DOA estimation algorithm based on orthogonal dipole is proposed in this paper. First, eigendecomposition of the covariance matrix is produced by the received data vectors of the polarization sensitive array. Then the signal subspace is divided into four subarrays, and the phase difference between one of the subarray and the others is obtained according to the ESPRIT algorithm. Then the phase difference between different subarrays is paired. Finally, the DOA estimation and polarization parameters of the signal are calculated according to the phase difference. The uniform linear array composed by orthogonal dipoles can not be two-dimensional DOA estimated by using the MUSIC algorithm and the traditional ESPRIT algorithm. The algorithm proposed in this paper solves this problem, and compared with the polarization MUISC algorithm greatly reduces the complexity of the algorithm. The simulation results verify the effectiveness of the proposed algorithm.
, doi: 10.11999/JEIT180971
[Abstract](84) [FullText HTML](49) [PDF 2492KB](15)
Abstract:
To solve the problems of low robustness and tracking accuracy in target tracking when interference factors occur such as target fast motion and occlusion in complex video scenes, an Adaptive Strategy Fusion Target Tracking algorithm is proposed based on multi-layer convolutional features (ASFTT). Firstly, the multi-layer convolutional features of frame images in Convolutional Neural Network(CNN) are extracted, which avoids the defect that the target information of the network is not comprehensive enough, so as to increase the generalization ability of the algorithm. Secondly, in order to improve the tracking accuracy of the algorithm, the multi-layer features are performed to calculate the correlation responses, which improves the tracking accuracy. Finally, the target position strategy in all responses are dynamically merged to locate the target through the adaptive strategy fusion algorithm in this paper. It comprehensively considers the historical strategy information and current strategy information of each responsive tracker to ensure the robustness. Experiments performed on the OTB2013 evaluation benchmark show that that the performance of the proposed algorithm are better than those of the other six state-of-the-art methods.
, doi: 10.11999/JEIT180925
[Abstract](59) [FullText HTML](34) [PDF 888KB](9)
Abstract:
Considering discrete-time chaotic dynamics systems, a new algorithm is proposed which is based on matrix eigenvalues and eigenvectors to configure Lyapunov exponents to be positive. The eigenvalues and eigenvectors of the discrete controlled matrix are calculated to design a general controller with positive Lyapunov exponents. The theory proves the boundedness of the system orbit and the finiteness of the Lyapunov exponents. The numerical simulation analysis of the linear feedback operator and the perturbation feedback operator verifies the correctness, versatility and effectiveness of the algorithm. Performance evaluations show that, compared with Chen-Lai methods, the proposed method can construct chaotic system with lower computation complexity and the running time is shorter and the outputs demonstrate strong randomness. Thus, a discrete chaotic system with no degradation and no merger is realized.
, doi: 10.11999/JEIT180904
[Abstract](155) [FullText HTML](57) [PDF 2422KB](17)
Abstract:
For the passive radar based on LTE signal, the received signal contains direct-path and multipath clutters interference of multiple co-channel base station, and the traditional passive radar signal processing flow is improved, and the processing steps of co-channel base station interference are added. A blind source separation algorithm based on convolutive mixtures is proposed. The algorithm can suppress the clutters interference of co-channel base station. It is assumed that the mixing matrix is a vector linear time-invariant filter matrix. The mutual information is used as a cost function. By finding the gradient of mutual information, it is iterated by the steepest descent method. The separation criterion is to minimize the mutual information between the separated signals. The simulation results show that the proposed algorithm can effectively suppress the clutters interference of the LTE signal co-channel base station, and provide a basis for the subsequent clutters cancellation processing of the main base station.
, doi: 10.11999/JEIT180850
[Abstract](123) [FullText HTML](66) [PDF 634KB](17)
Abstract:
The definition and security models of partial blind signcryption scheme in heterogeneous environment between CertificateLess Public Key Cryptography (CLPKC)and Traditional Public Key Infrastructure (TPKI) are propsed, and a construction by using the bilinear pairing is propsed. Under the random oracle model, based on the assumptions of Computational Diffie-Hellman Problem(CDHP) and Modifying Inverse Computational Diffie-Hellman(MICDHP), the scheme is proved to meeting the requirment of the unforgeability, confidentiality, partial blindness, and untraceability, undeniability. Finally, compared with the related scheme, the scheme increases the blindness and does not significantly increase the computational cost.
, doi: 10.11999/JEIT180793
[Abstract](135) [FullText HTML](65) [PDF 1985KB](23)
Abstract:
Ontology, as the superstructure of knowledge graph, has great significance in knowledge graph domain. In general, structural redundancy may arise in ontology evolution. Most of existing redundancy elimination algorithms focus on transitive redundancies while ignore equivalent relations. Focusing on this problem, a redundancy elimination algorithm based on super-node theory is proposed. Firstly, the nodes equivalent to each other are considered as a super-node to transfer the ontology into a directed acyclic graph. Thus the redundancies relating to transitive relations can be eliminated by existing methods. Then equivalent relations are restored, and the redundancies between equivalent and transitive relations are eliminated. Experiments on both synthetic dynamic networks and real networks indicate that the proposed algorithm can detect redundant relations precisely, with better performance and stability compared with the benchmarks.
, doi: 10.11999/JEIT180828
[Abstract](162) [FullText HTML](80) [PDF 2158KB](20)
Abstract:
In order to reconstruct natural image from Compressed Sensing(CS) measurements accurately and effectively, a CS image reconstruction algorithm based on Non-local Low Rank(NLR) and Weighted Total Variation(WTV) is proposed. The proposed algorithm considers the Non-local Self-Similarity(NSS) and local smoothness in the image and improves the traditional TV model, in which only the weights of image’s high-frequency components are set and constructed with a differential curvature edge detection operator. Besides, the optimization model of the proposed algorithm is built with constraints of the improved TV and the non-local low rank model, and a non-convex smooth function and a soft thresholding function are utilized to solve low rank and TV optimization problems respectively. By taking advantage of them, the proposed method makes full use of the property of image, and therefore conserves the details of image and is more robust and adaptable. Experimental results show that, compared with the CS reconstruction algorithm via non-local low rank, at the same sampling rate, the Peak Signal to Noise Ratio(PSNR) of the proposed method increases 2.49 dB at most and the proposed method is more robust, which proves the effectiveness of the proposed algorithm.
, doi: 10.11999/JEIT180874
[Abstract](191) [FullText HTML](96) [PDF 1132KB](18)
Abstract:
In order to solve the problem that users can not request to exit during the bitcoin confusion process, an anonymous revocation scheme for Bitcoin confusion is proposed. The commitment is used to bind the user with its destination address. When the user requests to quit the shuffle service, a zero-knowledge proof of the commitment is made using the accumulator and the signatures of knowledge. Finally, the shuffled output address of the user who quits the service is modified to its destination address. Security analysis shows that the scheme satisfies the anonymity of the user who quits the service based on the double discrete logarithm problem and the strong RSA assumption, and can be implemented without modifying the current bitcoin system. The scheme allows at most n–2 users to exit in the confusion process of n (n≥10) honest users participation.
, doi: 10.11999/JEIT180948
[Abstract](129) [FullText HTML](53) [PDF 2508KB](13)
Abstract:
Based on the analysis on the difference between vector tracking loop and scalar tracking loop on fault detection, it is pointed out that in vector receiver of Global Navigation Satellite System (GNSS), the detection statistic of Receiver Autonomous Integrity Monitoring (RAIM) algorithm is inaccurate because of the influence of noise, and the propagation of fault information in the loop makes it difficult to identify the fault source. To solve the problems, a double loop tracking structure based on pre-filter is proposed after modifying the structure of vector receiver. In the new receiver, the influence of noise is reduced by pre-filter based on cubature Kalman filtering algorithm, and the fault information is prevented from propagating to each other by switching the loop. Finally, the method is verified by simulation. Simulation results show that the improved vector receiver not only greatly reduces the mean and variance of RAIM detection statistics, but also improves the accuracy of fault identification. Thus, the performance of RAIM is significantly improved.
, doi: 10.11999/JEIT180942
[Abstract](133) [FullText HTML](65) [PDF 1249KB](10)
Abstract:
Data compression and decompression are widely used in modern communication and data transmission. However, how to decompress the damaged lossless compressed files is still a challenge. For the lossless data compression algorithm widely used in the general coding field, an effective method is proposed to repair the error and decompress and restore the corrupted LZSS files, and the theoretical basis is given. By using the residual redundancy left by the encoder to carry the check information, the method can repair the errors in LZSS compressed data without loss of any compression performance. The proposed method does not require additional bits or changes in coding rules and data formats, thus it is fully compatible with standard algorithms. That is, the data compressed by LZSS with error repair capability can still be decompressed by standard LZSS decoder. The experimental results verify the validity and practicability of the proposed algorithm.
, doi: 10.11999/JEIT180885
[Abstract](128) [FullText HTML](64) [PDF 1287KB](18)
Abstract:
A Priority Scheduling scheme based on Adaptive Random Linear Network Coding (PSARLNC) is proposed, to avoid the high computation complexity of the scheduling scheme based on Random Linear Network Coding (RLNC) and the high feedback dependence of the network performance. The characteristics of the video stream and RLNC adapted to multicast are combined in this scheme. Compared with the traditional RLNC, the computation complexity of this scheme is reduced. After the initial transmission, the transmission slots left of the data packet are comprehensively considered in the subsequent data recovery phase, and the maximum transmission node of the destination node gain is selected to maximize data transmission. At the same time, the decoding probability is available according to the different receiving situations in each relay node. According to the decoding probability value, the scheduling priority is determined, and the forwarding is completed. The transmission of each node is adaptively adjusted, and the feedback information is effectively reduced. The simulation results show that the performance of this scheme is approached to the full-feedback scheme, with the better advantages in the reducing computational complexity and the decreasing feedback dependence.
, doi: 10.11999/JEIT180836
[Abstract](130) [FullText HTML](73) [PDF 1219KB](17)
Abstract:
Large integer multiplication is the most important part in public key encryption, which often consumes most of the computing time in RSA, ElGamal, Fully Homomorphic Encryption (FHE) and other cryptosystems. Based on Schönhage-Strassen Algorithm (SSA), a design of high-speed 768 kbit multiplier is proposed in this paper. As the key component, an 64K-point Number Theoretical Transform (NTT) is optimized by adopting parallel architecture, in which only addition and shift operations are employed and thus the processing speed is improved effectively. The large integer multiplier design is validated on Stratix-V FPGA. By comparing its results with CPU using Number Theory Library(NTL) and GMP library, the correctness of this design is proved. The results also show that the FPGA implementation is about eight times faster than the same algorithm executed on the CPU.
, doi: 10.11999/JEIT180804
[Abstract](124) [FullText HTML](63) [PDF 2650KB](11)
Abstract:
Present researches on Distributed Luby’s Transmission codes(DLT) are restricted on several-sources and one-layer-relay networks, thus the Multiple Layers Distributed LT Code (MLDLT) for multiple-layers-relays networks is proposed. In MLDLT, sources are grouped and realys are layered in order that scores of sources could be connected to the only destination through the layered relays. By this scheme, the distributed communication between scores of sources and the destination could be performed. Through the and-or tree analysis, the linear procedures for the optimization of the relays' degree distributions are derived. On both lossless and lossy links, asymptotic performances of MLDLT are analized and the numberical simulations are experimented. The results demonstrate that MLDLT could achieve satisfying erasure floors on both lossless and lossy links. MLDLT is a feasible solution for the scores-sources and multiple-layers-realys networks.
, doi: 10.11999/JEIT180805
[Abstract](138) [FullText HTML](67) [PDF 836KB](20)
Abstract:
In a cloud database environment, data is usually encrypted and stored to ensure the security of cloud storage data. To encrypt the data query overhead is big, does not support the cipher text sorting, query and other shortcomings, this paper puts forward a kind of f - mOPE cryptograph database retrieval scheme. Based on mOPE sequential encryption algorithm, this scheme uses the idea of binary sort tree data structure to generate plaintext one-to-one corresponding sequential coding. Data is converted plaintext into ciphertext storage based on AES encryption scheme. The improved partial homomorphic encryption algorithm is used to improve the security of sequential encryption scheme. The security analysis and experimental results show that this scheme can not only resist statistical attack, but also reduce effectively server computing cost and improve database processing efficiency on the basis of guaranteeing data privacy.
, doi: 10.11999/JEIT180837
[Abstract](178) [FullText HTML](82) [PDF 1690KB](15)
Abstract:
Considering lacking of centralized and synergistic scheduling for time-slots and wavelength resources of the inter-TWDM-PONs, a novel Resource Allocation based on Bandwidth Prediction (RABP) strategy with software-defined centralized schedule is proposed. For intra-Optical Line Terminal (OLT), the BP neural network model based on Particle Swarm Optimization (PSO) algorithm is designed to predict the required bandwidth of each OLT in order to avoid the impact of delay between controller and OLT on real-time resource allocation. For inter-OLT, the slide cycle is dynamically set, and then the shared bandwidth of resource pool is counted in real-time according to the authorized information of optical network unit. In the process of wavelength scheduling, a wavelength scheduling mechanism with load balancing to achieve efficiently utilize of wavelength resource is designed. The simulation results show that the proposed strategy not only effectively improves the utilization of channel resources, but also reduce the average packet delay.
, doi: 10.11999/JEIT180722
[Abstract](162) [FullText HTML](70) [PDF 1394KB](13)
Abstract:
In Cloud Radio Access Network (Cloud-RAN), most of the existing work assumes Remote Radio Heads (RRHs) could not cache content. To better adapt the content-centric feature of next-generation communication networks, it is necessary to consider caching function for RRHs in Cloud-RAN. Motivated by this, this paper intends to design the suitable caching schemes and reduce the burden of fronthaul link burden through resource allocation. It is assumed the system utilizes Orthogonal Frequency Division Multiple Access (OFDMA) technique. A jointly optimization scheme of SubCarrier (SC) allocation, RRH selection, and transmission power is proposed to minimize the total downlink power consumption. To transform the original non-convex problem, a Lagrange dual decomposition is utilized to design the optimal allocation scheme. The experimental results show that the proposed algorithms can effectively improve the energy efficiency of the system, which meets the requirements of green communication in the future.
, doi: 10.11999/JEIT180818
[Abstract](159) [FullText HTML](72) [PDF 756KB](16)
Abstract:
A two-user single cell network employing Non-Orthogonal Multiple Access (NOMA) technique is studied. Accounting for time-varying channel fading and dynamic traffic arrival, a stochastic optimization issue is formulated, which aims to balance the queue delay of users and maximize the network total throughput. Based on Lyapunov optimization method, the closed-form optimal solution of the stochastic optimization issue is derived, and a low-complexity optimal delay equilibrium and power control method is proposed. The method is compared with a NOMA scheme with a non-optimal resource management method and a Time Division Multiple Access (TDMA) scheme with an optimal resource management method. Simulation results show that the proposed method can significantly improve network performance.
, doi: 10.11999/JEIT180807
[Abstract](148) [FullText HTML](64) [PDF 2141KB](8)
Abstract:
To satisfy the demand of the high precision time and frequency synchronization for engineering application, to reduce system complexity and ensure the construction of large-scale optical fiber network for time and frequency transmission, a method of high precision time and frequency integration transfer via optical fiber based on pseudo-code modulation is developed. The optical fiber time and frequency transfer system is designed and built. The unidirectional and bidirectional time and frequency transfer test via optical fiber are completed. In the unidirectional time-frequency transfer test, the influence of temperature change on the transmission delay of the system is analyzed. In the bidirectional time-frequency transfer test, the system additional time transfer jitter is 0.28 ps/s, 0.82 ps/1000 s, the additional frequency transfer instability is 4.94×10–13/s, and 6.39×10–17/40000 s. The results show that the proposed method achieves high precision time and frequency integration synchronization, and the system additional time transfer jitter is better than the current optical fiber time synchronization schemes.
, doi: 10.11999/JEIT180824
[Abstract](169) [FullText HTML](64) [PDF 500KB](9)
Abstract:
In order to comprehensively study the security of the Attribute-Based Encryption (ABE) scheme based on Learning With Errors (LWE) and test its ability to resist existing attacks, an analysis method for concrete security of ABE based on LWE is proposed. After consideration of the parameter restrictions caused by algorithms on lattices and noise expansion, this method applies the existing algorithms for solving LWE and the available program modules and it can quickly provide the specific parameters that satisfy the scheme and estimate the corresponding security level. In addition, it can output the specific parameters that satisfy the pre-given security level. Finally, four existing typical schemes are analyzed by this method. Experiments show that the parameters are too large to be applied to practical applications.
, doi: 10.11999/JEIT180944
[Abstract](181) [FullText HTML](101) [PDF 809KB](23)
Abstract:
Considering the existing attribute-based searchable encryption scheme lacks the authorization service to the cloud server, a multi-server Ciphertext Polity Attribute Base Encryption (CP-ABE) scheme with searchable is proposed based on authorization. The scheme implements search services through a cloud filter server, cloud search server and cloud storage server cooperation mechanism. The users send the authorization information to the cloud filter server at once, then the server creates the authorization information; the cloud search server creates the trapdoor information based on the trapdoor information sent by the users. Without decrypting the cipher text, the cloud filter server can detect all the cipher text. Multiple attribute authorities can be used to ensure efficient and fine-grained access under the premise of ensuring data confidentiality, solve the problem of leakage of data user keys. It can improve the data retrieval efficiency when people user the cloud server. Through security analysis, it is proved that the scheme can not steal sensitive information of data users while providing data retrieval services, can effectively prevent the leakage of data privacy.
, doi: 10.11999/JEIT180780
[Abstract](177) [FullText HTML](71) [PDF 1417KB](14)
Abstract:
Because of the problem that the target is prone to drift in complex background, a robust tracking algorithm based on spatial reliability constraint is proposed. Firstly, the pre-trained Convolutional Neural Network (CNN) model is used to extract the multi-layer deep features of the target, and the correlation filters are respectively trained on each layer to perform weighted fusion of the obtained response maps. Then, the reliability region information of the target is extracted through the high-level feature map, a binary matrix is obtained. Finally, the obtained binary matrix is used to constrain the search area of the response map, and the maximum response value in the area is the target position. In addition, in order to deal with the long-term occlusion problem, a random selection model update strategy with the first frame template information is proposed. The experimental results show that the proposed algorithm has good performance in dealing with similar background interference, occlusion, and other scenes.
, doi: 10.11999/JEIT180777
[Abstract](192) [FullText HTML](88) [PDF 1513KB](18)
Abstract:
As machine learning is widely applied to various domains, its security vulnerability is also highlighted. A PSO (Particle Swarm Optimization) based on adversarial example generation algorithm is proposed to reveal the potential security risks of Support Vector Machine (SVM). The adversarial examples, generated by slightly crafting the legitimate samples, can mislead SVM classifier to give wrong classification results. Using the linear separable property of SVM in high-dimensional feature space, PSO is used to find the salient features, and then the average method is used to map back to the original input space to construct the adversarial example. This method makes full use of the easily finding salient features of linear models in the feature space, and the interpretable advantages of the original input space. Experimental results show that the proposed method can fool SVM classifier by using the adversarial example generated by less than 7 % small perturbation, thus proving that SVM has obvious security vulnerability.
, doi: 10.11999/JEIT180851
[Abstract](130) [FullText HTML](68) [PDF 3290KB](16)
Abstract:
Benefit from the rapid development of digital frequency storage technology, the Intermittent Sampling Repeater Jamming(ISRJ) is widely used. Existing radar anti-jamming means are hard to against this jamming effectively. Based on the analysis of the principles of the ISRJ, for the discontinuities of ISRJ in time domain, an anti ISRJ method based on LFM segmented pulse compression is proposed. This method utilizes the orthogonality between LFM segmented signals, combines with cover waveform concept, distinguishes jamming and target through narrow band filter group, then suppresses jamming, finally accumulates signals in intra-pulse and inter-pulse. Theoretical analysis and experimental results show the anti ISRJ method can effectively resist the intermittent sampling interference which combine with different styles of multiple jammers.
, doi: 10.11999/JEIT180747
[Abstract](157) [FullText HTML](66) [PDF 821KB](17)
Abstract:
In view of the imaging quality of sparse ISAR imaging methods is limited by the inaccurate sparse representation of the scene to be imaged, the Dictionary Learning (DL) technique is introduced into ISAR sparse imaging to get better sparse representation of the scene. An off-line DL based imaging method and an on-line DL based imaging method are proposed. The off-line DL imaging method can obtain a better sparse representation via a dictionary learned from the available ISAR images. The on-line DL imaging method can obtain the sparse representation from the data currently considered by jointly optimizing the imaging and DL processes. The results of both simulated and real ISAR data show that the on-line DL imaging method and the off-line dictionary imaging method are both able to better sparsely represent the target scene leading to better imaging results. The off-line DL based imaging method works better than the on-line DL based imaging method with respect to both imaging quality and computational efficiency.