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

Display Method:

, doi: 10.11999/JEIT180595
[Abstract](15) [FullText HTML](6) [PDF 1387KB](3)
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 maximize data utility can only be established on the basis of K-anonymized data. Data owners participates in the protocol flow in a distributed and collaborative manner to achieve the transmission of SA after the Quasi-Identifier (QI) is anonymized. This reduces the probability that the data collector uses the QI to associate SA values and weakens the risk of privacy leakage caused by internal identity disclosure. It divides the coded value of the SA into two shares of a random anchor point and a compensation distance through the tree coding structure and the members of the equivalent class formed by K-anonymity elect two data leaders to aggregate and forward the two shares respectively, which releases the association between unique network identification and SA values and prevents leakage of privacy caused by external identification effectively. Formal rules are established that meet the characteristics of the protocol and analyze the protocol to prove that the protocol meets privacy protection requirements.
, doi: 10.11999/JEIT180775
[Abstract](24) [FullText HTML](9) [PDF 3323KB](3)
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/JEIT180931
[Abstract](19) [FullText HTML](7) [PDF 1589KB](4)
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/JEIT180796
[Abstract](15) [FullText HTML](7) [PDF 2423KB](3)
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/JEIT180874
[Abstract](93) [FullText HTML](47) [PDF 960KB](7)
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](60) [FullText HTML](31) [PDF 2508KB](6)
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](62) [FullText HTML](37) [PDF 1249KB](4)
Abstract:
Data compression and decompression is 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/JEIT180798
[Abstract](18) [FullText HTML](9) [PDF 3659KB](3)
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 Samcjez G. et al. (2017).
, doi: 10.11999/JEIT180885
[Abstract](59) [FullText HTML](30) [PDF 1287KB](4)
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/JEIT180812
[Abstract](21) [FullText HTML](7) [PDF 3633KB](3)
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/JEIT180571
[Abstract](151) [FullText HTML](69) [PDF 459KB](12)
Abstract:
In 2018, Wang Daxing and Teng Jikai proposed a certificateless aggregate signature scheme for vehicular ad-hoc networks, and proved that their scheme was existentially unforgeable in the random oracle model. To analyze the security of this scheme, three types of forgery attacks are given: " honest-but-curious” KGC attacks, malicious KGC and RSU coalition attacks, and internal signers’ coalition attacks. The analysis results show that the certificateless aggregate signature scheme designed by Wang Daxing and Teng Jikai is insecure against these three types of attacks. To resist these attacks, an improved certificateless aggregate signature scheme is further proposed. The new scheme not only satisfies existential unforgeability under adaptive chosen-message attacks, but also resists effectively coalition attacks.
, doi: 10.11999/JEIT180900
[Abstract](23) [FullText HTML](12) [PDF 1591KB](3)
Abstract:
ElectroEncephaloGram (EEG) is regarded as a " gold standard” of fatigue detection and drivers’ vigilance states can be detected through the analysis of EEG signals. However, due to the characteristics of non-linear, non-stationary and low spatial resolution of EEG signals, traditional machine learning methods still have the disadvantages of low recognition rate and complicated feature extraction operations in EEG-based fatigue detection task. To tackle this problem, a fatigue detection method with transfer learning based on the Electrode-Frequency Distribution Maps (EFDMs) of EEG signals is proposed. A deep convolutional neural network is designed and pre-trained with SEED dataset, and then it is used for fatigue detection with transfer learning strategy. Experimental results show that the proposed convolutional neural network can automatically obtain vigilance related features from EFDMs, and achieve much better recognition results than traditional machine learning methods. Moreover, based on the transfer learning strategy, this model can also be used for other recognition tasks, which is helpful for promoting the application of EEG signals to the driver fatigue detection system.
, doi: 10.11999/JELT180894
[Abstract](35) [FullText HTML](14) [PDF 1905KB](3)
Abstract:
In order to solve the unreasonable virtual resource allocation caused by the dynamic change of service request and delay of information feedback in wireless virtualized network, a traffic-aware algorithm which exploits historical service function chaining (SFC) queue information to predict future load state based on long short-term memory (LSTM) network is proposed. With the prediction results, the virtual network function (VNF) deployment and the corresponding computing resource allocation problems are studied, and a VNFs’ deployment method based on adaptive ant colony algorithm is developed. On the premise of satisfying the minimum resource demand for future queue non-overflow, the on-demand allocation method is used to maximize the computing resource utilization. Simulation results show that the prediction model based on LSTM neural network in this paper obtains good prediction results and realizes online monitoring of the network. The adaptive ant colony algorithm based VNF deployment method reduces effectively the bit loss rate and the average end-to-end delay caused by overall VNFs’ scheduling at the same time.
, doi: 10.11999/JEIT180899
[Abstract](23) [FullText HTML](13) [PDF 960KB](3)
Abstract:
Automatic Target Recognition(ATR) is an important research area in the field of radar information processing. Because the deep Convolution Neural Network(CNN) does not need to carry out feature engineering and the performance of image classification is superior, it attracts more and more attention in the field of radar automatic target recognition. The application of CNN to radar image processing is reviewed in this paper. Firstly, the related knowledges including the characteristics of the radar image is introduced, and the limitations of traditional radar automatic target recognition methods are pointed out. The principle, composition and development of CNN the field of computer vision are introduced. Then, the research status of CNN in radar automatic target recognition is provided. The detection and recognition method of SAR image are presented in detail. Then the challenge of radar automatic target recognition is analyzed. Finally, the new theory and model of convolution neural network, the new imaging technology of radar and the application to complex environments in the future are prospected.
, doi: 10.11999/JEIT180916
[Abstract](17) [FullText HTML](7) [PDF 1207KB](1)
Abstract:
A method for visualizing the weights of a reconstructed model is proposed to analyze how a deep convolutional network works. Firstly, a specific input is used in the original neural network during the forward propagation to get the prior information for model reconstruction. Then some of the structure of the original network is changed for further parameter calculation. After that, the parameters of the reconstructed model are calculated with a group of orthogonal vectors. Finally, the parameters are put into a special order to make them visualized. Experimental results show that the model reconstructed with the method proposed is totally equivalent to the original model during the forward propagation in the classification process. The feature of the weights of the reconstructed model can be observed clearly and the principle of the neural network can be analyzed with the feature.
, doi: 10.11999/JEIT180837
[Abstract](60) [FullText HTML](29) [PDF 1690KB](3)
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/JEIT180818
[Abstract](81) [FullText HTML](36) [PDF 756KB](4)
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/JEIT180722
[Abstract](102) [FullText HTML](49) [PDF 1394KB](7)
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/JEIT180735
[Abstract](80) [FullText HTML](39) [PDF 1232KB](11)
Abstract:
A sufficient condition for general quadratic polynomial systems to be topologically conjugate with Tent map is proposed. Base on this condition, the probability density function of a class of quadratic polynomial systems is provided and transformations function which can homogenize this class of chaotic systems is further obtained. The performances of both the original system and the homogenized system are evaluated. Numerical simulations show that the information entropy of the uniformly distributed sequences is closer to the theoretical limit and its discrete entropy remains unchanged. In conclusion, with such homogenization method all the chaotic characteristics of the original system is inherited and better uniformity is performed.
, doi: 10.11999/JEIT180695
[Abstract](155) [FullText HTML](96) [PDF 1212KB](27)
Abstract:
Velocity estimation of moving targets is a key part of ground moving target imaging and positioning in airborne single-antenna high-resolution SAR system. In order to solute the defects of traditional algorithms, such as high computation brought by searching and interpolation and low reliability caused by range cell migration, a novel method based on least square fitting of echo sequence is proposed. Range changes between adjacent echo sequences are extracted using envelope correlation, and coefficients of range change equation are obtained by least square linear fitting, from which radial velocity and along-track velocity can be derived. Compared with traditional algorithms, the new method has less computation and it can work without RCMC. In this paper, the mathematical model is presented and the principle of parameter selection is provided, and then accuracy, computation and applicable conditions of the algorithm are analyzed. The effectiveness of the proposed algorithm is validated by simulation and real data.
, doi: 10.11999/JEIT180592
[Abstract](130) [FullText HTML](69) [PDF 702KB](17)
Abstract:
Mobile Edge Computing (MEC) improves the quality of users experience by providing users with computing capabilities at the edge of the wireless network. However, computing offloading in MEC still faces some problems. In this paper, a joint optimization problem of offloading decision and resource allocation is proposed for the computation offloading problem in Ultra-Dense Networks (UDN) with MEC. To solve this problem, firstly, the coordinate descent method is used to formulate the optimization scheme for the offloading decision. Meanwhile, the improved Hungarian algorithm and greedy algorithm are used to allocate the channels to meet the user’s delay requirements. Finally, the problem of minimizing energy consumption is converted into a problem of minimizing power. Then it is converted into a convex optimization problem to get the user’s optimal transmission power. Simulation results show that the proposed scheme can minimize the energy consumption of the system while satisfying the users’ different delay requirements, and improve effectively the performance of the system.
, doi: 10.11999/JEIT180850
[Abstract](69) [FullText HTML](39) [PDF 634KB](10)
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](67) [FullText HTML](36) [PDF 1985KB](10)
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](88) [FullText HTML](47) [PDF 2158KB](5)
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/JEIT180804
[Abstract](67) [FullText HTML](34) [PDF 2650KB](6)
Abstract:
Present researches on Distributed Luby’s Transmission codes (DLT) codes 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 MDLT, 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/JEIT180807
[Abstract](78) [FullText HTML](36) [PDF 2130KB](5)
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/JEIT180777
[Abstract](106) [FullText HTML](50) [PDF 1513KB](13)
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/JEIT180747
[Abstract](72) [FullText HTML](39) [PDF 821KB](8)
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.
, doi: 10.11999/JEIT180766
[Abstract](129) [FullText HTML](62) [PDF 1577KB](18)
Abstract:
A joint real-valued beamspace-based method for angle estimation in bistatic Multiple-Input Multiple-Output (MIMO) radar is proposed. Instead of using the traditional Discrete Fourier Transform (DFT) beamspace filter, the proposed beamspace filter is designed through convex optimization, which can flexibly control the bandwidth and limit the sidelobe level. Based on this property, the mainlobe-to-sidelobe ratio of the proposed beamspace filter can be greatly improved, which results in the improved estimation performance. More importantly, the structure of the proposed beamspace matrix can be properly designed, which is indispensable in constructing the real-valued signal model. Finally, the mapping relationship to compensate the interpolation error is established. Simulation results verify the effectiveness of the proposed method.
, doi: 10.11999/JEIT180758
[Abstract](86) [FullText HTML](33) [PDF 2258KB](4)
Abstract:
This paper presents a method of low-altitude wind-shear speed estimation based on Generalized adjacent Multi-Beam (GMB) Adaptive Processing under aircraft yawing. The clutter range-dependence compensation method based on echo data is first used to correct the range dependence of clutter for estimating the clutter covariance matrix. Then the dimension-reduced transform matrix is calculated by combining adjacent multiple beams in the airspace and adjacent multiple Doppler channels in time domain simultaneously, and the radar echo data of the measured range bin is reduced in dimension, and then the optimal weight vector of the GMB adaptive processor is constructed to filter adaptively the dimension-reduced data. Finally, the accurate estimation of the wind speed under the aircraft yawing is got. The simulation results show that the proposed method can obtain an effective estimation of wind speed under aircraft yawing.
, doi: 10.11999/JEIT180842
[Abstract](17) [FullText HTML](6) [PDF 1071KB](4)
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/JEIT180771
[Abstract](24) [FullText HTML](10) [PDF 1634KB](2)
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/JEIT180491
[Abstract](191) [FullText HTML](87) [PDF 1929KB](17)
Abstract:
Focusing on the reconnaissance mission of Unmanned Aerial Vehicle (UAV) swarm under complex battlefield environment, the non-uniform energy consumption during the information transmission between UAVs affects the efficient implementation of the reconnaissance mission, thus a cluster-based algorithm of reconnaissance UAV swarm based on wireless ultraviolet secret communication is proposed. Combined the advantages of wireless ultraviolet scattering communication, this algorithm uses cluster topology management mechanism to balance the energy consumption of UAV swarm. Simulation results show that the algorithm can effectively balance the network energy consumption and improve the transmission efficiency of the network when compared with the existing algorithm, and the lifetime of swarm can be extended when selecting the appropriate packet length and node density.
, doi: 10.11999/JEIT180548
[Abstract](218) [FullText HTML](103) [PDF 1058KB](15)
Abstract:
FIR notch filter has many advantages such as linear phase, high precision and good stability. However, when the notch performance is required to be high, a higher order is usually required, resulting in increased greatly hardware complexity of the FIR notch filter. Based on sparse FIR filter design algorithm and common subexpression elimination, a novel algorithm is proposed for the design of low complexity sparse FIR notch filter. First, a sparse FIR notch benchmark filter that fulfills frequency response specifications is obtained from the sparse filter design algorithm. Then, each quantized filter coefficient is represented in Canonical Signed Digit (CSD). The sensitivities of all weight-two subexpressions and isolated nonzero digits of the quantized coefficient set are analyzed. Finally, the filter coefficient set with lower implementation cost is constructed by iteratively admitting subexpressions and isolated nonzero digits according to their sensitivities. Simulation results show that the proposed algorithm can save about 51% of adder compared with other low complexity filter design algorithms, which reduces effectively the implementation complexity and saves greatly the hardware cost.
, doi: 10.11999/JEIT180129
[Abstract](102) [FullText HTML](45) [PDF 380KB](11)
Abstract:
The misuse of signcryption ciphertext means that the malicious recipient uses the received signcryption ciphertext to forge a new ciphertext that has a different recipient. It is found that the Existential UnForgeability against adaptive Chosen Message Attack (EUF-CMA) model can not simulate misuse attacks on signcryption schemes, and many of the existing signcryption schemes, claimed provable secure, can not resist the misuse attack. By enhancing the capabilities of adversaries in the EUF-CMA model, an extended EUF-CMA model is defined which captures the security associated with the resistance to misuse attacks on signcryption schemes. This paper describes the misuse attack instances in several newly proposed heterogeneous signcryption schemes, analyzes the reasons for the attacks and proposes improvement approaches. Finally, using the enhanced EUF-CMA model, the unforgeability of an improved heterogeneous signcryption scheme is analyzed, and the procedure of simulating the misuse attack is demonstrated. The results indicate that the enhanced EUF-CMA model and the improvement approaches for signcryption schemes are reasonable and effective.
, doi: 10.11999/JEIT180521
[Abstract](221) [FullText HTML](104) [PDF 4119KB](19)
Abstract:
Due to the selection of dominant scatterers is easy to be affected by noise, a novel Inverse Synthetic Aperture Radar (ISAR) cross-range scaling algorithm based on image contrast maximization is proposed, which can realize the cross-range scaling while achieving the range spatial-variant phase autofocus. With the image contrast as cost function, the cross-range chirp rate of received signal can be estimated accurately using Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Based on the estimated results, the cross-range scaling of ISAR image and precise phase autofocus can be implemented. Both simulated and real data experiments confirm the effectiveness and robustness of the proposed algorithm.
, doi: 10.11999/JEIT180519
[Abstract](233) [FullText HTML](111) [PDF 1197KB](22)
Abstract:
The Grey Wolf Optimizer (GWO) algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature, and it is an algorithm with high level of exploration and exploitation capability. This algorithm has good performance in searching for the global optimum, but it suffers from unbalance between exploitation and exploration. An improved Chaos Grey Wolf Optimizer called CGWO is proposed, for solving complex classification problem. In the proposed algorithm, Cubic chaos theory is used to modify the position equation of GWO, which strengthens the diversity of individuals in the iterative search process. A novel nonlinear convergence factor is designed to replace the linear convergence factor of GWO, so that it can coordinate the balance of exploration and exploitation in the CGWO algorithm. The CGWO algorithm is used as the trainer of the Multi-Layer Perceptrons (MLPs), and 3 complex classification problems are classified. The statistical results prove the CGWO algorithm is able to provide very competitive results in terms of avoiding local minima, solution precision, converging speed and robustness.
, doi: 10.11999/JEIT180466
[Abstract](239) [FullText HTML](111) [PDF 2165KB](20)
Abstract:
With the rapid growth of Data Center Network (DCN) traffic, how to improve the performance and service quality of data center network become a research hotspot. However, when the network load increases, the existing traffic scheduling algorithm on the one hand may cause bandwidth fragmentation results in the network throughput decrease, on the other hand, it neglects the traffic application requirements to lead to poor QoS. Therefore, a dynamic traffic scheduling algorithm for bandwidth fragmentation minimization and QoS guarantee is proposed. The algorithm takes into account the different requirements of the bandwidth-sensitive large flows, and delay sensitive and packet-loss sensitive small flows. Firstly, the shortest path set is established according to the source address and destination address of the to-be-scheduled flow. Secondly, all the paths that satisfy the bandwidth requirement of the to-be-scheduled flow are selected. Then, the weight function is established for each path according to the free bandwidth of the path and the application requirements of the small flow. Finally, the forwarding path is selected based on the weight function value by roulette algorithm. The network simulation results show that when the network load increases, the proposed algorithm reduces the packet loss rate and delay of small flows, and improves the network throughput compared with other algorithms.
, doi: 10.11999/JEIT180485
[Abstract](296) [FullText HTML](170) [PDF 1983KB](36)
Abstract:
A multi-parameter convolutional neural network method is proposed for gesture recognition based on Frequency Modulated Continuous Wave (FMCW) radar. A multidimensional parameter dataset is constructed for gestures by performing time-frequency analysis of the radar signal to estimate the distance, Doppler and angle parameters of the gesture target. To realize feature extraction and classification accurately, an end-to-end structured Range-Doppler-Angle of Time (RDA-T) multi-dimensional parameter convolutional neural network scheme is further proposed using multi-branch network structure and high-dimensional feature fusion. The experimental results reveal that using the combined gestures information of distance, Doppler and angle for multi-parameter learning, the proposed scheme resolves the problem of low information quantity of single-dimensional gesture recognition methods, and its accuracy outperforms the single-dimensional methods in terms of gesture recognition by 5%～8%.
, doi: 10.11999/JEIT180713
[Abstract](186) [FullText HTML](88) [PDF 1391KB](20)
Abstract:
The millimeter-wave hybrid beamforming becomes a widely accepted beamforming method in millimeter-wave systems. However there is almost no hybrid beamforming algorithm based on security. Especially when the eavesdropper has multi-user decoding capability, the system security performance can not be guaranteed. To solve this problem, a security hybrid beamforming algorithm is proposed for millimeter wave downlink multiuser system based on artificial noise. First, the analog part and the digital part of the hybrid beamforming matrix are decoupled. Based on the channel characteristics, the analog and digital beamforming matrices of useful signals are designed by maximizing the user’s received signal energy and Zero-Forcing (ZF). Then, the artificial noise baseband digital precoding matrix is designed by Singular Value Decomposition (SVD), and the artificial noise is placed in null space of the legal users and worsening eavesdropping channel. Simulation results show that the artificial noise-assisted secure hybrid beamforming algorithm solves effectively the security problem of the system when there are multi-user decoding ability eavesdroppers.
, doi: 10.11999/JEIT180324
[Abstract](53) [FullText HTML](20) [PDF 2038KB](0)
Abstract:
To solve the long decoding latency caused by the serial nature of the decoding of polar codes, a pre-decoding based maximum-likelihood simplified successive-cancellation decoding algorithm is proposed. First, the signs of the likelihood values stored in the decoding tree nodes are extracted and grouped to obtain symbol vectors. Then comparing the symbol vectors and the values of some information bits, the distribution rules are found that positive and negative values stored in the vectors are one to one corresponding to the value of middle information bits of the node. Based on the above analysis, one or two bits in the middle of the constituent code are pre-decoded. Finally, the maximum likelihood decoding method is used to estimate the remaining information bits in the constituent code, and the final decoding results are obtained. Simulation results show that the proposed algorithm can effectively reduce the decoding delay compared with the existing algorithms without affecting the error performance.
, doi: 10.11999/JEIT180643
[Abstract](157) [FullText HTML](73) [PDF 6998KB](8)
Abstract:
A new prompt 2-D attitude steering approach for zero Doppler centroid of GEOsynchronous SAR (GEOSAR) is proposed. Large yaw angle of GEOSAR in traditional 2-D yaw steering condition can be solved by this method. It is suited to the large satellite such as GEOSAR. The GEOSAR can achieve broadside imaging when this method is applied. Compared to the traditional attitude steering approach, the steering angle and time are just 1/10 of it, and the developing difficulty of GEOSAR becomes lower through this new method. This approach is propitious to GEOSAR. When it is employed to SAR satellites with different altitudes, the residual Doppler centroid is accurate zero in all the conditions. Besides, an attitude selection reference standard is illustrated for different altitude orbital satellites.
, doi: 10.11999/JEIT180480
[Abstract](345) [FullText HTML](109) [PDF 1917KB](22)
Abstract:
For the problem of the finite word length effect of prototype filters in hardware implementation of the filter bank system, this paper studies how to improve the performance of roundoff noise caused by signal quantization for the FIR prototype filter, that is, to reduce the roundoff noise gain. An FIR filter optimization structure is proposed. By analyzing the source of roundoff noise, a polynomial parameterization method is used to derive the roundoff noise gain expression. The simulation example shows that the amplitude-frequency and phase-frequency response of the proposed structure filter are basically consistent with the ideal state under different constraint of word length. Compared with the existing algorithms, the proposed structure has a smaller roundoff noise gain.
, doi: 10.11999/JEIT180419
[Abstract](227) [FullText HTML](93) [PDF 1263KB](14)
Abstract:
Request acceptance rate and energy saving are the two most important indicators in the virtual network mapping process. However, the current virtual network embedding problem considers only a single index, ignoring the correlation and constraints between the two, resulting in a decrease in the overall performance of the virtual network embedding. This paper proposes a Multi-Objective Virtual Network Embedding algorithm based on Nash Bargaining (MOVNE-NB). Firstly negotiating the virtual network embedding problem in the framework of Nash bargaining by using game theory technology. Then a fair bargaining mechanism is put forward to avoid selfish decisions by players and lead to bargaining failures. Experiments show that the MOVNE-NB algorithm can not only produce a Pareto efficient solution, but also achieve a fair tradeoff between request acceptance rate and energy saving.
, doi: 10.11999/JEIT180505
[Abstract](200) [FullText HTML](69) [PDF 2650KB](4)
Abstract:
For the requirement of broadband interference suppression for passive sonar, a robust broadband interference suppression algorithm using few snapshots is proposed. Based on the estimated bearing of the broadband interference, the algorithm obtains the steered cross-spectral density matrix through multi-frequency data in the bandwidth and estimates the signal subspace, then uses the projection approach to correct the unit vector, and estimates the steering vector of interference through inversely transforming. Repeating above steps can obtain the interference steering vector set, thereby constructing the suppression matrix. The interference component of array data is eliminated by suppression matrix processing, and the final spatial spectrum can be obtained after spatial processing. The theoretical analysis, simulation and processing of sea trial data show that the proposed algorithm uses few, even single frequency domain snapshots processing, and still has good performance in environments where target motion, conditions rapid change and other conditions that time integration is unsuitable, at the same time, algorithm is robust for mismatches faced by space processing.
, doi: 10.11999/JEIT180474
[Abstract](199) [FullText HTML](119) [PDF 947KB](21)
Abstract:
The existing virtual network reconfiguration algorithms do not consider the fragment resources generated in the physical network, which results in the improvement of the performance of the online virtual network embedding algorithms is not obvious. To solve this problem, a definition of network resource fragmentation is given, and a Fragment-Aware Secure Virtual Network Reconfiguration (FA-SVNR) algorithm is proposed. In the process of reconfiguration, the virtual node set to be migrated is selected by considering the fragmentation of nodes in the physical network periodically, and the best virtual node migration scheme is selected by considering the reduction of the fragmentation of the physical network and the reduction of the embedding cost of the virtual network. Simulation results show that the proposed algorithm has the higher acceptance ratio and revenue to cost ratio compared with the existing virtual network reconfiguration algorithm, especially in the metric of revenue to cost ratio.
, doi: 10.11999/JEIT180463
[Abstract](235) [FullText HTML](84) [PDF 1387KB](8)
Abstract:
To establish effective backhaul connection in multi-tiers Heterogeneous Network (HetNet), by exploiting advanced Non-Orthogonal Multiple Access (NOMA) a novel in-band wireless backhaul scheme is proposed at full-duplex Small cell Base Stations (SBSs). Firstly, a K+1 HetNet is investigated, where the first tier consists of Macro Base Stations (MBSs) that are equipped with massive MIMO antennas and the remainder K tiers consist of the different types of single-antenna SBSs. The base stations of the whole network operate in full-duplex mode. Specially, the downlink transmission of MBSs is considered. Hence, at each SBS the backhaul signal is superposed over the downlink signal. Then, by using the method from stochastic geometry and modeling all network’s elements as independent homogeneous Poisson Point Processes (PPPs) in this HetNet model, the coverage probabilities of up access link and backhaul link of SBSs are investigated as well as the throughput of small cells. Finally, the presented simulations and numerical results show that the coverage probability of small cell backhaul is changing monotonously with the power sharing efficient, but the monotony is not held for the power of mobile users. Compared with the systems without NOMA, it is found that with reasonable power allocation factor, the NOMA-deployed ones achieve the evident throughput gain.
, doi: 10.11999/JEIT180477
[Abstract](225) [FullText HTML](71) [PDF 1757KB](7)
Abstract:
Considering the low recognition accuracy of behavior recognition from different perspectives at present, this paper presents a perspective-independent method for depth videos. Firstly, the fully connected layer of depth Convolution Neural Network (CNN) is creatively used to map human posture in different perspectives to high-dimensional space that is independent with perspective to achieve the Human Posture Modeling (HPM) of deep-performance video in spatial domain. Secondly, considering temporal-spatial correlation between video sequence frames, the Rank Pooling (RP) function is applied to the series of each neuron activated time to encode the video time sub-sequence, and then the Fourier Time Pyramid (FTP) is used to each pooled time series to produce the final spatio-temporal feature representation. Finally, different methods of behavior recognition classification are tested on several datasets. Experimental results show that the proposed method improves the accuracy of depth video recognition in different perspectives. In the UWA3DII datasets, the proposed method is 18% higher than the most recent method. The proposed method (HPM+RP+FTP) has a good generalization performance, achieving a 82.5% accuracy on dataset of MSR Daily Activity3D.
, doi: 10.11999/JEIT180406
[Abstract](211) [FullText HTML](70) [PDF 1679KB](15)
Abstract:
Trust based access control is a research hotspot in open network that access control is one of the importation technology of information security. For the interactive access behaviors of non-honest cooperation between network interactive entities in open network, the dynamic game access control model is established based on trust, and interactive entities are encouraged to rationally choose strategies expected by the system (the designer) driven by its own benefits through the designed mechanism. Taking benefits as the driven force, the mechanism rewards the honest nodes and punishes and restrains the non-honest nodes, and then reaches the general state of equalization between entities which meets the goal. The simulation experiment and result analysis show that the incentive and restraint mechanism is valid and necessary on the issue of non-honest access between network interactive entities.
, doi: 10.11999/JEIT180557
[Abstract](206) [FullText HTML](76) [PDF 1483KB](19)
Abstract:
Focusing on the clutter suppression problem of the airborne Multiple Input Multiple Output (MIMO) radar, an improved method based on Knowledge-Aided Space-Time Adaptive signal Processing (KA-STAP) algorithm is proposed. The clutter subspace is constructed offline according to the prior distribution of the clutter in the space-time plane, to replace that of estimation based on the Prolate Spheroidal Wave Function (PSWF), so that complex operations are avoided. Simulation results show that the proposed approach can not only reduce the computational complexity, but can obtain deeper notch and better side-lobe performance.
, doi: 10.11999/JEIT180529
[Abstract](231) [FullText HTML](90) [PDF 3260KB](14)
Abstract:
For the problem of hydrometeor classification in the presence of ground clutter, traditional methods produce large classification errors under different weather and environmental conditions. A new method for the classification of Hydrometeor based on Fuzzy Neural Network-Fuzzy C-Means (FNN-FCM) is proposed. Firstly, the FNN is trained by the clutter data received by the Dual-polarization weather radar in the clear sky mode. The parameters of the membership function of each polarization parameter of the clutter are calculated adaptively. Then the ground clutter in the rainfall mode is suppressed by the ground clutter membership function obtained by the training. Finally, FCM clustering algorithm is used to classify the Hydrometeor after clutter suppression. The processing results of the measured data show that the proposed method can effectively suppress ground clutter and obtain finer hydrometeor classification results.
, doi: 10.11999/JEIT180525
[Abstract](257) [FullText HTML](129) [PDF 7187KB](35)
Abstract:
The traditional methods based on CFAR and Kernel Density Estimation (KDE) for SAR ship candidate region extraction has the following defects: The choice of false alarm rate of CFAR depends on artificial experience; CFAR only models the sea clutter distribution, which poses a certain risk of missing detection to the target; When KDE is used to filter strong sea clutter, the threshold must be selected by artificial experience. These defects make the traditional method unable to adapt to complex scene, such as multi-satellite and multi-resolution. A candidate region extraction method for multi-satellite and multi-resolution SAR ships is proposed. In view of the defects of CFAR, an iterative method of mean dichotomy is proposed to approximate the target and calculate the segmentation threshold. The calculation efficiency of this method is more than 10 times higher than that of CFAR while overcoming the defects of CFAR; In view of the defects of KDE, block KDE combined with large threshold is used to filter strong sea clutter, and then seed point growth algorithm is used to reconstruct target. Because the large threshold has enough thresholds, the method can adapt to more complex scenarios. Experiments show that the proposed method has the advantages of no missed detection, self-adaptive threshold, high computational efficiency, and low false alarm rate. It has excellent multi-satellite and multi-resolution SAR ship candidate region extraction capability.
, doi: 10.11999/JEIT180444
[Abstract](345) [FullText HTML](107) [PDF 2107KB](14)
Abstract:
For the problems of the Composite Binary Offset Carrier (CBOC) signal pseudo code period and combination code sequence are difficult to estimate in a non-cooperative context, two blind methods are proposed based on power spectrum reprocessing and Radial Basis Function (RBF) neural networks. It can get the CBOC pseudo code period through two power spectrum calculations. Firstly, the received one pseudo code period is overlapped segmentation based on the estimated pseudo code period. Secondly, the learning coefficient is optimized selection and each segment of date vector as an input signal to the RBF neural networks to supervised adjustment. Finally, through the continuous input signal, it can restore the original combination code sequence according to the convergent weight vectors. Simulation results show that the pseudo code period can be estimated using the secondary power spectrum under low Signal-to-Noise Ratio (SNR). Compared with the Back Propagation (BP) neural networks and the Sanger neural networks, the proposed RBF neural networks improve the SNR by 1 dB and 3 dB respectively and the number of data groups required is less through RBF neural networks under the same condition.
, doi: 10.11999/JEIT181016
[Abstract](17) [FullText HTML](7) [PDF 1224KB](1)
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/JEIT180729
[Abstract](17) [FullText HTML](9) [PDF 1835KB](0)
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/JEIT180357
[Abstract](73) [FullText HTML](36) [PDF 1444KB](10)
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/JEIT181086
[Abstract](79) [FullText HTML](47) [PDF 1923KB](14)
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/JEIT180737
[Abstract](27) [FullText HTML](15) [PDF 946KB](3)
Abstract:
, doi: 10.11999/JEIT180971
[Abstract](32) [FullText HTML](21) [PDF 2492KB](8)
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/JEIT
[Abstract](24) [FullText HTML](18) [PDF 3367KB](5)
Abstract:
To solve the incompleteness of the salient region obtained by the existing saliency detection method in the frequency domain, a frequency saliency detection method of multi-scale analysis is proposed. Firstly, the quaternion hypercomplex is constructed by the input image feature channels. Then, the multi-scale decomposition of the quaternion amplitude spectrum is performed by wavelet transform, and the multi-scale visual saliency map is calculated. Finally, the better saliency map is fused based on the evaluation function, and central bias is used to generate the final visual saliency map. The experimental results show that the proposed method can effectively suppress the background interference, find significant target quickly and accurately, and have high detection accuracy.
, doi: 10.11999/JEIT180925
[Abstract](20) [FullText HTML](12) [PDF 888KB](5)
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/JEIT180805
[Abstract](58) [FullText HTML](34) [PDF 837KB](10)
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/JEIT180944
[Abstract](101) [FullText HTML](74) [PDF 809KB](16)
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/JEIT180831
[Abstract](61) [FullText HTML](36) [PDF 2700KB](6)
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/JEIT180836
[Abstract](67) [FullText HTML](43) [PDF 1219KB](9)
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/JEIT180896
[Abstract](29) [FullText HTML](19) [PDF 1853KB](2)
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 is 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/JEIT180535
[Abstract](63) [FullText HTML](33) [PDF 1839KB](6)
Abstract:
With the increasing complexity of radar signals, it is becoming more and more difficult to extract features of the real sequence, but when they are transformed to a symbol sequence, it is usually easier to mine the effective feature parameters. Therefore, a radar signal recognition method based on Multi-Scale Information Entropy (MSIE) is proposed. Firstly, the radar signal is transformed into symbolic sequence by Symbolic Aggregate approXimation (SAX) algorithm under different character number scales. Then, the information entropy of each symbol sequence is combined to form the MSIE feature vector. Finally, the k-Nearest Neighbor (k-NN) is used as a classifier to realize the classification and identification of radar signals. The simulation results of 6 typical radar signals show that the correct recognition rate of different radar signals is greater than 90% when Signal to Noise Ratio (SNR) is 5 dB, and have a good performance than the traditional identification based on complexity characteristics (box-dimension and sparseness).
, doi: 10.11999/JEIT180824
[Abstract](68) [FullText HTML](34) [PDF 501KB](5)
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/JEIT180933
[Abstract](49) [FullText HTML](26) [PDF 1032KB](4)
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/JEIT180922
[Abstract](45) [FullText HTML](22) [PDF 4662KB](0)
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/JEIT180904
[Abstract](40) [FullText HTML](28) [PDF 2422KB](4)
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/JEIT180832
[Abstract](47) [FullText HTML](23) [PDF 1439KB](4)
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/TEIT180875
[Abstract](59) [FullText HTML](27) [PDF 2154KB](6)
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/JEIT180522
[Abstract](247) [FullText HTML](109) [PDF 2574KB](20)
Abstract:
With the development of Network Function Virtualization (NFV), Virtual Network Functions (VNFs) can be deployed in a common platform such as virtual machines in the form of Service Function Chaining (SFC), providing flexibility for management. However for service providers, these come with high OPerational EXpenditure (OPEX), due to the complexity of the network infrastructure and the growing demand for services. To solve this problem, a strategy for OPEX optimization is proposed, which aims to minimize the startup cost, energy consumption, transmission cost and obtain VNF deployment and routing allocation optimization scheme. The VNF deployment problem as a new Mixed Integer Linear Programming (MILP) model is formulated, and three OPEX optimization algorithms are designed including Genetic Algorithm (GA). The OPEX of MILP model and optimization algorithms are compared under different resource allocation constraints. The calculation result shows that the GA can obtain the near-optimal solutions when node resource ratio is more than 60%.
, doi: 10.11999/JEIT180458
[Abstract](279) [FullText HTML](136) [PDF 1695KB](39)
Abstract:
When modeling user preferences, the current researches of group recommendation ignore the problem of modeling initialization and the review information accompanied with rating information for recommender models, integrating deep learning into the recommendation system becomes a hotspot of Point-Of-Interest (POI) recommendation. In this paper, a new POI recommendation model called Matrix Factorization Model integrated with Hybrid Neural Networks (MFM-HNN) is proposed. The model improves the performance of POI recommendation by fusing review text and check-in information based on Neural Network (NN). Specifically, the convolutional neural network is used to learn the feature representation of the review text and the check-in information is initialized by using the stacked denoising autoencoder. Furthermore, the extended matrix factorization model is exploited to fuse the review information feature and the initial value of the check-in information for POI recommendation. As is shown in the experimental results on real datasets, the proposed MFM-HNN achieves better recommendation performances than the other state-of-the-art POI recommendation algorithms.
, doi: 10.11999/JEIT180456
[Abstract](237) [FullText HTML](82) [PDF 5830KB](5)
Abstract:
In order to improve the ability of anti-chosen plaintext attack and decryption quality under unknown attack in current optical encryption technology, an optical image encryption algorithm based on chaotic Gyrator transform and differential mixed mask is proposed. The input plaintext is converted into its corresponding Quick Response (QR) code. The chaotic phase mask is generated according to the Logistic map. At the same time, the radial Hilbert and the zone plate phase function are combined to fuse with the chaotic phase mask for constructing the mixed phase mask. Then, a random sequence of Logistic chaotic maps is used to calculate the rotation angle of the Gyrator transformation, and the QR code is modulated to form Gyrator spectrum by combining the mixed phase mask. The Gyrator spectrum is divided into two components by introducing the equivalent decomposition technique, and two differential spiral phase masks are obtained by setting up different orders. Then, the Singular Value Decomposition (SVD) is introduced to process one of the Gyrator spectral components so that its corresponding orthogonal matrix is encoded by combining two differential phase masks. Finally, by combining the encoded orthogonal matrix and diagonal matrix, the encrypted cipher is outputted based on thereversible SVD technology. The ability of resisting plaintext attack and clipping attack, as well as the sensitivity level of the encryption results to key change is analyzed theoretically. Experimental results show that the algorithm has good security performance.
, doi: 10.11999/JEIT180541
[Abstract](205) [FullText HTML](95) [PDF 770KB](9)
Abstract:
In view of the problem that the existing methods are not applicable or are only feasible to the case where only a low ratio of data are missing in multivariable time series, a missing data prediction algorithm is proposed based on Kronecker Compressed Sensing (KCS) theory. Firstly, the sparse representation basis is designed to largely utilize both the temporal smoothness characteristic of time series and potential correlation between multiple time series. In this way, the missing data prediction problem is modeled into the problem of sparse vector recovery. In the solution part of the model, according to the location of missing data, the measurement matrix is designed suitable for the current application scenario and low correlation with the sparse representation basis. Then, the validity of the model is verified from two aspects: Whether the sparse representation vector is sufficiently sparse and the sensing matrix satisfies the restricted isometry property. Simulation results show that the proposed algorithm has good performance in the case where a high ratio of data are missing.
, doi: 10.11999/JEIT180328
[Abstract](164) [FullText HTML](63) [PDF 1937KB](5)
Abstract:
To test the radiated interference E-field threshold of Equipment Under Test (EUT) with common-mode interference of transmission lines in reverberation chambers and unify the test results with the open areas, the range of the maximum directivity of the lines with random loads is calculated by the derivation of the equation of the common-mode currents and decomposition of the currents into the corresponding characteristic ones. The calculated results are validated with the experiments performed in a reverberation chamber and an open area, respectively, with a single conductor line and a coaxial cable as the EUT. The theoretical and experimental results show that the test results in the two different areas can be unified with the calculated results. The common mode interference of two conductor lines and coaxial cables can be equivalent to single conductor lines and the bend of the lines almost has no influence on the test results.
, doi: 10.11999/JEIT180482
[Abstract](181) [FullText HTML](75) [PDF 1428KB](17)
Abstract:
In view of the strong anti-jamming capability of monopulse radar, the cross eye is used to interfere with monopulse radar. Monopulse radar is widely used in missile terminal guidance for precise attack on aircraft and ship targets. Based on the radar equation and the principle of monopulse radar angle measurement, the retrodirective cross-eye interference of the isolated target and the two point source under the target echo are modeled. Based on the analysis method of linear fitting, the general formula of two source reverse cross eye parameters and monopulse radar indicating angle are obtained. The influence of jammer power, signal phase difference, signal amplitude ratio and echo signal phase on the angle deception effect of monopulse radar is discussed through case simulation. The results show that: The phase difference of the two signals emitted by the jammer is closer to 180° and the amplitude ratio is closer to 1, the better the angle deception effect of the jammer is to the monopulse radar; with the increase of jammer power, the parameter tolerance of jammers is more relaxed when JSR (10～25 dB) is increased; and due to the influence of target echeo phase, the jamming effect of jammer is unstable; the mathematical model is consistent with the simulation model of monopulse radar receiver. This study can provide reference for the design of reverse cross eye jammers for aircraft and ships.
, doi: 10.11999/JEIT180516
[Abstract](183) [FullText HTML](86) [PDF 2262KB](16)
Abstract:
To improve the location resolution of electromagnetic radiation source, a ultra-short baseline network CASMA (Mini-Array by Chinese Academy of Sciences) is proposed for detection, utilizing optical fiber for timing. CASMA contains 5 electromagnetic detection stations and a control unit. The distance between each pair of stations is about 1 km, meaning that the length of baseline to the wavelength is about 0.1. The timing accuracy is about 10 ns. CASMA is applied to record the vertical electric field emitting by radio transmitters. CASMA utilizes interferometric imaging algorithm to calculate the transmitters’ azimuth. By experiment, the calculated azimuths approach the expected azimuths with deviations are less than 0.2°, showing many advantages over traditional systems or methods. Consequently, CASMA has accuracy direction finding resolution for electromagnetic radiation source. According to the results, the location accuracy may be expected to be 0.5%·R in a 2500 km scope where R is the distance between the electromagnetic radiation source and CASMA using two sets of CASMA for intersection positioning.
, doi: 10.11999/JEIT180562
[Abstract](168) [FullText HTML](74) [PDF 2163KB](8)
Abstract:
In order to realize safety, reliability and self-control of electromagnetic computing, the large-scale parallel MoM is studied based on domestically-made many-core supercomputer platform named " Tianhe-2”. A new LU decomposition algorithm named Block Diagonal matrix Pivoting LU decomposition (BDPLU) algorithm, is proposed by analyzing the diagonally dominant characteristics of the matrix generated through dispersing electric field integral equation of MoM, for the purpose of communication pressure reduction to computer cluster and solution acceleration to MoM integral equation during large-scale parallel computation. The BDPLU algorithm reduces the amount of calculation in the process of panel factorization. More importantly, the algorithm completely eliminates MPI communication when pivoting. Using BDPLU algorithm, the maximum number of CPU cores break through 6×105 CPU cores, which is the largest scale of parallel MoM computation in domestically-made and many-core supercomputing platform at present, and the parallel efficiency of solving matrix can reach 51.95%. Numerical results show that parallel MoM can accurately and efficiently solve large-scale electromagnetic field problems on domestic supercomputing platform.
, doi: 10.11999/JEIT180531
[Abstract](293) [FullText HTML](104) [PDF 1494KB](14)
Abstract:
In order to solve the dictionary mismatch problem of Compressive Sensing (CS) based multi-target Device-Free Localization (DFL) under the wireless localization environments, a Variational Expectation Maximization (VEM) based dictionary refinement method is proposed. Firstly, this method builds the dictionary based on the saddle surface model, and models the environment-related dictionary parameters as tunable parameters. Then, a two-layer hierarchical Gaussian prior model is imposed on the location vector to induce its sparsity. Finally, the VEM algorithm is adopted to estimate the posteriors of hidden variables and optimize the environment-related dictionary parameter, thus the estimation of target locations and dictionary refinement can be realized jointly. Compared with the conventional CS based multi-target DFL schemes, the simulation results demonstrate that the performance of the proposed algorithm is especially excellent in changing wireless localization environments.
, doi: 10.11999/JEIT180547
[Abstract](189) [FullText HTML](101) [PDF 2769KB](25)
Abstract:
Integration of radar and communication on the electronic war platform is an effective method to reduce volume and enhance spectrum usage and efficiency. A transmitted pattern based on OFDM-LFM MIMO radar is designed to realize the integration of radar and communication by changing initial frequency. The communication receiver interpretation of the bit is based on the initial frequency of the signal. In radar receiver, the same range resolution as tradition OFDM-LFM MIMO radar can be get with get the time domain synthetic bandwidth methods. The proposed method changes the initial frequency without changing the omnidirectional pattern because the orthogonal transmitted signals are nonoverlapping in the spectrum. Simulation examples are provided for performance evaluation and to demonstrate the effectiveness of the proposed information embedding technique.
, doi: 10.11999/JEIT180452
[Abstract](247) [FullText HTML](93) [PDF 3829KB](14)
Abstract:
Three dimensional interferometry of wide-band radar can provide crucial information for estimating the micro-motion and geometric parameters of targets. For estimation of the micro-motion parameters via three dimensional interferometry in the case of squint observing mode, an algorithm for micro-motion and geometric parameters based on squint calibration is proposed. The algorithm performs ranging and angle measuring for each antenna receiving echo in an L formation array. Moreover, the squint distortion is calibrated and three dimensional trajectories of scattering centers are obtained via establishing two elements and quadratic nonlinear equations and coordinate transformation. In addition, smoothing filtering and optimization are used to retrieve micro-motion and geometry parameters. The effectiveness and robustness of the proposed algorithm is confirmed via extensive experiments.
, doi: 10.11999/JEIT180905
[Abstract](52) [FullText HTML](24) [PDF 1679KB](13)
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/JEIT180780
[Abstract](91) [FullText HTML](39) [PDF 1417KB](9)
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/JEIT180751
[Abstract](76) [FullText HTML](34) [PDF 812KB](6)
Abstract:
To overcome the mechanism shortcomings of wormhole and white hole selection in the base Multi-Verse Optimizer (MVO), an Improved Multi-Universes Optimization Algorithm is proposed. To speed up global exploration ability and quick iteration ability, this thesis designs the existence mechanism of wormhole with fixed probability and the Travel Distance Rate (TDR) that its convergence from early stage's smoothly to later stage's fast. The random white hole selection mechanism is proposed; Black holes can revolve around selected white hole stars and is modelled to solve the problem of information communication of the Inter-generational Universes. The performance of IMVO is verified by comparison experiments in low-middle dimensions. Three benchmarks test functions are selected for comparison in large scale which are difficult to be optimized, the experimental results show that IMVO has good applicability and robustness with higher solving accuracy and success rate in large scale optimization problem.
, doi: 10.11999/JEIT180761
[Abstract](67) [FullText HTML](29) [PDF 283KB](3)
Abstract:
Discriminant Neighborhood Embedding (DNE) algorithm is introduced into the speaker recognition system. DNE is a manifold learning approach and aims at preserving the local neighborhood structure on the data manifold. As well, DNE has much more power in discrimination by sufficiently using the between-class discriminant information. The experimental results on the telephone-telephone core condition of the NIST 2010 Speaker Recognition Evaluation (SRE) dataset indicate the effectiveness of DNE algorithm.
, doi: 10.11999/JEIT180637
[Abstract](177) [FullText HTML](101) [PDF 1198KB](16)
Abstract:
The traditional fingerprinting localization algorithm has high construct time overhead and low positioning accuracy. Because of this problem, an adaptive fading memory based bluetooth sequence matching localization algorithm is proposed. Firsly, Pedestrian Dead Reckoning(PDR) and Nearest Neighbor Algorithm(NNA) are applied to perform position calibration and Received Signal Strength(RSS) mapping of Motion Sequences. Secoudly, according to the relevance of neighboring locations, a sequence recursive search method is used to construct fingerprint sequence database. Finally, an adaptive fading memory algorithm and initial sequence matching degree are considered to realize the position estimation of target. The experimental results show that this algorithm is able to consume low construct time overhead and achieve high indoor localization precision.
, doi: 10.11999/JEIT180702
[Abstract](73) [FullText HTML](41) [PDF 1951KB](6)
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/JEIT180703
[Abstract](69) [FullText HTML](36) [PDF 474KB](9)
Abstract:
Two constructions of Gaussian integer Zero Correlation Zone (ZCZ) sequence set are researched. In Construction I, the method of zero padding is implemented on the ZCZ sequence set, and then the Gaussian integer ZCZ can be obtained by the filtering operation. Furthermore, the degree of the Gaussian integer ZCZ sequence set is calculated in this paper. In Construction II, two constructions of Gaussian integer orthogonal matrix are proposed. In addition, the optimal Gaussian integer ZCZ sequence sets are constructed based on the orthogonal matrix. The two classes of Gaussian integer ZCZ sequence sets presented in this paper can be applied to many communication systems such as Quasi-Synchronous Code Division Multiple Access (QS-CDMA)、Orthogonal Frequency Division Multiplexing (OFDM) and Mutiple-Input Multiple-Output (MIMO) system to suppress the interference and improve the spectrum efficiency.
, doi: 10.11999/JEIT180716
[Abstract](99) [FullText HTML](57) [PDF 1832KB](4)
Abstract:
Considering the problem that using a large number of reserved paths which causing higher complexity in order to obtain better performance for polar code Successive Cancellation List (SCL) decoding, the adaptive SCL decoding algorithm at a high Signal to Noise Ratio (SNR) reduces a certain amount of calculations, however, brings a higher decoding delay. According to the order of polar code decoding, an SCL decoding algorithm combining segmentationCyclic Redundancy Check (CRC) with adaptively selecting the number of reserved paths is proposed. The simulation results show that compared with the traditional CRC-assisted SCL decoding algorithm and adaptive-SCL algorithm, when the code rate is R=0.5, the complexity under low SNR (–1 dB) is reduced by about 21.6%, and the complexity at high SNR (3 dB) is reduced by about 64%, at the same time, better decoding performance is obtained.
, doi: 10.11999/JEIT180488
[Abstract](131) [FullText HTML](54) [PDF 1297KB](17)
Abstract:
For the target DOA estimation under active deception jamming environment with limited samples, a novel DOA estimation method based on the combination of Adaptive Polarization Filter(APF) and Block Sparse Bayesian Learning(BSBL) algorithm is proposed. At first, the interference energy is suppressed by using APF. Then, the proposed method constructs a sparse Bayesian model under active deception jamming environment. And the target DOA is estimated by using the BSBL algorithm based on the neighbor time sampling correlation. Simulated and measured data processing results prove that the proposed method reduces the influence of interference on the BSBL algorithm, and has higher spatial resolution and higher angle measurement accuracy, comparing with the method based on the combination of APF and subspace-based DOA algorithms or maximum likelihood DOA algorithm.
, doi: 10.11999/JEIT180563
[Abstract](144) [FullText HTML](66) [PDF 3989KB](13)
Abstract:
Due to the 2-D vacancies with serious motion errors when processing 10 GHz ultra-wideband microwave photonic-based SAR, current motion error estimation methods directly estimating with phase error can not obtain correct estimation result. An ultra-high resolution SAR motion error estimation method joint envelope and phase is proposed in this paper, which can realize accurate estimation of motion error without inertial information. Firstly, the approximate 3-D motion error is obtained by applying the Least Squares Algorithm (LSA) and the Gradient Descent Algorithm (GDA) to the envelope information extracted by the Range Alignment Algorithm (RAA) before Range Curve Migration Correction (RCMC). Then, phase-based motion error estimation is performed on the data after rough compensation and RCMC. After eliminating the azimuth variant phase error, the 2-D space-variant phase error estimation method is used to obtain an accurate estimation of residual motion error. Processing of simulated data and real data acquired from vehicle-borne microwave photonic-based radar validates the effectiveness of the proposed methods.
, doi: 10.11999/JEIT180719
[Abstract](84) [FullText HTML](37) [PDF 2701KB](9)
Abstract:
A grating lobe suppression method of wideband real time delay pattern based on Particle Swarm Optimization(PSO) algorithm is proposed to solve the problem of grating lobe arise from inter-element is larger than wavelength. Firstly, the array energy pattern based on wideband real time delay is defined. Then, a fitness function is constructed with maximum sidelobe level of the array energy pattern. Finally, the grating lobe is further suppressed by optimizing the elements position distribution using particle swarm optimization algorithm. The simulation results show that the proposed grating lobes suppression method is more effectively than individually using the particle swarm optimization method or the wideband real time delay method. Furthermore, the influence of the element space, the element number, the time width and the center frequency of signal on the performance of grating lobe suppression are studied.
, doi: 10.11999/JEIT180723
[Abstract](147) [FullText HTML](72) [PDF 1680KB](19)
Abstract:
In order to realize underdetermined wideband Direction Of Arrival(DOA) estimation based on sparse array, an algorithm on account of Distributed Compressive Sensing(DCS) is proposed. Firstly, wideband signal processing model based on sparse array is deduced and the underdetermined wideband DOA estimation is formulated as a DCS problem. Then, the DCS-Simultaneous Orthogonal Matching Pursuit(DCS-SOMP) algorithm is utilized to solve this problem. Finally, the off-grid problem is considered and a joint DCS model containing off-grid parameters is established. Estimations of DOAs and off-grid parameters are achieved through iterative solution. Simulation results show that the proposed algorithm is effective and have advantages in resolution and computational complexity.
, doi: 10.11999/JEIT180429
[Abstract](75) [FullText HTML](52) [PDF 1563KB](10)
Abstract:
Focusing on the problem that convolutional auto-encoder network based anomaly detection ignores time information, a novel anomaly detection model based on Bayesian fusion of spatial-temporal stream is proposed. A convolution auto-encoder network is used in spatial stream model to reconstructs video frames, and a convolutional Long Short-Term Memory (LSTM) encoder-decoder network is used to reconstruct short-term optical sequence in the temporal stream model. Then, the reconstruction errors under spatial and temporal stream are calculated separately. Meanwhile, an adaptive thresholds is designed to obtain the reconstruction binary error maps. Finally, the Bayesian fusion strategy is developed to combine the reconstruction error of spatial and temporal stream to obtain the final fusion reconstruction error map based on which the abnormal behavior can be determined. Experimental results show that the proposed algorithm is superior to the existing anomaly detection algorithms in UCSD and Avenue datasets.
, doi: 10.11999/JEIT180752
[Abstract](80) [FullText HTML](41) [PDF 2397KB](12)
Abstract:
A 10 bit fully differential dual slope Analog-to-Digital Converter (ADC) for Time Delay Integration (TDI) CMOS image sensors is realized based on column-parallel single-slope ADC. Top plates of the two capacitors are used for sampling differential inputs, and the bottom plates are connected to ramp generator for conversion. Current steering is used to generate the rising and falling ramp with same step voltage simultaneously. The proposed ADC is fabricated in SMIC 0.18 μm CMOS process. Simulated spurious free dynamic range and effective number of bits are 87.92 dB and 9.84 bit with the input frequency of 1.32 kHz at 19.49 kS/s sampling rate, respectively. Measured results show that the ADC has a differential nonlinearity of –0.7/+0.6 LSB and integral nonlinearity of –2.6/+2.1 LSB.
, doi: 10.11999/JEIT180707
[Abstract](115) [FullText HTML](58) [PDF 2133KB](14)
Abstract:
The detection performance of ship targets by skywave Over-The-Horizon Radar (OTHR) is affected by the sea clutter seriously. Accurate and adaptive suppression of sea clutter is significant for improving the detection performance of ship target. To solve the non-adaptive shortness of the sea clutter suppression algorithm based on High-Order Singular Value Decomposition (HOSVD), a modified adaptive algorithm based on Peak Signal-to-Noise Ratio (PSNR)-HOSVD is proposed by introducing the PSNR. The modified algorithm has a smaller computational complexity than the one based on HOSVD, since only one projection matrix is established from the left singular vectors of the third-mode unfolding matrix. Meanwhile, the modified algorithm has a better performance than the HOSVD based one, because the components of sea clutter are only aggregated in the column space of the third-mode unfolding matrix. Experimental results based on two sets of measured data received in ideal and non-ideal situations in respective show that, the modified adaptive algorithm based on PSNR-HOSVD has a better performance than the peer algorithms.
, doi: 10.11999/JEIT180631
[Abstract](137) [FullText HTML](73) [PDF 1705KB](16)
Abstract:
For problems of not meeting the demand of sampling both large flows and small flows at the same time, and not distinguishing flash crowd from traffic attacks in building network traffic anomaly detection datasets based on probabilistic sampling methods, a probabilistic flow sampling method for traffic anomaly detection is proposed. On the basis of the classification of network data flows according to their destination and source IP addresses, the sampling probability for each class of data flows is set as the maximum of its destination and source IP address’s sampling probability, and the number of sampled data flows is ceiled to ensure that each class of data flows is sampled at least once, so that the sampled dataset can reflect the distributions of large, small flows and source, destination IP addresses in original traffics. Then, the source IP address entropy is used to characterize the source IP dispersion of anomaly flows, and the attack flow sampling algorithm is designed based on the threshold of the source IP address entropy, which reduces the sampling probability of non-attack anomaly flows caused by flash crowd. The simulation results show that the proposed method can satisfy the sampling requirements of both large flows and small flows, it has a high anomaly flows sampling ability, can sample all the suspicious source and destination IP addresses related to anomaly flows, and can effectively filter the non-attack anomaly flows.
, doi: 10.11999/JEIT180670
[Abstract](95) [FullText HTML](53) [PDF 1375KB](10)
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/JEIT180648
[Abstract](106) [FullText HTML](35) [PDF 1859KB](7)
Abstract:
A metameterial absorber is designed, fabricated and experimentally demonstrated to realized ultra-wideband absorption based on loading lumped resistances to raise the efficiency of absorber. The proposed structure comprises of an upper absorber and an under absorber by longitudinal cascade to expand bandwidth. The analysis of equivalent circuit show that the absorber has good impedance matching in a wide frequency band and the mechanism of wave absorption is verified by current analysis. The size of the unit is only about 0.089 \begin{document}$\lambda_ {\rm{L}}$\end{document} ×0.089 \begin{document}$\lambda_ {\rm{L}}$\end{document} , where \begin{document}$\lambda_ {\rm{L}}$\end{document} is the wavelength of the lowest frequency, and the total thickness of the absorber is only 0.078 \begin{document}$\lambda_ {\rm{L}}$\end{document} . Simulated and experimental results show that the absorber exhibits absorptivity above 90% from 2.24 GHz to 16.14 GHz, and the relative absorption bandwidth is about 151%. Measurement results show good agreement with the numerically simulated results.
, doi: 10.11999/JEIT180600
[Abstract](100) [FullText HTML](43) [PDF 653KB](4)
Abstract:
In the standard application mapping problem, it is assumed that the communicating traffic of a task is a fixed value. In the real applications, the communication traffic is uncertain due to the time-varying and bursty characters. Therefore, it has the practical significance modeling the task with communicating traffic uncertainty. Given the interval flow and a conservation factor, the robust application mapping problem is formulated by a min-max model, and then solved by a revised Tabu-based algorithm (Tabu-RAM) in this paper. The algorithm is verified under five benchmark instances. As the experimental results show, under the standard application scenarios, the Tabu-RAM performs better than other methods proposed in the literature. In addition, under the application scenarios with uncertain tasks, experimental results show that the Tabu-RAM performs better and more stable than the traditional tabu algorithm.
, doi: 10.11999/JEIT180661
[Abstract](75) [FullText HTML](39) [PDF 3447KB](10)
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/JEIT180656
[Abstract](92) [FullText HTML](50) [PDF 1169KB](3)
Abstract:
, doi: 10.11999/JEIT180593
[Abstract](153) [FullText HTML](51) [PDF 2147KB](5)
Abstract:
As the bandwidth of the traditional TEM cell can not satisfy the growing demand for broadband, a broadband electromagnetic radiation device working from DC to 6 GHz is designed based on the coaxial structure. According to circuit principle and impedance matching of the transmission line, the device adopts the taper transition structure between the N connector and circular coaxial connected, which achieves the advantages of good impedance matching. The device is simulated by the CST software, and has been fabricated and measured. The simulated results show that S11 is better than –10 dB in the frequency range of DC-6 GHz. Due to the machining error, test results are slightly biased at individual frequencies, which have good consistency with the simulated results and demonstrate the desirable transmission performance of the radiation device. The design has great application value in electromagnetic radiation system.
, doi: 10.11999/JEIT180641
[Abstract](142) [FullText HTML](68) [PDF 1782KB](17)
Abstract:
In order to reduce the functional verification cycle of application-specific integrated circuits and on-chip system, a method for accelerating functional verification with FPGA digital hard processor system is proposed. The proposed method combines the advantages of software simulation function verification and field programmable gate array prototype verification, and uses the hard processor system integrated in the on-chip system field programmable gate array device as the verification excitation generation and the function verification coverage analysis unit. It solves the problem that verification speed and flexibility can not be unified. Compared with software simulation verification, the proposed method can effectively shorten the functional verification time of digital circuits; it is superior to existing FPGA prototyping technology in terms of functional verification efficiency and verification of intellectual property reusability.
, doi: 10.11999/JEIT180666
[Abstract](105) [FullText HTML](60) [PDF 2750KB](17)
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/JEIT180680
[Abstract](135) [FullText HTML](84) [PDF 2236KB](14)
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: 10.12000/JRIT180605
[Abstract](178) [FullText HTML](96) [PDF 2184KB](16)
Abstract:
When multi-objective evolutionary clustering algorithms are applied to image segmentation, the image pixels are always utilized to be clustered. It results in a long running time. In addition, due to not considering the image region information, the image segmentation effect is not ideal. In order to improve the segmentation effect and time efficiency of the multi-objective evolutionary clustering algorithm, the image region information and some supervised information are introduced into multi-objective evolutionary clustering. Then a multi-objective evolutionary semi-supervised fuzzy clustering image segmentation algorithm driven by image region information is presented. First, the region information of the image is obtained through the super-pixel strategy. Second, two novel fitness functions are designed by introducing the supervised information and region information. Third, the multi-objective evolutionary strategy is used to optimize these two fitness functions to obtain an optimal solution set. Finally, an optimal solution evaluation index with region information and supervision information is constructed and utilized to select an optimal solution from the optimal solution set. Experimental results show the proposed algorithm outperforms comparison methods in segmentation performance and running efficiency.
, doi: 10.11999/JEIT180672
[Abstract](135) [FullText HTML](67) [PDF 1785KB](18)
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/JEIT180663
[Abstract](124) [FullText HTML](65) [PDF 2058KB](7)
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/JEIT180653
[Abstract](119) [FullText HTML](65) [PDF 1481KB](6)
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/JEIT180914
[Abstract](64) [FullText HTML](33) [PDF 2278KB](6)
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/JEIT180589
[Abstract](133) [FullText HTML](76) [PDF 1330KB](11)
Abstract:
The existing IP location technology determines the location of IP by querying IP to register information databases or using time-delay information. In fact, due to the influence of various factors, most of the IP in the network can not get accurate and reasonable positioning results. For this reason, a region recognition method of IP is proposed based on network structure features. This method obtains the network topology information between the two nodes by sending the Traceroute detection packet from the detection nodes to the IPs that need to be located Comparing the network structure features between the nodes to be located and the known geographical nodes determines where the nodes located. The actual test shows that this method can achieve better results.
, doi: 10.11999/JEIT180851
[Abstract](63) [FullText HTML](35) [PDF 3135KB](9)
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/JEIT180609
[Abstract](166) [FullText HTML](77) [PDF 1571KB](15)
Abstract:
An adaptive method of limiter design is proposed to suppress impulsive noise. With a purpose of maximizing the efficacy function, the proposed method searches for optimal thresholds of clipper and blanker, via adaptive line search. Firstly, based on analysis on the relationship between the efficacy and the nonlinearity, the key problem of optimization is proposed. Then, since the calculation of efficacy is hard, an adaptive algorithm based on linear search approach is developed based on linear search to optimize the efficacy. Considering the noise distribution is unknown, the proposed method employs the nonparametric kernel density estimation and works robustly in the presence of estimation error. Finally, numeric simulations demonstrate that the proposed method can obtain the optimal performance of clippers and blankers successfully. In the processing of real atmospheric noise from unknown distribution, the proposed method achieves the best detection performance when combining nonparametric kernel density estimation approach.
, doi: 10.11999/JEIT180677
[Abstract](169) [FullText HTML](88) [PDF 1645KB](36)
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/JEIT180590
[Abstract](125) [FullText HTML](56) [PDF 2172KB](6)
Abstract:
In order to improve the measurement accuracy of Micro Electro Mechanical Systems (MEMS) gyroscopes, the influence of measurement noise on them is suppressed. The error characteristics of a certain type of MEMS gyroscope are analyzed. A strong tracking self-feedback model based on Recursive Least Square (RLS) multiple wavelet decomposition reconstruction is proposed to establish a new soft threshold function. Since the model processed data has partial singular values, an improved median filtering algorithm is proposed. For the problem of gyro zero-bias noise, a zero-bias stability suppression algorithm is proposed. In this paper, the algorithm model is described in detail, and the experimental data of the train attitude measurement system in a project research is applied to the algorithm model. The test experiments are divided into static and dynamic groups. The results show that the algorithm reduces the noise in the signal, suppresses effectively the random drift of the MEMS gyroscope and improves the accuracy of the attitude calculation. It is affirmed the feasibility and effectiveness of using this method to remove the signal noise of the gyroscope output and improve the accuracy of the use.
, doi: 10.11999/JEIT180581
[Abstract](149) [FullText HTML](72) [PDF 1271KB](10)
Abstract:
In order to improve the performance of the large queue backlogs and low convergence rate in back pressure routing algorithm, the cross-layer optimization of joint congestion control, multi-path routing and power allocation in wireless multi-hop networks are investigated. The system is modeled as a network utility maximization problem under the constraints of flow balancing condition and power. Based on the Newton’s method, the problem is solved and an algorithm with superlinear convergence speed is proposed. With matrix splitting technology, the algorithm can be implemented distributedly further. The simulation results show that the algorithm can effectively increase the energy utility while achieving the maximum network utility, and can keep the queue length at a very low level to decrease the packet transmission delay.
, doi: 10.11999/JEIT180655
[Abstract](120) [FullText HTML](54) [PDF 1322KB](8)
Abstract:
The system biases degrade seriously the location precision for the multi-static passive radar system. A joint registration and passive localization algorithm based on Constrained Total Least Squares (CTLS) using Direction Of Arrival (DOA) and Time Difference Of Arrival (TDOA) measurements is developed to address the multi-static radar localization problem under the influence of system biases. Firstly, the nonlinear DOA and TDOA measurement equations are linearized by introducing auxiliary variables. Considering the statistical correlation properties of the noise matrix in the pseudo-linear equations, a joint biases registration and passive localization problem is formulated as a CTLS problem and the Newton’s method is applied to solving the CTLS problem. Moreover, a dependent least squares algorithm is designed to improve the target position estimation using the relationship between auxiliary variables and target position. An iterative post-estimate procedure is exploited to enhance further the estimation accuracy of the system biases. Finally, the theoretical error of the proposed algorithm is derived. Simulations demonstrate that the proposed algorithm can effectively estimate the system biases and target position.
, doi: 10.11999/JEIT180651
[Abstract](160) [FullText HTML](77) [PDF 2455KB](22)
Abstract:
In order to deal with these issues of the traditional Fuzzy C-Means (FCM) algorithm, such as without consideration of the spatial neighborhood information of pixels, noise sensitivity and low convergence speed, a suppressed non-local spatial intuitionistic fuzzy c-means image segmentation algorithm is proposed. Firstly, in order to improve the accuracy of segmentation image, the non-local spatial information of pixel is used to improve anti-noise ability, and to overcome the shortcomings of the traditional FCM algorithm in which only considers the gray characteristic information of single pixel. Secondly, by using the ‘voting model’ based on the intuitionistic fuzzy set theory, the hesitation degrees are adaptively generated as inhibitory factors to modify the membership degrees, and then the operating efficiency is increased. Experimental results show that the new algorithm is robust to noise and has better segmentation performance.
, doi: 10.11999/JEIT180580
[Abstract](183) [FullText HTML](80) [PDF 2408KB](12)
Abstract:
To address the problems of low spectrum utilization and high energy consumption caused by physical impairment in elastic optical networks, a service differentiated energy efficiency routing strategy with Link Impairment-Aware Spectrum Partition (LI-ASP) is proposed. For reducing the nonlinear impairment between different channels, a path weight formula jointly considering the link spectrum state and transmission impairment is designed to balance the load. A modulation level-layered auxiliary graph is constructed according to traffic’s spectrum efficiency and maximum transmission distance. Starting from the highest modulation in the auxiliary graph, the K link-disjoined maximum weight paths are selected for high quality requests, and the K link-disjoined shortest energy efficiency paths are selected for low quality requests. Then, LI-ASP strategy divides spectrum partition according to requests rate ratio. The First-Fit (FF) and Last-Fit (LF) spectrum allocation policies are used to reduce cross-phase modulation between the requests with different rate. The simulation results show that the proposed LI-ASP strategy can reduce the bandwidth blocking probability and energy consumption effectively.
, doi: 10.11999/JEIT180691
[Abstract](184) [FullText HTML](95) [PDF 1326KB](36)
Abstract:
In order to improve the comprehensive performance of the wireless network intrusion detection model, Recurrent Neural Network (RNN) algorithm is used to build a wireless network intrusion detection classification model. For the over-fitting problem of the classification model caused by the imbalance of training data samples distribution in wireless network intrusion detection, based on the pre-treatment of raw data cleaning, transformation, feature selection, etc., an instance selection algorithm based on window is proposed to refine the train data-set. The network structure, activation function and re-usability of the attack classification model are optimized experimentally, so the optimization model is obtained finally. The classification accuracy of the optimization model is 98.6699%, and the running time after the model reuse optimization is 9.13 s. Compared to other machine learning algorithm, the proposed approach achieves good results in classification accuracy and execution efficiency. The comprehensive performances of our model are better than that of traditional intrusion detection model.
, doi: 10.11999/JEIT180597
[Abstract](201) [FullText HTML](98) [PDF 1407KB](26)
Abstract:
The goal of Image Quality Assessment (IQA) research is to simulate the Human Visual System’s (HVS) perception process of assessing image quality and construct an objective evaluation algorithm that is as consistent as the subjective evaluation result. Many existing algorithms are designed based on local structural similarity, but human subjective perception of images is a high-level, semantic process, and semantic information is essentially non-local, so image quality assessment should take the non-local information of the image into consideration. This paper breaks through the classical framework based on local information, and proposes a framework based on non-local information. Under the proposed framework, an image quality assessment algorithm based on non-local gradient is also presented. This algorithm predicts image quality by measuring the similarity between the non-local gradients of reference image and the distorted image. The experimental results on the public test database TID2008, LIVE, and CSIQ show that the proposed algorithm can obtain better evaluation results.
, doi: 10.11999/JEIT180512
[Abstract](193) [FullText HTML](93) [PDF 1390KB](18)
Abstract:
By means of Zero-mean Microstructure Pattern Binarization (ZMPB), an image representation method based on image local microstructure binary pattern extraction is proposed. The method can express all the important patterns with visual meaning that may occur in the image. Moreover, through the dominant binary pattern learning model, the dominant feature pattern set adapted to the different data sets is obtained, which not noly achieves excellent ability in feature robustness, discriminative and representation, but also can greatly reduce the dimension of feature coding and improve the execution speed of the algorithm. The experimental results show that the proposed method has strong discriminative power and outperformes the traditional LBP and GIMMRP methods. Compared with many recent algorithms, the proposed method also presents a competitive advantage.
, doi: 10.11999/JEIT180861
[Abstract](117) [FullText HTML](64) [PDF 2166KB](16)
Abstract:
In order to deal with the limited capacity of Virtualized Network Function (VNF), hardware acceleration resources are adopted in Software-Defined Networking and Network Function Virtualization (SDN/NFV) architecture. The deployment of hardware acceleration resources enables VNF to provide service guarantees for increasing data traffic. To overcome the ignorance of the requirements for VNF with high processing throughput in service chain in existing researches, a model for VNF placement with hardware acceleration support is proposed. Based on the bearing characteristics of hardware acceleration resources, the model prioritizes the reuse of acceleration resources in the switch under the optimal placement of VNF without acceleration to commercial servers. And the mapping correlation between hardware acceleration resources and VNF is flexibly adjusted according to the requirements of network services. Simulation results show that the proposed model can bear more service flows and meet the high processing throughput needs of service chains than typical policies in the case of the same amount of resources, which improves effectively the resource utilization of the acceleration hardware deployed in the network.
, doi: 10.11999/JEIT180636
[Abstract](198) [FullText HTML](87) [PDF 1460KB](12)
Abstract:
In order to meet the diversified service requirements in future mobile communication networks and provide users with customized services while improving network economic efficiency, a resource allocation algorithm of network slicing based on online auction is proposed in this paper. The algorithm transforms the service requests of users into the corresponding bidding information according to the service types. For maximizing the social welfare of the auction participants, the slicing resource allocation problem is modeled as a multi-service based online winner determination problem. Combined with the resource allocation and price updating strategy, the optimal resources allocation based on online auction is achieved. The simulation results show that the proposed algorithm can improve the network economic efficiency and satisfy the service requirements of users.
, doi: 10.11999/JEIT180604
[Abstract](146) [FullText HTML](72) [PDF 1894KB](13)
Abstract:
Comparing with K-means, Fuzzy logic is introduced in Fuzzy C-Means to handle the information between clusters. It can obtain better cluster results. However, fuzzy logic makes observations could belong to more than just one cluster, which results FCM is especially sensitivity to the noisy and outlier and has poor generalization performance. So a Rrobust Fuzzy C-Means clustering integrated Between-cluster Information algorithm (RBI-FCM) is proposed. Taking advantage of the sparsity of K-means, RBI-FCM helps to reduce the interactions among different clusters and improve the separability of sample points which locate in the adjacent domains of different clusters. Beside minimizing the inner-cluster scattering condition, RBI-FCM considers the between-cluster information. The generalization performance of RBI-FCM can be improved. An effective iterative algorithm for solving the model is designed in this paper. The experimental results show that the RBI-FCM improves the robustness of FCM and reduce effectively its sensitivity to size-imbalance and differences on the distribution of clusters of FCM. The great clustering result is obtained.
, doi: 10.11999/JEIT180764
[Abstract](184) [FullText HTML](90) [PDF 1219KB](11)
Abstract:
The Dissimilar Redundancy Structure (DRS) based cyberspace security technology is an active defense technology, which uses features such as dissimilarity and redundancy to block or disrupt network attacks to improve system reliability and security. By analyzing how heterogeneity can improve the security of the system, the importance of quantification of heterogeneity is pointed out and the heterogeneity of DRS is defined as the complexity and disparity of its execution set. A new method which is suitable for quantitative heterogeneity is also proposed. The experimental results show this method can divide 10 execution sets into 9 categories, while the Shannon-Wiener index, Simpson index and Pielou index can only divide into 4 categories. This paper provides a new method to quantify the heterogeneity of DRS in theory, and provides guidance for engineering DRS systems.
, doi: 10.11999/JEIT180714
[Abstract](197) [FullText HTML](87) [PDF 2171KB](14)
Abstract:
To address track-to-track association problem of radar and Electronic Support Measurements (ESM) in the presence of sensor biases and different targets reported by different sensors, an anti-bias track-to-track association algorithm based on track vectors hierarchical clustering is proposed. Firstly, the equivalent measurement is derived in the Modified Polar Coordinates (MPC). Linear relationship between state estimates and real states, sensor biases, measurement errors are established based on the approximate expansion of the equivalent measurement. The track vectors are obtained by the real state cancellation method. The homologous tracks are extracted by the method of track vectors hierarchical clustering, according to the statistical characteristics of Gaussian random vectors. The effectiveness of the proposed algorithm are verified by Monte Carlo simulation experiments in the presence of sensor biases, targets densities and detection probabilities.
, doi: 10.11999/JEIT180644
[Abstract](220) [FullText HTML](99) [PDF 2294KB](17)
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/JEIT180564
[Abstract](165) [FullText HTML](87) [PDF 3052KB](11)
Abstract:
The target location accuracy is an important technical parameter of airborne SAR system, so the target location of airborne SAR image is important for application. The precision of the moving parameters will directly influence the precision of the SAR image target location algorithm based on the Range-Doppler (RD) model. The locating accuracy will be greatly affected if the navigation accuracy of the airborne platform is limited. To solve this problem, an airborne SAR image target location algorithm based on RD model parameter refining is proposed. Using the matching points of airborne SAR image matching with the reference image, the moving parameters are refined with better accuracy, and the locating accuracy is improved. The experiments show that the proposed algorithm is effective.
, doi: 10.11999/JEIT180554
[Abstract](151) [FullText HTML](67) [PDF 860KB](6)
Abstract:
Group signcryption is a cryptosystem which can realize group signature and group encryption. However, the message sender and receiver of existing group signcryption schemes are basically in the same cryptosystem, which does not meet the needs of the real environment and the public key encryption technology is basically used, public key encryption technology in encrypted long message efficiency is too low. Therefore, this paper proposes a hybrid group signcryption scheme based on heterogeneous cryptosystem from Identity-Based Cryptosystem (IBC) to CertificateLess Cryptosystem (CLC). In the scheme, The Private Key Generator (PKG) in the IBC cryptosystem and Key Generation Center (KGC) in the CLC cryptosystem generate their own system master keys, and group members can only solve signcryption through collaboration, which improves the security of the scheme. At the same time, the user can dynamically join the group without changing the group public key and other members’ private key. The scheme uses hybrid signcryption and has the ability to encrypt any long message. It is proved that the scheme satisfies confidentiality and unforgeability in computing the Diffie-hellman hard problem in the random oracle model. Theoretical and numerical analysis show that the scheme is more efficient and feasible.
, doi: 10.11999/JEIT180640
[Abstract](172) [FullText HTML](72) [PDF 1458KB](8)
Abstract:
To solve the problem of insufficient number of participants and poor data quality in the sensing mission, a mobile crowd sensing incentive model for mission cost difference is proposed. First of all, the fuzzy reasoning method is used to analyze the impact of data quantity, environmental conditions and equipment consumption on mission cost, and the sensing mission is divided into different levels on the basis of cost difference. Meanwhile, the method is used to prepare a budget for the requester and give the participant an appropriate reward. Then, the sensing mission is assigned to more appropriate participants to complete the sensing mission and upload the sensing data through credibility assessment and participants’ preference. Finally, the sensing data uploaded by participants is evaluated, and the credibility of participants is updated. Besides, the participants are paid according to the cost level of perceived missions. The simulation experiments based on the real data set show that the model can recruit more users to participate in the sensing mission effectively and promote participants to upload high-quality sensing data by using the mutual influence between different modules.
, doi: 10.11999/JEIT180646
[Abstract](119) [FullText HTML](58) [PDF 373KB](17)
Abstract:
Constructions of Gaussian integer periodic complementary sequences are presented in this paper. Based on the relationship between periodic complementary sequences and difference families, the sufficient condition of the existence of Gaussian integer periodic complementary sequences is proposed at first, then Gaussian integer periodic complementary sequences with degree 2 are constructed directly. To extend the number of Gaussian integer complementary sequences, Gaussian integer complementary sequences with degree 4 are constructed based on mappings. Compared with binary complementary sequences, there are more Gaussian integer complementary sequences, as a result, the presented methods will propose an abundance of complementary sequences for communication systems.
, doi: 10.11999/JEIT180628
[Abstract](141) [FullText HTML](77) [PDF 8293KB](24)
Abstract:
To solve the problems of complex feature extraction process and low characteristic expressiveness of traditional remote sensing image classification methods, a high resolution remote sensing image classification method based on deep convolution neural network and multi-kernel learning is proposed. Firstly, the deep convolution neural network is constructed to train the remote sensing image data set to learn the outputs of two fully connected layers, which will be taken as two high-level features of remote sensing images. Then, the multi-kernel learning is used to train the kernel functions for these two high-level features, so that they can be mapped to the high dimensional space, where these two features are fused adaptively. Finally, with the combined features, a remote sensing image classifier based on Multi-Kernel Learning-Support Vector Machine (MKL-SVM) is designed for remote sensing image classification. Experimental results show that compared with the existing deep learning based remote sensing classification methods, the proposed algorithm achieves improved results in terms of classification accuracy, error, and Kappa coefficient. On the experimental test set, the above three indicators reach 96.43%, 3.57%, and 96.25% respectively, and satisfactory results are obtained.