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Candidate Region Extraction Method for Multi-satellite and Multi-resolution SAR Ships
Yan HU, Zili SHAN, Feng GAO
, Available online  , doi: 10.11999/JEIT180525 doi: 10.11999/JEIT180525
[Abstract](135) [FullText HTML] (73) [PDF 7117KB](26)
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.
Radar HRRP Target Recognition Based on Deep Multi-Scale 1D Convolutional Neural Network
Chen GUO, Tao JIAN, Congan XU, You HE, Shun SUN
, Available online  , doi: 10.11999/JEIT180677 doi: 10.11999/JEIT180677
[Abstract](14) [FullText HTML] (10) [PDF 1645KB](4)
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.
Underdetermined Wideband DOA Estimation Based on Distributed Compressive Sensing
Ying JIANG, Bingqie WANG, Jun HAN, Yi HE
, Available online  , doi: 10.11999/JEIT180723 doi: 10.11999/JEIT180723
[Abstract](9) [FullText HTML] (4) [PDF 1682KB](2)
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.
Evil Waveform Evaluating Method for New GNSS Signals
Chengyan HE, Xiaochun LU, Ji GUO
, Available online  , doi: 10.11999/JEIT180656 doi: 10.11999/JEIT180656
[Abstract](4) [FullText HTML] (3) [PDF 1175KB](0)
The waveform characteristics of the navigation signals of Global Navigation Satellite Systems (GNSSs) will be of vital importance for signal quality, which plays an imperative and direct role in achieving high performance of GNSS services. These traditional methods for evaluating evil waveforms mainly deal with the amplitude and width of simple modulated signals such as Phase Shift Keying (PSK) signals. However, no research is done on the influences of waveform asymmetry on tracking errors and ranging errors. Based on the traditional thread models, such as Thread Model A (TMA), Thread Model B (TMB) and Thread Model C (TMC), adopted by International Civil Aviation Organization (ICAO), this paper provides a new extended general thread model suitable for new Binary Offset Carrier (BOC) modulated signals. Then a new evil waveform analysis method, Waveform Rising and Falling Edge Symmetry (WRaFES) Method, is proposed in this paper. The effects of WRaFES model are analyzed in detail in terms of time domain, correlation peak and S curve bias. Finally, taking the B1Cd signal of the first modernized BeiDou navigation satellite System (BDS) experimental satellite named M1-S as an example, tested results of WRaFES model and correlation curves are shown in detail. Results show that the proposed methods could be able to analyze the asymmetry of signal deformation and its impact on ranging performance with high accuracy. The research brings about a new reference for new satellite navigation signal evaluation and signal system optimized design. In addition, it can provide valuable suggestions and technical supports for GNSS users to choose reasonable receivers’ correlator spacing.
Design and Research of DC-6 GHz Broadband Electromagnetic Radiation Experimental Device
Shiqi WANG, Shaojun FANG, Peng CHEN
, Available online  , doi: 10.11999/JEIT180593 doi: 10.11999/JEIT180593
[Abstract](8) [FullText HTML] (5) [PDF 2161KB](4)
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.
Accelerating Functional Verification for Digital Circuit with FPGA Hard Processor System
Xiaoqiang LIU, Guoshun YUAN, Shushan QIAO
, Available online  , doi: 10.11999/JEIT180641 doi: 10.11999/JEIT180641
[Abstract](13) [FullText HTML] (7) [PDF 1751KB](2)
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.
A Model for Virtualized Network Function Placement with Hardware Acceleration Support
Yuxiang HU, Hongwei FAN, Julong LAN, Tong DUAN
, Available online  , doi: 10.11999/JEIT180861 doi: 10.11999/JEIT180861
[Abstract](37) [FullText HTML] (24) [PDF 2233KB](13)
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.
Suppressed Non-local Spatial Intuitionistic Fuzzy C-means Image Segmentation Algorithm
Rong LAN, Yang LIN
, Available online  , doi: 10.11999/JEIT180651 doi: 10.11999/JEIT180651
[Abstract](45) [FullText HTML] (29) [PDF 2456KB](8)
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.
Denoising of MEMS Gyroscope Based on Improved Wavelet Transform
Guangwu CHEN, Xiaobo LIU, Di WANG, Shede LIU
, Available online  , doi: 10.11999/JEIT180590 doi: 10.11999/JEIT180590
[Abstract](45) [FullText HTML] (22) [PDF 2154KB](4)
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.
Remote Sensing Image Classification Method Based on Deep Convolution Neural Network and Multi-kernel Learning
Xin WANG, Ke LI, Chen NING, Fengchen HUANG
, Available online  , doi: 10.11999/JEIT180628 doi: 10.11999/JEIT180628
[Abstract](44) [FullText HTML] (26) [PDF 8293KB](7)
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.
An Airborne SAR Image Target Location Algorithm Based on Parameter Refining
Yuan WU
, Available online  , doi: 10.11999/JEIT180564 doi: 10.11999/JEIT180564
[Abstract](70) [FullText HTML] (40) [PDF 3053KB](4)
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.
Resource Allocation Algorithm of Network Slicing Based on Online Auction
Liang LIANG, Yanfei WU, Gang FENG
, Available online  , doi: 10.11999/JEIT180636 doi: 10.11999/JEIT180636
[Abstract](83) [FullText HTML] (44) [PDF 1460KB](7)
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.
A Fast Convergent Cross-layer Resource Optimization Allocation Algorithm in Wireless Multi-hop Networks
Wei FENG, Yongxin XU, Hao LIU, Xiaorong XU, Yingbiao YAO
, Available online  , doi: 10.11999/JEIT180581 doi: 10.11999/JEIT180581
[Abstract](64) [FullText HTML] (33) [PDF 1270KB](5)
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.
Robust Fuzzy C-means Clustering Algorithm Integrating Between-cluster Information
Yunlong GAO, Chengyu YANG, Zhihao WANG, Sizhe LUO, Jinyan PAN
, Available online  , doi: 10.11999/JEIT180604 doi: 10.11999/JEIT180604
[Abstract](63) [FullText HTML] (33) [PDF 1894KB](10)
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.
A Motion Error Estimation Method Joint Envelope and Phase for 10 GHz Ultra-wideband Microwave Photonic-based SAR Image
Xiaoxiang CEHN, Mengdao XING, Guangcai SUN, Guobin JING
, Available online  , doi: 10.11999/JEIT180563 doi: 10.11999/JEIT180563
[Abstract](51) [FullText HTML] (27) [PDF 3989KB](7)
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.
Investigation on the Radiated Interference E-field Threshold Testing for Common-mode Interference of Transmission Lines in Reverberation Chambers
Dezhou HU, Guanghui WEI, Xiaodong PAN, Xinfu LU
, Available online  , doi: 10.11999/JEIT180328 doi: 10.11999/JEIT180328
[Abstract](77) [FullText HTML] (33) [PDF 1861KB](3)
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.
Constructions of Gaussian Integer Periodic Complementary Sequences Based on Difference Families
Tao LIU, Chengqian XU, Yubo LI
, Available online  , doi: 10.11999/JEIT180646 doi: 10.11999/JEIT180646
[Abstract](42) [FullText HTML] (22) [PDF 372KB](12)
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.
The Incentive Model for Mobile Crowd Sensing Oriented to Differences in Mission Costs
Jian WANG, Yue HUANG, Guosheng ZHAO, Zhongnan ZHAO
, Available online  , doi: 10.11999/JEIT180640 doi: 10.11999/JEIT180640
[Abstract](66) [FullText HTML] (32) [PDF 1404KB](4)
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.
Geographical Location Recognition of IP Based on Network Structure Features
Gaolei FEI, Yameng ZHANG, Zhiyu HU, Lei ZHOU, Guangmin HU
, Available online  , doi: 10.11999/JEIT180589 doi: 10.11999/JEIT180589
[Abstract](33) [FullText HTML] (18) [PDF 1363KB](6)
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.
Research on Spaceborne High Resolution Wide Swath Imaging Method Based on Relax Algorithm
, Available online  , doi: 10.11999/JEIT180596 doi: 10.11999/JEIT180596
[Abstract](107) [FullText HTML] (34) [PDF 2188KB](8)
The modern spaceborne SAR system requires both high resolution and wide swath, and the conventional single channel spaceborne SAR system has a contradiction between the two important indexes, so the azimuth multichannel method is proposed and used to solve the above problem. Based on the analysis of the azimuth multichannel echo model and the characteristics of the Relax algorithm, a spaceborne SAR High Resolution Wide Swath (HRWS) imaging method is proposed, and the iterative process of the new method is described in detail. By the simulation of point target echo, and comparing with the traditional azimuth multichannel HRWS reconstruction methods, the reliability and effectiveness of the proposed method are verified.
Energy Efficiency Routing Strategy with Lightpath Impairment Awareness in Service-Oriented Elastic Optical Networks
Huanlin LIU, Fei FANG, Jun HUANG, Yong CHEN, Min XIANG, Yue MA
, Available online  , doi: 10.11999/JEIT180580 doi: 10.11999/JEIT180580
[Abstract](94) [FullText HTML] (47) [PDF 2408KB](11)
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.
Security Analysis and Improvement of Certificateless Aggregate Signature Scheme for Vehicular Ad Hoc Networks
Xiaodong YANG, Tingchun MA, Chunlin CHEN, Jinli WANG, Caifen WANG
, Available online  , doi: 10.11999/JEIT180571 doi: 10.11999/JEIT180571
[Abstract](77) [FullText HTML] (33) [PDF 459KB](7)
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.
Parallel MoM Using the Six Hundred Thousand Cores on Domestically-made and Many-core Supercomputer
Zongjing GU, Haoxiang WU, Xunwang ZHAO, Zhongchao LIN, Yu ZHANG, Qi ZHANG
, Available online  , doi: 10.11999/JEIT180562 doi: 10.11999/JEIT180562
[Abstract](75) [FullText HTML] (41) [PDF 2163KB](8)
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.
A Fast Random-valued Impulse Noise Detection Algorithm Based on Deep Belief Network
Shaoping XU, Guizhen ZHANG, Chongxi LI, Tingyun LIU, Yiling TANG
, Available online  , doi: 10.11999/JEIT180558 doi: 10.11999/JEIT180558
[Abstract](150) [FullText HTML] (76) [PDF 3168KB](31)
To improve the detection accuracy and execution efficiency of the existing Random-Valued Impulse Noise (RVIN) detectors, a fast training-based RVIN detection algorithm is implemented by constructing a more descriptive feature vector and training a detection model with more accurate nonlinear mapping. On the one hand, multiple Rank-Ordered Logarithmic absolute Deviation (ROLD) statistics are extracted and combined with a statistical value reflecting the edge characteristics in the form of feature vector to describe how RVIN-like the center pixel of a patch is. The description ability of the feature vector is improved significantly while the computational complexity is just increased in small amount. On the other hand, an RVIN prediction model (RVIN detector) is obtained by training a Deep Belief Network (DBN) to map the feature vectors to noise labels, which is more accurate than the shallow prediction model. Extensive experimental results show that, compared with the existing RVIN detectors, the proposed one has better performance in terms of detection accuracy and execution efficiency.
Image Quality Assessment Algorithm Based on Non-local Gradient
Minjuan GAO, Hongshe DANG, Lili WEI, Xuande ZHANG
, Available online  , doi: 10.11999/JEIT180597 doi: 10.11999/JEIT180597
[Abstract](95) [FullText HTML] (46) [PDF 1407KB](12)
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.
Single-observer DOA/TDOA Registration and Passive Localization Based on Constrained Total Least Squares
Yan ZUO, Zhimeng CHEN, Liping CAI
, Available online  , doi: 10.11999/JEIT180655 doi: 10.11999/JEIT180655
[Abstract](21) [FullText HTML] (12)
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.
Geometry-based Modeling for Cooperative MIMO Channel in High-Speed Railway Scenarios
Cheng TAO, Zhenqiao ZHAO, Tao ZHOU
, Available online  , doi: 10.11999/JEIT180680 doi: 10.11999/JEIT180680
[Abstract](10) [FullText HTML] (8)
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.
Recurrent Neural Networks Based Wireless Network Intrusion Detection and Classification Model Construction and Optimization
Hongsong CHEN, Jingjiu CHEN
, Available online  , doi: 10.11999/JEIT180691 doi: 10.11999/JEIT180691
[Abstract](18) [FullText HTML] (9) [PDF 1417KB](7)
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.
Multi-objective Evolutionary Semi-supervised Fuzzy Clustering Image Segmentation Motivated by Region Information
Feng ZHAO, Mimi ZHANG, Hanqiang LIU
, Available online  , doi: 10.12000/JR180605 doi: 10.12000/JR180605
[Abstract](8) [FullText HTML] (7)
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.
Related-key Impossible Differential Cryptanalysis on Lightweight Block Cipher ESF
Min XIE, Qiya ZENG
, Available online  , doi: 10.11999/JEIT180576 doi: 10.11999/JEIT180576
[Abstract](131) [FullText HTML] (67) [PDF 2242KB](12)
Eight-Sided Fortress (ESF) is a lightweight block cipher with a generalized Feistel structure, which can be used in resource-constrained environments such as protecting Radio Frequency IDentification (RFID) tags in the internet of things. At present, the research on the security of ESF mainly adopts the impossible differential cryptanalysis. The ability of ESF to resist the related-key impossible differential cryptanalysis is studied based on the characteristics of its S-boxes and key schedule. By constructing an 11-round related-key impossible differential distinguisher, an attack on 15-round ESF is proposed by adding 2-round at the top and 2-round at the bottom. This attack has a time complexity of 240.5 15-round encryptions and a data complexity of 261.5 chosen plaintexts with 40 recovered key-bit. Compared with published results, the time complexity is decreased and the data complexity is ideal with the number of attack rounds increased.
Security Hybrid Beamforming Algorithm for Millimeter Wave Downlink Multiuser System
Kaizhi HUANG, Shaoyu WANG, Xiaoming XU, Yajun CHEN
, Available online  , doi: 10.11999/JEIT180713 doi: 10.11999/JEIT180713
[Abstract](50) [FullText HTML] (34) [PDF 1455KB](13)
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. The 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.
Integration of MIMO Radar and Communication with OFDM-LFM Signals
Bingfan LIU, Baixiao CHEN
, Available online  , doi: 10.11999/JEIT180547 doi: 10.11999/JEIT180547
[Abstract](108) [FullText HTML] (62) [PDF 2311KB](21)
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.
An Accurate Wideband Beampattern Synthesis Method Based on the Space-frequency Structure and the Space-time Structure Conversion
Xu WANG, Julan XIE, Zishu HE, Huiyong LI
, Available online  , doi: 10.11999/JEIT180545 doi: 10.11999/JEIT180545
[Abstract](111) [FullText HTML] (60) [PDF 1030KB](15)
An accurate wideband beampattern synthesis method based on the space-time structure is proposed. Making use of the property that the magnitude response can be translated into linear function under the condition of conjugate symmetric weights, the beampattern synthesis problem is transformed into the convex optimization problem. The weights of space-time structure can be obtained by utilizing the principle of relationship between the two structures, after the weights of space-frequency structure is calculated by the interior point method. The proposed method can realize the wideband beampattern synthesis accurately, meanwhile ensuring the linear phase characteristic of the array response. Simulation results demonstrate the effectiveness of the proposed method.
An Adaptive Consistent Iterative Hard Thresholding Alogorith for Audio Declipping
Xia ZOU, Penglong WU, Meng SUN, Xingyu ZHANG
, Available online  , doi: 10.11999/JEIT180543 doi: 10.11999/JEIT180543
[Abstract](135) [FullText HTML] (73) [PDF 1685KB](21)
Audio clipping distortion can be solved by the Consistent Iterative Hard Thresholding (CIHT) algorithm, but the performance of restoration will decrease when the clipping degree is large, so, an algorithm based on adaptive threshold is proposed. The method estimates automatically the clipping degree, and the factor of the clipping degree is adjusted in the algorithm according to the degree of clipping. Compared with the CIHT algorithm and the Consistent Dictionary Learning (CDL) algorithm, the performance of restoration by the proposed algorithm is much better than the other two, especially in the case of severe clipping distortion. Compared with CDL, the computational complexity of the proposed algorithm is low like CIHT, compared with CDL, it has faster processing speed, which is beneficial to the practicality of the algorithm.
Supervised Learning Based Truthful Auction Mechanism Design in Cloud Computing
Jixian ZHANG, Ning XIE, Xuejie ZHANG, Weidong LI
, Available online  , doi: 10.11999/JEIT180587 doi: 10.11999/JEIT180587
[Abstract](63) [FullText HTML] (31) [PDF 1937KB](10)
Auction based resource allocation can make resource provider get more profit, which is a major challenging problem for cloud computing. However, the resource allocation problem is NP-hard and can not be solved in polynomial time. Existing studies mainly use approximate algorithms or heuristic algorithms to implement resource allocation in auction, but these algorithms have the disadvantages of low computational efficiency or low allocate accuracy. In this paper, the classification and regression of supervised learning is used to model and analyze multi-dimensional cloud resource allocation, for the different scale of problem, three resource allocation predict algorithms based on linear regression, logistic regression and Support Vector Machine (SVM) are proposed. Through the learning of the small-scale training set, the predict model can guarantee that the social welfare, allocation accuracy, and resource utilization in the feasible solution are very close to the optimal allocation solution. The payment price algorithm based on the critical value theory is proposed which ensure the truthful property of the auction mechanism design. Final experimental results show that the proposed scheme has good effect for resource allocation in cloud computing.
A Direct Fusion Algorithm for Multiple Pieces of Evidence Based on Improved Conflict Measure
Li ZHOU, Xinming ZHANG, Weizhen GUO, Yan WANG
, Available online  , doi: 10.11999/JEIT180578 doi: 10.11999/JEIT180578
[Abstract](60) [FullText HTML] (30) [PDF 816KB](7)
In the light of the disadvantages that Jousselme’s evidential distance function can not describe the local conflicting information of evidence well and can not measure the conflict of high conflicting evidence accurately, an improved Jousselme’s evidential distance function is proposed. In the new function, Jousselme’s evidence distance function is improved by using the non-coincidence degree which can better describe the local conflict of evidence, so that the conflict measure result of evidence varies proportionally with the value of the non-coincidence degree and the scope of its change. Secondly, an improved fusion conflict measure function is constructed based on the conflict coefficient and the new improved Jousselme’s evidential distance function. On this basis, the weight coefficient formula of focal element is improved, and the local multi-dimensional conflicting information is assigned proportionately. Theoretical and application analysis results show that the new algorithm is a kind of evidence fusion algorithm with wide applicability and good anti-jamming performance.
Fractional Fourier Transform and Compressed Sensing Adaptive Countering Smeared Spectrum Jamming
Yang ZHAO, Chaoxuan SHANG, Zhuangzhi HAN, Ning HAN, Hui XIE
, Available online  , doi: 10.11999/JEIT180569 doi: 10.11999/JEIT180569
[Abstract](61) [FullText HTML] (31) [PDF 2038KB](4)
SMeared SPectrum (SMSP) jamming has lots of coupling in time and frequency domain with Linear Frequency Modulated (LFM) radar signals, which has a great jamming performance. This paper proposed a signal processing method for countering SMSP jamming in information domain. According to the formulation and characteristics of SMSP signal, this method changes the jamming dictionary automatically, matches the frequency modulation rate of LFM and SMSP signal at the same time, constructs the compressed sampling model and carries out reconstruction of signal based on convex optimization. Finally, the recognition of jamming signal and extraction of radar signal are achieved. The construction of redundant dictionary used Pei type fractional Fourier decomposition method. Modulation and demodulation between time and frequency domain are avoided in this method, which leads to an improve in fewer iteration times and higher arithmetic speed.
An Improved Knowledge-aided Space-time Adaptive Signal Processing Algorithm for MIMO Radar
Jing HOU, Mengkai HU, Ziwei WANG
, Available online  , doi: 10.11999/JEIT180557 doi: 10.11999/JEIT180557
[Abstract](103) [FullText HTML] (38) [PDF 1399KB](10)
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.
Cluster-Based Algorithm of Reconnaissance UAV Swarm Based on Wireless Ultraviolet Secret Communication
Taifei ZHAO, Shan XU, Yao QU, Jing WANG, Jie ZHANG
, Available online  , doi: 10.11999/JEIT180491 doi: 10.11999/JEIT180491
[Abstract](85) [FullText HTML] (45) [PDF 2023KB](17)
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.
Interference Modeling of Two Point Source Retrodirective Cross-eye Considering Target Echo
Liang ZHOU, Jin MENG, Hao WU, Yongcai LIU, Wei LIU
, Available online  , doi: 10.11999/JEIT180482 doi: 10.11999/JEIT180482
[Abstract](94) [FullText HTML] (40) [PDF 1334KB](11)
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.
Virus Propagation Model and Stability Under the Hybrid Mechanism of “Two-go and One-live”
Gang WANG, Shiwei LU, Xin HU, Runnian MA
, Available online  , doi: 10.11999/JEIT180381 doi: 10.11999/JEIT180381
[Abstract](284) [FullText HTML] (226) [PDF 1436KB](39)
With the development of network information system, virus propagation and immunization strategy become one of the hot topics in the field of network security. In this paper, a new virus with hybrid attacking is introduced, which can attack network in two modes. One is to attack and infect the network nodes directly, and the another is to hide itself in the nodes by hiding its viral characteristic. According to its characteristics, this type of virus is defined as " Two-go and One-live” and the corresponding virus propagation model is established. Moreover, the stability of the system is studied by solving the equilibrium points and analyzing the basic reproduction number R0. Numerical simulations are presented to verify effectiveness and stability of the novel model.
Frequency Sorting Algorithm Based on Dynamic Ring Oscillator Physical Unclonable Function Statistical Model
Jinfu XU, Jin WU
, Available online  , doi: 10.11999/JEIT180405 doi: 10.11999/JEIT180405
[Abstract](130) [FullText HTML] (70) [PDF 1255KB](8)
The existing Ring Oscillator (RO) Physical Unclonable Function (ROPUF) design has low reliability and uniqueness, resulting in poor application security. A statistical model for ROPUF is proposed, the factors of reliability and uniqueness are quantitatively analyzed, it is found that the larger delay difference can improve the reliability, and the lower process difference between RO units can improve the uniqueness. According to the conclusion of the model, a dynamic RO unit is designed based on the mesh topological structure. In combination with the frequency distribution characteristics of the RO array, a new frequency sorting algorithm is designed to increase the delay difference and reduce the process variation of the RO unit, thereby improving the reliability and uniqueness of ROPUF. The results show that compared with other improved ROPUF designs, the reliability and uniqueness of the proposed design has significant advantages, which can reach 99.642% and 49.1%, and temperature changes affect minimally them. It is verified by security analysis that the proposed design has strong anti-modeling attack capabilities.
WSN Timer Resolution Adjustment Based on UKF Approach
Hao HE, Weidong YI, Yongrui CHEN, Zhe WANG
, Available online  , doi: 10.11999/JEIT171049 doi: 10.11999/JEIT171049
[Abstract](109) [FullText HTML] (56) [PDF 1648KB](11)
During the radio-off periods of Wireless Sensor Network (WSN) node, the timer Interrupt ReQuest (IRQ) which used to maintain the system clock become an important energy consumption source of Micro Controller Unit (MCU), thus the IRQ frequency has a great influence on WSN node total energy consumption. A timer resolution adjustment method based on Unscented Kalman Filter (UKF) approach is proposed, which switches high and low IRQ frequencies according to the characteristics of the protocol. Being at a low frequency during sleep period, if a node needs to switch to wake-up period, it will first obtain the optimal estimation of the start time of high resolution timing period by UKF, then enter the high resolution timing period after a linear combination of a group of gradual-changing resolution timer IRQ. The simulations of ContikiMAC protocol on the Tmote platform are conducted. When the Radio Duty Cycle (RDC) is 0.53%, the proposed method reduces the total power consumption by 28.85% compared to the original protocol.
An Efficient Online Algorithm for Load Balancing in Software Defined Networks Based on Efficiency Range
Jiugen SHI, Hao XU, Jing ZHANG, Ji WANG
, Available online  , doi: 10.11999/JEIT180464 doi: 10.11999/JEIT180464
[Abstract](135) [FullText HTML] (82) [PDF 715KB](18)
Due to the limitation of individual controller’s processing capacity in large-scale complex Software Defined Networks (SDN), an efficient online algorithm for load balancing among controllers based on efficiency range is proposed to improve load balancing among controllers and reduce the propagation delay between a controller and the switch. In the initial static network, the initial set of controllers is selected by a greedy algorithm, then M improved Minimum Spanning Trees (MST) rooted at the initial set of controllers are constructed, so initial M subnets with load balancing are determined. With the dynamic changes of load, for the purpose of making the controller work within efficiency range at any time, several switches in different subnets are reassigned by Breadth First Search (BFS). The initial set of controllers is updated for minimizing propagation delay in the algorithms’ last step. The algorithm is based on the connectivity of intra-domain and inter-domain. Simulation results show that the proposed algorithms not only guarantee the load balancing among controllers, but also guarantee the lower propagation delay. As to compare to PSA algorithm, optimized K-Means algorithm, etc., it can make Network Load Balancing Index (NLBI) averagely increase by 40.65%.
Development and Prospect of Radar and Communication Integration
Bo XIAO, Kai HUO, Yongxiang LIU
, Available online  , doi: 10.11999/JEIT180515 doi: 10.11999/JEIT180515
[Abstract](162) [FullText HTML] (72) [PDF 2009KB](24)
Radar communication integration realizes radar detection and communication transmission via shared hardware equipment, the integration is easier to be integrated, miniaturized and utilize effectively spectrum compared with traditional individual radar and communication devices. This paper systematically introduces the principles and characteristics of radar communication integration, presents the urgent problems need to be solved within integration investigation, starting from typical radar communication integration signal based on Linear Frequency Modulation (LFM), this paper reviews comprehensively the related research on radar communication integration and primarily summarizes the research developments of Orthogonal Frequency Division Multiplexing (OFDM) and Multi-Input Multi-Output (MIMO) techniques in critical directions including waveform design, signal processing and integrated system conception. Finally, the potential developing trend and significant application scenario in military and civilian intelligent transportation field of radar communication integration is analyzed.
Multi-server Key Aggregation Searchable Encryption Scheme in Cloud Environment
Yulei ZHANG, Xiangzhen LIU, Xiaoli LANG, Yongjie ZHANG, Wenjuan CHEN, Caifen WANG
, Available online  , doi: 10.11999/JEIT180418 doi: 10.11999/JEIT180418
[Abstract](114) [FullText HTML] (60) [PDF 568KB](13)
Key aggregation searchable encryption can not only retrieve ciphertext through keywords, but also can reduce user key management costs and security risks. This paper analyzes a verifiable key aggregation searchable encryption scheme, noting that the scheme does not satisfy keyword guessing attacks, and that unauthorized internal users can guess the private keys of other users. In order to improve the security of the original scheme, a multi-server key aggregation searchable encryption scheme is proposed in the cloud environment. The new scheme not only improves the security of the original solution, but also adds multi-service features, and improves the storage and search efficiency. Therefore, it is more suitable for a one-to-many user environment.
Sample and Hold Front-end Circuit for 14-bit 210 MS/s Charge-domain ADC
Zhenhai CHEN, Jinghe WEI, Hongwen QIAN, Zongguang YU, Xiaobo SU, Yan XUE, Hong ZHANG
, Available online  , doi: 10.11999/JEIT180337 doi: 10.11999/JEIT180337
[Abstract](168) [FullText HTML] (72) [PDF 2118KB](15)
A high precision common mode level insensitive sample and hold front-end circuit for charge domain pipelined Analog-to-Digital Converter (ADC) is proposed. The sample and hold circuit can be used to compensate the common mode charge errors caused by the variation of input common mode level in charge domain pipelined ADCs. Based on the proposed sample and hold circuit, a 14-bit 210 MS/s charge domain pipelined ADC is designed and realized in a 1P6M 0.18 μm CMOS process. Test results show the 14-bit 210 MS/s ADC achieves the signal-to-noise ratio of 71.5 dBFS and the spurious free dynamic range of 85.4 dBc, with 30.1 MHz input single tone signal at 210 MS/s, while the ADC core consumes the power consumption of 205 mW and occupies an area of 3.2 mm2.
Action Recognition Based on Multi-model Voting with Cross Layer Fusion
Huilan LUO, Fei LU, Yuan YAN
, Available online  , doi: 10.11999/JEIT180373 doi: 10.11999/JEIT180373
[Abstract](133) [FullText HTML] (70) [PDF 882KB](16)
To solve the problem of the loss in the motion features during the transmission of deep convolution neural networks and the overfitting of the network model, a cross layer fusion model and a multi-model voting action recognition method are proposed. In the preprocessing stage, the motion information in a video is gathered by the rank pooling method to form approximate dynamic images. Two basic models are presented. One model with two horizontally flipping layers is called " non-fusion model”, and then a fusion structure of the second layer and the fifth layer is added to form a new model named " cross layer fusion model”. The two basic models of " non-fusion model” and " cross layer fusion model” are trained respectively on three different data partitions. The positive and negative sequences of each video are used to generate two approximate dynamic images. So many different classifiers can be obtained by training the two proposed models using different training approximate dynamic images. In testing, the final classification results can be obtained by averaging the results of all these classifiers. Compared with the dynamic image network model, the recognition rate of the non-fusion model and the cross layer fusion model is greatly improved on the UCF101 dataset. The multi-model voting method can effectively alleviate the overfitting of the model, increase the robustness of the algorithm and get better average performance.
Region Proposal Generation for Object Detection Using Tree-DDQN by Action Attention
Guoyu ZUO, Tingting DU, Lei MA, Jiahao LU, Daoxiong Gong
, Available online  , doi: 10.11999/JEIT180358 doi: 10.11999/JEIT180358
[Abstract](144) [FullText HTML] (70) [PDF 2245KB](9)
Considering the problem of object detection of robots in the home environments, a Tree-Double Deep Q Network (TDDQN) based on the attention action strategy is proposed to determine the locations of region proposals. It combines DDQN with hierarchical tree structure. First, DDQN is used to select the best action of current state and obtain the right region proposal with a few actions executed. According to the state obtained after executing the selected action, the above process is repeated to create multiple "best" paths of the hierarchical tree structure. The best region proposal is selected using non-maximum suppression on region proposals that meet the conditions. Experimental results on Pascal VOC2007 and Pascal VOC2012 show that the proposed method based on TDDQN has better detection performance than other methods for region proposals of different numbers, different Intersection-over-Union (IoU) values and objects of different sizes and kinds, respectively.
Study on Frequency Shift in Mutual Coupling Effect of Ultra-High-Frequency Radio Frequency IDentification Near-field System
Yigang HE, Peiliang SHE, Lei ZUO, Chaoqun ZHANG
, Available online  , doi: 10.11999/JEIT180375 doi: 10.11999/JEIT180375
[Abstract](198) [FullText HTML] (130) [PDF 3026KB](31)
In Near-Field (NF) applications of Ultra-High-Frequency Radio Frequency IDentification (UHF RFID) systems, due to the structural characteristics of the microstrip tag, the traditional inter-coil mutual impedance expression has a large error in the estimation of the mutual coupling effect such as the frequency shift of the prediction system, and the accuracy is not enough. Firstly, based on the transformer model, the mutual impedance expressions of the NF dense tags are derived from the perspective of radio energy transmission. Then, the electrical parameter values are obtained indirectly by establishing the electromagnetic simulation model combining with the NF inductance coupling tag. Finally, the derivation formula is verified and UHF RFID NF frequency shift is studied from the perspective of environmental factors that affect the mutual impedance between the two tags. The test results show that the derived mutual impedance expression is applied to the frequency offset calculation with error range in 1.6 MHz~7.3 MHz when the tags’ spacing is less than 30 mm. The results provide a reference for studying the mutual coupling effect between UHF RFID NF tags based on the mutual impedance between tags.
Progress in Research on Marine Oil Spills Detection Using Synthetic Aperture Radar
Yu LI, Jie CHEN, Yuanzhi ZHANG
, Available online  , doi: 10.11999/JEIT180468 doi: 10.11999/JEIT180468
[Abstract](135) [FullText HTML] (32) [PDF 1735KB](11)
Marine oil spill pollution is a serious threat to the marine ecological environment, human life and economic development. Synthetic Aperture Radar (SAR) becomes one of the main technologies for marine oil film detection because of its all-weather and high sensitivity observation capability. This article first introduces the research progress of oil film detection technology on single polarimetric, fully polarimetric and compact polarimetric SAR technologies, based on the basic principle of SAR oil slick detection. Then the main difficulties and challenges encountered in the current research are analyzed. Finally, the broad prospects for the future development of this technology are forecasted.
Adaptive Color Image Steganography Based on Dynamic Distortion Modification
Guangming TANG, Mingming JIANG, Yi SUN
, Available online  , doi: 10.11999/JEIT180388 doi: 10.11999/JEIT180388
[Abstract](242) [FullText HTML] (120) [PDF 3908KB](12)
Considering the possible security problems of directly extending steganographic schemes for gray-scale images to color images, an adaptive distortion-updated steganography method is put forward based on the Modification Strategy for Color Components (CCMS). First, the correlation between color components and RGB channels is analyzed, and the principle of distortion cost modification is proposed. Moreover, the optimal modification mode is conducted to maintain the statistical correlation of adjacent components. Finally, color image steganography schemes called CCMS are proposed. The experimental results show that the proposed HILL-CCMS and WOW-CCMS make great improvement over HILL and WOW methods under 5 embedding rates in resisting state-of-the-art color steganalytic methods such as CRM and SCCRM.
Visual Tracking Method Based on Reverse Sparse Representation under Illumination Variation
Hongyan WANG, Helei QIU, Jia ZHENG, Bingnan PEI
, Available online  , doi: 10.11999/JEIT180442 doi: 10.11999/JEIT180442
[Abstract](128) [FullText HTML] (78) [PDF 1385KB](23)
Focusing on the issue of heavy decrease of object tracking performance induced by illumination variation, a visual tracking method via jointly optimizing the illumination compensation and multi-task reverse sparse representation is proposed. The template illumination is firstly compensated by the developed algorithm, which is based on the average brightness difference between templates and candidates. In what follows, the candidate set is exploited to sparsely represent the templates after illumination compensation. Subsequently, the obtained multiple optimization issues associated with single template can be recast as a multi-task optimization one related to multiple templates, which can be solved by the alternative iteration approach to acquire the optimal illumination compensation coefficient and the sparse coding matrix. Finally, the obtained sparse coding matrix can be exploited to quickly eliminate the unrelated candidates, afterwards the local structured evaluation method is employed to achieve the accurate object tracking. As compared to the existing state-of-the-art algorithms, simulation results show that the proposed algorithm can improve the accuracy and robustness of the object tracking significantly in the presence of heavy illumination variation.
Message Signature Scheme for ZigBee Network Security Positioning
Yicai HUANG, Sensen LI, Bowu BAO, Bin YU
, Available online  , doi: 10.11999/JEIT180064 doi: 10.11999/JEIT180064
[Abstract](154) [FullText HTML] (64) [PDF 1457KB](8)
In view of the security of message in ZigBee network node location, a message signature scheme with privacy protection is proposed. The proposed scheme is based on Elliptic Curve Cryptosystem (ECC) without bilinear pairing, and location request message signature algorithm with identity privacy protection and location reference message signature algorithm with coordinate privacy protection are put forward. It is proved theoretically that the proposed scheme can not only resist the various external attacks, such as forgery attack, replay attack, etc., but also has the function of privacy protection and identity tracking. Performance analysis shows that the proposed scheme has the advantages of computing overhead and communication overhead over similar schemes.
Night-vision Image Fusion Based on Intensity Transformation and Two-scale Decomposition
Haoran ZHU, Yunqing LIU, Wenying ZHANG
, Available online  , doi: 10.11999/JEIT180407 doi: 10.11999/JEIT180407
[Abstract](163) [FullText HTML] (73) [PDF 4599KB](14)
In order to achieve more suitable night vision fusion images for human perception, a novel night-vision image fusion algorithm is proposed based on intensity transformation and two-scale decomposition. Firstly, the pixel value from the infrared image is used as the exponential factor to achieve intensity transformation of the visible image, so that the task of infrared-visible image fusion can be transformed into the merging of homogeneous images. Secondly, the enhanced result and the original visible image are decomposed into base and detail layers through a simple average filter. Thirdly, the detail layers are fused by the visual weight maps. Finally, the fused image is reconstructed by synthesizing these results. The fused image is more suitable for the visual perception, because the proposed method presents the result in the visual spectrum band. Experimental results show that the proposed method outperforms obviously the other five methods. In addition, the computation time of the proposed method is less than 0.2 s, which meet the real-time requirements. In the fused result, the details of the background are clear while the objects with high temperature variance are highlighted as well.
Area and Delay Optimization of Binary Decision Diagrams Mapped Circuit
Huihong ZHANG, Zhiwen CHEN, Pengjun WANG
, Available online  , doi: 10.11999/JEIT180443 doi: 10.11999/JEIT180443
[Abstract](191) [FullText HTML] (64) [PDF 763KB](11)
Binary Decision Diagrams (BDD) is a data structure that can be used to describe a digital circuit. By replacing each node in a BDD with a 2-to-1 Multiplexer (MUX), a BDD can be mapped to a digital circuit. An area and delay optimization method on BDD mapped circuit is presented. A traditional Boolean circuit is converted into BDD form, and then diamond structure constructed by nodes is searched in the BDD, corresponding nodes are deleted and control signals of the modified nodes are updated by paths optimization, finally, the result BDD is mapped to a MUX circuit. The proposed method is test by a number of Microelectronics Center of North Carolina (MCNC) Benchmarks. Compared with the classical synthesis tools Sequential Interactive System (SIS) and BDD-based logic optimization system (BDS), the average number of nodes by the proposed methods is 55.8% less than that of BDS, and average circuit’s area and delay are reduced by 39.3% and 44.4% than that of the SIS, respectively.
Service Function Chain Deployment Algorithm Based on Longest Effective Function Sequence
Dan LI, Julong LAN, Peng WANG, Yuxiang HU
, Available online  , doi: 10.11999/JEIT180402 doi: 10.11999/JEIT180402
[Abstract](227) [FullText HTML] (71) [PDF 1214KB](16)
The efficiency of Service Function Chain (SFC) depends closely on where functions are deployed and how to select paths for data transmission. For the problem of SFC deployment in a resource-constrained network, this paper proposes an optimization algorithm for SFC deployment based on the Longest Effective Function Sequence (LEFS). To optimize function deployment and bandwidth requirement jointly, the upper bound of path length is set and relay nodes are searched incrementally on the basis of LEFS until the service request is satisfied. Simulation results show that, the proposed algorithm can balance network resource and optimize the function deploymen rate and bandwidth utilization. Compared with other algorithms, the utilization of network resource decreases 10%, so that more service requests can be supported. What is more, the algorithm has a lower computation complexity and can response to service requests quickly.
Performance Analysis of the Satellite-based Navigation Signal Acquisition under the Non-complete Spatial Overlapped Interference
Haichuan ZHANG, Fangling ZENG
, Available online  , doi: 10.11999/JEIT180355 doi: 10.11999/JEIT180355
[Abstract](130) [FullText HTML] (74) [PDF 3784KB](14)
A non-complete spatial overlapped interference signal model is proposed based on the jamming research against satellite-based positioning receiver for the acquisition of navigation signal. Firstly, the interference model is introduced and analyzed, and the signal fission effect induced by non-complete spatial overlapped interference is demonstrated. Then, the relationship between SINR of satellite-based positioning receiver and spatial overlapped length is derived, and the monotonic relationship of them is deduced. Simulation results suggest that SINR of satellite-based positioning receiver is the monotonically increasing function of spatial overlapped length, and the short-spatial-overlapped interference can restrain the peak amplitude of three dimensional frequency and coded domain correlation, degrading the performance of acquisition of satellite-based positioning receiver.
Frequency Domain Blind Source Separation Permutation Algorithm Based on Regional Growth Correction
Tianqi ZHANG, Huawei ZHANG, Donghua LIU, Qun LI
, Available online  , doi: 10.11999/JEIT180386 doi: 10.11999/JEIT180386
[Abstract](98) [FullText HTML] (58) [PDF 2323KB](16)
The convolutive blind source separation can be effectively solved in frequency domain, but blind source separation in frequency domain must solve the problem of ranking ambiguity. A frequency-domain blind source separation sorting algorithm is proposed based on regional growth correction. First, the convolutional mixed signal short-time Fourier transform is used to establish an instantaneous model at each frequency point in the frequency domain for independent component analysis. Based on this, the correlation of the power ratio of the separated signal is used to sort all frequency points one by one replacement. Second, according to the threshold, the sorted result is divided into several small areas. Finally. regional replacement and merging is performed according to the regional growth method, and the correct separation signal is finally obtained. Regional growth correction minimizes the mis-proliferation of frequency sorting and improves separation results. The speech blind source separation experiments are performed in the simulated and real environments respectively. The results show the effectiveness of the proposed algorithm.
Single-polarization SAR Data Flood Water Detection Method Based on Markov Segmentation
Deke TANG, Feng WANG, Hongqi WANG
, Available online  , doi: 10.11999/JEIT180420 doi: 10.11999/JEIT180420
[Abstract](180) [FullText HTML] (68) [PDF 2828KB](10)
China is a flood disaster-prone country, where floods occur frequently every year, from July to August. Therefore, rapid disaster detection and assessment of floods affected areas is of great significance. GF-3 SAR satellite data has obvious advantages of all-day, all-weather imaging characteristics in flood disaster reduction applications because of its active observation technology. For the purpose of rapid water detection in flooding area, a rapid detection method of flood area based on GF-3 single-polarized SAR data is proposed, including SAR preprocessing, flood extraction based on Markov random fields, shadow false alarm removal. Its detecting accuracy is evaluated with manual detection result. The test results show that this method can realize the rapid and accurate extraction of waters in flood disaster area.
Research on Network Virtualization Scheme and Networking Algorithm of Advanced Metering Infrastructure for Water, Electricity, Gas, and Heat Meters
Zhiyuan HU, Xiaofeng SONG, Tiancong HUANG, Xiaodi LI, Ruifang ZHOU, Xin XU, Zhanyu MENG, Qiang PENG
, Available online  , doi: 10.11999/JEIT180396 doi: 10.11999/JEIT180396
[Abstract](138) [FullText HTML] (84) [PDF 1395KB](9)
In order to achieve service data isolation in advanced metering Infrastructure for water, electricity, gas, and heat Meters and improve the stability and coverage of local data collection network, a network virtualization scheme of Advanced Metering Infrastructure (AMI) is proposed. In this scheme, the end-to-end isolated service data collection channels are constructed utilizing virtual Access Point Name (APN) and Software Defined Network (SDN) slice technology. The micro-power wireless and low-voltage power line carriers are used to constructed a real-time and reliable local dual mode virtual network. Furthermore, the networking algorithm based on global link-state and hierarchical iterative algorithm are proposed. The simulation and experiments show the packet loss rate and transmission delay of collected data are decreased utilizing the proposed scheme, and business support capability is improved. Moreover, the service data isolation is implemented in AMI for water, electricity, gas, and heat Meters and multiplexing ability of communication network infrastructure is improved.
Fast Scene Matching Method Based on Scale Invariant Feature Transform
Yanxiong NIU, Mengqi CHEN, He ZHANG
, Available online  , doi: 10.11999/JEIT180440 doi: 10.11999/JEIT180440
[Abstract](177) [FullText HTML] (64) [PDF 1703KB](10)
The traditional feature-based image matching method has many problems such as many redundant points and low matching accuracy, which can hardly meet the real-time and robustness requirements. In this regard, a fast scene matching method based on Scale Invariant Feature Transform (SIFT) is proposed. In the feature detection phase, FAST (Features from Accelerated Segment Test) is used to detect characteristics in multi-scale, after then, combining with Difference Of Gauss (DOG) operators to filter characteristics again. From this, the feature search process is simplified. In feature matching phase, the affine transformation model is used to simulate the transformation relation and establish the geometric constraint, to overcome the mismatching because of ignoring the geometric information. The experimental results show that the proposed method is superior to the SIFT in efficiency and precision, also has good robustness to light, blur and scale transformation, achieves scene matching better.
Design of Wideband High Performance Quad-ridge Waveguide Polarizer
Jin WANG, Biao DU, Lijie SUN, Lei XIE
, Available online  , doi: 10.11999/JEIT180423 doi: 10.11999/JEIT180423
[Abstract](156) [FullText HTML] (56) [PDF 2554KB](4)
Circular polarizer is a key component in feed systems with circular polarization in radio astronomy telescope and satellite communication antennas. Conventional polarizers are capable of operating over a maximum bandwidth of 40% with an axial ratio value of 0.75 dB, which is unable to meet the growing demand for wide band applications. In this paper, the design of the wide band quad ridges waveguide polarizer is introduced, and the relationship between the phase constants of two orthogonal principal modes is analyzed. The broadband phase shift characteristics are achieved by employing different horizontal and vertical ridges dimensions. Based on this method, a C-Band polarizer is designed, which operates at 3.625~7.025 GHz, 64% bandwidth. The effects of main parameters on the polarizer performances are studied. A prototype of the polarizer is developed. The measurements of the prototype show that return losses are less than –21 dB for two orthogonal polarizations and the phase difference is 90°±3.8°, the corresponding axial ratio is less than 0.6 dB. Measured and simulated results show good agreements, thus validating the analysis and design methods.
Joint Blind Channel Estimation and Symbols Detection for SIMO-OFDM Systems Based on PARAFAC
Ruonan YANG, Weitao ZHANG, Shuntian LOU
, Available online  , doi: 10.11999/JEIT180432 doi: 10.11999/JEIT180432
[Abstract](111) [FullText HTML] (58) [PDF 656KB](20)
To solve the problem of the joint blind channel estimation and symbol detection for SIMO-OFDM systems, a PARAllel FACtor (PARAFAC) analysis model of the receive data matrix is established. Then, with the full row rank characteristic of the discrete Fourier transform matrix and the singular value decomposition of the receiving data matrix, a closed method is proposed for joint blind channel estimation and symbol detection. The proposed method has low computational complexity because it has no iteration. Furthermore, by the simultaneously calculated of channel and signals, the proposed method can avoid the performance reduction of signal estimation caused by channel estimation error. Simulation results show that the proposed method has lower computational complexity and better estimation performance compared with traditional methods.
RF Interference Cancellation Based on Multi-channel Least Mean Square for Multi-transmits and Single-receive Co-vehicle Radios
Huixian SUN, Jiancheng LIU, Peizhang CUI, Houde QUAN, Youxi TANG
, Available online  , doi: 10.11999/JEIT180356 doi: 10.11999/JEIT180356
[Abstract](145) [FullText HTML] (69) [PDF 1232KB](20)
The transmit radios would severely interfere the receive radios, only if they are simultaneously operating in the same tactical command vehicle. Considering this problem, the RF interference cancellation method for multi-transmits and single-receive co-vehicle radios, based on Multi-Channel Least Mean Square (MCLMS) algorithm, is proposed. Firstly, the analysis indicates that the situation of N-transmits and M-receives co-vehicle radios is the equivalent of M case of N-transmits and single-receive, by which the RF interference cancellation model of multi-transmits and single-receive is constructed. Secondly, the RF interference cancellation method based on MCLMS algorithm is presented, and the performance of this method is analyzed to obtain the mathematical relation expression between Mutual-Interference Cancellation Ratio (MICR) and transmit radio number N, convergence factor \begin{document}$\mu $\end{document} . Finally, the simulations demonstrate the validity of theory result, and indicate that the mutual-interference between transmit radios and receive radios is efficiently suppressed to enhance the electromagnetic compatibility of communication command vehicle.
Research of Discrimination Between Left and Right Hand Motor Imagery EEG Patterns Based on Tunable Q-Factor Wavelet Transform
Wanzhong CHEN, Xiaoxu WANG, Tao ZHANG
, Available online  , doi: 10.11999/JEIT171191 doi: 10.11999/JEIT171191
[Abstract](103) [FullText HTML] (36) [PDF 1310KB](7)
In view of the problem of low accuracy and mutual information in left and right hand motor imagery-based ElectroEncephaloGram (EEG), a new approach based on Tunable Q-factor Wavelet Transform (TQWT) is proposed to handle with the binary-class motor imagery EEGs. Firstly, the TQWT is utilized to decompose the filtered EEG signal. Then, several sub-band signals are extracted and followed by calculating their energy, AutoRegressive (AR) model coefficients and fractal dimension. Finally, a Linear Discriminant Analysis (LDA) classifier is used to classify these EEGs. Two Graz datasets of BCI Competition 2003 and 2005 are employed to verify the proposed method. The maximum accuracy of classifying EEGs of four subjects is 88.11%, 89.33%, 77.13% and 78.80%, respectively, and the maximum mutual information is 0.95, 0.96, 0.43 and 0.45. The high accuracies and mutual information demonstrate eventually the effectiveness of the proposed method.
Time of Arrival Estimation Based on Sparse Reconstruction Loop Matching Pursuit Algorithm
Weijia CUI, Peng ZHANG, Bin BA
, Available online  , doi: 10.11999/JEIT180460 doi: 10.11999/JEIT180460
[Abstract](193) [FullText HTML] (100) [PDF 1764KB](22)
Under Single Measurement Vector (SMV) and low Signal-to-Noise Ratio (SNR) conditions, the sparse reconstruction method can improve the estimation accuracy of Time Of Arrival (TOA). However, the existing reconstruction algorithms have some mistakes and missing in the selection of sparse support set elements, which leads to limited estimation accuracy. In order to solve this problem, this paper proposes an algorithm based on sparse reconstruction Loop Matching Pursuit (LMP), which improves the estimation accuracy of the direct path. The algorithm first establishes a sparse representation model of channel impulse response. Then, under the premise of having obtained initial support set, the elements in the support set are removed cyclically. In addition, according to the maximum value of the current residual within the product, the remaining elements are used to match and add the new elements until the residual product is the same. Finally, the estimate of the TOA is obtained using the relationship between the time delay value and the sparse support set. The simulation results show that the proposed algorithm has higher estimation accuracy than the traditional sparse reconstruction time delay estimation algorithm. At the same time, based on the USRP platform, the effectiveness of the proposed algorithm is verified by the actual signal.
Performance Analysis of Ultra-dense Networks Based on Coordinated Multiple-points Joint Transmission
Xiaoping ZENG, Feng YU, Xin JIAN, Shiqi LI, Derong DU, Xin JIANG, Wei FANG
, Available online  , doi: 10.11999/JEIT180398 doi: 10.11999/JEIT180398
[Abstract](124) [FullText HTML] (51) [PDF 1160KB](6)
The high-density characteristic of base stations in Ultra-Dense Networks (UDN) brings serious inter-cell interference. It is the current research hotspot that Coordinated Multiple-Points Joint Transmission (CoMP-JT) is applied to UDN for interference management. The impact of base station density on network performance with CoMP-JT is analyzed. Firstly, the probability density function of the distance between the base station and the user in 3D space is derived using the stochastic geometric method. It provides the cooperation mechanism’s basis for CoMP-JT that selecting the multiple base stations closest to the user to joint transmission. Then, the downlink interference model is carried out based on the bounded dual-slope path loss model, and the downlink coverage probability and network area spectrum efficiency are further derived. Thereafter, the impact of the parameters such as the number of cooperating base stations and the base station density on the performance of the system is investigated. Numerical simulations show that when the number of cooperative base stations is 2, the downlink coverage probability increases by 10%, and the network area spectral efficiency achieves a gain of 2 to 3 times. When the number of cooperating base stations is 3, the cost-effectiveness ratio is better, and the density of base stations that maximizes the network area spectral efficiency under CoMP-JT can be obtained. This paper provides theoretical support for the deployment of base stations in next-generation mobile communication networks.
A Novel Encoding and Decoding Method of LT Codes and Application to Cognitive Radio
Weiqing YAO, Benshun YI
, Available online  , doi: 10.11999/JEIT180427 doi: 10.11999/JEIT180427
[Abstract](173) [FullText HTML] (65) [PDF 2666KB](8)
As an efficient anti-interference technique, Luby Transform (LT) codes are applied to cognitive radio systems for reliable data transmission of secondary users. Encoding and decoding are critical issue for the anti-interference performance of LT codes. To improve the reliability and speed of data transmission, a novel encoding and decoding method Combined Poisson Robust Soliton Distribution-Hierarchical (CPRSD-H) for LT codes is proposed to apply to cognitive radio systems. In the process of encoding, the encoder first produces encoded symbols and generator matrix based on CPRSD, and then uses column vectors corresponding to degree–1 and degree–2 in the generator matrix to carry dual information: the relationship between the degree–1 and degree–2 encoded symbols and their connected input symbols; and part of the original data. Contrarily, in the decoding process, the decoder first uses the Belief Propagation (BP) algorithm to decode by the first information, and then correct some unrecovered bits by the second information. Simulation results show that the proposed method CPRSD-H and application to cognitive radio systems can significantly reduce the Bit Error Rate (BER) of LT codes, the goodput performance of secondary users and the encoding and decoding speed of LT codes.
Joint Blind Source Separation Based on Joint Diagonalization of Fourth-order Cumulant Tensors
Xiaofeng GONG, Lei MAO, Qiuhua LIN, Yougen XU, Zhiwen LIU
, Available online  , doi: 10.11999/JEIT180414 doi: 10.11999/JEIT180414
[Abstract](190) [FullText HTML] (74) [PDF 1522KB](12)
A new Joint Blind Source Separation (J-BSS) algorithm is proposed based on joint diagonalization of fourth-order cumulant tensors. This algorithm constructs first a set of fourth-order tensors by computing the fourth-order cross cumulant of the multiset signals. Then, based on the Jacobian successive rotation strategy, the highly nonlinear optimization problem of joint tensor diagonalization is transformed into a series of simple sub-optimization problems, each admitting a closed form solution. The multiset mixing matrices are hence updated via alternating iterations, which diagonalize jointly the data tensors. Simulation results show that the proposed algorithm has nice convergence pattern and higher accuracy than existing BSS and J-BSS algorithms of similar type. In addition, the algorithm works well in a real-world application to fetal ECG separation.
Spatial Smoothing Regularization for Bi-direction Long Short-term Memory Model
Wenjie LI, Fengpei GE, Pengyuan ZHANG, Yonghong YAN
, Available online  , doi: 10.11999/JEIT180314 doi: 10.11999/JEIT180314
[Abstract](165) [FullText HTML] (69) [PDF 598KB](15)
Bi-direction Long Short-Term Memory (BLSTM) model is widely used in large scale acoustic modeling recently. It is superior to many other neural networks on performance and stability. The reason may be that the BLSTM model gets complicated structure and computation with cell and gates, taking more context and time dependence into account during training. However, one of the biggest problem of BLSTM is overfitting, there are some common ways to get over it, for example, multitask learning, L2 model regularization. A method of spatial smoothing is proposed on BLSTM model to relieve the overfitting problem. First, the activations on the hidden layer are reorganized to a 2-D grid, then a filter transform is used to induce smoothness over the grid, finally adding the smooth information to the objective function, to train a BLSTM network. Experiment results show that the proposed spatial smoothing way achieves 4% relative reduction on Word Error Ratio (WER), when adding the L2 norm to model, which can lower the relative WER by 8.6% jointly.
Direct Position Determination for Coherently Distributed Noncircular Source Based on Symmetric Shift Invariance
Zhiyu LU, Jianhui WANG, Tianzhu QIN, Bin BA
, Available online  , doi: 10.11999/JEIT180433 doi: 10.11999/JEIT180433
[Abstract](148) [FullText HTML] (49) [PDF 1525KB](4)
The existing Direct Position Determination (DPD) algorithm of Coherently Distributed (CD) sources rely on the distribution model of CD sources with huge computation cost, which is not practical. To improve further the localization performance, a novel DPD algorithm of CD sources that profits from the characteristics of noncircular signals is proposed based on the symmetric shift invariance of the centrosymmetric array. With the parameterization assumption of CD sources, the direct position determination model is firstly constructed by combining the characteristics of noncircular signals. Then, it is proved that for any centrosymmetric array, the generalized steering vector of CD sources has the property of symmetric shift invariance. Base on this characteristic, the positions of CD sources are directly estimated by fusing the information of all observation stations with no need to consider the distribution model, which reduces the dimension of the parameter to be estimated. Simulation results validate that, compared with the existing localization algorithms of CD sources, the proposed algorithm improves the localization accuracy, and avoids the dependence on the distribution model of CD sources, which is of great practical value.
Direction of Arrival Estimation for Multiple Frequency Hopping Signals Based on Sparse Bayesian Learning
Ying GUO, Runze DONG, Kunfeng ZHANG, Ping SUI, Yinsong YANG
, Available online  , doi: 10.11999/JEIT180435 doi: 10.11999/JEIT180435
[Abstract](132) [FullText HTML] (82) [PDF 2279KB](21)
To solve the problem of spatial parameter estimation of multi-frequency hopping signals, the sparsity in spatial domain of frequency hopping signals is used to realize the Direction Of Arrival (DOA) estimation based on Sparse Bayesian Learning (SBL). First, the spatial discrete grid is constructed and the offset between the actual DOA and the grid points is modeled into it. The data model of the uniform linear array with multiple frequency hopping signals is established. Then the posterior probability distribution of the sparse signal matrix is obtained by the SBL theory, and the line sparsity of the signal matrix and the offset is controlled by the hyperparameters. Finally, The expectation maximization algorithm is used to iterate the hyper parameters, and the maximum posteriori estimation of the signal matrix is obtained to complete the DOA estimation. Theoretical analysis and simulation experiments show that this method has good estimation performance and can adapt to less snapshots.
A Novel Joint ISAR Cross-range Scaling and Phase Autofocus Algorithm Based on Image Contrast Maximization
Shuai SHAO, Lei ZHANG, Hongwei LIU
, Available online  , doi: 10.11999/JEIT180521 doi: 10.11999/JEIT180521
[Abstract](107) [FullText HTML] (54) [PDF 4119KB](11)
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.
Multi-scale Local Region Structure Dominant Binary Pattern Learning for Image Representation
Dongbo ZHANG, Liangling YI, Haixia XU, Ying ZHANG
, Available online  , doi: 10.11999/JEIT180512 doi: 10.11999/JEIT180512
[Abstract](95) [FullText HTML] (44) [PDF 1390KB](14)
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.
A Robust Broadband Interference Suppression Algorithm Based on Few Snapshots
Hao WANG, Xiaonan XU, Qiming MA
, Available online  , doi: 10.11999/JEIT180505 doi: 10.11999/JEIT180505
[Abstract](99) [FullText HTML] (36) [PDF 2650KB](2)
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.
Joint Optimization of Virtualized Network Function Placement and Routing Allocation for Operational Expenditure
Jiugen SHI, Jing ZHANG, Hao XU, Ji WANG, Li SUN
, Available online  , doi: 10.11999/JEIT180522 doi: 10.11999/JEIT180522
[Abstract](116) [FullText HTML] (53) [PDF 2574KB](11)
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. But 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%.
Hydrometeor Classification Method in Dual-polarization Weather Radar Based on Fuzzy Neural Network-Fuzzy C-Means
Hai LI, Jiawei REN, Jinlei SHANG
, Available online  , doi: 10.11999/JEIT180529 doi: 10.11999/JEIT180529
[Abstract](111) [FullText HTML] (55) [PDF 3260KB](12)
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.
Adaptive Design of Limiters for Impulsive Noise Suppression
Zhongtao LUO, Peng LU, Yangyong ZHANG, Gang ZHANG
, Available online  , doi: 10.11999/JEIT180609 doi: 10.11999/JEIT180609
[Abstract](71) [FullText HTML] (32) [PDF 1318KB](6)
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.
Design and Implementation of Hardware Trojan Detection Algorithm for Coarse-grained Reconfigurable Arrays
Yingjian YAN, Min LIU, Zhaoyang QIU
, Available online  , doi: 10.11999/JEIT180484 doi: 10.11999/JEIT180484
[Abstract](209) [FullText HTML] (136) [PDF 2887KB](37)
Hardware Trojan horse detection has become a hot research topic in the field of chip security. Most existing detection algorithms are oriented to ASIC circuits and FPGA circuits, and rely on golden chips that are not infected with hardware Trojan horses, which are difficult to adapt to the coarse-grained reconfigurable array consisting of large-scale reconfigurable cells. Therefore, aiming at the structural characteristics of Coarse-grained reconfigurable cryptographic logical arrays, a hardware Trojan horse detection algorithm based on partitioned and multiple variants logic fingerprints is proposed. The algorithm divides the circuit into multiple regions, adopts the logical fingerprint feature as the identifier of the region, and realizes the hardware Trojan detection and diagnosis without golden chip by comparing the multiple variant logic fingerprints of the regions in both dimensions of space and time. Experimental results show that the proposed detection algorithm has high detection success rate and low misjudgment rate for hardware Trojan detection.
Training Multi-layer Perceptrons Using Chaos Grey Wolf Optimizer
Fu YAN, Jianzhong XU, Fengshu LI
, Available online  , doi: 10.11999/JEIT180519 doi: 10.11999/JEIT180519
[Abstract](130) [FullText HTML] (73) [PDF 1197KB](17)
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 which is 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.
A Traffic Scheduling Algorithm for Bandwidth Fragmentation Minimization and QoS Guarantee in Data Center Network
Hong TANG, Xinxin WANG, Yixing LIU
, Available online  , doi: 10.11999/JEIT180466 doi: 10.11999/JEIT180466
[Abstract](141) [FullText HTML] (77) [PDF 2165KB](16)
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, and 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.
Gesture Recognition with Multi-dimensional Parameter Using a FMCW Radar
Yong WANG, Jinjun WU, Zengshan TIAN, Mu ZHOU, Shasha WANG
, Available online  , doi: 10.11999/JEIT180485 doi: 10.11999/JEIT180485
[Abstract](187) [FullText HTML] (126) [PDF 1714KB](30)
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%.
Missing Data Prediction Based on Kronecker Compressing Sensing in Multivariable Time Series
Yan GUO, Xiaoxiang SONG, Ning LI, Peng QIAN
, Available online  , doi: 10.11999/JEIT180541 doi: 10.11999/JEIT180541
[Abstract](128) [FullText HTML] (66) [PDF 770KB](7)
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.
Micro-motion Parameters Estimation of Ballistic Targets Based on Wide-band Radar Three Dimensional Interferometry
Jiaqi WEI, Lei ZHANG, Hongwei LIU
, Available online  , doi: 10.11999/JEIT180452 doi: 10.11999/JEIT180452
[Abstract](158) [FullText HTML] (57) [PDF 2521KB](12)
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 proposed algorithm is confirmed via extensive experiments.
An Improvement Project of Roundoff Noise Performance of FIR Filters Based on Structure Optimization
Ling ZHUANG, Juan GUAN, Jingyi MA, Guangyu WANG
, Available online  , doi: 10.11999/JEIT180480 doi: 10.11999/JEIT180480
[Abstract](200) [FullText HTML] (75) [PDF 1917KB](21)
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.
A Fragment-aware Secure Virtual Network Reconfiguration Method
Xinbo LIU, Buhong WANG, Zhixian YANG, Haiou SHEN
, Available online  , doi: 10.11999/JEIT180474 doi: 10.11999/JEIT180474
[Abstract](129) [FullText HTML] (77) [PDF 947KB](18)
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.
Blind Estimation of the Pseudo Code Period and Combination Code Sequence for Composite Binary Offset Carrier Signal
Tianqi ZHANG, Donghua LIU, Shuai YUAN, Sheng WANG
, Available online  , doi: 10.11999/JEIT180444 doi: 10.11999/JEIT180444
[Abstract](216) [FullText HTML] (79) [PDF 2107KB](8)
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.
Semantic Summarization of Reconstructed Abstract Meaning Representation Graph Structure Based on Integer Linear Pragramming
Hongchang CHEN, Tuosiyu MING, Shuxin LIU, Chao GAO
, Available online  , doi: 10.11999/JEIT180720 doi: 10.11999/JEIT180720
[Abstract](159) [FullText HTML] (84) [PDF 925KB](25)
In order to solve the incomplete semantic structure problem that occurs in the process of using the Abstract Meaning Representation (AMR) graph to predict the summary subgraph, a semantic summarization algorithm is proposed based on Integer Linear Programming (ILP) reconstructed AMR graph structure. Firstly, the text data are preprocessed to generate an AMR total graph. Then the important node information of the summary subgraph is extracted from the AMR total graph based on the statistical features. Finally, the ILP method is applied to reconstructing the node relationships in the summary subgraph, which is further utilized to generate a semantic summarization. The experimental results show that compared with other semantic summarization methods, the ROUGE index and Smatch index of the proposed are significantly improved, up to 9% and 14% respectively. This method improves significantly the quality of semantic summarization.
Direction Finding for Electromagnetic Radiation Source Using Ultra-short Baseline Array
Xiaodong QU, Yang SUN, Chong CHEN, Junlong SHI, Xin XU, Jutao LI, Wanhua ZHU, Guangyou FANG
, Available online  , doi: 10.11999/JEIT180516 doi: 10.11999/JEIT180516
[Abstract](94) [FullText HTML] (56) [PDF 2262KB](14)
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.
Hybrid Point of Interest Recommendation Algorithm Based on Deep Learning
Hao FENG, Kun HUANG, Jing LI, Rong GAO, Donghua LIU, Chengfang SONG
, Available online  , doi: 10.11999/JEIT180458 doi: 10.11999/JEIT180458
[Abstract](156) [FullText HTML] (86) [PDF 1695KB](30)
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.
Multi-objective Virtual Network Embedding Algorithm Based on Nash Bargaining
Mengyang HE, Lei ZHUANG, Weibing LONG, Guoqing WANG
, Available online  , doi: 10.11999/JEIT180419 doi: 10.11999/JEIT180419
[Abstract](116) [FullText HTML] (54) [PDF 1259KB](8)
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.
Backhaul Scheme and Performance Study of Full-duplex Multi-tier Heterogeneous Networks Based on Non-orthogonal Multiple Access
Xiangdong JIA, Shanshan JI, Qiaoling FAN, Xiaorong YANG
, Available online  , doi: 10.11999/JEIT180463 doi: 10.11999/JEIT180463
[Abstract](118) [FullText HTML] (52) [PDF 1385KB](6)
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.
A Perspective-independent Method for Behavior Recognition in Depth Video via Temporal-spatial Correlating
Peiliang WU, Xiao YANG, Bingyi MAO, Lingfu KONG, Zengguang HOU
, Available online  , doi: 10.11999/JEIT180477 doi: 10.11999/JEIT180477
[Abstract](109) [FullText HTML] (41) [PDF 1535KB](6)
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. And the proposed method (HPM+RP+FTP) has a good generalization performance, achieving a 82.5% accuracy on dataset of MSR Daily Activity3D.
Optical Image Encryption Algorithm Based on Differential Mixed Mask and Chaotic Gyrator Transform
Yanhao CHEN, Zhongyan LIU, Liyan ZHOU
, Available online  , doi: 10.11999/JEIT180456 doi: 10.11999/JEIT180456
[Abstract](140) [FullText HTML] (54) [PDF 5830KB](5)
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.
Sensing Matrix Optimization for Sparse Signal under Structured Noise Interference
Ruchun LI, Yunxiao CHENG, Yali QIN
, Available online  , doi: 10.11999/JEIT180513 doi: 10.11999/JEIT180513
[Abstract](139) [FullText HTML] (61) [PDF 1293KB](16)
To solve sparse signal processing problem with structural noise interference, a method of sensing matrix optimization design based on sparse Bayesian theory is proposed. Combining the sparse signal model with additive interference, the design of the sensing matrix is realized by minimizing the trace of the posterior covariance matrix and the energy constraint of sensing matrix. The effects of sensing matrix optimization on the reconstruction error and reconstruction time are simulated using difference sparse signal and reconstruction algorithms, and the effects of the sensing matrix optimization on the reconstruction effect are analyzed when there is a bias in the prior information. The simulation results show that the optimized sensing matrix can obtain the important information in the sparse signal, the mean square error of the signal reconstruction accuracy is reduced by about 15~25 dB, and the reconstruction time is reduced by about 40%.
Dictionary Refinement Method for Compressive Sensing Based Multi-target Device-free Localization
Dongping YU, Yan GUO, Ning LI, Sixing YANG, Xiaoxiang SONG
, Available online  , doi: 10.11999/JEIT180531 doi: 10.11999/JEIT180531
[Abstract](151) [FullText HTML] (57) [PDF 1494KB](9)
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.
Compressive Sensing Based Multi-target Device-free Passive Localization Algorithm Using Multidimensional Measurement Information
Dongping YU, Yan GUO, Ning LI, Jie LIU, Sixing YANG
, Available online  , doi: 10.11999/JEIT180333 doi: 10.11999/JEIT180333
[Abstract](132) [FullText HTML] (79) [PDF 1488KB](15)
Device-free passive localization is a key issue of the intruder detection, environmental monitoring, and intelligent transportation. The existing device-free passive localization method can obtain the multidimensional measurement information by channel state information, but the existing scheme can not fully exploit the frequency diversity on multiple channels to improve the localization performance. This paper proposes a Compressive Sensing (CS) based multi-target device-free passive localization algorithm using multidimensional measurement information. It takes advantage of the frequency diversity of multidimensional measurement information to improve the accuracy and robustness of localization results under the CS framework. The dictionary is built according to the saddle surface model, and the multi-target device-free passive localization problem is modeled as a joint sparse recovery problem based on multiple measurement vectors. The target location vector is estimated based on the multiple sparse Bayesian learning algorithm. Simulation results indicate that the proposed algorithm can make full use of the multidimensional measurement information to improve the localization performance.
Sub-Nyquist Sampling Recovery Algorithm Based on Kernel Space of the Random-compression Sampling Value Matrix
Jianxin GAI, Haochen DU, Qi LIU, Ziquan TONG
, Available online  , doi: 10.11999/JEIT180323 doi: 10.11999/JEIT180323
[Abstract](142) [FullText HTML] (71) [PDF 1942KB](16)
To solve the low performance problem of the existing Modulated Wideband Converter (MWC)-based sub-Nyquist sampling recovery algorithm, this paper proposes a support recovery algorithm based on the kernel space of sampling value and a random compression rank-reduction idea. Combining them, a high-performance sampling recovery algorithm is achieved. Firstly random compression transforms are used to convert the sampling equation into several new multiple-measurement-vector problems, without changing the sparsity of the unknown matrix. Then the orthogonal relationship between the kernel space of sampling value and the support vectors of sampling matrix is utilized to obtain joint sparse support set of the unknown. The final recovery is performed by the pseudo inversion. The proposed method is analyzed and verified by theory and experiment. Numerical experiments show that, compared with the traditional recovery algorithm, the proposal can improve the recovery success rate, and reduce the channel number required for high-probability recovery. Furthermore, in general, the recovery performance improves with the rise of compression times.
Hybrid Precoding Algorithm Based on Successive Interference Cancellation for Millimeter Wave MIMO Systems
Peizhong XIE, Rui SUN, Ting LI
, Available online  , doi: 10.11999/JEIT180379 doi: 10.11999/JEIT180379
[Abstract](203) [FullText HTML] (127) [PDF 1378KB](24)
This paper investigates the design of hybrid analog and digital precoder and combiner for multi-user millimeter wave MIMO systems. Considering the problem of signal interference between multiple users due to diffuse scattering of signal propagation, a robust hybrid precoding algorithm based on Successive Interference Cancellation (SIC) is proposed. By deducing the orthogonal decomposition formula of the channel matrix to eliminate the interference from the known users’ signals, the multi-user links optimization problem with nonconvex constraints can be decompose into multiple single-user link optimization problems. The phase extraction algorithm is then used to search each user’s optimal transmission link one by one, and the multi-user hybrid precoding matrix is obtained in combination with Minimum Mean Square Error (MMSE) criterion. Simulation results show that the proposed algorithm has significant performance advantages compared with the existing hybrid precoding algorithms under severe interference conditions.
Received Signal Strength Indication Difference Location Algorithm Based on Kalman Filter
Youlin GENG, Chengbo XIE, Chuan YIN, Lantu GUO, Xianyi WANG
, Available online  , doi: 10.11999/JEIT180268 doi: 10.11999/JEIT180268
[Abstract](163) [FullText HTML] (102) [PDF 1573KB](43)
The signal source position can only be estimated by passive monitoring of the signal in terms of that the signal monitored by the spectrum monitoring system can not be controlled and there is no prior knowledge. To address this issue, based on Received Signal Strength Indication Difference (RSSID) and using Kalman filtering, a location algorithm is proposed to improve its localization accuracy. The proposed algorithm transforms the RSSID between two base stations into the ratio of the distance from the location of the signal source to the two base stations, and the distances to construct the matrix of location equations is obtained according to the ratio, and then the least square method to find the signal source position is obtained. The simulation results show that the proposed algorithm has better performance than the classical RSSI localization algorithm, reducing the impact of environmental factors on the positioning accuracy, and better meet the positioning service needing fewer parameters. This algorithm can be effectively applied to the spectrum monitoring system. In addition, Kalman algorithm can effectively improve the system's positioning accuracy, and achieve the expected positioning effect.
Gridless Sparse Method for Direction of Arrival Estimation for Two-dimensional Array
Jianshu WANG, Yangyu FAN, Rui DU, Guoyun LÜ
, Available online  , doi: 10.11999/JEIT180340 doi: 10.11999/JEIT180340
[Abstract](146) [FullText HTML] (76) [PDF 2144KB](27)
For the fact that current gridless Direction Of Arrival (DOA) estimation methods with two-dimensional array suffer from unsatisfactory performance, a novel girdless DOA estimation method is proposed in this paper. For two-dimensional array, the atomic L0-norm is proved to be the solution of a Semi-Definite Programming (SDP) problem, whose cost function is the rank of a Hermitian matrix, which is constructed by finite order of Bessel functions of the first kind. According to low rank matrix recovery theorems, the cost function of the SDP problem is replaced by the log-det function, and the SDP problem is solved by Majorization-Minimization (MM) method. At last, the gridless DOA estimation is achieved by Vandermonde decomposition method of semidefinite Toeplitz matrix built by the solutions of above SDP problem. Sample covariance matrix is used to form the initial optimization problem in MM method, which can reduce the iterations. Simulation results show that, compared with on-grid MUSIC and other gridless methods, the proposed method has better Root-Mean-Square Error (RMSE) performance and identifiability to adjacent sources; When snapshots are enough and Signal-Noise-Ratio (SNR) is high, proper choice of the order of Bessel functions of the first kind can achieve approximate RMSE performance as that of higher order ones, and can reduce the running time.
Wide Area Difference Calibration Algorithm Based on Virtual Reference Station for Tri-satellite TDOA Geolocation System
Kaiqiang REN, Zhengbo SUN
, Available online  , doi: 10.11999/JEIT180289 doi: 10.11999/JEIT180289
[Abstract](94) [FullText HTML] (48) [PDF 1341KB](4)
A wide area difference calibration algorithm based on Virtual Reference Station (VRS) for tri-satellite Time Difference Of Arrival (TDOA) geolocation system is proposed to solve the problem that traditional difference calibration algorithm can not eliminate the location error caused by ephemeris error completely, especially when the emitter source is far away from the calibration station. Firstly, TDOA measurements of the VRS, which is in the vicinity of emitter source, is estimated by using TDOA measurements of reference station. Then, in order to remove the effect of ephemeris error and synchronization error on location error, TDOA measurements of the VRS is subtracted from that of emitter source. Simulation results demonstrate that the proposed algorithm can almost eliminate the effect of ephemeris error on location error of tri-satellite TDOA geolocation system in wide area.
Sensor Selection Method for TDOA Passive Localization
Benjian HAO, Linlin WANG, Zan LI, Yue ZHAO
, Available online  , doi: 10.11999/JEIT180293 doi: 10.11999/JEIT180293
[Abstract](103) [FullText HTML] (55) [PDF 901KB](9)
This paper focuses on the sensor selection optimization problem in Time Difference Of Arrival (TDOA) passive localization scenario. Firstly, the localization accuracy metric is given by the error covariance matrix of classical closed-form solution, which is introduced to convert the TDOA nonlinear equations into pseudo linear equations. Secondly, the problem of sensor selection can be mathematically transformed into the non-convex optimization problem, to minimize the trace of localization error covariance matrix under the condition that the number of active sensors is given. Then, the non-convex optimization problem is relaxed and transformed into a positive semi-definite programming problem so that the optimal subset of positioning nodes can be solved quickly and effectively. Simulation results validate that the performance of proposed sensor selection method is very close to the exhausted-search method, and overcomes the shortcomings of the high computation complexity and poor timeliness of the exhausted-search method.
Research of Chronic Obstructive Pulmonary Disease Monitoring System Based on Four-line Turbine-type
Rongjian ZHAO, Wang ZHOU, Minfang TANG, Xianxiang CHEN, Lidong DU, Zhan ZHAO, Ting YANF, Qingyuan ZHAN, Zhen FANG
, Available online  , doi: 10.11999/JEIT180315 doi: 10.11999/JEIT180315
[Abstract](144) [FullText HTML] (97) [PDF 2782KB](12)
To improve accuracy and reliability of the traditional turbine-vital capacity meter, a novel four-line turbine-detection method is presented for the high precision and high reliability Chronic Obstructive Pulmonary Disease (COPD) monitoring system. On the hardware, a four-line breath signal acquisition circuit is designed following the four-line turbine-type detection method, which improves the resolution of the optical path through reasonable components arrangement. On the software, a linear regression algorithm is used to obtain early screening and diagnostic indicators such as Forced Vital Capacity (FVC), Peak Expiratory Flow (PEF) and so on. The standard Fluke air flow analyzer is used for data calibration, compared with the traditional medical turbine-type lung function meter: FVC average relative error is reduced from 1.98% to 1.47% and PEF average relative error is reduced from 2.04% to 1.02%. It is showed that the expiratory parameters of the four-line turbine-type COPD monitoring system is more accurate and reliable than that of the traditional COPD system which is suitable for early screening and accurate diagnosis of COPD. Combined with pulse oxygen saturation, End-tidal CO2, it can be used to achieve the medical care for COPD and play an important role to early detect and control of disease for moderate or severe COPD patients.
Cardinalized Probability Hypothesis Density Filter Based on Pairwise Markov Chains
Jiangyi LIU, Chunping WANG
, Available online  , doi: 10.11999/JEIT180352 doi: 10.11999/JEIT180352
[Abstract](127) [FullText HTML] (78) [PDF 1055KB](19)
In view of the problem that the Cardinalized Probability Hypothesis Density (CPHD) probability hypothesis density filtering algorithm based on the Pairwise Markov Chains (PMC) model (PMC-CPHD) is not suitable for implementation, the PMC-CPHD algorithm is modified into a polynomial form to facilitate implementation, and the Gauss Mixture (GM) implementation of the improved algorithm is given. The experimental results show that the given GM implementation realizes multitarget tracking effectively, and improves the stability of the target number estimation compared with the GM implementation of the probability hypothesis density filtering algorithm based on the PMC model (PMC-PHD).
A Diverse Virtual Optical Network Mapping Strategy Based on Security Awareness in Elastic Optical Networks
Huanlin LIU, Zhenyu LIN, Xin WANG, Yong CHEN, Min XIANG, Yue MA
, Available online  , doi: 10.11999/JEIT180335 doi: 10.11999/JEIT180335
[Abstract](130) [FullText HTML] (90) [PDF 2396KB](19)
Due to the probabilistic failure of the optical fiber of the underlying network in the virtual environment, traditional full protection configures one protection path at least which leads to high resource redundancy and low acceptance rate of the virtual network. In this paper, a Security Awareness-based Diverse Virtual Network Mapping (SA-DVNM) strategy is proposed to provide security guarantee in the event of failures. In SA-DVNM, the physical node weight formula is designed by considering the hops between nodes and the bandwidth of adjacent links, besides, a path-balanced link mapping mechanism is proposed to minimize the overloaded link. For improving the acceptability of virtual network, SA-DVNM strategy designs a resource allocation mechanism that allows path cut when a single path is unavailable for low security. Considering the difference of time delay to ensure the security of delay-sensitive services, a multipath routing spectrum allocation method based on delay difference is designed to optimize the routing and spectrum allocation for SA-DVNM strategy. The simulation results show that the proposed SA-DVNM strategy can improve the spectrum utilization and virtual optical network acceptance rate in the probabilistic fault environment, and reduce the bandwidth blocking probability.
Deployment Algorithm of Service Function Chain of Access Network Based on Stochastic Learning
Qianbin CHEN, Youchao YANG, Yu ZHOU, Guofan ZHAO, Lun TANG
, Available online  , doi: 10.11999/JEIT180310 doi: 10.11999/JEIT180310
[Abstract](157) [FullText HTML] (98) [PDF 1673KB](18)
To solve problem of the high delay caused by the change of physical network topology under the 5G access network C-RAN architecture, this paper proposes a scheme about dynamic deployment of Service Function Chain (SFC) in access network based on Partial Observation Markov Decision Process (POMDP). In this scheme, the system observes changes of the underlying physical network topology through the heartbeat packet observation mechanism. Due to the observation errors, it is impossible to obtain all the real topological conditions. Therefore, by the partial awareness and stochastic learning of POMDP, the system dynamically adjust the deployment of the SFC in the slice of the access network when topology changes, so as to optimize the delay. Finally, point-based hybrid heuristic value iteration algorithm is used to find SFC deployment strategy. The simulation results show that this model can support to optimize the deployment of SFC in the access network side and improve the access network’s throughput and resource utilization.
Latest Research Progress of Honeypot Technology
Leyi SHI, Yang LI, Mengfei MA
, Available online  , doi: 10.11999/JEIT180292 doi: 10.11999/JEIT180292
[Abstract](184) [FullText HTML] (70) [PDF 368KB](19)
Honeypot technology is a network trap in cyber defense. It can attract and deceive attackers and record their attack behavior, so as to study the target and attack means of the adversary and protect real service resources. However, because of the static configuration and the fixed deployment in traditional honeypots, it is as easy as a pie for intruders to identify and escape those traps, which makes them meaningless. Therefore, how to improve the dynamic characteristic and the camouflage performance of honeypot becomes a key problem in the field of honeypot. In this paper, the recent research achievements in honeypot are summarized. Firstly, the development history of honeypot in four stages is summed up. Subsequently, by focusing on the key honeypot mechanism, the analysis on process, deployment, counter-recognition and game theory are carried out. Finally, the achievements of honeypot in different aspects are characterized and the development trends of honeypot technology is depicted.
Improved Metric Learning Algorithm for Person Re-identification Based on Equidistance
Zhiheng ZHOU, Kaiyi LIU, Junchu HUANG, Zengqun CHEN
, Available online  , doi: 10.11999/JEIT180336 doi: 10.11999/JEIT180336
[Abstract](299) [FullText HTML] (120) [PDF 1632KB](30)
In order to improve the robustness of MLAPG algorithm, a person re-identification algorithm, called Equid-MLAPG algorithm is proposed, which is based on the equidistance measurement learning strategy. Due to the imbalanced distribution of positive and negative sample pairs in the mapping space, sample spacing hyper-parameter of MLAPG algorithm is more affected by the distance of negative sample pairs. Therefore, Equid-MLAPG algorithm tends to map the positive sample pair to be a point in the transform space. That is, the distance of a positive sample pair in the transform space is mapped to be zero, resulting in no intersection in the distribution of positive and negative sample pairs in the transform space when algorithm convergences. Experiments show that the Equid-MLAPG algorithm can achieve better experimental results on commonly used person re-identification datasets with better recognition rate and wide applicability.
Magnetic Anomaly Detection Algorithm Based on Fractal Features in Geomagnetic Background
Luzhao CHEN, Wanhua ZHU, Peilin WU, Chunjiao FEI, Guangyou FANG
, Available online  , doi: 10.11999/JEIT180307 doi: 10.11999/JEIT180307
[Abstract](304) [FullText HTML] (207) [PDF 4298KB](70)
Magnetic Anomaly Detection (MAD) is a widely used passive target detection method. Its applications include surface warship target monitoring, underwater moving targets, and land target detection and identification. It is of great significance to research on the reliability detection method of weak magnetic anomaly signals based on geomagnetic background. This paper proposes a single sensor detection method based on the fractal characteristics of target magnetic anomaly signal based on the study of the differences in geomagnetic background and fractal characteristics of magnetic anomaly signals and conducts actual field test verification. The experimental results show that the method can accurately distinguish the geomagnetic background interference and magnetic anomaly signals, and can detect the weak magnetic anomaly signals in the geomagnetic background noise.
A Multiuser Noise Reduction Differential Chaos Shift Keying System
Gang ZHANG, Hexiang CHEN, Tianqi ZHANG
, Available online  , doi: 10.11999/JEIT171173 doi: 10.11999/JEIT171173
[Abstract](79) [FullText HTML] (45) [PDF 2359KB](3)
One of the major drawbacks of the conventional Multiuser Differential Chaos Shift Keying is the poor Bit Error Rate (BER), a MultiUser Noise Reduction Differential Chaos Shift Keying (MU-NRDCSK) system is proposed. At the transmitter, M/P chaotic samples are transmitted and then duplicated P times as a reference signal, all users share the same reference signal, and information signals are delayed by different times to distinguish different users. At the receiver, the received signal is averaged by a moving average filter, and then the resultant filtered signal is correlated to different time-delated replica. The scheme can enhance the performance of BER by reducing the variance of noise terms in the system. The theoretical BER formula of this new scheme is derived in Additive White Gaussian Noise (AWGN) channel and Rayleigh channel. Theoretical analysis and the simulation results show that the theoretical formula and the simulation result are in good agreement. The MU-NRDCSK scheme can enhance the performance of BER better and has good development prospects and research value in the chaotic communication field.
Random Addition-chain Based Countermeasure Against Side-channel Attack for Advanced Encryption Standard
Hai HUANG, Xinxin FENG, Hongyu LIU, Jiao HOU, Yuying ZHAO, Lili YIN, Jiuxing JIANG
, Available online  , doi: 10.11999/JEIT171211 doi: 10.11999/JEIT171211
[Abstract](129) [FullText HTML] (61) [PDF 1883KB](12)
Side channel attacks have serious threat to the hardware security of Advanced Encryption Standard (AES), how to resist the side channel attack becomes an urgent problem. Byte substitution operation is the only nonlinear operation in AES algorithm, so it is very important for the whole encryption algorithm to improve its security. In this paper, a countermeasure against side-channel attack is proposed based on random addition-chain for AES by replacing the fixed addition-chain with random addition-chain to realize the inverse operation of multiplication in a finite field GF(28). The impact of the random addition-chain on the security and effectiveness of the algorithm is studied. Experimental results show that the proposed random addition-chain based algorithm is more secure and effective than the previous fixed addition-chain based algorithms in defending against side channel attacks.
Malware Sandbox Evasion Detection Based on Code Evolution
Guanghui LIANG, Jianmin PANG, Zheng SHAN
, Available online  , doi: 10.11999/JEIT180257 doi: 10.11999/JEIT180257
[Abstract](156) [FullText HTML] (102) [PDF 627KB](15)
In order to resist the malware sandbox evasion behavior, improve the efficiency of malware analysis, a code-evolution-based sandbox evasion technique for detecting the malware behavior is proposed. The approach can effectively accomplish the detection and identification of malware by first extracting the static and dynamic features of malware software and then differentiating the variations of such features during code evolution using sandbox evasion techniques. With the proposed algorithm, 240 malware samples with sandbox-bypassing behaviors can be uncovered successfully from 7 malware families. Compared with the JOE analysis system, the proposed algorithm improves the accuracy by 12.5% and reduces the false positive to 1%, which validates the proposed correctness and effectiveness.
Energy Saving Mechanism with Offload Delay Aware in Cloudlet Enhanced Fi-Wi Access Network
Hong ZOU, Yishuang GAO, Junjie YAN
, Available online  , doi: 10.11999/JEIT180274 doi: 10.11999/JEIT180274
[Abstract](170) [FullText HTML] (70) [PDF 973KB](11)
In cloudlet enhanced Fiber-Wireless (FiWi) access network, there is a problem that the traditional energy saving mechanism does not match the offload traffic. An offload collaboration sleep mechanism with load transfer is proposed. By analyzing the load of the optical network unit and combining the transmission delay of the multi-hop in the wireless domain and the sending time of the report frame of the target optical network unit, the proposed mechanism can determine the sleeping and the destination optical network unit to complete load transfer. Then, the optical network unit jointly considers the arrival time of the returned data of the edge severs and the sending time of the control frame in the wireless domain to select the optimal sleep duration and reduce the controlling overhead. Simulation results show that the proposed mechanism can effectively reduce the network energy consumption while ensuring the delay performance of offload traffic.
Sonar Broadband Adaptive Beamforming Based on Enhanced Keystone Transform
Yuning QIAN, Yawei CHEN, Jun SUN
, Available online  , doi: 10.11999/JEIT180394 doi: 10.11999/JEIT180394
[Abstract](207) [FullText HTML] (155) [PDF 2428KB](26)
Keystone transform is an effective broadband array signal pre-processing method, but it has a main problem of array data missing. In order to solve this problem, an enhanced Keystone transform algorithm, which combines the autoregression model with traditional Keystone transform, is proposed in this paper for sonar broadband adaptive beamforming. After phase alignment of broadband array signal using traditional Keystone transform, autoregression models for each frequency are constructed to compensate the missing array data. Then, a robust adaptive beamforming approach is utilized to obtain the target bearing results. The results of simulation studies indicate that the proposed broadband adaptive beamforming algorithm based on enhanced Keystone transform outperforms the beamforming algorithms based on traditional Keystone transform, steered minimum variance and frequency focusing.
Stackelberg Game-based Resource Allocation Strategy in Virtualized Wireless Sensor Network
Ruyan WANG, Hongjuan LI, Dapeng WU
, Available online  , doi: 10.11999/JEIT180277 doi: 10.11999/JEIT180277
[Abstract](291) [FullText HTML] (155) [PDF 1478KB](56)
Virtualization is a new technology that can effectively solve the low resource utilization and service inflexibility problem in the current Wireless Sensor Network (WSN). For the resource competition problem in virtualized WSN, a multi-task resource allocation strategy based on Stackelberg game is proposed. According to the different Quality of Service (QoS) requirements of the business carried by Virtual Sensor Network Request (VSNR), the importance of multiple VSNRs is quantified. Then, the optimal price of WSN and the optimal resource requirements of VSNRs are obtained by using distributed iteration method. Finally, the resource corresponding to multiple VSNRs is acquired according to optimal price and optimal resource allocation determined by Nash equilibrium. The simulation results show that the proposed strategy can not only meet the diversified needs of users, but also improve the resource utilization of nodes and links.
A Collaborative Mapping Method for Service Chain Based on Matching Game
Hongqi ZHANG, Rui HUANG, Dexian CHANG
, Available online  , doi: 10.11999/JEIT180385 doi: 10.11999/JEIT180385
[Abstract](166) [FullText HTML] (107) [PDF 1334KB](27)
Considering that it is difficult to balance efficiency and resource utilization of Service Chain (SC) mapping problem in Software Defined Network (SDN)/Network Function Virtualization (NFV) environment, this paper proposes a collaborative mapping method for SC based on matching game. Firstly, it defines a SC mapping model named MUSCM to maximize the utility of network resources. Secondly, it divides the SC mapping problem into Virtual Network Function (VNF) deployment and connection parts. As for the VNF deployment part, an algorithm is designed to collaborate the selection of the SC and the service node based on many-to-one matching game, improving the mapping efficiency of SC and utilization of physical resource effectively. On the basis of it, an algorithm is designed based on segment routing strategy to accomplish the traffic steering between VNF instances to finish the VNF connection part, reducing the link transmission delay effectively. The experiment result shows that, compared with the classical algorithm, this algorithm ensures the mapping request received rate, and at the same time, it reduces the average transmission delay of the service chain and improves the physical resources utilization of the system effectively.
Estimation of Ionospheric Incoherent Scatter Spectrum and Autocorrelation Function
Lin LI, Chengjiao HAN, Zonghua DING, Hongbing JI, Yajie WANG
, Available online  , doi: 10.11999/JEIT180331 doi: 10.11999/JEIT180331
[Abstract](127) [FullText HTML] (79) [PDF 1379KB](11)
Incoherent scatter spectrum plays an important role in studying the physical parameters of the ionosphere. The conventional theoretical model of incoherent scatter spectrum for derivation and calculation is extremely complicated and the model of the autocorrelation function can not be obtained . In this paper, the simplified model of ionospheric incoherent scatter spectrum is re-derived and the corresponding autocorrelation function is proposed. In the procedure of traditional incoherent scattering radar signal processing, the autocorrelation function is imbalance at different delays. This is mainly because the range resolution of zero-lag is very low, which affects the estimated performance of ionospheric scatter spectrum. Focus on this problem, a method based on data fitting is proposed to estimate the autocorrelation at zero-lag. Considering the computational complexity, a fast implementation method by polynomial functions is proposed to approach the autocorrelation function. Finally, experimental results on real echo data demonstrate the correctness and efficiency of the proposed method, which is of great significance for ionospheric detection.
Efficient Workload Balance Technology on Many-core Crypto Processor
Zibin DAI, Anqi YIN, Tongzhou QU, Longmei NAN
, Available online  , doi: 10.11999/JEIT180623 doi: 10.11999/JEIT180623
[Abstract](116) [FullText HTML] (61) [PDF 3967KB](12)
Imbalanced workload distribution results in low resource utilization of many-core crypto-platform. Dynamic workload allocation can improve the resource utilization with some overhead. Therefore, a higher frequency of workload balancing is not equivalent to higher gains. This paper establishes a mathematical model for gain rate and frequency of workload balancing. Based on this model, a collision-free workload balancing policy is proposed for many-core crypto systems, and a hierarchical "expandable-portable" engine is put forward, which consists of "Inter-cluster micro-network and intra-cluster ring-array" adopting hardware job queue technology. Experiment results show that the proposed workload-balancing engine is 4.06, 7.17, 23.01% and 2.15 times higher than the software technology based on " job stealing” in terms of performance, delay power consumption, resource utilization and workload balance; 1.75, 2.45, 10.2%, and 1.41 times better compared with the hardware technology based on "job stealing". By contrast with the ideal hardware technology, the average throughput of encryption algorithms is only decreased by 5.67% (the lowest 3%). The experiment also proves the scalability and portability of the proposed technique.
High Spectrum Efficiency Full-duplex Two-way Relay Scheme for OFDM
Yi LIU, Jiong WU, Pu YANG, Haihan NAN, Hailin ZHANG
, Available online  , doi: 10.11999/JEIT180451 doi: 10.11999/JEIT180451
[Abstract](140) [FullText HTML] (67) [PDF 991KB](6)
For the full-duplex two-way relay network, a two-way relay transmission scheme that is robust to the relay residual self-interference signal is proposed. Firstly, the residual self-interference signal of the relay is analyzed, the infinite self-interfering signal is modeled as an equivalent multipath signal, and the cyclic prefix of OFDM is used to combat the equivalent multipath phenomenon to reduce the residual self-interference signal impact. Based on the equivalent multipath scheme, the paper aims at maximizing the SINR of the system, and deduces the optimal amplification factor solving method of the relay in bidirectional full-duplex relay transmission. Finally, the simulation verifies the correctness of the optimal amplification factor of relay, and the effectiveness of the proposed two-way relay transmission scheme is verified through simulation.
Efficient Ciphertext Deduplication and Auditing Scheme with Attribute-based Encryption
Hua MA, Qianlong DANG, Jianfeng WANG, Zhenhua LIU
, Available online  , doi: 10.11999/JEIT170935 doi: 10.11999/JEIT170935
[Abstract](106) [FullText HTML] (51) [PDF 577KB](10)
Existing attribute-based deduplication schemes can support neither auditing of cloud storage data nor revocation of expired users. On the other hand, they are less efficient for deduplication search and users decryption. In order to solve these problems, this paper proposes an efficient deduplication and auditing Attribute-Based Encryption (ABE) scheme. A third-party auditor is introduced to verify the integrity of cloud storage data. Through an agent auxiliary user revocation mechanism, the proposed scheme supports the revocation of expired users. Effective deduplication search tree is put forward to improve the search efficiency, and the proxy decryption mechanism is used to assist users to decrypt. Finally, the security analysis shows that the proposed scheme can achieve IND-CPA security in the public cloud and PRV-CDA security in the private cloud by resorting to the hybrid cloud architecture. The performance analysis shows that the deduplication search is more efficient and the computation cost of user encryption is smaller.
BeiDou-reflectometry Sea Wind and Wave Retrieval System and Experiment
Dongkai YANG, Xiangyu WANG, Jianhua LIU, Feng WANG
, Available online  , doi: 10.11999/JEIT180397 doi: 10.11999/JEIT180397
[Abstract](212) [FullText HTML] (99) [PDF 1948KB](15)
A low power and cost BeiDou-reflectometry used to retrieve Significiant Wave Height (SWH) and wind is designed and implemented. To improve the retrieval accuracy, a correction method based on the power function of the elevation angle sinusoidal and a delay correlation for the rapid change of wind speed is proposed. Moreover, combined observation of multi-satellite signals and single-side filtering for the observable are performed to improve further the retrieval accuracy. The experiment results of observating SWH and wind speed using reflected BeiDou signals show that designed and developed system could implement long-term and stable observation; the retrieval accuracies of SWH and wind speed retrieved by propsoed retrieval models and improvement methods of the retreival accuracy are 0.13 m and 1.28 m/s which are 0.13 m and 0.78 m/s higher than the methods proposed by Soulat et al.
Three-dimensional Interferometric Imaging and Micro-motion Feature Extraction of Rotating Space Targets Based on Narrowband Radar
Jian HU, Ying LUO, Qun ZHANG, Le KANG, Qifang He
, Available online  , doi: 10.11999/JEIT180372 doi: 10.11999/JEIT180372
[Abstract](131) [FullText HTML] (56) [PDF 2865KB](15)
Inspired by the idea of multi-antenna interferometric processing in Interferometric Inverse Synthetic Aperture Radar (InISAR), by utilizing an L-shaped three-antenna imaging model, a Three-Dimensional (3-D) interferometric imaging and micro-motion feature extraction method for rotating space targets is proposed. Based on the integration of micro-Doppler (m-D) effect theory and multi-antenna interferometry processing technology, the m-D curves corresponding to different scatterers are obtained on the time-frequency plane and separated via Viterbi algorithm effectively, and then the projected coordinates of scatterers along the direction of baselines are reconstructed by interferometric processing. The height information of scatterers is solved by ellipse fitting, and 3-D imaging for the rotating space target is realized. Meanwhile, some 3-D micro-motion features are exactly extracted during imaging. Simulation results validate the effectiveness and the robustness of the method.
Unitary ESPRIT Based Multiband Fusion ISAR Imaging
Di XIONG, Junling WANG, Lizhi ZHAO, Shan ZHONG, Meiguo GAO
, Available online  , doi: 10.11999/JEIT180438 doi: 10.11999/JEIT180438
[Abstract](169) [FullText HTML] (66) [PDF 3965KB](4)
Multiband fusion imaging can effectively improve the range resolution of Inverse Synthetic Aperture Radar (ISAR) imaging. The traditional Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) spectral estimation signal fusion algorithm uses only the complex measured data without using their conjugate data. This paper proposes to modify the unitary ESPRIT method, which is based on synthesizing complex observation data and its conjugate data, to achieve unitary ESPRIT based multiband fusion ISAR imaging. The unitary ESPRIT method makes full use of the information of complex observations, which is more beneficial to multiband frequency spectrum estimation and ISAR imaging. Furthermore, for the correction of Migration Through Resolution Cell (MTRC) of scatterers in multiband fusion, the traditional processing flow is adjusted and optimized. The migration through range cell correction and the migration through Doppler cell correction are performed before and after the multiband fusion respectively, which avoids the influence of the fast time frequency - slow time coupling in the echo and the phase compensation on the spectrum fusion processing, thereby a better multiband fusion ISAR image is obtained. Simulation and real data experimental results show that the proposed methods can not only get high quality ISAR images, but also have good antinoise performance and higher computational efficiency.
Improved Long-code Direct Acquisition Algorithm Based on Time-frequency Fusion
Fangling ZENG, Xiaofeng OUYANG, Hao XU, Daqian LÜ
, Available online  , doi: 10.11999/JEIT180119 doi: 10.11999/JEIT180119
[Abstract](117) [FullText HTML] (55) [PDF 4766KB](10)
At present, long-code with the characteristic of high bit rate and long period is widely used in satellite navigation system as military signal. To overcome the shortcomings of the indirect acquisition method for long-code, the limitations of the extended replication overlay acquisition algorithm and the mean acquisition algorithm, an improved time-frequency capture method based on the Extended replica Folding Acquisition Search Technique (XFAST) and the mean algorithm is proposed. The feasibility of the algorithm is simulated and the performance of the algorithm is analyzed. The simulation experiment shows the superiority of the proposed algorithm.
Fast Cross-range Scaling for ISAR Imaging Based on Pseudo Polar Fourier Transform
Dong LI, Chengxiang ZHANG, Di ZHAO, Xiaoheng TAN, Xichuan ZHOU, Muyang ZHAN
, Available online  , doi: 10.11999/JEIT180299 doi: 10.11999/JEIT180299
[Abstract](131) [FullText HTML] (79) [PDF 2752KB](9)
For the Inverse Synthetic Aperture Radar (ISAR) imaging, the ISAR image obtained by the Range-Doppler (RD) or time-frequency analysis methods can not display the target's real shape due to its azimuth relating to the target Doppler frequency, thus the cross-range scaling is required for ISAR image. In this paper, a fast cross-range scaling method for ISAR is proposed to estimate the Rotational Angular Velocity (RAV). Firstly, the proposed method utilizes efficient Pseudo Polar Fast Fourier Transform (PPFFT) to transform the rotational motion of two ISAR images from two different instant time into translation in the polar angle direction. Then, a new cost function called integrated correction is defined to obtain the RAV coarse estimation. Finally, the optimal RAV can be estimated using the Bisection method to realize the cross-range scaling. Compared with the available algorithms, the proposed method avoids the problems of precision loss and high computational complexity caused by interpolation operation. The results of computer simulation and real data experiments are provided to demonstrate the validity of the proposed method.
An Estimation Method of Rotation Frequency of Unmanned Aerial Vehicle Based on Auto-correlation and Cepstrum
Chen SONG, Liangjiang ZHOU, Yirong WU, Chibiao DING
, Available online  , doi: 10.11999/JEIT180399 doi: 10.11999/JEIT180399
[Abstract](166) [FullText HTML] (81) [PDF 2700KB](20)
Accurately estimating rotor rotation frequency of Unmanned Aerial Vehicle (UAV) is of great significance for UAV detection and recognition. For the UAV target echo model of LFMCW (Linear Frequency Modulated Continuous Wave) radar, this paper proposes an auto-correlation and cepstrum to estimate the rotor rotation frequency of UAV, which derives the mapping relationship between the rotor rotation frequency of UAV and the periodic delay in the radar echo cepstrum output, and more effectively estimates the rotor frequency of multi-rotor UAV by weighted equilibrium, making up for the shortages of traditional methods. The effectiveness of the method is verified by simulation and real scene experiments.
High-precision Digital Surface Model Inversion Approach in Forest Region Based on PolInSAR
Jinwei XIE, Zhiyong SUO, Zhenfang LI, Yuekun WANG
, Available online  , doi: 10.11999/JEIT180387 doi: 10.11999/JEIT180387
[Abstract](168) [FullText HTML] (101) [PDF 4110KB](14)
The general method for inversion of Digital Surface Model (DSM) in forest region has great errors due to the inestimable waves’ penetration depth. For this problem, an approach to inversion of high-precision DSM is proposed. First, the phases of high and low scattering phase centers of the waves in forest are obtained by maximizing the phase separation of the coherence optimization. Then, the normal height variation models of the high and low scattering centers with extinction factors are constructed. According to the models, the least penetration depth of the waves in forest is acquired. Eventually, by implementing the interferometric technique on the phase of high scattering phase center, a coarse DSM is retrieved, and a high-precision DSM is developed by compensating the least penetration depth to the coarse one. The validation of the method is investigated by simulated datasets of PolSARpro under different tree species and different forest heights and by airborne real datasets. It shows that the proposed method can improve the accuracy on the inversion of DSM effectively in forest region.
Assessment Method for Gaps and Other Surface Defects
Jingcheng ZHAO, Xinru FU, Tao YANG, Xu GAO, Junqiang AI
, Available online  , doi: 10.11999/JEIT180378 doi: 10.11999/JEIT180378
[Abstract](124) [FullText HTML] (53) [PDF 2513KB](8)
Surface defects such as gaps have a significant impact on the stealth performance of the aircraft. According to the scattering mechanism of surface defects such as gaps, a method of evaluating the surface defect targets based on vector cancellation is proposed. The carrier is regarded as the target background and is subtracted the scattering effect of the carrier, especially the strong scattering angle of the carrier, and then the scattering characteristic data of the surface defect class target in the whole angle range are obtained, which solves the problem that the complete scattering characteristic can not be obtained using the conventional method. The comparison between the numerical calculation and the experimental results shows that the vector cancellation method can effectively evaluate the electromagnetic scattering characteristics of defective targets. After vector cancellation, the scattering of the carrier is greatly reduced, and the calculation or measurement accuracy of the defective target is effectively improved. At the same time, due to reducing the influence of the scattering of the carrier itself, this method avoids the requirement of the carrier size and ultra-low scattering characteristics, and reduces effectively the processing cost of the carrier.
Incentive and Restraint Mechanism of Rewards and Punishment in Access Control Based on Game Theory
Bin ZHAO, Chuangbai XIAO, Wenyin ZHANG, Xue GU
, Available online  , doi: 10.11999/JEIT180406 doi: 10.11999/JEIT180406
[Abstract](92) [FullText HTML] (37) [PDF 1679KB](10)
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.
A Novel Design Algorithm for Low Complexity Sparse FIR Notch Filters
Wei XU, Anyu LI, Boya SHI
, Available online  , doi: 10.11999/JEIT180548 doi: 10.11999/JEIT180548
[Abstract](89) [FullText HTML] (41) [PDF 1026KB](3)
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). And 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. The 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.
A New Prompt 2-D Attitude Steering Approach for Zero Doppler Centroid of Geosynchronous SAR
Bingji ZHAO, Qingjun ZHANG, chao DAI, Liping LIU, Zhihua TANG, Weiping SHU, Chong NI
, Available online  , doi: 10.11999/JEIT180643 doi: 10.11999/JEIT180643
[Abstract](50) [FullText HTML] (26) [PDF 3259KB](1)
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 in this paper.
Hybrid Group Signcryption Scheme Based on Heterogeneous Cryptosystem
Shufen NIU, Xiyan YANG, Caifen WANG, Miao TIAN, Xiaoni DU
, Available online  , doi: 10.11999/JEIT180554 doi: 10.11999/JEIT180554
[Abstract](57) [FullText HTML] (19) [PDF 692KB](2)
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.
Flight Delay Prediction Model Based on Deep SE-DenseNet
Renbiao WU, Ting ZHAO, Jingyi QU
, Available online  , doi: 10.11999/JEIT180644 doi: 10.11999/JEIT180644
[Abstract](111) [FullText HTML] (58) [PDF 1880KB](13)
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%.
Heterogeneity Quantization Method of Cyberspace Security System Based on Dissimilar Redundancy Structure
Jiexin ZHANG, Jianmin PANG, Zheng ZHANG, Ming TAI, Hao LIU
, Available online  , doi: 10.11999/JEIT180764 doi: 10.11999/JEIT180764
[Abstract](91) [FullText HTML] (51) [PDF 1036KB](6)
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.
Anti-bias Track Association Algorithm of Radar and Electronic Support Measurements Based on Track Vectors Hierarchical Clustering
Baozhu LI, Lin ZHANG, Yunlong DONG, jian GUAN
, Available online  , doi: 10.11999/JEIT180714 doi: 10.11999/JEIT180714
[Abstract](113) [FullText HTML] (55) [PDF 1800KB](13)
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.