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A Novel Fuzzy Clustering Algorithm Based on Similarity of Attribute Space
Weifeng SHI, Jinbao ZHUO, Ying LAN
, Available online  , doi: 10.11999/JEIT180974 doi: 10.11999/JEIT180974
[Abstract](234) [FullText HTML] (166) [PDF 386KB](15)
With the attribute feature information of the fuzzy membership matrix and cluster centers after the iteration not fully utilized, the results of Fuzzy C-Means (FCM) Clustering and related modified algorithms are determined based on the principle of maximum fuzzy membership, causing bad influence on the clustering accuracy. To solve this problem, the improvement ideas are proposed: to improve classification principle of FCM. The formula definition of attribute similarity in binary topological subspaces is given. Then, the improved FCM algorithm based on the similarity of attribute space is proposed: First, samples with fuzzy membership degree lower than the clustering reliability are selected as suspicious samples. Next, the attribute similarity between the suspicious samples and the cluster centers after clustering are calculated. Finally, cluster labels of suspicious samples based on the principle of maximum attribute similarity are updated. The validity and superiority of the proposed algorithm is verified by the UCI sample set experiments and comparisons with other modified algorithms based on the principle of maximum fuzzy membership.
A Computation Offloading and Resource Allocation Mechanism Based on Minimizing Devices Energy Consumption and System Delay
Meiling DAI, Zhoubin LIU, Shaoyong GUO, Sujie SHAO, Xuesong QIU
, Available online  , doi: 10.11999/JEIT180970 doi: 10.11999/JEIT180970
[Abstract](291) [FullText HTML] (196) [PDF 1351KB](25)
To support the execution of computation-intensive, delay-sensitive computing task by moving down the computing and processing capability in mobile edge computing becomes the current trend. However, when serving a large number of mobile users, how to effectively use the edge nodes with limited computing resources to ensure Quality of service (QoS) of end-user has become a key issue. To solve this problem, the edge cloud and remote cloud are combined to build a layered edge cloud computing architecture. Based on this architecture, with the goal of minimizing mobile device energy consumption and task execution time, the problem which is proved to be convex is formulated to minimize the weight sum of energy and delay. A computation offloading and resource allocation mechanism based on multiplier method is proposed. Simulations are conducted to evaluate the proposed mechanism. Compared with local computing and computation offloading mechanism, the proposed mechanism can effectively reduce the energy consumption of mobile device and the delay of system by up to 60% and 10%, respectively.
Channel Estimation Algorithm Using Temporal-spatial Structure for Up-link of Massive MIMO Systems
Xinhua LU, Carles Navarro MANCHÓN, Zhongyong WANG, Chuanzong ZHANG
, Available online  , doi: 10.11999/JEIT180676 doi: 10.11999/JEIT180676
[Abstract](313) [FullText HTML] (224) [PDF 1065KB](20)
To deal with the estimation problem of non-stationary channel in massive Multiple-Input Multiple-Output (MIMO) up-link, the 2D channels’ sparse structure information in temporal-spatial domain is used, to design an iterative channel estimation algorithm based on Dirichlet Process (DP) and Variational Bayesian Inference (VBI), which can improve the accuracy under a lower pilot overhead and computation complexity. On account of that the stationary channel models is not suitable for massive MIMO systems anymore, a non-stationary channel prior model utilizing Dirichlet Process is constructed, which can map the physical spatial correlation channels to a probabilistic channel with the same sparse temporal vector. By applying VBI technology, a channel estimation iteration algorithm with low pilot overhead and complexity is designed. Experiment results show the proposed channel method has a better performance on the estimation accuracy than the state-of-art method, meanwhile it works robustly against the dynamic system key parameters.
Design of Convolutional Neural Networks Hardware Acceleration Based on FPGA
Huabiao QIN, Qinping CAO
, Available online  , doi: 10.11999/JEIT190058 doi: 10.11999/JEIT190058
[Abstract](304) [FullText HTML] (209) [PDF 1911KB](30)
Considering the large computational complexity and the long-time calculation of Convolutional Neural Networks (CNN), an Field-Programmable Gate Array(FPGA)-based CNN hardware accelerator is proposed. Firstly, by deeply analyzing the forward computing principle and exploring the parallelism of convolutional layer, a hardware architecture in which parallel for the input channel and output channel, deep pipeline for the convolution window is presented. Then, a full parallel multi-addition tree is designed to accelerate convolution and efficient window buffer to implement deep pipelining operation of convolution window. The experimental results show that the energy efficiency ratio of proposed accelerator reaches 32.73 GOPS/W, which is 34% higher than the existing solutions, as the performance reaches 317.86 GOPS.
Depth Estimation of Monocular Road Images Based on Pyramid Scene Analysis Network
Wujie ZHOU, Ting PAN, Pengli GU, Zhinian ZHAI
, Available online  , doi: 10.11999/JEIT180957 doi: 10.11999/JEIT180957
[Abstract](390) [FullText HTML] (328) [PDF 1200KB](17)
Considering the problem that the prediction accuracy is not accurate enough when the depth information is recovered from the monocular vision image, a method of depth estimation of road scenes based on pyramid pooling network is proposed. Firstly, using a combination of four residual network block, the road scene image features are extracted, and then through the sampling, the features are gradually restored to the original image size, and the depth of the residual block is increased. Considering the diversity of information in different scales, the features with same sizes extracted from the sampling process and the feature extraction process are merged. In addition, pyramid pooling network blocks are added to the advanced features extracted by four residual network blocks for scene analysis, and the feature graph output of pyramid pooling network blocks is finally restored to the original image size and input prediction layer together with the output of the upper sampling module. Through experiments on KITTI data set, the results show that the proposed method is superior to the existing method.
Performance of Rank Sum Nonparametric Detector at Clutter Edge
Xiangwei MENG
, Available online  , doi: 10.11999/JEIT190136 doi: 10.11999/JEIT190136
[Abstract](13) [FullText HTML] (7) [PDF 816KB](5)
The performance of a Constant False Alarm Rate (CFAR) detector is often evaluated in three typical backgrounds - homogeneous environment, multiple targets situation and clutter edges described by Prof. Rohling. However, there is a lack of the analytic expression of the false alarm rate for the Rank Sum (RS) nonparametric detector at clutter boundaries, and lack of a comparison of the ability for the RS detector to control the rise of the false alarm rate at clutter edges to that of the conventional parametric CFAR schemes, which is incomplete and imperfect for the detection theory of the nonparametric detectors. The analytic expression of the false alarm rate Pfa for the RS nonparametric detector at clutter edges is given in this paper, and the ability of the RS nonparametric detector to control the rise of the false alarm rate at clutter edges is compared to that of the Cell Averaing (CA) CFAR, the Greatest Of (GO) CFAR and the Ordered Statistic (OS) CFAR with incoherent integration. When both of the heavy and the weak clutters follow a Rayleigh distribution, it is shown that the rise of the false alarm rate for the RS detector at clutter edges lies between that of the CA-CFAR and that of the OS-CFAR with incoherent integration. If a non-Gaussian distributed clutter with a long tail moves into the reference window, the rise of the CA-CFAR, the GO-CFAR and the OS-CFAR with incoherent integration reaches a peak of more than 3 orders of magnitude, and can not return to the original pre-designed Pfa. However, the RS nonparametric detector exhibits its inherent advantage in such situation, it can maintain the constant false alarm rate even the distribution form of clutter becomes a different one.
Fully Digital Feedforward Background Calibration of Time Skew for Sub-Sampling Time-Interleaved Analog-to-Digital Converter
Honghui DENG, Hui YAN, Rui XIAO, Hongmei CHEN
, Available online  , doi: 10.11999/JEIT190052 doi: 10.11999/JEIT190052
[Abstract](7) [FullText HTML] (4) [PDF 1611KB](0)
A full digital feedforward Time-Interleaved Analog-to-Digital Converter (TIADC) time skew calibration algorithm is presented, the time skew estimation adopts the feedforward extraction method of the improved derivative module of time skew function, which can greatly improve the accuracy of skew estimation when the input signal frequency is high. At the same time, the time skew function is based on subtraction, in order to reduce the complexity of skew estimation unit. Finally, the time skew is corrected by using first-order Taylor compensation. The simulation results show that when the input signal is a multi-frequency signal, the Spurious-Free Dynamic Range (SFDR) increases from 48.6 dB to 80.7 dB, after adopting the proposed time skew correction for a 4-channal 14-bit TIADC system. Compared with the traditional feedforward calibration structure based on correlation operation, the effective calibration input signal bandwidth can be increased from 0.19 to 0.39, which greatly increases the application range of the calibration algorithm.
Research on the Adaptive Synchrosqueezing Algorithm
Lin LI, Lin WANG, Hongxia HAN, Hongbing JI, Li JIANG
, Available online  , doi: 10.11999/JEIT190146 doi: 10.11999/JEIT190146
[Abstract](12) [FullText HTML] (8) [PDF 2031KB](0)
The improvement of time-frequency resolution plays a crucial role in the analysis and reconstruction of multi-component non-stationary signals. For traditional time-frequency analysis methods with fixed window, the time-frequency concentration is low and hardly to distinguish the multi-component signals with fast-varying frequencies. In this paper, by adopting the local information of the signal, an adaptive synchrosqueezing transform is proposed for the signals with fast-varying frequencies. The proposed method is with high time-frequency resolution, superior to existing synchrosqueezing methods, and particularly suitable for multi-component signals with close and fast-varying frequencies. Meanwhile, by using the separability condition, the adaptive window parameters are estimated by local Rényi entropy. Finally, experiments on synthetic and real signals demonstrate the correctness of the proposed method, which is suitable to analyze and recover complex non-stationary signals.
A Moving Target Imaging Approach for the Multichannel in Azimuth High Resolution Wide Swath SAR System
Yuying WANG, Zhimin ZHANG, Ning LI, Huaitao FAN, Qingchao ZHAO
, Available online  , doi: 10.11999/JEIT190211 doi: 10.11999/JEIT190211
[Abstract](13) [FullText HTML] (9) [PDF 1728KB](0)
Since the echo characteristics of moving targets are different from that of stationary targets, the traditional reconstruction filter bank algorithm, i.e., the reconstruction filter algorithm, is not applicable. In this paper, a novel reconstruction approach of the moving target for a multichannel in azimuth High-Resolution Wide-Swath (HRWS) Synthetic Aperture Radar (SAR) system is proposed. The approach firstly analyzes the echo characteristics of the moving target for the multi-channel in azimuth SAR system and gives the main reason for the failure of the traditional reconstruction method in contrast to the form of the stationary target echo. By introducing the radial velocity of the moving target, the spectrum reconstruction of the uniform moving target is effectively realized, and the azimuth ambiguities of the uniform moving target for the multi-channel in azimuth SAR system is well suppressed. Space-borne simulated results confirm the effectiveness of the proposed reconstruction approach.
Energy Efficiency and System Capacity Based Multi-Objective Radio Resource Management in M2M Communications
Shaoyi XU, Shuai GAO
, Available online  , doi: 10.11999/JEIT181168 doi: 10.11999/JEIT181168
[Abstract](9) [FullText HTML] (5) [PDF 1390KB](0)
Machine-to-Machine (M2M) and Device-to-Device (D2D) communications are both key technologies in the fifth Generation (5G) mobile communication systems. In M2M communications, the Energy Efficiency (EE) especially needs to be improved to extend the life cycle of the M2M equipment. In this paper, the M2M and D2D technologies are combined and the D2D technology is used to realize M2M transmission. At the same time, M2M users are allowed to reuse spectrum resources with Human-to-Human (H2H) devices in the cellular networks. To guarantee the Quality of Service (QoS) of these two systems simultaneously, a Multi-Objective Optimization Problem (MOOP) is then formulated to maximize the sum throughput of H2H systems, sum EE of M2M systems and to minimize the interference from M2M communications to H2H networks. To solve this MOOP, the penalty function method is firstly adopted to relax the original binary variables, and then the ConCave-Convex Procedure (CCCP) method is used to convert the non-convex single-objective problems into convex problems. Finally, the weighted Tchebyshev algorithm is utilized to obtain the Pareto solution of the original MOOP. By comparing with the traditional weighted sum method, the effectiveness of the proposed method is proved by simulation results.
Online Blind Equalization Algorithm for Satellite Channel Based on Echo State Network
Ling YANG, Bin ZHAO, Liang CHEN, Yuan LI, Guolong ZHANG
, Available online  , doi: 10.11999/JEIT190034 doi: 10.11999/JEIT190034
[Abstract](231) [FullText HTML] (170) [PDF 2002KB](15)
Two online blind equalization algorithms based on Echo State Network (ESN) in this paper are proposed for the nonlinear satellite channel. These two algorithms take advantage of the good nonlinear approximation of ESN to bring the High-Order Statistics (HOS) of the transmitted signal into the ESN, and constructing cost function of blind equalization by combining Constant Modulus Algorithm (CMA) and Multi-Modulus Algorithm (MMA). Then, the Recursive Least Squares (RLS) algorithm is used to iteratively optimize the network output weights, and the online blind equalization of the constant modulus signals and the multi-modulus signals over the channel of Volterra satellite are realized. Experiments show that the proposed algorithms can effectively reduce the distortion of the transmitted signal by the nonlinear channel. Compared with the traditional Volterra filtering method, they have faster convergence speed and lower mean square error.
Calculation of Forced Vital Capacity Based on Turbine Air Flow Sensor
Chenshuo WANG, Guangqiang HE, Yueqi LI, Rongjian ZHAO, Xianxiang CHEN, Lidong DU, Zhan ZHAO, Zhen FANG
, Available online  , doi: 10.11999/JEIT190051 doi: 10.11999/JEIT190051
[Abstract](192) [FullText HTML] (140) [PDF 1335KB](2)
Currently, the turbine air flow sensors are widely used to record the human exhalation signals in spirometry, but test results would vary due to different expiratory flow for the same Forced Vital Capacity(FVC) measurements, and the differences are usually not in an acceptable range. To address this issue, the present study proposes a FVC velocity penalty model by introducing speed penalty items to the traditional mathematical model of turbine. Moreover, the authors propose to use an over-amplitude drop sampling approach to calculate the rotations of the turbine due to the needs for the velocity penalty model to be able to accurately obtain the number of turbine rotations. The performance of the proposed approach are evaluated by using a syringe dispenser of 3L capacity and results demonstrated that it could reduce the differences and meet the acceptable and accuracy criteria of the American Thoracic Society(ATS) and the European Respiratory Society(ERS) to some extent.
Traffic Flow Prediction Based on Hybrid Model of Auto-regressive Integrated Moving Average and Genetic Particle Swarm Optimization Wavelet Neural Network
Lisheng YIN, Shengqi TANG, Sheng LI, Yigang HE
, Available online  , doi: 10.11999/JEIT181073 doi: 10.11999/JEIT181073
[Abstract](390) [FullText HTML] (191) [PDF 1696KB](25)
In view of the nonlinear and stochastic characteristics of short-term traffic flow data, this article propose a prediction model and algorithm based on hybrid Auto-Regressive Integrated Moving Average (ARIMA) and Genetic Particle Swarm Optimization Wavelet Neural Network (GPSOWNN) in order to improve its prediction accuracy and rate of convergence. In terms of model construction, the ARIMA model prediction value and the historical data of the first three moments with strong correlation with gray correlation coefficient greater than 0.6 are used as input of the Wavelet Neural Network(WNN), and the structure of the model is simplified considering both the stationary and non-stationary historical data. In terms of algorithm, by using the genetic particle swarm optimization algorithm to select optimally the initial values of the wavelet neural network, the results can speed up the convergence of network training under the condition that it is not easy to fall into local optimum. The experimental results show that the proposed model is superior to hybrid ARIMA and GPSOWNN in terms of prediction accuracy, the genetic particle swarm optimization algorithm is superior to the genetic algorithm optimization model in terms of convergence speed.
A Capacitor-less Low Dropout Regulator with Fast Response
Xingyuan TONG, Mao LI, Siwan DONG
, Available online  , doi: 10.11999/JEIT181060 doi: 10.11999/JEIT181060
[Abstract](108) [FullText HTML] (50) [PDF 2064KB](2)
A novel technique for increasing the load response speed of Capacitor-Less Low-DropOut linear regulator (CL-LDO) is proposed to improve the transient response of CL-LDO when its load current changes. With an additional fast signal feedback path, the CL-LDO can achieve fast transient response so that the overshoot and undershoot of its output voltage can be dramatically reduced. A CL-LDO with fast response is realized in 0.18 μm CMOS and occupies an active area of 0.00529 mm2. The CL-LDO has an output voltage of 1.194 V when the input supply voltage ranges from 1.5 V to 2.5 V. When the load current changes from 100 μA to 10 mA with the rise and fall time of 1 μs, the output of LDO can be recovered from its overshoot and undershoot to a stable voltage within 489.537 ns and 960.918 ns, respectively. Compared with a traditional CL-LDO without this proposed technique, the transient response speed of this CL-LDO is increased by 7.41 times. The overshoot and undershoot of the output voltage is decreased by 35.3% and 78.1%, respectively.
Coherent Integration Algorithm Based on Adjacent Cross Correlation Function-Parameterized Centroid Frequency-Chirp Rate Distribution -Keystone Transform for Maneuvering Target in Passive Radar
Yongsheng ZHAO, Dexiu HU, Zhixin LIU, Yongjun ZHAO, Chuang ZHAO
, Available online  , doi: 10.11999/JEIT180858 doi: 10.11999/JEIT180858
[Abstract](19) [FullText HTML] (16) [PDF 2356KB](2)
Increasing the integration time can effectively improve the detection performance of passive radar. However, for maneuvering targets, the complex motions, such as high velocity, acceleration and jerk, will cause existing detection methods to suffer the Range Migration (RM) and Doppler Frequency Migration (DFM) during the integration time, which will deteriorate the detection performance. This paper addresses the long time coherent integration for a maneuvering target with high-order motion (e.g., jerk motion) in passive radar systems. A method based on Adjacent Cross Correlation Function (ACCF), Parameterized Centroid Frequency-Chirp Rate Distribution (PCFCRD) and Keystone Transform (KT)(ACCF-PCFCRD-KT), is proposed. Firstly, the signal model for the maneuvering targets is given, and the influence of the target velocity, acceleration and jerk on the coherent integration is analyzed. For the Doppler curvature induced by the jerk motion, the ACCF is firstly applied to reduce the order of RM and DFM. Then the PCFCRD operation is employed to estimate the acceleration and jerk parameters. After compensating the RM and DFM caused by the acceleration and jerk, the RM arising from the velocity is corrected via the KT operation and the target echo energy is coherently integrated. Simulation results demonstrate that the proposed method can effectively compensate the RM and DFM caused by the target motion parameters in passive radar, and for a maneuvering target with jerk motion, the proposed method achieves a better integration performance over existing methods.
An Optimal Number of Indices Aided gOMP Algorithm for Multi-user Detection in NOMA System
Bin SHEN, Hebiao WU, Taiping CUI, Qianbin CHEN
, Available online  , doi: 10.11999/JEIT190270 doi: 10.11999/JEIT190270
[Abstract](22) [FullText HTML] (17) [PDF 1772KB](2)
As one of the Key 5G technologies, Non-Orthogonal Multiple Access (NOMA) can improve spectrum efficiency and increase the number of user connections by utilizing the resources in a non-orthogonal manner. In the uplink grant-free NOMA system, the Compressive Sensing (CS) and generalized Orthogonal Matching Pursuit (gOMP) algorithm are introduced in active user and data detection, to enhance the system performance. The gOMP algorithm is literally generalized version of the Orthogonal Matching Pursuit (OMP) algorithm, in the sense that multiple indices are identified per iteration. Meanwhile, the optimal number of indices selected per iteration in the gOMP algorithm is addressed to obtain the optimal performance. Simulations verify that the gOMP algorithm with optimal number of indices has better recovery performance, compared with the greedy pursuit algorithms and the Gradient Projection Sparse Reconstruction (GPSR) algorithm. In addition, given different system configurations in terms of the number of active users and subcarriers, the proposed gOMP with optimal number of indices also exhibits better performance than that of the other algorithms mentioned in this paper.
A Small Moving Object Detection Algorithm Based on Track in Video Surveillance
Yifeng SUN, Jiang WU, Yanyan HUANG, Guangming TANG
, Available online  , doi: 10.11999/JEIT181110 doi: 10.11999/JEIT181110
[Abstract](301) [FullText HTML] (231) [PDF 3553KB](25)
To solve the problem that small moving object is difficult to be detected in video surveillance, a track-based detection algorithm is proposed. Firstly, in order to reduce missing alarm, an adaptive foreground extraction method combining regional texture features and difference probability is presented. Then, for reducing false alarm, the probability computing model of track correlation is designed to establish the correlation of suspected objects between frames, and double-threshold are set to distinguish between true and false positive. Experimental results show that compared with many classical algorithms, this algorithm can accurately detect small moving object within the quantitative range with lower missing and false alarm.
Scheduling Algorithm Based on Value Optimization for Phased Array Radar
Shanchao YANG, Kangsheng TIAN, Renzheng LIU, Yujun ZHENG
, Available online  , doi: 10.11999/JEIT190147 doi: 10.11999/JEIT190147
[Abstract](36) [FullText HTML] (24) [PDF 1620KB](5)
A task scheduling algorithm based on value optimization is proposed for phased array radar. Firstly, the schedulability of tracking tasks is obtained through feasibility analysis and selecting operation on the task queue, using the proposed schedulability parameters. Then, a dynamic task value function about the actual execution time is established according to the peak value and value changing slope of tasks. And a value optimization model for tracking task scheduling is constructed based on the task value function. Timeliness can be better achieved while adopting this model to assign execution time for tasks. Finally, searching tasks are scheduled using the idle time intervals between tracking tasks which are going to be executed. Simulation results show that proposed algorithm reduces the average time shift ratio, and improves the value achieving ratio compared with the traditional scheduling algorithms.
JPEG Compression Artifacts Reduction Algorithm Based on Multi-scale Dense Residual Network
Shuzhen CHEN, Yijun ZHANG, Qiusheng LIAN
, Available online  , doi: 10.11999/JEIT180963 doi: 10.11999/JEIT180963
[Abstract](296) [FullText HTML] (198) [PDF 1923KB](22)
In the case of high compression rates, the JPEG decompressed image can produce blocking artifacts, ringing effects and blurring, which affect seriously the visual effect of the image. In order to remove JPEG compression artifacts, a multi-scale dense residual network is proposed. Firstly, the proposed network introduces the dilate convolution into a dense block and uses different dilation factors to form multi-scale dense blocks. Then, the proposed network uses four multi-scale dense blocks to design the network into a structure with two branches, and the latter branch is used to supplement the features that are not extracted by the previous branch. Finally, the proposed network uses residual learning to improve network performance. In order to improve the versatility of the network, the network is trained by a joint training method with different compression quality factors, and a general model is trained for different compression quality factors. Experiments demonstrate that the proposed algorithm not only has high JPEG compression artifacts reduction performance, but also has strong generalization ability.
Discrete Dynamic System without Degradation -configure N Positive Lyapunov Exponents
Geng ZHAO, Hong LI, Yingjie MA, Xiaohong QIN
, Available online  , doi: 10.11999/JEIT180925 doi: 10.11999/JEIT180925
[Abstract](302) [FullText HTML] (187) [PDF 885KB](18)
Considering discrete-time chaotic dynamics systems, a new algorithm is proposed which is based on matrix eigenvalues and eigenvectors to configure Lyapunov exponents to be positive. The eigenvalues and eigenvectors of the discrete controlled matrix are calculated to design a general controller with positive Lyapunov exponents. The theory proves the boundedness of the system orbit and the finiteness of the Lyapunov exponents. The numerical simulation analysis of the linear feedback operator and the perturbation feedback operator verifies the correctness, versatility and effectiveness of the algorithm. Performance evaluations show that, compared with Chen-Lai methods, the proposed method can construct chaotic system with lower computation complexity and the running time is shorter and the outputs demonstrate strong randomness. Thus, a discrete chaotic system with no degradation and no merger is realized.
Research on Co-channel Base Station Interference Suppression Method of Passive Radar Based on LTE Signal
Xiaode LÜ, Hanliang ZHANG, Zhongsheng LIU, Zhenghao SUN, Pingyu LIU
, Available online  , doi: 10.11999/JEIT180904 doi: 10.11999/JEIT180904
[Abstract](400) [FullText HTML] (244) [PDF 2602KB](29)
For the passive radar based on LTE signal, the received signal contains direct-path and multipath clutters interference of multiple co-channel base station, and the traditional passive radar signal processing flow is improved, and the processing steps of co-channel base station interference are added. A blind source separation algorithm based on convolutive mixtures is proposed. The algorithm can suppress the clutters interference of co-channel base station. It is assumed that the mixing matrix is a vector linear time-invariant filter matrix. The mutual information is used as a cost function. By finding the gradient of mutual information, it is iterated by the steepest descent method. The separation criterion is to minimize the mutual information between the separated signals. The simulation results show that the proposed algorithm can effectively suppress the clutters interference of the LTE signal co-channel base station, and provide a basis for the subsequent clutters cancellation processing of the main base station.
Design a Wideband Matasurface Antenna Array with Low Scattering Characteristics
Tao LIU, Xiangyu CAO, Jun GAO, Junxiang LAN, Lili CONG
, Available online  , doi: 10.11999/JEIT180922 doi: 10.11999/JEIT180922
[Abstract](380) [FullText HTML] (203) [PDF 4842KB](11)
A broadband metasurface antenna array with wideband low Radar Cross Section (RCS) is proposed. Two kind metasurface antennas with nearly the same radiation performance are designed and fabricated in a chessboard configuration. For x -polarized incidence, the reflected energy from the two elements is dissipated based on destructive phase difference. For y -polarized incidence, the incident energy is absorbed by matching load. By this means, inherent wideband low RCS is achieved for both polarizations without redundant structures. The proposed antenna array is fabricated and measured. Simulated and measured results show that the working frequency band is 6.0~8.5 GHz. Meanwhile under x polarization the antenna monostatic RCS is reduced significantly 6 dB RCS reduction is achieved over the range of 6.2~10.5 GHz and the peak reduction is up to 21.07 dB. Under y polarization the antenna monostatic RCS is reduced over 3 dB RCS reduction in bandwidth. Both measured and simulated results verify the proposed antenna array is characterized with wideband low-RCS without degrading the radiation performance.
Multi-priority Based Joint Optimization Algorithm of Virtual Network Function Migration Cost and Network Energy Consumption
Lun TANG, Heng YANG, Runlin MA, Qianbin CHEN
, Available online  , doi: 10.11999/JEIT180906 doi: 10.11999/JEIT180906
[Abstract](363) [FullText HTML] (228) [PDF 1780KB](32)
After the Virtual Network Function (VNF) in the 5G access network is deployed, the resource requirements are dynamically changed, resulting in the problem that the Physical Machine (PM) resource utilization in the network is too high or too low. To solve the above problem, the resource usage of PM in the network is divided into five different partitions, and a multi-priority VNF migration request queue scheduling model is proposed. Secondly, based on the model, a joint optimization model is established to minimize the VNF migration cost and minimize the network energy consumption. Finally, a multi-priority VNF migration cost and network energy joint optimization algorithm based on 5G access network is presented to solve the above model. The simulation results show that the algorithm can effectively improve the PM resources utilization, ensure the PM performance and balance the PM load while effectively realizing a compromise between VNF migration cost and network energy consumption.
Integral Attack on Reduced-round Simeck Algorithm
Jiongjiong REN, Hang LI, Shaozhen CHEN
, Available online  , doi: 10.11999/JEIT180849 doi: 10.11999/JEIT180849
[Abstract](272) [FullText HTML] (178) [PDF 1445KB](14)
The security of lightweight block cipher Simeck against integral attack is evaluated in this paper. First, a 16-round and a 20-round high-order integral distinguisher of Simeck48 and Simeck64 are constructed by decrypting the existed integral distinguisher forward. Then, combined with the meet-in-the-middle strategy and subkey relationship, the integral attacks on 24-round Simeck48 and 29-round Simeck64 are first proposed utilizing the equivalent-subkey and partial-sum technologies based on the new integral distinguishers. The data, time and memory complexity of attacking 24-round Simeck48 are 246, 295 and 282.52 while the data, time and memory complexity of attacking 29-round Simeck64 are 263, 2127.3 and 2109.02. These new attacks greatly improve the results of the previous integral attack on Simeck. Compared with the known results of the integral attack on Simeck, the number of rounds of the integral attacks on Simeck48 and Simeck64 is increased by 3-round and 5-round, respectively.
A SDN Routing Optimization Mechanism Based on Deep Reinforcement Learning
Julong LAN, Changhe YU, Yuxiang HU, Ziyong LI
, Available online  , doi: 10.11999/JEIT180870 doi: 10.11999/JEIT180870
[Abstract](303) [FullText HTML] (202) [PDF 2020KB](24)
In order to achieve routing optimization in the Software Defined Network (SDN) environment, deep reinforcement learning is imposed to the SDN routing process and a mechanism based on deep reinforcement learning is proposed to optimize routing. This mechanism can improve network performance such as delay, throughput, and realize black-box optimization in continuous time, which is surely reduce network operation and maintenance costs. Besides, this the proposed routing optimization mechanism is evaluated through a series of experiments. The experimental results show that the proposed SDN routing optimization mechanism has good convergence and effectiveness, and can provide better routing configurations and performance stability than traditional routing protocols.
Signal Detection Based on Sigmoid Function in Non-Gaussian Noise
Zhen DAI, Pingbo WANG, Hongkai Wei
, Available online  , doi: 10.11999/JEIT190012 doi: 10.11999/JEIT190012
[Abstract](205) [FullText HTML] (146) [PDF 1248KB](16)
To solve the problem of weak signals detection in non-Gaussian background, a method based on sigmoid function is proposed which is named Sigmoid Function Detector (SFD). Firstly, the non-Gaussian background is modeled as a mixed Gaussian model. Based on this, the relationship between parameter k and SFD's performance and characteristics are systematically analyzed. It is pointed out that SFD will be a constant false alarm detector when its detection performance is optimal. Secondly, a new non-parametric detector is proposed via fixing the parameter k, which has a significant improvement over matched filter. Finally, simulation analysis is carried out to verify the effectiveness and superiority of SFD.
An Anti-Dense False Target Jamming Algorithm Based on Agile Frequency Joint Hough Transform
Yinghui QUAN, Xiada CHEN, Feng RUAN, Xia GAO, Yachao LI, Mengdao XING
, Available online  , doi: 10.11999/JEIT190010 doi: 10.11999/JEIT190010
[Abstract](289) [FullText HTML] (175) [PDF 1655KB](26)
Forwarding dense false target jamming disturbs the detection and recognition of real targets by generating multiple false targets in the range dimension. Because the false echo signal is highly correlated with the real signal, it is difficult for radar to recognize and suppress it effectively. Frequency agile radar improves greatly the low interception and anti-jamming ability of radar by randomly changing the carrier frequency of transmitting adjacent pulses. However, agile radar cannot completely eliminate the interference, some target echo pulses may be submerged by the interference, agile radar cannot complete coherent accumulation and target detection well either. To solve the above problems, an anti-jamming method of frequency agility combined with Hough transform is proposed. Firstly, the inter-pulse frequency agility technology is used to avoid most narrowband aiming and deceptive jamming. Then, according to the time discontinuity of the jamming signal, Hough transform and peak extraction are used to identify and suppress the jamming. Frequency agility is incompatible with traditional Moving Target Detection(MTD). Target detection is accomplished by sparse reconstruction. The simulation and actual radar and jammer countermeasure experiments show that the proposed method can achieve good anti-jamming performance and target detection performance.
Research on the Novel Ultra-wideband Power Divider Based on Beetle Antennae Search Algorithm
Jie LI, Yuepeng YAN, Xiaoxin LIANG, Jing WAN, Kuisong WANG
, Available online  , doi: 10.11999/JEIT181003 doi: 10.11999/JEIT181003
[Abstract](205) [FullText HTML] (181) [PDF 1343KB](9)
Based the study of the spur-line, a novel spurs-line structure is proposed. The design of a novel Ultra-WideBand (UWB) power divider is described based on the novel spur line structure for the 2.5~13.2 GHz frequency range. The designed device is compact and has a simple structure and good frequency response in the band. Its return loss insertion is less than –12 dB and its insertion loss is less than 3.5 dB. The equations used for the design are based on the concept of odd-even modes and transmission line analysis. The Beetle Antennae Search (BAS) algorithm is used to improve the efficiency and accuracy of the power divider design. In order to verify the accuracy of the design, an UWB power divider is designed by using material RO4003C as substrate. The results validatethe feasibility of the spur line-based design and demonstratthat the BAS algorithm has a shortened running time and improved precision compared to other optimization methods. It can be widely used in UWB power divider design.
A Reliability-guarantee Method for Service Function Chain Deployment Based on Joint Backup
Hongbo TANG, Hang QIU, Wei YOU, Xinsheng JI
, Available online  , doi: 10.11999/JEIT190013 doi: 10.11999/JEIT190013
[Abstract](195) [FullText HTML] (149) [PDF 1552KB](10)
In the Network Function Virtualization (NFV) environment, for the reliability problem of Service Function Chain (SFC) deployment, a joint optimization method is proposed for backup Virtual Network Function (VNF) selection, backup instance placement and service function chain deployment. Firstly, the method defines a virtual network function measurement standard named the unit cost reliability improvement value to improve the backup virtual network function selection method. Secondly, the joint backup mode is used to adjust the placement strategy between adjacent backup instances to reduce bandwidth resources overhead. Finally, the reliability-guarantee problem of the whole service function chain deployment is modeled as integer linear programming, and a heuristic algorithm based on the shortest path is proposed to overcome the complexity of integer linear programming. The simulation results show that the method optimizes resource allocation while prioritizing the network service reliability requirements, and improves the request acceptance rate.
A Hardware Trojan Detection Method Based on Compression Marginal Fisher Analysis
Xiaohan WANG, Tao WANG, Xiongwei LI, Yang ZHANG, Changyang HUANG
, Available online  , doi: 10.11999/JEIT190004 doi: 10.11999/JEIT190004
[Abstract](275) [FullText HTML] (149) [PDF 2502KB](5)
Against the problem of low detection rate to detect small hardware Trojan by side-channel in physical environment, the Marginal Fisher Analysis (MFA) is introduced. On the basis, a hardware Trojan detection method based on Compression Marginal Fisher Analysis (CMFA) is proposed. The projection space is constructed by reducing the distance between the sample and its same neighbor samples, and the distance between the same neighbor samples and the center of the same kind, and increasing the distance between the same neighbor samples of the center and the sample in different kind. Thus, the difference in the original data is found without any assumptions about data distribution, and the detection of hardware Trojan is achieved. The hardware Trojan detection experiment in AES encryption circuit shows that this method can effectively distinguish the statistical difference in side-channel signal between reference chip and Trojan chip and detect the hardware Trojan whose scale is 0.04% of the original circuit.
An Error Bound of Signal Recovery for Penalized Programs in Linear Inverse Problems
Huan ZHANG, Hong LEI
, Available online  , doi: 10.11999/JEIT181125 doi: 10.11999/JEIT181125
[Abstract](207) [FullText HTML] (136) [PDF 794KB](4)
Penalized programs are widely used to solve linear inverse problems in the presence of noise. For now, the study of the performance of panelized programs has two disadvantages: First, the results have some limitations on the tradeoff parameters; Second, the effect of the direction of the noise is not clear. This paper studies the performance of penalized programs when bounded noise is presented. A geometry condition which has been used to study the noise-free problems and constrained problems is provided. Under this condition, an explicit error bound which guarantees stable recovery (i.e., the recovery error is bounded by the observation noise up to some constant factor) is proposed. The results are different from many previous studies in two folds. First, the results provide an explicit bound for all positive tradeoff parameters, while many previous studies require that the tradeoff parameter is sufficiently large. Second, the results clear the role of the direction of the observation noise plays in the recovery error, and reveal the relationship between the optimal tradeoff parameters and the noise direction. Furthermore, if the sensing matrix has independent standard normal entries, the above geometry condition can be studied using Gaussian process theory, and the measurement number needed to guarantee stable recovery with high probability is obtained. Simulations are provided to verify the theoretical results.
Power Control Algorithm Based on Q-Learning in Femtocell
Yun LI, Ying TANG, Hanxiao LIU
, Available online  , doi: 10.11999/JEIT181191 doi: 10.11999/JEIT181191
[Abstract](228) [FullText HTML] (134) [PDF 2144KB](12)
The power control problem of mobile users in macro-femto heterogeneous cellular networks is studied. Firstly, an optimization model that maximizes the total energy efficiency of femtocells with the minimum received signal-to-noise ratio as the constraint is established. Then, a femtocell centralized Power Control algorithm based on Q-Learning (PCQL) is proposed. Based on reinforcement learning, the algorithm can adjust the transmit power of the user terminal without accurate channel state information simultaneously. The simulation results show that the algorithm can effectively control the power of the user terminal and improve system energy efficient.
Design of Convolutional Neural Networks Accelerator Based on Fast Filter Algorithm
Wei WANG, Kaili ZHOU, Yichang WANG, Guang WANG, Jun YUAN
, Available online  , doi: 10.11999/JEIT190037 doi: 10.11999/JEIT190037
[Abstract](279) [FullText HTML] (221) [PDF 3511KB](21)
In order to reduce the computational complexity of Convolutional Neural Network(CNN), the two-dimensional fast filtering algorithm is introduced into the CNN, and a hardware architecture for implementing CNN layer-by-layer acceleration on FPGA is proposed. Firstly, the line buffer loop control unit is designed by using the cyclic transformation method to manage effectively different convolution windows and the input feature map data between different layers, and starts the convolution calculation acceleration unit by the flag signal to realize layer-by-layer acceleration. Secondly, a convolution calculation accelerating unit based on 4 parallel fast filtering algorithm is designed. The unit is realized by a less complex parallel filtering structure composed of several small filters. Using the handwritten digit set MNIST to test the designed CNN accelerator circuit, the results show that on the xilinx kintex7 platform, when the input clock is 100 MHz, the computational performance of the circuit reaches 20.49 GOPS(109/s), and the recognition rate is 98.68%. It can be seen that the computational performance of the circuit can be improved by reducing the amount of calculation of the CNN.
Research on Shift Generation of Foreign Airlines Service Personnel Based on Tabu Search Algorithm
Xia FENG, Ling TANG, Min LU
, Available online  , doi: 10.11999/JEIT181196 doi: 10.11999/JEIT181196
[Abstract](211) [FullText HTML] (121) [PDF 1008KB](6)
To solve the problem for the large amount of tasks, complex constraint conditions and manual which is hard to generation shifts of airport foreign airline service personnel. A shift generation model is studied and constructed for multi-task hierarchical qualification which including employees have hierarchical qualifications for tasks and shift needs to meet all kinds of labor laws and regulations and others constraints to minimize the total working time of shifts for optimum. Tabu search algorithm is designed to solve the model. Experiments, based on the actual scheduling data set of the foreign airlines service department of capital airport, verify the practicability and effectiveness of the model and the algorithm. The results show that compared to the existing manual shifts schemes, shifts obtained by using the model can fulfill all constraint conditions, shorten the total working time, reduce the number of employees and improve the utilization rate of airport resources.
A Fast and Stable Design Method for Dense Focal Plane Array Feed
Shanhong HE, Mengqian JI, Liangyu XIE, Jin FAN, Chong FAN
, Available online  , doi: 10.11999/JEIT190026 doi: 10.11999/JEIT190026
[Abstract](237) [FullText HTML] (148) [PDF 3288KB](14)
The Dense Focal Plane Array Feed (DFPAF), which integrates the characters of multi-beam feed with multiple independent horns and Phased Array Feed (PAF), can simultaneously provide more fixed shaped beams and wider field of view than multi-beam feed with multiple independent horns and PAF. It attracts more attention in radio telescope, radar, electronic reconnaissance, satellite communication and so on. Its unique structure promotes the studies on special design method recently. Combing the theory of array antenna and inherent characteristic of parabolic reflector antenna, a fast design method with robust processing procedure is proposed in this paper. The design principle, calculated results, and comparison between DFPAF and the most representative multi-beam feed with multiple independent horns are presented. All these provide a theoretical basis and reference data for the design of giant reflector with DFPAF.
Semi-Markov Decision Process-based Resource Allocation Strategy for Virtual Sensor Network
Ruyan WANG, Hongjuan LI, Dapeng WU, Hongxia LI
, Available online  , doi: 10.11999/JEIT190016 doi: 10.11999/JEIT190016
[Abstract](246) [FullText HTML] (155) [PDF 940KB](21)
The close relationship between resource deployment and specific tasks in traditional Wireless Sensor Network(WSN) leads to low resource utilization and revenue. According to the dynamic changes of Virtual Sensor Network Request(VSNR), the resource allocation strategy based on Semi-Markov Decision Process(SMDP) is proposed in Virtual Sensor Network(VSN). Then, difining the state, action, and transition probability of the VSN, the expected reward is given by considering the energy and time to complete the VSNR, and the model-free reinforcement learning approach is used to maximize the long-term reward of the network resource provider. The numerical results show that the resource allocation strategy of this paper can effectively improve the revenue of the sensor network resource providers.
An Incremental Feature Extraction Method without Estimating Image Covariance Matrix
Xiaofeng WANG, Mingyue SUN, Weimin GE
, Available online  , doi: 10.11999/JEIT181138 doi: 10.11999/JEIT181138
[Abstract](230) [FullText HTML] (141) [PDF 1451KB](10)
To solve the problems that Two-Dimensional Principal Component Analysis (2DPCA) can not implement the on-line feature extraction and can not represent the complete structure information, an Incremental 2DPCA (I2DPCA) without estimating covariance matrices is presented by an iterative estimation method, not to deal with the image covariance matrices by the eigenvalue decomposition or the singular value decomposition. The complexity will be greatly reduced and the on-line feature extraction speed can be improved. The proposed I2DPCA can only extract the horizontal features, and thus another Incremental Row-Column 2DPCA (IRC2DPCA) is proposed to incrementally extract the longitudinal ones from the projected subspaces of the I2DPCA. The IRC2DPCA can preserve the horizontal and longitudinal features and implement the dimensionality reduction in both row and column directions. Finally, a series of experiments are carried out with the self-built block dataset, ORL and Yale face datasets, respectively. The results show that the proposed algorithms have significantly improved the performances of the convergence rate, the classification rate and the complexity. The convergence rate is over 99%, the classification rate can reach 97.6% and the average processing speed is about 29 frames per second, and it can meet the on-line feature extraction requirements for incremental learning.
Image Forgery Detection Algorithm Based on Cascaded Convolutional Neural Network
Xiuli BI, Yang WEI, Bin XIAO, Weisheng LI, Jianfeng MA
, Available online  , doi: 10.11999/JEIT190043 doi: 10.11999/JEIT190043
[Abstract](266) [FullText HTML] (165) [PDF 3318KB](18)
The image forgery detection algorithm based on convolutional neural network can implement the image forgery detection that does not depend on a single image attribute by using the learning ability of convolutional neural network, and make up for the defect that the previous image forgery detection algorithm relies on a single image attribute and has low applicability. Although the image forgery detection algorithm using a single network structure of deep layers and multiple neurons can learn more advanced semantic information, the result of detecting and locating forgery regions is not ideal. In this paper, an image forgery detection algorithm based on cascaded convolutional neural network is proposed. Based on the general characteristics exhibited by convolutional neural network, and then the deeper characteristics are further explored. The cascaded network structure of shallow layers and thin neurons figures out the defect of the single network structure of deep layers and multiple neurons in image forgery detection. The proposed detection algorithm in this paper consists of two parts: the cascade convolutional neural network and the adaptive filtering post-processing. The cascaded convolutional neural network realizes hierarchical forgery regions localization, and then the adaptive filtering post-processing further optimizes the detection result of the cascaded convolutional neural network. Through experimental comparison, the proposed detection algorithm shows better detection results and has higher robustness.
Facial Expression Recognition Method Based on Multi-scale Detail Enhancement
Xiaohui TAN, Zhaowei LI, Yachun FAN
, Available online  , doi: 10.11999/JEIT181088 doi: 10.11999/JEIT181088
[Abstract](201) [FullText HTML] (132) [PDF 2242KB](12)
Facial expression is the most intuitive description of changes in psychological emotions, and different people have great differences in facial expressions. The existing facial expression recognition methods use facial statistical features to distinguish among different expressions, but these methods are short of deep exploration for facial detail information. According to the definition of facial behavior coding by psychologists, it can be seen that the local detail information of the face determines the meaning of facial expression. Therefore, a facial expression recognition method based on multi-scale detail enhancement is proposed, because facial expression is much more affected by the image details than other information, the method proposed in this paper extracts the image detail information with the Gaussian pyramid firstly, thus the image is enhanced in detail to enrich the facial expression information. Secondly, for the local characteristics of facial expressions, a local gradient feature calculation method is proposed based on hierarchical structure to describe the local shape features of facial feature points. Finally, facial expressions are classified using a Support Vector Machine (SVM). The experimental results in the CK+ expression database show that the method not only proves the important role of image detail in facial expression recognition, but also obtains very good recognition results under small-scale training data. The average recognition rate of expressions reaches 98.19%.
Candidate Label-aware Partial Label Learning Algorithm
Hongchang CHEN, Tian XIE, Chao GAO, Shaomei LI, Ruiyang HUANG
, Available online  , doi: 10.11999/JEIT181059 doi: 10.11999/JEIT181059
[Abstract](205) [FullText HTML] (145) [PDF 3575KB](11)
In partial label learning, the true label of an instance is hidden in a label-set consisting of a group of candidate labels. The existing partial label learning algorithm only measures the similarity between instances based on feature vectors and lacks the utilization of the candidate labelset information. In this paper, a Candidate Label-Aware Partial Label Learning (CLAPLL) method is proposed, which combines effectively candidate label information to measure the similarity between instances during the graph construction phase. First, based on the jaccard distance and linear reconstruction, the similarity between the candidate labelsets of instances is calculated. Then, the similarity graph is constructed by combining the similarity of the instances and the label-sets, and then the existing graph-based partial label learning algorithm is presented for learning and prediction. The experimental results on 3 synthetic datasets and 6 real datasets show that disambiguation accuracy of the proposed method is 0.3~16.5% higher than baseline algorithm, and the classification accuracy is increased by 0.2~2.8%.
Depth Map Error Concealment for 3D High Efficiency Video Coding
Yang ZHOU, Jiayi WU, Yu LU, Haibing YIN
, Available online  , doi: 10.11999/JEIT180926 doi: 10.11999/JEIT180926
[Abstract](194) [FullText HTML] (126) [PDF 1575KB](6)
By using the intra-view and inter-view correlations and the motion vector-sharing, a depth map error concealment approach is proposed for 3D video coding based on the High Efficiency Video Coding (3D-HEVC) to combat the packet loss of the depth video transmission. Based on the Hierarchical B-frame Prediction (HBP) structure in 3D-HEVC and textured features of the depth map, all the lost coding units are firstly categorized into two classes, i.e., motion blocks and static blocks. Then, according to the outer boundary matching criterion combining the texture structure, the optimal motion/disparity vector is chosen for the damaged motion blocks to conduct the motion/disparity compensation based error concealment. Whereas, the direct copy is applied to conceal the damaged static blocks quickly. Finally, for the concealed blocks whose qualities are not ideal, the new motion/disparity compensation blocks reconstructing by the reference frames recombination are applied to improve the qualities of those blocks. The experimental results show that the repaired depth map concealed by the proposed approach can achieve 0.25~2.03 dB gain in term of the Peak-Signal-to-Noise Ratio (PSNR) and 0.001~0.006 gain in term of Structural Similarity Index Measure(SSIM). Moreover, the subjective visual quality of the repaired area is better in lines with the original depth maps.
Adaptive Knowledge Transfer Based on Classification-error Consensus Regularization
Shuang LIANG, Wenlong HANG, Wei FENG, Xuejun LIU
, Available online  , doi: 10.11999/JEIT181054 doi: 10.11999/JEIT181054
[Abstract](209) [FullText HTML] (127) [PDF 1014KB](12)
Most current transfer learning methods are modeled by utilizing the source data with the assumption that all data in the source domain are equally related to the target domain. In many practical applications, however, this assumption may induce negative learning effect when it becomes invalid. To tackle this issue, by minimizing the integrated squared error of the probability distribution of the source and target domain classification errors, the Classification-error Consensus Regularization (CCR) is proposed. Furthermore, CCR-based Adaptive knowledge Transfer Learning (CATL) method is developed to quickly determine the correlative source data and the corresponding weights. The proposed method can alleviate the negative transfer learning effect while improving the efficiency of knowledge transfer. The experimental results on the real image and text datasets validate the advantages of the CATL method.
Automatic Rank Estimation Based Riemannian Optimization Matrix Completion Algorithm and Application to Image Completion
Jing LIU, Han LIU, Kaiyu HUANG, Liyu SU
, Available online  , doi: 10.11999/JEIT181076 doi: 10.11999/JEIT181076
[Abstract](212) [FullText HTML] (148) [PDF 1922KB](8)
As an extension of Compressed Sensing(CS), Matrix Completion(MC) is widely applied to different fields. Recently, the Riemannian optimization based MC algorithm attracts a lot of attention from researchers due to its high accuracy in reconstruction and computational efficiency. Considering that the Riemannian optimization based MC algorithm assumes a fixed rank of the original matrix, and selects a random initial point for iteration, a novel algorithm is proposed, namely automatic rank estimation based Riemannian optimization matrix completion algorithm. In the proposed algorithm, the estimate of rank is obtained minimizing the objective function that involving the rank regulation, in addition, the iterative starting point is optimized based on Riemannian manifold. The Riemannian manifold based conjugate gradient method is then used to complete the matrix, thereby improving the reconstruction precision. The experimental results demonstrate that the image completion performance is significantly improved using the proposed algorithm, compared with several classical image completion methods.
Inverse Synthetic Aperture Radar Imaging with Non-Coherent Short Pulse Radar and its Sparse Recovery
Haibo WANG, Wenhua HUANG, Tao Ba, Yue JIANG
, Available online  , doi: 10.11999/JEIT180912 doi: 10.11999/JEIT180912
[Abstract](198) [FullText HTML] (116) [PDF 1782KB](14)
The microwave source of Non-Coherent Short Pulse (NCSP) radar transmits short pulse. Thus, as to the high velocity target, the motion effect in the pulse duration can be neglected, and the echo signal does not need special motion compensation. In order to use the NCSP radar signal for inverse synthetic aperture radar imaging, the compensation coherent processing method is applied to remove the uncertainty of the envelope time and the initial phase uncertainty. Assuming that, the echo is envelope-aligned and initially compensated by conventional methods, ISAR radar imaging can be performed using the Range-Doppler (RD) method, subsequently. The simulation verifies the feasibility of the compensation signal inverse synthetic aperture radar. However, the carrier-frequency random jitter factor of NCSP radar causes random-modulated side lobes in the Doppler dimension, which affects imaging quality. In this paper, the sparse recovery technique is used to perform sparse reconstruction of the target scattering center in the imaging space. The Orthogonal Matching Pursuit (OMP) algorithm and the Sparse Bayesian Learning (SBL) algorithm are used as the recovery algorithm for imaging simulation experiments. The simulation results show that the sparse recovery technique can suppress the imaging side lobes caused by non-coherence and improve the imaging quality.
Doppler Frequency Estimation Method Based on Chinese Remainder Theorem with Spectrum Correction
Chenghu CAO, Yongbo ZHAO, Zhiling SUO, Xiaojiao PANG, Baoqing XU
, Available online  , doi: 10.11999/JEIT181102 doi: 10.11999/JEIT181102
[Abstract](214) [FullText HTML] (166) [PDF 1993KB](22)
It makes the Pulse Doppler (PD) radar widely applied that the PD radar has the obvious advantages of detecting the Doppler frequency of the target and suppressing the clutter effectively. However, it is difficult for the PD radar to detect the target due to velocity ambiguity. Combining with the characteristic and stagger-period model of the PD radar, a Doppler frequency estimation method based on all phase DFT Closed-Form Robust Chinese Remainder Theorem (CFRCRT) with spectrum correction is proposed in this paper. Both theoretical analysis and simulation experiment demonstrate that the proposed method can satisfy the engineering demand in measure accuracy and real-time performance.
Analysis of Beam Wave Interaction in a Planar Metallic Grating Based on Cyclotron Resonance Enhancement Effect
Jing WANG, Yu FAN, Ding ZHAO, Chen YANG, Gang WANG, Jirun LUO
, Available online  , doi: 10.11999/JEIT181145 doi: 10.11999/JEIT181145
[Abstract](223) [FullText HTML] (134) [PDF 1025KB](7)
Based on the beam wave synchronization interaction in transverse and longitudinal directions at the same time and derived from Maxwell’s equation and linear Vlasov equation, the planar metallic grating beam-wave interaction " hot” dispersion equation considering both cyclotron resonance and Cherenkov resonance is deduced. Through the reasonable selection for geometric and electrical parameters, the numerical calculation and analysis of the " hot” dispersion equation show that the beam-wave interaction gain and frequency band with the cyclotron resonance enhancement effect are higher than those with only Cherenkov resonance radiation.
A Novel Micro-motion Multi-target Wideband Resolution Algorithm Based on Curve Overlap Extrapolation
Jiaqi WEI, Lei ZHANG, Hongwei LIU, Jialian SHENG
, Available online  , doi: 10.11999/JEIT190033 doi: 10.11999/JEIT190033
[Abstract](235) [FullText HTML] (130) [PDF 2040KB](11)
To solve the problem that the traditional micro-Doppler feature extraction technologies are generally hard to achieve resolution and parameter estimation of multi-target, a novel curve overlap extrapolation algorithm for wide-band resolution of micro-motion multi-target is proposed. According to the relative distance between filtering data points and the historical slope information of each curve, the point trace behind the overlapping location can be extrapolated to realize data association of micor-motion curve for each signal component. On this basis, the multi-target resolution can be realized by analyzing the difference of micor-motion characteristics between each curve. Extensive simulation experiments are provided to illustrate the effectiveness and robustnees of the proposed algorithm.
A Method of Establishing Mine Target Fingerprint Database Based on Distributed Compressed Sensing
Zijian TIAN, Fangyuan HE
, Available online  , doi: 10.11999/JEIT180857 doi: 10.11999/JEIT180857
[Abstract](275) [FullText HTML] (185) [PDF 1528KB](22)
A method of establishing a fingerprint database, which is based on distributed compressed sensing, is proposed to improve the low positioning accuracy and poor real-time positioning that exist in the current mine target positioning in China. Using the method, the fingerprint information of mine target fingerprint database can be reconstructed with high probability by collecting only a few fingerprint information (reference node IDs, Time Of Arrival (TOA) measurements based on electromagnetic wave and actual distance values) in the roadway in the off-line stage. Therefore, the data collection workload can be reduced and the work efficiency can be improved as well. In the subsequent on-line stage, according to the pattern matching method, the estimated distance between the target node and the reference nodes at the certain time can be obtained only by getting the reference node IDs and the real-time TOA measurements measured by the reference nodes at a certain moment, which guarantees the positioning accuracy and positioning real-time performance. Based on this method, an improved Compressive Sampling Modifying Matching Pursuit (CoSaMMP) algorithm is proposed to reconstruct the fingerprint information. The algorithm can effectively shorten the reconstruction time by using the folding method to increase the cutting force. The simulation results show that the proposed algorithm is feasible and effective.
Frequency-hopping Transmitter Classification Based on Chaotic Attractor Reconstruction and Low-rank Clustering
Ping SUI, Ying GUO, Hongguang LI, Yuzhou WANG
, Available online  , doi: 10.11999/JEIT180947 doi: 10.11999/JEIT180947
The transient signal without modulation information of the radiation source can characterize the unintentional modulation characteristics of the radiation source. The analysis of the transient signal can realize the radiation source identification. In the switching on and frequency conversion process of the frequency-hopping signal, there is a transient adjustment time without information transmission. In the transient adjustment moment, the signal transmitted by the transmitter is a non-linear, non-stationary and non-Gaussian signal without modulation information. This transient time series can reflect the device characteristics of the frequency-hopping transmitter, and the sequence often exhibits complex chaotic characteristics. Therefore, from the idea of chaotic time series analysis and Low-rank characteristics of transient signal, a frequency-hopping transmitter classification algorithm is proposed based on chaotic attractor reconstruction and Low-rank clustering. The experimental tests show that the transient signal of the frequency-hopping transmitter belongs to the chaotic time series. At the same time, the classification results of the frequency-hopping signals demonstrate the feasibility of the Low-rank clustering algorithm in frequency-hopping transmitter classification.
Design of a Novel Broadband Low RCS Array Based on Three Types of Reflective Cell Shared Aperture
Guowen ZHANG, Jun GAO, Xiangyu CAO, Huanhuan YANG, Sijia LI
, Available online  , doi: 10.11999/JEIT181049 doi: 10.11999/JEIT181049
[Abstract](247) [FullText HTML] (132) [PDF 3704KB](5)
A novel wideband low RCS new super-surface array based on three reflective cell shared aperture is designed, which is composed of three kinds of Artificial Magnetic Conductor (AMC). Compared with the traditional AMC array, the new array uses one of AMC as phasor interference unit. A new phase cancellation relation is presented, the new phase cancellation relation is used to extend the traditional array phase cancellation band. Then, the parameters of the cell structure are further optimized to realize the reduction of RCS and the improvement of bandwidth. The physical sample is processed and tested. The results of simulation and field test show that: the backward reduction of RCS in the range of 5.2~13.9 GHz reaches more than 10 dB, and the relative bandwidth reaches 91%. It is shown that the new array can overcome the defect of the discontinuous operating band of the traditional array and has broadband low scattering characteristics.
Construction of a Class of Linear Codes with Four-weight and Six-weight
Xiaoni DU, HongXia LÜ, Rong WANG
, Available online  , doi: 10.11999/JEIT180939 doi: 10.11999/JEIT180939
[Abstract](155) [FullText HTML] (118) [PDF 338KB](6)
Due to the wide applications in association schemes, authentication codes and secret sharing schemes etc., construction of the linear codes with a few weights is an important research topic. A class of linear codes with four-weight and six-weight over finite field \begin{document}${F_p}$\end{document} (p is an odd prime) is constructed by a proper selection of the defining set. The explicit weight distribution is obtained using Gauss sums, and some examples from Magma program to illustrate the validity of the conclusions are provided. The results show that these codes include almost optimal codes with respect to Singleton bound.
Energy-Efficient Scheduling Algorithm for All Optical IP Multicast Based on CDC-F ROADM Node
Huanlin LIU, Fei FANG, Yong CHEN, Min XIANG, Yue MA
, Available online  , doi: 10.11999/JEIT180937 doi: 10.11999/JEIT180937
[Abstract](212) [FullText HTML] (136) [PDF 1172KB](7)
In order to improve multicast’s spectrum energy-efficient of elastic optical network configured with Colorless, Directionless and Contentionless-Flexible Reconfigurable Optical Add/Drop Multiplexer (CDC-F ROADM) nodes, an All-optical Multicast Energy Efficiency Scheduling Algorithm (AMEESA) is proposed. In the routing phase, considering both energy consumption and link spectrum resource utilization, the link cost function is designed to establish the multicast tree with the least cost. In the spectrum allocation phase, a spectrum conversion method based on High Spectral Resolution (HSR) is designed by changing the spectrum slot index of adjacent links according to links availability of spectrum blocks. And an energy-saving spectrum conversion scheme is selected to allocate spectrum block resources for the multicast tree. Simulation analysis shows that the proposed algorithm can effectively improve the network energy efficiency and reduce the bandwidth blocking probability of IP multicast.
The Linear Complexity of a New Class of Generalized Cyclotomic Sequence of Order q with Period 2pm
Yan WANG, Gaina XUE, Shunbo LI, Feifei HUI
, Available online  , doi: 10.11999/JEIT180884 doi: 10.11999/JEIT180884
[Abstract](200) [FullText HTML] (137) [PDF 334KB](11)
Based on the theory of Ding - generalized circle, a new class of generalized cyclotomic sequences of \begin{document}$ 2{p^m}$\end{document} (\begin{document}$ p$\end{document} odd prime and m>1) with arbitrary prime order is constructed in this paper. The polynomial cyclotomic classes were analysis by algebra number theory method, moreover, the linear complexity of the new sequences were determined, which losely related to the division of quadratic residual classes and quadratic non-residual classes. Results show that the linear complexity of this kind of sequence is much larger than half of the period, hence, can fight Berlekamp-Massey’s security application attack that is a pseudo-random sequence with good properties in the sense of cryptography.
Control Resource Optimization Mechanism of SDN Based on Traffic Engineering
Yuxiang HU, Ziyong LI, Zongkui HU, Tao HU
, Available online  , doi: 10.11999/JEIT190276 doi: 10.11999/JEIT190276
In Software-Defined Networking (SDN) with distributed control plane, network expansion problems arise due to network domain management. To address this issue, a Traffic Engineering-based control Resource Optimization (TERO) mechanism of SDN is proposed. It analyzes the control resource consumption of flow requests processing with different path characteristics, and points out that the control resource consumption can be reduced by changing the association relationship between controllers and switches. The controller association mechanism is divided into two phases: firstly, a minimum set cover algorithm is designed to solve the controller association problem efficiently in large-scale network. Then, a coalitional game strategy is introduced to optimize the controller association relationship to reduce both control resource consumption and control traffic overhead. The simulation results demonstrate that while keeping control traffic overhead low, mechanism which in this paper can reduce control resource consumption by about 28% in comparison with the controller proximity mechanism.
Integrated Programmable Microwave Photonic Filter with High Shape-factor
, Available online  , doi: 10.11999/JEIT181156 doi: 10.11999/JEIT181156
[Abstract](144) [FullText HTML] (53) [PDF 2752KB](2)
In order to accommodate the development of new communication technology, an integrated programmable microwave photonic filter with high shape-factor is proposed in this paper. This filter is based on Silicon-On-Insulator (SOI) and an eight-tap finite impulse response. By controlling the thermal heaters on the amplitude modulator and phase modulator of each tap, A rectangular filter with tunable bandwidth and high shape-factor greater than 0.55 is obtained. Furthermore, the tunability of central frequency, bandwidth and variable pass-band shape can be also realized. Small size, light weight and flexibility are advantages of our filters, moreover, it can be applied in large bandwidth signal processing and an alternative method to part the channels. So it can be widely used in defense field and 5G networks.
Target Assignment Method for Phased Array Radar Network Based on Quality of Service
Shanchao YANG, Kangsheng TIAN, Changfei WU
, Available online  , doi: 10.11999/JEIT181133 doi: 10.11999/JEIT181133
[Abstract](181) [FullText HTML] (113) [PDF 1816KB](12)
The constraint conditions of target assignment model for phased array radar network are unreasonable and the performance of model solving algorithms are not good enough. To solve these problems, a target assignment model for radar network based on Quality of Service (QoS) is constructed in this paper, and a model solving algorithm based on strong majorant function approximation is proposed. Through the establishment of resource space and environment space in QoS model, radar resource constraints as well as the visibility constraints between radars and targets are described accurately. Then, sufficient conditions for the optimal solution of QoS model are derived by Karush-Kuhn-Tucker(KKT) condition, and a two-dimensional fast traversal method is used to approximate the strong concave function curve. Finally, the optimal assignment scheme is obtained by the stepwise iteration of operation setting points on the strong concave curve of each target. The simulation results show that the model proposed in this paper can effectively accomplish the target assignment of radar network, and model solving algorithm has better performance than the typical intelligent search algorithms.
Linear Complexity of Binary Sequences Derived from Euler Quotients Modulo 2pm
Xiaoni DU, Li LI, Fujun ZHANG
, Available online  , doi: 10.11999/JEIT190071 doi: 10.11999/JEIT190071
[Abstract](179) [FullText HTML] (98) [PDF 360KB](9)
Families of pseudorandom sequences derived from Euler quotients modulo an odd prime power possess sound cryptographic properties. In this paper, according to the theory of residue class ring, a new classes of binary sequences with period \begin{document}$2{p^{m + 1}}$\end{document} is constructed using Euler quotients modulo \begin{document}$2{p^m},$\end{document} where \begin{document}$p$\end{document} is an odd prime and integer \begin{document}$m \ge 1.$\end{document} Under the condition of \begin{document}${2^{p - 1}}\not \equiv 1 ({od}\; \;{p^2})$\end{document}, the linear complexity of the sequence is examined with the method of determining the roots of polynomial over finite field \begin{document}${F_2}$\end{document}. The results show that the linear complexity of the sequence takes the value \begin{document}$2({p^{m + 1}} - p)$\end{document} or \begin{document}$2({p^{m + 1}} - 1)$\end{document}, which is larger than half of its period and can resist the attack of Berlekamp-Massey (B-M) algorithm. It is a good sequence from the viewpoint of cryptography.
Optimal Scheme of Resource Allocation for Ultra-reliable and Low-latency in Machine Type Communications Based on Non-orthogonal Multiple Access with Short Block Transmission
Xianzhong XIE, Jia LI, Qian HUANG, Jie CHEN
, Available online  , doi: 10.11999/JEIT190128 doi: 10.11999/JEIT190128
[Abstract](248) [FullText HTML] (110) [PDF 1486KB](9)
For the service characteristics and Quality of Service (QoS) requirements of Machine Type Communications (MTC), short-packet/short-coded block transmission in MTC based on Non-Orthogonal Multiple Access (NOMA) is considered in this paper, and the resource optimization problem of the Ultra-Reliable and Low-Latency (URLL) in MTC based on NOMA is discussed. Currently, uplink transmission is a bottleneck of MTC based on NOMA. Firstly, considering the performance requirements supporting NOMA and high reliability and low latency in wireless cellular networks, a system model for uplink wireless resource optimization is established. Then, the uplink transmission delay is analyzed and the link reliability function based on distance is derived. Further, with the constraints of delay, reliability and bandwidth, a wireless resource allocation algorithm for maximizing the sum rates of central users is proposed, and also the convergence proof and complexity analysis of the algorithm are given. Finally, the simulation results show the performance advantages of the proposed optimal scheme.
LoRa Backscatter Communication Method Based on Direct Digital Frequency Synthesis
Xiaoqing TANG, Guihui XIE, Yajun SHE, Shuai ZHANG
, Available online  , doi: 10.11999/JEIT190001 doi: 10.11999/JEIT190001
[Abstract](188) [FullText HTML] (181) [PDF 2400KB](14)
LoRa (Long Range) Backscattering Communication (BC) not only has the advantages of low cost and low power consumption, but also has a long communication distance. However, the existing LoRa BC scheme is complex and cannot be applied to actual engineering. For this purpose, a new LoRa BC method is proposed. A Direct Digital frequency Synthesis (DDS) technique is used to generate a square wave with a linear frequency variation in this paper as a LoRa scattering modulation signal. And for the first time, the prototype of LoRa BC system based on MCU is demonstrated. Experimental results show that design can successfully backscatter at any position between the station and the receiver which are 208 meters apart, while being compatible with commodity LoRa chipset. In addition, the method is also applicable to an Application Specific Integrated Circuit (ASIC) design, which enables the LoRa backscattering IC to have higher robustness, lower cost, and lower power consumption.
Salient Object Detection Using Weighted K-nearest Neighbor Linear Blending
Wei LI, Quanlong LI, Zhengyi LIU
, Available online  , doi: 10.11999/JEIT190093 doi: 10.11999/JEIT190093
[Abstract](267) [FullText HTML] (166) [PDF 2218KB](23)
Salient object detection which aims at automatically detecting what attracts human’s attention most in a scene, bootstrap learning based on Support Vector Machine(SVM) has achieved excellent performance in bottom-up methods. However, it is time-consuming for each image must be trained once based on multiple kernel SVM ensemble. So a salient object detection model via Weighted K-Nearest Neighbor Linear Blending (WKNNLB) is proposed in the paper. First of all, existing saliency detection methods are employed to generate weak saliency maps and obtain training samples. Then, Weighted K-Nearest Neighbor (WKNN) is introduced to learning salient score of samples. WKNN model need no pre-training process, only need selecting K value and computing saliency value by the K-nearest neighbors labels of training sample and the distances between the K-nearest neighbors training samples and the testing sample. In order to reduce the influence of selecting K value, linear blending of multi-WKNNs is applied to generate strong saliency maps. Last, multi-scale saliency maps of weak and strong model are integrated together to further improve the detection performance. The experimental results on common ASD and complex DUT-OMRON datasets show that the algorithm is effective and superior in running time and performance. It can even perform favorable against the state-of-the-art methods when adopts better weak saliency map.
Super-Resolution Reconstruction of Deep Residual Network with Multi-Level Skip Connections
Xiaoqiang ZHAO, Zhaoyang SONG
, Available online  , doi: 10.11999/JEIT190036 doi: 10.11999/JEIT190036
[Abstract](137) [FullText HTML] (63) [PDF 1247KB](8)
The Fast Super-Resolution Convolutional Neural Network algorithm (FSRCNN) is difficult to extract deep image information due to the small number of convolution layers and the correlation lack between the feature information of adjacent convolutional layers. To solve this problem, a deep residual network super-resolution reconstruction method with multi-level skip connections is proposed. Firstly, a residual block with multi-level skip connections is designed to solve the problem that the characteristic information of adjacent convolutional layers lacks relevance. A deep residual network with multi-level skip connections is constructed on the basis of the residual block. Then, the deep residual network connected to the multi-level skip is trained by using the adaptive gradient rate strategy of Stochastic Gradient Descent (SGD) method and the network super-resolution reconstruction model is obtained. Finally, the low-resolution image is input into the deep residual network super-resolution reconstruction model with the multi-level skip connections, and the residual eigenvalue is obtained by the residual block connected the multi-level skip connections. The residual eigenvalue and the low resolution image are combined and converted into a high resolution image. The proposed method is compared with the bicubic, A+, SRCNN, FSRCNN and ESPCN algorithms in the Set5 and Set14 test sets. The proposed method is superior to other comparison algorithms in terms of visual effects and evaluation index values.
Security Analysis and Improvements of Hybrid Group Signcryption Scheme Based on Heterogeneous Cryptosystem
Yulei ZHANG, Xiangzhen LIU, Xiaoli LANG, Yongjie ZHANG, Caifen WANG
, Available online  , doi: 10.11999/JEIT190129 doi: 10.11999/JEIT190129
[Abstract](172) [FullText HTML] (80) [PDF 650KB](17)
Heterogeneous hybrid group signcryption can not only solve the confidentiality and unforgeability of data transmission under different cryptosystems, but also encrypt data of any length. Firstly, the security of a hybrid group signcryption scheme under heterogeneous cryptosystem is analyzed, and it is pointed out that the scheme does not satisfy the correctness, confidentiality and unforgeability. And a new efficient heterogeneous hybrid group signcryption scheme is proposed. Secondly, it is proved that the proposed scheme is safe under the random oracle model. Finally, the efficiency analysis shows that the proposed scheme reduces the computational cost while realizing all the functions of the original scheme.
The Combination and Pooling Based on High-level Feature Map for High-resolution Remote Sensing Image Retrieval
Yun GE, Lin MA, Shunliang JIANG, Famao YE
, Available online  , doi: 10.11999/JEIT190017 doi: 10.11999/JEIT190017
[Abstract](265) [FullText HTML] (155) [PDF 1374KB](13)
High-resolution remote sensing images have complex visual contents, and extracting feature to represent image content accurately is the key to improving image retrieval performance. Convolutional Neural Networks (CNN) have strong transfer learning ability, and the high-level features of CNN can be efficiently transferred to high-resolution remote sensing images. In order to make full use of the advantages of high-level features, a combination and pooling method based on high-level feature maps is proposed to fuse high-level features from different CNNs. Firstly, the high-level features are adopted as special convolutional features to preserve the feature maps of the high-level outputs under different input sizes, and then the feature maps are combined into a larger feature map to integrate the features learned by different CNNs. The combined feature map is compressed by max-pooling method to extract salient features. Finally, the Principal Component Analysis (PCA) is utilized to reduce the redundancy of the salient features. The experimental results show that compared with the existing retrieval methods, the features extracted by this method have advantages in retrieval efficiency and precision.
A Spatial and Temporal Optimal Method of Service Function Chain Orchestration Based on Overlay Network Structure
Yunjie GU, Yuxiang HU, Jichao XIE
, Available online  , doi: 10.11999/JEIT190145 doi: 10.11999/JEIT190145
[Abstract](242) [FullText HTML] (140) [PDF 2125KB](12)
With the introduction of Network Function Virtualization (NFV), the operating costs of operators can be greatly reduced. However, most existing Service Function Chain (SFC) orchestration researches can not optimize the resources utilization while guaranteeing the performance of service delay. A spatial and temporal optimal method of Service Function Chain (SFC) orchestration based on an overlay network structure is proposed. Based on the consideration of the restrictions such as computing resource, network resource and fine-grained end to end delay, this method separates the computing resource and network resource. The resources cost and related delay of SFC can be abstracted into the links weight of overlay network, which can help to convert the SFC orchestration problem into the shortest path problem that can be easily solved. As for the SFC requests set requiring batch processing, an Overlay Network based Simulated Annealing iterative optimal orchestration algorithm(ONSA) is designed. The simulation results demonstrate that the proposed orchestration scheme can reduce the end-to-end delay, the utilization ratio of link bandwidth resource and the operational expenditure by 29.5%, 12.4% and 15.2%, and the acceptance ratio of requests set can be improved by 22.3%. The performance of Virtual Network Function (VNF) load balancing can be significantly improved.
Joint Design of Quasi-cyclic Low Density Parity Check Codes and Performance Analysis of Multi-source Multi-relay Coded Cooperative System
Shunwai ZHANG, Qi WEI
, Available online  , doi: 10.11999/JEIT190069 doi: 10.11999/JEIT190069
[Abstract](363) [FullText HTML] (160) [PDF 1402KB](16)
To solve the problems of high encoding complexity and long encoding delay in the multi-source multi-relay Low Density Parity Check (LDPC) coded cooperative system, a special kind of structured LDPC codes—Quasi-Cyclic LDPC (QC-LDPC) codes based on generator matrix is proposed, which combines the characteristics of QC-LDPC codes and Generator-matrix-based LDPC (G-LDPC) codes. It can perform completely parallel encoding, which greatly reduces the encoding complexity and delay at the relays. Based on this, a joint parity check matrix corresponding to the QC-LDPC codes adopted by the sources and relays is deduced, and the matrix is further jointly designed based on the Greatest Common Divisor (GCD) theorem to eliminate all cycles of girth-4 and girth-6. Theoretical analysis and simulation results show that under the same conditions, the Bit Error Rate (BER) performance of the proposed system is better than that of the corresponding point-to-point system. The simulation results also show that the cooperative system with jointly designed QC-LDPC codes can obtain a higher coding gain than the system with explicitly constructed QC-LDPC codes or generally constructed QC-LDPC codes.
Research on D2D Multi-multiplex Communication Resource Blocks Allocation Algorithm Based on Unbalanced Solution
Zhihong QIAN, Liangshuai HU, Chunsheng TIAN, Xue WANG
, Available online  , doi: 10.11999/JEIT190171 doi: 10.11999/JEIT190171
[Abstract](303) [FullText HTML] (167) [PDF 1669KB](21)
In order to solve the problem of the D2D multi-multiplex communication resource blocks allocation in a cell, the resource blocks allocation scheme about D2D multi-multiplex mode based on non-equilibrium solution is proposed after analyzing a D2D user to multiplex two and three cells respectively. The problem of resource blocks partitioning is transformed into the problem of solving the joint revenue maximum value of the multiplexed cellular user by using game theory. When the Nash equilibrium solution does not exist, the objective function is analyzed, the "optimal solution" is solved in the feasible domain and the optimality of unbalanced solution processing is guaranteed. When the equilibrium solution exists, it is rounded up and used as the basis of the resource allocation scheme to maintain its optimality. The theoretical analysis and simulation results show that the proposed algorithm enhances significantly the system performance and sum rate.
Ciphertext Sorting Search Scheme Based on B+ Tree Index Structure on Blockchain
Shufen NIU, Jinfeng WANG, Bobin WANG, Xiangdong JIA, Xiaoni DU
, Available online  , doi: 10.11999/JEIT190038 doi: 10.11999/JEIT190038
[Abstract](252) [FullText HTML] (157) [PDF 773KB](22)
In order to overcome the problem that cloud storage is not trusted and the low efficiency of ciphertext retrieval in cloud storage, a searchable ciphertext sorting encryption scheme based on B+ tree on the block chain is proposed.Combined with the blockchain technology, the problem of establishing reliable trust in multiple parties that do not understand each other is solved. Using a vector space model reduces the complexity of the text and implements an efficient text retrieval system.The index structure of the B+ tree is used to improve the retrieval of ciphertext transactions on the blockchain.The ranking of multi-keyword query results is realized by the Term Frequency–Inverse Document Frequency (TF-IDF) algorithm. Under the random oracle model, it is proved that the scheme is adaptive and indistinguishable. Through the comparative analysis of efficiency, it shows that the scheme achieves efficient ciphertext retrieval on the blockchain.
Design and Implementation of Robust Particle Filter Algorithms under Student-t Measurement Distribution
Zongyuan WANG, Weidong ZHOU
, Available online  , doi: 10.11999/JEIT190144 doi: 10.11999/JEIT190144
[Abstract](148) [FullText HTML] (55) [PDF 1725KB](6)
Outliers are non-Gaussian measurement values far from the bulk of data. In practical transmission, the signals added with outlier often have the heavy-tailed property. Particle filter is based on the Bayesian framework and applicable to the non-linear and non-Gaussian system. However, measurement noise with outlier degrades the performance of particle filter. In this paper, student-t distribution is used to model the measurement noise, combined with Variational Bayes (VB), a novel particle filter marginalized particle filter with VB Mean(MPF-VBM) is designed, which can estimate all parameters of t-distributed measurement distribution including mean parameter as well as state. Further, particle filter with noise correlation (MPF-VBM-COR-1) at the same epoch which is applicable to time variant measurement noise is developed. For verifying the performances of the proposed algorithms, the simulations on the typical univariate non-stationary growth model are performed under the different noise conditions in detail. The outcomes show that the proposed two algorithms of MPF-VBM and MPF-VBM-COR-1 (MPF-VBM-Corrlation-1) have the superior performances to the compared ones.
Performance Analysis of Massive MIMO-OFDM System with Hybrid-Precision Analog-to-Digital Converter
Kai LIU, Guichao CHEN, Cheng TAO, Tao ZHOU
, Available online  , doi: 10.11999/JEIT181136 doi: 10.11999/JEIT181136
[Abstract](264) [FullText HTML] (146) [PDF 1279KB](16)
The spectral efficiency and energy efficiency of the uplink of massive MIMO-OFDM system is studied using mixed-precision Analog-Digital Converter (ADC) and Zero-Forcing (ZF) reception algorithm at the receiver. By using the additive quantization noise model to analyze the performance of the system, the approximate closed expression of the spectral efficiency and energy efficiency of the whole system is derived, and the correctness of the expression is proved by simulation. The research results show that the spectral efficiency of the system is related to the transmission power of each user, the number of antennas at the receiver and the quantization accuracy of the receiver. Numerical and simulation results also show that the performance loss caused by the low-precision ADC can be compensated by increasing the number of antennas at the base station.
Bistatic Radar Coincidence Imaging Based on Sparse Bayesian Learning
Rui LI, Qun ZHANG, Linghua SU, Jia LIANG, Ying LUO
, Available online  , doi: 10.11999/JEIT180933 doi: 10.11999/JEIT180933
[Abstract](414) [FullText HTML] (208) [PDF 1088KB](27)
Bistatic radar has the advantages of high concealment and strong anti-interference performance, and plays an important role in modern electronic warfare. Based on the principle of radar coincidence imaging, the problem of bistatic radar coincidence imaging of moving targets is studied. Firstly, based on the bistatic radar system that uses uniform linear array as the transmitting and receiving antenna, the characteristics of the moving target radar echo signal are analyzed under the condition of transmitting random frequency modulation signal, and a bistatic radar coincidence imaging parametric sparse representation model is established. Secondly, an iterative coincidence imaging algorithm based on sparse Bayesian learning is proposed for the parametric sparse representation model established. Based on the Bayesian model, the sparse reconstructed signal is obtained by Bayesian inference, so that the moving target imaging and accurate estimation of motion parameters can be achieved. Finally, the effectiveness of the proposed method is verified by simulation experiments.
The Application of the G-matrix Modification Methods in the Imaging of the 1-D Synthetic Aperture Microwave Radiometer
Aili ZHANG, Hao LIU, Lin WU, Lijie NIU, Cheng ZHANG, Xue CHEN, Ji WU
, Available online  , doi: 10.11999/JEIT181067 doi: 10.11999/JEIT181067
[Abstract](190) [FullText HTML] (55) [PDF 1669KB](4)
The G-matrix model method is usually used to achieve the brightness temperature reconstruction for the one-dimensional (1-D) synthetic aperture microwave radiometer system. For the 1-D radiometer system, the imaging process mainly includes: the radiometer instrument observes the full field of view of the 2-D target scene maps, and obtains the 1-D samples of the visibility, and then inverts the system parameter matrix G to realize the reconstruction of the 1-D image of the target scene. Since the system sampling baselines are only distributed in the 1-D of the spatial frequency domain, in the process of the brightness temperature image reconstruction, the matrix G needs to realize 2-D to 1-D conversion. Therefore, two G-matrix modification methods are proposed to improve the imaging quality for the 1-D synthetic aperture microwave radiometer. For the 8-element ground radiometer prototype system and the 10-element salinity radiometer system, theoretical analysis and simulation experiments have verified that the G-matrix modification methods proposed in this paper can effectively improve the imaging results, and can effectively suppress the imaging error caused by the side-lobed degradation of the antenna patterns.
Constructions of Maximal Distance Separable Matrices with Minimum XOR-counts
Shaozhen CHEN, Yifan ZHANG, Jiongjiong REN
, Available online  , doi: 10.11999/JEIT181113 doi: 10.11999/JEIT181113
[Abstract](232) [FullText HTML] (137) [PDF 449KB](12)
With the development of the internet of things, small-scale communication devices such as wireless sensors and the Radio Frequency IDentification(RFID) tags are widely used, these micro-devices have limited computing power, such that traditional cryptographic algorithms are difficult to implement on these devices. How to construct a high-efficiency diffusion layer became an urgent problem. With the best diffusion property, the Maximal Distance Separable (MDS) matrix is often used to construct the diffusion layer of block ciphers. The number of XOR operations (XORs) is an indicator of the efficiency of hardware applications. Combined with the XORs calculation method which can evaluate hardware efficiency more accurately and a matrix with special structure——Toeplitz matrix, efficient MDS matrices with less XORs can be constructed. Using the structural characteristics of the Toeplitz matrix, the constraints of matrix elements are improved, and the complexity while search matrices is reduced. The 4×4 MDS matrices and the 6×6 MDS matrices with the least XORs in the finite field \begin{document}${\mathbb{F}_{{2^8}}}$\end{document} are obtained, and the 5×5 MDS matrices with the XORs which is equal to the known optimal results are obtained too. The method of constructing MDS Toeplitz matrices with the least XORs proposed by this paper has significance on the design of lightweight cryptographic algorithms.
A Novel Radiometric Signature of Time-Division Multiple Access Signals and Its Application to Specific Emitter Identification
Yiwei PAN, Hua PENG, Tianyun LI, Wenya WANG
, Available online  , doi: 10.11999/JEIT190163 doi: 10.11999/JEIT190163
[Abstract](272) [FullText HTML] (181) [PDF 1743KB](15)
For Time-Division Multiple Access (TDMA) signals, the performance of Specific Emitter Identification (SEI) is primarily limited by burst duration. To remedy this shortcoming, a novel radiometric signature is presented, which reveals whether the users of the adjacent time slots are the same from a perspective of carrier phase, thereby providing the basis for data accumulation of the same user. First, the feature mechanism is introduced, as well as the extraction method. Thereafter, user identity detection of the adjacent slots is implemented with an adaptive threshold, which is derived from the distribution of the signature. Finally, a new SEI processing procedure is designed with data accumulation, which breaks the routine of identifying only one slot at a time. Simulation results demonstrate that the proposed signature is resilient against the noise, and can accurately detect the user identity of the adjacent slots. Compared with the traditional processing procedure, the proposed one can effectively improve the SEI performance of TDMA signals.
Deep Convolution Blind Separation of Acoustic Signals Based on Joint Diagonalization
Yang LI, Weitao ZHANG, Shuntian LOU
, Available online  , doi: 10.11999/JEIT190067 doi: 10.11999/JEIT190067
[Abstract](248) [FullText HTML] (177) [PDF 1119KB](7)
The propagation of acoustic signal in space has a strong multipath effect, and the receiver often overlaps in the form of convolution. Especially in strong reverberation conditions such as ocean and theatre, where the length of impulse response of hybrid filter will increase significantly. In order to eliminate the problem that long impulse response leads to the failure of the frequency domain convolution blind separation algorithm, two Short-Time Fourier Transforms (STFT) are applied to the observed signal. The first STFT shortens the length of the hybrid filter. The second STFT converts the signal model into instantaneous blind separation. Finally, the separation matrix is estimated by Joint Diagonalization (JD) technique. Compared with the existing methods, this method solves the problem of model failure under deep convolution mixing, and can obtain better separation performance when the number of source signals is large or additive noise exists. The simulation results verify the effectiveness and performance advantages of the proposed method.
Traceable Lightweight and Fine-grained Access Control in Named Data Networking
Jiangtao LUO, Chen HE, Junxia WANG
, Available online  , doi: 10.11999/JEIT181160 doi: 10.11999/JEIT181160
[Abstract](222) [FullText HTML] (158) [PDF 1094KB](6)
Due to the feature of in-network caching in Named Data Networking (NDN), any consumer might fetch the cached contents from NDN routers, but the content producers have no idea about details of certain contents being accessed. Considering these problems, a fine-grained Traceable and Lightweight Access Control (TLAC) scheme is presented. In the TLAC scheme, an anonymous and secure " three-way handshake” authentication protocol is presented by collaboratively leveraging the combined public key and the Schnorr signature, and an improved secret sharing method is used to distribute the key efficiently. Finally, the experimental results prove the efficiency of TLAC scheme.
A Service Function Chain Deployment Method Against Side Channel Attack
Peng YI, Jichao XIE, Zhen ZHANG, Yunjie GU, Dan ZHAO
, Available online  , doi: 10.11999/JEIT190127 doi: 10.11999/JEIT190127
[Abstract](239) [FullText HTML] (165) [PDF 2196KB](9)
Side channel attack is the primary way to leak information between tenants in current cloud computing environment. However, existing Service Function Chain (SFC) deployment methods do not fully consider the side channel attack problem faced by the Virtual Network Function (VNF) in the multi-tenant environment. A SFC deployment method is proposed against side channel attack. A tenant classification strategy based on average time and a deployment strategy considering historical information are introduced. Under the resource constraints of the SFC, the optimization model is established with the goal of minimizing the number of servers that the tenant can cover. And a deployment algorithm is designed based on the greedy choice. The experimental results show that, compared with other deployment methods, this method can significantly improve the difficulty and cost of malicious tenant to realize co-residence, and reduces the risk of side channel attack faced by tenants.
Robust Visual Tracking Algorithm Based on Siamese Network with Dual Templates
Zhiqiang HOU, Lilin CHEN, Wangsheng YU, Sugang MA, Jiulun FAN
, Available online  , doi: 10.11999/JEIT181018 doi: 10.11999/JEIT181018
[Abstract](245) [FullText HTML] (203) [PDF 2486KB](22)
In recent years, the Siamese networks has drawn great attention in visual tracking community due to its balanced accuracy and speed. However, most Siamese networks model are not updated, which caused tracking errors. In view of this deficiency, an algorithm based on the Siamese network with double templates is proposed. First, the base template R which is the initial frame target with stable response map score and the dynamic template T which is using the improved APCEs model update strategy to determine are kept. Then, the candidate targets region and the two template matching results are analyzed, meanwhile the result response maps are fused, which could ensure more accurate tracking results. The experimental results on the OTB2013 and OTB2015 datasets show that comparing with the 5 current mainstream tracking algorithms, the tracking accuracy and success rate of the proposed algorithm are superior. The proposed algorithm not only displays better tracking effects under the conditions of scale variation, in-plane rotation, out-of-plane rotation, occlusion, and illumination variation, but also achieves real-time tracking at a speed of 46 frames per second.
Research on Efficient FPGA Bitstream Generation System Based on Mode Matching and Hierarchical Mapping
Kaihui TU, Zhihong HUANG, Zhengrong HOU, Haigang YANG
, Available online  , doi: 10.11999/JEIT190143 doi: 10.11999/JEIT190143
[Abstract](174) [FullText HTML] (126) [PDF 2352KB](7)
Bitstream generator in FPGA Electronic Design Automation(EDA) offers precise configuration information, which enables the application circuits to be implemented on the target device. On one hand, modern FPGAs tend to have larger device scale and more configuration bits, on the other hand, embedded applications (like eFPGAs) require better configuration efficiency and smaller, more adaptive database. In order to meet these new requirements, a bit-stream generation method is proposed which firstly models the configurable resources by configuration modes and matches the netlist with these models, then hierarchical mapping strategy is used to search every bit on a dynamically generated database determined by the array floorplan. This method well meets the challenges that embedded applications may bring-the surge of configuration bit count and the changeable size of the array. Compared to flattened modelling and mapping method, its time complexity is reduced from O(n) to O(lgn).
IBeacon/INS Data Fusion Location Algorithm Based on Unscented Kalman Filter
Shouhua WANG, Mingchi LU, Xiyan SUN, Yuanfa JI, Dingmei HU
, Available online  , doi: 10.11999/JEIT180748 doi: 10.11999/JEIT180748
[Abstract](251) [FullText HTML] (192) [PDF 1998KB](17)
In order to overcome the accumulation error in Micro-Electro-Mechanical System-Inertial Navigation System (MEMS-INS) and the jump error in iBeacon fingerprint positioning, an iBencon/MEMS-INS data fusion location algorithm based on Unscented Kalman Filter (UKF) is proposed. The new algorithm solves the distance between the iBeacon anchor and the locating target. The solution of attitude matrix and position are obtained respectively by using accelerometer and gyroscope data. Bluetooth anchor position vector, the carrier speed error and other information constitute state variables. Inertial navigation location and bluetooth system distance information constitute measure variables. Based on state variables and measure variables, the UKF is designed to realize iBencon/MEMS-INS data fusion indoor positioning. The experimental results show that the proposed algorithm can effectively solve the problem of the large accumulation error of INS and the jump error of iBeacon fingerprint positioning, and this algorithm can realize 1.5 m positioning accuracy.
Cognitive Emotion Interaction Model of Robot Based on Game Theory
Hongcheng HUANG, Ning LIU, Min HU, Yang TAO, Lan KOU
, Available online  , doi: 10.11999/JEIT180867 doi: 10.11999/JEIT180867
[Abstract](278) [FullText HTML] (189) [PDF 993KB](18)
To solve the problems of the existing in the process of human-computer interaction system, such as lack of emotion and low participation, a cognitive emotion interaction model based on game theory in PAD emotion space is proposed. Firstly, the interactive input emotion of participant is evaluated and some influence factors such as friendship and resonance are extracted to analyze the current human-computer interaction relationship. Secondly, modeling the emotional generation process of participants and robots by simulating the psychological game process in interpersonal communication, and the optimal emotional strategy of the robot is obtained by using the sub-game perfection equilibrium of the embedded game. Finally, the emotional state transition probability of the robot is updated according the optimal emotional strategy. The spatial coordinates of the six basic emotional states are used as labels to obtain the PAD spatial coordinate of the robot emotional state after emotional stimulate, The results of experiment show that compared with the others emotional interaction model, the proposed model can reduce the dependence of robots on external emotional stimuli and effective guide participants to participate in human-computer interaction, which provides some ideas for the emotion cognition model of robot in human-computer interaction.
Joint Clustering and Content Deployment Algorithm for Cellular D2D Communication Based on Delay Optimization
Rong CHAI, Ling WANG, Minglong CHEN, Qianbin CHEN
, Available online  , doi: 10.11999/JEIT180408 doi: 10.11999/JEIT180408
[Abstract](112) [FullText HTML] (64) [PDF 1236KB](17)
Due to the limited transmission performance of cellular network and the buffering capabilities of the Base Station (BS), it is very difficult to achieve the Quality of Service (QoS) requirements of multi-user content requests. In this paper, a joint user association and content deployment algorithm is proposed for cellular Device-to-Device (D2D) communication network. Assuming that multiple users located in a specific area may have content requests for the same content, a clustering and content deployment mechanism is presented in order to achieve efficient content acquisition. A joint clustering and content deployment optimization model is formulated to minimize total user service delay, which can be solved by Lagrange partial relaxation, iterative algorithm and Kuhn-Munkres algorithm, and the joint clustering and content deployment optimization strategies can be obtained. Finally, the effectiveness of the proposed algorithm is verified by MATLAB simulation.
Transfer Weight Based Conditional Adversarial Domain Adaptation
Jin WANG, Ke WANG, Zijian MIN, Kaiwei SUN, Xin DENG
, Available online  , doi: 10.11999/JEIT190115 doi: 10.11999/JEIT190115
[Abstract](94) [FullText HTML] (65) [PDF 1080KB](4)
Considering the failure of the Conditional adversarial Domain AdaptatioN(CDAN) to fully utilize the sample transferability, which still struggle with some hard-to-transfer source samples disturbed the distribution of the target domain samples, a Transfer Weight based Conditional adversarial Domain AdaptatioN(TW-CDAN) is proposed. Firstly, the discriminant results in the domain discriminant model as the main factor are employed to measure the transfer performance. Then the weight is applied to class loss and minimum entropy loss. It is for eliminating the influence of hard-to-transfer samples of the model. Finally, experiments are carried out using the six domain adaptation tasks of the Office-31 dataset and the 12 domain adaptation tasks of the Office-Home dataset. The proposed method improves the 14 domain adaptation tasks and increases the average accuracy by 1.4% and 3.1% respectively.
Research and Development on Applications of Convolutional Neural Networks of Radar Automatic Target Recognition
Fengshou HE, You HE, Zhunga LIU, Cong’an XU
, Available online  , doi: 10.11999/JEIT180899 doi: 10.11999/JEIT180899
[Abstract](455) [FullText HTML] (250) [PDF 900KB](76)
Automatic Target Recognition(ATR) is an important research area in the field of radar information processing. Because the deep Convolution Neural Network(CNN) does not need to carry out feature engineering and the performance of image classification is superior, it attracts more and more attention in the field of radar automatic target recognition. The application of CNN to radar image processing is reviewed in this paper. Firstly, the related knowledges including the characteristics of the radar image is introduced, and the limitations of traditional radar automatic target recognition methods are pointed out. The principle, composition and development of CNN the field of computer vision are introduced. Then, the research status of CNN in radar automatic target recognition is provided. The detection and recognition method of SAR image are presented in detail. Then the challenge of radar automatic target recognition is analyzed. Finally, the new theory and model of convolution neural network, the new imaging technology of radar and the application to complex environments in the future are prospected.
Two Dimensional DOA Estimation Based on Polarization Sensitive Array and Uniform Linear Array
Lutao LIU, Chuanyu WANG
, Available online  , doi: 10.11999/JEIT180832 doi: 10.11999/JEIT180832
[Abstract](336) [FullText HTML] (171) [PDF 1434KB](19)
To solve the problem that polarization sensitive array of defective electromagnetic vector sensor estimate multi parameter, a two-dimensional DOA estimation algorithm based on orthogonal dipole is proposed in this paper. First, eigendecomposition of the covariance matrix is produced by the received data vectors of the polarization sensitive array. Then the signal subspace is divided into four subarrays, and the phase difference between one of the subarray and the others is obtained according to the ESPRIT algorithm. Then the phase difference between different subarrays is paired. Finally, the DOA estimation and polarization parameters of the signal are calculated according to the phase difference. The uniform linear array composed by orthogonal dipoles can not be two-dimensional DOA estimated by using the MUSIC algorithm and the traditional ESPRIT algorithm. The algorithm proposed in this paper solves this problem, and compared with the polarization MUISC algorithm greatly reduces the complexity of the algorithm. The simulation results verify the effectiveness of the proposed algorithm.
Adaptive Strategy Fusion Target Tracking Based on Multi-layer Convolutional Features
Yanjing SUN, Yunkai SHI, Xiao YUN, Xuran ZHU, Sainan WANG
, Available online  , doi: 10.11999/JEIT180971 doi: 10.11999/JEIT180971
[Abstract](374) [FullText HTML] (201) [PDF 2570KB](37)
To solve the problems of low robustness and tracking accuracy in target tracking when interference factors occur such as target fast motion and occlusion in complex video scenes, an Adaptive Strategy Fusion Target Tracking algorithm (ASFTT) is proposed based on multi-layer convolutional features. Firstly, the multi-layer convolutional features of frame images in Convolutional Neural Network(CNN) are extracted, which avoids the defect that the target information of the network is not comprehensive enough, so as to increase the generalization ability of the algorithm. Secondly, in order to improve the tracking accuracy of the algorithm, the multi-layer features are performed to calculate the correlation responses, which improves the tracking accuracy. Finally, the target position strategy in all responses are dynamically merged to locate the target through the adaptive strategy fusion algorithm in this paper. It comprehensively considers the historical strategy information and current strategy information of each responsive tracker to ensure the robustness. Experiments performed on the OTB2013 evaluation benchmark show that that the performance of the proposed algorithm are better than those of the other six state-of-the-art methods.
Person Re-identification Based on Attribute Hierarchy Recognition
Hongchang CHEN, Yancheng WU, Shaomei LI, Chao GAO
, Available online  , doi: 10.11999/JEIT180740 doi: 10.11999/JEIT180740
[Abstract](412) [FullText HTML] (213) [PDF 2461KB](18)
In order to improve the accuracy rate of person re-identification, a pedestrian attribute hierarchy recognition neural network is proposed in this paper based on attention model. Compared with the existing algorithms, the model has the following three advantages. Firstly, the attention model is used in this paper to identify the pedestrian attributes, and to extract of pedestrian attribute information and degree of significance. Secondly, the attention model in used in this paper to classify the attributes according to the significance of the pedestrian attributes and the amount of informationcontained. Thirdly, this paper analyzes the correlation between attributes, and adjust the next level identification strategy according to the recognition results of the upper level. It can improve the recognition accuracy of small target attributes, and the accuracy of pedestrian recognition is improved. The experimental results show that the proposed model can effectively improve the first accuracy rate (rank-1) of person re-identification compared with the existing methods. On the Market1501 dataset, the first accuracy rate is 93.1%, and the first accuracy rate is 81.7% on the DukeMTMC dataset.
Surface Acoustic Wave Resonator Echo Signal Frequency Estimation
Boquan LIU, Jiajia GUO, Zhiming LUO
, Available online  , doi: 10.11999/JEIT180875 doi: 10.11999/JEIT180875
[Abstract](389) [FullText HTML] (169) [PDF 2530KB](24)
Surface Acoustic Wave (SAW) resonator measuring technology can be used in high temperature and high pressure, strong electromagnetic radiation and strong electromagnetic interference to realize wireless passive parameter detection. Based on the the non-stationary characteristics of the SAW signal, a kind of echo signal frequency estimation method, Digital Frequency Significant Place Tracking (DFSPT) method is put forward. Compared with the existing methods based on Fast Fourier Transform (FFT) and Singular Value Decomposition (SVD), the simulation results show that the method can determine the number of significant digits of digital frequency according to the difference of signal-to-noise ratio. Thus, it can increases stability and accuracy. The experiment of wireless SAW temperature sensor shows that the frequency estimation standard deviation of this method is small and the robustness is high.
Blind Recognition of Code Length and Synchronization of Turbo Codes on Trellis Termination at Low SNR
Zhaojun WU, Limin ZHANG, Zhaogen ZHONG, Keyuan YU, Yuncheng YANG
, Available online  , doi: 10.11999/JEIT180903 doi: 10.11999/JEIT180903
[Abstract](297) [FullText HTML] (176) [PDF 1707KB](18)
In order to overcome the shortcomings of low fault-tolerance and high computational complexity in the process of parameter identification such as code length and synchronization of Turbo code, a new algorithm based on Differential Likelihood Difference (DLD) at low Signal-to-Noise Ratio (SNR) is proposed. Firstly, the concept of DLD is defined, and the analysis matrix is constructed to identify the code length by using the characteristic that the DLD between two codes in Turbo frame terminal is positive ("+"); secondly, a method based on the minimum error decision criterion to decide DLD "+" position is proposed to complete frame synchronization. From the engineering practice, the possible values of the number of registers are traversed to realize the recognition of the code rate, the number of registers and the interleaving length. Simulation results show that the proposed algorithm is effective in identifying parameters such as code length and frame synchronization, the position distribution of DLD ‘+’ is consistent with the data structure characteristics of the analysis, and the threshold can effectively determine the position of DLD ‘+’. At the same time, the algorithm has strong fault-tolerant performance. Under the condition of SNR –5 dB, the identification of code length, frame synchronization and other parameters can reach more than 90%, and the complexity of the algorithm is far less than the existing algorithms.
3D Human Motion Prediction Based on Bi-directionalGated Recurrent Unit
Haifeng SANG, Zizhen CHEN
, Available online  , doi: 10.11999/JEIT180978 doi: 10.11999/JEIT180978
[Abstract](238) [FullText HTML] (173) [PDF 2318KB](13)
In the field of computer vision, predicting human motion is very necessary for timely human–computer interaction and personnel tracking. In order to improve the performance of human–computer interaction and personnel tracking, an encoder-decoder model called Bi–directional Gated Recurrent Unit Encoder–Decoder (EBiGRU–D) based on Gated Recurrent Unit (GRU) is proposed to learn 3D human motion and give a prediction of motion over a period of time. EBiGRU–D is a deep Recurrent Neural Network (RNN) in which the encoder is a Bidirectional GRU (BiGRU) unit and the decoder is a unidirectional GRU unit. BiGRU allows raw data to be simultaneously input from both the forward and reverse directions and then encoded into a state vector, which is then sent to the decoder for decoding. BiGRU associates the current output with the state of the front and rear time, so that the output fully considers the characteristics of the time before and after, so that the prediction is more accurate. Experimental results on the human3.6m dataset demonstrate that EBiGRU–D not only improves greatly the error of 3D human motion prediction but also increases greatly the time for accurate prediction.
Virtual Network Function Dynamic Deployment Algorithm Based on Prediction for 5G Network Slicing
Lun TANG, Yu ZHOU, Youchao YANG, Guofan ZHAO, Qianbin CHEN
, Available online  , doi: 10.11999/JELT180894 doi: 10.11999/JELT180894
[Abstract](377) [FullText HTML] (227) [PDF 1859KB](33)
In order to solve the unreasonable virtual resource allocation caused by the dynamic change of service request and delay of information feedback in wireless virtualized network, a traffic-aware algorithm which exploits historical Service Function Chaining (SFC) queue information to predict future load state based on Long Short-term Memory (LSTM) network is proposed. With the prediction results, the Virtual Network Function (VNF) deployment and the corresponding computing resource allocation problems are studied, and a VNFs’ deployment method based on Maximum and Minimum Ant Colony Algorithm (MMACA) is developed. On the premise of satisfying the minimum resource demand for future queue non-overflow, the on-demand allocation method is used to maximize the computing resource utilization. Simulation results show that the prediction model based on LSTM neural network in this paper obtains good prediction results and realizes online monitoring of the network. The Maximum and Minimum Ant Colony Algorithm based VNF deployment method reduces effectively the bit loss rate and the average end-to-end delay caused by overall VNFs’ scheduling at the same time.
Neighbor Information Constrained Node Scheduling in Stochastic Heterogeneous Wireless Sensor Networks
Ningning QIN, Lei JIN, Jian XU, Fan XU, Le YANG
, Available online  , doi: 10.11999/JEIT190094 doi: 10.11999/JEIT190094
[Abstract](220) [FullText HTML] (141) [PDF 1999KB](14)
Considering coverage redundancy problem existed in random heterogeneous sensor networks with high density deployment, a Node Scheduling algorithm for Stochastic Heterogeneous wireless sensor networks(NSSH) is proposed. The Delaunary triangulation is constructed based on the network prototype topology to work out a local subset of nodes for localization scheduling. Independent configuration of the perceived radius is achieved by discounting the radius of the circumcircle with the adjacent node. The concept of geometric line and plane is introduced, and the overlapping area and the effective constrained arcs are used to classify and identify the grey and black nodes. So the node only relies on local and neighbor information for radius adjustment and redundant node sleep. The simulation results show that NSSH can approximately match the dropping redundancy of greedy algorithm at the cost of low complexity, and exhibit low sensitivity to network size, heterogeneous span and parameter configuration.
Meteorological Radar Noise Image Semantic Segmentation Method Based on Deep Convolutional Neural Network
Hongyun YANG, Fengyan WANG
, Available online  , doi: 10.11999/JEIT190098 doi: 10.11999/JEIT190098
[Abstract](349) [FullText HTML] (257) [PDF 3060KB](47)
Considering the problem that the scattering echo image of the new generation Doppler meteorological radar is reduced by the noise echoes such as non-rainfall, the accuracy of the refined short-term weather forecast is reduced. A method for semantic segmentation of meteorological radar noise image based on Deep Convolutional Neural Network(DCNN) is proposed. Firstly, a Deep Convolutional Neural Network Model (DCNNM) was designed. The training set data of the MJDATA data set are used for training, and the feature is extracted by the forward propagation process, and the high-dimensional global semantic information of the image is merged with the local feature details. Then, the network parameters are updated by using the training error value back propagation iteration to optimize the convergence effect of the model. Finally, the meteorological radar image data are segmented by the model. The experimental results show that the proposed method has better denoising effect on meteorological radar images, and compared with the optical flow method and the Fully Convolutional Networks (FCN), the method has high recognition accuracy for meteorological radar image real echo and noise echo, and the image pixel precision is high.
A Novel Clutter Spectrum Compensation Method for End-fire Array Airborne Radar Based on Space-time Interpolation
Yongwei LI, Wenchong XIE
, Available online  , doi: 10.11999/JEIT181131 doi: 10.11999/JEIT181131
[Abstract](215) [FullText HTML] (155) [PDF 3085KB](8)
End-fire array antenna is extremely suitable for forward-looking or backward-looking blind compensation of airborne radar due to its low wind resistance and high-gain characteristics, while the forward-looking or backward-looking placement of antenna can not avoid the problem of range-dependent clutter. In this paper, in view of the fact that the conventional Space-Time INterpolation Technique(STINT) can not be directly applied to end-fire array clutter compensation in range ambiguity situation, a novel method of end-fire array clutter compensation based on space-time interpolation is proposed based on the characteristics of clutter spectrum for end-fire array airborne radar. The method takes full account of the ambiguous clutter of each range gate and takes the arc corresponding to the main lobe of the long-range stationary clutter ridge as the interpolation reference subspace. Furthermore, it also refines the constrained object of moving target constraints, which achieves effective compensation for the non-stationary clutter of end-fire array in range ambiguity situation. Computer simulation results verify the effectiveness of the proposed method.
Performance Analysis of Short Reference Orthogonal Multiuser Differential Chaotic Shift Keying Scheme
Gang ZHANG, Changchang ZHAO, Tianqi ZHANG
, Available online  , doi: 10.11999/JEIT181038 doi: 10.11999/JEIT181038
[Abstract](181) [FullText HTML] (130) [PDF 1978KB](6)
Considering the shortcomings of Differential Chaos Shift Keying (DCSK) transmission rate and further improving the system error performance, a Short reference Orthogonal Multiuser DCSK(SOM-DCSK) communication system is proposed. The system shortens the reference signal to 1/P of each information bearing signal, and transmits multiple users by different delay times. Then the orthogonality of Hilbert transform is used in each information slot to achieve the purpose of transmitting a two-bit information signal. The Bite Error Rate (BER) formula of SOM-DCSK system in Additive White Gaussian Noise (AWGN) and Rayleigh fading channel is derived and experimentally simulated. The simulation results show that the scheme has obvious improvement compared with the traditional multi-user system under the same conditions, and it has good practical value.
Research on Channel Selection and Power Control Strategy for D2D Networks
Zhihong QIAN, Chunsheng TIAN, Xin WANG, Xue WANG
, Available online  , doi: 10.11999/JEIT190149 doi: 10.11999/JEIT190149
[Abstract](219) [FullText HTML] (157) [PDF 2077KB](21)
Considering the resource allocation problem for Device-to-Device (D2D) communications, a channel selection and power control strategy for D2D communications is investigated. On the premise of guaranteeing the Quality of Service (QoS) of cellular users, a heuristic based D2D channel selection algorithm is proposed to find the suitable channel reusing resources for D2D users in the system. At the same time, the optimal transmission power of D2D users is obtained by using the Lagrange dual method. Simulation results demonstrate that when the cellular user shares channel resources with multiple pairs of D2D users, the system throughput can be dramatically improved. The performance of this algorithm outperforms the exiting algorithms under the same conditions.
Student’s t Mixture Cardinality Balanced Multi-target Multi-Bernoulli Filter
Shuxin CHEN, Lei HONG, Hao WU, Zhuowei LIU, Longhua YUE
, Available online  , doi: 10.11999/JEIT181121 doi: 10.11999/JEIT181121
[Abstract](196) [FullText HTML] (116) [PDF 2355KB](13)
The filtering performance of Gaussian Mixture Cardinality Balanced Multi-target Multi-Bernoulli (GM-CBMeMBer) filter can be effected by the heavy-tailed process noise and measurement noise. To solve this problem, a new STudent’s t Mixture Cardinality Balanced Multi-target Multi-Bernoulli (STM-CBMeMBer) filter is proposed. The process noise and measurement noise approximately obey the Student’s t distribution in the filter, where the Student’s t mixture model is used to describe approximately the posterior intensity of the multi-target. The predictive intensity and posterior intensity of Student’s t mixture form are deduced theoretically, and the closed recursive framework of cardinality balanced multi-target multi-Bernoulli filter is established. The simulation results show that, in the presence of the heavy-tailed process noise and the measurement noise, the filter can effectively suppress its interference, its tracking accuracy is superior over the traditional methods.
Scheduling Method Based on Markov Decision Process for Multi-sensor Cooperative Detection and Tracking
Gongguo XU, Ganlin SHAN, Xiusheng DUAN, Chenglin QIAO, Haotian WANG
, Available online  , doi: 10.11999/JEIT181129 doi: 10.11999/JEIT181129
[Abstract](228) [FullText HTML] (148) [PDF 1590KB](13)
In order to solve the problem of sensor scheduling in the multi-task scenario, a multi-sensor scheduling method for target cooperative detection and tracking is proposed. Firstly, the sensor scheduling model is built based on the Partially Observable Markov Decision Process (POMDP) and an objective function is designed based on Posterior Carmér-Rao Lower Bound (PCRLB). Then, considering sensor switching time and the change of target number, the randomly distributed particles are used to calculate the detection probability of new target, and the sensor scheduling methods are given for the situations with fixed target number and time-varying target number. At last, to meet the real-time requirement of online scheduling, an Adaptive Multi-swarm Cooperative Differential Evolution (AMCDE) algorithm is used to solve the sensor scheduling scheme. Simulation results show that the method can effectively deal with multi-task scenarios and realize reasonable scheduling of multi-sensor resources.
Stochastic Resonance Detection Method for the Dual-Sequence Frequency Hopping Signal under Extremely Low Signal-to-Noise Radio
Guangkai LIU, Houde QUAN, Huixian SUN, Peizhang CUI, Kuo CHI, Shaolin YAO
, Available online  , doi: 10.11999/JEIT190157 doi: 10.11999/JEIT190157
[Abstract](261) [FullText HTML] (137) [PDF 1945KB](9)
Considering the problem that the Dual-Sequence Frequency Hopping (DSFH) can not communicate at extremely low Signal-to-Noise Ratio (SNR), a Stochastic Resonance (SR) detection method is proposed. The SR takes full advantage of the physical characteristics of DSFH signal to improve the detection performance. Firstly, the SR is constructed by analyzing signals of transmission, reception and the Intermediate Frequency (IF). The scale transaction is used to adjust the IF signal to fit the SR. Secondly, the non-autonomous Fokker-Plank Equation (FPE) is transformed into an autonomous equation by introducing the decision time. Therefore, the analytical solution of the probability density function with the parameter of decision time is obtained. Finally, the detection probability, false alarm probability and Receiver Operating Characteristics (ROC) curve are obtained, when the criterion is the Maximum A Posterior probability (MAP) Simulation analysis results show three conclusions: (1) The SNR of DSFH signal can be as low as –18 dB, which uses the matched SR detection. (2) Method for combining DSFH with the matched SR is suitable to detect the signals with SNR of –18 ~–14 dB. (3) In the case of –14 dB SNR, the DFSH signal detection performance increases by 25.47%, when using SR. The proposed method effectiveness is proved with simulation results.
A Satellite Calibration Method for the Baseline Coordinate and Phase Difference of Distributed Radar Array
Lu LU, Meiguo GAO
, Available online  , doi: 10.11999/JEIT181152 doi: 10.11999/JEIT181152
[Abstract](260) [FullText HTML] (157) [PDF 2111KB](22)
In the system of distributed radar array system using phase interference angle measurement, the phase center coordinate error of arrays and the phase difference error have relatively large influence on the angle measurement. And the phase center position is often inconsistent with physical center position. Thus it is necessary to compensate these errors precisely. Far field radiation sources are often used to calibrate radar in traditional calibration methods. However, it is usually hard to achieve far field radiation sources for distributed radar array with large space between units surveilling space targets. In this paper, a calibration method based on the precise ephemeris of refined orbit satellites without measuring with special instruments is proposed. The phase error caused by coordinate error can be whitened by the precise ephemeris of multiple arcs, and the coordinate and phase difference will be searched out by matching the minimum variance. This method can get the errors easily. The simulation results and actual data verify that angle measurement accuracy gets large improvement by the method.
Saliency Detection Using Wavelet Transform in Hypercomplex Domain
Ying YU, Qinglong WU, Kaixuan SHAO, Yuxing KANG, Jian YANG
, Available online  , doi: 10.11999/JEIT180738 doi: 10.11999/JEIT180738
[Abstract](334) [FullText HTML] (191) [PDF 7822KB](22)
To solve the incompleteness of the salient region obtained by the existing saliency detection method in the frequency domain, a frequency saliency detection method of multi-scale analysis is proposed. Firstly, the quaternion hypercomplex is constructed by the input image feature channels. Then, the multi-scale decomposition of the quaternion amplitude spectrum is performed by wavelet transform, and the multi-scale visual saliency map is calculated. Finally, the better saliency map is fused based on the evaluation function, and central bias is used to generate the final visual saliency map. The experimental results show that the proposed method can effectively suppress the background interference, find significant target quickly and accurately, and have high detection accuracy.
Semi-supervised Indoor Fingerprint Database Construction Method Based on the Nonhomogeneous Distribution Characteristic of Received Signal Strength
Shibao LI, Shengzhi WANG, Jianhang LIU, Tingpei HUANG, Xin ZHANG
, Available online  , doi: 10.11999/JEIT180599 doi: 10.11999/JEIT180599
[Abstract](359) [FullText HTML] (161) [PDF 1933KB](10)
The radio map construction is time consuming and labor intensive, and the conventional semi-supervised based methods usually ignore the influence of the uneven distribution of high-dimensional Received Signal Strength (RSS). In order to solve that problem, a semi-supervised radio map construction approach which is based on the nonhomogeneous distribution characteristic of RSS is proposed. The approach utilizes the RSS local scale and common neighbors similarities to calculate the weighted matrix. Thus, the weighted graph that reflects accurately the structure of RSS data manifold is presented. In addition, the weighted graph is used to find the optimal solution of the objective function to calibrate the locations of plenty of unlabeled data by a small number of labeled RSS. The extensive experiments demonstrate that the proposed method is capable of not only construct an accurate radio map at a low manual cost, but also achieve a high localization accuracy.
Multi-party Contract Signing Protocol Based on Certificateless
Suzhen CAO, Fei WANG, Xiaoli LANG, Rui WANG, Xueyan LIU
, Available online  , doi: 10.11999/JEIT190166 doi: 10.11999/JEIT190166
[Abstract](149) [FullText HTML] (105) [PDF 1018KB](14)
Online contract signing is becoming more and more popular in e-commerce. It is not easy to sign a contract between two parties who do not trust each other. Many of these protocols involve the participation of third parties, but they are not advantageous in efficiency and prone to security problems. Currently, contract signing agreements with third-party participation are replaced by block chain technology, but the public verification of block chain challenges the sensitive information of both the signer and the contract to be signed. And most of the agreements are for the signing of contracts between the two parties. With the increase of the number of signatories, the communication cost and complexity of the agreements increase sharply. Combined with the existing protocols, this paper proposes an efficient multi-party contract signing protocol. In the protocol, an efficient aggregation signature scheme based on no certificate is used to improve the signature verification efficiency of the signer under the block chain, and only the temporary key of the signer is disclosed on the block chain to reduce the system overhead. The protocol satisfies the requirements of correctness, security, fairness, privacy and high efficiency.
Driver Fatigue Detection Through Deep Transfer Learning in an Electroencephalogram-based System
Fei WANG, Shichao WU, Shaolin LIU, Yahui ZHANG, Ying WEI
, Available online  , doi: 10.11999/JEIT180900 doi: 10.11999/JEIT180900
[Abstract](330) [FullText HTML] (221) [PDF 1568KB](32)
ElectroEncephaloGram (EEG) is regarded as a " gold standard” of fatigue detection and drivers’ vigilance states can be detected through the analysis of EEG signals. However, due to the characteristics of non-linear, non-stationary and low spatial resolution of EEG signals, traditional machine learning methods still have the disadvantages of low recognition rate and complicated feature extraction operations in EEG-based fatigue detection task. To tackle this problem, a fatigue detection method with transfer learning based on the Electrode-Frequency Distribution Maps (EFDMs) of EEG signals is proposed. A deep convolutional neural network is designed and pre-trained with SEED dataset, and then it is used for fatigue detection with transfer learning strategy. Experimental results show that the proposed convolutional neural network can automatically obtain vigilance related features from EFDMs, and achieve much better recognition results than traditional machine learning methods. Moreover, based on the transfer learning strategy, this model can also be used for other recognition tasks, which is helpful for promoting the application of EEG signals to the driver fatigue detection system.
Matrix Metric Learning for Person Re-identification Based on Bidirectional Reference Set
Ying CHEN, Xiaoyue XU
, Available online  , doi: 10.11999/JEIT190159 doi: 10.11999/JEIT190159
Targeting the problem of inaccurate feature representation caused by indistinctive appearance difference in person re-identification domain, a new matrix metric learning based on bidirectional reference set is proposed. Firstly, reciprocal-neighbor reference sets in different camera views are respectively constructed by the reciprocal-neighbor scheme. To ensure the robustness of reference sets, the reference sets in different camera views are jointly considered to generate the Bidirectional Reference Set (BRS). With hard samples which are mined by the BRS to represent feature descriptors, accurate appearance difference representations could be obtained. Finally, these representations are utilized to conduct more effective matrix metric learning. Experimental results on several public datasets demonstrate the superiority of the proposed method.
A Hierarchical Vertical Handover Algorithm Based on Fuzzy Logic in Heterogeneous Wireless Networks
Bin MA, Shangru LI, Xianzhong XIE
, Available online  , doi: 10.11999/JEIT190190 doi: 10.11999/JEIT190190
[Abstract](163) [FullText HTML] (106) [PDF 2031KB](2)
In the heterogeneous wireless networks, the parameter weight is difficult to determine for the vertical handover algorithm considering the parameters of the network and the user, at the same time, the vertical handover algorithm based on fuzzy logic has high complexity. Considering this problem, a hierarchical vertical handover algorithm based on fuzzy logic is proposed. Firstly, the Received Signal Strength (RSS), bandwidth and delay are input into the first-level fuzzy logic system. Combining with the rule adaptive matching, the QoS fuzzy value is inferred, and the network is initially filtered by the QoS fuzzy value to obtain the candidate network set; Then, the second-level fuzzy logic system is triggered by the trigger mechanism, and the QoS fuzzy value, network load rate and user access cost of the candidate network are input into the second-level fuzzy logic system. At the same time, the output decision value is obtained by combining the adaptive rule matching, so as to select the best access network. Finally, the experimental results show that the algorithm can guarantee the network performance while reducing the time cost of the system.
Joint User Association and Power Allocation Algorithm for Network Slicing Based on NOMA
Lun TANG, Runlin MA, Heng YANG, Qianbin CHEN
, Available online  , doi: 10.11999/JEIT180770 doi: 10.11999/JEIT180770
[Abstract](228) [FullText HTML] (181) [PDF 2220KB](23)
To satisfy the diversity of requirements for different network slices and realize dynamic allocation of wireless virtual resource, an algorithm for network slice joint user association and power allocation is proposed in Non-Orthogonal Multiple Access(NOMA) C-RAN. Firstly, by considering imperfect Channel State Information(CSI), a joint user association and power allocation algorithm is designed to maximize the average total throughput in C-RAN with the constraints of slice and user minimum required rate, outage probability and fronthaul capacity limits. Secondly, we design a joint user association and power allocation algorithm according to the current slot by transforming the probabilistic mixed optimalization problem into a non-probabilistic optimalization problem and using Lyapunov optimization. Finally, for user association problem, a greedy algorithm is proposed to find a feasible suboptimal solution; the power allocation problem is transformed into a convex optimization problem by using successive convex approximation, then a dual decomposition approach is exploited to obtain a power allocation strategy. Simulation results demonstrate that the proposed algorithm can effectively improve the average total throughput of system while guaranteeing the network slice and user requirement.
RGB-D Saliency Detection Based on Optimized ELM and Depth Level
Zhengyi LIU, Tianze XU
, Available online  , doi: 10.11999/JEIT180826 doi: 10.11999/JEIT180826
[Abstract](236) [FullText HTML] (156) [PDF 1553KB](12)
Currently, many saliency-detection methods focus on 2D-image. But, these methods cannot be applied in RGB-D image. Based on this situation, new methods which are suitable for RGB-D image are needed. This paper presents a novel algorithm based on Extreme Learning Machine(ELM), feature-extraction and depth-detection. Firstly, feature-extraction is used for getting a feature, which contains 4-scale superpixels and 4096 dimensions. Secondly, according to the 4-sacle superpixels, the RGB, LAB and LBP feature of RGB image are computed, and LBE feature of depth image. Thirdly, weak salient map with LBE and dark-channel features are computed, and the foreground objects is strengthened in every circle. Fourthly, according to weak salient map, both foreground seeds and background seeds are chosen, and then, put these seeds into ELM to compute the first stage salient map. Finally, depth-detection and graph-cut are used for optimizing the first stage salient map and getting the second stage salient map.
Binaural Target Sound Source Localization Based on Time-frequency Units Selection
Ruwei LI, Tao LI, Xiaoyue SUN, Dengcai YANG, Qi WANG
, Available online  , doi: 10.11999/JEIT181127 doi: 10.11999/JEIT181127
[Abstract](188) [FullText HTML] (128) [PDF 569KB](7)
The performance of the existing target localization algorithms is not ideal in complex acoustic environment. In order to improve this problem, a novel target binaural sound localization algorithm is presented. First, the algorithm uses binaural spectral features as input of a time-frequency units selector based on deep learning. Then, to reduce the negative impact of the time-frequency unit belonging to noise on the localization accuracy, we employ the selector to select the reliable time-frequency units from binaural input sound signal. At the same time, a Deep Neural Network (DNN)-based localization system map the binaural cues of each time-frequency unit to the azimuth posterior probability. Finally, the target localization is completed according to the azimuth posterior probability belonging to the reliable time-frequency units. Experimental results show that the performance of the proposed algorithm is better than comparison algorithms and achieves a significant improvement in target localization accuracy in low Signal-to-Noise Ratio(SNR) and various reverberation environments, especially when there is noise similar to the target sound source.
Image Saliency Detection Based on Object Compactness and Regional Homogeneity Strategy
Hongmei TANG, Biying WANG, Liying HAN, Yatong ZHOU
, Available online  , doi: 10.11999/JEIT190101 doi: 10.11999/JEIT190101
[Abstract](194) [FullText HTML] (132) [PDF 3888KB](6)
Considering the inaccurate description of feature differences between nodes in the graph-based saliency detection algorithm, an image saliency detection algorithm combining object compactness and regional homogeneity strategy is proposed. Different from the commonly used graph-based model, a sparse graph-based structure closer to the human visual system and a novel regional homogeneity graph-based structure are established. They are used to describe the correlation within the foreground and the difference between foreground and background. Therefore, many redundant connections of nodes are eliminated and the local spatial relationship of nodes is strengthened. Then the clusters are combined to form a saliency map by means of manifold ranking. Finally, the background confidence is introduced for saliency optimization by the similarity of the background region clusters and the final detection result is obtained. Compared with 4 popular graph-based algorithms on the four benchmark datasets, the proposed algorithm can highlight the salient regions clearly and has better performance in the evaluation of multiple comprehensive indicators.
Energy-saving Virtual Network Embedding Algorithm Based on Sliding Region Particle Swarm
Lei ZHUANG, Shuaikui TIAN, Mengyang HE, Yu SONG, Guoqing WANG, Wentan LIU, Ling MA
, Available online  , doi: 10.11999/JEIT190168 doi: 10.11999/JEIT190168
[Abstract](177) [FullText HTML] (152) [PDF 1456KB](15)
Considering the problem of scattered node mapping and more hops of link mapping in the traditional virtual network energy-saving embedding, the node and link are mapped simultaneously by using the minimum spanning tree topology of the virtual network request, and Energy-saving Virtual Network Embedding algorithm based on Sliding Region Particle Swarm (EVNE_SRPS) is proposed. When a virtual network request arrives, the minimum spanning tree topology is generated, the root node is the node with the shortest path length; Multiple regions are randomly selected as the particle object in the substrate network, and the minimum spanning tree topology of the virtual network request is mapped in the regional center; The fitness of the particles is calculated. The optimal solution of the group and the individual is finded, and the sliding direction and the location of the update region under the guidance of the optimal solution are determined. After the iteration, the mapping scheme of the virtual network is obtained. The experimental results show that compared with the existing algorithms, the network energy consumption is reduced, and the internet service providers revenue to cost ratio is improved.
Blind Reconstruction of Convolutional Code Based on Partitioned Walsh-Hadamard Transform
Zhigang YAO, Hui XIE, Zhuangzhi HAN, Lin SHI, Yuanwei YIN
, Available online  , doi: 10.11999/JEIT181139 doi: 10.11999/JEIT181139
[Abstract](226) [FullText HTML] (125) [PDF 1117KB](6)
The Walsh-Hadamard transform can be used to solve binary domain error-containing equations, and the method can be used for blind identification of convolutional codes. However, when the number of system unknowns is large, the requirement of computer memory makes it difficult to apply this method in practice. Therefore, a convolutional code recognition method based on partitioned Walsh-Hadamard transform is proposed. By segmenting the high-dimensional coefficient vectors of the equations into two low-dimensional coefficient vectors, the problem of solving the high-dimensional equations by Walsh-Hadamard transformation is decomposed into the problem of solving the two low-dimensional equations, and it is proved that the combination of the solution vectors of the two low-dimensional equations is the solution of the high-dimensional equations. The algorithm reduces effectively the need for computer memory, and the simulation results verify the effectiveness of the proposed algorithm, and the algorithm has a good error code adaptability.
A Doppler Resampling Based Imaging Algorithm for High Squint SAR with Constant Acceleration
Ning LI, Bowen BIE, Mengdao XING, Guangcai SUN
, Available online  , doi: 10.11999/JEIT180953 doi: 10.11999/JEIT180953
[Abstract](205) [FullText HTML] (153) [PDF 2356KB](11)
A modified SPECtral ANalysis (SPECAN) algorithm based on Doppler resampling is proposed to deal with the azimuth Space-Variant (SV) phase coefficients of the High Squint (HS) SAR data acquired from maneuvering platform. Firstly, for HS SAR with constant acceleration, an orthogonal coordinate slant range model is presented, which can handle the coordinate rotation caused by the traditional method of Range Walk Correction (RWC), and solve the mismatch between the range model and the signal after RWC. Then azimuth Doppler resampling is used to correct the SV phase coefficients. The focused image is achieved by SPECAN technique. Finally, the proposed algorithm is validated by processing of simulated SAR data, and has significant improvement on focusing quality over the reference one.
Research on Dynamic Threat Tracking and Quantitative Analysis Technology Based on Attribute Attack Graph
Yingjie YANG, Qiang LENG, Ruixuan PAN, Hao HU
, Available online  , doi: 10.11999/JEIT181117 doi: 10.11999/JEIT181117
[Abstract](307) [FullText HTML] (198) [PDF 1821KB](19)
Network multi-alarm information fusion processing is one of the most important methods to implement effectively network dynamic threat analysis. Focusing on this, a mechanism for dynamic threat tracking and quantitative analysis by using network system multi-alarm information is proposed. Firstly, the attack graph theory is used to construct the system dynamic threat attribute attack graph. Secondly, based on the privilege escalation principle, Antecedent Predictive Algorithm(APA), the Consequent Predictive Algorithm(CPA) and the Comprehensive Alarm Information Inference Algorithm(CAIIA) are designed integrate the multi-alarm information and do threat analysis. Then, the network dynamic threat tracking graph is generated to visualize the threat change situation. Finally, the effectiveness of the mechanism and algorithm is validates through experiments.
A Lossy Frame Memory Compression Algorithm Using Directional Interpolation Prediction Variable Length Coding
Yu LUO, Zhenzhen ZHANG
, Available online  , doi: 10.11999/JEIT181195 doi: 10.11999/JEIT181195
[Abstract](346) [FullText HTML] (157) [PDF 573KB](8)
A lossy frame memory compression algorithm using Direction Interpolation Prediction Variable Length Coding (DIPVLC) is proposed to improve frame memory compression performance. Firstly, the prediction residual is obtained by adaptive texture directional interpolation. Then, a new rate-distortion is optimized to quantize prediction residual. Finally, the run length Golomb method is used to entropy coding for quantized residual. Simulation results show that compared with parallel Content Aware Adaptive Quantization (CAAQ) oriented lossy frame memory recompression for HEVC, the proposed algorithm improves the compression rate by 10.05% and reduces the encoding time by 10.62% with less PSNR reduction.
A Distributed Node Localization Algorithm for Large Scale Sensor Networks
Junzheng JIANG, Yangjian LI, Haibing ZHAO, Shan OUYANG
, Available online  , doi: 10.11999/JEIT181101 doi: 10.11999/JEIT181101
[Abstract](236) [FullText HTML] (178) [PDF 783KB](26)
A distributed algorithm based on modified Newton method is proposed to solve the nodes localization problem in large scale Wireless Sensor Network(WSN). The algorithm includes network partitioning and distributed algorithm. Firstly, the network is divided into several overlapping subregions according to the nodes positions and the distance information between the sensors. The localization problem of subregions is formulated into an unconstrained optimization problem and each subregion can be calculated independently. Then distributed algorithm is used to determine nodes positions in subregions and merge the subregions. Simulation results indicate that the proposed algorithm is superior to the existing algorithms in terms of accuracy in large scale network, which can meet the needs of nodes localization in large scale network.
Total Variation Regularized Reconstruction Algorithms for Block Compressive Sensing
Derong CHEN, Haibo LÜ, Qiufu LI, Jiulu GONG, Zhiqiang LI, Xiaojun HAN
, Available online  , doi: 10.11999/JEIT180931 doi: 10.11999/JEIT180931
[Abstract](321) [FullText HTML] (187) [PDF 1584KB](28)
In order to improve the quality of reconstruction image by Block Compressed Sensing (BCS), a Total Variation Iterative Threshold regularization image reconstruction algorithm (BCS-TVIT) is proposed. Combining the properties of local smoothing and bounded variation of the image, BCS-TVIT use the minimization l0 norm and total variation to construct the objective function. To solve the problem that l0 norm term and the block measurement constraint cannot be optimized directly, the iterative threshold method is used to minimize the l0 norm of the reconstructed image, and the convex set projection was employed to guarantee the block measurement constraint condition. Experiments show that BCS-TVIT has better performance than BCS-SPL in PSNR by 2 dB. Meanwhile, BCS-TVIT can eliminate the " bright spot” effect of BCS-SPL, having better visual effect. Comparing with the minimum total variation, the proposed algorithm increases PSNR by 1 dB, and the reconstruction time is reduced by two orders of magnitude.
Nonlinear Distortion Suppression in Cooperative Jamming Cancellation System
Chenxing LI, Wenbo GUO, Ying LIU, Ying SHEN, Hongzhi ZHAO, Youxi TANG
, Available online  , doi: 10.11999/JEIT180919 doi: 10.11999/JEIT180919
[Abstract](286) [FullText HTML] (167) [PDF 1131KB](24)
In Cooperative Jamming (CJ) system, the Power Amplifier (PA) in the jamming transmitter works in nonlinear region, which results in a large number of nonlinear components in the Self-Interference (SI) signal received by the near-end receiver. To solve the problem of nonlinear distortion suppression, a nonlinear model is established at the receiver. Then, the reconstructed nonlinear signal based on the estimated parameters is subtracted from the received signal to suppress the nonlinear interference in CJ. Simulation and experimental results indicate that the nonlinear suppression scheme proposed in this paper can further suppress the nonlinear interference under the residual frequency offset in CJ, and verify the effectiveness and feasibility of the proposed scheme.
A Virtual Node Migration Method for Sensing Side-channel Risk
Kaizhi HUANG, Qirun PAN, Quan YUAN, Wei YOU
, Available online  , doi: 10.11999/JEIT180905 doi: 10.11999/JEIT180905
[Abstract](329) [FullText HTML] (170) [PDF 1662KB](23)
In order to defend against Side-Channel Attacks (SCA) in Network Slicing (NS), the existing defense methods based on dynamic migration have the problem that the conditions for sharing of physical resources between different virtual nodes are not strict enough, a virtual node migration method is proposed for sensing side-channel risk. According to the characteristics of SCA, the entropy method is used to evaluate the side-channel risks and migrate the virtual node from a server with large deviation from average risk. The Markov decision process is used to describe the migration of virtual nodes for network slicing, and the Sarsa learning algorithm is used to solve the optimal migration scheme. The simulation results show that this method can separates malicious network slice instances from other target network slice instances to achieve the purpose of defense side channel attacks.
A Method to Visualize Deep Convolutional Networks Based on Model Reconstruction
Jiaming LIU, Mengdao XING, Jixiang FU, Dan XU
, Available online  , doi: 10.11999/JEIT180916 doi: 10.11999/JEIT180916
[Abstract](252) [FullText HTML] (154) [PDF 1562KB](26)
A method for visualizing the weights of a reconstructed model is proposed to analyze how a deep convolutional network works. Firstly, a specific input is used in the original neural network during the forward propagation to get the prior information for model reconstruction. Then some of the structure of the original network is changed for further parameter calculation. After that, the parameters of the reconstructed model are calculated with a group of orthogonal vectors. Finally, the parameters are put into a special order to make them visualized. Experimental results show that the model reconstructed with the method proposed is totally equivalent to the original model during the forward propagation in the classification process. The feature of the weights of the reconstructed model can be observed clearly and the principle of the neural network can be analyzed with the feature.
Performances Analysis in Uplink Non-orthogonal Multiple Access System with Imperfect Successive Interference Cancellation
Xiyu WANG, Xiaoming Xu, Yajun CHEN
, Available online  , doi: 10.11999/JEIT181165 doi: 10.11999/JEIT181165
[Abstract](238) [FullText HTML] (176) [PDF 1171KB](10)
Non-Orthogonal Multiple Access (NOMA) serves multiple transmitters using the same resource block, and the receiver decodes the information from different transmitters through Successive Interference Cancellation (SIC). However, most of the researches on NOMA systems are based on perfect SIC assumption, in which the impact of imperfect SIC on NOMA system is not considered. Focusing on this problem, a framework is provided to analyze the performance of single-cell uplink NOMA system under the assumption of imperfect SIC. Firstly, the Binomial Point Process (BPP) is used to model the spatial distribution of base station and user equipment in uplink NOMA system. Based on this model, the interference cancellation order which is based on large-scale fading is adopted, and then the error of interference cancellation is analyzed. Then, based on stochastic geometry theory and order statistics theory, the expression of coverage probability of user equipment which is at rank k in terms of the distance from the base station is derived, besides, the average coverage probability is adopted to reflect the reliability of NOMA transmission system. The analytical and simulation results show the influence of system parameters such as distance order and base station radius on transmission reliability. Also, the validity of theoretical deduction is verified.
Quality of Service-aware Elastic Flow Aggregation Based on Enhanced Rough k-means
Zheng WU, Yuning DONG, Wei TIAN, Pingping TANG
, Available online  , doi: 10.11999/JEIT181169 doi: 10.11999/JEIT181169
[Abstract](174) [FullText HTML] (141) [PDF 1860KB](8)
Facing changeable network environment, current Quality of Service (QoS)-aware flow aggregation scheme is lack of flexibility. A dynamic flow aggregation method to overcome present problems is proposed. An Enhanced Rough k-Means (ERKM) algorithm is used to aggregate network flows properly. Importantly, it is able to adjust degree of membership to face ever-changing internet environment to make algorithm more flexible. Internet scheduler experiment is carried out and a comparison is made with existing methods. Experimental results suggest that proposed method has advantages not only on flexibility of aggregation but on assurance of QoS of Internet flows. In addition, the consistency of QoS allocation under different network environment is investigated.
Measurement Error Correction of the Orthogonal Magnetic Loop Antenna for Lightning-direction Finding
Miao HU, Zehui RUAN, Peng LI, Baofeng CAO, Xuefang ZHOU, Jiaqi SUN, Ximing HU, Sheng YE
, Available online  , doi: 10.11999/JEIT181016 doi: 10.11999/JEIT181016
[Abstract](264) [FullText HTML] (151) [PDF 1189KB](7)
The measurement accuracy for lightning direction finding by the Orthogonal Magnetic Loop Antenna (OMLA) is continuously improved, which results in the Angle Measurement Error (AME) caused by the OMLA machining error increasing. A theoretical model is established for the relationship between the machining error and AME of OMLA. With the compensation coefficient and equivalent non-orthogonal angle error, a AME correction method for OMLA is proposed. The AME of the conventional measurement way and the corrected measurement way are compared through three groups of data experimentally. The experimental results show that the AME by the corrected measurement way is significantly reduced by about 50%. Therefore, this correction method can help the OMLA with the same hardware condition to obtain higher measurement accuracy for lightning direction finding.
Research on Synchronous Excitation and Detection Method for Synthetic Multi-frequency Magnetic Induction Signals
Qiang DU, Kehao ZHANG, Li KE, Chenyang WANG
, Available online  , doi: 10.11999/JEIT181083 doi: 10.11999/JEIT181083
[Abstract](194) [FullText HTML] (146) [PDF 1683KB](9)
Magnetic induction detection technology is a non-contact and non-invasive electrical impedance detection technology. Multi-frequency synchronous detection can simultaneously obtain the impedance information of the tested object at different frequencies. Firstly, the principle of multi-frequency synchronous excitation and detection of magnetic induction signal are studied. Five-frequency excitation signal is synthesized based on Walsh function. Secondly, the performance of synthesized multi-frequency synchronous detection is analyzed, and a synthesized multi-frequency magnetic induction signal synchronous detection system is designed. Finally, the detection experiments of NaCl solution with different conductivities are carried out by synthesizing five-frequency excitation signal and synchronous detection system. The results show that the measurement results of five main harmonics of synthesized five-frequency excitation signal have good linearity. It provides an excitation-detection method for multi-frequency synchronous detection of magnetic induction signal.
Real-time Estimation of Tropospheric Scattering Slant Delay of Low-elevation Obtained by Improved Ray Tracing
Wenyi WU, Fangping ZHONG, Wanpeng WANG, Xihong CHEN, Dan ZHU
, Available online  , doi: 10.11999/JEIT190014 doi: 10.11999/JEIT190014
[Abstract](195) [FullText HTML] (129) [PDF 2058KB](14)
Considering the disadvantage of oblique delay estimation of tropospheric scattering at arbitrary stations, which is difficult to obtain real-time sounding meteorological data, an oblique delay estimation algorithm of tropospheric scattering based on improved ray tracing method with ground meteorological parameters is proposed. In order to get rid of the method’s dependence on radiosonde data, the algorithm infers the relationship between refractive index and altitude through the formula of meteorological parameters in the model of medium latitude atmosphere. The interpolation of meteorological parameters in the model of UNB3m is used to gain the coefficient of temperature and water vapor pressure. Meteorological data for 2012 from 6 International GNSS Service (IGS) stations in Asia are selected to test the applicability of new method, the results suggest that precision is less than 1 cm. Then, the tropospheric slant delays of three parts observation stations under different angles of incidence (0°~5°) are calculated by the modified algorithm. The results suggest that the maximum delay is 17.03~33.10 m in a single way time transfer. In two way time transfer, when the delay can counteract 95%, time delay is 2.88~5.52 ns.
An Improved Four-component Decomposition Method Based on the Characteristic of Polarization and the Optimal Parameters of PolInSAR
Yu WANG, Weidong YU, Xiuqing LIU
, Available online  , doi: 10.11999/JEIT190108 doi: 10.11999/JEIT190108
[Abstract](247) [FullText HTML] (154) [PDF 5051KB](6)
The backscattering of the radar targets is sensitive to the relative geometry between orientations of the targets and the radar line of sight. When the orientations of the same target are different from the radar line of sight, the scattering characteristics are quite different. Targets such as inclined ground and inclined buildings may reverse the polarization base of the backscattered echo, which causes the cross-polarization component to be too high and the volume scattering component of the image is overestimated. In this paper, a polarimetric interferometric decomposition method based on polarimetric parameters (\begin{document}$ H/{\alpha} $\end{document}) and Polarimetric Interferometric Similarity Parameters (PISP) is proposed to solve the overestimation problem. The method makes full use of the scattering diversity of the scatterer in the radar line of sight. The cross-polarization components generated by targets such as inclined grounds and inclined buildings with different orientations are better adapted to obtain better decomposition results. Finally, the effectiveness of the proposed method in polarimetric interferometric decomposition is verified by the airborne C-band PolInSAR data obtained by the Institute of Electronics, Chinese Academy of Sciences. The experimental results show that the proposed improved algorithm can distinguish the scattering characteristics of terrain types effectively and correctly.
A Cluster Algorithm Based on Interference Increment Reduction in Ultra-Dense Network
Yanxia LIANG, Jing JIANG, Changyin SUN, Xin LIU, Yongbin XIE
, Available online  , doi: 10.11999/JEIT181144 doi: 10.11999/JEIT181144
[Abstract](229) [FullText HTML] (143) [PDF 1199KB](12)
Ultra-Dense Networks (UDNs) shorten the distance between terminals and nodes, which improve greatly the spectral efficiency and expand the system capacity. But the performance of cell edge users is seriously degraded. Reasonable planning of Virtual Cell (VC) can only reduce the interference of moderate scale UDNs, while the interference of users under overlapped base stations in a virtual cell needs to be solved by cooperative user clusters. A user clustering algorithm with Interference Increment Reduction (IIR) is proposed, which minimizes the sum of intra-cluster interference and ultimately maximizes system sum rate by continuously switching users with maximum interference between clusters. Compared with K-means algorithm, this algorithm, no need of specifying cluster heads, avoids local optimum without increasement of the computation complexity. The simulation results show that the system sum rate, especially the throughput of edge users, can be effectively improved when the network is densely deployed.
Design and Implementation of High Speed PCIe Cipher Card Supporting GM Algorithms
Jun ZHAO, Xuewen ZENG, Zhichuan GUO
, Available online  , doi: 10.11999/JEIT190003 doi: 10.11999/JEIT190003
[Abstract](207) [FullText HTML] (229) [PDF 744KB](9)
Cipher cards play an important role in the field of information security. However, the performance of cipher cards are insufficient, and it is difficult to meet the needs of high-speed network security services. A design and system implementation method of high-speed PCIe cipher card based on MIPS64 multi-core processor is proposed, which supports the GM algorithm SM2/3/4 and international cryptographic algorithms, such as RSA, SHA and AES. The implemented system includes module of hardware, cryptographic algorithm, host driver and interface calling. An optimization scheme for the implementation of SM3 is proposed, the performance is improved by 19%. And the host to send requests in Non-Blocking mode is supported, so a single-process application can get the cipher card’s full load performance. Under 10-core CPU, the speed of SM2 signature and verification are 18000 and 4200 times/s, SM3 hash speed is 2200 Mbps, SM4 encryption/decryption speed is 8/10 Gbps, multiple indicators achieve higher level; When using 16-core CPU @1300 MHz, SM2/3 performance can be improved by more than 100%, and the speed of SM2 signature could achieve 105 times/s with 48-core CPU.
A Circuit Optimization Method of Improved Lookup Table for Highly Efficient Resource Utilization
Lijiang GAO, Haigang YANG, Wei LI, Yanan HAO, Changlong LIU, Caixia SHI
, Available online  , doi: 10.11999/JEIT190095 doi: 10.11999/JEIT190095
[Abstract](261) [FullText HTML] (136) [PDF 2331KB](8)
The circuit structure optimization method for Basic programmable Logic Element (BLE) of FPGA is studied. Considering finding the solution to the bottleneck problem of low resource utilization efficiency in logic and arithmetic operations with 4-input Look Up Table (LUT), some efforts to improve BLE design based on 4-input LUT are explored. A high area-efficient LUT structure is proposed, and the possible benefits of such a new structure are analyzed theoretically and simulated. Further, a statistical method for evaluation of the post synthesis and mapping netlist is also proposed. Finally, a number of experiments are carried out to assess the proposed structure based on the MCNC and VTR benchmarks. The results show that, compared with Intel Stratix series FPGAs, the optimized structure proposed in this paper improves respectively the area efficiency of the FPGA by 10.428% and 10.433% in average under the MCNC and VTR benchmark circuits.
Robust Sidelobe Suppression Method for Cognitive Radar Based on Sequential Optimization
Songpo JIN, Shanna ZHUANG
, Available online  , doi: 10.11999/JEIT181091 doi: 10.11999/JEIT181091
[Abstract](241) [FullText HTML] (148) [PDF 1289KB](16)
Range sidelobes may lead to weak targets masked by strong targets and false alarm. This paper proposes a sequential optimization method against to the sidelobe suppression of cognitive radar. First, the region to detect is divided according to range cell. Second, the transmit waveform and receive filter are optimized jointly based on the principle of minimum mean square error against one range cell. The optimized transmit and receive systems are used in Radar Cross Section (RCS) estimation for the scatter in the current range cell. The above process is carried out in each range cell in the scene sequentially. The acquired RCS estimate is used in the sidelobe suprresion for the following range cells. The RCS estimation for all the range cells in the scene is obtained in a bootstrapping way successively and updated circularly. The proposed method forms a closed loop detection system. The transmitting and receiving systems are adjusted according to the feedback scene information in real time. The sensing ability about the environment can be enhanced. The detection performance and robustness against noise can be improved. The efficiency and validity are verified by the simulation results.
Sparse Reconstruction OFDM Delay Estimation Algorithm Based on Bayesian Automatic Relevance Determination
Weijia CUI, Peng ZHANG, Bin BA
, Available online  , doi: 10.11999/JEIT181181 doi: 10.11999/JEIT181181
[Abstract](200) [FullText HTML] (134) [PDF 1978KB](13)
Considering the problem of Orthogonal Frequency Division Multiplexing (OFDM) signal delay estimation with only a Single Measurement Vector (SMV) in a complex environment, a sparse reconstruction time delay estimation algorithm based on Bayesian Automatic Relevance Determination (BARD) is proposed. The Bayesian framework is used to start from the perspective of further mining useful information, and asymmetric Automatic Relevance Determination(ARD) priori is introduced to integrate into the parameter estimation process, which improves the accuracy of time delay estimation under SMV and low Signal-to-Noise Ratio (SNR) conditions. Firstly, a sparse real-domain representation model is constructed based on the estimated frequency domain response of the OFDM signal physical layer protocol data unit. Then, probability hypothesis for the noise and sparse coefficient vectors are made in the model, and Automatic Relevance Determination (ARD) prior is introduced. Finally, according to the Bayesian framework, the Expectation Maximization (EM) algorithm is used to solve the hyperparameters to estimate the delay. The simulation experiments show that the proposed algorithm has better estimation performance and is closer to the Cramér–Rao Bound (CRB). At the same time, based on the Universal Software Radio Peripheral (USRP), the effectiveness of the proposed algorithm is verified by the actual signal.
Multi-feature Map Pyramid Fusion Deep Network for Semantic Segmentation on Remote Sensing Data
Fei ZHAO, Wenkai ZHANG, Zhiyuan YAN, Hongfeng YU, Wenhui DIAO
, Available online  , doi: 10.11999/JEIT190047 doi: 10.11999/JEIT190047
[Abstract](338) [FullText HTML] (435) [PDF 1192KB](35)
Utilizing multiple data (elevation information) to assist remote sensing image segmentation is an important research topic in recent years. However, the existing methods usually directly use multivariate data as the input of the model, which fails to make full use of the multi-level features. In addition, the target size varies in remote sensing images, for some small targets, such as vehicles, houses, etc., it is difficult to achieve detailed segmentation. Considering these problems, a Multi-Feature map Pyramid fusion deep Network (MFPNet) is proposed, which utilizes optical remote sensing images and elevation data as input to extract multi-level features from images. Then the pyramid pooling structure is introduced to extract the multi-scale features from different levels. Finally, a multi-level and multi-scale feature fusion strategy is designed, which utilizes comprehensively the feature information of multivariate data to achieve detailed segmentation of remote sensing images. Experiment results on the Vaihingen dataset demonstrate the effectiveness of the proposed method.
Image Semantic Segmentation Based on Region and Deep Residual Network
Huilan LUO, Fei LU, Fansheng KONG
, Available online  , doi: 10.11999/JEIT190056 doi: 10.11999/JEIT190056
[Abstract](357) [FullText HTML] (235) [PDF 3206KB](21)
An image semantic segmentation model based on region and deep residual network is proposed. Region based methods use multi-scale to create overlapping regions, which can identify multi-scale objects and obtain fine object segmentation boundary. Fully convolutional methods learn features automatically by using Convolutional Neural Network (CNN) to perform end-to-end training for pixel classification tasks, but typically produce coarse segmentation boundaries. The advantages of these two methods are combined: firstly, candidate regions are generated by region generation network, and then the image is fed through the deep residual network with dilated convolution to obtain the feature map. Then the candidate regions and the feature maps are combined to get the features of the regions, and the features are mapped to each pixel in the regions. Finally, the global average pooling layer is used to classify pixels. Multiple different models are obtained by training with different sizes of candidate region inputs. When testing, the final segmentation are obtained by fusing the classification results of these models. The experimental results on SIFT FLOW and PASCAL Context datasets show that the proposed method has higher average accuracy than some state-of-the-art algorithms.
A Security-oriented Dynamic and Heterogeneous Scheduling Method for Virtual Network Function
Xinsheng JI, Shuiling XU, Wenyan LIU, Qing TONG, Lingshu LI
, Available online  , doi: 10.11999/JEIT181130 doi: 10.11999/JEIT181130
[Abstract](198) [FullText HTML] (131) [PDF 1961KB](12)
Network Function Virtualization (NFV) brings flexibility and dynamics to the construction of service chain. However, the software and virtualization may cause security risks such as vulnerabilities and backdoors, which may have impact on Service Chain (SC) security. Thus, a Virtual Network Function (VNF) scheduling method is proposed. Firstly, heterogeneous images are built for every virtual network function in service chain, avoiding widespread attacks using common vulnerabilities. Then, one network function is selected dynamically and periodically. The executor of this network function is replaced by loading heterogeneous images. Finally, considering the impact of scheduling on the performance of network functions, Stackelberg game is used to model the attack and defense process, and the scheduling probability of each network function in the service chain is solved with the goal of optimizing the defender’s benefit. Experiments show that this method can reduce the rate of attacker’s success while controlling the overhead generated by the scheduling within an acceptable range.
Research into Low Thermal Gradient Oriented 3D FPGA Interconnect Channel Architecture Design
Lijiang GAO, Haigang YANG, Chao ZHANG
, Available online  , doi: 10.11999/JEIT181134 doi: 10.11999/JEIT181134
[Abstract](186) [FullText HTML] (147) [PDF 3929KB](11)
To solve the problem of heat dissipation in Three Dimensional Field Programmable Gate Array Technology (3D FPGA), an interconnect channel architectural design method with low thermal gradient feature is proposed. A thermal resistance network model is established for the 3D FPGA, and theoretical studies and thermal simulation experiments are carried out on the influence of different types of channels on the thermal performance of 3D FPGA. Further, non-uniform vertical direction channel structures of 3D FPGA are proposed. Experiments indicate that 3D FPGA designed using the method proposed can reduce the maximum temperature gradient between different layers by 76.8% and the temperature gradient within the same layer by 10.4% compared with the traditional channel structure of 3D FPGA.