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Articles in press have been peer-reviewed and accepted, which are not yet assigned to volumes /issues, but are citable by Digital Object Identifier (DOI).
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Subspace Projection Based Track-Before-Detect Scheme for Small Moving Target in Complex Underwater Environment
Huajie Chen, Haoran Bai
 doi: 10.11999/JEIT200446
[Abstract](0) [FullText HTML](0) [PDF 2703KB](0)
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The small moving target detection in complex underwater environment is complicated due to the weak target signal strength and low signal-to-clutter ratio. A Track-Before-Detect (TBD) algorithm based on subspace projection is proposed to solve these problems. A sequence motion track fragment is extracted from the original data, and then projected from the 3D space-time onto the 2D subspace. The morphological features in 2D subspace are applied in preliminary screening to remove most of the clutters and locate the local motion areas of the target. The 3D space-time track is reconstructed from 2D subspace in these local motion areas. During the above 3D track backtracking process, the motion continuity characteristics are also extracted to further remove the clutters and select the effective target track fragments. Through the above-mentioned hierarchical processing, a fast and high-precision target track fragment detection algorithm is achieved. By combing this track fragment detection algorithm with the foreground detection and Hierarchical Agglomerative Clustering (HAC) based long-time track detection algorithms, a complete TBD scheme for small moving targets detection is constructed. The accuracy and speed of this detection scheme are verified on the real sonar image data.
Joint Design of the Transmit and Receive Beamforming via Alternating Direction Method of Multipliers for LPI of Frequency Diverse Array MIMO Radar in the Presence of Clutter
Pengcheng GONG, Zhaobin WANG, Haiming TAN, Wenqin WANG
 doi: 10.11999/JEIT200445
[Abstract](50) [FullText HTML](6) [PDF 1603KB](22)
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In view of the limitation of the phased-array on suppressing the range-dependent interference, a joint design of the transmit and receive beamforming via the Alternating Direction Method of Multipliers (ADMM) method for Low Probability of Intercept (LPI) of Multiple-Input Multiple-Output with Frequency Diverse Array (FDA-MIMO) radar in the presence of clutter is proposed. The problem of joint design is to maximize the performance of target parameter estimation, and minimize the transmit energy at the target region which enhances LPI capability. Following a weighted sum of the performance metric, the original problem is firstly recasts to a multiple-ratio Fractional Programming (FP) problem. Subsequently, an iterative algorithm is developed. Concretely, at each iteration, the transmit beamforming matrix is optimized by employing ADMM method and the quadratic approximation algorithm. Moreover, the computational complexity of the proposed algorithm is discussed. Numerical simulations are provided to demonstrate the effectiveness of the proposed algorithm.
Two Low-complexity Symbol Flipping Decoding Algorithms for Non-Binary LDPC Codes
Haiqiang CHEN, Yaoling WANG, Wenjuan WEI, Bingxu JIANG, Youming SUN, Xiangcheng LI, Tuanfa QIN
 doi: 10.11999/JEIT191008
[Abstract](12) [FullText HTML](5) [PDF 1622KB](0)
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Two low-complexity symbol flipping decoding algorithms, the Improved Weighted-Algorithm B algorithm (Iwtd-AlgB) and the Truncation-based Distance-Symbol-Flipping-Decoding with Prediction (TD-SFDP) algorithm, are presented for non-binary LDPC codes. For the Iwtd-AlgB algorithm, the scaling factor of the flipping metric can be replaced by the simple sums of the extrinsic information and the distance-based parameter, which can avoid the multiplication operations in the iterations and thus can reduce the decoding complexity. For the presented TD-SFDP algorithm, the variable nodes and the finite field symbols are truncated and classified based on the extrinsic information frequency and the flipping function. Only those nodes/symbols that satisfy the designed conditions can be involved in the message updating process. Simulations and numeric results show that, the presented two decoding algorithms can reduce the computational complexity at each iteration with a controllable performance degradation, thus can make efficient trade-offs between performance and complexity.
ReliefF-Pearson Based Olfactory ElectroEncephaloGram Channel Selection
Xiaonei ZHANG, Wenpeng ZHAI, Huirang HOU, Qinghao MENG
 doi: 10.11999/JEIT200413
[Abstract](11) [FullText HTML](3) [PDF 798KB](1)
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The study of odor recognition based on ElectroEncephaloGram (EEG) signals has important application value in objectively evaluating olfactory function and diagnosing olfactory disorders. Because of the inconvenience caused by using too many EEG channels in practical application scenarios, it is particularly important to study how to choose EEG channels. In this paper, a new ReliefF-Pearson channel selection algorithm was proposed to solve the channel selection problem in the classification of olfactory EEG signals. The algorithm combined the weight idea of ReliefF and the correlation principle of Pearson coefficient to select EEG channels. Experimental results showed that compared with the traditional ReliefF-based channel selection algorithm, the proposed algorithm could significantly reduce the number of channels used while ensuring a certain classification accuracy, and the result of channel selection did not depend on human experience and classifiers. In addition, the spatial distribution of the selected channels was consistent with the existing olfactory neurophysiological position, which further confirmed the scientificity and effectiveness of this method. The proposed method provides new idea for the research of olfactory EEG channel selection.
Multichannel MI-EEG Feature Decoding Based on Deep Learning
Jun YANG, Zhengmin MA, Tao SHEN, Zhuangfei CHEN, Yaolian SONG
 doi: 10.11999/JEIT190300
[Abstract](10) [FullText HTML](2) [PDF 4461KB](0)
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Regarding as the measure of the electrical fields produced by the active brain, ElectroEncephaloGraphy (EEG) is a brain mapping and neuroimaging technique widely used inside and outside of the clinical domain, which is also widely used in Brain–Computer Interfaces (BCI). However, low spatial resolution is regarded as the deficiency of EEG signified from researches, which can fortunately be made up by synthetic analysis of data from different channels. In order to efficiently obtain subspace features with discriminant characteristics from EEG channel information, a Multi-Channel Convolutional Neural Networks (MC-CNN) model is proposed for MI-EEG decoding, firstly input data is pre-processed form selected multi-channel signals, then the time-spatial features are extracted using a novel 2D Convolutional Neural Networks (CNN). Finally, these features are transformed to discriminant sub-space of information with Auto-Encoder (AE) to guide the identification network. The experimental results show that the proposed multi-channel spatial feature extraction method has certain advantages in recognition performance and efficiency.
Low-complexity Joint Channel Estimation and Decoding for LDPC Codes Via Sliding-Window Belief-Propagation over Non-stationary Channels
Yang YANG, Yong FANG, Bowei SHAN
 doi: 10.11999/JEIT200406
[Abstract](66) [FullText HTML](14) [PDF 1631KB](13)
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With the continuous increase of possible usage scenarios of mobile networks, non-stationary channels become more and more common transmission environments, and reliable transmission over non-stationary channels relies on accurate channel estimation. Based on the Sliding-Window Belief-Propagation (SWBP) algorithm used to cope with source parameter estimation and source correlation estimation, a Joint Channel Estimation and Decoding (JCED) algorithm for LDPC codes over non-stationary channels is proposed. Two fast algorithms to set adaptively the window size in each JCED iteration are also proposed based on cross entropy and Discrete Fourier Transform (DFT), respectively. Simulation results reveal that, without the aid of pilots, the performance of the proposed algorithm approaches that of Belief-Propagation (BP) decoding under ideal channel estimation, and has the advantages of high efficiency, low complexity, strong robustness and not incurring error-floor.
Construction of Low-rank Circulant Matrices and Their Associated Nonbinary LDPC Codes
Hengzhou XU, Hai ZHU, Dan FENG, Bo ZHANG, Manjie ZHOU
 doi: 10.11999/JEIT200351
[Abstract](23) [FullText HTML](2) [PDF 1041KB](8)
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In image processing, the redundant information of low-rank matrices can be used for image recovery and image feature extraction, and redundant rows of the parity-check matrices can accelerate the convergence rate in iterative decoding. A class of low-rank circulant matrices with easy hardware implementation is studied. Circulant matrices are first converted into position sets, the search space of position sets is pruned based on isomorphism theory, and then construction of circulant matrices is proposed based on the bit shift method. Considering the relationship between the column assignment of non-zero field elements and the matrix rank, circulant matrices whose Tanner graphs have no cycles of length 4 are chosen, and according to the column assignment of non-zero field elements, construction of nonbinary LDPC codes over various finite fields and with different code rates is presented. Numerical simulation results show that, compared with binary LDPC codes constructed based on the PEG algorithm, the proposed nonbinary LDPC codes have 0.9 dB gain at Word Error Rate (WER) of 10-5 when the modulation is BPSK, and the performance gap becomes large by combining with high order modulations. Furthermore, the performance gap of the proposed codes between 5 iterations and 50 iterations is negligible, and it provides a promising coding scheme for low-latency and high-reliability communications.
A Novel Non-contact AC Voltage Detector Based on Concentric Double-layer Spherical Shell Structure
Pengfei YANG, Xiaolong WEN, Xiaoming NI, Chunrong PENG
 doi: 10.11999/JEIT200286
[Abstract](16) [FullText HTML](3) [PDF 1350KB](6)
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A novel non-contact AC voltage detector based on the electric field probe of concentric double-layer spherical shell structure is presented. The concentric double-layer spherical shell structure is similar to the differential structure, which can eliminate the influence of common mode interference noise. The theoretical model of the electric field distribution of the double-layer spherical shell structure was established, and the induced charge density of the outer spherical shell surface was analyzed. Then the sensitivity expression of the electric field probe was obtained. Furthermore, the equivalent circuit model of the electric field probe was proposed and the interface circuit was designed. Finally, a prototype of the not-contact AC voltage detector was successfully developed. The test results show that there is a good linear relationship between the output of the prototype and the applied electric field, with a linearity of 0.66%, and the test results are in good agreement with the calculated results. Additionally, when the prototype rotates within the range of 0~45°, the output voltage is only reduced by a maximum of 4.0%, which indicates that the small angle rotation of the AC voltage detector does not affect the accuracy of electricity testing. Besides, the closer to the transmission line, the faster the output voltage of the prototype increases, and the threshold is easy to identify, suggesting that it is easier to verify the electricity.
Aircraft Target Detection in Remote Sensing Image Based on Multi-scale Circle Frequency Filter and Convolutional Neural Network
Junzhi YANG, Jinliang WU, Jun ZHI
 doi: 10.11999/JEIT200144
[Abstract](43) [FullText HTML](16) [PDF 4342KB](15)
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In view of the problems of missed alarm and false alarm caused by the different scales of aircrafts in aircraft target detection tasks for remote sensing images, a multi-scale aircraft target automatic detection algorithm is proposed based on the shape characteristics and gray-scale changes of aircraft targets. Firstly, the multi-scale circle frequency filter is used to filter out the complex background of remote sensing images to extract the candidate region of aircraft targets on different scales. Then, the Convolutional Neural Network (CNN) model is constructed to realize the effective classification of candidate regions, and finally the aircraft target position is accurately determined. The target detection algorithm is experimentally verified based on the obtained real remote sensing images. It shows that the aircraft target detection rate and the false alarm rate are 94.38% and 3.76% respectively. The experimental results fully verify the effectiveness of the proposed algorithm which can provide important technical support for airport supervision, military reconnaissance and other applications.
Image Blind Deblurring Algorithm Based on Deep Multi-level Wavelet Transform
Shuzhen CHEN, Shipeng CAO, Meiyue CUI, Qiusheng LIAN
 doi: 10.11999/JEIT190947
[Abstract](7) [FullText HTML](1) [PDF 3008KB](1)
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In recent years, convolutional neural networks are widely used in single image deblurring problems. The receptive field size and network depth of convolutional neural networks can affect the performance of image deblurring algorithms. In order to improve the performance of image deblurring algorithm by increasing the receptive field, an image blind deblurring algorithm based on deep multi-level wavelet transform is proposed. Embedding the wavelet transform into the encoder-decoder architecture enhances the sparsity of the image features while increasing the receptive field. In order to reconstruct high-quality images in the wavelet domain, the paper leverges to multi-scale dilated dense block to extract multi-scale information of images, and introduces feature fusion blocks to adaptively fuse features between encoder and decoder. In addition, due to the difference in representation of image information between the wavelet domain and the spatial domain, in order to fuse these different feature representations, we present spatial domain reconstruction module to further improve the quality of the reconstructed image in the spatial domain. The experimental results show that the proposed method has better performance on SSIM and PSNR, and has better visual effects on real blurred images.
A Multipath TDOA Estimation Algorithm based on Correntropy under Impulsive Noise Environment
Sen LI, Jifu WANG, Bin LIN
 doi: 10.11999/JEIT200358
[Abstract](10) [FullText HTML](1) [PDF 0KB](1)
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In order to realize the high-resolution multipath TDOA estimation which is not limited by the resolution limit of correlation method under impulsive noise environment, a Correntropy Expectation-Maximum (CEM) high resolution multipath TDOA estimation algorithm is proposed based on the maximum correntropy criterion. The multipath TDOA is estimated by transforming multi-dimensional optimization problems into multiple one-dimensional optimization problems. The simulation results show that the CEM algorithm has good estimation performance under strong impulsive noise and low SNR environment, and the selection of kernel size in CEM algorithm is not depend on the prior information of the impulsive noise.
Decision Threshold-aided Fast Z-Forward in Wireless Multirelay Communications
Jianrong BAO, Yunxuan LIN, Chao LIU, Bing JIANG, Fang Zhu, Jianhai He
 doi: 10.11999/JEIT200183
[Abstract](7) [FullText HTML](4) [PDF 0KB](0)
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In consideration of improper power allocation and insufficient relay selection in the current Z-Forward (ZF) scheme, an efficient Decision Threshold-aided Fast Z-Forward (DT-FZF) scheme is proposed to improve power and transmission efficiency. When the absolute value of the Log-Likelihood Ratio (LLR) of a source-relay reception is less than the decision threshold, the relay remains quiet. Otherwise, it directly sends the truncated LLR to the destination. In addition, the proposed DT-FZF scheme incorporates the Amplify-Forward(AF), Decode-Forward(DF), Piecewise-Forward(PF) and ZF schemes, all of which can be the special case of the proposed scheme. At a BER of 10–3, the DT-FZF scheme outperforms the ZF scheme by approximately 0.6 dB in a three-relay system.
A Three-Dimensional Imaging Algorithm of Downward-looking Sparse Linear Array SAR Based on Low-rank Tensor
Siqian ZHANG, Meiting YU, Gangyao KUANG
 doi: 10.11999/JEIT200274
[Abstract](4) [FullText HTML](4) [PDF 0KB](0)
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In order to solving the problems of the inner structure damage and the high computation load brought by the vectorizing or matrixing of 3-D sparse data, the 3-D signal model is established in tensor space for downward-looking sparse linear array three-dimensional SAR. Based on this signal model, a three-dimensional SAR sparse imaging algorithm is proposed in this paper. The missing data firstly can be recovered by tensor completion on the assumption that the echo tensor is essentially low rank. Then, the resulting 3-D images can be well focused by any Fourier transform-based 3-D imaging algorithms with the recovered full-sampled data tensor. The proposed algorithm achieves not only high resolution and low-level side-lobes but also the ideal computational cost and memory consumption, which verified by several numerical simulations and multiple comparative studies on real data.
The Winner-Take-All Neural Network Based on DNA Strand Displacement
Bin WANG, Ya LI, Hongwei ZHAO
 doi: 10.11999/JEIT200579
[Abstract](20) [FullText HTML](7) [PDF 2850KB](6)
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DNA strand displacement technology is widely used in biological computing, and it has excellent performance in computing power and information processing. However, the use of DNA strand displacement technology in some calculations, such as signal amplification, restoration, and comparison, not only increases the number of DNA strands, but also brings additional calculation costs. Therefore, in order to reduce the number of DNA strands used, a Winner-Take-All (WTA) neural network based on DNA strand displacement is constructed. Firstly, the logic operations AND, NAND, and OR are realized through neurons, and the linear inseparable problem is solved by cascading them into a WTA neural network. By comparing with the results with others, the effectiveness of the method is proved, and stable and intuitive results are obtained in Visual DSD (DNA strand displacement). Then, in order to test the scalability of the neuron cascade, a three-person voter was designed and the scientists were classified. The paper shows how the molecular system demonstrates the ability to think in a similar way to the brain, and finally proves the accuracy is higher than other methods.
3D Radar Imaging Based on Target Scenario Structer Sparse Reconstruction
Yan ZHANG, Baoping WANG, Yang FANG, Jiahui WANG, Zuxun SONG
 doi: 10.11999/JEIT200071
[Abstract](24) [FullText HTML](14) [PDF 2273KB](5)
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The three-Dimensional (3D) radar imaging mathods based on sparse representation by the scattering intensity of imaging sceen has a poor representation of geometric details on the shape of the target, which isn’t conducive to target recognition. Firstly, the structural characteristics of scattering intensity in the imaging Scenario are analyzed in this paper. Then, by the structured sparse representation with the gradient information of scattering points, a structured sparse reconstruction model is constructed. Finally, the 3D imaging result is reconstructed by a improved joint orthogonal matching pursuit algorithm. The experimental results show that the proposed method has good anti-noise and imaging quality, and can reflect the geometric details of the target.
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2020, (11): 1-4.  
[Abstract](17) [PDF 257KB](7)
Abstract:
Interference Recognition Based on Singular Value Decomposition and Neural Network
Man FENG, Zinan WANG
2020, 42(11): 2573-2578.   doi: 10.11999/JEIT190228
[Abstract](681) [FullText HTML](762) [PDF 1465KB](45)
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The anti-interference technology in wireless communication is great significance to the stability and security of communication. As an important part of anti-interference technology, interference recognition is a research hotspot. An interference recognition method based on singular value decomposition and neural network is proposed. This method only calculates the singular value of the signal matrix as the feature. Compared with the traditional method, it saves the computational complexity of multiple spectral features. The simulation results show that the recognition accuracy based on singular value decomposition and neural network is 10%~25% higher than the traditional method under the condition of jamming-signal ratio at 0 dB.
Two-dimensional DOA Estimation Method for L-shaped Array of Coherent Signals Based on Main Singular Vector
Xiaojie TANG, Minghao HE, Mingyue FENG, Changxiao CHEN, Jun HAN
2020, 42(11): 2579-2586.   doi: 10.11999/JEIT190455
[Abstract](84) [FullText HTML](43) [PDF 1905KB](9)
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In order to handle the problem that the existing DOA estimation algorithm for L-shaped array of coherent signals is not accurate and the aperture loss is large, a method named L-shaped array Principal-singular-vector Utilization for Modal Analysis (L-PUMA) and its modified algorithm named L-shaped array Modified PUMA (L-MPUMA) are proposed. L-PUMA algorithm first denoises the cross-covariance matrix, then obtains the two-dimensional main singular vector by singular value decomposition, and then obtains the polynomial coefficient of the linear prediction equation by weighted least squares method. The root of the linear prediction equation is the DOA estimation of the signals. Finally, a new pairing algorithm is proposed to realize the pairing of elevation and azimuth. L-MPUMA algorithm uses the inverse conjugate transform to obtain the augmented main singular vector, which further improves the data utilization rate and overcomes the problem that the performance of L-PUMA deteriorates seriously when the signals are completely coherent. Simulation experiments verify the efficiency of the proposed algorithm.
Wideband DOA Estimation via Cyclic Correntropy and Sparse Reconstruction in the Presence of Impulsive Noise
Jiacheng ZHANG, Tianshuang QIU, Shengyang LUAN, Jingchun LI, Rong LI
2020, 42(11): 2587-2591.   doi: 10.11999/JEIT190521
[Abstract](90) [FullText HTML](41) [PDF 1019KB](19)
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To deal with wideband band Direction Of Arrival (DOA) estimation in the presence of impulsive noise and co-channel interferences, a novel method is proposed with the help of Cyclic CorrEntropy (CCE) and sparse reconstruction. Firstly, the received signal model of wideband sources is analyzed and a virtual array output is constructed, which shows resistance to impulsive noise and co-channel interferences via the characteristics of CCE. Then, to extract the DOA of wideband signals, the virtual array output with a sparse structure is represented and the Normalized Iterative Hard Thresholding (NIHT) is utilized to solve the sparse reconstruction problem. Comprehensive simulation results demonstrate that the proposed method has efficient suppression on impulsive noise and co-channel interference and it can improve both accuracy and efficiency than existing methods.
A Discrete Side-lobe Clutter Recognition Method Using Space-time Steering Vectors for Space Based Radar System
Weiwei WANG, Chongdi DUAN, Xin ZHANG, Yu LI, Xiaochao YANG
2020, 42(11): 2592-2599.   doi: 10.11999/JEIT190562
[Abstract](28) [FullText HTML](22) [PDF 3470KB](16)
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On account of the large coverage of space based radar, a lot of discrete strong side-lobe clutter, which shares familiar Doppler feature with the real moving targets, can be received by the radar system and hence results in false alarms. For this problem, a discrete side-lobe clutter recognition method using space-time steering vectors for space based radar system is proposed. In this method, the “Suspected targets”, including both the real moving targets and discrete side-lobe clutter, are detected after suppressing clutter by employing the Space-Time Adaptive Processing (STAP). The range-Doppler cells where “suspected targets” located in or around are selected. Afterwards, the space time steering vectors of them are obtained based on the coupling relationship between Doppler frequencies and space angles of clutter. Lastly, the above range-Doppler cells are processed again by the adaptive processing filters which are derived from the new space-time steering vectors. Obviously, the signal-clutter-noise ratio of real moving target will be reduced significantly, while it will not change much for the discrete side-lobe clutter. Therefore, the discrete side-lobe clutter can be identified by using the proposed method. Theoretical analyses and multi-channel airborne radar experiments demonstrate the effectiveness and stability of this method.
A Novel DOA Estimation Method for Coherently Distributed Sources Based on Correntropy in the Impulsive Noise
Ruiyan CAI, Li YANG, Yang QIAN
2020, 42(11): 2600-2606.   doi: 10.11999/JEIT200325
[Abstract](49) [FullText HTML](21) [PDF 869KB](18)
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To solve the problem of passive wireless monitoring and positioning in complex electromagnetic environments, a generalized auto-correntropy for suppressing the impulsive noise in the array output signals is proposed and its properties are derived. To obtain the estimates of both central Direction Of Arrival (DOA) and angular spread for coherently distributed sources in the impulsive noise, a novel DOA estimation method based on the generalized auto-correntropy is proposed, and its boundedness is proved. To improve the robustness of the proposed algorithm, a new adaptive kernel function, which only depends on the array output signals, is also derived. The simulation results show that the proposed algorithm can obtain the joint estimation for coherently distributed sources under impulsive noise environments, and has higher estimation accuracy and robustness than existing algorithms.
The Effects of Complex Weather and Sea Wind on the Performance of Space-borne Passive Interferometric Microwave Detection System
Guangnan SONG, Hailiang LU, Hao LI, Yinan LI, Liang LANG, Siqiao DONG, Pengfei LI, Rongchuan LÜ
2020, 42(11): 2607-2614.   doi: 10.11999/JEIT190534
[Abstract](203) [FullText HTML](127) [PDF 2325KB](11)
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Based on the target detection equation of the passive interferometric microwave system, the effects of complex weather on the detection ability of the passive interferometric microwave system are discussed for the sea surface target, such as clouds, fog and rain, and sea winds. Quantitative simulations are also performed to assess the effects of these previous mentioned factors. The experiments are also performed to demonstrate the passive interferometric microwave system penetrating the clouds. Both theoretical and simulation results show that complex weather has a negative impact on the passive interferometric microwave systems in the sea target detection, such as clouds, fog and rain. However, the impacts can be neglected in low frequency, since the impacts of clouds in low frequency is very small. On the other hand, rainfall will seriously degrade the system’s target detection capability. Sea winds have a positive impact in the metallic target detection. However, sea winds have a negative impact and reduce the system’s detection capability for the stealthy target detection.
Research on Mesh Generation Optimization of Finite Element Model of Human Body Based on Energy Error
Hong’an WEI, Xiaoqing WU, Ang ZHANG
2020, 42(11): 2615-2620.   doi: 10.11999/JEIT190765
[Abstract](234) [FullText HTML](125) [PDF 6524KB](10)
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Meshing is the most important part of the finite element modeling and analysis process, and it also has the largest workload, which directly affects the accuracy and time of the finite element analysis. Based on the research of adaptive meshing and finite element discrete errors, three-dimensional human body models of different complexity are established in the environment of high-voltage power transmission fields. By comparing the results of the electric field simulation between the adaptive meshing of the human body model and the manual meshing, the trend of energy error changes is analyzed, so as to guide the establishment of the human body model and the setting of the optimal mesh size.The research results have certain reference significance to the optimization research of other finite element splitting schemes.
A Synthesis Method of Hybrid Reflector Antenna for Satellite Communications
Jianjun LI, Pengfei YIN, Xianbin ZHAO
2020, 42(11): 2621-2628.   doi: 10.11999/JEIT190564
[Abstract](53) [FullText HTML](18) [PDF 2544KB](23)
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A hybrid reflector antenna is presented to generate a contoured beam over service area, an un-scanned and a scanned pencil beam from two shaped reflectors and three feeds, simultaneously. The shaped main reflector is shared by three beams, and the antenna is equivalent to two single-reflector antennas with single-feed for each beam and a pair of dual offset Gregorian shaped reflector antennas, and generating the contoured, un-scanned and scanned pencil beam, respectively. The proposed method is successfully applied to a 1.2 m hybrid reflector antenna. Simulations and experimentations of each beam has been performed. The Edge of Coverage(EoC) directivity over service area is 27.5 dBi for contoured beam in Tx and Rx working frequency of Ku-band, and the un-scanned pencil beam has a aperture efficiency higher than 70% in Tx and Rx working frequency of C-band. Meanwhile, the scanned pencil beam inside and outside the service area is realized by the lateral defocus of the sub-reflector and the corresponding feed in Tx and Rx working frequency of Ka-band. Simulation results show that the hybrid reflector antenna can realize C/Ku/Ka-band communication tasks simultaneously.
Study on the Characteristics of Composite Electromagnetic Scattering From Soil Surface and Combinatorial Target Placed on It
Xincheng REN, Peng LIU, Xiaomin ZHU, Pengju YANG, Ye ZHAO
2020, 42(11): 2629-2635.   doi: 10.11999/JEIT190645
[Abstract](746) [FullText HTML](430) [PDF 2378KB](24)
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In order to meet the needs of measuring and detecting combinatorial target placed on the rough surface, Dobson semi-empirical model and dielectric complex permittivity formula are used to represent the real and imaginary parts of the soil dielectric constant, the soil surface is simulated with the model of exponential distribution and Monte Carlo method. The strategy of the Finite Difference Time Domain (FDTD) method for calculating the composite scattering from rough surface with target and the modeling method are presented with their validity evaluated by the method of moment, then the composite scattering of soil surface and combinatorial target placed on it is studied by this method, the angular distribution curve of the composite scattering coefficient is obtained. The results show that the composite scattering coefficient oscillates with the scattering angle, and the scattering enhancement effect occurs in the mirror reflection direction; the larger the root mean square of the fluctuation of soil surface, the larger the composite scattering coefficient; the larger the correlation length, the smaller the composite scattering coefficient; the larger the soil moisture content, the smaller the composite scattering coefficient; the influence of the scale and dielectric constant of combinatorial target, incident angle on composite scattering coefficient is complex. The results obtained in this paper can be used to solve the composite electromagnetic scattering from rough land surface and rough sea surface with any target placed on it. Compared with other numerical methods, the finite difference time domain method can not only obtain higher accuracy, but also reduce the calculation time and the amount of memory occupying.
Research on Optimum Multi-hop Relay of Wireless Ultraviolet Communication in Military Vehicle Secret Formation
Taifei ZHAO, Yongming LI, Shan XU, Shiqi WANG
2020, 42(11): 2636-2642.   doi: 10.11999/JEIT190172
[Abstract](624) [FullText HTML](367) [PDF 1981KB](27)
Abstract:
Wireless ultraviolet communication becomes an effective means of communication under strong electromagnetic interference, which meets the need of reliable and secret communication between vehicles when the fleet performs strategic material transportation and the missile vehicle fleet of concealed driving vehicles in a complex battlefield environment. Each vehicle acts as a relay for other vehicles while driving, and establishes a stable and reliable communication link between non-line-of-sight vehicles through a multi-hop model. Therefore, based on the single-scattering model of ultraviolet, the optimal multi-hop relay problem is studied, and the relationship between the elevation angle of the transmitting and receiving and the spectral efficiency is theoretically analyzed. According to the principle of maximizing the spectral efficiency, the approximate expression of the optimum number of hops is obtained. The simulation results show that the optimum number of hops correspond to different distance shift range and elevation angles. Compared with the optimum energy calculation method, the proposed method has better transmission capability in low power transmission and achieves the requirement of power saving. In the long-distance ultraviolet communication, system performance does not increase with the number of cooperative relays. The system can obtain a higher transmission capacity by selecting a suitable number of relays and a small transmission elevation angle and a large receiving elevation angle.
Automatic Decoding Algorithm of Morse Code Based on Deep Neural Network
Ling YOU, Weihao LI, Wenlin ZHANG, Keren WANG
2020, 42(11): 2643-2648.   doi: 10.11999/JEIT190658
[Abstract](1120) [FullText HTML](301) [PDF 883KB](53)
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In the military and civilian fields, the Morse telegraph is always as an important means of short-wave communication, but the current automatic decoding algorithms still have problems such as low accuracy, inability to adapt to low signal-to-noise ratio and unstable signals. A deep learning method is introduced to construct a Morse code automatic recognition system. The neural network model consists of convolutional neural network, bidirectional long short-term memory network and connectionist temporal classification layer. The structure is simple and can implement end-to-end training. Related experiments show that the decoding system can achieve good recognition results under different signal-to-noise ratio, code rate, frequency drift and code length deviation caused by different sending manipulation, and the performance is better than the traditional recognition algorithms.
Probability Approximation Message Passing Detection Algorithm Based on Early Termination of Iteration
Min SHEN, Xiyuan REN, Yun HE
2020, 42(11): 2649-2655.   doi: 10.11999/JEIT190471
[Abstract](84) [FullText HTML](80) [PDF 2916KB](9)
Abstract:
As a key technology of the fifth generation communication system, large-scale Multi-Input and Multi-Output(MIMO) technology can effectively improve spectrum utilization. The base station side uses the Message Passing Detection (MPD) algorithm to achieve good detection performance. However, the computational complexity of the MPD algorithm increases with the increase of the modulation order and the number of user antennas, and the Probability Approximation Message Passing Detection (PA-MPD) algorithm can reduce the computational complexity of the MPD algorithm. In order to further reduce the complexity of PA-MPD algorithm, this paper introduces an early termination iteration strategy based on PA-MPD algorithm, and proposes an Improved PA-MPD (IPA-MPD) algorithm. Firstly, the convergence rate of the symbol probability of different users in the iterative process is determined, and then the convergence probability is used to determine whether the user’s symbol probability reaches the best convergence. Finally, the user termination algorithm that the symbol probability reaches the best convergence is iterated. The simulation results show that the computational complexity of the IPA-MPD algorithm can be reduced to 52%~77% of the PA-MPD algorithm under different single-antenna user configurations without loss of the detection performance of the algorithm.
Energy Efficient Power Allocation with NOMA in Downlink Heterogeneous Networks
Shuang ZHANG, Guixia KANG
2020, 42(11): 2656-2663.   doi: 10.11999/JEIT190492
[Abstract](93) [FullText HTML](31) [PDF 1478KB](22)
Abstract:
This paper proposes a power allocation scheme for energy efficiency maximization in a downlink Non-Orthogonal Multiple Access (NOMA)-based Heterogeneous Network (HetNets) with considering the out-of-cell interference and in-cell interference. The scheme contains mainly two parts. One is the power allocation between the users at the same sub-channel, where Difference of Convex (DC) functions -programming is exploited to solve the problem. Another is the power allocation between the sub-channels, in which ConCave–Convex Procedure (CCCP) method and Lagrangian multiplier method are combined to solve the problem. The simulation results show that the fast convergence property, and demonstrate that the EE obtained by the proposed algorithms based on NOMA is at least 44% higher than that obtained by the conventional orthogonal multiple access scheme.
Mobility Prediction Based Computation Offloading Handoff Strategy for Vehicular Edge Computing
Bo LI, Li NIU, Xin HUANG, Hongwei DING
2020, 42(11): 2664-2670.   doi: 10.11999/JEIT190483
[Abstract](109) [FullText HTML](43) [PDF 2375KB](25)
Abstract:
In the vehicular cloud computing environments, computation offloading faces the problems such as high network delay and large load of the remote cloud. The vehicular edge computing takes advantage of the edge servers to be close to the vehicular terminals, and provides the cloud computing service to solve the problem mentioned above. However, due to the dynamic change of communication environment caused by vehicle movement, the task completion time will increase. For this reason, this paper proposes a Mobility Prediction-based computation Offloading Handoff Strategy (MPOHS), which tries to minimize the average completion time of offloaded tasks by migrating tasks among edge servers according to the prediction of vehicle movement. The experimental results show that, compared with the existing research, the proposed strategy can reduce the average task completion time, cut down the handoff times and handoff time overhead, and effectively reduce the impact of vehicle movement on the performance of computation offloading.
Deployment Algorithm of Service Function Chain Based on Transfer Actor-Critic Learning
Lun TANG, Xiaoyu HE, Xiao WANG, Qianbin CHEN
2020, 42(11): 2671-2679.   doi: 10.11999/JEIT190542
[Abstract](670) [FullText HTML](257) [PDF 3260KB](19)
Abstract:
To solve the problem of high system delay caused by unreasonable resource allocation because of randomness and unpredictability of service requests in 5G network slicing, this paper proposes a deployment scheme of Service Function Chain (SFC) based on Transfer Actor-Critic (A-C) Algorithm (TACA). Firstly, an end-to-end delay minimization model is built based on Virtual Network Function (VNF) placement, and joint allocation of computing resources, link resources and fronthaul bandwidth resources, then the model is transformed into a discrete-time Markov Decision Process (MDP). Next, A-C learning algorithm is adopted in the MDP to adjust dynamically SFC deployment scheme by interacting with environment, so as to optimize the end-to-end delay. Furthermore, in order to realize and accelerate the convergence of the A-C algorithm in similar target tasks (such as the arrival rate of service requests is generally higher), the transfer A-C algorithm is adopted to utilize the SFC deployment knowledge learned from source tasks to find quickly the deployment strategy in target tasks. Simulation results show that the proposed algorithm can reduce and stabilize the queuing length of SFC packets, optimize the system end-to-end delay, and improve resource utilization.
A Low Latency Random Access Mechanism for 5G New Radio in Unlicensed Spectrum
Zhenghang ZHU, Jianxin JIA, Zhenhong LI, Hua QIAN, Kai KANG
2020, 42(11): 2680-2688.   doi: 10.11999/JEIT190515
[Abstract](710) [FullText HTML](432) [PDF 2213KB](41)
Abstract:
For the 5G New Radio in Unlicensed (NR-U) spectrum scenario, a novel random access mechanism is proposed, which first adds the channel idle timer in Random Access Response Window (RARW) and contention resolution window to reduce the accessing delay caused by the contention-based accessing and employs the Request To Send/Clear To Send (RTS /CTS) mechanism to address the hidden node issue. The mechanism can alleviate the latency incurred by the legacy mechanism which did not consider the intrinsic attribute of unlicensed band and the hidden node problem. Specifically, the legacy random access mechanism applied to NR-U is analyzed. Then, the detailed elaboration of the network entity interaction sequence defined in novel mechanism is proposed. Finally, the performance evaluation processes are carried out in the way of mathematical modeling and experimental simulation, and the analysis result demonstrates that the novel scheme outperforms the benchmark one in the respect of the average random access delay.
Signal Quality Assessment of BDS-3 Preliminary System
Chengeng SU, Shuren GUO, Xunan LIU, Yongnan RAO, Meng WANG
2020, 42(11): 2689-2697.   doi: 10.11999/JEIT190683
[Abstract](1180) [FullText HTML](921) [PDF 4451KB](32)
Abstract:
The Signal-In-Space (SIS) quality affects directly the user performance of Global Navigation Satellite System (GNSS). Unlike BDS-2, the BDS-3 satellites not only broadcast old signals, but also broadcast new signals such as B1C and B2a at the same time. The signal structure of BDS-3 with multi-frequency, multi-signal and multi-component is more complex than BDS-2, which is a great challenge to signal quality control of BDS-3 satellites. By the end of 2018, 18 BDS-3 satellites were successfully launched and the BDS-3 preliminary system is completed to provide global services. It is necessary to evaluate the signal quality of BDS-3. Traditional signal quality assessment methods focus on the qualitative assessment of a single item, but lacks systematic and quantitative analysis results for the complex signal structure of BDS-3. Based on the Interface Control Document (ICD) of BDS, this paper studies the influence of different parameter configurations on the evaluation results from the aspects of power characteristics, frequency characteristics, time characteristics, correlation characteristics and signal consistency, and forms a set of quantitative evaluation methods for new modulations and multi-frequency, multi-component signals. Based on the signal quality assessment system with 40-meter aperture antenna, 18 MEO satellites of BDS-3 preliminary system were monitored, and the signal quality of BDS-3 satellites were comprehensively and quantitatively evaluated for the first time. The results show that, signal qualities of BDS-3 satellites are good, and the 18 MEOs have a good consistency, which can meet the requirements of ICD and GNSS users. The evaluation methods can be also used to quantitatively evaluate the signal quality of other satellites.
Adaptive Secure Non-zero Inner Product Encryption Scheme with Small-scale Public Parameters
Haiying GAO, Duo WEI
2020, 42(11): 2698-2705.   doi: 10.11999/JEIT190510
[Abstract](71) [FullText HTML](30) [PDF 680KB](11)
Abstract:
Inner product encryption is a kind of function encryption which supports inner product form. The public parameter scale of the existing inner product encryption schemes are large. In order to solve this problem, based on prime-order bilinear entropy expansion lemma and Double Pairing Vector Space (DPVS), an inner product encryption scheme is proposed in this paper, which has fewer public parameters and adaptive security. In the private key generation algorithm of the scheme, the components of the user’s attribute with the main private key are combined to generate a vector that can be combined with the key components in the entropy expansion lemma, and in encryption algorithm of the scheme, each component of the inner product vector is combined with ciphertext component in the entropy expansion lemma. Finally, under the condition of prime order bilinear entropy extension lemma and \begin{document}$\textstyle{{\rm{MDDH}}_{k, k + 1}^n}$\end{document} difficult assumption, the adaptive secure of the scheme is proved. The proposed scheme has only 10 group elements as public parameters, which is the smallest compared with the existing inner product encryption schemes.
Research on Intrusion Detection Technology Based on Densely Connected Convolutional Neural Networks
Xianghua MIAO, Xiaoche SHAN
2020, 42(11): 2706-2712.   doi: 10.11999/JEIT190655
[Abstract](1425) [FullText HTML](414) [PDF 1260KB](53)
Abstract:
Convolutional Neural Network (CNN) is widely used in the field of intrusion detection technology. It is generally believed that the deeper the network structure, the more accurate in feature extraction and detection accuracy. However, it is accompanied with the problems of gradient dispersion, insufficient generalization ability and low accuracy of parameters. In view of the above problems, the Densely Connected Convolutional Network (DCCNet) is applied into the intrusion detection technology, and achieve the purpose of improving the detection accuracy by using the hybrid loss function. Experiments are performed with the KDD 99 data set, and the experimental results are compared with the commonly used LeNet neural network and VggNet neural network structure. Finally, the analysis shows that the accuracy of detection is improved, and the problem of gradient vanishing during training is alleviated.
Certificateless Puklic Key Encryption With Equality Test of Supporting Keyword Search
Yulei ZHANG, Wenjuan CHEN, Yongjie ZHANG, Xuewei ZHANG, Caifen WANG
2020, 42(11): 2713-2719.   doi: 10.11999/JEIT190752
[Abstract](399) [FullText HTML](185) [PDF 1008KB](26)
Abstract:
Public Key Encryption with Equality Test (PKEET) is an important method to achieve the equality test of ciphertexts which are generated by the different public key aiming to the same plaintext in cloud environment. In other words, it can tests the plaintext corresponding to the two ciphertext’s equivalence without decrypting the ciphertext, but does not supply the searchable function. Nowadays, the existing PKEET scheme takes directly the message to generate a trapdoor as the proof of equality test, which has low test accuracy and search efficiency. To solve the above problems, a certificateless public key encryption with equality test scheme supporting keyword search (CertificateLess Equality test EncrypTion with keyword Search, CLEETS) is proposed. The scheme determines whether it contains information needed by the user through the keyword search, then performs the equality test according to the search result, which can avoid invalid test. Then, it is proved that the scheme satisfies the indistinguishability of adaptive selection of keywords under the random oracle model. Finally, the comparison analyses of function and efficiency are performed. The results indicate the computation cost of CLEETS scheme is less efficient. Fortunately, it can realizes the function of keyword search in encryption with equality test, which can remedies the inefficiency.
Signal Separation for Automatic Dependent Surveillance-Broadcast Using Improved Single Antenna Project Algorithm
Wenyi WANG, Yushi SHAO
2020, 42(11): 2720-2726.   doi: 10.11999/JEIT190673
[Abstract](105) [FullText HTML](48) [PDF 1182KB](17)
Abstract:
The Automatic Dependent Surveillance-Broadcast (ADS-B), as a new surveillance technology, is being vigorously promoted by International Civil Aviation Organization (ICAO). However, overlapping among multiple signals is inevitable because of the randomness of ADS-B signal transmission. An improved Project Algorithm Single Antenna (PASA) algorithm which separates the overlapping signals with one single channel is proposed. Firstly, a new matrix reconstruction method for the data that received by single channel is proposed to decrease the requirement of relative time delay and frequency difference between two ADS-B signals, and then the overlapping signals can be separated by utilizing the projection algorithm. The effectiveness of the algorithm is verified by simulation experiments.
ADS-B Anomalous Data Detection Model Based on PSO-MKSVDD
Buhong WANG, Peng LUO, Tengyao LI, Jiwei TIAN, Fute SHANG
2020, 42(11): 2727-2734.   doi: 10.11999/JEIT190767
[Abstract](1543) [FullText HTML](321) [PDF 2368KB](33)
Abstract:
As a new generation of Air Traffic Management(ATM) communication protocol, Automatic Dependent Surveillance-Broadcast(ADS-B) is the key technology of ATM monitoring system in the future. At present, the security of ADS-B is challenged because it broadcasts data in plaintext format. Because ADS-B is susceptible to spoofing, the difference between ADS-B position data and synchronous Secondary Surveillance Radar(SSR) data is taken as sample data. Using Multi-Kernel Support Vector Data Description(MKSVDD) to train samples, a hypersphere classifier is obtained, which can detect anomalous data in ADS-B test samples. In addition, Particle Swarm Optimization (PSO) is used to optimize GaussLapl and GaussTanh MKSVDD penalty factors, coefficients of multi-kernel functions and kernel parameters.The performance of anomaly detection is improved. Experimental results show that PSO-MKSVDD can detect anomalous data of random position deviation, fixed position deviation, Denial Of Service(DOS) attack and replay attack. In addition, compared with other machine learning and deep learning methods, this model has better adaptability and better recall rate and detection rate of anomaly detection.It is proved that this model can be used to detect ADS-B anomalous data.
Sparse Multinomial Logistic Regression Algorithm Based on Centered Alignment Multiple Kernels Learning
Dajiang LEI, Jianyang TANG, Zhixing LI, Yu WU
2020, 42(11): 2735-2741.   doi: 10.11999/JEIT190426
[Abstract](145) [FullText HTML](49) [PDF 511KB](22)
Abstract:
As a generalized linear model, Sparse Multinomial Logistic Regression (SMLR) is widely used in various multi-class task scenarios. SMLR introduces Laplace priori into Multinomial Logistic Regression (MLR) to make its solution sparse, which allows the classifier to embed feature selection in the process of classification. In order to solve the problem of non-linear data classification, Kernel Sparse Multinomial Logistic Regression (KSMLR) is obtained by kernel trick. KSMLR can map nonlinear feature data into high-dimensional and even infinite-dimensional feature spaces through kernel functions, so that its features can be fully expressed and eventually classified effectively. In addition, the multi-kernel learning algorithm based on centered alignment is used to map the data in different dimensions through different kernel functions. Then center-aligned similarity can be used to select flexibly multi-kernel learning weight coefficients, so that the classifier has better generalization ability. The experimental results show that the sparse multinomial logistic regression algorithm based on center-aligned multi-kernel learning is superior to the conventional classification algorithm in classification accuracy.
Fast Training Adaboost Algorithm Based on Adaptive Weight Trimming
Lubin YU, Qiliang DU, Lianfang TIAN
2020, 42(11): 2742-2748.   doi: 10.11999/JEIT190473
[Abstract](86) [FullText HTML](28) [PDF 2064KB](19)
Abstract:
The Adaboost algorithm provides noteworthy benefits over the traditional machine algorithms for numerous applications, including face recognition, text recognition, and pedestrian detection. However, it takes a lot of time during the training process that affects the overall performance. Adaboost fast training algorithm based on adaptive weight (Adaptable Weight Trimming Adaboost, AWTAdaboost) is proposed in this work to address the aforementioned issue. First, the algorithm counts the current sample weight distribution of each iteration. Then, it combines the maximum value of current sample weights with data size to calculate the adaptable coefficients. The sample whose weight is less than the adaptable coefficients is discarded, that speeds up the training. The experimental results validate that it can significantly speed up the training speed while ensuring the detection effect. Compared with other fast training algorithms, the detection effect is better when the training time is close to each other.
Maneuvering Target Tracking Algorithm Based on the Adaptive Augmented State Interracting Multiple Model
Hong XU, Wenchong XIE, Huadong YUAN, Keqing DUAN, Yongliang WANG
2020, 42(11): 2749-2755.   doi: 10.11999/JEIT190516
[Abstract](108) [FullText HTML](48) [PDF 1627KB](26)
Abstract:
The existing Augmented State-Interracting Multiple Model (AS-IMM) algorithm suffers from the problem that it relies on the prior information of the covariance matrix of the measurement noise. When the prior information is unavailable or inaccurate, the tracking performance of AS-IMM will be degraded. In order to overcome this problem, a novel adaptive Bayesian Variational Augmented State-Interracting Multiple Model (VB-AS-IMM) algorithm is proposed. Firstly, the variational Bayesian inference probabilistic model of the augmented state and the covariance matrix of the measurement noise for the jump Markovarian system is presented. Secondly, the probabilistic model is proven to be non-conjugated. Finally, by introducing a novel post processing method, the suboptimal solution to calculate the joint posterior distribution is proposed. The proposed algorithm can estimate the unknown covariance matrix of the measurement noise online, thus it is more robust and has higher adaptability. Simulation result verifies good performance of the proposed algorithm.
Selective Ensemble Method of Extreme Learning Machine Based on Double-fault Measure
Pingfan XIA, Zhiwei NI, Xuhui ZHU, Liping NI
2020, 42(11): 2756-2764.   doi: 10.11999/JEIT190617
[Abstract](239) [FullText HTML](107) [PDF 1053KB](13)
Abstract:
Extreme Learning Machine (ELM) has unique advantages such as fast learning speed, simplicity of implementation, and excellent generalization performance. However, the performance of a single ELM is unstable in classification. Ensemble learning can effectively improve the classification ability of single ELMs, but it may incur the rapid increase in memory space and computational overheads as the increase of the data size and the number of ELMs. To address this issue, a Selective Ensemble approach of ELM based on Double-Fault measure (DFSEE) is proposed, and it is evaluated by theoretical and experimental analysis simultaneously. Firstly, multiple training subsets extracted from a training dataset are obtained employing the bootstrap sampling method, and an initial pool of base ELMs is constructed by independently training multiple ELMs on different training subsets; Secondly, the ELMs in pool are sorted in ascending order according to their double-fault measures of those ELMs. Finally, it starts with one ELM and grows the ensemble by adding new base ELMs according to the order, the final ensemble of ELMs can be achieved with the best classification ability, and the theoretical basis of DFSEE is analyzed. Experimental results on 10 benchmark classification tasks show that DFSEE can achieve better results with less number of ELMs by comparing with other approaches, and its validity and significance.
A Nonparametric Bayesian Dictionary Learning Algorithm with Clustering Structure Similarity
Daoguang DONG, Guosheng RUI, Wenbiao TIAN, Yang ZHANG, Ge LIU
2020, 42(11): 2765-2772.   doi: 10.11999/JEIT190496
[Abstract](117) [FullText HTML](56) [PDF 4811KB](36)
Abstract:
Making use of image structure information is a difficult problem in dictionary learning, the traditional nonparametric Bayesian algorithms lack the ability to make full use of image structure information, and faces problem of inefficiency. To this end, a dictionary learning algorithm called Structure Similarity Clustering-Beta Process Factor Analysis (SSC-BPFA) is proposed in this paper, which completes efficient learning of the probabilistic model via variational Bayesian inference and ensures the convergence and self-adaptability of the algorithm. Image denoising and inpainting experiments show that this algorithm has significant advantages in representation accuracy, structure similarity index and running efficiency compared with the existing nonparametric Bayesian dictionary learning algorithms.
Image Compressed Sensing Reconstruction Based on Structural Group Total Variation
Hui ZHAO, Xiaojun YANG, Jing ZHANG, Chao SUN, Tianqi ZHANG
2020, 42(11): 2773-2780.   doi: 10.11999/JEIT190243
[Abstract](2129) [FullText HTML](551) [PDF 2363KB](45)
Abstract:
To solve the problem that the traditional Compressed Sensing (CS) algorithm based on Total Variation (TV) model can not effectively restore details and texture of image, which leads to over-smoothing of reconstructed image, an image Compressed Sensing (CS) reconstruction algorithm based on Structural Group TV (SGTV) model is proposed. The proposed algorithm utilizes the non-local self-similarity and structural sparsity of image, and converts the CS recovery problem into the total variation minimization problem of the structural group constructed by non-local self-similar image blocks. In addition, the optimization model of the proposed algorithm is built with regularization constraint of the structural group total variation model, and it uses the split Bregman iterative algorithm to separate it into multiple sub-problems, and then solves them respectively. The proposed algorithm makes full use of the information and structural sparsity of image to protects the image details and texture. The experimental results demonstrate that the proposed algorithm achieves significant performance improvements over the state-of-the-art total variation based algorithm in both PSNR and visual perception.
High Efficiency Video Coding Intra Prediction Optimization Algorithm Based on Region of Interest
Renjie SONG, Yuandong ZHANG
2020, 42(11): 2781-2787.   doi: 10.11999/JEIT190330
[Abstract](318) [FullText HTML](168) [PDF 1847KB](14)
Abstract:
For the high complexity of High Efficiency Video Coding (HEVC) intra prediction coding algorithm, an HEVC intra prediction optimization algorithm based on Region Of Interest (ROI) is proposed. Firstly, the algorithm divides the Region Of Interest and Non-Region Of Interest (NROI) of the current frame according to image saliency; Then, the final grading depth of the current coding unit is determined by the proposed fast Coding Unit (CU) partitioning algorithm based on spatial correlation in the ROI, and the unnecessary CU partitioning process is skipped. Finally, the proposed Prediction Unit (PU) mode fast selection algorithm is used to calculate the energy and direction of the current PU based on the ROI, and the current PU prediction mode is determined according to the energy and direction, and the correlation calculation of the rate distortion cost is reduced, Achieving the purposes of reducing coding complexity and saving coding time. The experimental results show that the proposed algorithm can reduce the coding time by 47.37% on average when the Peak Signal-to-Noise Ratio (PSNR) loss is only 0.0390 dB.
Moving Object Detection Method Based on Low-Rank and Sparse Decomposition in Dynamic Background
Hongyan WANG, Haikun ZHANG
2020, 42(11): 2788-2795.   doi: 10.11999/JEIT190452
[Abstract](78) [FullText HTML](47) [PDF 3323KB](30)
Abstract:
Focusing on the issue that the detection accuracy of moving object is significantly reduced by background motion, a low-rank and sparse decomposition based moving object detection method is developed. Firstly, in order to solve the problem that the nuclear norm over-penalizing large singular values lead to the optimal solution of the obtained minimization problem can not be obtained and then the detection performance is decreased, the gamma norm (\begin{document}$\gamma {\rm{ - norm}}$\end{document}) is introduced to acquire almost unbiased approximation of rank function. In what follows, the \begin{document}${L_{{1 / 2}}}$\end{document} norm is used to extract the sparse foreground object to enhance the robustness to noise, and the spatial continuity constraint is proposed to suppress dynamic background pixels such that the moving object detection model can be constructed on the basis of the sparse and spatially discontinuous nature of the false alarm pixels. After that, the Augmented Lagrange Multiplier (ALM) method, which is the extension of the Alternating Direction Minimizing (ADM) strategy, can be employed to deal with the acquired constrained minimization problem. Compared with some state-of-the-art algorithms, the experimental results show that the proposed method can significantly improve the accuracy of moving object detection in the case of dynamic background.
Video Dehazing Algorithm via Haze-line Prior with Spatiotemporal Correlation Constraint
Tingting YAO, Yue LIANG, Xiaoming LIU, Qing HU
2020, 42(11): 2796-2804.   doi: 10.11999/JEIT190403
[Abstract](231) [FullText HTML](128) [PDF 6820KB](35)
Abstract:
Because of the existent video dehazing algorithm lacks the analysis of the video structure correlation constraint and inter-frame consistency, it is easy to cause the dehazing results of continuous frames to have sudden changes in color and brightness. Meanwhile, the edge of foreground target is also prone to degradation. Focus on the aforementioned problems, a novel video dehazing algorithm via haze-line prior with spatiotemporal correlation constraint is proposed, which improves the accuracy and robustness of video dehazing result by bringing the structural relevance and temporal consistency of each frame. Firstly, the dark channel and haze-line prior are utilized to estimate the atmospheric light vector and initial transmission image of each frame. Then a weighted least square edge preserving smoothing filter is introduced to smooth the initial transmission image and eliminate the influence of singularities and noises on the estimated results. Furthermore, the camera parameters are calculated to describe the time series variation of the transmission image between continuous frames, and the independently obtained transmission image of each frame is corrected with temporal correlation constraint. Finally, according to the physical model, the video dehazing results are obtained. The experimental results of qualitative and quantitative comparison show that the proposed algorithm could make the inter-frame transition more smooth, and restore the color of each frame more accurately. Besides, more details are displayed at the edge of the dehazing results.
Research on Fuzzy Image Instance Segmentation Based on Improved Mask R-CNN
Weidong CHEN, Weiran GUO, Hongwei LIU, Qiguang ZHU
2020, 42(11): 2805-2812.   doi: 10.11999/JEIT190604
[Abstract](111) [FullText HTML](37) [PDF 1744KB](54)
Abstract:
Mask R-CNN is a relatively mature method for image instance segmentation at this stage. For the problems of segmentation boundary accuracy and poor robustness of fuzzy pictures in Mask R-CNN algorithm, an improved Mask R-CNN method for image instance segmentation is proposed. This method first proposes that on the Mask branch, Convolution Condition Random Field(ConvCRF) is used to optimize the Mask branch, and the candidate area is further segmented, and uses FCN-ConvCRF branch to replace the original branch. Then, a new anchor size and IOU standard are proposed to enable the RPN candidate box cover all the instance areas. Finally, a training method is used to add a part of data transformed by the transformation network. Compared with the original algorithm, the total mAP value is improved by 3%, and the accuracy and robustness of segmentation boundary are improved to some extent.
A Geodesic Locality Canonical Correlation Analysis Method for Image Recognition
Huan XU, Shuzhi SU, Wenjing YAN, Yinghao DENG, Jun XIE
2020, 42(11): 2813-2818.   doi: 10.11999/JEIT200123
[Abstract](178) [FullText HTML](91) [PDF 363KB](22)
Abstract:
Canonical Correlation Analysis (CCA) is a classic multi-modal feature learning method, which can learn low-dimensional features with the maximum correlation from different modalities. However, it is difficult for CCA to find the nonlinear manifold structures hidden in the sample spaces. This paper proposes a multi-modal feature learning method based on geodesic manifolds, namely Geodesic Locality Canonical Correlation Analysis (GeoLCCA).The geodesic distances are used to construct the geodesic scatters of low-dimensional correlation features, and the nonlinear correlation features with better discriminative power are learned by maximizing the between-modal correlation and minimizing the within-modal geodesic scatters. This paper not only analyzes the proposed method in theory, but also verifies the effective of the proposed method on the real-world image datasets.
High Resolution Digital Pulse Width Modulation Design for Digital DC-DC Converter
Zhang ZHANG, Minghui CUI, Bin LI, Xin CHENG, Guangjun XIE
2020, 42(11): 2819-2826.   doi: 10.11999/JEIT190482
[Abstract](125) [FullText HTML](40) [PDF 2770KB](16)
Abstract:
The advantages of digital control in the field of power electronics lead to an increasing use of Digital Pulse Width Modulation (DPWM). However, the insufficient resolution of DPWM is one of the main factors that constrain the development of digital control technology in the field of switch mode power supplies. For the application requirements of high-resolution DPWM, this paper proposes a high-resolution DPWM circuit based on high-speed carry chain structure. The circuit comprises of counters, comparators, fixed phase shift PLL units and high-speed carry chains, which can effectively increase resolution. The circuit is also implemented on Altera’s Cyclone IV low-cost Field-Programmable Gate Array (FPGA) devices. The experimental results show that the resolution of the structure can reach 56 ps with 70 MHz input reference clock. In addition, the circuit also has wide switching frequency adjustment range and good linearity.
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