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, Available online  , doi: 10.11999/JEIT190127 doi: 10.11999/JEIT190127
[Abstract](3) [FullText HTML] (4) [PDF 2196KB](2)
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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.
, Available online  , doi: 10.11999/JEIT190058 doi: 10.11999/JEIT190058
[Abstract](2) [FullText HTML] (3)
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Considering the large computational complexity and the long-time calculation of Convolutional Neural Networks (CNN), an 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.
, Available online  , doi: 10.11999/JEIT181021 doi: 10.11999/JEIT181021
[Abstract](173) [FullText HTML] (88) [PDF 1693KB](28)
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In complex indoor environment, the measured Received Signal Strength (RSS) values will fluctuate in different degrees, which lead to inaccurate characterization of wireless signal propagation model. To solve this problem, a universal coarse grained localization method is proposed based on the Wi-Fi ranging location model. This method gets the signal propagation model by fitting the measured RSS value. On this basis, the distance between the unknown node and the Access Point (AP) is calculated, then the location of the unknown node is realized by the beetle antennae search algorithm. The performance of the propagation model and the effectiveness of the optimization algorithm are verified by simulation.
, Available online  , doi: 10.11999/JEIT190163 doi: 10.11999/JEIT190163
[Abstract](42) [FullText HTML] (35)
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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.
, Available online  , doi: 10.11999/JEIT190094 doi: 10.11999/JEIT190094
[Abstract](37) [FullText HTML] (28) [PDF 2035KB](7)
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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 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.
, Available online  , doi: 10.11999/JEIT180953 doi: 10.11999/JEIT180953
[Abstract](49) [FullText HTML] (36) [PDF 2356KB](7)
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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.
, Available online  , doi: 10.11999/JEIT181117 doi: 10.11999/JEIT181117
[Abstract](101) [FullText HTML] (79) [PDF 1821KB](11)
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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.
, Available online  , doi: 10.11999/JEIT180957 doi: 10.11999/JEIT180957
[Abstract](60) [FullText HTML] (65) [PDF 1200KB](11)
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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.
, Available online  , doi: 10.11999/JEIT181195 doi: 10.11999/JEIT181195
[Abstract](50) [FullText HTML] (48) [PDF 573KB](6)
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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.
, Available online  , doi: 10.11999/JEIT180921 doi: 10.11999/JEIT180921
[Abstract](72) [FullText HTML] (52) [PDF 2705KB](9)
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Correlation Filters (CF) are efficient in visual tracking, but its performance is badly affected by boundary effects. Focusing on problem, the adaptive regularized correlation filters for visual tracking based on sample quality estimation is proposed. Firstly, the proposed algorithm adds spatial regularization matrix to the training process of the filters, and constructs color and gray histogram templates to compute the sample quality factor. Then, the regularization term adaptively changes with the sample quality coefficient, so that the samples of different quality are subject to different degrees of punishment. Then, by thresholding the sample quality coefficient, the tracking results and model update strategy are optimized. The experimental results on OTB2013 and OTB2015 indicate that, compared with the state-of-the-art tracking algorithm, the average success ratio of the proposed algorithm is the highest. The success ratio is raised by 9.3% and 9.9% contrasted with Spatially RegularizeD Correlation Filters(SRDCF) algorithm respectively on OTB2013 and OTB2015.
, Available online  , doi: 10.11999/JEIT181152 doi: 10.11999/JEIT181152
[Abstract](47) [FullText HTML] (36) [PDF 2111KB](9)
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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.
, Available online  , doi: 10.11999/JEIT181101 doi: 10.11999/JEIT181101
[Abstract](59) [FullText HTML] (53) [PDF 783KB](10)
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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.
, Available online  , doi: 10.11999/JEIT190063 doi: 10.11999/JEIT190063
[Abstract](59) [FullText HTML] (51) [PDF 1844KB](12)
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Mobile network authentication protocol attacks continue to emerge. For the new 5G network protocol EAP-AKA', an EAP-AKA' security analysis method based on Lowe’s taxonomy is proposed. Firstly, 5G network, EAP-AKA', communication channel and adversary are formally modeled. Then Lowe authentication property is formally modeled. Using the TAMARIN prover, objectives of the security anchor key KSEAF are analyzed, such as Lowe’s taxonomy, perfect forward secrecy, confidentiality, etc. Four attack paths under 3GPP implicit authentication mode are discovered. Two improved schemes are proposed for the discovered security problems and their security is verified. Finally, the security of the two authentication protocols EAP-AKA’ and 5G AKA of the 5G network is compared, and it’s found that the former is safer in terms of Lowe authentication property.
, Available online  , doi: 10.11999/JEIT181139 doi: 10.11999/JEIT181139
[Abstract](36) [FullText HTML] (29) [PDF 1123KB](5)
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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 algorithm, and the algorithm has a good error code adaptability.
, Available online  , doi: 10.11999/JEIT180332 doi: 10.11999/JEIT180332
[Abstract](49) [FullText HTML] (46) [PDF 1105KB](12)
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Since the performance of adaptive beamforming algorithm for coherent signals degrades when the estimation error in the Direction Of Arrival (DOA) of the desired signal is large, a new multistage blocking based beamforming algorithm for coherent interference suppression is proposed. Firstly, the blocking matrix is constructed and the principle of multistage blocking is derived, with which the received signal is processed to remove the desired signal component. Then the mapping between the array manifold of sub-aperture array and the full-aperture array is analyzed when only the desired signal exists in space. On this basis, the extension transformation is derived with its effectiveness proved in the presence of interference signals. At last, the optimal weight vector of the adaptive beamformer for coherent interference is obtained by extension transformation. Requiring no prior information of the DOA of the interference signals, the new method is robust to the DOA estimation error, and can avoid the loss of array aperture. The effectiveness and superiority of the new algorithm are verified by simulation analysis.
, Available online  , doi: 10.11999/JEIT180900 doi: 10.11999/JEIT180900
[Abstract](128) [FullText HTML] (76) [PDF 1576KB](15)
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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.
, Available online  , doi: 10.11999/JELT180894 doi: 10.11999/JELT180894
[Abstract](161) [FullText HTML] (84) [PDF 1904KB](23)
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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.
, Available online  , doi: 10.11999/JEIT181113 doi: 10.11999/JEIT181113
[Abstract](31) [FullText HTML] (25) [PDF 447KB](3)
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With the development of the internet of things, small-scale communication devices such as wireless sensors and the Radio Frequency Identification 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.
, Available online  , doi: 10.11999/JEIT181018 doi: 10.11999/JEIT181018
[Abstract](32) [FullText HTML] (29) [PDF 2469KB](5)
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In recent years, the Siamese networks drawn great attention in visual tracking community due to its balanced accuracy and speed. However, most Siamese networks model were 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.
, Available online  , doi: 10.11999/JEIT190038 doi: 10.11999/JEIT190038
[Abstract](32) [FullText HTML] (25) [PDF 801KB](7)
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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 weighted statistics (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.
, Available online  , doi: 10.11999/JEIT190034 doi: 10.11999/JEIT190034
[Abstract](27) [FullText HTML] (19)
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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.
, Available online  , doi: 10.11999/JEIT190093 doi: 10.11999/JEIT190093
[Abstract](34) [FullText HTML] (27)
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Salient object detection which aims at automatically detecting what attracts human’s attention most in a scene, bootstrap learning based on 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.
, Available online  , doi: 10.11999/JEIT180842 doi: 10.11999/JEIT180842
[Abstract](119) [FullText HTML] (79) [PDF 1639KB](18)
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A novel scheme termed Hybrid Power Allocation Strategy (H-PAS), which integrated with Statistical Channel State Information (S-CSI) and Instantaneous Channel State Information (I-CSI), is proposed for Non-Orthogonal Multiple Access (NOMA) based cooperative relaying systems to achieve a better performance-complexity tradeoff. Simulation results demonstrate that, with the proposed H-PAS, on the one hand, NOMA shows a distinct advantage on the sum-rate when compared with conventional orthogonal multiple access techniques in which only the knowledge of S-CSI is available; on the other hand, NOMA reduces the signaling overhead and computational complexity at the expense of marginal sum rate degradation when compared with the cases in which only the knowledge of I-CSI is available for it.
, Available online  , doi: 10.11999/JEIT190098 doi: 10.11999/JEIT190098
[Abstract](61) [FullText HTML] (50)
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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. DCNN method for semantic segmentation of meteorological radar noise image based on Deep Convolutional Neural Network is proposed. Firstly, a Deep Convolutional Neural Network Model (DCNNM) was designed. The training set data of the MJDATA data set is 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 is 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.
, Available online  , doi: 10.11999/JEIT181136 doi: 10.11999/JEIT181136
[Abstract](40) [FullText HTML] (29)
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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.
, Available online  , doi: 10.11999/JEIT190037 doi: 10.11999/JEIT190037
[Abstract](37) [FullText HTML] (34)
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In order to reduce the computational complexity of Convolutional Neural Network, 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 start 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.
, Available online  , doi: 10.11999/JEIT181041 doi: 10.11999/JEIT181041
[Abstract](119) [FullText HTML] (74) [PDF 1437KB](8)
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In order to improve the utilization of non-contiguous virtual array elements in the underdetermined DOA estimation of the coprime array, a DOA estimation method based on Toeplitz covariance matrix reconstruction is proposed. First, the virtual array element distribution characteristics of the matrix are analyzed from the perspective of the difference coarray. Additionally, according to the correspondence between the difference coarray and the wave path difference, the covariance matrix is extended to a Toeplitz array covariance matrix, of which some elements are zero. Then, the Toeplitz matrix is recovered to the full covariance matrix according to the low rank matrix completion theory. Finally, the root-MUSIC method is employed for the DOA estimation. Theoretical analysis and simulation results show that this method can increase the number of the resolvable signals by increasing the number of virtual array elements, eliminate the effect of the off-grid effect without discretization of the angle domain, and avoid regularization parameter selection. Therefore, the estimation accuracy and resolution are improved.
, Available online  , doi: 10.11999/JEIT180906 doi: 10.11999/JEIT180906
[Abstract](173) [FullText HTML] (98) [PDF 1776KB](25)
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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.
, Available online  , doi: 10.11999/JEIT180925 doi: 10.11999/JEIT180925
[Abstract](90) [FullText HTML] (69) [PDF 885KB](14)
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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.
, Available online  , doi: 10.11999/JEIT180903 doi: 10.11999/JEIT180903
[Abstract](97) [FullText HTML] (59) [PDF 1552KB](7)
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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.
, Available online  , doi: 10.11999/JEIT180922 doi: 10.11999/JEIT180922
[Abstract](145) [FullText HTML] (85) [PDF 4616KB](8)
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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.
, Available online  , doi: 10.11999/JEIT180692 doi: 10.11999/JEIT180692
[Abstract](83) [FullText HTML] (66) [PDF 2175KB](10)
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To solve the problems of Apriori algorithm and FP-Growth algorithm in the process of mining the maximal frequent itemsets, which refer to inefficient operation, high memory consumption, difficulty in adapting to the process of dense datasets, and affecting the time-effectiveness of large data value mining, this paper proposes an maximal frequent itemsets mining algorithm based on adjacency table. The algorithm only needs to traverse the database once and adopts the hash table to store the adjacency table, which reduces the memory consumption. Theoretical analysis and experimental results show that the algorithm has lower time and space complexity and improves the mining rate of maximal frequent itemsets, especially when dealing with dense datasets.
, Available online  , doi: 10.11999/JEIT180978 doi: 10.11999/JEIT180978
[Abstract](71) [FullText HTML] (60) [PDF 2318KB](5)
Abstract:
In the field of computer vision, predicting human motion is very necessary for timely human–computer interaction and personnel tracking. In order to improve the performance of human–computer interaction and personnel tracking, an encoder-decoder model called 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.
, Available online  , doi: 10.11999/JEIT180931 doi: 10.11999/JEIT180931
[Abstract](155) [FullText HTML] (88) [PDF 1584KB](24)
Abstract:
In order to improve the quality of reconstruction image by Block Compressed Sensing (BCS), a Total Variation Iterative Threshold regularization image reconstruction algorithm (BCS-TVIT) is proposed. Combining the properties of local smoothing and bounded variation of the image, BCS-TVIT use the minimization l0 norm and total variation to construct the objective function. To solve the problem that l0 norm term and the block measurement constraint cannot be optimized directly, the iterative threshold method is used to minimize the l0 norm of the reconstructed image, and the convex set projection was employed to guarantee the block measurement constraint condition. Experiments show that BCS-TVIT has better performance than BCS-SPL in PSNR by 2 dB. Meanwhile, BCS-TVIT can eliminate the " bright spot” effect of BCS-SPL, having better visual effect. Comparing with the minimum total variation, the proposed algorithm increases PSNR by 1 dB, and the reconstruction time is reduced by two orders of magnitude.
, Available online  , doi: 10.11999/JEIT181131 doi: 10.11999/JEIT181131
[Abstract](46) [FullText HTML] (35) [PDF 3089KB](2)
Abstract:
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.
, Available online  , doi: 10.11999/JEIT181038 doi: 10.11999/JEIT181038
[Abstract](26) [FullText HTML] (17) [PDF 2006KB](3)
Abstract:
Considering the shortcomings of Differential Chaos Shift Keying (DCSK) transmission rate and further improving the system error performance, a Short reference Orthogonal Multiuser DCSK(SOM-DCSK) communication system is proposed. The system shortens the reference signal to 1/P of each information bearing signal, and transmits multiple users by different delay times. Then the orthogonality of Hilbert transform is used in each information slot to achieve the purpose of transmitting a two-bit information signal. The Bite Error Rate (BER) formula of SOM-DCSK system in Additive White Gaussian Noise (AWGN) and Rayleigh fading channel is derived and experimentally simulated. The simulation results show that the scheme has obvious improvement compared with the traditional multi-user system under the same conditions, and it has good practical value.
, Available online  , doi: 10.11999/JEIT180520 doi: 10.11999/JEIT180520
[Abstract](97) [FullText HTML] (67) [PDF 1011KB](14)
Abstract:
Appropriate warhead structure modeling is the basis for warhead parameters estimation. In this paper, the warhead is modeled by the blunt-nosed chamfered cone model, which regarding the spherical center and the chamfer scattering centers as the sliding centers and taking the influence of the side of the cone into account, the general form of the position of the scattering centers is given based on the model. Then, the micro-motion of the scattering centers in the blunt-nosed chamfered cone model is derived. Based on this, a nonlinear optimization method is proposed to estimate the target's motion parameters and structural parameters. Finally, simulation results verify the correctness of the model and the effectiveness of the parameter estimation method.
, Available online  , doi: 10.11999/JEIT180857 doi: 10.11999/JEIT180857
[Abstract](75) [FullText HTML] (57) [PDF 1535KB](6)
Abstract:
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.
, Available online  , doi: 10.11999/JEIT180971 doi: 10.11999/JEIT180971
[Abstract](120) [FullText HTML] (69) [PDF 2570KB](19)
Abstract:
To solve the problems of low robustness and tracking accuracy in target tracking when interference factors occur such as target fast motion and occlusion in complex video scenes, an Adaptive Strategy Fusion Target Tracking algorithm (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.
, Available online  , doi: 10.11999/JEIT181053 doi: 10.11999/JEIT181053
[Abstract](76) [FullText HTML] (49) [PDF 1359KB](8)
Abstract:
In Software Defined Networks (SDN), latency and load are important factors for Controller Placement Problem (CPP). To reduce the transmission latency between controllers, the propagation latency and queuing latency of flow requests, and balance the controller load, a strategy on how to place and adjust the controller is proposed. It mainly includes Genetic Algorithm (GA) and Balanced Control Region Algorithm (BCRA) which are used to place the initial controller and one Algorithm of Dynamic Online Adjustment (ADOA), that is an online adjusting algorithm in term dynamic controlling. The above algorithms all base on the network connectivity. The simulation results show that in initial controller placement situation, under the premise of guaranteeing the lower propagation latency, queue latency and controller transmission latency of flow request, when BCRA is deployed in small and medium-sized networks, its load balancing performance is similar to that of GA and superior to k-center and k-means algorithm; when GA is deployed in large networks, compared with BCRA, k-center and k-means, the load balancing rate increases averagely 49.7%. In the dynamic situation, ADOA can guarantee lower queuing delay and running time, and still make the load balance parameter less than 1.54.
, Available online  , doi: 10.11999/JEIT180919 doi: 10.11999/JEIT180919
[Abstract](119) [FullText HTML] (70) [PDF 1131KB](20)
Abstract:
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.
, Available online  , doi: 10.11999/JEIT180905 doi: 10.11999/JEIT180905
[Abstract](166) [FullText HTML] (68) [PDF 1662KB](22)
Abstract:
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.
, Available online  , doi: 10.11999/JEIT180916 doi: 10.11999/JEIT180916
[Abstract](123) [FullText HTML] (65) [PDF 1562KB](20)
Abstract:
A method for visualizing the weights of a reconstructed model is proposed to analyze how a deep convolutional network works. Firstly, a specific input is used in the original neural network during the forward propagation to get the prior information for model reconstruction. Then some of the structure of the original network is changed for further parameter calculation. After that, the parameters of the reconstructed model are calculated with a group of orthogonal vectors. Finally, the parameters are put into a special order to make them visualized. Experimental results show that the model reconstructed with the method proposed is totally equivalent to the original model during the forward propagation in the classification process. The feature of the weights of the reconstructed model can be observed clearly and the principle of the neural network can be analyzed with the feature.
, Available online  , doi: 10.11999/JEIT180849 doi: 10.11999/JEIT180849
[Abstract](95) [FullText HTML] (62) [PDF 1446KB](9)
Abstract:
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.
, Available online  , doi: 10.11999/JEIT190033 doi: 10.11999/JEIT190033
[Abstract](27) [FullText HTML] (23) [PDF 2096KB](1)
Abstract:
To solve the problem that the traditional micro-Doppler feature extraction technologies are generally hard to achieve resolution and parameter estimation of multi-target, a novel curve overlap extrapolation algorithm for wide-band resolution of micro-motion multi-target is proposed. According to the relative distance between filtering data points and the historical slope information of each curve, the point trace behind the overlapping location can be extrapolated to realize data association of micor-motion curve for each signal component. On this basis, the multi-target resolution can be realized by analyzing the difference of micor-motion characteristics between each curve. Extensive simulation experiments are provided to illustrate the effectiveness and robustnees of the proposed algorithm.
, Available online  , doi: 10.11999/JEIT180832 doi: 10.11999/JEIT180832
[Abstract](159) [FullText HTML] (69) [PDF 1434KB](13)
Abstract:
To solve the problem that polarization sensitive array of defective electromagnetic vector sensor estimate multi parameter, a two-dimensional DOA estimation algorithm based on orthogonal dipole is proposed in this paper. First, eigendecomposition of the covariance matrix is produced by the received data vectors of the polarization sensitive array. Then the signal subspace is divided into four subarrays, and the phase difference between one of the subarray and the others is obtained according to the ESPRIT algorithm. Then the phase difference between different subarrays is paired. Finally, the DOA estimation and polarization parameters of the signal are calculated according to the phase difference. The uniform linear array composed by orthogonal dipoles can not be two-dimensional DOA estimated by using the MUSIC algorithm and the traditional ESPRIT algorithm. The algorithm proposed in this paper solves this problem, and compared with the polarization MUISC algorithm greatly reduces the complexity of the algorithm. The simulation results verify the effectiveness of the proposed algorithm.
, Available online  , doi: 10.11999/JEIT181127 doi: 10.11999/JEIT181127
[Abstract](18) [FullText HTML] (13)
Abstract:
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 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.
, Available online  , doi: 10.11999/JEIT190143 doi: 10.11999/JEIT190143
[Abstract](18) [FullText HTML] (9)
Abstract:
Bitstream generator in FPGA electronic design automation 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 simplified from O(n) to O(lgn).
, Available online  , doi: 10.11999/JEIT190101 doi: 10.11999/JEIT190101
[Abstract](22) [FullText HTML] (12)
Abstract:
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.
, Available online  , doi: 10.11999/JEIT190051 doi: 10.11999/JEIT190051
[Abstract](25) [FullText HTML] (13)
Abstract:
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 and the European Respiratory Society to some extent.
, Available online  , doi: 10.11999/JEIT190010 doi: 10.11999/JEIT190010
[Abstract](47) [FullText HTML] (30) [PDF 1557KB](8)
Abstract:
Forwarding dense false target jamming disturbs the detection and recognition of real targets by generating multiple false targets in the range dimension. Because the false echo signal is highly correlated with the real signal, it is difficult for radar to recognize and suppress it effectively. Frequency agile radar improves greatly the low interception and anti-jamming ability of radar by randomly changing the carrier frequency of transmitting adjacent pulses. However, agile radar cannot completely eliminate the interference, some target echo pulses may be submerged by the interference, agile radar cannot complete coherent accumulation and target detection well either. To solve the above problems, an anti-jamming method of frequency agility combined with Hough transform is proposed. Firstly, the inter-pulse frequency agility technology is used to avoid most narrowband aiming and deceptive jamming. Then, according to the time discontinuity of the jamming signal, Hough transform and peak extraction are used to identify and suppress the jamming. Frequency agility is incompatible with traditional Moving Target Detection(MTD). Target detection is accomplished by sparse reconstruction. The simulation and actual radar and jammer countermeasure experiments show that the proposed method can achieve good anti-jamming performance and target detection performance.
, Available online  , doi: 10.11999/JEIT180775 doi: 10.11999/JEIT180775
[Abstract](120) [FullText HTML] (65) [PDF 3346KB](10)
Abstract:
In order to overcome the vulnerability of Physical Unclonable Function (PUF) to modeling attacks, a controlled PUF architecture based on sensitivity confusion mechanism is proposed. According to the Boolean function definition of PUF and Walsh spectrum theory, it is derived that each excitation bit has different sensitivity, and the position selection rules related to the parity of the confound value bit width are analyzed and summarized. This rule guides the design of the Multi-bit Wide Confusion Algorithm (MWCA) and constructs a controlled PUF architecture with high security. The basic PUF structure is evaluated as a protective object of the controlled PUF. It is found that the response generated by the controlled PUF based on the sensitivity confusion mechanism has better randomness. Logistic regression algorithm is used to model different PUF attack. The experimental results show that compared with the basic ROPUF, the arbiter PUF and the OB-PUF based on the random confusion mechanism, the controlled PUF based on the sensitivity confusion mechanism can significantly improve the PUF resistance capabilities for modeling attack.
, Available online  , doi: 10.11999/JEIT180771 doi: 10.11999/JEIT180771
[Abstract](161) [FullText HTML] (87) [PDF 1983KB](16)
Abstract:
To solve the problem of lacking efficient and dynamic resource allocation schemes for 5G Network Slicing (NS) in Cloud Radio Access Network (C-RAN) scenario in the existing researches, a virtual resource allocation algorithm for NS in virtualized C-RAN is proposed. Firstly, a stochastic optimization model in virtualized C-RAN network is established based on the Constrained Markov Decision Process (CMDP) theory, which maximizes the average sum rates of all slices as its objective, and is subject to the average delay constraint for each slice as well as the average network backhaul link bandwidth consumption constraint in the meantime. Secondly, in order to overcome the issue of having difficulties in acquiring the accurate transition probabilities of the system states in the proposed CMDP optimization problem, the concept of Post-Decision State (PDS) as an " intermediate state” is introduced, which is used to describe the state of the system after the known dynamics, but before the unknown dynamics occur, and it incorporates all of the known information about the system state transition. Finally, an online learning based virtual resource allocation algorithm is presented for NS in virtualized C-RAN, where in each discrete resource scheduling slot, it will allocate appropriate Resource Blocks (RBs) and caching resource for each network slice according to the observed current system state. The simulation results reveal that the proposed algorithm can effectively satisfy the Quality of Service (QoS) demand of each individual network slice, reduce the pressure of backhaul link on bandwidth consumption and improve the system throughput.
, Available online  , doi: 10.11999/JEIT180738 doi: 10.11999/JEIT180738
[Abstract](96) [FullText HTML] (63) [PDF 7830KB](15)
Abstract:
To solve the incompleteness of the salient region obtained by the existing saliency detection method in the frequency domain, a frequency saliency detection method of multi-scale analysis is proposed. Firstly, the quaternion hypercomplex is constructed by the input image feature channels. Then, the multi-scale decomposition of the quaternion amplitude spectrum is performed by wavelet transform, and the multi-scale visual saliency map is calculated. Finally, the better saliency map is fused based on the evaluation function, and central bias is used to generate the final visual saliency map. The experimental results show that the proposed method can effectively suppress the background interference, find significant target quickly and accurately, and have high detection accuracy.
, Available online  , doi: 10.11999/JEIT180729 doi: 10.11999/JEIT180729
[Abstract](114) [FullText HTML] (58) [PDF 1837KB](11)
Abstract:
LiCi algorithm is a newly lightweight block cipher. Due to its new design idea adopted by Patil et al, it has the advantages of compact design, low energy consumption and less chip area, which is especially suitable for resource-constrained environments. Currently, its security receives extensively attention, and Patil et al. claimed that the 16-round reduced LiCi can sufficiently resist both differential attack and linear attack. In this paper, a new 10-round impossible differential distinguisher is constructed based on the differential characteristics of the S-box and the meet-in-the-middle technique. Moreover, on the basis of this distinguisher, a 16-round impossible differential attack on LiCi is proposed by respectively extending 3-round forward and backward via the key scheduling scheme. This attack requires a time complexity of about 283.08 16-round encryptions, a data complexity of about 259.76 chosen plaintexts, and a memory complexity of 276.76 data blocks, which illustrates that the 16-round LiCi cipher can not resist impossible differential attack.
, Available online  , doi: 10.11999/JEIT180904 doi: 10.11999/JEIT180904
[Abstract](181) [FullText HTML] (79) [PDF 2603KB](24)
Abstract:
For the passive radar based on LTE signal, the received signal contains direct-path and multipath clutters interference of multiple co-channel base station, and the traditional passive radar signal processing flow is improved, and the processing steps of co-channel base station interference are added. A blind source separation algorithm based on convolutive mixtures is proposed. The algorithm can suppress the clutters interference of co-channel base station. It is assumed that the mixing matrix is a vector linear time-invariant filter matrix. The mutual information is used as a cost function. By finding the gradient of mutual information, it is iterated by the steepest descent method. The separation criterion is to minimize the mutual information between the separated signals. The simulation results show that the proposed algorithm can effectively suppress the clutters interference of the LTE signal co-channel base station, and provide a basis for the subsequent clutters cancellation processing of the main base station.
, Available online  , doi: 10.11999/JEIT190168 doi: 10.11999/JEIT190168
[Abstract](20) [FullText HTML] (18)
Abstract:
Considering at 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.
, Available online  , doi: 10.11999/JEIT180826 doi: 10.11999/JEIT180826
[Abstract](23) [FullText HTML] (19)
Abstract:
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.
, Available online  , doi: 10.11999/JEIT181129 doi: 10.11999/JEIT181129
[Abstract](33) [FullText HTML] (22)
Abstract:
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.
, Available online  , doi: 10.11999/JEIT190157 doi: 10.11999/JEIT190157
[Abstract](25) [FullText HTML] (17)
Abstract:
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) is the criterion. Simulation Theory and 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 dB～-14 dB. (3) In the case of -14 dB SNR, the DFSH signaldetection performance increases by 25.47%, when using SR. The proposed method effectiveness is proved with simulation results.
, Available online  , doi: 10.11999/JEIT180770 doi: 10.11999/JEIT180770
[Abstract](63) [FullText HTML] (45)
Abstract:
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.
, Available online  , doi: 10.11999/JEIT181003 doi: 10.11999/JEIT181003
[Abstract](37) [FullText HTML] (35)
Abstract:
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.
, Available online  , doi: 10.11999/JEIT190149 doi: 10.11999/JEIT190149
[Abstract](34) [FullText HTML] (29)
Abstract:
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 sharing 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.
, Available online  , doi: 10.11999/JEIT190004 doi: 10.11999/JEIT190004
[Abstract](36) [FullText HTML] (28)
Abstract:
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.
, Available online  , doi: 10.11999/JEIT181125 doi: 10.11999/JEIT181125
[Abstract](29) [FullText HTML] (29)
Abstract:
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.
, Available online  , doi: 10.11999/JEIT180870 doi: 10.11999/JEIT180870
[Abstract](50) [FullText HTML] (37) [PDF 2517KB](8)
Abstract:
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.
, Available online  , doi: 10.11999/JEIT180748 doi: 10.11999/JEIT180748
[Abstract](92) [FullText HTML] (79) [PDF 2012KB](10)
Abstract:
In order to overcome the accumulation error in Micro-Electro-Mechanical System-Inertial Navigation System (MEMS-INS) and the jump error in iBeacon fingerprint positioning, an iBencon/INS data fusion location algorithm based on Unscented Kalman Filter (UKF) is proposed. The new algorithm solves the distance between the iBeacon anchor and the locating target. The solution of attitude matrix and position are obtained respectively by using accelerometer and gyroscope data. Bluetooth anchor position vector, the carrier speed error and other information constitute state variables. Inertial navigation location and bluetooth system distance information constitute measure variables. Based on state variables and measure variables, the UKF is designed to realize iBencon/INS data fusion indoor positioning. The experimental results show that the proposed algorithm can effectively solve the problem of the large accumulation error of INS and the jump error of iBeacon fingerprint positioning, and this algorithm can realize 1.5 m positioning accuracy.
, Available online  , doi: 10.11999/JEIT181165 doi: 10.11999/JEIT181165
[Abstract](112) [FullText HTML] (72) [PDF 1171KB](9)
Abstract:
Non-Orthogonal Multiple Access (NOMA) serves multiple transmitters using the same resource block, and the receiver decodes the information from different transmitters through Successive Interference Cancellation (SIC). However, most of the researches on NOMA systems are based on perfect SIC assumption, in which the impact of imperfect SIC on NOMA system is not considered. Focusing on this problem, a framework is provided to analyze the performance of single-cell uplink NOMA system under the assumption of imperfect SIC. Firstly, the Binomial Point Process (BPP) is used to model the spatial distribution of base station and user equipment in uplink NOMA system. Based on this model, the interference cancellation order which is based on large-scale fading is adopted, and then the error of interference cancellation is analyzed. Then, based on stochastic geometry theory and order statistics theory, the expression of coverage probability of user equipment which is at rank k in terms of the distance from the base station is derived, besides, the average coverage probability is adopted to reflect the reliability of NOMA transmission system. The analytical and simulation results show the influence of system parameters such as distance order and base station radius on transmission reliability. Also, the validity of theoretical deduction is verified.
, Available online  , doi: 10.11999/JEIT181169 doi: 10.11999/JEIT181169
[Abstract](55) [FullText HTML] (43) [PDF 1860KB](6)
Abstract:
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.
, Available online  , doi: 10.11999/JEIT181016 doi: 10.11999/JEIT181016
[Abstract](118) [FullText HTML] (57) [PDF 1189KB](6)
Abstract:
The measurement accuracy for lightning direction finding by the Orthogonal Magnetic Loop Antenna (OMLA) is continuously improved, which results in the Angle Measurement Error (AME) caused by the OMLA machining error increasing. A theoretical model is established for the relationship between the machining error and AME of OMLA. With the compensation coefficient and equivalent non-orthogonal angle error, a AME correction method for OMLA is proposed. The AME of the conventional measurement way and the corrected measurement way are compared through three groups of data experimentally. The experimental results show that the AME by the corrected measurement way is significantly reduced by about 50%. Therefore, this correction method can help the OMLA with the same hardware condition to obtain higher measurement accuracy for lightning direction finding.
, Available online  , doi: 10.11999/JEIT190013 doi: 10.11999/JEIT190013
[Abstract](35) [FullText HTML] (32) [PDF 1552KB](4)
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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.
, Available online  , doi: 10.11999/JEIT190012 doi: 10.11999/JEIT190012
[Abstract](32) [FullText HTML] (35)
Abstract:
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.
, Available online  , doi: 10.11999/JEIT181171 doi: 10.11999/JEIT181171
[Abstract](46) [FullText HTML] (35) [PDF 2900KB](8)
Abstract:
In order to solve the problem of speed and position error divergence in the integrated navigation system based on MicroElectro Mechanical Systems (MEMS) inertial device and GPS system combined positioning, an improved Adaptive Unsecnted Kalman Filter (AUKF) enhanced by the Radial Basis Function(RBF) neural network based on Artificial Bee Colony(ABC) algorithm is proposed. When the GPS signal is out of lock, the trained network outputs predictied information to perform error correction on the Strapdown Inertial Navigation System(SINS). Finally, the performance of the method is verified by vehicle-mounted semi-physical simulation experiments. The experimental results show that the proposed method has a significant inhibitory effect on the error divergence of the strapdown inertial navigation system in the case of loss of lock.
, Available online  , doi: 10.11999/JEIT181083 doi: 10.11999/JEIT181083
[Abstract](80) [FullText HTML] (50) [PDF 1683KB](7)
Abstract:
Magnetic induction detection technology is a non-contact and non-invasive electrical impedance detection technology. Multi-frequency synchronous detection can simultaneously obtain the impedance information of the tested object at different frequencies. Firstly, the principle of multi-frequency synchronous excitation and detection of magnetic induction signal are studied. Five-frequency excitation signal is synthesized based on Walsh function. Secondly, the performance of synthesized multi-frequency synchronous detection is analyzed, and a synthesized multi-frequency magnetic induction signal synchronous detection system is designed. Finally, the detection experiments of NaCl solution with different conductivities are carried out by synthesizing five-frequency excitation signal and synchronous detection system. The results show that the measurement results of five main harmonics of synthesized five-frequency excitation signal have good linearity. It provides an excitation-detection method for multi-frequency synchronous detection of magnetic induction signal.
, Available online  , doi: 10.11999/JEIT190026 doi: 10.11999/JEIT190026
[Abstract](30) [FullText HTML] (26)
Abstract:
The Dense Focal Plane Array Feed (DFPAF), which integrates the characters of multi-beam feed with multiple independent horns and Phased Array Feed (PAF), can simultaneously provide more fixed shaped beams and wider field of view than multi-beam feed with multiple independent horns and PAF. It attracts more attention in radio telescope, radar, electronic reconnaissance, satellite communication and so on. Its unique structure promotes the studies on special design method recently. Combing the theory of array antenna and inherent characteristic of parabolic reflector antenna, a fast design method with robust processing procedure is proposed in this paper. The design principle, calculated results, and comparison between DFPAF and the most representative multi-beam feed with multiple independent horns are presented. All these provide a theoretical basis and reference data for the design of giant reflector with DFPAF.
, Available online  , doi: 10.11999/JEIT180936 doi: 10.11999/JEIT180936
[Abstract](84) [FullText HTML] (51) [PDF 2278KB](19)
Abstract:
Generally speaking, Three Dimension (3D) imaging of spinning space target is obtained by performing matrix factorization method on the scattering trajectories obtained from sequential radar images. Because of the errors of scattering center extraction and association, the 3D reconstruction accurate is reduced or even fail. In addition, the scattering center trajectory from turntable target consists with circle nature, which is inconsistent with the elliptic property of the scattering center trajectory obtained by optical geometry projection. To tackle these problems, this paper proposes a short time 3D reconstruction method of space target. Firstly, the retrieved trajectory is fitted with 2D circular nature to make the trajectory smooth and closer to the theoretical curve. Then the radar Line Of Sight (LOS) is estimated by multiple views and the circular curve is converted into elliptical curve by multiplying the coefficients calculated by the LOS. The 3D reconstruction can be obtained by performing matrix factorization method on elliptical curves. Finally, the simulations verify the effectiveness of the proposed method.
, Available online  , doi: 10.11999/JEIT190014 doi: 10.11999/JEIT190014
[Abstract](79) [FullText HTML] (45) [PDF 2058KB](8)
Abstract:
Considering the disadvantage of oblique delay estimation of tropospheric scattering at arbitrary stations, which is difficult to obtain real-time sounding meteorological data, an oblique delay estimation algorithm of tropospheric scattering based on improved ray tracing method with ground meteorological parameters is proposed. In order to get rid of the method’s dependence on radiosonde data, the algorithm infers the relationship between refractive index and altitude through the formula of meteorological parameters in the model of medium latitude atmosphere. The interpolation of meteorological parameters in the model of UNB3m is used to gain the coefficient of temperature and water vapor pressure. Meteorological data for 2012 from 6 International GNSS Service (IGS) stations in Asia are selected to test the applicability of new method, the results suggest that precision is less than 1 cm. Then, the tropospheric slant delays of three parts observation stations under different angles of incidence (0°～5°) are calculated by the modified algorithm. The results suggest that the maximum delay is 17.03～33.10 m in a single way time transfer. In two way time transfer, when the delay can counteract 95%, time delay is 2.88～5.52 ns.
, Available online  , doi: 10.11999/JEIT190108 doi: 10.11999/JEIT190108
[Abstract](62) [FullText HTML] (47) [PDF 5051KB](4)
Abstract:
The backscattering of the radar targets is sensitive to the relative geometry between orientations of the targets and the radar line of sight. When the orientations of the same target are different from the radar line of sight, the scattering characteristics are quite different. Targets such as inclined ground and inclined buildings may reverse the polarization base of the backscattered echo, which causes the cross-polarization component to be too high and the volume scattering component of the image is overestimated. In this paper, a polarimetric interferometric decomposition method based on polarimetric parameters (\begin{document}$H/{\alpha}$\end{document}) and Polarimetric Interferometric Similarity Parameters (PISP) is proposed to solve the overestimation problem. The method makes full use of the scattering diversity of the scatterer in the radar line of sight. The cross-polarization components generated by targets such as inclined grounds and inclined buildings with different orientations are better adapted to obtain better decomposition results. Finally, the effectiveness of the proposed method in polarimetric interferometric decomposition is verified by the airborne C-band PolInSAR data obtained by the Institute of Electronics, Chinese Academy of Sciences. The experimental results show that the proposed improved algorithm can distinguish the scattering characteristics of terrain types effectively and correctly.
, Available online  , doi: 10.11999/JEIT181144 doi: 10.11999/JEIT181144
[Abstract](63) [FullText HTML] (36) [PDF 1199KB](9)
Abstract:
Ultra-Dense Networks (UDNs) shorten the distance between terminals and nodes, which improve greatly the spectral efficiency and expand the system capacity. But the performance of cell edge users is seriously degraded. Reasonable planning of Virtual Cell (VC) can only reduce the interference of moderate scale UDNs, while the interference of users under overlapped base stations in a virtual cell needs to be solved by cooperative user clusters. A user clustering algorithm with Interference Increment Reduction (IIR) is proposed, which minimizes the sum of intra-cluster interference and ultimately maximizes system sum rate by continuously switching users with maximum interference between clusters. Compared with K-means algorithm, this algorithm, no need of specifying cluster heads, avoids local optimum without increasement of the computation complexity. The simulation results show that the system sum rate, especially the throughput of edge users, can be effectively improved when the network is densely deployed.
, Available online  , doi: 10.11999/JEIT181078 doi: 10.11999/JEIT181078
[Abstract](77) [FullText HTML] (39) [PDF 2199KB](6)
Abstract:
High Frequency Surface Wave Radar (HFSWR) utilizes electromagnetic wave diffracting along the earth to detect targets over the horizon. However, the increase of target distance will decrease the received echo energy, and this will degrade the detection capability. A joint domain matrix Constant False Alarm Rate (CFAR) detector is proposed to improve the detection performance. It employs the multi-dimensional information of signal in azimuth, Doppler velocity and range domain to detect target, and Log-Determinant Divergence (LDD) and Symmetrized Log-Determinant Divergence (SLDD) are used to replace the Riemannian Distance (RD) as the measure of distance. Finally, the experiment results show that the detector presented by the paper can improve the detection performance effectively.
, Available online  , doi: 10.11999/JEIT190003 doi: 10.11999/JEIT190003
[Abstract](65) [FullText HTML] (36) [PDF 744KB](4)
Abstract:
Cipher cards play an important role in the field of information security. However, the performance of cipher cards are insufficient, and it is difficult to meet the needs of high-speed network security services. A design and system implementation method of high-speed PCIe cipher card based on MIPS64 multi-core processor is proposed, which supports the GM algorithm SM2/3/4 and international cryptographic algorithms, such as RSA, SHA and AES. The implemented system includes module of hardware, cryptographic algorithm, host driver and interface calling. An optimization scheme for the implementation of SM3 is proposed, the performance is improved by 19%. And the host to send requests in Non-Blocking mode is supported, so a single-process application can get the cipher card’s full load performance. Under 10-core CPU, the speed of SM2 signature and verification are 18000 and 4200 times/s, SM3 hash speed is 2200 Mbps, SM4 encryption/decryption speed is 8/10 Gbps, multiple indicators achieve higher level; When using 16-core CPU @1300 MHz, SM2/3 performance can be improved by more than 100%, and the speed of SM2 signature could achieve 105 times/s with 48-core CPU.
, Available online  , doi: 10.11999/JEIT190095 doi: 10.11999/JEIT190095
[Abstract](92) [FullText HTML] (49) [PDF 2331KB](5)
Abstract:
The circuit structure optimization method for Basic programmable Logic Element (BLE) of FPGA is studied. Considering finding the solution to the bottleneck problem of low resource utilization efficiency in logic and arithmetic operations with 4-input Look Up Table (LUT), some efforts to improve BLE design based on 4-input LUT are explored. A high area-efficient LUT structure is proposed, and the possible benefits of such a new structure are analyzed theoretically and simulated. Further, a statistical method for evaluation of the post synthesis and mapping netlist is also proposed. Finally, a number of experiments are carried out to assess the proposed structure based on the MCNC and VTR benchmarks. The results show that, compared with Intel Stratix series FPGAs, the optimized structure proposed in this paper improves respectively the area efficiency of the FPGA by 10.428% and 10.433% in average under the MCNC and VTR benchmark circuits.
, Available online  , doi: 10.11999/JEIT181073 doi: 10.11999/JEIT181073
[Abstract](152) [FullText HTML] (76) [PDF 1689KB](16)
Abstract:
In view of the nonlinear and stochastic characteristics of short-term traffic flow data, this article propose a prediction model and algorithm based on hybrid Auto-Regressive Integrated Moving Average (ARIMA) and Genetic Particle Swarm Optimization Wavelet Neural Network (GPSOWNN) in order to improve its prediction accuracy and rate of convergence. In terms of model construction, the ARIMA model prediction value and the historical data of the first three moments with strong correlation with gray correlation coefficient greater than 0.6 are used as input of the Wavelet Neural Network, and the structure of the model is simplified considering both the stationary and non-stationary historical data. In terms of algorithm, by using the genetic particle swarm optimization algorithm to select optimally the initial values of the wavelet neural network, the results can speed up the convergence of network training under the condition that it is not easy to fall into local optimum. The experimental results show that the proposed model is superior to hybrid ARIMA and GPSOWNN in terms of prediction accuracy, the genetic particle swarm optimization algorithm is superior to the genetic algorithm optimization model in terms of convergence speed.
, Available online  , doi: 10.11999/JEIT180946 doi: 10.11999/JEIT180946
[Abstract](108) [FullText HTML] (66) [PDF 2701KB](15)
Abstract:
With the development of light and small Unmanned Aerial Vehicles (UAV), the detection method of Mini SAR based on UAV platform will bring a revolutionary impact on information acquisition mode. In this paper, a W-band Mini SAR system for UAV is proposed, including the system design proposal and composition, high linearity analog phase-locked frequency modulation, MilliMeter Wave (MMW) substrate integrated waveguide antenna, 3D integration and motion compensation methods to solve the key problems of Mini SAR. A W-band Mini SAR prototype is developed and the imaging test based on Multi-rotor UAV is proceeded. The results show the resolution, volume and the weight of Mini SAR prototype is at the industry-leading level. A high SNR imaging with perfect focusing effect is obtained from flight test.
, Available online  , doi: 10.11999/JEIT180740 doi: 10.11999/JEIT180740
[Abstract](30) [FullText HTML] (26)
Abstract:
In order to improve the accuracy rate of person re-identification. A pedestrian attribute hierarchy recognition neural network is proposed in this paper based on attention model. Compared with the existing algorithms, the model has the following three advantages. Firstly, the attention model is used in this paper to identify the pedestrian attributes, and to extract of pedestrian attribute information and degree of significance. Secondly, the attention model in used in this paper to classify the attributes according to the significance of the pedestrian attributes and the amount of informationcontained. Thirdly, this paper analyzes the correlation between attributes, and adjust the next level identification strategy according to the recognition results of the upper level. It can improve the recognition accuracy of small target attributes, and the accuracy of pedestrian recognition is improved. The experimental results show that the proposed model can effectively improve the first accuracy rate (rank-1) of person re-identification compared with the existing methods. On the Market1501 dataset, the first accuracy rate is 93.1%, and the first accuracy rate is 81.7% on the DukeMTMC dataset.
, Available online  , doi: 10.11999/JEIT181102 doi: 10.11999/JEIT181102
[Abstract](44) [FullText HTML] (32) [PDF 2017KB](5)
Abstract:
It makes the Pulse Doppler (PD) radar widely applied that the PD radar has the obvious advantages of detecting the Doppler frequency of the target and suppressing the clutter effectively. However, it is difficult for the PD radar to detect the target due to velocity ambiguity. Combining with the characteristic and stagger-period model of the PD radar, a Doppler frequency estimation method based on all phase DFT Closed-Form Robust Chinese Remainder Theorem (CFRCRT) with spectrum correction is proposed in this paper. Both theoretical analysis and simulation experiment demonstrate that the proposed method can satisfy the engineering demand in measure accuracy and real-time performance.
, Available online  , doi: 10.11999/JEIT181138 doi: 10.11999/JEIT181138
[Abstract](19) [FullText HTML] (25)
Abstract:
To solve the problems that Two-Dimensional Principal Component Analysis (2DPCA) can not implement the on-line feature extraction and can not represent the complete structure information, an Incremental 2DPCA (I2DPCA) without estimating covariance matrices is presented by an iterative estimation method, not to deal with the image covariance matrices by the eigenvalue decomposition or the singular value decomposition. The complexity will be greatly reduced and the on-line feature extraction speed can be improved. The proposed I2DPCA can only extract the horizontal features, and thus another Incremental Row-Column 2DPCA (IRC2DPCA) is proposed to incrementally extract the longitudinal ones from the projected subspaces of the I2DPCA. The IRC2DPCA can preserve the horizontal and longitudinal features and implement the dimensionality reduction in both row and column directions. Finally, a series of experiments are carried out with the self-built block dataset, ORL and Yale face datasets, respectively. The results show that the proposed algorithms have significantly improved the performances of the convergence rate, the classification rate and the complexity. The convergence rate is over 99%, the classification rate can reach 97.6% and the average processing speed is about 29 frames per second, and it can meet the on-line feature extraction requirements for incremental learning.
, Available online  , doi: 10.11999/JEIT190016 doi: 10.11999/JEIT190016
[Abstract](26) [FullText HTML] (22)
Abstract:
The close relationship between resource deployment and specific tasks in traditional Wireless Sensor Network(WSN) leads to low resource utilization and revenue. According to the dynamic changes of Virtual Sensor Network Request(VSNR), the resource allocation strategy based on Semi-Markov Decision Process(SMDP) is proposed in Virtual Sensor Network(VSN). Then, difining the state, action, and transition probability of the VSN, the expected reward is given by considering the energy and time to complete the VSNR, and the model-free reinforcement learning approach is used to maximize the long-term reward of the network resource provider. The numerical results show that the resource allocation strategy of this paper can effectively improve the revenue of the sensor network resource providers.
, Available online  , doi: 10.11999/JEIT180926 doi: 10.11999/JEIT180926
[Abstract](41) [FullText HTML] (26) [PDF 1578KB](3)
Abstract:
By using the intra-view and inter-view correlations and the motion vector-sharing, a depth map error concealment approach is proposed for 3D video coding based on the High Efficiency Video Coding (3D-HEVC) to combat the packet loss of the depth video transmission. Based on the Hierarchical B-frame Prediction (HBP) structure in 3D-HEVC and textured features of the depth map, all the lost coding units are firstly categorized into two classes, i.e., motion blocks and static blocks. Then, according to the outer boundary matching criterion combining the texture structure, the optimal motion/disparity vector is chosen for the damaged motion blocks to conduct the motion/disparity compensation based error concealment. Whereas, the direct copy is applied to conceal the damaged static blocks quickly. Finally, for the concealed blocks whose qualities are not ideal, the new motion/disparity compensation blocks reconstructing by the reference frames recombination are applied to improve the qualities of those blocks. The experimental results show that the repaired depth map concealed by the proposed approach can achieve 0.25～2.03 dB gain in term of the Peak-Signal-to-Noise Ratio (PSNR) and 0.001～0.006 gain in term of Structural Similarity Index Measure(SSIM). Moreover, the subjective visual quality of the repaired area is better in lines with the original depth maps.
, Available online  , doi: 10.11999/JEIT181121 doi: 10.11999/JEIT181121
[Abstract](32) [FullText HTML] (21) [PDF 2346KB](5)
Abstract:
The filtering performance of Gaussian Mixture Cardinality Balanced Multi-Target Multi-Bernoulli (GM-CBMeMBer) filter can be effected by the heavy-tailed process noise and measurement noise. To solve this problem, a new STudent’s t Mixture Cardinality Balanced Multi-Target Multi-Bernoulli (STM-CBMeMBer) filter is proposed. The process noise and measurement noise approximately obey the Student’s t distribution in the filter, where the Student’s t mixture model is used to describe approximately the posterior intensity of the multi-target. The predictive intensity and posterior intensity of Student’s t mixture form are deduced theoretically, and the closed recursive framework of cardinality balanced multi-target multi-Bernoulli filter is established. The simulation results show that, in the presence of the heavy-tailed process noise and the measurement noise, the filter can effectively suppress its interference, its tracking accuracy is superior over the traditional methods.
, Available online  , doi: 10.11999/JEIT181196 doi: 10.11999/JEIT181196
[Abstract](31) [FullText HTML] (17) [PDF 1008KB](2)
Abstract:
To solve the problem for the large amount of tasks, complex constraint conditions and manual which is hard to generation shifts of airport foreign airline service personnel. A shift generation model is studied and constructed for multi-task hierarchical qualification which including employees have hierarchical qualifications for tasks and shift needs to meet all kinds of labor laws and regulations and others constraints to minimize the total working time of shifts for optimum. Tabu search algorithm is designed to solve the model. Experiments, based on the actual scheduling data set of the foreign airlines service department of capital airport, verify the practicability and effectiveness of the model and the algorithm. The results show that compared to the existing manual shifts schemes, shifts obtained by using the model can fulfill all constraint conditions, shorten the total working time, reduce the number of employees and improve the utilization rate of airport resources.
, Available online  , doi: 10.11999/JEIT181191 doi: 10.11999/JEIT181191
[Abstract](37) [FullText HTML] (25) [PDF 2157KB](6)
Abstract:
The power control problem of mobile users in macro-femto heterogeneous cellular networks is studied. Firstly, an optimization model that maximizes the total energy efficiency of femtocells with the minimum received signal-to-noise ratio as the constraint is established. Then, a femtocell centralized Power Control algorithm based on Q-Learning (PCQL) is proposed. Based on reinforcement learning, the algorithm can adjust the transmit power of the user terminal without accurate channel state information simultaneously. The simulation results show that the algorithm can effectively control the power of the user terminal and improve system energy efficient.
, Available online  , doi: 10.11999/JEIT180676 doi: 10.11999/JEIT180676
[Abstract](38) [FullText HTML] (25) [PDF 912KB](3)
Abstract:
To deal with the estimation problem of non-stationary channel in massive Multiple-Input Multiple-Output (MIMO) up-link, the 2D channels’ sparse structure information in temporal-spatial domain is used, to design an iterative channel estimation algorithm based on Dirichlet Process (DP) and Variational Bayesian Inference (VBI), which can improve the accuracy under a lower pilot overhead and computation complexity. On account of that the stationary channel models is not suitable for massive MIMO systems anymore, a non-stationary channel prior model utilizing Dirichlet Process is constructed, which can map the physical spatial correlation channels to a probabilistic channel with the same sparse temporal vector. By applying VBI technology, a channel estimation iteration algorithm with low pilot overhead and complexity is designed. Experiment results show the proposed channel method has a better performance on the estimation accuracy than the state-of-art method, meanwhile it works robustly against the dynamic system key parameters.
, Available online  , doi: 10.11999/JEIT190043 doi: 10.11999/JEIT190043
[Abstract](34) [FullText HTML] (21) [PDF 3318KB](6)
Abstract:
The image forgery detection algorithm based on convolutional neural network can implement the image forgery detection that does not depend on a single image attribute by using the learning ability of convolutional neural network, and make up for the defect that the previous image forgery detection algorithm relies on a single image attribute and has low applicability. Although the image forgery detection algorithm using a single network structure of deep layers and multiple neurons can learn more advanced semantic information, the result of detecting and locating forgery regions is not ideal. In this paper, an image forgery detection algorithm based on cascaded convolutional neural network is proposed. Based on the general characteristics exhibited by convolutional neural network, and then the deeper characteristics are further explored. The cascaded network structure of shallow layers and thin neurons figures out the defect of the single network structure of deep layers and multiple neurons in image forgery detection. The proposed detection algorithm in this paper consists of two parts: the cascade convolutional neural network and the adaptive filtering post-processing. The cascaded convolutional neural network realizes hierarchical forgery regions localization, and then the adaptive filtering post-processing further optimizes the detection result of the cascaded convolutional neural network. Through experimental comparison, the proposed detection algorithm shows better detection results and has higher robustness.
, Available online  , doi: 10.11999/JEIT181049 doi: 10.11999/JEIT181049
[Abstract](60) [FullText HTML] (35) [PDF 3562KB](3)
Abstract:
A novel wideband low RCS new super-surface array based on three reflective cell shared aperture is designed, which is composed of three kinds of Artificial Magnetic Conductor (AMC). Compared with the traditional AMC array, the new array uses one of AMC as phasor interference unit. A new phase cancellation relation is presented, the new phase cancellation relation is used to extend the traditional array phase cancellation band. Then, the parameters of the cell structure are further optimized to realize the reduction of RCS and the improvement of bandwidth. The physical sample is processed and tested. The results of simulation and field test show that: the backward reduction of RCS in the range of 5.2～13.9 GHz reaches more than 10 dB, and the relative bandwidth reaches 91%. It is shown that the new array can overcome the defect of the discontinuous operating band of the traditional array and has broadband low scattering characteristics.
, Available online  , doi: 10.11999/JEIT180937 doi: 10.11999/JEIT180937
[Abstract](46) [FullText HTML] (32) [PDF 1180KB](4)
Abstract:
In order to improve multicast’s spectrum energy-efficient of elastic optical network configured with Colorless, Directionless and Contentionless-Flexible Reconfigurable Optical Add/Drop Multiplexer (CDC-F ROADM) nodes, an All-optical Multicast Energy Efficiency Scheduling Algorithm (AMEESA) is proposed. In the routing phase, considering both energy consumption and link spectrum resource utilization, the link cost function is designed to establish the multicast tree with the least cost. In the spectrum allocation phase, a spectrum conversion method based on High Spectral Resolution (HSR) is designed by changing the spectrum slot index of adjacent links according to links availability of spectrum blocks. And an energy-saving spectrum conversion scheme is selected to allocate spectrum block resources for the multicast tree. Simulation analysis shows that the proposed algorithm can effectively improve the network energy efficiency and reduce the bandwidth blocking probability of IP multicast.
, Available online  , doi: 10.11999/JEIT180912 doi: 10.11999/JEIT180912
[Abstract](51) [FullText HTML] (32) [PDF 1783KB](4)
Abstract:
The microwave source of Non-Coherent Short Pulse (NCSP) radar transmits short pulse. Thus, as to the high velocity target, the motion effect in the pulse duration can be neglected, and the echo signal does not need special motion compensation. In order to use the NCSP radar signal for inverse synthetic aperture radar imaging, the compensation coherent processing method is applied to remove the uncertainty of the envelope time and the initial phase uncertainty. Assuming that, the echo is envelope-aligned and initially compensated by conventional methods, ISAR radar imaging can be performed using the Range-Doppler (RD) method, subsequently. The simulation verifies the feasibility of the compensation signal inverse synthetic aperture radar. However, the carrier-frequency random jitter factor of NCSP radar causes random-modulated side lobes in the Doppler dimension, which affects imaging quality. In this paper, the sparse recovery technique is used to perform sparse reconstruction of the target scattering center in the imaging space. The Orthogonal Matching Pursuit (OMP) algorithm and the Sparse Bayesian Learning (SBL) algorithm are used as the recovery algorithm for imaging simulation experiments. The simulation results show that the sparse recovery technique can suppress the imaging side lobes caused by non-coherence and improve the imaging quality.
, Available online  , doi: 10.11999/JEIT181076 doi: 10.11999/JEIT181076
[Abstract](45) [FullText HTML] (30) [PDF 1860KB](3)
Abstract:
As an extension of Compressed Sensing(CS), Matrix Completion(MC) is widely applied to different fields. Recently, the Riemannian optimization based MC algorithm attracts a lot of attention from researchers due to its high accuracy in reconstruction and computational efficiency. Considering that the Riemannian optimization based MC algorithm assumes a fixed rank of the original matrix, and selects a random initial point for iteration, a novel algorithm is proposed, namely automatic rank estimation based Riemannian optimization matrix completion algorithm. In the proposed algorithm, the estimate of rank is obtained minimizing the objective function that involving the rank regulation, in addition, the iterative starting point is optimized based on Riemannian manifold. The Riemannian manifold based conjugate gradient method is then used to complete the matrix, thereby improving the reconstruction precision. The experimental results demonstrate that the image completion performance is significantly improved using the proposed algorithm, compared with several classical image completion methods.
, Available online  , doi: 10.11999/JEIT181061 doi: 10.11999/JEIT181061
[Abstract](45) [FullText HTML] (25) [PDF 2309KB](3)
Abstract:
With the development of earth remote sensing technology, SAR system is required to obtain high resolution and wide swath simultaneously, the space borne array SAR combined with Digital Beam Forming(DBF) technology provides a good solution to solve the problem. However, the phase error between channels will degrade the quality of DBF, and the traditional compensation methods suffer from large error or limited application. In this paper, a compensation method based on antenna pattern and Doppler correlation coefficient is proposed, using the antenna pattern and meanwhile utilizing the Doppler correlation coefficient. By minimizing the combined cost function, the phase error between channels are estimated. Simulation results using RADAR-SAT data validate the effectiveness of the proposed method.
, Available online  , doi: 10.11999/JEIT181088 doi: 10.11999/JEIT181088
[Abstract](47) [FullText HTML] (36) [PDF 2210KB](7)
Abstract:
Facial expression is the most intuitive description of changes in psychological emotions, and different people have great differences in facial expressions. The existing facial expression recognition methods use facial statistical features to distinguish among different expressions, but these methods are short of deep exploration for facial detail information. According to the definition of facial behavior coding by psychologists, it can be seen that the local detail information of the face determines the meaning of facial expression. Therefore, a facial expression recognition method based on multi-scale detail enhancement is proposed, because facial expression is much more affected by the image details than other information, the method proposed in this paper extracts the image detail information with the Gaussian pyramid firstly, thus the image is enhanced in detail to enrich the facial expression information. Secondly, for the local characteristics of facial expressions, a local gradient feature calculation method is proposed based on hierarchical structure to describe the local shape features of facial feature points. Finally, facial expressions are classified using a Support Vector Machine (SVM). The experimental results in the CK+ expression database show that the method not only proves the important role of image detail in facial expression recognition, but also obtains very good recognition results under small-scale training data. The average recognition rate of expressions reaches 98.19%.
, Available online  , doi: 10.11999/JEIT180796 doi: 10.11999/JEIT180796
[Abstract](112) [FullText HTML] (56) [PDF 2365KB](16)
Abstract:
For the passive detection of underwater line-spectrum target, the information such as the azimuth, frequency and number of the line-spectrum signals is usually unknown, and the line-spectrum detection performance is affected by broadband interferences and noise. For this issue, a method of detecting the unknown line-spectrum target by space-time domain processing is proposed. Firstly, a space-time filter that autonomously matches the unknown line-spectrum signals is constructed to filter out the broadband interferences and noise. Secondly, the conventional frequency domain beamforming is performed on the filtered signals, and then a space-time two-dimensional beam output with relatively pure line-spectrum spectral peaks is obtained. The line-spectrum signals are extracted from the space-time two-dimensional beam output, and the spatial spectrum is calculated using the extracted line-spectrum information. Then, the detection of the line-spectrum target is realized. Theoretical derivation and simulation results verify that the proposed method performs the spatiotemporal filtering on the unknown line-spectrum signals in the minimum mean square error sense, and fully utilizes the line-spectrum information for the passive detection of underwater line-spectrum target. Compared with the existing line-spectrum target detection methods utilizing the line-spectrum features, the proposed method requires lower Signal to Noise Ratio (SNR), and has better detection performance under the complex multi-target multi-spectrum-line conditions.
, Available online  , doi: 10.11999/JEIT180983 doi: 10.11999/JEIT180983
[Abstract](43) [FullText HTML] (31) [PDF 1508KB](4)
Abstract:
, Available online  , doi: 10.11999/JEIT180891 doi: 10.11999/JEIT180891
[Abstract](84) [FullText HTML] (55) [PDF 1556KB](13)
Abstract:
In order to solve the problem of classification and recognition of unknown interference types under large samples, an adaptive recognition method for unknown interference based on signal feature space is proposed. Firstly, the interference signal is processed and the interference signal feature space is established with the Hilbert signal space theory. Then the projection theorem is used to approximate the unknown interference. The classification algorithm based on signal feature space with Probabilistic Neural Network (PNN) is proposed, and the processing flow of unknown interference classifier is designed. The simulation results show that compared with two kinds of traditional methods, the proposed method improves the classification accuracy of the known interference by 12.2% and 2.8% respectively. The optimal approximation effect of the unknown interference varies linearly with the power intensity in the condition, and the overall recognition rate of the designed classifier reaches 91.27% in the various types of interference satisfying the optimal approximation, and the speed of processing interference recognition is improved significantly. When the signal-to-noise ratio reaches 4 dB, the accuracy of unknown interference recognition is more than 92%.
, Available online  , doi: 10.11999/JEIT181110 doi: 10.11999/JEIT181110
[Abstract](83) [FullText HTML] (67) [PDF 3553KB](10)
Abstract:
To solve the problem that small moving object is difficult to be detected in video surveillance, a track-based detection algorithm is proposed. Firstly, in order to reduce missing alarm, an adaptive foreground extraction method combining regional texture features and difference probability is presented. Then, for reducing false alarm, the probability computing model of track correlation is designed to establish the correlation of suspected objects between frames, and double-threshold are set to distinguish between true and false positive. Experimental results show that compared with many classical algorithms, this algorithm can accurately detect small moving object within the quantitative range with lower missing and false alarm.
, Available online  , doi: 10.11999/JEIT181091 doi: 10.11999/JEIT181091
[Abstract](65) [FullText HTML] (61) [PDF 1289KB](11)
Abstract:
Range sidelobes may lead to weak targets masked by strong targets and false alarm. This paper proposes a sequential optimization method against to the sidelobe suppression of cognitive radar. First, the region to detect is divided according to range cell. Second, the transmit waveform and receive filter are optimized jointly based on the principle of minimum mean square error against one range cell. The optimized transmit and receive systems are used in Radar Cross Section (RCS) estimation for the scatter in the current range cell. The above process is carried out in each range cell in the scene sequentially. The acquired RCS estimate is used in the sidelobe suprresion for the following range cells. The RCS estimation for all the range cells in the scene is obtained in a bootstrapping way successively and updated circularly. The proposed method forms a closed loop detection system. The transmitting and receiving systems are adjusted according to the feedback scene information in real time. The sensing ability about the environment can be enhanced. The detection performance and robustness against noise can be improved. The efficiency and validity are verified by the simulation results.
, Available online  , doi: 10.11999/JEIT181181 doi: 10.11999/JEIT181181
[Abstract](75) [FullText HTML] (54) [PDF 1978KB](11)
Abstract:
Considering the problem of Orthogonal Frequency Division Multiplexing (OFDM) signal delay estimation with only a Single Measurement Vector (SMV) in a complex environment, a sparse reconstruction time delay estimation algorithm based on Bayesian Automatic Relevance Determination (BARD) is proposed. The Bayesian framework is used to start from the perspective of further mining useful information, and asymmetric Automatic Relevance Determination(ARD) priori is introduced to integrate into the parameter estimation process, which improves the accuracy of time delay estimation under SMV and low Signal-to-Noise Ratio (SNR) conditions. Firstly, a sparse real-domain representation model is constructed based on the estimated frequency domain response of the OFDM signal physical layer protocol data unit. Then, probability hypothesis for the noise and sparse coefficient vectors are made in the model, and Automatic Relevance Determination (ARD) prior is introduced. Finally, according to the Bayesian framework, the Expectation Maximization (EM) algorithm is used to solve the hyperparameters to estimate the delay. The simulation experiments show that the proposed algorithm has better estimation performance and is closer to the Cramér–Rao Bound (CRB). At the same time, based on the Universal Software Radio Peripheral (USRP), the effectiveness of the proposed algorithm is verified by the actual signal.
, Available online  , doi: 10.11999/JEIT181059 doi: 10.11999/JEIT181059
[Abstract](45) [FullText HTML] (39) [PDF 3575KB](5)
Abstract:
In partial label learning, the true label of an instance is hidden in a label-set consisting of a group of candidate labels. The existing partial label learning algorithm only measures the similarity between instances based on feature vectors and lacks the utilization of the candidate labelset information. In this paper, a Candidate Label-Aware Partial Label Learning (CLAPLL) method is proposed, which combines effectively candidate label information to measure the similarity between instances during the graph construction phase. First, based on the jaccard distance and linear reconstruction, the similarity between the candidate labelsets of instances is calculated. Then, the similarity graph is constructed by combining the similarity of the instances and the label-sets, and then the existing graph-based partial label learning algorithm is presented for learning and prediction. The experimental results on 3 synthetic datasets and 6 real datasets show that disambiguation accuracy of the proposed method is 0.3～16.5% higher than baseline algorithm, and the classification accuracy is increased by 0.2～2.8%.
, Available online  , doi: 10.11999/JEIT181054 doi: 10.11999/JEIT181054
[Abstract](42) [FullText HTML] (28) [PDF 1014KB](8)
Abstract:
Most current transfer learning methods are modeled by utilizing the source data with the assumption that all data in the source domain are equally related to the target domain. In many practical applications, however, this assumption may induce negative learning effect when it becomes invalid. To tackle this issue, by minimizing the integrated squared error of the probability distribution of the source and target domain classification errors, the Classification-error Consensus Regularization (CCR) is proposed. Furthermore, CCR-based Adaptive knowledge Transfer Learning (CATL) method is developed to quickly determine the correlative source data and the corresponding weights. The proposed method can alleviate the negative transfer learning effect while improving the efficiency of knowledge transfer. The experimental results on the real image and text datasets validate the advantages of the CATL method.
, Available online  , doi: 10.11999/JEIT190047 doi: 10.11999/JEIT190047
[Abstract](77) [FullText HTML] (50) [PDF 1192KB](20)
Abstract:
Utilizing multiple data (elevation information) to assist remote sensing image segmentation is an important research topic in recent years. However, the existing methods usually directly use multivariate data as the input of the model, which fails to make full use of the multi-level features. In addition, the target size varies in remote sensing images, for some small targets, such as vehicles, houses, etc., it is difficult to achieve detailed segmentation. Considering these problems, a Multi-Feature map Pyramid fusion deep Network (MFPNet) is proposed, which utilizes optical remote sensing images and elevation data as input to extract multi-level features from images. Then the pyramid pooling structure is introduced to extract the multi-scale features from different levels. Finally, a multi-level and multi-scale feature fusion strategy is designed, which utilizes comprehensively the feature information of multivariate data to achieve detailed segmentation of remote sensing images. Experiment results on the Vaihingen dataset demonstrate the effectiveness of the proposed method.
, Available online  , doi: 10.11999/JEIT190056 doi: 10.11999/JEIT190056
[Abstract](86) [FullText HTML] (54) [PDF 3206KB](17)
Abstract:
An image semantic segmentation model based on region and deep residual network is proposed. Region based methods use multi-scale to create overlapping regions, which can identify multi-scale objects and obtain fine object segmentation boundary. Fully convolutional methods learn features automatically by using Convolutional Neural Network (CNN) to perform end-to-end training for pixel classification tasks, but typically produce coarse segmentation boundaries. The advantages of these two methods are combined: firstly, candidate regions are generated by region generation network, and then the image is fed through the deep residual network with dilated convolution to obtain the feature map. Then the candidate regions and the feature maps are combined to get the features of the regions, and the features are mapped to each pixel in the regions. Finally, the global average pooling layer is used to classify pixels. Multiple different models are obtained by training with different sizes of candidate region inputs. When testing, the final segmentation are obtained by fusing the classification results of these models. The experimental results on SIFT FLOW and PASCAL Context datasets show that the proposed method has higher average accuracy than some state-of-the-art algorithms.
, Available online  , doi: 10.11999/JEIT181130 doi: 10.11999/JEIT181130
[Abstract](78) [FullText HTML] (46) [PDF 1961KB](10)
Abstract:
Network Function Virtualization (NFV) brings flexibility and dynamics to the construction of service chain. However, the software and virtualization may cause security risks such as vulnerabilities and backdoors, which may have impact on Service Chain (SC) security. Thus, a Virtual Network Function (VNF) scheduling method is proposed. Firstly, heterogeneous images are built for every virtual network function in service chain, avoiding widespread attacks using common vulnerabilities. Then, one network function is selected dynamically and periodically. The executor of this network function is replaced by loading heterogeneous images. Finally, considering the impact of scheduling on the performance of network functions, Stackelberg game is used to model the attack and defense process, and the scheduling probability of each network function in the service chain is solved with the goal of optimizing the defender’s benefit. Experiments show that this method can reduce the rate of attacker’s success while controlling the overhead generated by the scheduling within an acceptable range.
, Available online  , doi: 10.11999/JEIT181134 doi: 10.11999/JEIT181134
[Abstract](71) [FullText HTML] (43) [PDF 3929KB](6)
Abstract:
To solve the problem of heat dissipation in Three Dimensional Field Programmable Gate Array Technology (3D FPGA), an interconnect channel architectural design method with low thermal gradient feature is proposed. A thermal resistance network model is established for the 3D FPGA, and theoretical studies and thermal simulation experiments are carried out on the influence of different types of channels on the thermal performance of 3D FPGA. Further, non-uniform vertical direction channel structures of 3D FPGA are proposed. Experiments indicate that 3D FPGA designed using the method proposed can reduce the maximum temperature gradient between different layers by 76.8% and the temperature gradient within the same layer by 10.4% compared with the traditional channel structure of 3D FPGA.
, Available online  , doi: 10.11999/JEIT181103 doi: 10.11999/JEIT181103
[Abstract](142) [FullText HTML] (87) [PDF 713KB](17)
Abstract:
Most existing searchable encryption schemes only support the search for keyword sets, and the data users can not quickly identify the file keyword information returned by the server. Meanwhile, considering the server has strong computing power, it may judge keyword information from single keywords and the identity of the data consumer is not verified. In this paper, the data user and data owner are delegated server to verify whether the data ueer is a legitimate user; if legal, the delegated server can detect the validity of the return ciphertext with data user. The data user uses the server public key, keywords and pseudo-keywords to generate trapdoor, in order to ensure the indistinguishable of the keywords, a delegated multi-keyword searchable encryption scheme is designed, which is resistant to keyword guessing of data user authentication. Meanwhile, when the data owner encrypts, the public key of the cloud server, the delegated server, and the data user can be used to prevent collusion attacks. In the random oracle model the security of the proposed scheme is proved. The experiment results show that the scheme is efficient under the multi-keyword environment.
, Available online  , doi: 10.11999/JEIT181025 doi: 10.11999/JEIT181025
[Abstract](108) [FullText HTML] (77) [PDF 2410KB](9)
Abstract:
Firstly, a network Dynamic Threat Attribute Attack Graph (DT-AAG) analysis model is constructed by using Attribute Attack Graph theory. On the base of the comprehensive description of system vulnerability and network service-induced threat transfer relationship, a threat transfer probability measurement algorithm is designed in combination with Common Vulerability Scoring System (CVSS) vulnerability evaluation criteria and Bayesian probability transfer method. Secondly, based on the model, a Dynamic Threat Attribute Attack Graph generation Algorithm (DT-AAG-A) is designed by using the relationship between the threat and the vulnerability as well as the service. What’s more, to solve the problem that threat transfer loop existing in the generated attribute attack graph, the loop digestion mechanism is designed. Finally, the effectiveness of the proposed model and algorithm is verified by experiments.
, Available online  , doi: 10.11999/JEIT180886 doi: 10.11999/JEIT180886
[Abstract](114) [FullText HTML] (76) [PDF 1303KB](18)
Abstract:
The structure of Tree-Augmented Naïve Bayes (TAN) forces each attribute node to have a class node and a attribute node as parent, which results in poor classification accuracy without considering correlation between each attribute node and the class node. In order to improve the classification accuracy of TAN, firstly, the TAN structure is proposed that allows each attribute node to have no parent or only one attribute node as parent. Then, a learning method of building the tree-like Bayesian classifier using a decomposable scoring function is proposed. Finally, the low-order Conditional Independency (CI) test is applied to eliminating the useless attribute, and then based on improved Bayesian Information Criterion (BIC) function, the classification model with acquired the parent node of each attribute node is established using the greedy algorithm. Through comprehensive experiments, the proposed classifier outperforms Naïve Bayes (NB) and TAN on multiple classification, and the results prove that this learning method has certain advantages.
, Available online  , doi: 10.11999/JEIT181014 doi: 10.11999/JEIT181014
[Abstract](97) [FullText HTML] (62) [PDF 1532KB](8)
Abstract:
The basis of the identification of network security situation element is to perform the feature extraction of situation data effectively. Considering the problem that the Back Propagation(BP) neural networks have excessive dependence on data labels when it has a learning of massive security situation information data, a network security situation element identification method, is proposed which combines deep stack encoder and BP algorithm. It trains the network layer by layer through unsupervised learning algorithm. On this basis the deep track encoder by stacking can be obtained. The unsupervised training of the network is realized when using the encoder to extract the characteristic of the data sets. It is verified by simulation experiments that the method can improve the performance and accuracy of situational awareness effectively.
, Available online  , doi: 10.11999/JEIT181097 doi: 10.11999/JEIT181097
[Abstract](105) [FullText HTML] (82) [PDF 3174KB](26)
Abstract:
In order to improve the recognition rate of banknotes, the improved banknote recognition algorithm based on Deep Convolutional Neural Network(DCNN) is proposed. Firstly, the algorithm constructs a deep convolution layer by integrating transfer learning, Leaky-Rectified Liner Unit (Leaky ReLU) function, Batch Normalization(BN) and multi-level residual unit that perform stable and fast feature extraction and learning on input different size banknotes. Secondly, a fixed-size output representation of the extracted banknote features is obtained by using the improved multi-level spatial pyramid pooling algorithm. Finally, the banknote classification is implemented by the full connection layer and the softmax layer of the network. The experimental results show that the proposed algorithm can effectively improve the recognition rate of banknotes, and has better generalization ability and robustness than the traditional banknote classification method. At the same time, the algorithm can meet the real-time requirements of the banknote sorting system.
, Available online  , doi: 10.11999/JEIT181090 doi: 10.11999/JEIT181090
[Abstract](149) [FullText HTML] (103) [PDF 1304KB](26)
Abstract:
The MUltiple SIgnal Classification (MUSIC) algorithm is a classical spatial spectrum estimation algorithm. Taking L-shaped array as an example, an improved 2D-MUSIC algorithm is proposed for the problem that 2D-MUSIC algorithm often fails to estimate accurately targets in close proximity among multiple targets when the signal-to-noise ratio is low.The algorithm identifies the target location through spectrum peak search by first performing conjugate recombination on the covariance matrix generated by the classical 2D-MUSIC algorithm, then calculating the mean of sum of square of the recombined one and the original one as the new matrix, whose corresponding noise subspace then weighted by applying appropriate coefficients to obtain a new noise subspace. The computer simulation results show that compared with the 2D-MUSIC algorithm, the improved algorithm performs well on DOA estimation for the targets in close proximity among multiple targets when the received signal has low signal-to-noise ratio, which improves the resolution of 2D-DOA estimation with L-shaped array, with better engineering application value.
, Available online  , doi: 10.11999/JEIT180392 doi: 10.11999/JEIT180392
[Abstract](139) [FullText HTML] (70) [PDF 2807KB](14)
Abstract:
The Mann-Whitney rank sum test based Wireless Local Area Network (WLAN) indoor mapping and localization approach is proposed. Firstly, according to the localization accuracy requirement, this approach performs the motion paths segmentation in target area, and meanwhile merges the similar motion path segments based on the Mann-Whitney rank sum test. Then, a signal clustering algorithm based on the similar Received Signal Strength (RSS) sequence segments is adopted to guarantee the physical adjacency of the RSS samples in the same cluster. Finally, the backbone nodes based diffusion mapping is used to construct the mapping relations between the physical and signal spaces, and the motion user localization is consequently achieved. The experimental results indicate that compared with the existing WLAN indoor mapping and localization approaches, the proposed one is able to achieve higher mapping and localization accuracy without motion sensor assistance or location fingerprint database construction.
, Available online  , doi: 10.11999/TEIT180875 doi: 10.11999/TEIT180875
[Abstract](222) [FullText HTML] (71) [PDF 2138KB](20)
Abstract:
Surface Acoustic Wave (SAW) resonator measuring technology can be used in high temperature and high pressure, strong electromagnetic radiation and strong electromagnetic interference to realize wireless passive parameter detection. Based on the the non-stationary characteristics of the SAW signal, a kind of echo signal frequency estimation method, Digital Frequency Significant Place Tracking method (DFSPT) is put forward. Compared with the existing methods based on Fast Fourier Transform (FFT) and Singular Value Decomposition (SVD), the simulation results show that the method can determine the number of significant digits of digital frequency according to the difference of signal-to-noise ratio. Thus, it can increase stability and accuracy. The experiment of wireless SAW temperature sensor shows that the frequency estimation standard deviation of this method is small and the robustness is high.
, Available online  , doi: 10.11999/JEIT180737 doi: 10.11999/JEIT180737
[Abstract](108) [FullText HTML] (57) [PDF 931KB](18)
Abstract:
, Available online  , doi: 10.11999/JEIT180914 doi: 10.11999/JEIT180914
[Abstract](170) [FullText HTML] (84) [PDF 3247KB](18)
Abstract:
Because of restricted earth-based tracking network, Tracking, Telemetry and Command (TT&C) for lunar orbit micro-satellite is depended on Unified S/X Band (USB) antennas in China Chang’E-4 lunar exploration. Based on analysis of the geometry between relay satellite, micro-satellite and earth-based antennas during earth-moon transfer orbit, an applicable method to acquire delay observable through Same-Beam Interferometry (SBI) tracking by China deep space network is discussed. Benefited from more kinds of tracking resources and high accuracy orbit of relay satellite, delay observable for angular position measurement of micro-satellite in the order of 1 ns is obtained, which improves the micro-satellite orbit determination accuracy from 2 km to less than 1 km and improves orbit prediction accuracy from 6 km to 2 km. SBI tracking plays an important role in short arc orbit determination of micro-satellite.
, Available online  , doi: 10.11999/JEIT180898 doi: 10.11999/JEIT180898
[Abstract](55) [FullText HTML] (39) [PDF 1288KB](9)
Abstract:
In order to solve the problem of member information leakage in multi-party cooperative design of integrated circuits, a orthogonal obfuscation scheme of multi-hardware IPs core security protection is proposed. Firstly, the orthogonal obfuscation matrix generates orthogonal key data, and the obfuscated key of the hardware IP core is designed with the physical feature of the Physical Unclonable Function (PUF) circuit. Then the security of multiple hardware IP cores are realized by the orthogonal obfuscation state machine. Finally, the validity of orthogonal aliasing is verified using the ISCAS-85 circuit and cryptographic algorithm. The multi-hardware IP core orthogonal obfuscation scheme is tested under Taiwan Semiconductor Manufacturing Company (TSMC) 65 nm process, the difference of Toggle flip rate between the correct key and the wrong key is less than 5%, and the area and power consumption of the larger test circuit are less than 2%. The experimental results show that orthogonal obfuscation can improve the security of multi-hardware IP cores, and can effectively defend against member information leakage and state flip rate analysis attacks.
, Available online  , doi: 10.11999/JEIT180897 doi: 10.11999/JEIT180897
[Abstract](146) [FullText HTML] (78) [PDF 1518KB](28)
Abstract:
Recently, the mobile charging and data collecting by using Mobile Equipment (ME) in Wireless Sensor Networks (WSNs) is a hot topic. Existing studies determine usually the traveling path of ME according to the charging requirements of sensor nodes firstly, and then handle the data collecting. In this paper, charging requirement and data collecting are taken into consideration simultaneously. A one-to-many charging and data collecting model for ME are established with two optimization objectives, maximizing the total energy utilization and minimizing the average delay of data collecting. Due to the limited energy of the ME, the path planning strategy and the equalization charging strategy are designed. An improved multi-objective ant colony algorithm is proposed to solve the problem. Experiments show that the objective values, the number of Pareto solutions, the homogeneity of Pareto solutions and the distribution of Pareto solutions obtained by the proposed algorithm are all superior to NSGA-II algorithm.
, Available online  , doi: 10.11999/JEIT180812 doi: 10.11999/JEIT180812
[Abstract](107) [FullText HTML] (47) [PDF 3633KB](10)
Abstract:
For the problem of Direct Sequence-Code Division Multiple Access (DS-CDMA) signal in traditional asynchronous single-channel, including blind estimation of the Pseudo-Noise (PN) sequence and information sequence, a method using multi-channel synchronous and asynchronous based on PARAllel FACtor (PARAFAC) is proposed. Firstly, the signal is modeled as a multi-channel receiving model, then the observed data matrix is equivalent to a factor model. Finally, the iterative least squares algorithm is applied to decompose the parallel factor, and the information sequence and PN sequences of DS-CDMA signals are further estimated. The simulation results show that the proposed method not only can effectively estimate the PN sequence and information sequence of the short code DS-CDMA signal, but also the estimation of 6 user PN sequences can be realized under the condition of the number of channels is 10 and the Signal-to-Noise Ratio (SNR) is –10 dB.
, Available online  , doi: 10.11999/JEIT180798 doi: 10.11999/JEIT180798
[Abstract](112) [FullText HTML] (49) [PDF 3659KB](11)
Abstract:
In order to encode better the depth maps in 3D video, the 3D-High Efficiency Video Coding (3D-HEVC) standard is introduced in Depth Modeling Modes(DMMs), which increase the quality of original algorithm while improving the encoding complexity. The traditional architecture of DMM-1 encoder circuit has a longer coding period and can only meet real-time coding requirements of lower resolution and frame rate. In order to improve the performance of DMM-1 encoder, the structure of DMM-1 algorithm is researched and a five-stage pipeline architecture of DMM-1 encoder is proposed. The pipeline architecture can reduce the coding cycles. The architecture is implemented by Verilog HDL. Experiments show that this architecture can reduce the coding cycle by at least 52.3%, at the cost of 1568 gates compared to previous work by Sanchez G. et al. (2017).
, Available online  , doi: 10.11999/JEIT180933 doi: 10.11999/JEIT180933
[Abstract](147) [FullText HTML] (69) [PDF 1032KB](14)
Abstract:
Bistatic radar has the advantages of high concealment and strong anti-interference performance, and plays an important role in modern electronic warfare. Based on the principle of radar coincidence imaging, the problem of bistatic radar coincidence imaging of moving targets is studied. Firstly, based on the bistatic radar system that uses uniform linear array as the transmitting and receiving antenna, the characteristics of the moving target radar echo signal are analyzed under the condition of transmitting random frequency modulation signal, and a bistatic radar coincidence imaging parametric sparse representation model is established. Secondly, an iterative coincidence imaging algorithm based on sparse Bayesian learning is proposed for the parametric sparse representation model established. Based on the Bayesian model, the sparse reconstructed signal is obtained by Bayesian inference, so that the moving target imaging and accurate estimation of motion parameters can be achieved. Finally, the effectiveness of the proposed method is verified by simulation experiments.
, Available online  , doi: 10.11999/JEIT180850 doi: 10.11999/JEIT180850
[Abstract](151) [FullText HTML] (77) [PDF 634KB](18)
Abstract:
The definition and security models of partial blind signcryption scheme in heterogeneous environment between CertificateLess Public Key Cryptography (CLPKC)and Traditional Public Key Infrastructure (TPKI) are propsed, and a construction by using the bilinear pairing is propsed. Under the random oracle model, based on the assumptions of Computational Diffie-Hellman Problem(CDHP) and Modifying Inverse Computational Diffie-Hellman(MICDHP), the scheme is proved to meeting the requirment of the unforgeability, confidentiality, partial blindness, and untraceability, undeniability. Finally, compared with the related scheme, the scheme increases the blindness and does not significantly increase the computational cost.
, Available online  , doi: 10.11999/JEIT180793 doi: 10.11999/JEIT180793
[Abstract](163) [FullText HTML] (86) [PDF 1985KB](25)
Abstract:
Ontology, as the superstructure of knowledge graph, has great significance in knowledge graph domain. In general, structural redundancy may arise in ontology evolution. Most of existing redundancy elimination algorithms focus on transitive redundancies while ignore equivalent relations. Focusing on this problem, a redundancy elimination algorithm based on super-node theory is proposed. Firstly, the nodes equivalent to each other are considered as a super-node to transfer the ontology into a directed acyclic graph. Thus the redundancies relating to transitive relations can be eliminated by existing methods. Then equivalent relations are restored, and the redundancies between equivalent and transitive relations are eliminated. Experiments on both synthetic dynamic networks and real networks indicate that the proposed algorithm can detect redundant relations precisely, with better performance and stability compared with the benchmarks.
, Available online  , doi: 10.11999/JEIT180828 doi: 10.11999/JEIT180828
[Abstract](181) [FullText HTML] (89) [PDF 2158KB](21)
Abstract:
In order to reconstruct natural image from Compressed Sensing(CS) measurements accurately and effectively, a CS image reconstruction algorithm based on Non-local Low Rank(NLR) and Weighted Total Variation(WTV) is proposed. The proposed algorithm considers the Non-local Self-Similarity(NSS) and local smoothness in the image and improves the traditional TV model, in which only the weights of image’s high-frequency components are set and constructed with a differential curvature edge detection operator. Besides, the optimization model of the proposed algorithm is built with constraints of the improved TV and the non-local low rank model, and a non-convex smooth function and a soft thresholding function are utilized to solve low rank and TV optimization problems respectively. By taking advantage of them, the proposed method makes full use of the property of image, and therefore conserves the details of image and is more robust and adaptable. Experimental results show that, compared with the CS reconstruction algorithm via non-local low rank, at the same sampling rate, the Peak Signal to Noise Ratio(PSNR) of the proposed method increases 2.49 dB at most and the proposed method is more robust, which proves the effectiveness of the proposed algorithm.
, Available online  , doi: 10.11999/JEIT180874 doi: 10.11999/JEIT180874
[Abstract](209) [FullText HTML] (111) [PDF 1132KB](18)
Abstract:
In order to solve the problem that users can not request to exit during the bitcoin confusion process, an anonymous revocation scheme for Bitcoin confusion is proposed. The commitment is used to bind the user with its destination address. When the user requests to quit the shuffle service, a zero-knowledge proof of the commitment is made using the accumulator and the signatures of knowledge. Finally, the shuffled output address of the user who quits the service is modified to its destination address. Security analysis shows that the scheme satisfies the anonymity of the user who quits the service based on the double discrete logarithm problem and the strong RSA assumption, and can be implemented without modifying the current bitcoin system. The scheme allows at most n–2 users to exit in the confusion process of n (n≥10) honest users participation.
, Available online  , doi: 10.11999/JEIT180948 doi: 10.11999/JEIT180948
[Abstract](144) [FullText HTML] (68) [PDF 2508KB](15)
Abstract:
Based on the analysis on the difference between vector tracking loop and scalar tracking loop on fault detection, it is pointed out that in vector receiver of Global Navigation Satellite System (GNSS), the detection statistic of Receiver Autonomous Integrity Monitoring (RAIM) algorithm is inaccurate because of the influence of noise, and the propagation of fault information in the loop makes it difficult to identify the fault source. To solve the problems, a double loop tracking structure based on pre-filter is proposed after modifying the structure of vector receiver. In the new receiver, the influence of noise is reduced by pre-filter based on cubature Kalman filtering algorithm, and the fault information is prevented from propagating to each other by switching the loop. Finally, the method is verified by simulation. Simulation results show that the improved vector receiver not only greatly reduces the mean and variance of RAIM detection statistics, but also improves the accuracy of fault identification. Thus, the performance of RAIM is significantly improved.