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

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, doi: 10.11999/JEIT200056
[Abstract](36) [FullText HTML](34) [PDF 2055KB](4)
Abstract:
The application of Unmanned Air Vehicles (UAV)-enabled Cognitive Radio (CR) is widely used due to the convenience and high mobility of the UAV. In the UAV-based Cognitive Radio Network (CRN), the throughput optimization scheme in single radian is firstly investigated, in which the sensing radian is optimized to maximize the average throughput of UAV. Then, a multi-radian throughput optimization scheme based on Cooperative Spectrum Sensing (CSS) is studied to improve the sensing performance under the non-ideal channel, and the throughput of the UAV is maximized by utilizing an Alternative Iterative Optimization (AIO) algorithm. The simulation results show that the proposed scheme has better performance on improving the throughput of the UAV and ensuring the Quality-of-Service (QoS) of the Primary User (PU) when the channel fading is serious.
, doi: 10.11999/JEIT190850
[Abstract](30) [FullText HTML](21) [PDF 813KB](5)
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Currently, a main problem in software is repackaging or plagiarization, which means attackers can add malicious payloads or advertisements into legitimate APPs through piggybacking, it greatly threatens the users and original developers. In this paper, a novel Software Watermarking method based on Program Execution Time (SW_PET) is proposed. By generating a variety of effect-canceling operations, the watermark information can be encoded into the form of program execution time, and can be embedded into Android APPs. In the detection process, the watermark information is extracted and compared with the original watermark to check whether the APP is repackaged. This method can be combined with other types of watermarks (e.g., picture-based watermarks) in order to enhance the robustness. Finally, the effectiveness of the proposed approach is verified, and the overhead introduced by the watermark is measured, which is demonstrated to be minimal.
, doi: 10.11999/5EIT190649
[Abstract](9) [FullText HTML](4) [PDF 770KB](1)
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To ensure the security of MIMO heterogeneous networks while facing the active multi-antenna eavesdropping, a robust secure transmission scheme based on artificial noise is proposed. Firstly, considering that the eavesdropper sends uplink pilot interference signal, the influence of uplink pilot interference on the channel estimation of legitimate users is studied. Then, based on the channel estimation results, the precoding matrix of downlink data and noise signals of macro base station and micro base station are designed, respectively. The expression of system security rate in this case is derived; after that, for maximizing the system security rate, the transmission power of downlink data and noise signal of base station are optimized, and a solution method based on one-dimensional linear search is proposed. Furthermore, considering the situation that the eavesdropper sends the uplink pilot interference and then sends the noise to interfere with the downlink communication of the legitimate user, a discrete zero-sum game method is proposed to obtain the optimal transmission power design. The simulation results verify the security and robustness of the proposed scheme.
, doi: 10.11999/JEIT190935
[Abstract](18) [FullText HTML](10) [PDF 2212KB](3)
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To solve the problem of blind identification of polar codes’ parameters, a blind recognition algorithm of polar codes based on zero space matrix matching is proposed. The construction of polar codes’ generation matrix is certain, and all the generation matrices are full rank square matrices, first delete the rows corresponding to the frozen bit codes by using the channel reliability estimation in the polar code encoding. Then find out the null space matrix of this matrix in the binary field as the supervision matrix under the code length. Multiply iteratively the code word by the supervision matrix of different code lengths, according to the proportion of "1" in the product result; the code length, number and position distribution of information bits of the code word are determined. The simulation results show that for the 200 groups of polar code with 64-code-length and 30-information-bits, the recognition rate can be kept above 80% when the maximum bit error rate is less than 0.06.
, doi: 10.11999/JEIT190755
[Abstract](12) [FullText HTML](8) [PDF 8216KB](0)
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Rainy days and other severe weather will seriously affect the image quality, thus affecting the performance of vision processing algorithms. In order to improve the imaging quality of rain images, a rain removal algorithm based on multi-channel multi-scale convolution neural network to extract rain line features is proposed. Firstly, the rain images are decomposed by wavelet threshold-guided bilateral filtering to obtain high-frequency rain line images and low-frequency background images with high contour preservation. Then, in order to make the rain line information in the high-frequency part of the image more obvious and reduce the background misjudgment in the high-frequency image during the rain line feature learning, the obtained high-frequency rain line image is passed through a filter again to obtain a higher-frequency rain line image with reduced background information and enhanced rain line information. Secondly, in view of the large amount of raindrop imprint left on the low-frequency background image, it is proposed to send the low-frequency background image and the higher-frequency rain line image together into the convolution neural network for feature learning, in which multi-scale feature information is extracted from the image, finally, a more complete restoration image with rain line removal is obtained. At the same time, when constructing the network model, hole convolution is used instead of standard convolution to extract the feature information of the image, thus obtaining richer image features and improving the rain removal performance of the algorithm. From the experimental results, after removing rain, the image is clear and the detail retention is high.
, doi: 10.11999/JEIT190751
[Abstract](24) [FullText HTML](22) [PDF 4740KB](4)
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In the ultra-dense heterogeneous wireless network composed of heterogeneous cellular networks and wireless local area networks, vehicle terminals with variable speeds will face more frequent handovers, resulting in the deterioration of user’s Quality of Service (QoS). For the above problems, firstly, the Gauss Markov mobility model is used to predict the position of the vehicle terminal at the next moment, and the candidate network set that meets the terminal service quality is selected to make the intersection with the current candidate network set. Secondly, if the current access network is not in the intersection, the variable-step firefly algorithm is used to find the best network. Thirdly, the terminal that fails to switch due to the prediction error is migrated to the macro cellular to ensure the continuity of communication. Simulation results show that the proposed algorithm can reduce the frequent handoff phenomenon, such as ping pong handoff in the ultra-dense heterogeneous wireless network. Meanwhile, it can improve the user service quality and network throughput.
, doi: 10.11999/JEIT190853
[Abstract](36) [FullText HTML](39) [PDF 352KB](5)
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As a kind of important information security products, the cryptographic technique adopted by cryptographic products guarantees the confidentiality, integrity and non-repudiation of information. The side channel attack is an important security threat against cryptographic products. It mainly utilizes the leakage of side information (such as time, power consumption, etc.) during the operation of cryptographic algorithm, and attacks by analyzing the dependence between side information and secret information. It has become an important test content to evaluate the ability of cryptographic products to defend against the side channel attack. The development of side channel evaluation of cryptographic products is introduced from three aspects of attack test, general evaluation and formal verification. The attack test is the most popular way adopted in side channel evaluation, which aims to recover the secret imformation such as the key by executing specific attack process. The latter two methods are not for the purpose of recovering secret information, but focus on assessing whether there is any side information leakage in the cryptographic implementation. They are more general than the attack test because they do not require the evaluator to go into the details of the attack process and implementation. The general evaluation is to describe the degree of information leakage by means of statistical test and information entropy calculation. For example, Test Vector Leakage Assessment (TVLA) technology is widely used at present. The formal method is a new development direction to evaluate the effectiveness of side channel protection strategy which has the advantage that it can automatically/semi-automatically evaluate whether the cryptographic implementation has side channel attack vulnerability. The latest results of formal verification for different protection strategies such as software mask, hardware mask and fault protection is introduced in this paper, mainly including program verification, type inference and model counting.
, doi: 10.11999/JEIT190870
[Abstract](17) [FullText HTML](16) [PDF 2618KB](6)
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With the continuous development of smart card technology, the security of smart card chip is facing more and more challenges. Among many encryption algorithms, Data Encryption Standard(DES) algorithm is a widely used symmetric encryption and decryption algorithm. In order to resist all kinds of side channel attacks, the most widely used method is to eliminate correlation of the real key and power consumption through the masking technology in the algorithm. A new cyclic mask scheme for DES is proposed. Compared with the pre-calculated mask scheme in the previous literature, not only the pre-calculation amount is greatly reduced, but also the intermediate data in the whole DES operation process is masked. After the mask is split, it can also protect against high-order attacks.
, doi: 10.11999/JEIT190893
[Abstract](85) [FullText HTML](68) [PDF 1625KB](8)
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For the security of industrial control system, a framework for Numerical Control System(NCS) network security protection technology is proposed. The SM2, SM3 and SM4 algorithms in the domestic cryptographic algorithms are used to design and establish the AUTH-VRF model of the Computerized Numerical Control(CNC) network, which provides security protection for both internal and external sides. The external side conducts the security authentication for communication and transmission between CNC network devices to achieve network segment isolation. The internal side verifies communication protocol integrity to ensure that the operating procedures received by the field devices are correct and valid. The external protection device designed and deployed based on the SM2, SM3 and SM4 algorithms provides identity authentication and file encryption transmission for communication between the Distributed Numerical Control(DNC) device and the CNC system. At the same time, for the proprietary industrial communication protocol data in the CNC network, the SM3 algorithm is used to verify its integrity. The network attack experiments prove that the AUTH-VRF model can provide effective security certification and integrity protection for industrial production data in CNC networks. It also provides a practical technical approach to meet the requirements of ‘secure and controllable both for domestic and foreign products’, as well as ‘applying security technique to all layers of Industrial Control Systems’ for protecting the critical infrastructure.
, doi: 10.11999/JEIT190534
[Abstract](21) [FullText HTML](14) [PDF 2222KB](0)
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Based on the target detection equation of the passive interferometric microwave system, the effects of complex weather on the detection ability of the passive interferometric microwave system are discussed for the sea surface target, such as clouds, fog and rain, and sea winds. Quantitative simulations are also performed to assess the effects of these previous mentioned factors. The experiments are also performed to demonstrate the passive interferometric microwave system penetrating the clouds. Both theoretical and simulation results show that complex weather has a negative impact on the passive interferometric microwave systems in the sea target detection, such as clouds, fog and rain. However, the impacts can be neglected in low frequency, since the impacts of clouds in low frequency is very small. On the other hand, rainfall will seriously degrade the system’s target detection capability. Sea winds have a positive impact in the metallic target detection. However, sea winds have a negative impact and reduce the system’s detection capability for the stealthy target detection.
, doi: 10.11999/JEIT190880
[Abstract](30) [FullText HTML](18) [PDF 4477KB](1)
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DNA strand displacement technology has the characteristics of spontaneity, parallelism, programmability and dynamic cascade, which is widely used to solve mathematical problems. In this paper, a two-bit subtracter is designed by using Gray code encoding, and DNA strand displacement technology extends the operation of DNA subtraction. Finally, Visual DSD software is used to simulate the two-bit subtracter. The circuit, with the strong parallelism and expansibility, achieves the expected function. It can be used in combination with other biochemical circuits.
, doi: 10.11999/JEIT190969
[Abstract](18) [FullText HTML](15) [PDF 2112KB](0)
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In view of the current deployment of the Service Function Chain (SFC), the failure importance of the Virtual Network Function (VNF) is not considered,an SFC reliable deployment algorithm based on deep reinforcement learning is proposed. Firstly, establish a reliable mapping model of VNF and virtual links, set high reliability requirements for important VNFs, and ensure the reliability requirements of virtual links as much as possible through link deployment length restrictions. Secondly, taking load balancing as the resource coordination principle, joint optimization with VNF reliability. Finally use deep reinforcement learning to get the service function chain deployment strategy. In addition, node backup and link backup strategies based on importance are proposed to deal with situations where VNF/link reliability is difficult to meet during deployment. Simulation results show that the reliable deployment algorithm in this paper can effectively reduce the failure SFC loss on the basis of ensuring the reliability requirements, and at the same time make the virtual network more stable and reliable.
, doi: 10.11999/JEIT190728
[Abstract](46) [FullText HTML](40) [PDF 1514KB](5)
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With the rapid development of the Internet of Things (IoT), Mobile Edge Computing (MEC) becomes increasingly effective in improving processing capacity and providing low-latency computing services. However, in the time-varying MEC-IoT environment, heterogeneous devices and applications cause serious challenges on efficient task offloading and resource allocation. A Distributed Dynamic Heterogeneous task offloading Methodology (D2HM) algorithm is proposed in this paper by exploiting Game theory and Lyapunov optimization, which can achieves heterogeneous control and allocation of computation resources by dynamic quote price mechanism. Simulation results show that the proposed algorithm can meet the diverse computing needs of heterogeneous tasks and reduce the average delay of the system while ensuring network stability.
, doi: 10.11999/JEIT191009
[Abstract](28) [FullText HTML](17) [PDF 1146KB](1)
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Under the directed network topology, the consensus of the second order multi-agent system with large communication delay is studied, the protocol of delayed-state-derivative feedback with weighting term is proposed, which improves the problem of system oscillation caused by large communication delay. Firstly, the delayed-state-derivative feedback protocol with weighting term is introduced, and the closed-loop form of the second-order multi-agent system is given. Then, the sufficient and necessary conditions for the asymptotic steady-state consensus of the second-order multi-agent system are obtained by using the frequency domain analysis method, and it is proved that the second-order multi-agent system can tolerate the greater communication delay under the delayed-state-derivative feedback protocol with weighting term. Finally, the advantage of the delayed-state-derivative feedback protocol with weighting term is verified by numerical simulation.
, doi: 10.11999/JEIT190743
[Abstract](16) [FullText HTML](18) [PDF 824KB](1)
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For the faultiness that the recent branch obfuscation method is only efficient on branch condition formed by integer comparison. The relations between the binary representation and big or small comparison of floats are analyzed. The idea that the floats in float interval has prefix matching relation with the prefix set which comes from the binary representation interval of the floats is proved. By protecting the prefix set with hash function, and based on the comparison of prefix-hash, a new branch obfuscation method which works well on the branch formed by float number comparison is proposed. The new obfuscation method is powerful on symbolic execution combating and obfuscation recovery combating. At last, the obfuscation proposed in this paper is confirmed to be practical, and is useful to be against symbolic execution and obfuscation recovery.
, doi: 10.11999/JEIT190349
[Abstract](52) [FullText HTML](50) [PDF 3241KB](9)
Abstract:
With the intelligent development of urban traffic, accurate and efficient access to available parking spaces is essential to solve the increasingly difficult problem of parking difficulties. Therefore, this paper proposes a deep convolutional neural network parking occupancy detection algorithm based on non-local operation. For the image characteristics of parking spaces, non-local operations are introduced, the similarity between distant pixels is measured, and the high-frequency features of the edges are directly obtained. The local details are obtained by using small convolution kernels, and the network is trained in an end-to-end manner. In the experiment, the network structure is optimized by setting different convolution kernel sizes and non-local module layers. The experimental results show that compared with the traditional texture feature-based parking space occupancy detection algorithm, the proposed algorithm has significant advantages in both prediction accuracy and generalization performance of the model. At the same time, compared with the currently widely used convolutional neural network based on local feature extraction, the algorithm also has great advantages. In real scenes, the algorithm also has high precision and has practical application value.
, doi: 10.11999/JEIT190117
[Abstract](65) [FullText HTML](55) [PDF 2249KB](13)
Abstract:
The major drawback of Short Reference Differential Chaos Shift Keying (SR-DCSK) system is the low data transmission rate. To solve the problem, a Short Reference Multi-User Differential Chaos Shift Keying (SR-MUDCSK) communication scheme is proposed. The orthogonality of Walsh code is used to transmit the information of multiple users, which effectively improves the data transmission rate. The theoretical Bit Error Rate (BER) performance is analyzed, and experimental simulation is carried out under AWGN and multipath Rayleigh fading channel environment, respectively. The results show that the system has a significant improvement in transmission rate, and the energy efficiency is also significantly improved. Therefore, it is of great value in application.
, doi: 10.11999/JEIT190273
[Abstract](111) [FullText HTML](101) [PDF 5195KB](14)
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The generative adversarial network receives extensive attention in the study of computer vision such as image fusion and image super-resolution, due to its strong ability of generating high quality images. At present, the remote sensing image fusion method based on generative adversarial network only learns the mapping between the images, and lacks the unique Pan-sharpening domain knowledge. This paper proposes a remote sensing image fusion method based on optimized generative adversarial network with the integration of the spatial structure information of panchromatic image. The proposed algorithm extracts the spatial structure information of the panchromatic image by the gradient operator. The extracted feature would be added to both the discriminator and the generator which uses a multi-stream fusion architecture. The corresponding optimization objective and fusion rules are then designed to improve the quality of the fused image. Experiments on images acquired by WorldView-3 satellites demonstrate that the proposed method can generate high quality fused images, which is better than the most of advanced remote sensing image fusion methods in both subjective visual and objective evaluation indicators.
, doi: 10.11999/JEIT190306
[Abstract](73) [FullText HTML](61) [PDF 1277KB](10)
Abstract:
, doi: 10.11999/JEIT190309
[Abstract](111) [FullText HTML](84) [PDF 3194KB](20)
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The micro-Doppler modulation generated by the rotor rotation of UAV can reflect the micro-movement characteristics of such targets. Accurately estimating the rotor length and rotation frequency of the UAV is of great significance for UAV detection and recognition. In this paper, a method for estimating micro-movement parameters of multi-rotor UAV based on Concentration of Time-Frequency (CTF) is proposed for FMCW radar system. The mapping relationship between dynamic parameters of UAV rotor and signal parameters of micro-Doppler component is deduced. Based on time-frequency concentration index in time-frequency rotation domain, the discrimination of micro-motion components is improved. Compared with the traditional methods, the proposed method can improve the accuracy of multi-component micro-Doppler parameter. Furthermore, it has good robustness in low SNR. The validity of the method is verified by simulation and field test.
, doi: 10.11999/JEIT190320
[Abstract](96) [FullText HTML](69) [PDF 1442KB](14)
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For blind modulation recognition of Multiple Input Multiple Output (MIMO) signals in non-cooperative communication, a modulation recognition method based on Independent Component Analysis (ICA) and feature extraction is proposed. According to the signal independence of each transmitting antenna in space division multiplexing MIMO system, the ICA algorithm is used to separate the transmitting signal from the received mixed signal. In order to realize modulation recognition under completely blind condition, the Minimum Description Length (MDL) criterion is used to estimate the number of transmitting antennas before ICA separation. After obtaining the transmitted signal, four characteristic parameters are constructed by using six-order cumulant, cyclic spectrum and fourth-power spectrum algorithm, and then the modulation type of the signal is identified by using hierarchical neural network classifier. The simulation results show that the proposed method can effectively recognize {2PSK, 2ASK, 2FSK, 4PSK, 4ASK, MSK, 8PSK, 16QAM} eight MIMO signals at low SNR. When the number of transmitting antennas is 2, the number of receiving antennas is 5 and the SNR is 2dB, the recognition rate can reach more than 98%.
, doi: 10.11999/JEIT190345
[Abstract](75) [FullText HTML](45) [PDF 4830KB](4)
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For security of stereoscopic video, a video encryption and information hiding algorithm based on Context-based Adaptive Binary Arithmetic Coding (CABAC) is proposed. Firstly, with the analysis of Multi-view Video Coding (MVC), the physical mechanism of error drift is investigated. By applying the stereoscopic masking effect, the frames to be encrypted and the frames to be embedded are determined. Secondly, by using a CABAC bin-string substitution technique, the encryption and data hiding of stereoscopic video are implemented. Experimental implementation reveals that the video stream has the format compatibility and the bit-rate remains unchanged after encryption and data hiding. The video quality degradation is negligible. The algorithm has significant advantages in computational complexity and rate increase.
, doi: 10.11999/JEIT190634
[Abstract](64) [FullText HTML](43) [PDF 1495KB](11)
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In multistatic radar system, the real target echoes are independent of each other in different node radars under long-term baseline conditions, but the amplitudes of the deception jamming signals in different node radars are completely correlated because of the jamming signals generated from the same jammer. The difference is used to realize the recognition of deception jamming in multistatic radar system on the stage of tracking is proposed in this paper. Correlation measurement and parameter estimation are carried out on the amplitude sequence of the received signals by different nodes, and the test statistic is constructed to realize the recognition of deception jamming under the given false alarm probability. The simulation results show that the proposed method has a good performance on the recognition of deception jamming. Compared to the Anderson-Darling (AD) test based on goodness-of-fit, the recognition probability increases by an average of 18.63%.
, doi: 10.11999/JEIT190330
[Abstract](57) [FullText HTML](46) [PDF 1846KB](5)
Abstract:
For the high complexity of High Efficiency Video Coding (HEVC) intra prediction coding algorithm, an HEVC intra prediction optimization algorithm based on Region Of Interest (ROI) is proposed. Firstly, the algorithm divides the Region Of Interest and Non-Region Of Interest (NROI) of the current frame according to image saliency; Then, the final grading depth of the current coding unit is determined by the proposed fast CU (Coding Unit) partitioning algorithm based on spatial correlation in the ROI, and the unnecessary CU partitioning process is skipped. Finally, the proposed PU (Prediction Unit) mode fast selection algorithm is used to calculate the energy and direction of the current PU based on the ROI, and the current PU prediction mode is determined according to the energy and direction, and the correlation calculation of the rate distortion cost is reduced, Achieving the purposes of reducing coding complexity and saving coding time. The experimental results show that the proposed algorithm can reduce the coding time by 47.37% on average when the Peak Signal-to-Noise Ratio (PSNR) loss is only 0.0390 dB.
, doi: 10.11999/JEIT190533
[Abstract](613) [FullText HTML](612) [PDF 1918KB](6)
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In order to solve the problem of inter-core crosstalk in Space Division Multiplexing Elastic Optical Network (SDM-EON), which leads to the decline of service transmission quality and the increase of blocking probability, a routing, fiber core and spectrum allocation method for reducing inter-core crosstalk through sparse configuration spectrum converter at nodes is proposed in the paper. This method configures the spectrum converter according to the node’s centrality sparseness in SDM-EON. During service routing, a weighting method for optical path selection considering both optical path load and node spectrum conversion capability is designed.to reduce crosstalk. In the core spectrum allocation stage, a method of fiber core grouping and spectrum partition allocation is utilized. Finally, spectrum conversion is used to reduce traffic crosstalk and improve bandwidth blocking probability for services with high crosstalk. The simulation results show that the proposed algorithm can effectively improve the spectrum utilization and reduce the bandwidth blocking probability caused by fibers inter-core crosstalk.
, doi: 10.11999/JEIT190558
[Abstract](607) [FullText HTML](256) [PDF 2271KB](11)
Abstract:
To eliminate the blocking effects in the dynamic recovery of the streaming signals observed from multiple tasks in time domain, a streaming multi-task sparse Bayesian learning based algorithm and its robust enhanced version are proposed in this paper, where the former extends Lapped Orthogonal Transform (LOT) sliding window in time domain to multi-task condition, and decouples the estimation of unknown noise accuracy from signal reconstruction by Bayesian probability modeling and omits it, the latter further introduces the measurement of reconstructed uncertainty, which improves the robustness of the algorithm and the ability to suppress the accumulation of errors. Experimental results based on measured meteorological data shows that the proposed algorithms have significantly higher reconstruction accuracy, success rate and running speed than the representative algorithms in the field of compressed sensing from multiple measurement vectors, namely, the Temporal Multiple Sparse Bayesian Learning (TMSBL) algorithm and the Multi-Task-Compressed Sensing (MT-CS) algorithm, under different conditions of Signal-to-Noise Ratios, number of observations and tasks.
, doi: 10.11999/JEIT190561
[Abstract](498) [FullText HTML](216) [PDF 2208KB](9)
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Survivable virtual optical network mapping is an important technology to improve the optical network response to disaster failures. In order to solve the problem of bandwidth capacity loss caused by multi-area faults resulted from disasters in Elastic Optical Networks (EONs), a multi-area disaster fault model of survivable virtual network based on risk assessment is established, and a Disaster Fault Model through Ant Colony Optimization for Virtual Network Mapping (DFM-ACO-VNM) algorithm is proposed in the paper. An optical node ranking mapping criterion based on node resources and global potential failure probability of adjacent links in EONs is designed. Then, a heuristic information formula is designed to realize cooperative mapping of virtual nodes and virtual links with minimum bandwidth capacity loss under multi-area faults. The simulation results show that the proposed algorithm can decrease the bandwidth capacity loss, reduce the bandwidth blocking probability and improve the spectrum utilization in multi-area faults.
, doi: 10.11999/JEIT190427
[Abstract](778) [FullText HTML](441) [PDF 3006KB](43)
Abstract:
The monolithic signal processing circuit system for Light Detection And Ranging (LiDAR) measurement has significant practical values in terms of improving LiDAR measurement accuracy and data rate, shortening measurement time, and reducing equipment size and power consumption. As the environment interface problem is less considered, the appropriate input interface model must be established to break through the technology difficulty to associate circuit system with photodetectors, die chip, package, transmission line, test board and so on in the operating frequency range. By the combination of theoretical analysis and model simulation, the real working environment of circuit systemfor LiDAR signal processing can be simulated reasonably. Furthermore, based on CMOS technology, the signal processing circuit chip is tested with different photodetector parasitic capacitances. The well agreements between simulation and the testing results validate the feasibility of the input interface model.
, doi: 10.11999/JEIT190532
[Abstract](1480) [FullText HTML](746) [PDF 3469KB](14)
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Integrated Circuits (ICs) are suffering severer threats caused by Hardware Trojans (HTs), some of which hide in routine operations by coercing firmware or hardware. Along with conventional side-channel detection not always getting golden-chip, HTs become more difficult to detect. An improved Support Vector Machine (SVM) machine learning frameworks for this is proposed using system-level side-channel analysis. Cross validation experimental results on Field Programmable Gate Array (FPGA) show that in the condition of golden-chip, supervised SVM achieves 85.8% test accuracy in average. After grouping, outlier-removing and normalization, it rises by 4%. Even if golden-chip is out of hand, semi-supervised SVM has accuracy to judge HTs existence, averaging in 52.9%-79.5% under different test modes. Comparing with existing researches, this work verifies the efficiency of SVM for HT detection in instruction level, and points out the relationship between diversified learning conditions with detection performance.
, doi: 10.11999/JEIT190578
[Abstract](612) [FullText HTML](374) [PDF 1427KB](15)
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In encrypted email system, the public key searchable encryption technology can effectively solve the problem of searching for encrypted emails without decryption. In view of the complex key management problem of public key searchable encryption, an identity-based cryptosystem is introduced in the encrypted mail system. For the offline keyword guessing attack problem of searchable encryption, the method of encrypting keywords and generating trapdoors are adopted at the same time, and the server is designated to search for encrypted emails. At the same time, under the random oracle model, based on the decisional bilinear Diffie-Hellman assumption, the scheme is proved to satisfy the trapdoor and ciphertext indistinguishable security. The numerical experiments show that the scheme has higher computational efficiency than the existing schemes in the keyword trapdoor generation and keyword ciphertext test phase.
, doi: 10.11999/JEIT190356
[Abstract](65) [FullText HTML](42) [PDF 4294KB](5)
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A motion compensation method based on Kirchhoff migration imaging algorithm for MIMO array is proposed. In the method, the traditional Kirchhoff intergral equation is introduced into the MIMO array and the forward Green function is replaced by the backward Green function to solve the problem of the large view field of the small array. The echo signal of the reference unit is directly recieved by adding a reference channel to estimate the position change information of the moving target, while the signal of the receiving units is sampled through the microwave switch time divition multiplexing. Then 3D imaging is performed after motion compensation for the data of the MIMO channel. which avoids effectively the problem of defocusing due to target migration and the deviation of the imaging result from the real position.
, doi: 10.11999/JEIT190393
[Abstract](65) [FullText HTML](57) [PDF 2159KB](11)
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Co-saliency object detection aims to discover common and salient objects in an image group which contain two or more relevant images. In this paper, a method of using machine learning is proposed to detect co-saliency object. Firstly, some simple images are selected to form a simple image set based on four scoring indicators. Secondly, positive and negative samples are extracted from the simple images set based on co-coherence characteristics, and high-dimensional semantic features are extracted by the deep learning model which receives RGBD four-channels input. Thirdly, the co-saliency classifier is trained by positive and negative samples, and co-saliency maps are generated by testing all the superpixels in the images by the co-saliency classifier. Finally, a smooth fusion operation is adopted to generate the final co-saliency map. Experimental results on public benchmark dataset show that the proposed algorithm is superior to the state-of-the-art methods in terms of accuracy and efficiency, and it is robust.
, doi: 10.11999/JEIT200058
[Abstract](228) [FullText HTML](121) [PDF 1158KB](19)
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With the rapid development of mobile communication technologies and the commercial use of 5G, cybersecurity issues are increasingly prominent. For the essence of operation in 5G cybersecurity, this paper analyzes current researches on cybersecurity confrontation and game from the aspects of basic models including static game, dynamic game, evolutionary game, and graph-based game, as well as the typical confrontation issues including eavesdropping and anti-eavesdropping and jamming and anti-jamming. Furthermore, some potential research directions are also set forth in establishing 5G cybersecurity confrontation theory and general law. Finally, the necessity and challenges of security and game research in 5G networks are discussed, so as to provide new sights for the research of confrontation in 5G cyberspace.
, doi: 10.11999/JEIT190778
[Abstract](86) [FullText HTML](59) [PDF 2826KB](8)
Abstract:
An Orthogonal MultiUser Short Reference Differential Chaos Shift Keying (OMU-SR-DCSK) communication system is proposed to overcome the dominant drawbacks of DCSK system relating to low transmission rate and energy efficiency. The proposed system shortens the reference signal to 1/P of information bearing signal. Two consecutive information time slots are added after the reference time slot. Due to the excellent features of Walsh codes, the system sends information from N users in one information time slot. Meanwhile, the use of orthogonality of the Walsh code eliminates completely intra-signal interference and enhances the performance of Bit Error Rate (BER) better. The theoretical BER formula of OMU-SR-DCSK over Additive White Gaussian Noise (AWGN) channel and Rayleigh fading channel are derived and simulations are carried out respectively. The coincidence between the simulation results and the theoretical derivations proves the correctness of the theoretical derivation, providing a theoretical basis for the application of OMU-SR-DCSK to multiuser serial transmission system.
, doi: 10.11999/JEIT191013
[Abstract](66) [PDF 0KB](3)
Abstract:
Considering the problem of information entropy being low and easily disturbed by environmental factors in the traditional Physical Unclonable Function (PUF), a PUF scheme is designed to generate multiple stable information entropy. By analyzing the frequency data generated by the ring oscillator on the FPGA, the feature bits representing the characteristics of the ring are extracted from each ring as information entropy. By studying the temperature characteristics of the inverter, a new oscillating ring is formed by the current hungry inverter and the conventional inverter to reduce the influence of temperature on the reliability of the generated information entropy. Through Cadence IC simulation and experiments on zynq7000 series FPGA development platform, the results show that the improved PUF structure can generate more information entropy with the same number of oscillatory rings, and its reliability and uniqueness are improved.
, doi: 10.11999/JEIT190748
[Abstract](79) [FullText HTML](56) [PDF 1866KB](5)
Abstract:
In multi-cell massive Multiple Input Multiple Output (MIMO) systems, pilot contamination has become the bottleneck which restricts the performance of the whole system, so the reasonable usage of pilot resources can mitigate the pilot contamination of the system. In order to find the pilot allocation method that maximizes the total transmission capacity of edge users, a pilot allocation scheme based on Hysteretic Noise Chaotic Neural Network (HNCNN) is proposed for the first time. HNCNN is a famous optimization tool, and its optimization ability is related to the designed energy function. This scheme combines the characteristics of pilot resource usage and the calculation method of maximizing the total transmission capacity of edge users to design a new energy function. The simulation results show that the proposed network can converge to a better pilot allocation mode after a certain number of iterations. Compared with other literature pilot allocation solution, the pilot allocation method based on HNCNN can further reduce the influence of pilot contamination and improve the system performance.
, doi: 10.11999/JEIT190608
[Abstract](74) [FullText HTML](73) [PDF 4679KB](5)
Abstract:
The color-ring resistor is one of the most commonly used electronic components in Printed Circuit Board (PCB). It is featured by sequential color rings, which often brings assembling errors, however. Manual detection of color-ring resistors has low efficiency and high false detection rate. Traditional image-based automatic detection methods have difficulties in dealing with PCB images under various illuminations, imaging distance and views. To solve this problem, an automatic detection and localization method for PCB color-ring resistor is proposed based on Convolution Neural Network (CNN). Firstly, the encoder-decoder CNN model is established and trained using weighted cross-entropy loss function. With CNN, color-ring resistors are segmented from PCB images with complex illumination and scenes. Secondly, each color-ring resistor is localized using minimum area bounding rectangle, and its position is adjusted to the vertical direction by affine transformation. Finally, the localization of color rings on the resistor is achieved by Gaussian template matching. The proposed method is tested and verified by 1270 PCB images, and the result is compared with that of the traditional method (method based on geometric contour, and method based on template matching). It is shown that the proposed method has obvious advantages in performance indices, including recall rate, precision, and intersection of unions, which can meet the requirements of practical applications.
, doi: 10.11999/JEIT190972
[Abstract](97) [FullText HTML](67) [PDF 2041KB](3)
Abstract:
In order to deploy fault-tolerant software-defined networks, many controllers must be physically distributed among different network devices. However, a large number of controllers bring huge costs, which limits severely the application of the fault-tolerant control plane to the real networks. In order to solve the above problems, the fault-tolerant control plane is analyzed and a mathematical model that covers all switches using the least number of controllers is constructed. Then, a heuristic controller placement algorithm based on the local search strategy is proposed to avoid the local optimal solution. The experimental results show that compared with other algorithms, the proposed algorithm can effectively reduce the number of required controllers while ensuring network fault tolerance requirements in different scale networks.
, doi: 10.11999/JEIT190986
[Abstract](129) [FullText HTML](75) [PDF 1883KB](9)
Abstract:
While projecting 3D shapes to 2D images is irreversible due to the abandoned dimension amid the projection process, there are rapidly growing interests across various vertical industries for 3D reconstruction techniques, from visualization purposes to computer aided geometric design. The traditional 3D reconstruction approaches based on depth map or RGB image can synthesize visually satisfactory 3D objects, while they generally suffer from several problems: (1)The 2D to 3D learning strategy is brutal-force;(2)Unable to solve the effects of differences in appearance from different viewpoints of objects;(3)Multiple images from distinctly different viewpoints are required. In this paper, an end-to-end View-Aware 3D (VA3D) reconstruction network is proposed to address the above problems. In particular, the VA3D includes a multi-neighbor-view synthesis sub-network and a 3D reconstruction sub-network. The multi-neighbor-view synthesis sub-network generates multiple neighboring viewpoint images based on the object source view, while the adaptive fusional module is added to resolve the blurry and distortion issues in viewpoint translation. The 3D reconstruction sub-network introduces a recurrent neural network to recover the object 3D shape from multi-view sequence. Extensive qualitative and quantitative experiments on the ShapeNet dataset show that the VA3D effectively improves the 3D reconstruction results based on single-view.
, doi: 10.11999/JEIT190831
[Abstract](363) [FullText HTML](173) [PDF 2339KB](39)
Abstract:
The existence of acceleration and descent velocity makes the imaging parameters of high-squint SAR mounted on maneuvering platform have obvious two-dimensional spatial variability, which affects seriously the focus depth of the scene. To solve this problem, a maneuvering SAR imaging method based on Keystone transform and azimuth perturbation resampling is proposed. First of all, the range azimuth decoupling and the azimuth spectrum de aliasing are realized by the range walk correction and de-acceleration processing. Then the spatial-variant range cell migration is corrected by the Keystone transform in the azimuth time domain; In the process of azimuth compression, the second- and third-order spatial variabilities of Doppler parameters are removed by introducing the high-order perturbation factor in the time domain, and then the first-order spatial variability of the Doppler parameters is removed by the azimuth resampling processing in the azimuth frequency domain. The proposed method can effectively correct the two-dimensional spatial variability of range cell migration trajectory and azimuth focus parameters, and realize the large scene imaging of high-squint maneuvering SAR. Simulation analysis verifies the effectiveness of the proposed method.
, doi: 10.11999/JEIT190674
[Abstract](467) [FullText HTML](157) [PDF 1968KB](15)
Abstract:
The system transfer function of high-squint SAR system with curved trajectory has complex multi-dimensional spatial variability. The existing efficient frequency-domain echo simulation algorithms are difficult to achieve high-precision echo simulation of extended scenes. Therefore, a fast echo simulation method based on sub-aperture Keystone transform is proposed for maneuvering SAR. Based on the time-domain range compression function after range cell migration correction, the method calculates efficiently the range compression echo of scene, and then realizes the echo simulation of extended scene by high precision range inverse processing. In order to reduce the influence of residual range cell migration on the accuracy of echo simulation, the method of the Keystone transform of sub-aperture is used in range processing to achieve accurate correction of space-variant range cell migration in extended scenes. The theoretical analysis and simulation results show that the proposed method can quickly and accurately simulate the original echo data of extended scenes for high-squint SAR mounted on a maneuvering platform.
, doi: 10.11999/JEIT190441
[Abstract](377) [FullText HTML](140) [PDF 2656KB](23)
Abstract:
The non-coherent integration detectors for coherent radar systems can promote the detection rate of the radar and meet the required real-time processing, however, these detectors are not constant false alarm rate (CFAR) with respect to the reference cell number, the accumulated pulse number, the clutter speckle covariance matrix, and the shape parameter of the sea clutter model. Based on block-whitening method to whiten the sea clutter, a Pre-Whitening Cell-Averaging CFAR (PWCA-CFAR) detector and a Pre-Whitening Cell-Median CFAR (PWCM-CFAR) detector are proposed where the detection thresholds matching the reference cell number, accumulated pulse number and shape parameter are used. The experiment results show that the PWCM-CFAR detector attains better detection performance than the PWCA-CFAR detector when there exist abnormal cells.
, doi: 10.11999/JEIT190931
[Abstract](495) [FullText HTML](193) [PDF 3188KB](42)
Abstract:
Considering further improve the discrimination ability of the correlation filtering algorithm and the ability to deal with fast motion and occlusion, a tracking framework based on adaptive context selection and multiple detection areas is proposed. Firstly, the peak value of the detected response map is analyzed. When the response is single peak, four areas surrounding the target are extracted as negative samples to train the model. When the response is multi-peak, the peak value extraction technology and threshold selection are used to extract several larger peak areas as negative samples. In order to further improve the ability to deal with occlusion, a multi detection area search strategy is proposed. Combining the framework with the traditional correlation filter algorithm, the experimental results show that the proposed algorithm improves the accuracy by 6.9% and the success rate by 6.3%.
, doi: 10.11999/JEIT190731
[Abstract](408) [FullText HTML](165) [PDF 3223KB](21)
Abstract:
Most existing link prediction methods in directed networks fail to consider the structural properties of directed networks when calculating node similarity, nor do they differentiate the contributions of directed neighbors on link formation, resulting in the limitation on prediction performance. To solve these problems, a novel link prediction method in directed networks based on linear programming is proposed. The contributions of three types of directed neighbors are quantified, then the linear programming problem is established based on network topological property. The similarity index is deduced by solving the optimal solution of the linear programming problem. Experimental results on nine real-world directed networks show that the proposed method outperforms nine benchmarks on both accuracy and robustness under two evaluation metrics.
, doi: 10.11999/JEIT190971
[Abstract](263) [FullText HTML](107) [PDF 2871KB](27)
Abstract:
Transmission delay and packet loss rate are critical issues in reliable transmission of power communication services. A minimum path selection routing control strategy for software-defined power communication networks is proposed. Combining the characteristics of the centralized control structure of the software-defined power communication network, a Link Bandwidth Occupancy Predictive model based on Graph Convolutional Network (LBOP-GCN) is built to analyze the route paths bandwidth occupancy in the next period. Calculate the selectivity (Q) of different transmission paths from the source node to the destination node by using Triangle Modular Operator (TMO) to fuse the transmission delay of the path, the path bandwidth occupancy at the current moment and the path bandwidth occupancy at the next moment. Then the path with the lowest Q value is used as the flow table of the OpenFlow switch delivered by the Software Defined Network (SDN) controller. Experiments show that the proposed routing control strategy can effectively reduce service transmission delay and packet loss rate.
, doi: 10.11999/JEIT290752
[Abstract](203) [FullText HTML](87) [PDF 796KB](8)
Abstract:
Public Key Encryption with Equality test (PKEE) is an important method to achieve the equality test of ciphertexts which are generated by the different public key aiming to the same plaintext in cloud environment. In other words, it can tests the plaintext corresponding to the two ciphertext’s equivalence without decrypting the ciphertext, but does not supply the searchable function. Nowadays, the existing PKEE scheme directly takes the message to generate a trapdoor as the proof of equality test, which has low test accuracy and search efficiency. To solve the above problems, a certificateless public key encryption with equality test scheme supporting keyword search (CertificateLess Equality test EncrypTion with keyword Search, CLEETS) is proposed. The scheme determines whether it contains information needed by the user through the keyword search, then performs the equality test according to the search result, which can avoid invalid test. Then, it is proved that the scheme satisfies the indistinguishability of adaptive selection of keywords under the random oracle model. Finally, the comparison analyses of function and efficiency are shown. The results indicate the computation cost of CLEETS scheme is less efficient. Fortunately, it can realizes the function of keyword search in encryption with equality test, which can remedies the inefficiency.
, doi: 10.11999/JEIT190658
[Abstract](900) [FullText HTML](169) [PDF 856KB](36)
Abstract:
In the military and civilian fields, the Morse telegraph is always as an important means of short-wave communication, but the current automatic decoding algorithms still have problems such as low accuracy, inability to adapt to low signal-to-noise ratio and unstable signals. A deep learning method is introduced to construct a Morse code automatic recognition system. The neural network model consists of convolutional neural network, bidirectional long short-term memory network and connectionist temporal classification layer. The structure is simple and can implement end-to-end training. Related experiments show that the decoding system can achieve good recognition results under different signal-to-noise ratio, code rate, frequency drift and code length deviation caused by different sending manipulation, and the performance is better than the traditional recognition algorithms.
, doi: 10.11999/JEIT190707
[Abstract](412) [FullText HTML](118) [PDF 2419KB](15)
Abstract:
The transmission performance of nodes in the satellite Internet of Things(IoT) is limited due to the long-distance transmission and the power-constrained terminal. A collaborative beamforming technique is proposed based on the node selection algorithm to improve the transmission performance of nodes. An average far-field beampattern for collaborative beamforming is derived by considering the location information error in practical scenario. Furthermore, the difference between average beampattern and instantaneous beampattern is analyzed by the system parameters. On this basis, a node selection algorithm is proposed based on region grouping not only to meet the requirement of satellite link, but also to suppress the sidelobe. Simulation results show better performance of the proposed algorithm compared with the traditional node selection algorithms in the actural error model.
, doi: 10.11999/JEIT190655
[Abstract](1175) [FullText HTML](283) [PDF 1243KB](36)
Abstract:
Convolutional neural network is widely used in the field of intrusion detection technology. It is generally believed that the deeper the network structure, the more accurate in feature extraction and detection accuracy. However, it is accompanied with the problems of gradient dispersion, insufficient generalization ability and low accuracy of parameters. In view of the above problems, the Densely Connected Convolutional Network (DCCNet) is applied into the intrusion detection technology, and achieve the purpose of improving the detection accuracy by using the hybrid loss function. Experiments are performed with the KDD 99 Data Set, and the experimental results are compared with the commonly used LeNet neural network and VggNet neural network structure. Finally, the analysis shows that the accuracy of detection is improved, and the problem of gradient vanishing during training is alleviated.
, doi: 10.11999/JEIT190123
[Abstract](149) [FullText HTML](93) [PDF 1081KB](5)
Abstract:
Use of FM radio of additional information channel as a carrier laies a solid foundation for time pass for FM radio. Based on the research of FM radio additional channels, a new design method of time-sharing spread spectrum code is proposed and the content of spread spectrum code is designed in detail. It not only conforms to the requirements of the channel conditions, and is helpful for accurate tracking capture at the receiving end, and for the time information transmission and timing functions. The measured results show the feasibility and accuracy of the method.
, doi: 10.11999/JEIT190761
[Abstract](723) [FullText HTML](1199) [PDF 5140KB](12)
Abstract:
The infrared imaging system of ultrawide field of view has large monitoring range and is not limited by illumination, but there are diverse scales and abundant small objects. For accurately detecting them, a multi-scale infrared pedestrian detection method is proposed with the ability of background-awareness, which can improve the detection performance of small objects and reduce the redundant computation. Firstly, a four scales feature pyramid network is constructed to predict object independently and supplement detail features with higher resolution. Secondly, attention module is integrated into the horizontal connection of feature pyramid structure to generate salient features, suppress feature response of irrelevant areas and enhance the object features. Finally, the anchor mask generation subnetwork is constructed on the basis of salient coefficient to the location of the anchors, eliminate the flat background, and improve the processing efficiency. The experimental results show that the salient generation subnetwork only increases the processing time by 5.94%, and has the lightweight characteristic. The Average-Precision is 93.20% on the Ultrawide Field Of View (U-FOV) infrared pedestrian dataset, 26.49% higher than that of YOLOv3. Anchor box constraint strategy can save 18.05% of processing time. The proposed method is lightweight and accurate, which is suitable for detecting multi-scale infrared objects in ultrawide field of view camera.
, doi: 10.11999/JEIT190739
[Abstract](429) [FullText HTML](509) [PDF 3117KB](23)
Abstract:
In order to solve the problems caused by the traditional data analysis based on the centralized algorithm in the IoT, such as excessive bandwidth occupation, high communication latency and data privacy leakage, considering the typical linear regression model of elastic net regression, a distributed learning algorithm for Internet of Things (IoT) is proposed in this paper. This algorithm is based on the the Alternating Direction Method of Multipliers (ADMM) framework. It decomposes the objective problem of elastic net regression into several sub-problems that can be solved independently by each IoT node using its local data. Different from traditional centralized algorithms, the proposed algorithm does not require the IoT node to upload its private data to the server for training, but rather the locally trained intermediate parameters to the server for aggregation. In such a collaborative manner, the server can finally obtain the objective model after several iterations. The experimental results on two typical datasets indicate that the proposed algorithm can quickly converge to the optimal solution within dozens of iterations. As compared to the localized algorithm in which each node trains the model solely based on its own local data, the proposed algorithm improves the validity and the accuracy of training models; as compared to the centralized algorithm, the proposed algorithm can guarantee the accuracy and the scalability of model training, and well protect the individual private data from leakage.
, doi: 10.11999/JEIT190650
[Abstract](228) [FullText HTML](779) [PDF 1683KB](8)
Abstract:
In Specific Emitter Identification (SEI), the stability of individual features and final correct identification rate are always declined due to the influence of the primary signal with high energy on the individual features. To solve the problem above, a primary signal suppression algorithm based on synchrosqueezed wavelet transform is exploited for specific emitter identification in this paper. Firstly, a denoising method based on stationary wavelet transform is applied to preprocess the noised signal; Then, the detection and suppression of the primary signal from time-frequency distribution are developed, where root mean square error and Pearson correlation coefficient are used as numerical indicators to measure the effectiveness of the proposed primary signal suppression algorithm; Finally, a feature extraction based on box-counting dimension and a classification based on support vector machine are exploited to verify the identification performance. The simulation results show that the correct identification rate of SEI using the proposed primary signal suppression outperforms the conventional SEI with 10%, which proves the practical improvement of the proposed primary signal suppression algorithm on specific emitter identification.
, doi: 10.11999/JEIT190691
[Abstract](118) [FullText HTML](106) [PDF 900KB](4)
Abstract:
Device to Device (D2D) communications can effectively offload base station traffic. In D2D networks, the popular content is not only needs to be shared, but also the individual content is needs to be cached. In this paper, the problem of cache content selection is researched. A Content Social Value prediction method based on feature perception is proposed. Value prediction can not only reduce latency, but also reduce the number of cache replacements and reduce cache costs. Firstly, the current value of content is calculated by combining user features and content features, and then future value is calculated through user social relationships. The small base station provides the user with a personalized content caching service according to the value of the content, and the base station selects a content with a larger value in the individual cache content of each small base station as popular content. Simulation results show that the caching strategy based on the proposed method can alleviate the base station traffic effectively, and reduce the delay by about 20%–40%.
, doi: 10.11999/JEIT190916
[Abstract](420) [FullText HTML](102) [PDF 2142KB](7)
Abstract:
In order to solve the problem of target propeller features extraction under Alpha stable distribution noise, a method based on fractional low-order cyclic spectrum is proposed. Firstly, the low-order cyclic spectrum of ship radiation noise in impulse noise is derived, and the relationship between the propeller features and the peak value in the fractional low-order cyclic spectrum is given. Based on this, a propeller feature estimation method based on fractional low-order cyclic spectrum is proposed. Finally, the performance of method is verified by simulation experiments, and the effectiveness of the algorithm is further verified by the actual data.
, doi: 10.11999/JEIT190602
[Abstract](462) [FullText HTML](172) [PDF 1701KB](5)
Abstract:
In order to reduce the computation complexity and storage capacity of the Kernel Affine Projection P-norm (KAPP) algorithm, and improve the convergence rate and steady-state performance of the algorithm when the input signal is strongly correlated, a Kernel Normalization Decorrelated Affine Projection P-norm algorithm based on Gaussian Kernel Explicit Mapping (KNDAPP-GKEM) is proposed. The correlation of the input signal is eliminated in advance by the normalized correlation method. The explicit kernel function is approximated by Gaussian kernel explicit mapping method, which eliminates the dependence on historical data and solves the problem that the computation and storage capacity of the KAPP algorithm are too high due to the continuous growth of structure. The simulation results of nonlinear system identification under α-stable distribution noise environment show that when the training data scale is large, the KNDAPP-GKEM algorithm still maintains a fast convergence rate and the low identification mean square error of nonlinear system. Moreover, its training time is linearly and slowly increased, which is more conducive to the practical application of nonlinear system identification.
, doi: 10.11999/JEJT190638
[Abstract](673) [FullText HTML](136) [PDF 3081KB](22)
Abstract:
The space-borne synthetic aperture radar sparse flight three-dimensional (3-D) imaging technology through the multiple observations in cross-track direction obtains the 3-D spatial distribution of the observed scene. In this paper, the orbit distribution of single satellite SAR sparse flight is given. In order to shorten effectively the satellite revisit time, the formation of double star SAR orbit distribution is given. The corresponding cross-track equivalent aperture length is 20 km. A sparse 3-D imaging method based on interferometry and compressed sensing is proposed. The referential complex image is formed by using part of the echoes of the sparse flight, and the SAR 3-D image signals which are to be reconstruct are processed by interferometry. This method makes the signal sparse in the frequency domain. Under the large orbit distribution range, the frequency domain range direction and cross-track linear measurement matrix is established, which is beneficial to the CS theory to solve jointly the image frequency spectrum under sparse representation, and avoid the echoes coupling between the range and cross-track direction. Inversely transforming the resulting spectrum into the spatial domain, the reconstruction result can be obtained. Simulation results show that under the condition of sparse sampling rate of 74.4%, the imaging performance of the proposed method is still comparable to that of full sampling.
, doi: 10.11999/JEIT190474
[Abstract](1937) [FullText HTML](1058) [PDF 1879KB](38)
Abstract:
Parameter estimation is very important for radar to detect and recognize targets. In this paper, a high speed multi-target parameter estimation method for Frequency Agility-Orthogonal Frequency Division Multiplexing(FA-OFDM) radar based on Expectation Maximization(EM) algorithm is proposed. Firstly, a promising idea is to combine narrowband Orthogonal Frequency Division Multiplexing (OFDM) signals and frequency agility, multiple subcarriers that frequency hopping randomly are simultaneously transmitted within each pulse width. Then, all echoes of a single pulse are compressed and sparsely reconstructed to achieve 1-demension high range resolution. Subsequently, the high resolution range of multiple targets at each pulse time are obtained to constitute the observation data, and Gauss mixture model is established. EM algorithm is applied to estimate the parameters of the model and the range and velocity of multiple targets. Also, multiple time-range lines are fitted at the same time, and the slope of the line corresponds to the velocity of the target, as well as, the vertical intercept of the line corresponds to the initial range of the target, separately. Finally, the influence of the Signal-to-Noise Ratio (SNR) on detection probability and the target velocity on relative error of estimation are analyzed, respectively. Simulations are provided to verify the effectiveness of the proposal.
, doi: 10.11999/JEIT190405
[Abstract](1025) [FullText HTML](609) [PDF 1265KB](7)
Abstract:
, doi: 10.11999/JEIT190662
[Abstract](986) [FullText HTML](165) [PDF 2245KB](16)
Abstract:
The most critical issue in the deployment of Mobile Wireless Sensor Networks (MWSN) is how to provide maximum regional coverage.To solve the problem that the existing coverage control algorithm has unsatisfactory coverage, low deployment efficiency and high energy consumption, an efficient deployment strategy is proposed.The first stage uses the voronoi diagram to obtain the coverage hole of the entire network, and detects the uncovered area in the voronoi polygon, and provides virtual force to drive the sensor movement, and uses the dynamic adjustment strategy to change the moving step size, thereby reducing energy loss;The second stage proposes a detection mechanism that uses a delaunay triangulation to detect local coverage holes between sensors and repair them.The simulation results show that the algorithm accelerates the convergence speed while improving the network coverage, and provides a new solution for deploying mobile wireless sensor networks.
, doi: 10.11999/JEIT190589
[Abstract](902) [FullText HTML](252) [PDF 1027KB](19)
Abstract:
In recent year, the Many-objective Optimization Problems (MaOPs) have become an increasingly hot research area in evolutionary computation. However, it is still a difficult problem to achieve a good balance between convergence and diversity on solving various kinds of MaOPs. To alleviate this issue mentioned above, a Decomposition and dominance relation based many-objective Evolutionary Algorithm(DdrEA) is proposed in this paper. Firstly, the population is decomposed into numbers of sub-populations by using a set of uniform weight vectors, in which they are optimized in a cooperative manner. Then, the fitness value of solution in each sub-population is calculated by angle dominance relation and angle. Finally, elite selection strategy is performed according to its corresponding fitness value. That is, in each subspace, the solution with the smallest fitness value is selected as the elite solution to enter the next generation. Comparing with several high-dimensional and multi-objective evolutionary algorithms (NSGA-II/AD, RVEA, MOMBI-II), the experimental results show that the performance of the proposed algorithm DdrEA is better than that of the comparison algorithm, and the convergence and diversity of the population can be effectively balanced.
, doi: 10.11999/JEIT190528
[Abstract](522) [FullText HTML](219) [PDF 1071KB](9)
Abstract:
Accurate prediction of low-frequency sky-wave has significance for the lower ionosphere detection and remote navigation timing. The characteristics of sky-wave propagation time delay in the Earth-ionosphere waveguide are studied in this paper based on the traditional wave-hop theory and FDTD method. Time delay variations of 100 kHz one-hop sky waves are given under homogeneous/exponentially graded isotropic ionosphere waveguide models. The great-circle distance between the transmitter and the receiver is within 200 km. Together with a sky- and ground-wave separation technique in the time domain, the narrow-band Loran-C signals are employed in two methods. Compared to the results of wave-hop theory, the method in this paper has higher calculation accuracy by considering the influence of irregular earth and inhomogeneous distribution of ionospheric day-night parameters at the same time.
, doi: 10.11999/JEIT190566
[Abstract](728) [FullText HTML](443) [PDF 1225KB](15)
Abstract:
, doi: 10.11999/JEIT190822
[Abstract](420) [FullText HTML](115) [PDF 1701KB](10)
Abstract:
To solve the problem of track correlation in practical engineering effectively, a new concept of generalized space-time cross point of track pair is defined in this paper. Then a new algorithm which utilize space-time cross-point as feature points and realizes the track correlation through feature point matching is proposed in this paper. The test experiments with measured data illustrate the proposed algorithm of which the performance is effective, stable and robust, can elimi-nate redundant tracks and provide a unified situation.
, doi: 10.11999/JEIT190642
[Abstract](380) [FullText HTML](172) [PDF 2911KB](4)
Abstract:
In view of the problem of high complexity for non-sinusoidal time domain modulation algorithms based on Prolate Spheroidal Wave Functions (PSWFs), spatial mapping is introduced to analyze the complete orthogonality and derive the minimum number of sampling points of PSWFs in the frequency domain. On this basis, the complex domain mapping and FFT/IFFT signal processing framework are introduced, and the PSWFs frequency domain modulation and demodulation method are proposed. The proposed method extends PSWFs signal processing from time domain to frequency domain, providing a possibility for exploring and studying the application of PSWFs signal to 5G, beyond 5G which use frequency domain signal processing. Theory and numerical analysis show that, compared with the time domain modulation, the proposed method can reduces the complexity of the algorithm from O(2Qg2) to O(g2+glog2g) without changing the system spectral efficiency, system error performance, modulation signal energy aggregation, and peak-to-average power ratio.
, doi: 10.11999/JEJT190722
[Abstract](811) [FullText HTML](95) [PDF 1854KB](4)
Abstract:
For the problem of blind extraction of rolling bearing fault signals under complex working conditions, an adaptive selection method of non-linear functions in Independent Component Analysis (ICA) is proposed, which solves the problem that Equivariant Adaptive Separation via Independence(EASI) can not separate bearing fault signals when multiple vibration sources coexist. In addition, in order to balance the steady-state error and convergence rate of the online blind separation algorithm, an adaptive iterative step selection method based on fuzzy logic is proposed, which improves greatly the convergence speed of the learning algorithm and reduces the steady-state error. The simulation results of blind extraction of bearing fault data verify the performance of the proposed algorithm.
, doi: 10.11999/JEIT190724
[Abstract](465) [FullText HTML](917) [PDF 3477KB](7)
Abstract:
Botnets have become one of the main threats to cyberspace security. Although they can be detected by techniques such as reverse engineering, botnets using covert technologies such as fast-flux can successfully bypass existing security detection and continue to survive. The existing fast-flux botnet detection methods are mainly divided into active and passive, the former will cause a large network load, and the latter has the problem of cumbersome feature value extraction. In order to effectively detect fast-flux botnets and alleviate the problems in traditional detection methods, a fast-flux botnet detection method based on spatiotemporal features of network traffic is proposed, combined with convolutional neural networks and recurrent neural network models, the fast-flux botnet is detected from both spatial and temporal dimensions. Experiments performed on the CTU-13 and ISOT public data sets show that compared with other methods, the accuracy rate of our proposed method is 98.3%, the recall rate is 96.7%, and the accuracy is 97.5%.
, doi: 10.11999/JEIT190683
[Abstract](850) [FullText HTML](722) [PDF 4331KB](9)
Abstract:
The Signal-In-Space (SIS) quality directly affects the user performance of Global Navigation Satellite System (GNSS). Unlike BDS-2, the BDS-3 satellites not only broadcast old signals, but also new signals such as B1C and B2a at the same time. The signal structure of BDS-3 with multi-frequency, multi-signal and multi-component is more complex than BDS-2, which is a great challenge to signal quality control of BDS-3 satellites. By the end of 2018, 18 BDS-3 satellites were successfully launched and the BDS-3 preliminary system is completed to provide global services. It is necessary to evaluate the signal quality of BDS-3. Traditional signal quality assessment methods focus on the qualitative assessment of a single item, but lacks systematic and quantitative analysis results for the complex signal structure of BDS-3. Based on the Interface Control Document (ICD) of BDS, this paper studies the influence of different parameter configurations on the evaluation results from the aspects of power characteristics, frequency characteristics, time characteristics, correlation characteristics and signal consistency, and forms a set of quantitative evaluation methods for new modulations and multi-frequency, multi-component signals. Based on the signal quality assessment system with 40-meter aperture antenna, 18 MEO satellites of BDS-3 preliminary system were monitored, and the signal quality of BDS-3 satellites were comprehensively and quantitatively evaluated for the first time. The results show that, signal qualities of BDS-3 satellites are good, and the 18 MEOs have a good consistency, which can meet the requirements of ICD and GNSS users. The evaluation methods can be also used to quantitatively evaluate the signal quality of other satellites.
, doi: 10.11999/JEIT190645
[Abstract](541) [FullText HTML](331) [PDF 1648KB](15)
Abstract:
In order to meet the needs of measuring and detecting combinatorial target placed on the rough surface, Dobson semi-empirical model and dielectric complex permittivity formula are used to represent the real and imaginary parts of the soil dielectric constant, the soil surface is simulated with the model of exponential distribution and Monte Carlo method. The strategy of the finite difference time domain method for calculating the composite scattering from rough surface with target and the modeling method are presented with their validity evaluated by the method of moment, then the composite scattering of soil surface and combinatorial target placed on it is studied by this method, the angular distribution curve of the composite scattering coefficient is obtained. The results show that the composite scattering coefficient oscillates with the scattering angle, and the scattering enhancement effect occurs in the mirror reflection direction; the larger the root mean square of the fluctuation of soil surface, the larger the composite scattering coefficient; the larger the correlation length, the smaller the composite scattering coefficient; the larger the soil moisture content, the smaller the composite scattering coefficient; the influence of the scale and dielectric constant of combinatorial target, incident angle on composite scattering coefficient is complex. The results obtained in this paper can be used to solve the composite electromagnetic scattering from rough land surface and rough sea surface with any target placed on it. Compared with other numerical methods, the finite difference time domain method can not only obtain higher accuracy, but also reduce the calculation time and the amount of memory occupying.
, doi: 10.11999/JEIT190539
[Abstract](549) [FullText HTML](156) [PDF 2510KB](35)
Abstract:
Considering the requirement for weak signal detection in non-Gaussian background interference, after conceptions and ideas summarizing for Gaussianization processing and extended matched filter, two Gaussianizaiton filters and corresponding detections are proposed based on symmetric alpha-stable distribution modeling, comparing with those under Gaussian mixture modeling proposed earlier. All these Gaussianization filters and extended matched filters are realized in simulation. Their performances, such as Gaussianzing effect, detecting capability and running time are studies in system. Some conclusions are reached: Gaussianization can improve the detecting performance of the succeeding matched filter because of its restraining of big impulsive samples; Gaussianization filters under symmetric alpha-stable distribution modeling have higher operatiing efficiency while their peformance are similar to those under Gaussian mixture modeling.
, doi: 10.11999/JEIT190429
[Abstract](1403) [FullText HTML](417) [PDF 1208KB](41)
Abstract:
In order to suppress effectively the interference signal and improve further the performance of radar system, a joint transmitting subarray partition and beamforming design method based on two-dimensional phased-MIMO radar is proposed. Firstly, the transmitting array of MIMO radar system is equally partitioned into a number of non-overlapping subarrays and the transmit power of each antenna is equal, so as to guarantee that the transmit signal has constant modulus characteristic. Then, the optimization model for subarray structure of transmitting array, transmit beamformer weight vectors and receive beamformer weight vector is established by maximizing the output signal-to-interference-plus-noise ratio of the receive beamformer under certain constraint conditions. Simulation results demonstrate the correctness and effectiveness of the proposed method.
, doi: 10.11999/JEIT190675
[Abstract](1192) [FullText HTML](1007) [PDF 2632KB](30)
Abstract:
In the passive tracking using acoustic arrays, we are committed to continuous and stable tracking of targets. In complex underwater environments, there are inevitably many trajectory interruptions, outliers, interference and target azimuth crossings in the bearing detection results, due to interference, noise, and arrays aperture limitations. In this paper, a multi-target passive tracking algorithm based on unmanned underwater vehicle is proposed. The particle sampling prediction method based on the motion information of the vehicle is used to perform the interruption prediction. The observation threshold setting method based on the motion information of the vehicle is used to adaptively set the tracking threshold. The block association tracking method is used for association of trajectory break and azimuth cross. The experimental results show that the proposed algorithm achieves correct multi-target tracking.
, doi: 10.11999/JEIT190767
[Abstract](1282) [FullText HTML](177) [PDF 2372KB](15)
Abstract:
, doi: 10.11999/JEIT190570
[Abstract](216) [FullText HTML](264) [PDF 513KB](12)
Abstract:
In this paper, the S-box of Keccak is generalized into n-variable Keccak-like S-box, and the linear properties of n-variable Keccak-like S-box have been studied. It is proved that all the values of correlation advantages of this kind of S-box are 0 or ${2^{ - k}}$, where $k \in Z$ and ${\rm{0}} \le k \le \left\lfloor {{2^{ - 1}}n} \right\rfloor$, and for any k in this range, there is an input mask and an output mask that make the correlation advantage be ${2^{ - k}}$. Furthermore, we prove that when the output mask is fixed, the values of the nontrivial correlation advantages of the S-box are determined. Then we present the necessary and sufficient condition as well as the count for the nontrivial correlation advantage to reach the maximum value ${2^{ - 1}}$. Finally, the value distribution of the Walsh spectrum of Keccak-like S-box is presented.
, doi: 10.11999/JEIT190845
[Abstract](458) [FullText HTML](369) [PDF 507KB](14)
Abstract:
The elliptic curve Diffie-Hellman key exchange protocol enjoys great advantages since it could achieve the same security level with significantly smaller size of parameters compared with other public key cryptosystems. In real-world scenarios, this type of protocol requires less bandwidth and storage which leads to more application especially in computing resource constrained environments. Hence, it is important to evaluate the threat aroused by the partial information leakage during the establishment of shared keys. In this paper, the bit security of elliptic curve Diffie-Hellman with knowledge of partial inner bits based on the combination of hidden number problem and lattice-based cryptanalysis technique is recisited. 11/12 of the inner bits of the x-coordinate of the elliptic curve Diffie-Hellman key are approximately as hard to compute as the entire key. Moreover, the explicit relationship between the leakage fraction and the leakage position is elaborated. This result which relaxes the restriction on the location of leakage position dramatically improves the trivial one which stemmed from prior work.
, doi: 10.11999/JEIT190805
[Abstract](1999) [FullText HTML](449) [PDF 2752KB](13)
Abstract:
A micro-displacement measurement algorithm is proposed based on the Orientation Code Matching (OCM) and Edge Enhanced Matching (EEM) algorithms for monitoring the structural damage of tall buildings after earthquake. The algorithm first fuses the gradient information of the original image with the pixel intensity to enhance the image information; then uses the phase correlation method to perform the matching operation, the matching speed is 96.1% higher than the normalized cross-correlation method; finally, the sub-pixel interpolation method is used to make the measurement achieve sub-pixel accuracy. Experimental results show that the proposed algorithm avoids the loss of image gradient information during the quantization of OCM and EEM algorithms, greatly improves the template matching accuracy, and the matching speed is 43.3% higher than OCM and 19.6% higher than EEM.
, doi: 10.11999/JEIT190913
[Abstract](3339) [FullText HTML](571) [PDF 1927KB](24)
Abstract:
The depth of neural network is positively correlated with the recognition effect in a certain range. In order to solve the problem that model recognition accuracy decreases when the number of network layers increases after exceeding the range. A neural network model with efficient micro internal blocks structure and residual network structure are proposed, which is used for recognition of ship targets based on High Range Resolution Profile (HRRP) data. In this method, the convolution module with a small scale convolution kernel is used to automatically extract the stable and separable features of target. And the intra-class distance of the target is constrained by the joint loss function to improve the recognition ability. Simulation results show that compared with other common network structures, this model has better recognition performance and stronger noise robustness with fewer model parameters.
, doi: 10.11999/JEIT190681
[Abstract](1162) [FullText HTML](307) [PDF 2561KB](44)
Abstract:
As the azimuth angular resolution is limited by the antenna length in automotive radars, a novel imaging approach for improving azimuth angular resolution of automotive radars is proposed based on multi-beam real-aperture radar images combination processing. Firstly, the antenna beam of the phased array antenna is electronically scanned to obtain forward-looking real-aperture radar images. Afterwards, multiple real-aperture radar images are coherent accumulated according to the imaging geometry of automotive radar to improve azimuth angular resolution. Simulation results validate the proposed imaging approach to improve the azimuth angular resolution of automotive radar.
, doi: 10.11999/JEIT190574
[Abstract](290) [FullText HTML](447) [PDF 1123KB](8)
Abstract:
In order to reveal the complex electromagnetic environment effects mechanism of communication radio, the blocking effect of single-frequency and out-of-band dual-frequency electromagnetic radiation for the ultra-short wave digital communication radio is experimentally studied by irradiation method. The rule of single-frequency electromagnetic radiation effect and the susceptible bandwidth are determined. The experimental data show that the tested radio is 9～23 dB more susceptible to out-of-band dual-frequency third-order intermodulation blocking than single-frequency electromagnetic radiation blocking. A sensitive phenomenon of dual-frequency electromagnetic radiation which can neither be explained by the dual-frequency non-intermodulation superposition mechanism nor by the third-order intermodulation mechanism has been found in the experiment.
, doi: 10.11999/JEIT190690
[Abstract](1131) [PDF 0KB](2)
Abstract:
To solve the problem that the existing algorithms for recognition of RS codes need to transform the code characters among different domains and poor performance, a new algorithm based on soft decision is proposed. Firstly, starting from the definition of RS codes, the equivalent conversion mode of the check relation of RS code from GF (2m) to GF (2) is given, which avoids the complex symbol transformation in different domains. Secondly, the average check conformity which can measure the validity of the check relationship is introduced and based on its statistical characteristics and minimax decision criteria, the possible code length and corresponding m-level primitive polynomials are traversed to match the initial code root, as the results, the code length and primitive polynomial are recognized. Finally, under the identified code length and the primitive polynomial, the GF (2m) is constructed, and the continuous code root matching decision is made, then the generation polynomial is recognized. The simulation results show that the derived statistical characteristics of the average check conformity are consistent with the actual situation, and the proposed algorithm can effectively recognize parameter under low Signal-to-Noise Ratio (SNR). At the same time, the proposed algorithm has good adaptability to low signal-to-noise ratio. At SNR of 6 dB, the recognition rate of common RS codes in engineering can reach more than 90%. Compared with the existing methods, the performance of this algorithm is better than hard-decision algorithm, besides, it is improved by more than 1 dB compared by traditional algorithms.
, doi: 10.11999/JEIT190626
[Abstract](1062) [FullText HTML](588) [PDF 2451KB](11)
Abstract:
In order to solve the problem of the long decoding delay for the Spatially-Coupled Low-Density Parity-Check (SC-LDPC) code with long code length, a Layered Sliding Window Decoding (LSWD) algorithm is proposed. By exploring the quasi-cyclic characteristics of the SC-LDPC sub-codeblock and the hierarchical structure of the check matrix in the sliding window, the part of check matrix in the sliding window is layered to optimize the message transfer between two neighbor layers, with the aim of accelerating the convergence of the iterative procedure and reducing the number of decoding iterations. Simulation and analysis results show that the number of iterations in the proposed LSWD algorithm is less than that in the SWD, under the same Signal-to-Noise Ratio (SNR) and the bit error ratio. In the high SNR region, especially, the number of iterations in the proposed LSWD is about half of that in the SWD, hence the global decoding delay of the former is effectively shorten. In addition, the decoding performance of the LSWD algorithm was better than the SWD algorithm under the same number of decoding iterations, and the overall computational complexity was slightly increased.
, doi: 10.11999/JEIT190438
[Abstract](771) [FullText HTML](557) [PDF 2719KB](5)
Abstract:
, doi: 10.11999/JEIT190635
[Abstract](1124) [FullText HTML](555) [PDF 1830KB](10)
Abstract:
In view of the lack of deployment and management of slicing in vehicular network, a slice coordination agent of vehicular network slicing structure is designed. Firstly, based on the K-means++ clustering algorithm, the vehicle network communication services are clustered according to the similarity and then mapped into different slices. Secondly, considering the imbalance of radio resource utilization caused by the space-time characteristic among application scenarios, a shared proportional fairness scheme is proposed to utilize radio resources efficiently and differently. Finally, in order to ensure the requirements of slicing service, linear programming obstacle method is used to solve the optimal slice weight distribution to maximize the slice load variation tolerance. Simulation results show that the shared proportional fairness scheme has smaller average Bit Transmission Delay (BTD) than the static slicing scheme, and the optimal slice weight distribution can be obtained under different user load distribution scenarios. The BTD gain achieves 1.4038 in the uniform user load scenario with 30 users per slice.
, doi: 10.11999/JEIT190747
[Abstract](967) [FullText HTML](1120) [PDF 1845KB](5)
Abstract:
The traditional Least Squares-Estimating Signal Parameter via Rotational Invariance Techniques (LS-ESPRIT) algorithm is not effective while estimating parameters of the Geometric Theory of Diffraction (GTD) at lower SNR. To solve this problem, an improved LS-ESPRIT algorithm is proposed in this paper. Firstly, a Hankel matrix is constructed by the echo data of radar targets.Secondly,a low- rank reconstructed Hankel matrix is obtained,which is solved by the nuclear norm convex optimization method. Finally, the traditional LS-ESPRIT algorithm is used to process the data after noise reduction and estimate the parameters of the GTD model. Moreover,the reconstructed Radar Cross Section (RCS) can be obtained by the traditional LS-ESPRIT algorithm and the improved LS-ESPRIT algorithm. The influence of different bandwidths on parameter estimation is also analyzed in this paper. Simulation results show that the estimation accuracy and noise resistance of the improved LS-ESPRIT algorithm is better than the traditional LS-ESPRIT algorithm and the traditional TLS-ESPRIT algorithm. Furthermore, the amplitude error and phase angle error of the RCS which is reconstructed by the improved algorithm are smaller than the traditional algorithm. Different bandwidths also have influences on parameter estimation accuracy, the more wider bandwidth is, the more accurate parameters can be estimated.
, doi: 10.11999/JEIT190522
[Abstract](524) [FullText HTML](360) [PDF 2362KB](15)
Abstract:
Considering the shortcomings on the Bit Error Rate (BER) and the compression ratio of the existing asymmetric Distributed Source Coding (DSC) schemes, a scheme named Distributed Source Coding Using Improved Side Information (DSCUISI) is proposed. At the sender, the source sequence is sampled and divided into a sampled and an un-sampled sub-sequences. The un-sampled sub-sequence is compressed by arithmetic coder while the syndrome of the sampled sub-sequence is calculated. The receiver exploits the correlation between the side information and the un-sampled sub-sequence to estimate the sampled symbols, so that the correlation between the estimated sequence and the original sampled sub-sequence is improved, finally, the syndromes and the estimated sequence are used to recover the sampled sub-sequence. Experiment results show that the DSCUISI can reach high compression ratio, when the correlation among neighboring symbols is strong. The BER of the reconstructed sequence can be kept low when the correlation between sources are weak. It is an efficient, practical DSC scheme and is easy to be implemented.
, doi: 10.11999/JEIT190172
[Abstract](444) [FullText HTML](277) [PDF 1970KB](2)
Abstract:
Wireless ultraviolet communication becomes an effective means of communication under strong electromagnetic interference, which meets the need of reliable and secret communication between vehicles when the fleet performs strategic material transportation and the missile vehicle fleet of concealed driving vehicles in a complex battlefield environment. Each vehicle acts as a relay for other vehicles while driving, and establishes a stable and reliable communication link between non-line-of-sight vehicles through a multi-hop model. Therefore, based on the single-scattering model of ultraviolet, the optimal multi-hop relay problem is studied, and the relationship between the elevation angle of the transmitting and receiving and the spectral efficiency is theoretically analyzed. According to the principle of maximizing the spectral efficiency, the approximate expression of the optimum number of hops is obtained. The simulation results show that the optimum number of hops correspond to different distance shift range and elevation angles. Compared with the optimum energy calculation method, the proposed method has better transmission capability in low power transmission and achieves the requirement of power saving. In the long-distance ultraviolet communication, system performance does not increase with the number of cooperative relays. The system can obtain a higher transmission capacity by selecting a suitable number of relays and a small transmission elevation angle and a large receiving elevation angle.
, doi: 10.11999/JEIT190687
[Abstract](524) [FullText HTML](169) [PDF 4013KB](19)
Abstract:
The problem that single-channel Synthetic Aperture Radar (SAR) cannot effectively suppress FM slope mismatch jamming is studied in this paper. According to the difference between the jamming spectrum and the real echo spectrum, a method for suppressing the mismatch jamming of SAR FM slope based on matched filtering in the frequency domain is proposed. Based on the accurate measurement of the slope of jamming FM, the sparse super-resolution estimation of the jamming position is carried out to obtain a more accurate phase of jamming delay by utilizing the sparsity of the jamming signal and the characteristics of low Signal-to-Interference Ratio (SIR). Then, the spectrum of the interference signal is reconstructed by the obtained slope of interference frequency modulation and phase delay. Based on this, the orthogonal matched filter is designed to suppress the interference signal in the frequency domain and reconstruct the undistorted scene image. Finally, computer simulation experiments verify the effectiveness of the proposed method.
, doi: 10.11999/JEIT190556
[Abstract](924) [FullText HTML](1102) [PDF 1751KB](33)
Abstract:
Noise is one of the most important influences for clustering. Existing fuzzy clustering methods try to reduce the impact of noise by relaxing the constraint condition of membership. But there are still two basic problems need to be solved. First, how to evaluate the probability that a sample point is a noise. Second, how to retain the effect of normal points while suppressing the impact of noise. To solve these two problems, Robust Fuzzy C-Means based on Adaptive Relaxation (AR-RFCM) is proposed. The new model estimates the reliability of sample points by the method of the K-Nearest Neighbor (KNN). It adaptively adjusts the relaxation parameters to reduce the impact of noise, and keeps the effect of reliable sample points at the same time. In addition, AR-RFCM utilizes the sparsity of membership in k-means to improve the effect of reliable sample points. Therefore, the compactness of clusters is improved and the impact of noise is suppressed. Experiments demonstrate that AR-RFCM has a good robustness for noise, and also achieves higher rand index in all 25 UCI data sets, even averagely higher than FCM 7.7864%.
, doi: 10.11999/JEIT190704
[Abstract](1577) [FullText HTML](194) [PDF 1469KB](20)
Abstract:
In Adjacent Channel Interference (ACI) suppression, in order to obtain the nonlinear characteristics of interference signal for reconstruction and cancellation, the receiver needs to use high-sampling-rate wideband Analog-to-Digital Converter (ADC) to sample interference signal, which will greatly increase the cost of the receiver. To solve the problem, a ACI suppression method based on deconvolution of interference signal’s out-of-band component is proposed in this paper. By using the known out-of-band nonlinear component, the influence between adjacent frames is calculated and eliminated, and then the narrow band linear convolution frame is constructed from the partial convolution frame. Finally, the original wide band signal is recovered by regularized least square method, thus reducing the ADC sampling rate. The simulation results show that when the sampling rate is only 1/3 of the traditional scheme, the residual interference brought by the proposed method is not higher than the noise floor of 6 dB.
, doi: 10.11999/JEIT190623
[Abstract](1140) [FullText HTML](362) [PDF 1232KB](13)
Abstract:
Many optimization problems develop into high-dimensional large-scale optimization problems in the process of the development of artificial intelligence. Although the high-dimensional problem can avoid the algorithm falling into local optimum, it has no advantage in convergence speed and time feasibility. Therefore, the natural computing method for Nonlinear Dimension Reduction (NDR) is proposed. This strategy does not depend on specific algorithm and has universality. In this method, the initialized N individuals are regarded as a matrix of N rows and D columns, and then the maximum linear independent group is calculated for the column vector of the matrix, so as to reduce the redundancy of the matrix and reduce the dimension. In this process, since any remaining column vector group can be represented by the maximum linearly independent group, a random coefficient is applied to the maximum linearly independent group to maintain the diversity and integrity of the population. The standard genetic algorithm and particle swarm optimization using NDR strategy compare with Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and the four mainstream algorithms for dimension optimization. Experiments show that the improved algorithm has strong global convergence ability and better time complexity for most standard test functions.
, doi: 10.11999/JEIT190550
[Abstract](273) [FullText HTML](302) [PDF 2116KB](9)
Abstract:
In view of the great difference between classical trajectory and real-time trajectory, a robust trajectory similarity measurement method is proposed based on the longest common subsequence theory. Firstly, the distance between point and line segment is used to judge whether the point of classical trajectory is consistent with the line segment of real-time trajectory. Secondly, the longest common subsequence length between classical trajectory and real-time trajectory is calculated by using the improved multi-to-one longest common subsequence algorithm. Finally, the ratio of the longest common subsequence length to the number of points of the classical trajectory is taken as the similarity between the classical trajectory and the real-time trajectory. Experiments show that the algorithm is robust and can effectively solve the problem of similarity measurement between the classical trajectories and real-time trajectories.
, doi: 10.11999/JEIT190228
[Abstract](367) [FullText HTML](614) [PDF 1433KB](23)
Abstract:
The anti-interference technology in wireless communication is great significance to the stability and security of communication. As an important part of anti-interference technology, interference recognition is a research hotspot. An interference recognition method based on singular value decomposition and neural network is proposed. This method only calculates the singular value of the signal matrix as the feature. Compared with the traditional method, it saves the computational complexity of multiple spectral features. The simulation results show that the recognition accuracy based on singular value decomposition and neural network is 10%~25% higher than the traditional method under the condition of jamming-signal ratio at 0 dB.
, doi: 10.11999/JEIT190580
[Abstract](1380) [FullText HTML](539) [PDF 1572KB](30)
Abstract:
Considering the shortage of traditional medical ultrasound image despeckle methods, an adaptive multi-exposure fusion framework and feedforward convolutional neural network model image despeckle method is proposed. Firstly, an ultrasound image training data set is produced. Then, a multi-exposure fusion framework with adaptive enhancement factors is proposed to enhance the image for effective feature extraction.Finally, a speckle model is trained through the network and a speckle image is obtained. Experimental results show that, compared with the existing methods, this paper can more effectively remove speckle noise in medical ultrasound images and retain more image details.
, doi: 10.11999/JEIT190541
[Abstract](519) [FullText HTML](139) [PDF 2006KB](13)
Abstract:
The existing similar entity search method has poor adaptability to the length of the observed sequence, and the data storage overhead in the search process is too large, and the accuracy of the search result is insufficient. To this end, an efficient search method is proposed for the IoT entity with similarity adaptive estimation. Firstly, in order to reduce the storage overhead of the entity observation sequence, a lightweight method of segmentation representation of the observation sequence is designed to perform a lightweight segmentation compression representation of the original observation sequence of the entity collected by the sensor. Then, in order to achieve an accurate estimation of the similarity of entities with different observation sequence lengths, an adaptive estimation method for observation sequence similarity is proposed. Finally, by exploiting the designed efficient similar entity search matching method, the exact search matching of the entity is completed according to the estimated entity similarity. The simulation results show that the proposed method can greatly improve the efficiency of similar entity search.
, doi: 10.11999/JEIT190842
[Abstract](2276) [FullText HTML](484) [PDF 2166KB](21)
Abstract:
In order to improve the reception performance of Secondary Surveillance Radar (SSR) replies in high-density signal environment, a separation algorithm is proposed, which constructs the separating matrix with estimating the source number and the direction of arrival of signal. Firstly, the number of overlapping signals is determined with the eigenvalues distribution of the measurements. Secondly, the mixing matrix with the DOA of signals, which is estimated by peak value searching in MUSIC algorithm. Finally, the separating matrix is estimated by calculating the Moore-Penrose inverse of the reconstructed mixing matrix, achieving separation of overlapping signals. Simulation is done based on uniform linear array with 6 elements. The results show that the proposed separation algorithm can achieve more than 90 percent success rate to separate two short Mode S replies, and the separating performance is similar to the independent component analysis algorithm and is better than Projection Algorithm (PA). The amount of calculation is less than 10 percent of Independent Component Analysis (ICA) algorithm, thus the proposed separation algorithm is easier to engineering application.
, doi: 10.11999/JEIT190243
[Abstract](1932) [FullText HTML](467) [PDF 2250KB](25)
Abstract:
To solve the problem that the traditional compressed sensing algorithm based on Total Variation (TV) model can not effectively restore details and texture of image, which leads to over-smoothing of reconstructed image, an image Compressed Sensing (CS) reconstruction algorithm based on Structural Group TV (SGTV) model is proposed. The proposed algorithm utilizes the non-local self-similarity and structural sparsity of image, and converts the CS recovery problem into the total variation minimization problem of the structural group constructed by non-local self-similar image blocks. In addition, the optimization model of the proposed algorithm is built with regularization constraint of the structural group total variation model, and it uses the split Bregman iterative algorithm to separate it into multiple sub-problems, and then solves them respectively. The proposed algorithm makes full use of the information and structural sparsity of image to protects the image details and texture. The experimental results demonstrate that the proposed algorithm achieves significant performance improvements over the state-of-the-art total variation based algorithm in both PSNR and visual perception.
, doi: 10.11999/JEIT190508
[Abstract](1587) [FullText HTML](341) [PDF 2834KB](6)
Abstract:
Considering the problem of multi-group maneuvering target tracking, a fast tracking method based on Interactive Multiple Maneuvering Gaussian Mixture Probability Hypothesis Density (IMM-GM-PHD) algorithm is proposed. Firstly, based on the completion of the IMM-GM-PHD algorithm prediction process, the density detection mechanism is added, and the correlation domain is used to select effective measurement for all predicted Gaussian components, and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is combined to detect whether a new formation target appears. Secondly, based on the completion of the state update of the IMM-GM-PHD algorithm, the update of the model probability is completed by updating the composition of the Gaussian component. Finally, in the process of state estimation optimization, combined with the characteristics of formation targets, the similarity discrimination technique is added, and the Jensen-Shannon (JS) divergence is used to measure the similarity between Gaussian components, and the Gaussian components without similar components are eliminated, and the estimation results are further optimized. The simulation results show that the proposed algorithm can track multi-group maneuvering targets quickly and effectively, and has better tracking performance.
, doi: 10.11999/JEIT190856
[Abstract](1203) [PDF 0KB](11)
Abstract:
Password guessing attack is the most direct way to break information systems. Using appropriate methods to generate password dictionaries can accurately evaluate the security of password sets. This paper proposes a new approach to the Chinese password set security evaluation that is named Chinese Syllables and Neural Network-based password generation (CSNN). In CSNN, each chinese syllable is treated as an integral element, and the spelling rules of chinese syllable can be used to parse and process the passwords. The processed passwords are then trained in the neural network model of Long Short-Term Memory (LSTM), which is used to generate password dictionaries (guessing sets). To evaluate the performance of CSNN, the hit rates of guessing sets generated by CSNN is compared with the two classical approaches (i.e., Probability Context-Free Grammar (PCFG) and 5th-order markov chain model). In the hit rate experiment, guessing sets of different scales were selected; the results show that the comprehensive performance of guessing sets generated by CSNN is better than PCFG and 5th-order markov chain model. Compared with PCFG, different scales of CSNN guessing sets can improve 5.1%～7.4% in hit rate on some test sets by 107 guesses (average 6.3%); compared with 5th-order markov chain model, the CSNN guessing sets increased its hit rate by 2.8% to 12% (with an average of 8.2%) by 8×105 guesses.
, doi: 10.11999/JEIT190495
[Abstract](911) [FullText HTML](359) [PDF 2090KB](18)
Abstract:
Infrared thermal imaging system has obvious advantages in target recognition and detection at night, and the motion defocus blur caused by dynamic environment on mobile platform affects the application of the above imaging system. In order to solve the above problems, based on the research of infrared image restoration method after motion defocusing using using generating confrontation network, a Infrared thermal image Multi scale deblurGAN(IMdeblurGAN) is proposed to suppress motion defocusing blurring effectively while preserving the image by using generating confrontation network to suppress the motion defocusing blurring of infrared image. Hold the contrast of infrared image details, improve the detection and recognition ability of night targets on motion platform. The experimental results show that compared with the existing optimal restoration methods for blurred images, Peak Signal to Noise Ratio (PSNR) of the image is increased by 5%, the Structure SIMilarity (SSIM) is increased by 4%, and the confidence score of YOLO for target recognition is increased by 6%.
, doi: 10.11999/JEIT190494
[Abstract](629) [FullText HTML](188) [PDF 3453KB](23)
Abstract:
The Yellow River is an important water resource in China. Using radar remote sensing to monitor the runoff of the Yellow River can conveniently reflect the changing trend of drought and flood, which has important practical significance. At present, Radar Altimeter (RA) commonly used to construct a water depth-runoff model in runoff inversion. This method ignores the influence of river surface change on runoff fluctuation and has certain limitations. A Multi-source Radar Remote Sensing Runoff Calculation Model (MRRS-RCM) is proposed. In this study, RA technology and Synthetic Aperture Radar (SAR) technology are used to construct MRRS-RCM model on the basis of the Manning’s equation to realize runoff inversion. Three stations are selected for experiments in the lower reaches of the Yellow River. The results show that the Relative Root Mean Square Error (RRMSE) of MRRS-RCM runoff inversion reaches 13.969%, which is better than the accuracy requirement of traditional runoff monitoring of 15%-20%.
, doi: 10.11999/JEIT190542
[Abstract](525) [FullText HTML](158) [PDF 3253KB](9)
Abstract:
To solve the problem of high system delay caused by unreasonable resource allocation because of randomness and unpredictability of service requests in 5G network slicing, this paper proposes a deployment scheme of Service Function Chain (SFC) based on Transfer Actor-Critic Algorithm (TACA). Firstly, an end-to-end delay minimization model is built based on Virtual Network Function (VNF) placement, and joint allocation of computing resources, link resources and fronthaul bandwidth resources, then the model is transformed into a discrete-time Markov Decision Process (MDP). Next, A-C learning algorithm is adopted in the MDP to adjust dynamically SFC deployment scheme by interacting with environment, so as to optimize the end-to-end delay. Furthermore, in order to realize and accelerate the convergence of the A-C algorithm in similar target tasks (such as the arrival rate of service requests is generally higher), the transfer A-C algorithm is adopted to utilize the SFC deployment knowledge learned from source tasks to find quickly the deployment strategy in target tasks. Simulation results show that the proposed algorithm can reduce and stabilize the queuing length of SFC packets, optimize the system end-to-end delay, and improve resource utilization.
, doi: 10.11999/JEIT190515
[Abstract](489) [FullText HTML](251) [PDF 2026KB](23)
Abstract:
For the 5G New Radio in Unlicensed spectrum scenario (NR-U), a novel random access mechanism is proposed, which first adds the channel idle timer in Random Access Response (RAR) window and contention resolution window to reduce the accessing delay caused by the contention-based accessing and employs the Request To Send/Clear To Send (RTS /CTS) mechanism to address the hidden node issue. The mechanism can alleviate the latency incurred by the legacy mechanism which did not consider the intrinsic attribute of unlicensed band and the hidden node problem. Specifically, the legacy random access mechanism applied to NR-U is analyzed. Then, the detailed elaboration of the network entity interaction sequence defined in novel mechanism is proposed. Finally, the performance evaluation processes are carried out in the way of mathematical modeling and experimental simulation, and the analysis result demonstrates that the novel scheme outperforms the benchmark one in the respect of the average random access delay.
, doi: 10.11999/JEIT190569
[Abstract](1476) [FullText HTML](214) [PDF 5101KB](23)
Abstract:
In order to solve the problems of lower precision of target location in short-term occlusion and inaccurate of scale estimation of target in rotation by Spatial-Temporal Regularized Correlation Filters (STRCF), an object tracking algorithm with channel reliability and target response adaptation is proposed in this paper. In this algorithm, target response regularization is added to train target model. By updating the ideal target response function in the process of solving model, the target can be tracked again after being occluded for a short time. The reliability of each feature channel is evaluated by coefficient of channel reliability, which can improves the model's expression of the target. Scale filters can be trained in log-polar coordinates to improve the accuracy of scale estimation when target is rotating. The experimental results show that the proposed algorithm reduces 28.54 pixels in the average center position error and improves the average overlap rate by 22.8% compared with STRCF.
, doi: 10.11999/JEIT190719
[Abstract](615) [FullText HTML](177) [PDF 3709KB](8)
Abstract:
Through the research of a true random number generator (TRNG), which is a low-power and high-noise source, a new type of low-frequency clock is designed. It can amplify the thermal noise of resistance more than 100 times, thus reducing the bandwidth and resistance value of the circuit, reducing the area and power consumption of the circuit, and making the jitter of low-frequency clock reach 58.2 ns. The circuit is designed by SMIC 40 nm CMOS technology. The flow sheet and test are completed. The output speed of TRNG ranges from 1.38 to 3.33 Mbit/s. The overall power consumption of the circuit is 0.11 mW and the area is 0.00789 mm2. The output of random number meets the test requirement of AIS31 true random number entropy source, and passes the security test of National Secret 2.
, doi: 10.11999/JEIT190384
[Abstract](359) [FullText HTML](284) [PDF 460KB](8)
Abstract:
The multipath delay for different propagation mode is 0.5～2.0 ms, and the multipath delay for the same propagation mode is analyzed. Taking into account the earth magnetic field effects, the refractive index of High frequency propagation in ionosphere is combined with ray tracing, and then a new numerical iteration algorithm is given. The multipath delay caused by ionosphere dispersion is analyzed by numerical method, and the simulation is realized. Thus the analogue bandwidth of wideband communication for high frequency should be 48 kHz.
, doi: 10.11999/JEIT190734
[Abstract](705) [FullText HTML](344) [PDF 2376KB](27)
Abstract:
The existing SMeared SPectrum (SMSP) jamming suppression algorithms take a jammed echo whose length equal to radar transmitting signal as the processing object and do not involve the whole echo within the coherent processing interval. For this problem, a jamming suppression algorithm based on fast and slow time domain joint processing is proposed under the background of Linear Frequency Modulation (LFM) coherent radar countering SMSP jamming. The time and frequency domain characteristics of SMSP are studied and the effect on coherent radar is analyzed on the condition of self screening jamming. On this basis, four processing steps are designed to suppress the SMSP jamming. Firstly, the jamming fast time location is estimated by calculating the differential entropy of slow time signal. Secondly, the real jamming parameter is found based on the maximum correlation coefficient criterion. Then the jamming signals are reconstructed using Biorthogonal Fourier Transform. Finally, the SMSP jamming is suppressed by cancellation. The simulation results show that the proposed algorithm model is highly consistent with the actual radar processing flow, and the efficiency is further verified through algorithms comparison.
, doi: 10.11999/JEIT190882
[Abstract](536) [FullText HTML](201) [PDF 2747KB](8)
Abstract:
Emotion has always been a research hot spot in many disciplines such as psychology, education, and information science. EEG signal has received extensive attention in the field of emotion recognition because of its objective and not easy to disguise. Since human emotions are generated by the interaction of multiple brain regions in the brain, this paper propose an algorithm — Support Tensor Machine based on Synchronous Brain Network (SBN-STM) for emotion classification. The algorithm uses Phase Locking Value (PLV) to construct a synchronous brain network, in order to analyze the synchronization and correlation between multi-channel EEG signals, and generate a second-order tensor sequence as a training set. The Support Tensor Machine (STM) model can distinguish a two-category of positive and negative emotions. Based on the DEAP EEG emotion database, this paper analyzes the selection method of synchronic brain network tensor sequence, the research on the size and position of the optimal tensor sequence window solves the problem of traditional emotion classification algorithm which always exists feature redundancy, and improves the model training speed. The results show that the accuracy of the emotional classification method based on SBN-STM is better than support vector machine, C4.5 decision tree, artificial neural network, and K-nearest neighbor which using vectors as input feature.
, doi: 10.11999/JEIT190676
[Abstract](657) [FullText HTML](328) [PDF 1631KB](7)
Abstract:
Considering the problem of agents’ network selection for Human-to-Human(H2H) and Machine-to-Machine (M2M) traffic in heterogeneous wireless networks, an agents’ network selection scheme based on the characteristic of traffic is designed. Game theory is adopted to solve the problem of network selection to satisfy difference in traffic’s Quality of Service (QoS) requirements. The existence and feasibility of the Nash Equilibrium (NE) of the proposed game are also analyzed. Then, a distributed algorithm with limited feedback based on learning automata is presented to obtain the NE of the proposed game. In simulations, the proposed algorithm can achieve a near optimal performance compared to the exhaustive search, satisfy the QoS requirements of different types of traffic, and improves the efficiency of network resources.
, doi: 10.11999/JEIT190582
[Abstract](681) [FullText HTML](470) [PDF 764KB](4)
Abstract:
Paroxysmal Atrial Fibrillation (PAF) is a kind of accidental arrhythmia, and its high missed detection rate leads to the increase of heart-related diseases. An automatic detection method is proposed based on kernel sparse coding, which can identify PAF attacks based only on short RR interval data. A special geometric structure is presented to analyze the high-dimensional characteristics of the data, and the covariance matrix is calculated as a feature descriptor to find the Riemannian manifold structure contained in the data; Based on the Log-Euclidean framework, a manifold method is used to map the manifold space to a high-dimensional renewable kernel Hilbert space to obtain a more accurate sparse representation to quickly identify PAF. After verification by the Massa-chusetts Institute of Technology-Beth Israel Hospital atrial fibrillation database, the sensitivity is 98.71%, the specificity is 98.43%, and the total accuracy rate is 98.57%. Therefore, this study has a substantial improvement in the detection of transient PAF and shows good potential for clinical monitoring and treatment.
, doi: 10.11999/JEIT190585
[Abstract](575) [FullText HTML](455) [PDF 3198KB](6)
Abstract:
A detection algorithm based on spatio-temporal context information is proposed to reduce the influence of non-uniform illumination and random jitter on the accuracy of target hole detection. The light equalization is carried out by using the spatial context information of target and its neighborhood, and the temporal motion context information between chest bitmap sequences is extracted for dithering correction. In order to improve the stability of chest bitmaps, a mul-ti-parameter fusion method is proposed to perform pixel-level fusion of jitter corrected sequence images. Then, rough ex-traction of bullet hole area, energy screening and overlapping bullet holes discrimination are carried out to obtain the location distribution of bullet holes. The experimental results show that the algorithm can effectively suppress the noise caused by non-uniform illumination and random jitter, and has great ability of bullet hole extraction.
, doi: 10.11999/JEIT190712
[Abstract](2228) [FullText HTML](1246) [PDF 1905KB](22)
Abstract:
Chronic cardiovascular diseases such as arrhythmia seriously affect human health. The automatic classification of Electrocardiogram(ECG) signals can effectively improve the diagnostic efficiency of such diseases and reduce labor costs. To tackle this problem, an improved long-short term memory to achieve automatic classification of one dimensional ECG signals is proposed. Firstly, deep convolutional neural network is designed to deeply encode the ECG signal, and ECG signal morphological features are extracted. Secondly, the long-short term memory classification network is used to realize automatic classification of arrhythmia of ECG signal features. In Experimental studies based on the MIT-BIH arrhythmia database show that the training duration is significantly shortened and more than 99.2% classification accuracy is obtained. Sen and other evaluation parameters are improved to meet the real-time and efficient requirements for automatic classification of ECG signals.
, doi: 10.11999/JEIT190517
[Abstract](555) [FullText HTML](256) [PDF 590KB](13)
Abstract:
Based on the characteristics of Whitened Swap−Or−Not (WSN) construction, the maximum expected differential probability (MEDP) of Bent whItened Swap Or Not -like (BISON-like) algorithm proposed by Canteaut et al. is analyzed in this paper. In particular, the ability of BISON-like algorithm with balanced nonlinear components against linear cryptanalysis is also investigated. Notice that the number of iteration rounds of BISON algorithm is rather high (It usually needs to iterate 3n rounds, n is the block length of data) and Bent function (unbalanced) is directly used to XOR with the secret key bits. In order to overcome these shortcomings, a kind of balanced Boolean functions that has small absolute value indicator, high nonlinearity and high algebraic degree is selected to replace the Bent functions used in BISON algorithm. Moreover, the abilities of this new variant BISON algorithm against both the differential cryptanalysis and the linear cryptanalysis are estimated. It is shown that the new variant BISON algorithm only needs to iterate n-round function operations; If n is relative large (e.g. n=128 or n=256), Its abilities against both the differential cryptanalysis and the linear cryptanalysis almost achieve ideal value. Furthermore, due to the balanced function is directly XORed with the secret key bits of the variant algorithm, it attains a better local balance indeed.
, doi: 10.11999/JEIT190654
[Abstract](396) [FullText HTML](253) [PDF 1316KB](23)
Abstract:
To solve the problem of increasing the digital currency of mobile terminals based on limited residual resources of the system, a task offloading scheme is proposed based on node residual resources and network delay in the joint blockchain and fog computing system. In order to offload the task in an optimal way, the expected revenue of mobile terminals is firstly analyzed based on the amount of tasks. Secondly, based on the remaining computing resources, storage resources, power resources and network delays, the expenditure of mobile terminal is analyzed. Then, a mathematical optimization model is established to maximize the digital currency income of the mobile terminal. The Simulated Annealing (SA) algorithm is employed to deal with the suboptimal model, respectively. Simulation results demonstrate the effectiveness of the proposed scheme.
, doi: 10.11999/JEIT190591
[Abstract](276) [FullText HTML](229) [PDF 825KB](17)
Abstract:
An improved fuzzy c-means clustering algorithm is proposed in this study in accordance with the characteristics of interval uncertain data. First, the interval data is transformed into real data composed of 2p dimension feature, which is mapped from that of p dimension feature. Second, a method for calculating sample distance, which realizes the interval sample clustering by fuzzy c-mean algorithm, is designed while considering the relationship between interval median value and interval size. Theoretical analysis and comparison experiments show that the presented algorithm surpaes the compared algorithms by more than 10% on average in terms of the partition coefficient (PC) and Correct Rank(CR) value. These results indicate that the algorithm presents in this study has better clustering accuracy and provides a new solution for the classification of uncertain data in current big data environments.
, doi: 10.11999/JEIT190721
[Abstract](925) [FullText HTML](202) [PDF 1904KB](24)
Abstract:
Considering the problem that it is difficult to accurately and effectively extract the quality features of mixed distortion image, an image quality assessment method based on spatial distribution analysis is extracted. Firstly, the brightness coefficients of the image are normalized, and the image is divided into blocks. While the Convolutional Neural Network (CNN) is used for end-to-end depth learning, the multi-level stacking of convolution cores is applied to acquire image quality perception features. The feature is mapped to the mass fraction of the image block through the full connection layer, then the quality pool is obtained by aggregating the quality of the block. Through the analysis of the spatial distribution of local quality in the quality pool, the features that can represent its spatial distribution are extracted, and then the mapping model from local quality to overall quality is established by the neural network to aggregate the local quality of the image. Finally, the effectiveness of the algorithm is verified by the performance tests in MLIVE, MDID2013 and MDID2016 mixed distortion image databases and better than the related algorithms.
, doi: 10.11999/JEIT190372
[Abstract](1456) [FullText HTML](211) [PDF 3103KB](13)
Abstract:
Electromagnetic vortices are introduced into wireless communication to improve spectral efficiency and anti-interference capability. In this paper, the basic principle and characteristics of orbital angular momentum and electromagnetic eddy are introduced firstly. The principle of generating orbital angular momentum from supersurface is given, and the methods and research status of generating orbital angular momentum based on supersurface are summarized. The transmission performance, receiving and detecting method, multiplexing and demultiplexing performance of orbital angular momentum are summarized. Finally, the key problems to be solved in the future application of wireless communication orbital angular momentum are discussed.
, doi: 10.11999/JEIT190486
[Abstract](633) [FullText HTML](332) [PDF 2314KB](8)
Abstract:
Optimal node recovery is an effective measure to control cascading failure of interdependent networks. In view of the fact that the previous recovery model does not consider the node load, this paper analyzes first the cascading failure process including dependent failure and overload failure, and constructs the recovery model of interdependent network under load. Then, considering the structure and dynamic properties of the mutual boundary nodes, a Preferential Recovery method based on Capacity and Connectivity Link (PRCCL) is proposed Experiment results show that in scale-free independent networks, the recovery effect of PRCCL is better than benchmark methods, the recovery time is shorter, and the recovered networks have higher average degree and robustness. In the independent network composed of Power grid and Internet network, the recovery effect of PRCCL method is also better than the benchmark methods. The advantages of PRCCL are proportional to the recovery ratio, load control parameters and inversely proportional to the tolerance coefficient. The experimental results verify the validity of the PRCCL method, which has scientific guidance value for the recovery of interdependent networks in reality.
, doi: 10.11999/JEIT190432
[Abstract](70) [PDF 0KB](1)
Abstract:
For the DOA estimation problem of low-elevation target of VHF radar, a new multi-frame phase feature enhancement based method is proposed, which solves effectively the phase feature ambiguity of direct signal, and thus improves the accuracy of DOA estimation. By learning the complex mapping relationship between the phase distribution of the multi-frame data and ideal phase distribution of the direct signal, the fuzzy phase information is enhanced and is used to reconstruct a new data matrix with original amplitude information. The DOA is estimated by conventional methods using new data matrix, which effectively improves the DOA estimation accuracy of the low-elevation target. The effectiveness of proposed method is validated by computer simulation experiments and real data, and it shows higher accuracy compared with physics-driven methods including MUSIC method and state-of-the-art data-driven method including feature reversal and SVR (Support Vector Regression).
, doi: 10.11999/JEIT190435
[Abstract](811) [FullText HTML](251) [PDF 2284KB](33)
Abstract:
In the passive location of moving target, the closed-form solution can reach Cramér-Rao Lower Bound (CRLB) under the low noise level, but these algorithms often can not adapt to the large measurement noise condition. For this problem, this paper proposes a passive positioning algorithm based on the Semi-Definite Relaxation (SDR) using Time Difference Of Arrival (TDOA) and Frequency Difference Of Arrival (FDOA). Firstly, this method constructs the pseudo-linear equation of the typical closed-form solution. Secondly, the idea of Stochastic Robust Least Squares (SRLS) and the nonlinear relationship between the target parameters and the additional variables are used to transform the localization problem into the least squares problem with quadratic equality. Using Semi-Definite Programming (SDP) technique, constrained least squares problem is then converted to the SDP problem, which is finally solved by the optimization toolbox. The proposed method does not require an initial priori information and simulations show the effectiveness of the proposed method.
, doi: 10.11999/JEIT190398
[Abstract](427) [FullText HTML](241) [PDF 1787KB](23)
Abstract:
, doi: 10.11999/JEIT190453
[Abstract](336) [FullText HTML](225) [PDF 2603KB](12)
Abstract:
To solve the failure of existing evaluation methods of infrared and visible fusion image caused by high brightness halation information in night vision halation scene, a novel fusion image quality evaluation method based on adaptive partition is proposed. In this method, the adaptive coefficient is automatically determined according to the halation degree of visible image, and then, the fusion image is divided into halo regions and non-halo region by iterative calculation of the critical halation gray value. In the halo region, the effectiveness of halation elimination is evaluated by halation elimination index designed, while in the non-halo region, the enhancement effect of detailed information such as texture and color is evaluated from three aspects including: characteristics of fusion image itself, retention degree of original image information and human visual effect. Based on evaluation and analysis of fusion images obtained by 4 different anti-halation algorithms, nine objective indexes are selected to construct a quality evaluation system of night vision anti-halation fused image. Experimental results in different night vision halation scenes show that the proposed method could evaluate anti-halation image quality of infrared and visible fusion comprehensively and reasonably, and could solve the problem that the more thorough halation elimination of fusion image, the worse objective evaluation results. This method could also be suitable for evaluating merits and demerits of different anti-halation fusion algorithms.
, doi: 10.11999/JEIT190383
[Abstract](666) [FullText HTML](461) [PDF 2397KB](9)
Abstract:
The BDS-2 (BeiDou-2 System) officially provided services to the Asia-Pacific region in 2012. The GEO-3 satellite has been retired and replaced by the GEO-7 satellite. Studying the signal quality characteristics of the satellite during the pro-retirement period can not only analyze the satellite payload status of the BDS-2, but also provide important reference value for other signal characteristics of the pro-retirement satellite. At the same time, it has important enlightenment and reference significance for the signal quality controlling and optimizing of the GEO satellite load of the BDS-3. Using the multi-method monitoring data of the 40-meter large-diameter antenna of the Hao-ping Radio Observatory(HRO), the power spectrum, the ground receiving power and S-Curve Bias (SCB) of the GEO-3 satellite B1 civil signal are analyzed. The long-term trend of these characteristics are given, and the corresponding statistical results are given. Relevant suggestions for satellite payload signal quality optimization are proposed.
, doi: 10.11999/5EIT190445
[Abstract](558) [FullText HTML](248) [PDF 1761KB](9)
Abstract:
Many researches demonstrate that cell-edge users are more susceptible to pilot contamination than the cell-center users in massive MIMO systems. Therefore, this paper proposes a dynamic pilot allocation scheme for Joint User Grouping and Alliance Game (JUG-AG) to mitigate pilot contamination. According to the user signal strength, the users are divided into two groups, namely A and B. Users with weak strength of the received Base Stations (BSs) signals are recorded as group A, and the remaining users are group B. The users of group A use mutually orthogonal pilots, and the users of group B reuse the remaining orthogonal pilots by means of alliance game. In the alliance game for the users of group B, users are divided into several disjoint user sub-alliances, users belonging to different sub-alliances are allocated different orthogonal pilot sequences, and users in the same sub-alliance reuse the same pilot sequence. Compared with existing pilot allocation schemes, the proposed JUG-AG scheme is more flexible and can be used for scenarios that all users are randomly distributed. Moreover, the algorithm can obtain the overall optimal solution through cyclic searching. The simulation results demonstrate that the JUG-AG scheme can effectively reduce the average Root Mean Square Error (RMSE) of user signal detection in the uplink and improve the average service rate of users.
, doi: 10.11999/JEIT190598
[Abstract](855) [FullText HTML](439) [PDF 1974KB](23)
Abstract:
As the key object in the process of template analysis, power traces have the characteristics of high dimension, less effective dimension and unaligned. Before effective preprocessing, template attack is difficult to work. Based on the characteristics of energy data, a global alignment method based on manifold learning is proposed to preserve the changing characteristics of power traces, and then the dimensionality of data is reduced by linear projection. The method is validated in Panda 2018 challenge1 standard datasets respectively. The experimental results show that the feature extraction effect of this method is superior than that of traditional PCA and LDA methods. Finally, the method of template analysis is used to recover the key, and the recovery success rates can reach 80% with only two traces.
, doi: 10.11999/JEIT190502
[Abstract](1223) [FullText HTML](836) [PDF 2776KB](41)
Abstract:
As a new type of neural network, Extreme Learning Machine (ELM) has extremely fast training speed and good generalization performance. Considering the problem that the Extreme Learning Machine has high computational complexity and huge memory demand when dealing with high dimensional data, a Batch inheritance Extreme Learning Machine (B-ELM) algorithm is proposed. Firstly, the dataset is divided into different batches, and the automatic encoder network is used to reduce the dimension of each batch. Secondly, the inheritance factor is introduced to establish the relationship between adjacent batches. At the same time, the Lagrange optimization function is constructed by combining the regularization framework to realize the mathematical modeling of batch ELM. Finally, the MNIST, NORB and CIFAR-10 datasets are used for the test experiment. The experimental results show that the proposed algorithm not only has higher classification accuracy, but also reduces effectively computational complexity and memory consumption.
, doi: 10.11999/JEIT181011
[Abstract](558) [FullText HTML](348) [PDF 1902KB](11)
Abstract:
In order to achieve a higher level of autonomy for Unmanned Combat Air Vehicle (UCAV) in autonomous air combat, an autonomous maneuvering decision system is established in this paper. Firstly, the factor function of maneuvering decision-making is established by using fuzzy logic, and then the prediction model of enemy aircraft maneuvering is designed. The air combat game is regarded as a Markov process, and the air combat situation is effectively calculated by using Bayesian inference (BI) theory. Finally, the whole air combat maneuvering decision-making process is carried out by Moving Horizon Optimization (MHO) method. Modeling and Simulation of short-range air combat are carried out. The results show that the proposed method can effectively improve the situation advantages of UCAV and has obvious advantages.
, doi: 10.11999/JEIT180705
[Abstract](2010) [FullText HTML](737) [PDF 2449KB](43)
Abstract:
Considering the problem that the traditional circularly polarized microstrip antenna has narrow Axial Ratio (AR) bandwidth and small system capacity, a new type of broadband dual circularly polarized printed antenna is proposed. The antenna structure is simple with a dual port microstrip feed mode, consisting of only two radiating patches and an improved ground plane, and the entire size of antenna is 48 mm× 48 mm× 1 mm. By optimizing the shape of the radiating patch and adding a circular structure on the ground plane, the impedance bandwidth and the axial ratio bandwidth of the antenna can be effectively increased, achieving the dual circular polarization characteristics. The design process of antenna is given, and the circular polarization mechanism of the antenna is analyzed from the surface current distributions. The simulated and measured results show that the antenna has a very wide impedance bandwidth and axial ratio bandwidth. The working frequency band of the antenna is 1.9～9.6 GHz (the relative bandwidth is 133.9%), and the 3 dB AR bandwidth is 1.9～6.6 GHz (the relative bandwidth is 110.6%). The radiation performance and gain characteristics of the antenna are measured. The measured results agree well with the simulated results, which proves the effectiveness of the antenna. The antenna can be applied to Ultra WideBand (UWB) wireless communication systems and satellite communication systems.

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2020, 42(6): 1 -4
[Abstract](146) [FullText HTML](112) [PDF 197KB](37)
Abstract:
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