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DNA Aptamer-based Fluorescence Biosensor
Wenxiao HU, Mengyao QIAN, Yue WANG, Yafei DONG
, doi: 10.11999/JEIT190860
[Abstract](0) [FullText HTML](0) [PDF 326KB](0)
In recent years, with the rapid development of DNA nanotechnology, fluorescence biosensors based on DNA as aptamer have been studied and constructed by a large number of scholars in order to realize the sensitive and rapid detection of target materials. As a new branch of DNA nanotechnology, fluorescence biosensors based on DNA aptamer have great application. The fluorescence biosensors based on DNA aptamers are summarized. The realization of fluorescence signal contains fluorescent dyes and non-fluorescent dye labeled. Enhancement of fluorescence signals involve enzyme-assisted, chain replacement reaction and both of all mediated target circulation and signals amplification strategy. On this basis, the fluorescence biosensor based on DNA aptamer is prospected and some suggestions are put forward.
Synthesis of Sparse Rectangular Planar Arrays with Multiple Constraints Based on Dynamic Parameters Differential Evolution Algorithm
Minli YAO, Xujian WANG, Fenggan ZHANG, Dingcheng DAI
, doi: 10.11999/JEIT190346
[Abstract](147) [FullText HTML](81) [PDF 1109KB](10)
For solving the problem of the synthesis of sparse rectangular planar arrays with multiple constraints, this paper proposes a Dynamic Parameters Differential Evolution (DPDE) algorithm. Firstly, to improve searching efficiency and accuracy of Differential Evolution (DE), the proposed method introduces dynamically changing strategies to the scaling factor and the crossover probability of the traditional Differential Evolution algorithm. Secondly, a modified matrix mapping method and the redefinition of mapping principles are presented to make up the defects of strong randomness and low accuracy in existing methods. Finally, simulation experiments of antenna arrays are performed to validate the effectiveness of the proposed method, and the results demonstrate that the proposed method out performs the existing methods in the respect of reducing peak sidelobe level of antenna arrays.
A Software-Defined Networking Packet Forwarding Verification Mechanism Based on Programmable Data Plane
Zhibin ZUO, Chaowen CHANG, Xianwei ZHU
, doi: 10.11999/JEIT190381
[Abstract](131) [FullText HTML](99) [PDF 1792KB](15)
For the fixed and limited number of OpenFlow protocol matching fields, and the lack of effective forwarding verification mechanism for data packet forwarding in the Software-Defined Networking (SDN), a SDN packet forwarding verification mechanism based on programmable data plane is proposed. By adding a cipher identification to the data packet, the P4 forwarding device joins the OpenFlow-based SDN network to control accurately and sample network traffic flow without affecting the normal forwarding of the data flow. The controller verifies the integrity of the sampled packet, and sends flow rules to the OpenFlow forwarding device to control the abnormal data flow such as malicious tampering and forgery. Finally, the forwarding verification prototype and simulation network based on P4 forwarding device and Open vSwitch forwarding device are constructed and tested. The experimental results show that the mechanism can effectively detect the forwarding abnormal behaviors such as packet tampering and forgery. Compared with similar verification mechanisms, in the case of the same security verification processing overhead, it can achieve more fine-grained flow precise control sampling and lower forwarding delay.
Cryptographic Approaches for Privacy-Preserving Machine Learning
Han JIANG, Yiran LIU, Xiangfu SONG, Hao WANG, Zhihua ZHENG, Qiuliang XU
, doi: 10.11999/JEIT190887
[Abstract](4) [FullText HTML](4) [PDF 1122KB](0)
The characteristics of the new generation of artificial intelligence technology are shown as follows: with the help of GPU computing, cloud computing and other high-performance distributed computing capabilities, machine learning algorithms represented by deep learning algorithms are used for learning and training on big data to simulate, extend and expand human intelligence. Different data sources and computing physical locations make the current machine learning face serious privacy leakage problem, so the privacy protection of machine learning has become a widely concerned research area. Using cryptography technology to solve the problem of privacy in machine learning is an important technology to protect the privacy of machine learning. This paper introduces cryptographic tools using in privacy-preserving machine learning, such as general secure multi-party computing, privacy protection set operation and homomorphic encryption, describes the status and developments applying the tools to solve the problems of privacy protection in various stages of machine learning, such as data processing, model training, model testing, and data prediction.
A Low Latency Random Access Mechanism for 5G New Radio in Unlicensed Spectrum
Zhenghang ZHU, Jianxin JIA, Zhenghong LI, Hua QIAN, Kai KANG
, doi: 10.11999/JEIT190515
[Abstract](6) [FullText HTML](5) [PDF 2026KB](3)
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.
An Object Tracking Algorithm with Channel Reliability and Target Response Adaptation
Peng WANG, Mengyu SUN, Haiyan WANG, Xiaoyan LI, Zhigang LV
, doi: 10.11999/JEIT190569
[Abstract](4) [FullText HTML](3) [PDF 5101KB](0)
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.
A True Random Number Design of Low Power and High Noise Source
Zikui WEI, Yi HU, Xin JIN, Zhenguo LI, Wennan FENG, Xi FENG, Xiaoke TANG
, doi: 10.11999/JEIT190719
[Abstract](23) [FullText HTML](17) [PDF 3709KB](2)
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.
High Frequency Channel Multipath Analysis Based on Ionosphere Dispersion
Yonghong WU, Chenglin WANG, Yuanbo REN, Fuhou ZHOU
, doi: 10.11999/JEIT190384
[Abstract](15) [FullText HTML](13) [PDF 460KB](1)
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.
Optical Image Encryption Based on Spiral Phase Transform and Generalized Fibonacci Chaos
Yuan GUO, Xin XU, Shiwei JING, Tao JIN, Mei JIN
, doi: 10.11999/JEIT1900514
[Abstract](163) [FullText HTML](35) [PDF 9856KB](4)
In this paper, an optical image encryption algorithm based on spiral phase transform and new generalized fibonacci chaotic system is proposed to solve the problems of the fresnel domain double random phase coding system is insensitive to the first diffraction distance, uneven distribution of chaotic sequences and weak resistance to choice plaintext attack. The plaintext image is encoded as phase information and spiral phase transformed to overcame the insensitivity of the first random phase template and diffraction distance of the fresnel diffraction transform-double random phase encoding system and improves the increase the sensitivity of the optical keys. The weighted interference between secure image and plaintext image is added to further increase the sensitivity of of the optical keys and dimension of key . A generalized Fibonacci chaotic system, which could generate uniform sequences, was constructed to generate phase templates to overcame uneven distribution of logistic chaos and improve the efficiency of key transmission and the sensitivity of the keys. The chaotic initial value and parameters of spiral phase transform were related to SHA-256. It made the keys changed with the plaintext and achieved the effect of "one encryption at a time", and enhanced the sensitivity of the plaintext and the ability of the resistance to choice plaintext attack and avalanche effect.Experimental comparison shows that this method can effectively increase the plaintext sensitivity and key sensitivity. This method’ robustness and the key space are sufficiently secure, is a high security optical image encryption method.
Joint Estimation Algorithm for Azimuth Velocity and Normal Velocity of Moving Targets in Airborne Multi-channel SAR
Wen JIANG, Jie NIU, Yirong WU, Xingdong LIANG
, doi: 10.11999/JEIT190672
[Abstract](291) [FullText HTML](47) [PDF 2492KB](9)
Parameter estimation is essential for SAR imaging of moving targets. The existing algorithms mainly estimate the radial velocity and azimuth velocity of the moving target, but the normal velocity of the three-dimensional moving target can not be estimated. In this paper, a joint estimation algorithm of azimuth velocity and normal velocity is proposed by using an airborne multi-channel SAR system with L-shaped baseline. The algorithm extracts the moving target signal in Range-Doppler domain, and estimate the azimuth and normal velocity jointly using the phase differences between multiple SAR images. The algorithm does not rely on image registration, does not need to solve Doppler ambiguity. Therefore, the algorithm has high estimation accuracy and robustness, and has strong practical significance and application value.
Fast-slow Time Domain Joint Processing Suppressing Smeared Spectrum Jamming
Liang ZHANG, Guohong WANG, Xiangyu ZHANG, Siwen LI
, doi: 10.11999/JEIT190734
[Abstract](267) [FullText HTML](53) [PDF 2376KB](19)
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.
A Connection-oriented Fast Multi-dimensional Packet Classification Algorithm
Bin ZHANG, Haoming WU
, doi: 10.11999/JEIT190434
[Abstract](31) [FullText HTML](23) [PDF 1652KB](1)
In order to increase the classification speed of Aggregated Bit Vector (ABV) algorithm, an Improved Aggregated Bit Vector (IABV) algorithm is proposed, which is connection-oriented. Based on the characteristic that the packets which belong to the same connection have similar classification results, IABV establishes a Hash table-rule set two-level searching structure. It first searches in the Hash table to check the packet classification rule and then finds the matching rule in the rule set when the Hash table lookup fails. To avoid the accumulation of rules in the table, a collision handling mechanism is proposed. It judges whether to overwrite the Hash table entry which is collision according to the last hit time of the entry; Secondly, for the purpose of accelerate rule set searching, IABV divides each dimension into multiple intervals equally and employs array to index these intervals; Finally, the prefix in the rule is converted into range to reduce the complexity of the search structure, so that the time and memory consumption of the algorithm can be decreased. The experiment result shows that the performance of the algorithm can be improved by converting prefix into range and the time performance of IABV algorithm is significantly improved compared with the ABV algorithm under the same conditions.
Error Correction of Lempel-Ziv-Welch Compressed Data
Gang WANG, Yanqing JIN, Hua PENG, Guangwei ZHANG
, doi: 10.11999/JEIT190520
[Abstract](25) [FullText HTML](24) [PDF 769KB](0)
Lossless data compression system is prone to bit error and causes error spread during communication transmission, which affects its application to file system and wireless communication. For the lossless data compression algorithm Lempel-Ziv-Welch (LZW), which is widely used in the field of general coding, analyzes and utilizes the redundancy of LZW compressed data, carries the check code by selecting part of the codeword and dynamically adjusting the length of its corresponding compressed string. A lossless data compression method Carrier-LZW(CLZW) with error correction capability is proposed. This method does not need additional data, does not change the data specification and coding rules, and is compatible with the standard LZW algorithm. The experimental results show that the file compressed by this method can still be decompressed by the standard LZW decoder. In the range of error correction capability, the method can effectively correct the error of LZW compressed data.
Research on Support Tensor Machine Based on Synchronous Brain Network for Emotion Classification
Liya HUANG, Yibo SU, Junkai MA, Wei DING, Chuancheng SONG
, doi: 10.11999/JEIT190882
[Abstract](40) [FullText HTML](20) [PDF 2747KB](3)
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.
Recent Advances in Zero-Shot Learning
Hong LAN, Zhiyu FANG
, doi: 10.11999/JEIT190485
[Abstract](1843) [FullText HTML](851) [PDF 2877KB](63)
Deep learning has shown excellent performance in the field of artificial intelligence. In the supervised identification task, deep learning algorithms can achieve unprecedented recognition accuracy by training massive tagged data. However, owing to the high cost of labeling massive data and the difficulty of obtaining massive data of rare categories, it is still a serious problem how to identify unknown class that is rarely or never seen during training. In view of this problem, the researches of Zero-Shot Learning (ZSL) in recent years was reviewed and illustrated from the aspects of research background, model analysis, data set introduction and performance analysis in this article. Some solutions of mainstream problem and prospects of future research are provided. Meanwhile, the current technical problems of ZSL was analyzed, which can offer some references to beginners and researchers of ZSL.
Robust Altitude Estimation Based on Spatial Sign Transform in the Presence of Diffuse Multipath for Very High Frequency Radar
Genhua CHEN, Baixiao CHEN
, doi: 10.11999/JEIT190554
[Abstract](103) [FullText HTML](65) [PDF 1368KB](18)
A robust spatial sign transform-based maximum likelihood method for low-elevation target altitude measurement is proposed in the presence of the non-Gaussian diffuse multipath component for Very High Frequency (VHF) radar. The spatial sign transform is implemented to the antenna array snapshots, reducing the influence of the outliers on array covariance matrix and the low elevation estimation algorithms, followed by computing the spatial Sign Covariance Matrix(SCM). Then the application of SCM to the Maximum Likelihood method(SCM-ML) is presented on the basis of the affine equivalence and preservation of the eigenstructure for robust low elevation estimation and height finding of VHF radar. The proposed method effectively solves the non-Gaussian property of the diffuse multipath component and improves the robustness of low elevation estimation. Simulation result and real data demonstrate the robustness and validation of the SCM-ML method.
A Novel Imaging Approach for Improving Azimuth Angular Resolution of Automotive Radars
Tongjun WANG, Feng WU, Wei XU
, doi: 10.11999/JEIT190681
[Abstract](233) [FullText HTML](48) [PDF 2566KB](18)
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.
Occluded Pedestrian Detection Based on Joint Attention Mechanism of Channel-wise and Spatial Information
Yong CHEN, Xi LIU, Huanlin LIU
, doi: 10.11999/JEIT190606
[Abstract](857) [FullText HTML](88) [PDF 1464KB](15)
Pedestrian detector performance is damaged because occlusion often leads to missed detection. In order to improve the detector's ability to detect pedestrian, a single-stage detector based on feature-guided attention mechanism is proposed. Firstly, a feature attention module is designed, which preserves the association between the feature channels while retaining spatial information, and guides the model to focus on visible region. Secondly, the attention module is used to fuse shallow and deep features, then high-level semantic features of pedestrians are extracted. Finally, pedestrian detection is treated as a high-level semantic feature detection problem. Pedestrian location and scale are obtained through heat map prediction, then the final prediction bounding box is generated. This way, the proposed method avoids the extra parameter settings of the traditional anchor-based method. Experiments show that the proposed method is superior to other comparison algorithms for the accuracy of occlusion target detection on CityPersons and Caltech pedestrian database. At the same time, the proposed method achieves a faster detection speed and a better balance between detection accuracy and speed.
Performance Prediction Based on Random Forest for the Stream Processing Checkpoint
Zheng CHU, Jiong YU
, doi: 10.11999/JEIT190552
[Abstract](317) [FullText HTML](52) [PDF 1669KB](11)
Since real-time processing scenarios for ever-increasing amount and type of streaming data caused by the development of the Internet of Things (IoT) keep increasing, and strategies based on empirical knowledge for checkpoint configuration are deficiencies, the strategy faces huge challenges, such as time-consuming, labor-intensive, causing system anomalies, etc. To address these challenges, regression algorithm-based prediction is proposed for checkpoint performance. Firstly, six kinds of features, which have a huge influence on the performance, are analyzed, and then feature vectors of the training set are input into the regression algorithms for training, finally, test sets are used for the checkpoint performance prediction. Compared with other machine learning algorithms, the experimental results illustrat that the random forest has lower errors, higher accuracy and faster execution on CPU intensive benchmark, memory intensive benchmark and network intensive benchmark.
A Reliability-aware 5G Network Slice Reconfiguration and Embedding Algorithm
Guofan ZHAO, Lun TANG, Yanjuan HU, Peipei ZHAO, Qianbin CHEN
, doi: 10.11999/JEIT190500
[Abstract](97) [FullText HTML](104) [PDF 1647KB](13)
Considering the problems of low resource utilization and poor reliability of traditional network slice embedding, a reliability-aware Network Slice (NS) reconfiguration and embedding strategy is proposed. Firstly, a utility function of reliable embedding oriented reliability and available resources is established. Then, considering the resource requirements and the location constraints of Virtual Network Function (VNF), a method is proposed to quantify the reliability requirement of VNF. Based on the above works, the reliable network slice embedding problem is formulated as an integer linear programming which maximizes the profits of reliable VNF deployment while minimizing the consumption of link bandwidth resource. Finally, according to different types of network slices, a network slice reliable embedding algorithm based on neighborhood search and a network slice reconfiguration embedding algorithm based on key VNF backup are proposed. Simulation results show that the proposed algorithms improve the resources utilization and reduce the embedding cost while meeting the reliability of VNF.
A Flexible Network Access Scheme in Heterogeneous Cell Networks with H2H and M2M Coexistence
Hui TIAN, Lei HE, Wenfeng MA, Cong WANG
, doi: 10.11999/JEIT190676
[Abstract](200) [FullText HTML](32) [PDF 1631KB](4)
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.
Public Accountable Redactable Signature Scheme
Jinhua MA, Xinyi HUANG, Junpeng XU, Wei WU
, doi: 10.11999/JEIT190836
[Abstract](71) [FullText HTML](42) [PDF 685KB](4)
Redactable Signature Scheme (RSS) with accountability allows a redactor to delete some portions of the signed data, and generate a valid signature for the remained data without any interaction with the original signer. It supports to trace the data producer, and is an effective solution to the malicious redaction problem of RSS. A novel design of Public Accountable Redactable Signature Scheme (PA-RSS) is proposed, and its security model is formally defined. The first concrete design of PA-RSS is presented by using traditional digital signature scheme, which can add public accountability to any RSS without accountability. Its unforgeability, privacy, signer's public accountability, and redactor's public accountability are proved. Compared with the existing public accountable RSS, the presented scheme with less communication cost is more efficient, and much more applicable to realize the public accountability of authenticated data redaction in an open and sharable environment.
NAND Gate Computational Model Based on the DNA Origami Template
Zhixiang YIN, Zhen TANG, Qiang ZHANG, Jianhong CUI, Jing YANG, Risheng WANG, Shouwei ZHAO, Juli ZHANG
, doi: 10.11999/JEIT190825
[Abstract](143) [FullText HTML](75) [PDF 5241KB](12)
The essence of NAND gate is the superposition of AND gate and NOT gate. The AND gate operation is performed first, and then the NOT gate is performed. It is the basis of the DNA computer. In order to realize the computing of NAND gate, a NAND gate computational model is established based on the DNA origami template. The inputs of the logic value are completed by the Hybridization Chain Reaction (HCR) on the DNA origami template. The input strands first react with the AND gate region and then react with the NOT gate region. The result of the reaction is shown by dynamically separation of the gold nanoparticles on the DNA origami template. The simulation of the model through Visual DSD shows that the system has the advantages of high feasibility.
Indoor Localization Algorithm Based on Array Antenna and Sparse Bayesian Learning
Kun LIU, Jianxin WU, Jie ZHEN, Tong WANG
, doi: 10.11999/JEIT190314
[Abstract](175) [FullText HTML](125) [PDF 1621KB](16)
Due to the influence of many factors such as multipath and multi-source, the traditional indoor localization algorithms based on Bluetooth signal strength have low performance in accuracy and stability. In order to solve the location problem in complex indoor environment based on Bluetooth signal, an indoor localization algorithm based on low-cost array antenna is developed. The algorithm utilizes single-channel using switch-antenna polarization sensitive array to sample Bluetooth signal, then combines the accurate array manifold measured in dark room and the algorithm of Polarized Fast Converging Sparse Bayesian Learning (P-FCSBL) to estimate the source’s angle, and finally gets the target location by angle. This algorithm makes full use of polarization information and angle information to separate target and multipath signal, and simultaneous sampling of one source ensures estimation stability. Finally, the effectiveness of the method is verified by the real data.
A Deep Convolutional Network for Saliency Object Detection with Balanced Accuracy and High Efficiency
Wenming ZHANG, Zhenfei YAO, Yakun GAO, Haibin LI
, doi: 10.11999/JEIT190229
[Abstract](813) [FullText HTML](599) [PDF 2332KB](14)
It is difficult for current salient object detection algorithms to reach a good balance performance between accuracy and efficiency. To solve this problem, a deep convolutional network for saliency object detection with balanced accuracy and high efficiency is produced. First, through replacing the traditional convolution with the decomposed convolution, the computational complexity is greatly reduced and the detection efficiency of the model is improved. Second, in order to make better use of the characteristics of different scales, sparse cross-layer connection structure and multi-scale fusion structure are adopted to improve the detection precision. A wide range of evaluations show that compared with the existing methods, the proposed algorithm achieves the leading performance in efficiency and accuracy.
Pre-filter Design for Sum Secrecy Rate Optimization in Time-reversal Multiuser Systems
Weijia LEI, Miaomiao YANG
, doi: 10.11999/JEIT190339
[Abstract](128) [FullText HTML](106) [PDF 1194KB](0)
Utilizing the characteristics of the wireless channel to achieve secure transmission of information through physical layer technology is a way to realize security communications. The time-reversed transmission has natural anti-jamming and anti-eavesdropping capability due to its unique spatial and temporal focusing property, so a good secrecy performance can be obtained even when the transmitter is equipped with single transmitting antenna. This paper studies the optimization of the transmit filter impulse response in a two-user time-reversed downlink multiple access secure transmission system. The joint optimization problem of two transmitting filters is transformed into the independent optimization problem of each filter based on reciprocity principles. This problem is further converted into the problem of finding the largest eigenvalue and its corresponding eigenvector which is solved by iterative algorithm. The simulation results show that by optimizing the pre-filter for sum secrecy rate, the system's sum secrecy rate is promoted and is obviously higher than that of the conventional time-reversed pre-processing filter system and direct transmission system.
An Improved Provable Secure Certificateless Aggregation Signature Scheme for Vehicular Ad Hoc NETworks
Yong XIE, Xiang LI, Songsong ZHANG, Libing WU
, doi: 10.11999/JEIT190184
[Abstract](254) [FullText HTML](137) [PDF 702KB](15)
Vehicular Ad hoc NETworks (VANETs) which is an important part of smart cities are large networks that organize wireless communication and information exchange between vehicles and X (X: cars, roads, pedestrians, and the Internet). The security and efficiency of the message authentication algorithm are crucial to the VANETs. After analyzing the security shortage of Wang Daxing et al VANETs message authentication scheme, an improved provable secure certificateless aggregation signature scheme for VANETs is proposed. The scheme constructs a secure certificateless aggregation authentication scheme by using Elliptic Curve Cryptography (ECC) and reduces the complexity of the cryptographic operation process, while achieving user’s conditional privacy protection. Rigid security analysis proves that the scheme satisfies the security requirements of VANETs. The performance analysis shows the proposed scheme considerably reduces the computational cost of message signature, single verification and aggregation verification algorithm, and reduces the communication cost when compared with Wang schemes.
Design and Implementation of CRC with Variable Computing Width Based on Formula Recursive Algorithm
Rong CHEN, Lan CHEN, Arfan Haider WAHLA
, doi: 10.11999/JEIT190503
[Abstract](541) [FullText HTML](386) [PDF 951KB](32)
Cyclic Redundancy Check (CRC) is used in cascade with channel coding to improve the convergence of the decoding. In the new generation of wireless communication systems, such as 5G, both code length and code rate are diverse. To improve the decoding efficiency of cascaded systems, a CRC parallel algorithm with variable computing width is proposed in this paper. Based on the existing fixed bit-width parallel algorithm, this algorithm combines the parallel calculation of feedback data and input data in the formula recursive method, realizing a highly parallel CRC check architecture with variable bit-width CRC calculation. Compared with the existing parallel algorithms, the merged algorithm saves the overhead of circuit resources. When the bit-width is fixed, the resource saving effect is obvious, and at the same time, the feedback delay is also optimized by nearly 50%. When the bit-width is variable, the use of resources is also optimized accordingly.
Image-to-image Translation Based on Improved Cycle-consistent Generative Adversarial Network
Jinglei ZHANG, Yawei HOU
, doi: 10.11999/JEIT190407
[Abstract](670) [FullText HTML](322) [PDF 5038KB](39)
Image-to-image translation is a method to convert images in different domains. With the rapid development of the Generative Adversarial Network(GAN) in deep learning, GAN applications are increasingly concerned in the field of image-to-image translation. However, classical algorithms have disadvantages that the paired training data is difficult to obtain and the convert effect of generation image is poor. An improved Cycle-consistent Generative Adversarial Network(CycleGAN++) is proposed. New algorithm removes the loop network, and cascades the prior information of the target domain and the source domain in the image generation stage, The loss function is optimized as well, using classification loss instead of cycle consistency loss, realizing image-to-image translation without training data mapping. The evaluation of experiments on the CelebA and Cityscapes dataset show that new method can reach higher precision under the two classical criteria—Amazon Mechanical Turk perceptual studies(AMT perceptual studies) and Full-Convolutional Network score(FCN score), than the classical algorithms such as CycleGAN, IcGAN, CoGAN, and DIAT.
WLAN Indoor Intrusion Detection Approach Based on Multiple Kernel Maximum Mean Discrepancy Transfer Learning
Mu ZHOU, Yaoping LI, Liangbo XIE, Qiaolin PU, Zengshan TIAN
, doi: 10.11999/JEIT190358
[Abstract](560) [FullText HTML](281) [PDF 2222KB](37)
Wireless Local Area Network (WLAN) indoor intrusion detection technique is one of the current research hotspots in the field of intelligent detection, but the conventional database construction based intrusion detection technique does not consider the time-variant property of WLAN signal in the complicated indoor environment, which results in the low robustness of WLAN indoor intrusion detection system. To address this problem, a Multiple Kernel Maximum Mean Discrepancy (MKMMD) transfer learning based WLAN indoor intrusion detection approach is proposed. First of all, the offline labeled and online pseudo-labeled Received Signal Strength (RSS) features are used to construct source and target domains respectively. Second, the optimal transfer matrix is constructed to minimize the MKMMD of the joint distributions of RSS features in source and target domains. Third, a classifier trained from the transferred RSS features and the corresponding labels in source domain is used to classify the transferred RSS features in target domain, and meanwhile the label set corresponding to target domain is obtained. Finally, the label set corresponding to target domain is updated in an iterative manner until the proposed algorithm converges, and then the intrusion detection in target environment is achieved. The experimental results indicate that the proposed approach is able to preserve high detection accuracy as well as overcome the impact of time-variant signal property on the detection performance.
Detection of Paroxysmal Atrial Fibrillation Based on Kernel Sparse Coding
Ming LIU, Xianhui MENG, Peng XIONG, Xiuling LIU
, doi: 10.11999/JEIT190582
[Abstract](45) [FullText HTML](18) [PDF 764KB](0)
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.
Inversion of Yellow River Runoff Based on Multi-source Radar Remote Sensing Technology
Lin MIN, Ning WANG, Lin WU, Ning LI, Jianhui ZHAO
, doi: 10.11999/JEIT190494
[Abstract](57) [FullText HTML](12) [PDF 4966KB](1)
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%.
Online Anomaly Detection for Virtualized Network Slicing
Weili WANG, Qianbin CHEN, Lun Tang
, doi: 10.11999/JEIT190531
[Abstract](88) [FullText HTML](49) [PDF 2029KB](9)
In virtualized network slicing scenario, one anomaly Physical Node (PN) or Physical Link (PL) in substrate networks will cause performance degradation of multiple network slices. For new measurements are achieved in each period, two online anomaly detection algorithms to monitor the working states of substrate networks in real time are designed. An online One-Class Support Vector Machine (OCSVM) algorithm is first proposed in this paper to detect the working states of PNs. Without requiring any labeled data, the model parameters of OCSVM can be updated based on the new measurements of Virtual Nodes (VNs) in each iteration. Then, an online Canonical Correlation Analysis (CCA) based PL anomaly detection algorithm is proposed according to the natural correlation of measurements between neighboring VNs of virtual links. With a small amount of labeled data, the algorithm can accurately analyze the working states of PLs. The simulation results verify the effectiveness and robustness of the proposed online anomaly detection algorithms for the virtualized network slicing.
Side Channel Cube Attack Improvement and Application on Cryptographic Algorithm
Yongjuan WANG, Tao WANG, Qingjun YUAN, Yang GAO, Xiangbin WANG
, doi: 10.11999/JEIT181075
[Abstract](100) [FullText HTML](26) [PDF 701KB](3)
The complexity of the pre-processing phase of the cubic attack grows exponentially with the number of output bit algebras, and the difficulty of finding an effective cube set increases. In this paper, the algorithm of preprocessing stage in cubic attack is improved. In the cube set search, from random search to target search, a new target search optimization algorithm is designed to optimize the computational complexity of the preprocessing stage. In turn, the offline phase time complexity is significantly reduced. The improved cubic attack combined with the side-channel method is applied to the MIBS block cipher algorithm. The algorithm characteristics of MIBS are analyzed from the perspective of side-channel attack. The leak location is selected in the third round, and the overdetermined linear equations from initial key and output bit are established, which can directly recover 33bit key. Then the 6bit key can be recovered by quadric-detecting. The amount of plaintext required is 221.64, time complexity is 225. This result is greatly improved compared with the existing results, the number of keys recovered is increased, and the time complexity of the online phase is reduced.
Decomposition and Dominance Relation Based Many-objective Evolutionary Algorithm
Hui ZhAO, Tianlong WANG, Yanzhou LIU, Cheng HUANG, Tianqi ZhANG
, doi: 10.11999/JEIT190589
[Abstract](168) [FullText HTML](32) [PDF 1050KB](3)
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 is proposed in this paper, named as DdrEA. 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.
Study on Time Delay Characteristics of LF One-Hop Sky Waves in the Isotropic Ionosphere
Lili ZHOU, Jingjing YAN, Zhonglin MU, Qiaoqiao WANG, Chenglin LIU, Lifeng HE
, doi: 10.11999/JEIT190528
[Abstract](83) [FullText HTML](22) [PDF 1078KB](4)
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.
Motion Defocus Infrared Image Restoration Based on Multi Scale Generative Adversarial Network
Shi YI, Zhijuan WU, Jinjing ZHU, Xinrong LI, Xuesong YUAN
, doi: 10.11999/JEIT190495
[Abstract](24) [FullText HTML](33) [PDF 2306KB](2)
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 (PNSR) 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%.
Detection Algorithm of Chest Bitmap Based on Spatio-temporal Context Information
Hongyu WANG, Yang CHENG
, doi: 10.11999/JEIT190585
[Abstract](29) [FullText HTML](19) [PDF 3198KB](1)
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.
Prediction of Drug Synergy and Antagonism Based on Drug-Drug Interaction Network
Wenbin LIU, Jie CHEN, Gang FANG, Xiaolong SHI, Peng XU
, doi: 10.11999/JEIT190867
[Abstract](256) [FullText HTML](81) [PDF 2495KB](6)
Accurately predicting the synergistic and antagonistic relationship of drugs is helpful to the safety of drug use and the development of drug combination. A method for predicting drug synergy and antagonistic is proposed, which based on the Drug-Drug Interaction Network (DDINet) and its topological structure. From the result of feature selection, it can be seen that the feature constructed based on the interaction between the drug and its common neighbor node shows an obvious difference in the distribution of positive and negative samples, which can effectively reflect the drug synergy or antagonism. In the classification results using different feature classifiers, the optimal Area Under the Curve (AUC) and classification accuracy value reached 0.9687 and 0.9187 respectively. In the prediction results of synergy and antagonism, the prediction accuracy also reached above 0.45 and 0.75. This shows that the method based on network topology can effectively classify and predict the synergistic and antagonistic effects of drugs. Compared with traditional methods based on similarity features of drug function, structure, target gene, etc., this method is simple and efficient to calculate, and will effectively promote the development of combination drugs.
Joint Congestion Control and Resource Allocation Dynamic Scheduling Strategy for Network Slices in H-CRAN
Lun TANG, Yannan WEI, Qi TAN, Rui TANG, Qianbin CHEN
, doi: 10.11999/JEIT190439
[Abstract](288) [FullText HTML](115) [PDF 1835KB](12)
For online dynamic radio resources optimization for network slices in H-CRAN, by comprehensively considering traffic admission control, congestion control, resource allocation and reuse, the problem is formulated as a stochastic optimization programming which maximizes network average total throughput subject to Base Station (BS) transmit power, system stability, Quality of Service (QoS) requirements of different slices and resource allocation constraints. Then, a joint congestion control and resource allocation dynamic scheduling algorithm is proposed which will dynamically allocate resources to users in network slices with distinct performance requirements within each resource scheduling time slot. The simulation results show that the proposed algorithm can improve the network overall throughput while satisfying the QoS requirement of each slice user and maintaining network stability. Besides, it could also flexibly strike a dynamic balance between delay and throughput by simply tuning an introduced control parameter.
Shared Data Auditing Scheme Supports Efficient Revocation of Group Members via Multi-participation
Junfeng TIAN, Xuan JING
, doi: 10.11999/JEIT190468
[Abstract](346) [FullText HTML](176) [PDF 1333KB](13)
In the view of the integrity verification problem of data sharing on the cloud platform, a Shared Data auditing scheme supports efficient Revocation of group Members via multi-participation (SDRM) is proposed. First, through the Shamir secret sharing method, multiple group members participate in revoking the illegal group members, ensuring the equal rights between the group members. Second, this scheme combined with algebraic signature technology, the file identifier identifies the data owner’s upload data record and the normal group member’s access record, enabling the data owner to efficiently update all of its data. Finally, theoretical analysis and experimental verification of the correctness, safety and effectiveness of the scheme show that the scheme meets the requirement of efficient cancellation of group members, at the same time, as the number of data owners increases, the efficiency of updating data in this scheme is significantly higher than that of NPP.
Stochastic Average Gradient Descent Contrast Source Inversion Based Nonlinear Inverse Scattering Method for Complex Objects Reconstruction
Huilin ZHOU, Tao OUYANG, Jian LIU
, doi: 10.11999/JEIT190566
[Abstract](71) [FullText HTML](94) [PDF 1221KB](2)
When using the nonlinear Contrast Source Inversion (CSI) algorithm to solve the electromagnetic inverse scattering problem, each iteration involves finding the differential of the dissolution radiation field data about the contrast source and the total field, i.e., the Jacobi matrix. the solution of the matrix leads to the problem of large computational cost and slow convergence speed of the algorithm. in this paper, a Contrast Source Inversion algorithm based on Stochastic Average Gradient Descent (SAG-CSI) is used instead of the original full gradient alternating Conjugate Gradient algorithm to reconstruct the spatial distribution information of the dielectric constant of the dielectric target under the CSI framework. the method only needs to calculate the gradient information of the randomly selected part of the measurement data in the objective function in each iteration, while the objective function keeps the gradient information of the unscented measurement data, and the optimal value of the objective function is solved together with the above two parts of the gradient information. The simulation results show that the proposed method reduces the computational cost and improves the convergence speed of the algorithm when compared with the traditional CSI method.
Arrhythmia Classification Based on CNN and LSTM Networks
Li KE, Danni WANG, Qiang DU, Chudi JIANG
, doi: 10.11999/JEIT190712
[Abstract](42) [FullText HTML](87) [PDF 1905KB](2)
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.
The Mobility Management Strategies by Integrating Mobile Edge Computing and CDN in Vehicular Networks
Haibo ZHANG, Yan CHENG, Kaijian LIU, Xiaofan HE
, doi: 10.11999/JEIT190571
[Abstract](363) [FullText HTML](126) [PDF 1622KB](10)
Due to the popularity of vehicle applications and the increase of the number of vehicles, the physical resources of roadside infrastructure are limited. When a large number of vehicles are connected to the vehicle networks, the energy consumption and latency are simultaneously increased. The framework for integrating the Content Delivery Network (CDN) and Mobile Edge Computing (MEC) can reduce the latency and energy consumption. In vehicle network, vehicle mobility poses a major challenge to the continuity of cloud services. Therefore, Mobility Management (MM) to is proposed to deal with this problem. The Dynamic Channel Allocation (ODCA) algorithm with Overhead selection avoids the ping-pong effect and reduces the handover time of vehicles between cells. Use cooperative game algorithms based on RoadSide Unit (RSU) for virtual machine migration and develop a learning-based price control mechanism to process vehicular computation resources efficiently. The simulation results show that the proposed algorithm can improve resource utilization and reduce overhead compared with the existing algorithms.
A Variant BISON Block Cipher Algorithm and Its Analysis
Haixia ZHAO, Yongzhuang WEI, Zhenghong LIU
, doi: 10.11999/JEIT190517
[Abstract](94) [FullText HTML](31) [PDF 590KB](6)
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.
Survey on Applications of List Decoding in Cryptography
Zhuoran ZHANG, Huang ZHANG, Fangguo ZHANG
, doi: 10.11999/JEIT190851
[Abstract](147) [FullText HTML](50) [PDF 614KB](8)
Since the conception of list decoding being proposed in the 1950s, list decoding has not only been applied in communication and coding theory, but also plays a significant role in computational complexity and cryptography. In recent years, with the rapid development of quantum computing, traditional cryptographic schemes based on factorization and other difficult problems have been greatly threatened. The code-based cryptosystems, whose security relies on the NP-hard problems in coding theory, are attracting more and more attention as a candidate of the post-quantum cryptography, and so does the list decoding algorithm. This paper systematically reviews the applications of list decoding in cryptography, including early applications in proving that any one-way function has hard-core bits, designing traitor tracing schemes, designing public key schemes using polynomial reconstruction as cryptographic primitives, improving traditional code-based cryptosystems and solving discrete logarithm problems, and recent applications in designing secure communication interactive protocols, solving the elliptic curve discrete logarithm problem, and designs new cryptographic schemes based on error correction codes. At the end of the paper, the new research issues of the algorithm improvement of list decoding, its application in the design of cryptographic protocol and cryptoanalysis, and the exploration of new application scenarios are discussed.
Drug Recommendation Based on Individual Specific Biomarkers
Wenbin LIU, Qian WU, Yugai DU, Gang FANG, Xiaolong SHI, Peng XU
, doi: 10.11999/JEIT190837
[Abstract](318) [FullText HTML](83) [PDF 4035KB](11)
Drug recommendation research based on personalized markers can help to achieve personalized medicine and promote the development of precision medicine. In this paper, a method for calculating the weight of drugs on personalized markers is proposed, which first uses gene expression profile data and protein network information to filter out personalized network markers based on gene two-dimensional Gaussian distribution and then uses the importance degree of genes and the drugs side effect data to calculate the weight of drugs. This method is applied in lung adenocarcinoma, kidney renal clear cell carcinoma and uterine corpus endometrial carcinoma. Through the iterative process, a list of important drug recommendations for each disease sample is got. The results showed that there are some differences in the recommended drug list and the ordering importance of drugs in different cases of the same kind of cancer, which indicated the importance and necessity of personalized drugs in the treatment of diseases. By querying the relationship between drugs from the drug database, many of the drug combinations screened by this method have a positive effect on the treatment of specific diseases, which further proves the accuracy of our drug recommendation methods based on personalized network markers. This study will effectively promote the development of precision medicine.
Optimal Task Offloading Scheme Based on Network Delay and Resource Management in Joint Blockchain and Fog Computing System
Tong LIU, Lun TANG, Xiaoqiang HE, Qianbin CHEN
, doi: 10.11999/JEIT190654
[Abstract](46) [FullText HTML](25) [PDF 1316KB](5)
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.
Natural Computing Method Based on Nonlinear Dimension Reduction
Weidong JI, Xiaoqing SUN, Ping LIN, Qiang LUO, Haotian XU
, doi: 10.11999/JEIT190623
[Abstract](36) [FullText HTML](29) [PDF 1318KB](1)
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.
Crosstalk-aware Spectrum Converters Sparse Configuration and Resource Allocation for Space Division Multiplexing Elastic Optical Networks
Huanlin LIU, Lixiang DU, Yong CHEN, Huixia HU
, doi: 10.11999/JEIT190533
[Abstract](31) [FullText HTML](20) [PDF 1894KB](1)
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 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.
Initial Sensitive Dynamics in Memristor Synapse-coupled Hopfield Neural Network
Mo CHEN, Chengjie CHEN, Bocheng BAO, Quan XU
, doi: 10.11999/JEIT190858
[Abstract](83) [FullText HTML](63) [PDF 3825KB](12)
The initial sensitive dynamics in a Hopfield Neural Network (HNN) with three neurons under the action of electromagnetic induction current is reported. A simple 4-D memristive HNN is constructed by using a non-ideal memristor synapse to imitate the electromagnetic induction current caused by membrane potential difference between two adjacent neurons. By means of theoretical analyses and numerical simulations, the complex dynamical behaviors under different coupling strengths of the memristor synapse are researched, and special phenomena closely related to the initial values are revealed. Finally, the analog equivalent realization circuit of the memristive HNN model is designed, and the correctness of MATLAB numerical simulation is verified by PSIM circuit simulations.
Research of Low Sampling Frequency Broadband Digital Predistortion with Cyclostationary Characteristics
Rong LAN, Xin HU, Feng ZOU, Gang WANG, Jirun LUO
, doi: 10.11999/JEIT190105
[Abstract](207) [FullText HTML](124) [PDF 843KB](7)
In order to reduce the sampling rate of the Traveling Wave Tube (TWT) of the Analog to Digital Converter (ADC) in the feedback loop of Digital PreDistortion (DPD), the nonlinear parameters of the power amplifier model are proved to be estimated with the undersampled output signal based on the cyclostationary of digital modulation signal. The output signal similar to high sampling rate can be obtained by combining the nonlinear parameters of the power amplifier model with the input signal. The DPD of the power amplifier is implemented through indirect learning architecture. To validate the method, a 55 W X-band Traveling Wave Tube Amplifier (TWTA) is driven by a 20 MHz LTE signal. The sampling rate of ADC in the DPD feedback loop is reduced from 61.44 Msps to 6.144 Msps and 3.072 Msps, but the linearization effect has little change, which shows the validation of the undersampling method.
An Improved Fuzzy Clustering Method for Interval Uncertain Data
Mansheng XIAO, Longxin ZHANG, Xiaoli ZHANG, Yongxiang HU
, doi: 10.11999/JEIT190591
[Abstract](54) [FullText HTML](34) [PDF 825KB](5)
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.
Memristive Digital Logic Circuit Design
Xiaoyuan WANG, Chenxi JIN, Pengfei ZHOU
, doi: 10.11999/JEIT190864
[Abstract](577) [FullText HTML](309) [PDF 3549KB](5)
A brief overview of the theory of memristor, the state of applied research and its current status in the field of electronic technology are proposed. The importance of memristor in the design of digital logic circuits is also introduced. Combined with the binary characteristics and circuit characteristics of Hewlett Packard(HP) memristor, the development status, trend and applicable prospects of memristor in digital logic circuit design are reviewed, which provide certain reference for further research based on memristor in digital logic circuit design and other related applications.
Research Progress on Chaos, Memory and Neural Network Circuits Based on Memristor
Chunhua WANG, Hairong LIN, Jingru SUN, Ling ZHOU, Chao ZHOU, Quanli DENG
, doi: 10.11999/JEIT190821
[Abstract](313) [FullText HTML](126) [PDF 1007KB](23)
Memristor is the fourth basic electronic component in addition to resistor, capacitor and inductor. It is a nonlinear device with memory characteristics, which can be used to design chaotic circuits, memory devices and neural networks. The design of memristor-based chaos circuits, memory and neural systems, and some research of neural dynamics in this field are reviewed, and their research prospects are also given.
Robust Discriminative Feature Subspace Learning Based on Low Rank Representation
Ao LI, Xin LIU, Deyun CHEN, Yingtao ZHANG, Guanglu SUN
, doi: 10.11999/JEIT190164
[Abstract](226) [FullText HTML](320) [PDF 1965KB](9)
Feature subspace learning is a critical technique in image recognition and classification tasks. Conventional feature subspace learning methods include two main problems. One is how to preserve the local structures and discrimination when the samples are projected into the learned subspace. The other hand when the data are corrupted with noise, the conventional learning models usually do not work well. To solve the two problems, a discriminative feature learning method is proposed based on low rank representation. The novel method includes three main contributions. It explores the local structures among samples via low rank representation, and the representation coefficients are used as the similarity measurement to preserve the local neighborhood existed in the samples; To improve the anti-noise performance, a discriminative learning item is constructed from the recovered samples via low rank representation, which can enhance the discrimination and robustness simultaneously; An iterative numerical scheme is developed with alternating optimization, and the convergence can be guaranteed effectively. Extensive experimental results on several visual datasets demonstrate that the proposed method outperforms conventional feature learning methods on both of accuracy and robustness.
Spectrum Sensing Based on Signal Envelope of Rayleigh Multi-path Fading Channels
Yiming ZHOU, Yingshun LI, Xiaoping TIAN
, doi: 10.11999/JEIT190065
[Abstract](218) [FullText HTML](160) [PDF 1073KB](9)
In order to improve the correlation between signal samplings and reduce the influence of noise on sensing performance, a spectrum sensing algorithm based on signal envelope autocorrelation matrix is proposed in the paper. Firstly, the sampling signals are intercepted at equal intervals, the signal autocorrelations are calculated by means of the adjacent interval samples, and an approximate autocorrelation matrix is constructed. Secondly, the statistic is constructed according to the properties of the sub-diagonal elements of the matrix. The detection probability distribution function and the false alarm probability distribution function of the statistic are calculated respectively. The detection performances of the spectrum sensing algorithm are analyzed. The algorithm optimizes the calculation of signal correlation and reduces the impact of noise on detection performance. Finally, the effects of different parameters on detection probability and false alarm probability are verified by simulation, and some measures are proposed to improve detection performance.
Energy EfficientBased Resource Optimization Algorithm for Two-tier Non-Orthogonal Multiple Access Network
Dong GAO, Zilin LIANG
, doi: 10.11999/JEIT190048
[Abstract](281) [FullText HTML](152) [PDF 957KB](4)
A subchannel matching method based on bilateral matching and a power allocation algorithm based on Stackelberg game are proposed for two-tier Non-Orthogonal Multiple Access (NOMA) network. Firstly, the resource optimization problem is decomposed into two subproblems—sub-channel matching and power allocation. In the power allocation, the macro base station layer and small base station layer are regarded as the leader and followers in the Stackelberg game. Then, the non-convex optimization problem is converted into a way to be easily solved, and the power allocation of the both layers are obtained respectively. Finally, the global power allocation scheme of the system is obtained by using Stackelberg game. The simulation results show that the proposed resource optimization algorithms can effectively improve the energy efficiency of the two-tier NOMA system.
No-reference Image Quality Evaluation for Multiply-distorted Images Based on Spatial Domain Coding
Yon CHEN, Kaixin ZHU, Hao FAN, Huanlin LIU
, doi: 10.11999/JEIT190721
[Abstract](309) [FullText HTML](35) [PDF 1904KB](10)
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.
A Survey of Orbital Angular Momentum in Wireless Communication
Xi LIAO, Chenhong ZHOU, Yang WANG, Shasha LIAO, Jihua ZHOU, Jie ZHANG
, doi: 10.11999/JEIT190372
[Abstract](29) [FullText HTML](26) [PDF 3103KB](3)
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.
Blockchain User Anonymity and Traceability Technology
Peili LI, Haixia XU
, doi: 10.11999/JEIT190813
[Abstract](331) [FullText HTML](89) [PDF 670KB](30)
Blockchain has the advantages of transparency, data integrity, tamper resistance, etc., and has important application value in the fields of finance, government, and military. There are many work study the privacy protection of the blockchain, typically including Monero, Zerocash, Mixcoin, and more. Their privacy protection methods can be used to protect the identity of the user and the amount of the transaction. The privacy protection scheme is a double-edged sword. On the one hand, it is the perfect protection of the privacy of legitimate users. On the other hand, if it is completely out of supervision, it is the appeasement and connivance of illegal crimes such as money laundering and extortion. In response to the various endangered privacy protection schemes on the blockchain, regulation must also keep pace with the times. In view of this, the privacy protection and supervision methods of blockchain user’s identity is studied, and anonymity and traceability technology to promote the application of blockchain in practice is proposed.
Design of Memristor Based Multiplier Circuits
Guangyi WANG, Shuhang SHEN, Gongzhi LIU, Fupeng LI
, doi: 10.11999/JEIT190811
[Abstract](281) [FullText HTML](81) [PDF 2993KB](8)
As a new non-volatile electronic device, memristor has a good application prospect in digital logic circuits. At present, memristor based logic circuits mainly involve the research of full adder, multiplier, exclusive-OR (XOR) and equivalence (XNOR), etc., among which there is little research on memristor based multiplier. The 2-bit binary multiplier circuit is designed in two different ways based on memristor. One is to design a 2-bit binary multiplier circuit by using the improved XOR and AND multifunctional logic modules. The other is to design a 2-bit binary multiplier by combining a new type of ratio logic, i.e. a unit gate circuit consisting of one memristor and one NMOS transistor. The two multipliers are compared and validated by LTSPICS simulation. The multiplier designed in this paper only uses 2 N-Metal-Oxide-Semiconductor (NMOS) and 18 memristors (the other is 6 NMOS and 28 memristors). Compared with previous memristor based multipliers, the multipliers in this paper reduce the number of transistors.
State View Based Efficient Hilbert Encoding and Decoding Algorithms
Lianyin JIA, Mingxian CHEN, Mengjuan LI, Jinguo YOU, Jiaman DING
, doi: 10.11999/JEIT190501
[Abstract](93) [FullText HTML](62) [PDF 1476KB](7)
Hilbert curve is an important method for high-dimensional reduction to one-dimensional. It has good characteristics of spatial aggregation and spatial continuity and is widely used in geographic information system, spatial databases and information retrieval. Existing Hilbert encoding or decoding algorithms do not consider the differences between input data, thus treating them equally. To this end, an efficient Hilbert coding algorithm Front-Zero-Free Hilbert Encoding(FZF-HE) and an efficient decoding algorithm Front-Zero-Free Hilbert Decoding(FZF-HD) are proposed. FZF-HE and FZF-HD can quickly identify the 0 s of the front part of input data before iterative calculation by combining efficient state views and first bit-1 detection algorithm, thus reducing the number of iterations and the complexity of the algorithm, and improving the encoding and decoding efficiency. The experimental results show that efficiencies of these two algorithms are slightly higher than existing algorithms for uniform distributed data, and are much higher than existing algorithms for skew distributed data.
Dynamic Resource Allocation and Energy Management Algorithm for Hybrid Energy Supply in Heterogeneous Cloud Radio Access Networks
Qianbin CHEN, Qi TAN, Yannan WEI, Lanqin HE, Lun TANG
, doi: 10.11999/JEIT190499
[Abstract](132) [FullText HTML](82) [PDF 1755KB](13)
Considering the dynamic resource allocation and energy management problem in the 5G Heterogeneous Cloud Radio Access Networks(H-CRANs) architecture for hybrid energy supply, a dynamic network resource allocation and energy management algorithm based on deep reinforcement learning is proposed. Firstly, due to the volatility of renewable energy and the randomness of user data service arrival, taking into account the stability of the system, the sustainability of energy and the Quality of Service(QoS) requirements of users, the resource allocation and energy management issues in the H-CRANs network as a Constrained infinite time Markov Decision Process (CMDP) is modeled with the goal of maximizing the average net profit of service providers. Then, the Lagrange multiplier method is used to transform the proposed CMDP problem into an unconstrained Markov Decision Process (MDP) problem. Finally, because the action space and the state space are both continuous value sets, the deep reinforcement learning to solve the above MDP problem is used. The simulation results show that the proposed algorithm can effectively guarantee the QoS and energy sustainability of the system, while improving the average net income of the service provider and reducing energy consumption.
Interference Efficiency-based Base Station Selection and Power Allocation Algorithm for Multi-cell Heterogeneous Wireless Networks
Guoquan LI, Yongjun XU, Qianbin CHEN
, doi: 10.11999/JEIT190419
[Abstract](365) [FullText HTML](84) [PDF 1939KB](21)
To solve interference management and efficiency improvement of multi-cell multi-user heterogeneous wireless networks, the downlink Base Station (BS)-user matching and power allocation problem is studied to maximize the interference efficiency of femtocells. Firstly, consideration of quality of service of macro cell users and femtocell users, the problem is formulated as a multivariate mixed integer nonlinear programming problem. Secondly, the problem is decomposed into two subproblems. The BS selection problem is solved by convex optimization technique. The power allocation problem is firstly converted into a convex one by using quadratic transformation method and Dinkelbach approach, then the problem is resolved by using Lagrange dual methods and subgradient methods. Simulations results show the effectiveness of the proposed algorithm by comparing with the existing algorithms in terms of interference efficiency and interference management.
Bursting Oscillations and Bifurcation Mechanism in Memristor-based Shimizu–Morioka System with Multi-frequency Slow Excitations
Zhijun LI, Siyuan FANG, Chengyi ZHOU
, doi: 10.11999/JEIT190855
[Abstract](250) [FullText HTML](148) [PDF 3244KB](34)
In order to study the bursting oscillations and its formation mechanism of memristor-based system, a multi-timescale memristor-based S-M system is established by introducing a memristor device and two slowly changing periodic excitations into the Shimizu-Morioka (S-M) system. Firstly, the bursting behavior and bifurcation mechanism of S-M system under single excitation are studied, and a symmetric bursting pattern of "sub-Hopf/sub-Hopf" is obtained. Then the multi-frequency excitation system is transformed into single frequency excitation system by using De Moivre formula, and the influence of additional excitation amplitude and frequency on "sub Hopf / sub Hopf" bursting mode is analyzed by using the Fast-slow analysis method. As a result, two new bursting patterns named as twisted “sub-Hopf/sub-Hopf” bursting and nested “sub-Hopf/sub-Hopf” are found under different amplitudes of the additional excitation. The corresponding bursting mechanisms are analyzed with time history diagram, bifurcation diagram and transformation phase diagram. Finally, Multisim simulation results, which are in good agreement with the numerical simulation results, are provided to verify the validity of the study.
Bursting, Coexistence Analysis and DSP Implementation of Duffing System Based on Hyperbolic-tangent Memristor
Mengjiao WANG, Yong DENG, Zhijun LI, Yicheng ZENG
, doi: 10.11999/JEIT190631
[Abstract](61) [FullText HTML](67) [PDF 4972KB](6)
Memristor is first proposed by Chua as the fourth basic circuit element, which provides a novel idea for the design and engineering application of chaotic circuits. In this paper, a novel memristive Duffing nonautonomous system is obtained by introducing a hyperbolic-tangent memristor into the Homles type Duffing system. By using the transformed phase portraits, phase portraits, Lyapunov exponents, etc., it is revealed that the system has novel dynamical behaviors such as bursts with controllable number of oscillation spikes, non-completely symmetrical bilateral bursts, coexistence of non-completely symmetrical bursts, multiple coexistence of chaos and period. And the mechanism of bursting is studied by analysis of equilibrium point and bifurcation diagram. Multisim circuit simulation and Digital Signal Processing Platform (DSP) are used to implement the system in hardware, and the experimental results basically consistent with the theoretical analysis prove that the system is feasible and physically achievable.
Disaster Prediction-based Survivable Virtual Optical Network Mapping for Multi-Area Faults
Huanlin LIU, Lixiang DU, Yong CHEN, Zhanpeng WANG
, doi: 10.11999/JEIT190561
[Abstract](39) [FullText HTML](21) [PDF 2183KB](3)
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.
Multi-channel Memristive Pulse Coupled Neural Network based Multi-frame Super-resolution Reconstruction Algorithm
Zhekang DONG, Chenjie DU, Huipin Lin, Chun sing LAI, Xiaofang HU, Shukai DUAN
, doi: 10.11999/JEIT190868
[Abstract](803) [FullText HTML](219) [PDF 6870KB](9)
The high-resolution image is the prerequisite of information acquisition and precise analysis. Multi-frame super-resolution images reconstruction technologies are able to address many image degraded issues (caused by external shooting environment), such as detail information lost, blurred edges, and so forth. According to the nanoscale memristor, a multi-channel memristive pulse coupled neural network model is proposed in this paper. This model is able to simulate the adaptive-variable linking coefficient in pulse coupled neural network. Meanwhile, the proposed network is applied into the multi-frame super resolution reconstruction for fusing the registered low- resolution images. Furthermore, the sparse coding based super resolution method is performed to improve the original high-resolution image. Finally, a series of computer experiments and the relevant subjective/objective analysis jointly illustrate the validity and effectiveness of the entire scheme.
Characteristics Analysis and DSP Implementation of Fractional-order Memristive Hypogenetic Jerk System
Kehui SUN, Chuan QIN, Huihai WANG
, doi: 10.11999/JEIT190904
[Abstract](155) [FullText HTML](33) [PDF 3549KB](0)
To investigate the dynamic characteristics of this type of system in the fractional-order case, fractional-order calculus is introduced into memristive hypogenetic Jerk system, which adds one degree of freedom and improves system performance. The dynamical characteristics of the system are analyzed by phase diagram, bifurcation diagram, Lyapunov exponent spectrum, complexity chaotic diagram, etc., and the digital circuit of the system is realized by employing DSP technology. The research results show that when the system is extended to the fractional order, the system presents a period doubling bifurcation path with the initial value, and the evolution path of the system changes abruptly at some specific initial values, showing infinite coexistence of attractors.
Review of Sign Language Recognition Based on Deep Learning
Shujun ZHANG, Qun ZHANG, Hui LI
, doi: 10.11999/JEIT190416
[Abstract](2664) [FullText HTML](408) [PDF 1219KB](57)
Sign language recognition involves computer vision, pattern recognition, human-computer interaction, etc. It has important research significance and application value. The flourishing of deep learning technology brings new opportunities for more accurate and real-time sign language recognition. This paper reviews the sign language recognition technology based on deep learning in recent years, formulates and analyzes the algorithms from two branches - isolated words and continuous sentences. The isolated-word recognition technology is divided into three structures: Convolutional Neural Network (CNN), Three-Dimensional Convolutional Neural Network (3D-CNN) and Recurrent Neural Network (RNN) based method. The model used for continuous sentence recognition has higher complexity and is usually assisted with certain kind of long-term temporal sequence modeling algorithm. According to the major structure, there are three categories: the bidirectional LSTM, the 3D convolutional network model and the hybrid model. Common sign language datasets at home and abroad are summarized. Finally, the research challenges and development trends of sign language recognition technology are discussed, concluding that the robustness and practicality on the premise of high-precision still requires to be promoted.
Global Navigation Satellite System Spoofing Mitigation Method by Utilizing Signal Reconstruction
Dan LU, Tianlin BAI
, doi: 10.11999/JEIT190321
[Abstract](465) [FullText HTML](242) [PDF 1799KB](24)
Spoofing misleads the receiver to generate the wrong position information by trans-mitting signals similar to authentic satellite signals, which has great harm. In this paper, a single-antenna spoofing mitigation algorithm based on signal reconstruction is proposed for meaconing. Firstly, the carrier frequency and code phase of spoofing signal are obtained by parameter estimation method, and then the orthogonal projection matrix of spoofing signal subspace is constructed to suppress spoofing. The simulation results show that the algorithm has a good suppression effect on spoofing and ensure the receiver can locate effectively in the interference environment, and the algorithm also has lower computational complexity.
Differential Fault Attack on the Lightweight Block Cipher PUFFIN
Qingjun YUAN, Xuncheng ZHANG, Yang GAO, Yongjuan WANG
, doi: 10.11999/JEIT190506
[Abstract](76) [FullText HTML](58) [PDF 514KB](9)
The lightweight block cipher algorithm PUFFIN based on substitution-permutation network structure is widely used in resource-constrained hardware environments. Differential fault attack is a more effective attack method for hardware cryptographic algorithms. The multi-bit fault model for PUFFIN algorithm is improved. By constructing the relationship between the output difference and the possible input values, the single input value of a single S-box can be determined by injecting 5 faults. The probability of successfully recovering the round key is 78.64%, and the initial key can be recovered.
A Firewoks Algorithm-Back Propagation Fault Diagnosis Algorithm for System-level Fault Diagnosis
Weixia GUI, Qian LU, Meili SU
, doi: 10.11999/JEIT190484
[Abstract](768) [FullText HTML](132) [PDF 2087KB](12)
In order to diagnose fault units in the large-scale multiprocessor systems more quickly and accurately, a system-level fault diagnosis algorithm—FireWorks Algorithm-Back Propagation Fault Diagnosis (FWA-BPFD) based on fireworks algorithm and Back Propagation(BP) neural network is proposed. Firstly, two population strategy, cooperative operator and optimal operator are introduced into fireworks algorithm. A new fitness function is designed, and the mutation operator, mapping rule and selection strategy are optimized. Then, the optimization process of weight and threshold value in BP neural network is optimized by the self-regulating mechanism of global and local searching ability of fireworks algorithm. Simulation results show that compared with other algorithms, this algorithm not only reduces the number of iterations and training time, but also improves the accuracy of diagnosis.
Measurement of Node Influence Based on Three-level Neighbor in Complex Networks
Shuxin YANG, Wen LIANG, Kaili ZHU
, doi: 10.11999/JEIT190440
[Abstract](694) [FullText HTML](80) [PDF 3534KB](11)
There are some limitations in the existing metric methods for measuring node influence. A measurement method of node influence with three-level neighbors is proposed, which is based on the principle of three-degree influence, and considering the appropriate level of local measurement and the scalability of the large-scale network. Firstly, the neighbors with propagation attenuation characteristics in the second and third level of a node are regarded as a whole, which is used to measure the influence of the node. Then, an algorithm for measure called Three-level Influence Measurement (TIM) is proposed. Finally, in order to validate the effectiveness of the algorithm, the experiments on three datasets are conducted by using susceptible-infected-recovered model and independent cascade model. The experimental results show that the proposed algorithm is superior in consistency of influence, discrimination, sorting performance and other evaluation indexes. Furthermore, the TIM is applied to effectively solve the problem of maximizing influence.
Joint 2D-DOA and Polarization Estimation with Sparsely Stretched L-shaped Polarization Sensitive Array
Huihui MA, Haihong TAO
, doi: 10.11999/JEIT190208
[Abstract](217) [FullText HTML](167) [PDF 1791KB](22)
In order to reduce the serious mutual coupling effect across the elements of the existing collocated vector sensor array and further improve the parameter estimation accuracy, a Sparsely Stretched L-shaped Polarization Sensitive Array (SSL-PSA) is proposed in this paper, and a novel method for estimating the azimuth-elevation angles as well as polarization parameters is presented accordingly. Firstly, the signal model of SSL-PSA is established. Then, the SSL-PSA is divided into 6 subarrays, thus the ESPRIT algorithm can be utilized to estimate the Rotational Invariant Factors (RIFs). On this basis, a set of fine but ambiguous estimates and four sets of unambiguous coarse estimates of direction cosine are obtained by a series of mathematical operations. Then, four corresponding steering vectors can be reconstructed and the correct coarse direction-cosine estimation can be determined according to the orthogonality of the steering vector and the noise subspace. Finally, the estimates of Direction-Of-Arrival (DOA) and polarization can be achieved by the existing disambiguate method. Compared to the existing polarization sensitive array consists of collocated vector sensor, the proposed one has no collocated configuration, which can reduce the mutual coupling effect. Additionally, the proposed method can also extend the spatial aperture and refine the direction-finding accuracy without adding any redundant antennas. Simulations are carried out to verify the effectiveness of the proposed method.
Copy-move Forgeries Detection Based on Polar Sine Transform
Jie MA, Binbin ZHONG, Yanan JIAO
, doi: 10.11999/JEIT190481
[Abstract](368) [FullText HTML](228) [PDF 2221KB](8)
Polar Sine Transform (PST) is used to detect Copy-move forgeries in the paper, and the image to be detected is transformed into gray scale image and feature extraction is carried out by PST. Improved PatchMatch, a fast approximate nearest neighbor search algorithm, is used to match feature descriptors to overcome the problem of long time consuming caused by matching global descriptors. Experiments show that the proposed method is not only effective for linear Copy-move forgeries and rotation interference forgeries, but also robust to noise and JPEG compression interference forgeries. Finally, the experimental results of synthetic interference forgeries show that the accuracy can reach 98.0% when the synthetic forgeries range is small.
Quantization and Energy Optimization Strategy of Wireless Sensor Networks
Jingxiang LÜ, Wenlang LUO
, doi: 10.11999/JEIT190185
[Abstract](376) [FullText HTML](314) [PDF 1631KB](20)
Due to the limitation of energy and bandwidth in Wireless Sensor Networks(WSN), the direct transmission of analog signals in the network is greatly restricted. Therefore, quantization of analog signals is an important means to save network energy and ensure effective bandwidth. To this end, based on the principle of minimum absolute mean reconstruction error a network quantization and energy optimization method is designed in this paper. Firstly, for single sensor, the optimal quantization bit number is derived under the condition of fixed energy and the optimal energy distribution is derived under the condition of fixed quantization bit number. Secondly, on the basis of single sensor, the optimal quantization bit number and optimal energy allocation are further deduced in multi-sensor case. In both cases, the sensor measurement noise and channel fading loss are considered. Finally, the numerical simulation results show that the proposed method is correct and better than the equal energy distribution.
A Distributed Space Target Tracking Algorithm Based on Asynchronous Multi-sensor Networks
Jingqi HUANG, Chen HU, Shanpeng SUN, Xiang GAO, Bing HE
, doi: 10.11999/JEIT190460
[Abstract](475) [FullText HTML](293) [PDF 2353KB](26)
To solve the problem of asynchronous sampling and communication delay of sensor network in space target tracking, an Asynchronous Distributed algorithm based on Information Filtering (ADIF) is proposed. First, local state information and measurement information with sampling time are transmitted between local sensor and adjacent nodes in a certain topology structure. Then, the local sensor sorts the received asynchronous information by time, and uses ADIF algorithm to calculate the target state respectively. This method is simple to implement, the frequency of communication between sensors is small, and it supports the real-time change of network topology, which is suitable for multi-target tracking. In this paper, single target and multi-target tracking are simulated respectively. The results show that the algorithm can effectively solve the problem of asynchronous sensor filtering, and the distributed filtering accuracy converges to the centralized result.
Certificateless Authentication Searchable Encryption Scheme for Multi-user
Yulei ZHANG, Long WEN, Haohao WANG, Yongjie ZHANG, Caifen WANG
, doi: 10.11999/JEIT190437
[Abstract](472) [FullText HTML](155) [PDF 1207KB](27)
The searchable encryption technology enables users to encrypt data and store it in the cloud, and can directly retrieve ciphertext data. Most of the existing searchable encryption schemes are single-to-single mode, and the searchable encryption scheme in some multi-user environments is based on public key cryptography or identity-based public key cryptosystem. Such schemes have certificate management and key escrow issues and scheme are vulnerable to suffer internal keyword guessing attacks. Public key authentication encryption and proxy re-encryption technology are combined, and an efficient certificateless authentication searchable encryption scheme is proposed for multi-user environment. The scheme uses proxy re-encryption technology to re-encrypt portion of ciphertexts, so that authorized users can generate trapdoor with the keywords to query ciphertext. In the random oracle model, the scheme is proved that it has the ability to resist the internal keyword guessing of two type attackers in the certificateless public key cryptosystem, and the calculation and communication efficiency of the scheme is better than the similar scheme.
Virus Propagation Model and Security Performance Optimization Strategy of Multi-operating System Heterogeneous Network
Gang WANG, Yun FENG, Shiwei LU, Runnian MA
, doi: 10.11999/JEIT190360
[Abstract](302) [FullText HTML](215) [PDF 3272KB](17)
In view of the fact that worm viruses can only infect specific operating systems, the virus propagation rule and security performance optimization strategy in multi-operating system heterogeneous network are studied in this paper. First, considering that most viruses can only spread in link between the same operation system, the parameters of heterogeneous edges ratio are introduced into the Susceptible Infected Remove Susceptible (SIRS) virus transmission model, and the influence of heterogeneous edges and network security performance on the single system virus transmission is studied through system equilibrium solution and basic regeneration number analysis. Secondly, according to the moving target defense thought and technology, the network security optimization strategies is designed for non-isomeric random interrupt, non-isomeric random reconnecting and single operating system random node migration, and analyzes the variation of the same ratio and the basic number of regenerated numbers in the three strategies and the impact on the safety of the network. Finally, the correctness of the virus propagation model is verified by simulation, and the network security performance optimization effects of the three strategies are analyzed.
Integrated Navigation Algorithm for Large Concave Obstacles
Qinghua LI, Yue YOU, Yaqi MU, Zhao ZHANG, Chao FENG
, doi: 10.11999/JEIT190179
[Abstract](476) [FullText HTML](299) [PDF 1739KB](16)
For the problem that mobile robot can not avoid large concave obstacles during navigation, this paper proposes a multi-state integrated navigation algorithm. The algorithm classifies the running state of mobile robot into running state, switching state and obstacle avoidance state according to different moving environment, and defines the state double switching conditions based on the running speed and running time of the mobile robot. The Artificial Potential Field Method (APFM) is used to navigate and observe the geometric configuration of adjacent obstacles in real time. When encountering an obstacle, the switching state is used to determine whether the state switching condition is satisfied, and the obstacle avoidance algorithm is executed to enter the obstacle avoidance state and enter the obstacle avoidance state to implement the obstacle avoidance algorithm. After the obstacle avoidance is completed, the state automatically switches back to the running state to continue the navigation task. The proposal of multi-state can solve the problem of local oscillation of traditional artificial potential field method in the process of avoiding large concave obstacles. Furthermore, the double-switching condition determination algorithm based on running speed and running time can realize smooth switching between states and optimize the path. The experimental results show that the algorithm can not only solve the local oscillation problem, but also reduce the obstacle avoidance time and improve the efficiency of the navigation algorithm.
Conditional Empirical Mode Decomposition and Serial Parallel CNN for Electro Encephalo Gram Signal Recognition
Xianlun TANG, Wei LI, Weichang MA, Desong KONG, Yiwei MA
, doi: 10.11999/JEIT190124
[Abstract](365) [FullText HTML](326) [PDF 1575KB](17)
For the non-linear and non-stationary characteristics of motor imagery Electro Encephalo Gram (EEG) signals, an EEG signal recognition method based on Conditional Empirical Mode Decomposition (CEMD) and Serial Parallel Convolutional Neural Network (SPCNN) is proposed. In the CEMD process, the correlation coefficient between the Intrinsic Mode Functions (IMFs) and the original signal is used as the first condition to select IMFs. Based on this, the relative energy occupancy rates between the IMFs are proposed as the second condition to select IMFs. Further, to consider the characteristics between the EEG signal channels and highlight the features in each EEG signal channel, a SPCNN model is proposed to classify the processed EEG signals. The experimental results show that the average recognition rate reaches 94.58% on the dataset collected by ourselves. And the average recognition rate reaches 82.13% on the BCI competition IV 2b dataset, which is 3.85% higher than the average recognition rate of convolutional neural network. Finally, the online control experiments are carried out on the designed intelligent wheelchair platform, which proves the effectiveness of the proposed algorithm for EEG signals recognition.
Specific Emitter Identification Using Signal Trajectory Image
Yiwei PAN, Sihan YANG, Hua PENG, Tianyun LI, Wenya WANG
, doi: 10.11999/JEIT190329
[Abstract](852) [FullText HTML](335) [PDF 5490KB](33)
The radio frequency fingerprinting of the emitter is complex, and the performance of Specific Emitter Identification (SEI) is subjected to the present expertise. To remedy this shortcoming, this paper presents a novel SEI algorithm based on signal trajectory image, which realizes joint extraction of multiple complex fingerprints using deep learning architecture. First, this paper analyses the visual characteristics of multiple emitter imperfections in the signal trajectory image. Thereafter, signal trajectory grayscale image is used as the signal representation. Finally, a deep residual network is constructed to learn the visual characteristics reflected in the images. The proposed method overcomes the limitations of existing knowledge, and combines high information integrity with low computational complexity. Simulation results demonstrate that, compared with the existing algorithms, the proposed one can remarkably improve the SEI performance with a gain of about 30%.
Performance Analysis of Physical Layer Security for Cognitive Radio Non-Orthogonal Multiple Access Random Network
Baoquan YU, Yueming CAI, Jianwei HU
, doi: 10.11999/JEIT190049
[Abstract](268) [FullText HTML](159) [PDF 881KB](20)
This paper analyzes the security communication performance of secondary user communication pairs in Cognitive Radio Non-Orthogonal Multiple Access (CR-NOMA) networks, where interference sources and eavesdropping nodes are randomly distributed. The stochastic geometry theory is used to model the eavesdropping nodes and the interfering nodes as a homogeneous Poisson Point Processes (PPP). Firstly, to ensure the reliability of the primary user communication pairs, the power allocation coefficient set of the sender is obtained, and the closed expressions of the connection outage probability and the secrecy outage probability of the secondary user are further obtained. Then, the variation of the power distribution coefficient with the constraint of the primary user’s reliability is analyzed. Finally, the relationship between outage probability of secondary user communication pairs and the density of the eavesdropping nodes and the transmission power is studied. The research shows that the enhancement of interfering signal reduces the reliability of the system, but brings about a significant improvement of security performance. The simulation results verify the correctness of the theoretical analysis.
An Affine Projection Algorithm with Multi-scale Kernels Learning
Qunsheng LI, Yan ZHAO, Lei KOU, Jinda WANG
, doi: 10.11999/JEIT190023
[Abstract](219) [FullText HTML](120) [PDF 1274KB](15)
In order to improve the ability of noise elimination and channel equalization of strong non-linear signals, a Multi-scale Kernels learning Affine Projection filtering Algorithm based on Surprise Criterion (SC-MKAPA) is proposed on the basis of kernel learning adaptive filtering method. Based on the kernel affine projection filtering algorithm, the structure of the kernel combination function is improved, and the bandwidths of several different Gaussian kernels are taken as variable parameters to participate in the update of the filter together with the weighted coefficients.The calculation results are sparsed by using the surprise criterion, and the surprise measure is improved according to the constraints of the affine projection algorithm, which simplifies the variance term and reduces the calculation complexity. The algorithm is applied to noise cancellation, channel equalization, and Mackey Glass (MG) time series prediction The simulation results are compared with the traditional adaptive filtering algorithm and the kernel learning adaptive filtering algorithm, it proves the superiority of the proposed algorithm.
Passive Fingerprint Indoor Positioning Based on CSI Amplitude-phase
Xiaoping JIANG, Miaoyu WANG, Hao DING, Chenghua LI
, doi: 10.11999/JEIT180871
[Abstract](245) [FullText HTML](140) [PDF 2553KB](12)
Indoor positioning technology based on Channel State Information (CSI) receives much attention in recent years. The existing indoor positioning solution is continuously innovative and improved in terms of deployment implementation and positioning accuracy. This paper proposes a passive one-transmitter two-receivers fingerprint indoor positioning system. The CSI data is collected by two fixed receiving end-devices. In the signal preprocessing stage, the CSI amplitude is singular value removed and low pass filtered, and the CSI phase is corrected by a linear fitting method, and the CSI amplitude and phase information obtained by the two receiving ends is collectively used as a fingerprint. The fingerprint samples are finally trained through the fully connected neural network, and matched with the collected real-time data. Experiments show that the matching recognition rate reaches 98% by using two receivers and the combination of amplitude and phase positioning, and the positioning accuracy is 0.69 m. It proves that the system can accurately and effectively achieve indoor positioning.
The Role of Parasitic Elements in Fading Memory of A Charge Controlled Memristor
Yiran SHEN, Fupeng LI, Guangyi WANG
, doi: 10.11999/JEIT190865
[Abstract](259) [FullText HTML](164) [PDF 5001KB](20)
In the presence of parasitic elements, fading memory may occur in charge controlled memristors. The effects of parasitic resistance and capacitance on the dynamic characteristics of memristor are studied by using the dynamic route map and simulation method. The oretical and simulation analysis shows that the ideal charge controlled (current controlled) memristor does not have fading memory when the parasitic resistance or capacitance exists alone under the excitation of DC and AC, but fading memory occurs when the parasitic resistance and capacitance exist at the same time. The mechanism is that the parasitic elements form discharge path, which leads to fading memory of the charge controlled memristor.
A Simple Inductor-free Memristive Chaotic Circuit and Its Characteristics
Yicheng ZENG, Dewu CHENG, Qiwei TAN
, doi: 10.11999/JEIT190859
[Abstract](248) [FullText HTML](162) [PDF 6052KB](13)
A simple two-memristor chaotic circuit without inductance (only five electronic components) is designed by using a non-ideal active voltage control memristor and a flux-controlled smooth cubic nonlinear memristor. When the circuit parameters change, the basic dynamic behaviors of the system are studied in detail by the means of conventional nonlinear analysis, such as the analysis of equilibrium stability, phase diagram, Lyapunov exponent spectrum and bifurcation diagram. With the parameters changing, the proposed system can produce various phenomena of dynamics such as multi-scrolls, multi-wings and transient transition behaviors. Furthermore, the multistability characteristics of the system are also studied in the condition of changing the initial state of two memristors in system respectively, and some meaningful results are obtained. In order to verify the feasibility and stability of the circuit, the analog equivalent circuit of each memristor is constructed, and it is applied to the proposed chaotic circuit. The experimental results of the hardware circuit and the circuit simulation results of the Multisim are in good agreement with the theoretical analysis.
Planar Sparse Array Constraint Optimization Based on Hybrid Trigonometric Mutation Differential Evolution Algorithm
Zhikun CHEN, Kang DU, Dongliang PENG, Xinting ZHU
, doi: 10.11999/JEIT190705
[Abstract](235) [FullText HTML](144) [PDF 1778KB](10)
For the problems of sparse planar array optimization with side-lobe concave nulls constraints and premature algorithm, a Hybrid Trigonometric Mutation Differential Evolution (HTMDE) algorithm is proposed based on the idea of parameter adaptation. By introducing side-lobe concave nulls constraints matrix, adaptive penalty function is constructed. Time-varying weight combination mutation strategy and crossover strategy improve the initial global search ability and late convergence ability of the algorithm. The constrained optimization of the planar array with peak side lobe level and side-lobe concave nulls is finally realized. The simulation results show that, compared with the algorithm before the hybrid trigonometric mutation strategy, the algorithm not only optimizes the peak side-lobe level of sparse array, but also designs concave nulls in specified side-lobe area to reduce the influence of active interference.
Research on High Reflective Imaging Technology Based on Compressed Sensing
Jianying FAN, Mingyang MA, Shoubo ZHAO
, doi: 10.11999/JEIT190512
[Abstract](128) [FullText HTML](106) [PDF 2412KB](10)
When imaging a highly reflective object, the light intensity reflected easily exceeds the maximum quantized value of the light intensity received by the sensor, which causing image distortion of the captured image in the saturated region of light intensity and seriously affects the quality of information transmission. In order to improve the data loss in the high-reflection imaging saturation region, a compression-sensing of high-reflection imaging method based on the new sampling theory of compressed sensing is proposed. A specific measurement matrix is used to conduct linear sampling of the target image, and the single light intensity sampling value of the CCD image sensor is combined with the distribution data in the measurement matrix, and the integrated data is restored and reconstructed with the algorithm to achieve the imaging of the measured target in the high-light environment. The peak signal to noise ratio and gray histogram were used as objective evaluation criteria. Experiments show that this imaging method is robust and feasible, with the proportion of saturated pixels in histogram detection being 0% and the peak signal to noise ratio being 58.37 dB, realizing the imaging without saturated light in the high-light environment, providing a new direction for the application of compressed sensing in imaging.
A Preferential Recovery Method of Interdependent Networks under Load
Fengzeng LIU, Bing XIAO, Shisi CHEN, Jiaxun CHEN
, doi: 10.11999/JEIT190486
[Abstract](102) [FullText HTML](73) [PDF 2314KB](4)
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.
Low-elevation DOA Estimation for VHF Radar Based on Multi-frame Phase Feature Enhancement
Houhong XIANG, Baixiao CHEN, Ting YANG, Minglei YANG
, doi: 10.11999/JEIT190432
[Abstract](158) [FullText HTML](97) [PDF 4316KB](11)
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).
Low-elevation DOA Estimation for VHF Radar Based on Multi-frame Phase Feature Enhancement
Houhong XIANG, Baixiao CHEN, Ting YANG, Minglei YANG
, doi: 10.11999/JEIT190432
[Abstract](12) [PDF 0KB](0)
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).
A TDOA-FDOA Passive Positioning Algorithm Based on the Semi-Definite Relaxation Technique
Ting SUN, ChunXi DONG, Yu MAO
, doi: 10.11999/JEIT190435
[Abstract](138) [FullText HTML](83) [PDF 2284KB](18)
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.
Distributed Coherent Radar LFM Wideband Stretch Parameter Estimation Method
Baoliang ZHOU
, doi: 10.11999/JEIT190398
[Abstract](133) [FullText HTML](85) [PDF 1787KB](16)
Wideband distributed coherent radar technology can effectively improve target measurement accuracy and recognition performance, has important research value. For existing radar equipment generally does not have the ability to stretch the positive and negative frequency modulation rate wideband LFM signal simultaneously, that is the problem of the delay phase value of wideband signal transmit coherent can not be obtained by wideband stretch method in receive coherent synthesis phase. This article uses the delay difference between the unit radar and the target, equivalents the unit radar transmitting wideband LFM signal to the target signal, performs cross-correlation processing on the received signal to obtain the value of transmit coherent parameter. Through modeling and simulation realizes receive coherent and transmits coherent synthesis processing, and carries out the aircraft target two input single output coherent detection test, obtaines the ideal test results. The method has the advantages of high estimation accuracy,small calculation amount and good real-time performance, can be applied to distributed coherent radar engineering implementation.
Group-Label-Specific Features Learning Based on Label-Density Classification Margin
Yibin WANG, Gensheng PEI, Yusheng CHENG
, doi: 10.11999/JEIT190343
[Abstract](775) [FullText HTML](131) [PDF 1227KB](7)
The label-specific features learning avoids the same features prediction for all class labels, it is a kind of framework for extracting the specific features of each label for classification, so it is widely used in multi-label learning. For the problems of large label dimension and unbalanced label distribution density, the existing multi-label learning algorithm based on label-specific features has larger time consumption and lower classification accuracy. In order to improve the performance of classification, a Group-Label-Specific Features Learning method based on Label-Density Classification Margin (GLSFL-LDCM) is proposed in this paper. Firstly, the cosine similarity is used to construct the label correlation matrix, and the class labels are grouped by spectral clustering to extract the label-specific features of each label group to reduce the time consumption for calculating the label-specific features of all class labels. Then, the density of each label is calculated to update the label space matrix, the label-density information is added to the original label space. And the classification margin between the positive and negative labels is expanded, so the imbalance label distribution density problem is effectively solved by the method of label-density classification margin. Finally, the final classification model is obtained by inputting the group-label-specific features and the label-density matrix into the extreme learning machine. The comparison experiment results verify fully the feasibility and stability of the proposed algorithm.
Quality Evaluation of Night Vision Anti-halation Fusion Image Based on Adaptive Partition
Quanmin GUO, Gaixia CHAI, Hanshan LI
, doi: 10.11999/JEIT190453
[Abstract](121) [FullText HTML](87) [PDF 2603KB](5)
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.
Researches on Pro-retirement Signal Quality of BeiDou Navigation Satellite System GEO-3 Satellite B1 Signal
Huihui SHI, Meng WANG, Yongnan RAO, Xiaochun LU, Xue WANG
, doi: 10.11999/JEIT190383
[Abstract](144) [FullText HTML](166) [PDF 2397KB](8)
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.
Dynamic Pilot Allocation Scheme for Joint User Grouping and Alliance Game in Massive MIMO Systems
Hui ZHI, Feiyue WANG, Ziju HUANG
, doi: 10.11999/5EIT190445
[Abstract](137) [FullText HTML](101) [PDF 1761KB](5)
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.
Amplifying Circuit Interface Model for LIDAR Signal Processing Systems
Ruqing LIU, Yan JIANG, Chenghao JIANG, Feng LI, Jingguo ZHU
, doi: 10.11999/JEIT190427
[Abstract](214) [FullText HTML](103) [PDF 2712KB](5)
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.
Insulator Orientation Detection Based on Deep Learning
Cailin LI, Qinghua ZHANG, Wenhe CHEN, Xiaobin JIANG, Bin YUAN, Changlei YANG
, doi: 10.11999/JEIT190350
[Abstract](675) [FullText HTML](250) [PDF 3977KB](50)
In order to solve the problem of inaccurate location in insulator target detection, this paper proposes an insulator orientation recognition algorithm based on deep learning. By adding angle information to the axis alignment detection frame, it can effectively solve the problem that conventional deep learning algorithm can not accurately locate the target. First, the angular rotation parameters are introduced into the axially aligned rectangular detection frame to form a directional detection frame. Then the parameter offset is added as the fifth parameter to the loss function for iterative regression. At the same time, in order to improve the detection accuracy, Adam algorithm is used to replace Stochastic Gradient Descent (SGD) to optimize the loss function. Finally, the insulator directional detection model can be obtained. The experimental results show that the orientation detection frame with rotation angle can effectively locate the insulator target accurately.
An Improved Template Analysis Method Based on Power Traces Preprocessing with Manifold Learning
Qingjun YUAN, An WANG, Yongjuan WANG, Tao WANG
, doi: 10.11999/JEIT190598
[Abstract](608) [FullText HTML](262) [PDF 1974KB](15)
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.
Intuitionistic Fuzzy Clustering Image Segmentation Based on Flower Pollination Optimization with Nearest Neighbor Searching
Feng ZHAO, Wenjing SUN, Hanqiang LIU, Zhe ZENG
, doi: 10.11999/JEIT190428
[Abstract](474) [FullText HTML](292) [PDF 5644KB](40)
In order to overcome shortcomings of the traditional fuzzy clustering algorithm for image segmentation, such as this easily affected by noise, sensitive to the initial value of clustering center, easily falling into local optimum, and inadequate ability of fuzzy information processing, an intuitionistic fuzzy clustering image segmentation algorithm is proposed based on flower pollination optimization with nearest neighbor searching. Firstly, a novel extraction strategy of image spatial information is proposed, and then an intuitionistic fuzzy clustering objective function with image spatial information is constructed to improve the algorithm’s robustness against noise and enhance the ability of the algorithm to process the image fuzzy information. In order to overcome the defects of sensitivity to clustering centers and easily falling into local optimum, a flower pollination algorithm based on nearest neighbor learning search mechanism is proposed. Experimental results show that the proposed method can get satisfactory segmentation results on a variety of noisy images.
A Batch Inheritance Extreme Learning Machine Algorithm Based on Regular Optimization
Bin LIU, Youheng YANG, Zhibiao ZHAO, Chao WU, Haoran LIU, Yan WEN
, doi: 10.11999/JEIT190502
[Abstract](964) [FullText HTML](642) [PDF 2776KB](32)
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.
Variable Tailing Nonlinear Transformation Design Based on Exponential Function in Impulsive Noise
Zhongtao LUO, Yanmei ZHAN, Renming GUO, Yangyong ZHANG
, doi: 10.11999/JEIT190401
[Abstract](351) [FullText HTML](256) [PDF 1903KB](8)
A novel design of nonlinear transformation function for the signal detection in impulsive noise is proposed. The proposed method takes the advantage of adjustable fading factors of the exponential function, it can be effective for different models of impulsive noise. By introducing the efficacy as the objective function, nonlinear design is converted into the problem of optimizing the threshold and bottom parameters to maximize the efficacy. Since the efficacy is continuous, derivative, and unimodal, the optimization problem can be easily solved by the traditional optimization methods, such as the Nelder-Mead simplex method. Analysis shows that the proposed design can obtain the optimal performance in the widely-used models of impulsive noise, including the symmetric α-stable model, the Class A model, and the Gaussian mixture model. Simulation on real atmospheric noise demonstrates that the proposed design is obviously better than the traditional clipper and blanker. Thus, this paper proposes an optimal and uniform solution for suppressing impulsive noise of various models.
Research on Suppressing Co-channel Interference of Passive Radar Based on Blind Source Separation Using Second Order Statistics
Xiaode LYU, Zhenghao SUN, Zhongsheng LIU, Hanliang ZHANG, Pingyu LIU
, doi: 10.11999/JEIT190178
[Abstract](357) [FullText HTML](203) [PDF 2335KB](23)
Aiming at the problem of co-frequency base station interference in passive radar based on Long Term Evolution (LTE) signal, an algorithm based on blind source separation using second statistics is proposed. The presented algorithm is based on convolution mixed model, and achieves the minimum correlation among separated signals through multi-channel Least-Mean-Square (LMS) algorithm. Without statistical correlation among the signals of each transmitting base station, the separation of the observed signals is completed when the separated signals achieve the minimum correlation. On this base, the traditional signal processing for passive radar is improved. The steps of separating co-frequency interference clutter consisting of both direct-path and multipath clutter are added, which can suppress the clutter interference of co-channel base station. Simulation and analysis verify the effectiveness of the algorithm. The algorithm provides a reference for data processing of passive radar based on LTE signal.
An Image Encryption Algorithm Based on Chaos Set
Fupeng LI, Jingbiao LIU, Guangyi WANG, Kangtai WANG
, doi: 10.11999/JEIT190344
[Abstract](612) [FullText HTML](405) [PDF 3876KB](44)
A novel image encryption algorithm is proposed based on a chaos set which consists of discrete chaotic systems and continuous chaotic systems. The chosen combination of chaotic system is dependent on the encryption intensity. The pixel mean value and pixel coordinate value of images are exploited to control the generation of key, thus enhancing the relationship between chaotic key and plain text. In addition, the octet of cipher text pixel is divided into three parts, and then hided into a processed public image, which can promote the external characteristics of cipher text. The image histogram analysis, correlation analysis, and information entropy analysis methods are adopted to identify the security performance, which indicates the effectiveness of the proposed image encryption algorithm and potential application in the image security transmission.
Maneuvering Decision for Autonomous Air Combat of UCAV Based on BI and MHO
Changqiang HUANG, Le WANG
, doi: 10.11999/JEIT181011
[Abstract](294) [FullText HTML](173) [PDF 1902KB](3)
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.
Controllable Magnification for Visual Saliency Object Based on Virtual Optics
Jiazhen CHEN, Weimin WU, Zihua ZHENG, Feng YE, Guiren LIAN, Li XU
, doi: 10.11999/JEIT190469
[Abstract](664) [FullText HTML](297) [PDF 2806KB](10)
A high-resolution controllable magnification method for visual saliency object based on virtual optics is proposed in this paper. The original image is placed on the virtual object plane. Firstly, the diffractive wave of the original image on the virtual diffraction plane is obtained by inverse diffraction calculation, and then the forward diffraction calculation is carried out after the virtual diffraction wave is irradiated by spherical wave. The original images with different magnification can be reconstructed by changing the position of the observation plane. The simulation results show that compared with the general interpolation method, the magnified image shows a good visual perception effect, especially in the saliency region. When the degraded face image is used as the signal to be reconstructed, the significant areas such as eyes and nose are clearer than the general method. The local salient region in the original image is segmented by the level set method combined with salient map, and the magnification and contour extraction are performed. The contours show good smoothness.
A Mobile Crowdsensing Data Security Delivery Model Based on Tangle Network
Guosheng ZHAO, Hui ZHANG, Jian WANG
, doi: 10.11999/JEIT190370
[Abstract](916) [FullText HTML](529) [PDF 1952KB](25)
Considering at the security risks and privacy leaks in the process of data and reward in the Mobile CrowdSensing (MCS), a distributed security delivery model based on Tangle network is proposed. Firstly, in the data perception stage, the local outlier factor detection algorithm is used to eliminate the anomaly data, cluster the perception data and determine the trusted participant. Then, in the transaction writing stage, Markov Monte Carlo algorithm is used to select the transaction and verify its legitimacy. The anonymous identity data is uploaded by registering with the authentication center, and the transaction is synchronously written to the distributed account book. Finally, combined with Tangle network cumulative weight consensus mechanism, when the security of transaction reaches its threshold, task publishers can safely deliver data and rewards. The simulation results show that the model not only protects user privacy, but also enhances the ability of secure delivery of data and reward. Compared with the existing sensing platform, the model reduces the time complexity and task publishing cost.
RGB-D Image Saliency Detection Based on Multi-modal Feature-fused Supervision
Zhengyi LIU, Quntao DUAN, Song SHI, Peng ZHAO
, doi: 10.11999/JEIT190297
[Abstract](1327) [FullText HTML](814) [PDF 2713KB](47)
RGB-D saliency detection identifies the most visually attentive target areas in a pair of RGB and Depth images. Existing two-stream networks, which treat RGB and Depth data equally, are almost identical in feature extraction. As the lower layers Depth features with a lot noise, it causes image features not be well characterized. Therefore, a multi-modal feature-fused supervision of RGB-D saliency detection network is proposed, RGB and Depth data are studied independently through two-stream , double-side supervision module is used respectively to obtain saliency maps of each layer, and then the multi-modal feature-fused module is used to later three layers of the fused RGB and Depth of higher dimensional information to generate saliency predicted results. Finally, the information of lower layers is fused to generate the ultimate saliency maps. Experiments on three open data sets show that the proposed network has better performance and stronger robustness than the current RGB-D saliency detection models.
Quasi-periodic, Chaotic-torus Bursting Oscillations and SlowPassage Effect in Memristive High-pass Filter Circuit
Fangyuan LI, Mo CHEN, Huagan WU
, doi: 10.11999/JEIT190373
[Abstract](632) [FullText HTML](432) [PDF 3669KB](18)
A memristive high-pass filter circuit is presented, which is composed of an active high-pass RC filter parallelly coupling with a memristor emulator of diode-bridge cascaded by LC oscillator. The circuit equations and system model are established. Based on bifurcation diagram, phase plane plot, and Poincaré mapping, bifurcation analysis with the feedback gain as adjustable parameter is performed, from which bursting oscillating behaviors including quasi-period, chaotic-torus, chaos, and multiple period that exist in such a memristive high-pass filter circuit are disclosed. Furthermore, through fast-slow analysis method, Hopf bifurcation set of the fast sub-system is derived, with which the formation mechanism of slow passage effect in the memristive high-pass filter circuit is expounded. Finally, the numerical simulation results are validated based on Multisim circuit simulations.
Range Spread Target Detection Based on OnlineEstimation of Strong Scattering Points
Pengcheng GUO, Zheng LIU, Dingli LUO, Jianpu LI
, doi: 10.11999/JEIT190417
[Abstract](919) [FullText HTML](361) [PDF 1531KB](16)
The traditional range-extended target detection is usually completed under the condition of scattering point density or scattering point number priori. The detection performance will be greatly reduced when the scattering point information of the target is completely unknown. To solve this problem, a Range Spread Target Detection method based on Online Estimation of Strong Scattering(OESS-RSTD) points is proposed. Firstly, the unsupervised clustering algorithm in machine learning is used to estimate the number of strong scattering points and the first detection threshold adaptively. Then, the second detection threshold is determined according to false alarm rate. Finally, the existence of the target is determined through two detection thresholds. In this paper, the simulation data and the measured data are used to verify and compare with other algorithms. By comparing the Signal-to-Noise Ratio (SNR) -detection probability curves of various methods with a given false alarm probability, it is verified that the proposed method in this paper has higher robustness than the traditional algorithm, and the method does not need any priori information of target scattering points.
Design of a Novel Broadband Dual Circularly Polarized Antenna
Zhenya LI, Xiaosong ZHU, Chengyou YIN, Wei WU, Yong WANG
, doi: 10.11999/JEIT180705
[Abstract](731) [FullText HTML](464) [PDF 2449KB](31)
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(3): 1 -4  
[Abstract](68) [FullText HTML](50) [PDF 203KB](11)
A Moving Target Imaging Approach for the Multichannel in Azimuth High Resolution Wide Swath SAR System
Yuying WANG, Zhimin ZHANG, Ning LI, Huaitao FAN, Qingchao ZHAO
2020, 42(3): 541 -546   doi: 10.11999/JEIT190211
[Abstract](1132) [FullText HTML](595) [PDF 1587KB](40)
Since the echo characteristics of moving targets are different from that of stationary targets, the traditional reconstruction filter bank algorithm, i.e., the reconstruction filter algorithm, is not applicable. In this paper, a novel reconstruction approach of the moving target for a multichannel in azimuth High-Resolution Wide-Swath (HRWS) Synthetic Aperture Radar (SAR) system is proposed. The approach firstly analyzes the echo characteristics of the moving target for the multi-channel in azimuth SAR system and gives the main reason for the failure of the traditional reconstruction method in contrast to the form of the stationary target echo. By introducing the radial velocity of the moving target, the spectrum reconstruction of the uniform moving target is effectively realized, and the azimuth ambiguities of the uniform moving target for the multi-channel in azimuth SAR system is well suppressed. Space-borne simulated results confirm the effectiveness of the proposed reconstruction approach.
Error Analysis of Dynamic Sea Surface Height Measurement by Near-nadir Interferometric SAR
Yao CHEN, Mo HUANG, Xiaoqing WANG, Haifeng HUANG
2020, 42(3): 547 -554   doi: 10.11999/JEIT190191
[Abstract](819) [FullText HTML](519) [PDF 2544KB](57)
The wide-swath interferometric altimeter working at near-nadir is a newly developed ocean surface topography measurement technology in recent years. Different from land elevation measurement, for the dynamic ocean surface waves, they move randomly all the time and this brings bias in Synthetic Aperture Radar (SAR) imaging and interferometric processes and leads to the final height measurement errors. For the requirement of centimeter-level precision, this error is the main source of measurement errors. The errors due to the characteristics of ocean surface and their impact on near-nadir InSAR’s precision are investigated. The motion error theoretical model is established combining the characteristics of the ocean surface and InSAR working mechanism, and the electromagnetic bias and layover bias are also taken into consideration. The error models in different SAR modes under various sea states are simulated. The error model is validated by the interferometric SAR full-link experimental simulation and the simulation results are consistent with the theoretical values. The results show that the errors are approximately linear changing with the Doppler centroid frequency and are proportional to the radial velocity of targets modulated by scattering. The errors are not only related to the characteristics of the waves, but also related to system parameters. This work can provide the feasible suggestions for future system design, error budget and data processing.
Algebraic Solution for 3D Localization of Multistatic Passive Radar in the Presence of Sensor Position Errors
Yan ZUO, Xialei ZHOU, Taoran JIANG
2020, 42(3): 555 -562   doi: 10.11999/JEIT190292
[Abstract](510) [FullText HTML](428) [PDF 1733KB](34)
An observer is placed on the airborne in the multistatic passive radar localization system. The error in observer position may seriously affect the localization accuracy. An algebraic closed-form solution is proposed for 3D localization of multistatic passive radar in the presence of sensor position errors. Firstly, the nonlinear Bistatic Range Difference (BRD) measurement equations are linearized by proper additional parameters and a pseudo-linear estimation model is given accordingly. Then a modified Two-Step Weighted Least Squares (TS-WLS) algorithm is developed with considering the statistic characteristics of the observer position measurement noises. Finally the Cramer-Rao Lower Bound (CRLB) and the theoretical error of the algorithm are derived. Simulation results show that the proposed algorithm can achieve the CRLB in a moderate level of noises.
Research on the Detection Algorithm for Sea Surface Targets Based on Passive Interferometric Microwave Images
Hailiang LU, Zhiqiang WANG, Chao GAO, Yinan LI, Taiyang HU, Jiakun WANG, Xiaojiao YANG, Rongchuan LÜ, Hao LI
2020, 42(3): 563 -572   doi: 10.11999/JEIT190256
[Abstract](705) [FullText HTML](535) [PDF 6994KB](50)
To effectively detect sea surface targets by the passive interferometric microwave technology considered as an important complement for the space-based early warning system of China, a detection algorithm is proposed based on the Passive Interferometric Microwave Images (PIMI). First, the mathematical model of PIMI is established for the sea background and sea surface target. Second, the detection algorithm is introduced in detail, and numerical simulations are performed to demonstrate the feasibility of the proposed algorithm. Finally, the air-borne experiments are also carried out. Both theoretical and experimental results demonstrate that the proposed algorithm is feasible, can effectively detect sea surface targets, and show good performance. That also exhibits that the moving metal vessels on the sea surface show a “hot” and “low” characteristic in PIMI, which can be used to improve the detection probability. The proposed detection algorithm can provide a reference for space-based PIMI to detect sea surface targets.
Magnetic Dipole Object Tracking Algorithm Based on Magnetometer Array in Geomagnetic Background
Luzhao CHEN, Yongqiang FENG, Ruijie GUO, Wanhua ZHU, Guangyou FANG
2020, 42(3): 573 -581   doi: 10.11999/JEIT190236
[Abstract](510) [FullText HTML](273) [PDF 3997KB](27)
In order to solve the problem of geomagnetic interference and model nonlinearity in the tracking process of magnetic dipole under geomagnetic background, Monte Carlo Kalman Filter (MCKF) tracking method based on differential magnetic anomaly is proposed in this paper. The new tracking method takes the difference of magnetic field measured by sensor array as the observation signal, and uses Monte Carlo Kalman Filtering (MCKF) algorithm to solve the nonlinear problem of the model to realize the real-time tracking of magnetic dipole targets. The simulation results show that the proposed method is more accurate than the traditional Extended Kalman Filter (EKF) or Untracked Kalman Filter (UKF) in the stable tracking process. The results of real geomagnetic background tracking experiments show that the proposed algorithm has better tracking performance under low SNR.
Design of Ka-band Linear Tapered Slot Antennas Based on Substrate Integrated Waveguide Feed
Honggang HAO, Jiang LI, Ting ZHANG, Wei RUAN
2020, 42(3): 582 -588   doi: 10.11999/JEIT190218
[Abstract](798) [FullText HTML](483) [PDF 4402KB](28)
Linear Tapered Slot Antennas (TSA) have significant advantages over traditional horn antennas, dielectric rod antenna when used as feed elements in Focal Plane Arrays (FPA) of Passive MilliMeter Wave(PMMW) imaging. In this paper, a novel Antipodal Linear Tapered Slot Antenna(ALTSA) is designed and optimized. The proposed antenna, the gain of which is improved by loading metamaterial structure, is fed by the Substrate Integrated Waveguide(SIW). Simulation and measure analysis show that the good impedance characteristics, low sidelobe levels, high and smooth gain are all achieved in a wide frequency band. Meanwhile, the designed antenna has a smaller aperture width and is easier to form a denser feed array in the focal plane to improve the spatial resolution of passive millimeter wave imaging.
Sparse and Low Rank Recovery Based Robust DOA Estimation Method
Hongyan WANG, Ruonan YU
2020, 42(3): 589 -596   doi: 10.11999/JEIT190263
[Abstract](1046) [FullText HTML](557) [PDF 1160KB](49)
Focusing on the problem of rather large estimation error in the traditional Direction Of Arrival (DOA) estimation algorithm induced by finite subsampling, a robust DOA estimation method based on Sparseand Low Rank Decomposition (SLRD) is proposed in this paper. Following the low-rank matrix decomposition method, the received signal covariance matrix is firstly modeled as the sum of the low-rank noise-free covariance matrix and sparse noise covariance one. After that, the convex optimization problem associated with the signal and noise covariance matrix is constructed on the basis of the low rank recovery theory. Subsequently, a convex model of the estimation error of the sampling covariance matrix can be formulated, and this convex set is explicitly included into the convex optimization problem to improve the estimation performance of signal covariance matrix such that the estimation accuracy and robustness of DOA can be enhanced. Finally, with the obtained optimal noiseless covariance matrix, the DOA estimation can be implemented by employing the Minimum Variance Distortionless Response (MVDR) method. In addition, exploiting the statistical characteristics of the sampling covariance matrix estimation error subjecting to the asymptotic normal distribution, an error parameter factor selection criterion is deduced to reconstruct the noise-free covariance matrix preferably. Compared with the traditional Conventional BeamForming (CBF), Minimum Variance Distortionless Response(MVDR), MUltiple SIgnal Classification (MUSIC) and Sparse and Low-rank Decomposition based Augmented Lagrange Multiplier(SLD-ALM) algorithms, numerical simulations show that the proposed algorithm has higher DOA estimation accuracy and better robustness performance under finite sampling snapshot.
Adaptive Null Broadening Algorithm Based on Sidelobes Cancellation
Yunhe CAO, Yongqiang GUO, Shuai LIU, Yutao LIU
2020, 42(3): 597 -602   doi: 10.11999/JEIT190296
[Abstract](570) [FullText HTML](243) [PDF 1142KB](7)
In existing null broadening algorithm, the taper matrix does not contain phase information, and when it is used to against strong directional and large deviation angle interference, the null depth becomes shallow and the interference suppression performance drops seriously. An adaptive null broadening algorithm for sidelobe canceller is proposed based on dense disturbance in virtual airspace. The algorithm reconstructs the self-covariance matrix of the auxiliary array data and the co-covariance matrix of the main and auxiliary array data at the same time to realize the adaptive control of the null region. The taper matrix is only related to the position and width of the array elements, and it can be generated offline without disturbing information and occupying no computing resources of the system. The simulation results show that this method can achieve adaptive broadening of the null region and improve the robustness of non-stationary interference suppression.
Indoor Through-the-wall Passive Human Target Detection Algorithm
Xiaolong YANG, Shiming WU, Mu ZHOU, Liangbo XIE, Jiacheng WANG
2020, 42(3): 603 -612   doi: 10.11999/JEIT190378
[Abstract](409) [FullText HTML](409) [PDF 4977KB](34)
In through-the-wall scene, due to the serious attenuation of signal caused by wall, the energy of target reflection signal in the received signal decreases significantly and the received signal is submerged in the direct signal of the transceiver and the reflection signal of indoor furniture, making the target behind wall is hard to be detected. In view of the above problems, a novel Through-the-Wall Multiple human targets Detection (TWMD) algorithm based on multidimensional signal features fusion is proposed. Firstly, the received Channel State Information(CSI) is preprocessed to eliminate the phase error and amplitude noise, and the multidimensional signal features are fully extracted from the correlation coefficient matrix by using time correlation and subcarrier correlation of CSI. Finally, the mapping between features and detection results is established by BP neural network. The experimental results show that the recognition accuracy of this algorithm in the environment with glass wall, brick wall and concrete wall is above 0.98, 0.90, 0.85, respectively. According to the detection results of 4000 samples, compared with the existing detection algorithms based on single signal feature, the proposed algorithm achieves an average accuracy improvement of 0.45 in the detection of different number of moving targets.
High-performance Co-prime Spectral Analysis Method Based on Parallelled All-phase Point-pass Filtering
Xiangdong HUANG, Yuxuan SHAN, Jian WANG
2020, 42(3): 613 -620   doi: 10.11999/JEIT190317
[Abstract](500) [FullText HTML](206) [PDF 2609KB](8)
In order to completely remove the spurious-peak side effect in the undersampling based wide-band spectral analysis, this paper proposes a high-performance co-prime spectral analysis method based on paralleled all-phase point-pass filtering. On basis of a deep analysis on the mechanism of the classical co-prime spectral analysis, it is discovered that this spurious-peak side effect arises from those redudant overlapping boundary-bands related to distinct polyphase filtering branches between the up data path and the down data path. Therefore, through replacing the prototype filters in the classical co-prime spectral analysis by the all-phase point-pass filtering banks, a novel co-prime analysis dataflow is derived based on paralleled all-phase point-pass filtering. Both theoretic analysis and numerical simulation show that the proposed spectral analysis method achieves remarkable performance improvement: it can not only completely remove the spurious-peak side effect, but also obtain a much higher spectral resolution than the classical co-prime analysis, thereby possessing another merit of distinguishing dense spectral components. The proposed spectral analysis method possesses vast potentials in the software-defined radio, radar detection, passive positioning and marine wireless communication etc.
An Optimal Number of Indices Aided gOMP Algorithm for Multi-user Detection in NOMA System
Bin SHEN, Hebiao WU, Taiping CUI, Qianbin CHEN
2020, 42(3): 621 -628   doi: 10.11999/JEIT190270
[Abstract](837) [FullText HTML](413) [PDF 1769KB](37)
As one of the key 5G technologies, Non-Orthogonal Multiple Access (NOMA) can improve spectrum efficiency and increase the number of user connections by utilizing the resources in a non-orthogonal manner. In the uplink grant-free NOMA system, the Compressive Sensing (CS) and generalized Orthogonal Matching Pursuit (gOMP) algorithm are introduced in active user and data detection, to enhance the system performance. The gOMP algorithm is literally generalized version of the Orthogonal Matching Pursuit (OMP) algorithm, in the sense that multiple indices are identified per iteration. Meanwhile, the optimal number of indices selected per iteration in the gOMP algorithm is addressed to obtain the optimal performance. Simulations verify that the gOMP algorithm with optimal number of indices has better recovery performance, compared with the greedy pursuit algorithms and the Gradient Projection Sparse Reconstruction (GPSR) algorithm. In addition, given different system configurations in terms of the number of active users and subcarriers, the proposed gOMP with optimal number of indices also exhibits better performance than that of the other algorithms mentioned in this paper.
A Hierarchical Vertical Handover Algorithm Based on Fuzzy Logic in Heterogeneous Wireless Networks
Bin MA, Shangru LI, Xianzhong XIE </