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Articles in press have been peer-reviewed and accepted, which are not yet assigned to volumes /issues, but are citable by Digital Object Identifier (DOI).
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A Decoupling and Dimension Dividing Multi-parameter Estimation Method for Cross-band SAR Scattering Centers
Yiyuan XIE, Yuexin GAO, Mengdao XING, Liang GOU, Guangcai SUN
 doi: 10.11999/JEIT200319
[Abstract](8) [FullText HTML](1) [PDF 2380KB](3)
Microwave photonics radar generates cross-band signals with large bandwidth, providing a basis for precise electromagnetic characteristics description and accurate identification of targets. Meanwhile, a corresponding electromagnetic model parameter extraction method is urgently required in the case of large-bandwidth and wide-angle. For the same scene, the amount of return signals will increase considerably in cross-band condition comparing with narrow-bandwidth condition. Furthermore, the signals under cross-band condition may exhibit complex range-azimuth coupling. Under such a condition, it is of difficulty to estimate high dimensional physical parameters of scattering centers in the scene from return signals. To solve this problem, a multi-parameter estimation of cross-band SAR scattering centers method is proposed. The Polar Format Algorithm (PFA) and the attributed scattering center model are combined to construct a two-dimensional decoupled wavenumber domain model. With this scattering model, the estimation procedure is transformed into an optimization problem with multiple variables. This complex multi-variable optimization problem is divided into a set of single variable optimization problems by using the Coordinate Descent Algorithm (CDA). The separation effectively reduces dictionary dimensions and estimation complexity. Moreover, the Hooke-Jeeves algorithm is introduced to enhance estimation accuracy in each single variable optimization problem. Consequently, the proposed estimator for scattering parameters is not only efficient, but also accurate. The structure and location of each scattering center can be identified according to the parameter estimation results. Simulation results confirm the validity of the proposed method.
Traffic Modeling for Low Earth Orbit Satellite Constellation Internet of Things
Yifan CHENG, Zhicheng QU, Gengxin ZHANG
 doi: 10.11999/JEIT200091
[Abstract](6) [FullText HTML](4) [PDF 1186KB](3)
With the continuous development of the Internet of Things(IoT), its business demands show a trend of diversification and globalization. As the ground Internet of Things cannot cover the whole world, the satellite IoT, especially the Low Earth Orbit Satellite Constellation (LEOSC) IoT, can supplement and extend the ground network. Due to the wide coverage and high dynamic characteristics of the LEOSC IoT system, there are significant differences between it and the ground IoT in terms of traffic statistics. In order to make reasonable and efficient use of limited resources on board, the traffic model of global Internet of Things based on LEOSC is studied in this paper. Combined with diversified traffic characteristics and satellite communication system characteristics, the framework of global IoT traffic model is obtained by using statistical modeling theory. What’s more, an access strategy based on the highest priority is proposed to enable the device node to select the satellite in real time. The simulation results show that the Poisson process can be used to approximately simulate the superposition process of asynchronous traffic commonly exist in LEOSC IoT, and due to the high dynamic nature of low earth orbit satellite, its traffic source changes at high speed, resulting in high Peak-to-Average Ratio(PAR) of traffic.
Energy Efficiency Model and Mapping Algorithm of Block Cipher for Cipher Specific Programmable Logic Array
Wei LI, Jiahao GAO, Yiran DU, Tao CHEN
 doi: 10.11999/JEIT200079
[Abstract](97) [FullText HTML](62) [PDF 1841KB](10)
Cipher Specific Programmable Logic Array (CSPLA) is a data stream-driven cryptographic processing structure. The relations between cryptographic mapping energy efficiency and array structures of different scales is considered in this paper. First, based on the specific hardware structure of CSPLA and block ciphers, an energy efficiency model of block cipher algorithm mapping based on this structure is established and related factors affecting energy efficiency are analyzed. Then the basic process of algorithm mapping on the array structure is discussed and a mapping algorithm is proposed. Finally, several typical block cipher algorithms are selected to perform mapping experiments on arrays of different scales. The results show that larger scale CSPLA does not necessarily bring higher energy efficiency. When the CSPLA scale is about 4×4~4×6 which achieves the best energy efficiency. In order to obtain the best energy efficiency, the scale parameter of the array should match the specific hardware resource constraints and cryptographic algorithm parameters. The optimal energy efficiency of AES algorithm is 33.68 Mbps/mW. CSPLA has better energy efficiency characteristics compared with other cryptographic processing structures.
A Glioma Detection and Segmentation Method in MR Imaging
Hao CHEN, Guang LI, Yang LIU, Yongqian QIANG
 doi: 10.11999/JEIT200033
[Abstract](144) [FullText HTML](89) [PDF 3399KB](10)
The glioma detection and focus segmentation in Magnetic Resonance Imaging (MRI) has important value for the therapeutic schedule selection and the surgical operations. In order to improve the detection efficiency and segmentation accuracy for glioma, this paper proposes a two-stage calculating method. First, a light convolutional neural network is designed to implement rapidly detection and localization for the glioma in MR images. Then, the peritumoral edema, non-enhancing tumor, enhancing tumor, and normal are classified and segmented from each other through an ensemble learning process. In order to improve the accuracy of segmentation, 416 radiomics features extracted from multi-modal MR images and 128 CNN features extracted by a convolutional neural network are mixed. The feature vector consisting of 298 features for classification learning are formed after a feature reduction process. In order to verify the performance of the proposed algorithm, experiments are carried out on the BraTS2017 dataset. The experimental results show that the proposed method can quickly detect and locate the tumor. The overall segmentation accuracy is improved distinctly with respect to 4 state-of-the-art approaches.
Collaborative Parameter Update Based on Average Variance Reduction of Historical Gradients
Tao XIE, Chunjiong ZHANG, Yongjian XU
 doi: 10.11999/JEIT200061
[Abstract](126) [FullText HTML](93) [PDF 1922KB](8)
The Stochastic Gradient Descent (SGD) algorithm randomly picks up a sample to estimate gradients, creating big variance which reduces the convergence speed and makes the training unstable. A Distributed SGD based on Average variance reduction, called DisSAGD is proposed. The method uses the average variance reduction based on historical gradients to update parameters in the machine learning model, requiring little gradient calculation and additional storage, but using the asynchronous communication protocol to share parameters across nodes. In order to solve the “update staleness” problem of global parameter distribution, a learning rate with an acceleration factor and an adaptive sampling strategy are included: on the one hand, when the parameter deviates from the optimal value, the acceleration factor is increased to speed up the convergence; on the other hand, when one work node is faster than the other ones, more samples are sampled for the next iteration, so that the node has more time to calculate the local gradient. Experiments show that the DisSAGD reduces significantly the waiting time of loop iterations, accelerates the convergence of the algorithm being faster than that of the controlled methods, and obtains almost linear acceleration in distributed cluster environments.
An Efficient and Robust Algorithm to Generate Initial Center of Bisecting K-means for High-dimensional Big Data Based on Random Integer Triangular Matrix Mappings
Min LI, Tingting HE
 doi: 10.11999/JEIT200043
[Abstract](55) [FullText HTML](45) [PDF 2092KB](0)
The algorithm of Bisecting K-means obtains multiple clustering results by using a set of initial center pairs to segment a cluster, and then selects the best from them to mitigate the adverse effect of the local optimal convergence on the performance of the algorithm. However, the current methods of random sampling to generate initial center pairs for Bisecting K-means have some problems, such as low efficiency, poor stability, missing values and so on, which are not competent for big data clustering. In order to solve these problems, firstly the lower triangular matrix composed by the pairs of initial centers and the lower triangular matrix composed by serial numbers of the pairs of initial centers are created. Then, by establishing several mappings between the elements and their positions in the two matrices, a linear complexity algorithm is proposed to generate initial center pairs from the set of random integers. Both theoretical analysis and experimental results show that the time efficiency and efficiency stability of this method are significantly better than the current methods of random sampling, so it is particularly suitable for these scenarios of high-dimensional big data clustering.
Robust Energy Efficiency Optimization Algorithm for NOMA-based D2D Communication With Simultaneous Wireless Information and Power Transfer
Yongjun XU, Zijian LIU, Guoquan LI, Qianbin CHEN, Jinzhao LIN
 doi: 10.11999/JEIT200175
[Abstract](72) [FullText HTML](31) [PDF 1686KB](11)
In order to resolve the problems of spectrum shortage, large power consumption, and excessive load at base stations, a Simultaneous Wireless Information and Power Transfer (SWIPT)-based Robust Energy Efficiency (EE) Algorithm (SREA) with imperfect channel state information is proposed to maximize the total EE in Non-Orthogonal Multiple Access (NOMA) assisted Device-to-Device (D2D) networks. Considering the users' Quality of Service (QoS) constraints and maximum transmit power constraints, a robust EE maximization-based resource allocation model is established based on random channel uncertainties. Moreover, the original NP-hard problem is transformed into a deterministic convex optimization problem by using Dinkelbach’s method and the variable substitution method. And the analytical solutions are obtained through Lagrange dual theory. Simulation results demonstrated that the proposed algorithm can effectively improve the system EE and the robustness of D2D users while ensuring the communication quality of cellular users.
The Optimization of Wireless Sensor Network Topology Based on FW-PSO Algorithm
Ying ZHANG, Guangyuan YANG
 doi: 10.11999/JEIT191039
[Abstract](54) [FullText HTML](36) [PDF 1116KB](4)
Wireless Sensor Network (WSN) has the characteristics of scale-free network, usually works in an unattended open environment, and is vulnerable to a variety of deliberate attacks. The attack causes the network to break down, and even causes the whole network to be paralyzed. In this paper, the scale-free network in complex network is taken as the research object, and a scale-free wireless sensor network model is constructed. Using the advantages of Fireworks algorithm and Particle Swarm Optimization (PSO) algorithm, such as search ability and population diversity, the FW-PSO (FireWorks and Particle Swarm Optimization) algorithm is proposed, which has good performance in global search ability and convergence speed. For the scale-free network model, FW-PSO algorithm is used to optimize the network topology. Under different attack strategies, the performance of the network before and after the optimization is analyzed from dynamic and static invulnerability respectively. Simulation results show that, compared with other similar algorithms, the dynamic and static invulnerability of wireless sensor network optimized by the proposed algorithm has obvious advantages.
QoE-based Resource Allocation for Multi-cell Hybrid NOMA Networks
Hongxiang SHAO, Youming SUN, Jihao CAI
 doi: 10.11999/JEIT20032
[Abstract](111) [FullText HTML](76) [PDF 1803KB](3)
Resource allocation in Multi-Cell hybrid Non-Orthogonal Multiple Access-orthogonal multiple access (MC-hybrid NOMA) networks is studied in this paper. To satisfy the Quality of Experience (QoE) of different service types of users, an algorithm joint user-BS association, sub-channel assignment and power allocation is proposed to maximize the sum Mean Opinion Scores (MOSs) of users in the networks. A low-complexity two-step approach based on matching game theory and developed a power allocation strategy based on QoE proportional fairness are proposed. Simulation results demonstrate that the proposed algorithm can effectively improve the system performance and fairness.
Robust Nonnegative Least Mean Square Algorithm Based on Sigmoid Framework
Kuan’gang FAN, Haiyun QIU
 doi: 10.11999/JEIT200018
[Abstract](78) [FullText HTML](42) [PDF 4036KB](7)
Impulsive noise causes nonnegative algorithms to yield excessive error during iterations, which will damage the stability of the algorithm and causes performance degradation. In the paper, a NonNegative Least Mean Square algorithm based on the Sigmoid framework (SNNLMS) is proposed. The algorithm embeds the conventional nonnegative cost function into the Sigmoid framework to receive a new cost function. The new cost function has the characteristics of suppressing the impact of impulse noise. In addition, in order to enhance the robustness of the SNNLMS algorithm under sparse system identification, the Inversely-Proportional Sigmoid NonNegative Least Mean Square (IP-SNNLMS) is proposed based on the inversely-proportional function. Simulation results demonstrate that the SNNLMS algorithm effectively solves the problem of misadjustment caused by impulsive noise. IP-SNNLMS enhances the robustness of the algorithm and improves the defect of the convergence rate of the SNNLMS algorithm under the sparse system identification.
A Privacy-preserving Computation Offloading Method Based on k-Anonymity
Xing ZHAO, Jianhua PENG, Wei YOU, Lu CHEN
 doi: 10.11999/JEIT191046
[Abstract](48) [FullText HTML](33) [PDF 2913KB](4)
Users’ offloading tasks and offloading frequencies in Mobile Edge Computing(MEC) may cause users to be locked out. A privacy-preserving computation offloading method based on k-anonymity is proposed in this paper. Firstly, based on the differences between offloading tasks and their frequencies, this method proposes privacy constraint to establish a privacy-preserving computation offloading model based on offloading frequency; Then, a Privacy-preserving Computation Offloading algorithm based on Simulated Annealing (PCOSA) is utilized to obtain the optimal k-anonymous groups and the privacy constraint frequency of each task; Finally, the user’s original offloading frequencies are changed to meet the privacy constraint while minimizing terminal energy consumption. Simulation results validate that the PCOSA can find out k users with the closest offloading performance to form anonymous sets, which protects effectively the privacy of all users.
A Nonparametric Bayesian Dictionary Learning Algorithm with Clustering Structure Similarity
Daoguang DONG, Guosheng RUI, Wenbiao TIAN, Yang ZHANG, Ge LIU
 doi: 10.11999/JEIT190496
[Abstract](36) [FullText HTML](25) [PDF 7305KB](4)
Making use of image structure information is a difficult problem in dictionary learning, the traditional nonparametric Bayesian algorithms lack the ability to make full use of image structure information, and faces problem of inefficiency. To this end, a dictionary learning algorithm called Structure Similarity Clustering-Beta Process Factor Analysis (SSC-BPFA) is proposed in this paper, which completes efficient learning of the probabilistic model via variational Bayesian inference and ensures the convergence and self-adaptability of the algorithm. Image denoising and inpainting experiments show that this algorithm has significant advantages in representation accuracy, structure similarity index and running efficiency compared with the existing nonparametric Bayesian dictionary learning algorithms.
Research on Fuzzy Image Instance Segmentation Based on Improved Mask R-CNN
Weidong CHEN, Weiran GUO, Hongwei LIU, Qiguang ZHU
 doi: 10.11999/JEIT190604
[Abstract](53) [FullText HTML](9) [PDF 1744KB](20)
Mask R-CNN is a relatively mature method for image instance segmentation at this stage. Aiming at the problems of segmentation boundary accuracy and poor robustness of fuzzy pictures in Mask R-CNN algorithm, an improved Mask R-CNN method for image instance segmentation is proposed. This method first proposes that on the Mask branch, Convolution Condition Random Field(ConvCRF) is used to optimize the Mask branch, and the candidate area is further segmented, and uses FCN-ConvCRF branch to replace the original branch. Then, a new anchor size and IOU standard are proposed to enable the RPN candidate box cover all the instance areas. Finally, a training method is used to add a part of data transformed by the transformation network. Compared with the original algorithm, the total mAP value is improved by 3%, and the accuracy and robustness of segmentation boundary are improved to some extent.
A Synthesis Method of Hybrid Reflector Antenna for Satellite Communications
Jianjun LI, Pengfei YIN, Xianbin ZHAO
 doi: 10.11999/JEIT190564
[Abstract](15) [FullText HTML](5) [PDF 2762KB](10)
A hybrid reflector antenna is presented to generate a contoured beam over service area, an un-scanned and a scanned pencil beam from two shaped reflectors and three feeds, simultaneously. The shaped main reflector is shared by three beams, and the antenna is equivalent to two single-reflector antennas with single-feed for each beam and a pair of dual offset Gregorian shaped reflector antennas, and generating the contoured, un-scanned and scanned pencil beam, respectively. The proposed method is successfully applied to a 1.2 m hybrid reflector antenna. Simulations and experimentations of each beam has been performed. The Edge of Coverage(EoC) directivity over service area is 27.5 dBi for contoured beam in Tx and Rx working frequency of Ku-band, and the un-scanned pencil beam has a aperture efficiency higher than 70% in Tx and Rx working frequency of C-band. Meanwhile, the scanned pencil beam inside and outside the service area is realized by the lateral defocus of the sub-reflector and the corresponding feed in Tx and Rx working frequency of Ka-band. Simulation results show that the hybrid reflector antenna can realize C/Ku/Ka-band communication tasks simultaneously.
A Survey on Personality in Cyber Security
Tong WU, Kangfeng ZHENG, Chunhua WU, Xiujuan WANG, Heci ZHENG
 doi: 10.11999/JEIT190806
[Abstract](19) [FullText HTML](1) [PDF 1917KB](12)
Cyberspace is a collection of all information systems, which refers to the information environment for human survival. Cyberspace security has expanded from physical and information domain security to human-centered social and cognitive domain security. The research on human security has become an inevitable trend of Cyberspace security. The characteristics of human are complex and changeable. The personality, as a stable psychological characteristic, is an appropriate breakthrough point for human security research. This paper investigates and untangles relevant personality research in Cyberspace security. The concepts of Cyberspace security and personality are introduced concisely. A research framework of personality in Cyberspace security is proposed, including theoretical research, technological research and technological application. The technological research mainly includes three parts: personality measurement, personality vulnerability and protection methods in Cyber security. Additionally, this paper discusses the current research status and problems of personality in Cyber security in detail. Finally, the future research directions and development trends of personality in Cyber security are explored.
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2020, 42(9): 1-4.  
[Abstract](2) [PDF 212KB](0)
Overview of Cyber Security Threats and Defense Technologies for Energy Critical Infrastructure
Jianhua LI
2020, 42(9): 2065-2081.   doi: 10.11999/JEIT191055
[Abstract](145) [FullText HTML](96) [PDF 2787KB](20)
Energy critical infrastructure has undergone transformative rapid development in the context of the rapid development of information technology, and has been deeply integrated with new technologies such as Artificial Intelligence (AI), big data, and the Internet of Things. While information technology significantly improves the efficiency and performance of energy critical infrastructure, it also brings new types of security threats that are more persistent and covert. An urgent problem is how to establish a systematic and intelligent security defense system for energy critical infrastructure. This paper starts with the development trend of energy critical infrastructure, and analyzes the mechanism of the traditional and new security threat mechanisms it faces. On this basis, insightful analysis on the research status and evolution trends of defense technologies for energy critical infrastructures is made.
Android Malware Detection Based on Deep Learning: Achievements and Challenges
2020, 42(9): 2082-2094.   doi: 10.11999/JEIT200009
[Abstract](108) [FullText HTML](65) [PDF 471KB](14)
With the prosperous of Android applications, Android malware has been scattered everywhere, which raises the serious security risk to users. On the other hand, the rapid developing of deep learning fires the combat between the two sides of malware detection. Inducing deep learning technologies into Android malware detection becomes the hottest topic of society. This paper summarizes the existing achievements of malware detection from four aspects: Data collection, feature construction, network structure and detection performance. Finally, the current limitations and facing challenges followed by the future researches are discussed.
Domain-Independent Intelligent Planning Technology and Its Application to Automated Penetration Testing Oriented Attack Path Discovery
Yichao ZHANG, Tianyang ZHOU, Junhu ZHU, Qingxian WANG
2020, 42(9): 2095-2107.   doi: 10.11999/JEIT191056
[Abstract](156) [FullText HTML](112) [PDF 443KB](11)
Attack path discovery is an important research direction in automated penetration testing area. This paper introduces the research progress of domain independent intelligent planning technology and its application to the field of automated penetration testing oriented attack paths discovery. Firstly, the basic concept of attack path discovery is introduced and the related algorithms are divided into domain-specific and domain-independent intelligent planning based attack path discovery algorithms separately. Secondly, the domain-independent planning algorithms are classified into deterministic planning, uncertain planning and game planning, where each of which is described from principle, development and application aspect in detail. Thirdly, this paper summarizes the characteristics of automated penetration testing and compares the advantages and disadvantages of domain independent intelligent planning algorithms adopted in automated penetration testing. Lastly, the development of automated penetration testing oriented attack path discovery is prospected. It is hoped that this paper could contribute future research works on attack path discovery.
Protecting Android Native Code Based on Instruction Virtualization
Xiaohan ZHANG, Yuan ZHANG, Xinjian CHI, Min YANG
2020, 42(9): 2108-2116.   doi: 10.11999/JEIT191036
[Abstract](197) [FullText HTML](127) [PDF 3887KB](14)
Android system is now increasingly used in different kinds of smart devices, such as smart phones, smart watches, smart TVs and smart cars. Unfortunately, reverse attacks against Android applications are also emerging, which not only violates the intellectual right of application developers, but also brings security risks to end users. Existing Android application protection methods such as naming obfuscation, dynamic loading, and code hiding can protect Java code and native (C/C++) code, but are relatively simple and easy to be bypassed. A more promising method is to use instruction virtualization, but previous binary-based methods target specific architecture (x86), and cannot be applied to protect Android devices with different architectures. An architecture-independent instruction virtualization method is proposed, a prototype named Virtual Machine Packing Protection (VMPP) to protect Android native code is designed and implemented. VMPP includes a register-based fix-length instruction set, an interpreter to execute virtualized instructions, and a set of tool-chains for developers to use to protect their code. VMPP is tested on a large number of C/C++ code and real-world Android applications. The results show that VMPP can effectively protect the security of Android native code for different architectures with low overhead.
An Anti-cheat Method of Game Based on Windows Kernel Events
Jianming FU, Zheng YANG, Chenke LUO, Jianwei HUANG
2020, 42(9): 2117-2125.   doi: 10.11999/JEIT190695
[Abstract](242) [FullText HTML](162) [PDF 2646KB](7)
In view of many limitations of current client anti plug-in methods, an anti-cheat method based on kernel events is proposed, and the network game anti-cheat system called CheatBlocker is implemented. This method uses the kernel event monitoring provided by Windows to intercept the abnormal access between processes and the injection of abnormal modules. At the same time, the anti-cheat Dynamic Loaded Library (DLL) injected from the kernel can block the simulation of the mouse keyboard. The experimental results show that CheatBlocker can defend against process module injection cheating and user input simulation cheating, and has low performance overhead. Moreover, CheatBlocker does not need to modify the kernel data or code which ensures the integrity of the kernel and is more compatible than the current anti-cheat systems.
Method for Generating Malicious Code Adversarial Samples Based on Genetic Algorithm
Jia YAN, Chujiang NIE, Purui SU
2020, 42(9): 2126-2133.   doi: 10.11999/JEIT191059
[Abstract](190) [FullText HTML](112) [PDF 1580KB](13)
Machine learning is widely used in malicious code detection and plays an important role in malicious code detection products. Constructing adversarial samples for malicious code detection machine learning models is the key to discovering defects in malicious code detection models, evaluating and improving malicious code detection systems. This paper proposes a method for generating malicious code adversarial samples based on genetic algorithms. The generated samples combat effectively the malicious code detection model based on machine learning, while ensuring the consistency of the executable and malicious behavior of malicious code samples, and improving effectively the authenticity of the generated adversarial samples and the accuracy of the model adversarial evaluation are presented. The experiments show that the proposed method of generating adversarial samples reduces the detection accuracy of the MalConv malicious code detection model by 14.65%, and can directly interfere with four commercial machine-based malicious code detection engines in VirusTotal. Among them, the accuracy rate of Cylance detection is only 53.55%.
Research on Target Failure Link Location Method in Inter-domain Routing System Cascading Failure
Ziyi ZENG, Han QIU, Junhu ZHU, Qingxian WANG, Di CHEN
2020, 42(9): 2134-2141.   doi: 10.11999/JEIT200008
[Abstract](85) [FullText HTML](54) [PDF 1499KB](2)
Coordinated Cross Plane Session Termination (CXPST) repeatedly implements Low rate Denial of Service (LDoS) attacks on multiple target critical links, causing the cascading failure of the inter-domain routing system and the collapse of the internet. In the early stages of an attack, accurately locating the critical link under attack and carrying out targeted defense can prevent the occurrence of cascading failures. The research on existing locating methods is mainly based on the single-source hypothesis, and does not consider the impact of simultaneous failure of multiple target links on path withdrawal, so the locating accuracy is limited. To solve the above problems, a locating method is proposed based on Weighted Statistical Fit Score (WSFS). Using the target link selection strategy of cascading failure attack as inferring basis, scores are weighted by the reciprocal of the length of the withdrawal paths. The simulation results based on the actual network topology and vantage point location show that WSFS can improve the average accuracy rate by 5.45% compared with the current optimal method. Experimental results prove that WSFS is more suitable for locating target failure links in inter-domain routing system cascading failure than other locating methods.
Zero-knowledge Location Proof Based on Blockchain
Rongwei YU, Boxiao ZHOU, Lina WANG, Xinyan ZHU, Huihua XIE, Hongjun XIE
2020, 42(9): 2142-2149.   doi: 10.11999/JEIT191054
[Abstract](70) [FullText HTML](53) [PDF 1293KB](5)
Due to the proliferation of geographic location virtual software and the easy simulation or tampering of civil satellite positioning signals, it is difficult to realize the trusted authentication of geographic location. In view of the security risk of single-point failure in the existing location certification scheme using centralized architecture, a zero-knowledge location certification method based on blockchain is proposed, combining with zero knowledge certification protocol, to achieve a decentralized, privacy protected, highly accurate, review offset geographic location certification service, so as to ensure the accuracy of the location provided by users. This method not only ensures the confidentiality of the location data, but also proves that the location data can not tamper once it is linked. The results of the test analysis show that the average performance of the whole proving process is about 5 s/time, and the total time of proof generation and verification is 50.5~55.5 ms. Therefore, the algorithm has better performance overhead, which can meet the actual application requirements.
Blind Recognition of RS Codes Based on Soft Decision
Zhaojun WU, Limin ZHANG, Zhaogen ZHONG, Chuanhui LIU
2020, 42(9): 2150-2157.   doi: 10.11999/JEIT190690
[Abstract](1236) [FullText HTML](274) [PDF 2316KB](4)
To solve the problem that the existing algorithms for recognition of RS codes need to transform the code characters among different domains and poor performance, a new algorithm based on soft decision is proposed. Firstly, starting from the definition of RS codes, the equivalent conversion mode of the check relation of RS code from GF (2m) to GF (2) is given, which avoids the complex symbol transformation in different domains. Secondly, the average check conformity which can measure the validity of the check relationship is introduced and based on its statistical characteristics and minimax decision criteria, the possible code length and corresponding m-level primitive polynomials are traversed to match the initial code root, as the results, the code length and primitive polynomial are recognized. Finally, under the identified code length and the primitive polynomial, the GF (2m) is constructed, and the continuous code root matching decision is made, then the generation polynomial is recognized. The simulation results show that the derived statistical characteristics of the average check conformity are consistent with the actual situation, and the proposed algorithm can effectively recognize parameter under low Signal-to-Noise Ratio (SNR). At the same time, the proposed algorithm has good adaptability to low SNR. At SNR of 6 dB, the recognition rate of common RS codes in engineering can reach more than 90%. Compared with the existing methods, the performance of this algorithm is better than hard-decision algorithm, besides, it is improved by more than 1 dB compared by traditional algorithms.
Survivability Coordinated Mapping Based on Node Centrality and Spectrum Dispersion Awareness for Virtual Optical Networks
Huanlin LIU, Huixia HU, Yong CHEN, Meng WEN, Zhanpeng WANG
2020, 42(9): 2166-2172.   doi: 10.11999/JEIT190543
[Abstract](114) [FullText HTML](84) [PDF 1993KB](6)
The mapping strategy of virtual network has important effect on the resource availability and survivability of the Elastic Optical Network (EON). A survivable virtual optical network Coordinated Mapping based on the Distance and Spectrum Dispersion Awareness (CM-DSDA) between nodes is proposed in the paper. A physical node weighted sorting strategy is studied, which not only considers the number of physical node computing resources, but also considers the location centrality of the physical nodes in the EON topology. And a method of spectrum dispersion is designed to evaluate the link’s spectrum fragmentation. During the virtual link’s survivability mapping, the working and protection optical paths adjacent the position of the mapped physical nodes with the minimum number of spectrum usage and the lowest frequency spectrum dispersion are selected to coordinated mapping the virtual optical networks. Simulation results show that the CM-DSDA can effectively increase the EON’s spectrum utilization and reduce bandwidth blocking probability.
Deep Reinforcement Learning Based Migration Mechanism for Service Function Chain in Operator Networks
Zhuo CHEN, Gang FENG, Ying HE, Yang ZHOU
2020, 42(9): 2173-2179.   doi: 10.11999/JEIT190545
[Abstract](211) [FullText HTML](128) [PDF 1876KB](26)
To improve the service experience provided by the operator network, this paper studies the online migration of Service Function Chain(SFC). Based on the Markov Decision Process(MDP), modeling analysis is performed on the migration of multiple Virtual Network Functions(VNF) in SFC. By combining reinforcement learning and deep neural networks, a double Deep Q-Network(double DQN) based service function chain migration mechanism is proposed. This method can make online migration decisions and avoid over-estimation. Experimental result shows that when compared with the fixed deployment algorithm and the greedy algorithm, the double DQN based SFC migration mechanism has obvious advantages in end-to-end delay and network system revenue, which can help the mobile operator to improve the quality of experience and the efficiency of resources usage.
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
2020, 42(9): 2180-2185.   doi: 10.11999/JEIT190654
[Abstract](639) [FullText HTML](377) [PDF 1346KB](33)
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.
A Robust Secure Transmission Scheme Based onArtificial Noise for Resisting Active Eavesdropper in MIMO Heterogeneous Networks
Bo ZHANG, Kaizhi HUANG, Shengbin LIN, Ming YI, Yajun CHEN
2020, 42(9): 2186-2193.   doi: 10.11999/5EIT190649
[Abstract](180) [FullText HTML](116) [PDF 755KB](10)
To ensure the security of MIMO heterogeneous networks while facing the active multi-antenna eavesdropping, a robust secure transmission scheme based on artificial noise is proposed. Firstly, considering that the eavesdropper sends uplink pilot interference signal, the influence of uplink pilot interference on the channel estimation of legitimate users is studied. Then, based on the channel estimation results, the precoding matrix of downlink data and noise signals of macro base station and micro base station are designed, respectively. The expression of system security rate in this case is derived; After that, for maximizing the system security rate, the transmission power of downlink data and noise signal of base station are optimized, and a solution method based on one-dimensional linear search is proposed. Furthermore, considering the situation that the eavesdropper sends the uplink pilot interference and then sends the noise to interfere with the downlink communication of the legitimate user, a discrete zero-sum game method is proposed to obtain the optimal transmission power design. The simulation results verify the security and robustness of the proposed scheme.
An Improved Virtual Force Relocation Coverage Enhancement Algorithm
Fei ZHOU, Haotian GUO, Yi YANG
2020, 42(9): 2194-2200.   doi: 10.11999/JEIT190662
[Abstract](1080) [FullText HTML](227) [PDF 2260KB](17)
The most critical issue in the deployment of Mobile Wireless Sensor Networks (MWSN) is how to provide maximum regional coverage.To solve the problem that the existing coverage control algorithm has unsatisfactory coverage, low deployment efficiency and high energy consumption, an efficient deployment strategy is proposed.The first stage uses the Voronoi diagram to obtain the coverage hole of the entire network, and detects the uncovered area in the Voronoi polygon, and provides virtual force to drive the sensor movement, and uses the dynamic adjustment strategy to change the moving step size, thereby reducing energy loss;The second stage proposes a detection mechanism that uses a Delaunay triangulation to detect local coverage holes between sensors and repair them.The simulation results show that the algorithm accelerates the convergence speed while improving the network coverage, and provides a new solution for deploying mobile wireless sensor networks.
Feature-Aware D2D Content Caching Strategy
Jing YANG, Jinke LI
2020, 42(9): 2201-2207.   doi: 10.11999/JEIT190691
[Abstract](278) [FullText HTML](205) [PDF 925KB](15)
Device to Device (D2D) communications can effectively offload base station traffic. In D2D networks, the popular content is not only needs to be shared, but also the individual content is needs to be cached. In this paper, the problem of cache content selection is researched. A Content Social Value Prediction(CSVP) method based on feature perception is proposed. Value prediction can not only reduce latency, but also reduce the number of cache replacements and reduce cache costs. Firstly, the current value of content is calculated by combining user features and content features, and then future value is calculated through user social relationships. The small base station provides the user with a personalized content caching service according to the value of the content, and the base station selects a content with a larger value in the individual cache content of each small base station as popular content. Simulation results show that the caching strategy based on the proposed method can alleviate the base station traffic effectively, and reduce the delay by about 20%~40%.
MIMO Signal Modulation Recognition Algorithm Based on ICA and Feature Extraction
Tianqi ZHANG, Congcong FAN, Wanying GE, Tian ZHANG
2020, 42(9): 2208-2215.   doi: 10.11999/JEIT190320
[Abstract](317) [FullText HTML](183) [PDF 1577KB](33)
For blind modulation recognition of Multiple Input Multiple Output (MIMO) signals in non-cooperative communication, a modulation recognition method based on Independent Component Analysis (ICA) and feature extraction is proposed. According to the signal independence of each transmitting antenna in space division multiplexing MIMO system, the ICA algorithm is used to separate the transmitting signal from the received mixed signal. In order to realize modulation recognition under completely blind condition, the Minimum Description Length (MDL) criterion is used to estimate the number of transmitting antennas before ICA separation. After obtaining the transmitted signal, four characteristic parameters are constructed by using six-order cumulant, cyclic spectrum and fourth-power spectrum algorithm, and then the modulation type of the signal is identified by using hierarchical neural network classifier. The simulation results show that the proposed method can effectively recognize {2PSK, 2ASK, 2FSK, 4PSK, 4ASK, MSK, 8PSK, 16QAM} eight MIMO signals at low SNR. When the number of transmitting antennas is 2, the number of receiving antennas is 5 and the SNR is 2dB, the recognition rate can reach more than 98%.
Multi-User Detection Based on Sparsity Adaptive Matching Pursuit Compressive Sensing for Uplink Grant-free Non-Orthogonal Multiple Access
Qianzhu WANG, Dong FANG, Guangfu WU
2020, 42(9): 2216-2222.   doi: 10.11999/JEIT190505
[Abstract](175) [FullText HTML](93) [PDF 1754KB](9)
Grant-free Non-Orthogonal Multiple Access (NOMA) combined with Multi-User Detection (MUD) technology can meet the requirements of large connection volume, low signaling overhead and low latency transmission in massive Machine Type Communications (mMTC) scenarios. In the MUD algorithm based on Compressed Sensing (CS), the number of active users is often used as known information, but it is difficult to accurately estimate in the actual communication system. Based on this, this paper proposes a multi-user algorithm (Modified Sparsity Adaptive Matching Pursuit MUD, MSAMP-MUP) to improve the adaptive matching of sparsity. Firstly, the algorithm uses the generalized Dice coefficient matching criterion to select the atom that best matches the residual, and updates the user support set. When the residual energy is close to the noise energy, the iteration is terminated to obtain the final support set; Otherwise, the above criteria are used to update the user support set, and the estimation accuracy of the active users in the support set is improved. In the iteration process, different iteration steps are selected according to the ratio of the last two residual energies, so as to reduce the number of detection iterations. The simulation results show that, compared with the traditional CS-based MUD algorithm, the proposed algorithm reduces the bit error rate by about 9% and the number of iterations by about 10%.
Adaptive Incremental Kalman Filter Based on Innovation
Xiaojun SUN, Han ZHOU, Guangming YAN
2020, 42(9): 2223-2230.   doi: 10.11999/JEIT190493
[Abstract](113) [FullText HTML](87) [PDF 2792KB](14)
Under certain environmental conditions, the unknown system errors often occur and yield to larger filtering errors when the unverified or uncalibrated measurement equation is used. Incremental equation can be introduced, which can effectively solve the problem of state estimation for the systems under poor observation condition. In this paper, the linear discrete incremental system with unknown noise statistics is considered. Firstly, a noise statistics estimation algorithm is proposed based on innovation. The unbiased estimation of system noise statistics can be obtained. Furthermore, a new incremental system adaptive Kalman filtering algorithm is proposed. Compared with the existing adaptive incremental filtering algorithm, the state estimation accuracy of the proposed algorithm is higher. Two simulation examples prove its effectiveness and feasibility.
Estimation of Volume Target Length in Alpha Distribution Noise
Bin WANG, Yuesheng HOU
2020, 42(9): 2231-2238.   doi: 10.11999/JEIT190327
[Abstract](163) [FullText HTML](121) [PDF 2220KB](5)
In order to estimate the length of moving ship targets in Alpha stable distribution noise, a moving target length estimation method based on Generalized Time-Frequency Analysis (G-TFA) and least squares estimation is proposed, which utilizes nonlinear transform to suppress impulsive noise and Doppler effect to estimate target motion characteristics. The method uses G-TFA to obtain the Doppler frequency of moving targets in a stable distributed noise environment. Then, the least squares method is used to estimate the target speed and the closest point of approach time of different positions. Finally, the target length is calculated using the above estimation results. Taking Generalized Winger-Ville Distribution (G-WVD) as an example, the ability of G-TFA to extract Doppler features in Alpha stable distributed noise is theoretically derived. The robustness of the proposed method under low-to-medium mixed signal-to-noise ratio is verified by simulation experiments. Compared with the existing methods, the proposed method does not need to estimate the noise characteristic index, and the performance is better than the methods based on the traditional time-frequency analysis.
Study of Gaussianization Processing Based on Symmetric Alpha-stable Distribution Modeling
Pingbo WANG, Zhen DAI, Hongkai WEI
2020, 42(9): 2239-2245.   doi: 10.11999/JEIT190539
[Abstract](645) [FullText HTML](219) [PDF 3186KB](38)
Considering the requirement for weak signal detection in non-Gaussian background interference, after conceptions and ideas summarizing for Gaussianization processing and extended matched filter, two Gaussianizaiton filters and corresponding detections are proposed based on symmetric alpha-stable distribution modeling, comparing with those under Gaussian mixture modeling proposed earlier. All these Gaussianization filters and extended matched filters are realized in simulation. Their performances, such as Gaussianzing effect, detecting capability and running time are studies in system. Some conclusions are reached: Gaussianization can improve the detecting performance of the succeeding matched filter because of its restraining of big impulsive samples; Gaussianization filters under symmetric alpha-stable distribution modeling have higher operatiing efficiency while their peformance are similar to those under Gaussian mixture modeling.
A New Method of Anti-FM Slope Mismatch Jamming for Single Channel Synthetic Aperture Radar
Zhichao MENG, Jingyue LU, Shuaiqin ZHANG, Lei ZHANG, Hongxian WANG
2020, 42(9): 2246-2252.   doi: 10.11999/JEIT190687
[Abstract](646) [FullText HTML](256) [PDF 4043KB](27)
The problem that single-channel Synthetic Aperture Radar (SAR) can not effectively suppress FM slope mismatch jamming is studied in this paper. According to the difference between the jamming spectrum and the real echo spectrum, a method for suppressing the mismatch jamming of SAR FM slope based on matched filtering in the frequency domain is proposed. Based on the accurate measurement of the slope of jamming FM, the sparse super-resolution estimation of the jamming position is carried out to obtain a more accurate phase of jamming delay by utilizing the sparsity of the jamming signal and the characteristics of low Signal-to-Interference Ratio (SIR). Then, the spectrum of the interference signal is reconstructed by the obtained slope of interference frequency modulation and phase delay. Based on this, the orthogonal matched filter is designed to suppress the interference signal in the frequency domain and reconstruct the undistorted scene image. Finally, computer simulation experiments verify the effectiveness of the proposed method.
Study on 3D Imaging and Motion Compensation Algorithm for UWB-MIMO Through-wall Radar
Xin LIU, Kun YAN, Guangyao YANG, Shengbo YE, Qunying ZHANG, Guangyou FANG
2020, 42(9): 2253-2260.   doi: 10.11999/JEIT190356
[Abstract](267) [FullText HTML](144) [PDF 4471KB](22)
A motion compensation method based on Kirchhoff migration imaging algorithm for MIMO array is proposed. In the method, the traditional Kirchhoff intergral equation is introduced into the MIMO array and the forward Green function is replaced by the backward Green function to solve the problem of the large view field of the small array. The echo signal of the reference unit is directly recieved by adding a reference channel to estimate the position change information of the moving target, while the signal of the receiving units is sampled through the microwave switch time divition multiplexing. Then 3D imaging is performed after motion compensation for the data of the MIMO channel, which avoids effectively the problem of defocusing due to target migration and the deviation of the imaging result from the real position.
Sub-aperture Keystone Transform Based Echo Simulation Method for High-squint SAR with a Curve Trajectory
Gen LI, Yanheng MA, Jianqiang HOU, Gongguo XU
2020, 42(9): 2261-2268.   doi: 10.11999/JEIT190674
[Abstract](618) [FullText HTML](247) [PDF 2022KB](27)
The system transfer function of high-squint SAR system with curved trajectory has complex multi-dimensional spatial variability. The existing efficient frequency-domain echo simulation algorithms are difficult to achieve high-precision echo simulation of extended scenes. Therefore, a fast echo simulation method based on sub-aperture Keystone transform is proposed for maneuvering SAR. Based on the time-domain range compression function after range cell migration correction, the method calculates efficiently the range compression echo of scene, and then realizes the echo simulation of extended scene by high precision range inverse processing. In order to reduce the influence of residual range cell migration on the accuracy of echo simulation, the method of the Keystone transform of sub-aperture is used in range processing to achieve accurate correction of space-variant range cell migration in extended scenes. The theoretical analysis and simulation results show that the proposed method can quickly and accurately simulate the original echo data of extended scenes for high-squint SAR mounted on a maneuvering platform.
Deep Convolutional Neural Network for Parking Space Occupancy Detection Based on Non-local Operation
Xuanjing SHEN, Zhe SHEN, Yongping HUANG, Yu WANG
2020, 42(9): 2269-2276.   doi: 10.11999/JEIT190349
[Abstract](216) [FullText HTML](187) [PDF 3243KB](26)
With the intelligent development of urban traffic, accurate and efficient access to available parking spaces is essential to solve the increasingly difficult problem of parking difficulties. Therefore, this paper proposes a deep convolutional neural network parking occupancy detection algorithm based on non-local operation. For the image characteristics of parking spaces, non-local operations are introduced, the similarity between distant pixels is measured, and the high-frequency features of the edges are directly obtained. The local details are obtained by using small convolution kernels, and the network is trained in an end-to-end manner. In the experiment, the network structure is optimized by setting different convolution kernel sizes and non-local module layers. The experimental results show that compared with the traditional texture feature-based parking space occupancy detection algorithm, the proposed algorithm has significant advantages in both prediction accuracy and generalization performance of the model. At the same time, compared with the currently widely used convolutional neural network based on local feature extraction, the algorithm also has great advantages. In real scenes, the algorithm also has high precision and has practical application value.
RGBD Image Co-saliency Object Detection Based on Sample Selection
Zhengyi LIU, Junlei LIU, Peng ZHAO
2020, 42(9): 2277-2284.   doi: 10.11999/JEIT190393
[Abstract](269) [FullText HTML](162) [PDF 2427KB](16)
Co-saliency object detection aims to discover common and salient objects in an image group which contains two or more relevant images. In this paper, a method of using machine learning is proposed to detect co-saliency objects. Firstly, some simple images are selected to form a simple image set based on four scoring indicators. Secondly, positive and negative samples are extracted from the simple images set based on co-coherence characteristics, and high-dimensional semantic features are extracted by the deep learning model which receives RGBD four-channels input. Thirdly, the co-saliency classifier is trained by positive and negative samples, and co-saliency maps are generated by testing all the superpixels in the images by the co-saliency classifier. Finally, a smooth fusion operation is adopted to generate the final co-saliency map. Experimental results on the public benchmark dataset show that the proposed algorithm is superior to the state-of-the-art methods in terms of accuracy and efficiency, and it is robust.
Research on Rain Removal Method for Single Image Based on Multi-channel and Multi-scale CNN
Changyuan LIU, Qi WANG, Xiaojun BI
2020, 42(9): 2285-2292.   doi: 10.11999/JEIT190755
[Abstract](251) [FullText HTML](164) [PDF 8551KB](21)
Rainy days and other severe weather will seriously affect the image quality, thus affecting the performance of vision processing algorithms. In order to improve the imaging quality of rain images, a rain removal algorithm based on multi-channel multi-scale convolution neural network to extract rain line features is proposed. Firstly, the rain images are decomposed by wavelet threshold-guided bilateral filtering to obtain high-frequency rain line images and low-frequency background images with high contour preservation. Then, in order to make the rain line information in the high-frequency part of the image more obvious and reduce the background misjudgment in the high-frequency image during the rain line feature learning, the obtained high-frequency rain line image is passed through a filter again to obtain a higher-frequency rain line image with reduced background information and enhanced rain line information. Secondly, in view of the large amount of raindrop imprint left on the low-frequency background image, it is proposed to send the low-frequency background image and the higher-frequency rain line image together into the convolution neural network for feature learning, in which multi-scale feature information is extracted from the image, finally, a more complete restoration image with rain line removal is obtained. At the same time, when constructing the network model, hole convolution is used instead of standard convolution to extract the feature information of the image, thus obtaining richer image features and improving the rain removal performance of the algorithm. From the experimental results, after removing rain, the image is clear and the detail retention is high.
Light Field All-in-focus Image Fusion Based on Edge Enhanced Guided Filtering
Yingchun WU, Yumei WANG, Anhong WANG, Xianling ZHAO
2020, 42(9): 2293-2301.   doi: 10.11999/JEIT190723
[Abstract](125) [FullText HTML](83) [PDF 7626KB](4)
Affected by the micro-lens geometric calibration accuracy of the light field camera, the decoding error of the 4D light field in the angular direction will cause the edge information loss of the integrated refocused image, which will reduce the accuracy of the all-in-focus image fusion. In this paper, a light field all-in-focus image fusion algorithm based on edge-enhanced guided filtering is proposed. Through multi-scale decomposition of the digital refocused images and guided filtering optimization of the feature layer decision map, the final all-in-focus image is obtained. Compared with the traditional fusion algorithm, the edge information loss caused by the 4D light field calibration error is compensated in the presented method. In the step of multi-scale decomposition of the refocused image, the edge layer extraction is added to accomplish the high-frequency information enhancement. Then the multi-scale evaluation model is established to optimize the edge layer’s guided filtering parameters to obtain a better light field all-in-focus image. The experimental results show that the edge intensity and the perceptual sharpness of the all-in-focus image can be improved without significantly reducing the similarity between the all-in-focus image and the original image.
Method for Color-ring Resistor Detection and Localization in Printed Circuit Board Based on Convolutional Neural Network
Xiaoyan LIU, Zhaoming LI, Jiaxu DUAN, Tianyuan XIANG
2020, 42(9): 2302-2311.   doi: 10.11999/JEIT190608
[Abstract](291) [FullText HTML](213) [PDF 4673KB](14)
The color-ring resistor is one of the most commonly used electronic components in Printed Circuit Board (PCB). It is featured by sequential color rings, which often brings assembling errors, however. Manual detection of color-ring resistors has low efficiency and high false detection rate. Traditional image-based automatic detection methods have difficulties in dealing with PCB images under various illuminations, imaging distance and views. To solve this problem, an automatic detection and localization method for PCB color-ring resistor is proposed based on Convolution Neural Network (CNN). Firstly, the encoder-decoder CNN model is established and trained using weighted cross-entropy loss function. With CNN, color-ring resistors are segmented from PCB images with complex illumination and scenes. Secondly, each color-ring resistor is localized using minimum area bounding rectangle, and its position is adjusted to the vertical direction by affine transformation. Finally, the localization of color rings on the resistor is achieved by Gaussian template matching. The proposed method is tested and verified by 1270 PCB images, and the result is compared with that of the traditional method (method based on geometric contour, and method based on template matching). It is shown that the proposed method has obvious advantages in performance indices, including recall rate, precision, and intersection of unions, which can meet the requirements of practical applications.
4.5 bit Sub-stage Circuit for 14 bit 210 MS/s Charge-domain ADC
Yan XUE, Zongguang YU, Zhenhai CHEN, Jinghe WEI, Hongwen QIAN
2020, 42(9): 2312-2318.   doi: 10.11999/JEIT190592
[Abstract](85) [FullText HTML](52) [PDF 6922KB](3)
A 4.5 bit sub-stage circuit for high speed high precision charge domain pipelined Analog-to-Digital Converter (ADC) is proposed. Instead of the high-performance opamps used in traditional switched-capacitor pipelined ADCs, charge transfer and residue charge calculation is realized with Boosted Charge Transfer (BCT) circuit in the proposed 4.5 bit sub-stage. Therefore, the power consumption of the 4.5 bit sub-stage circuit can be reduced remarkably. The proposed 4.5 bit sub-stage circuit is used as the 1st stage circuit for a 14 bit 210 MS/s charge domain pipelined ADC and realized in a 1P6M 0.18 μm CMOS process. Test results show the 14 bit 210 MS/s ADC achieves the signal-to-noise ratio of 71.5 dBFS and the spurious free dynamic range of 85.4 dB, with 30.1 MHz input single tone signal at 210 MS/s, while the ADC core consumes the power consumption of 205 mW and occupies an area of 3.2 mm2.
A Steganography Algorithm in Encrypted Stereoscopic Video Based on Entropy Coding
Wei GAO, Gangyi JIANG, Mei YU, Ting LUO
2020, 42(9): 2158-2165.   doi: 10.11999/JEIT190345
[Abstract](246) [FullText HTML](129) [PDF 4706KB](7)

For security of stereoscopic video, a video encryption and information hiding algorithm based on entropy coding is proposed. Firstly, with the analysis of Multi-view Video Coding (MVC), the physical mechanism of error drift is investigated. By applying the stereoscopic masking effect, the frames to be encrypted and the frames to be embedded are determined. Secondly, by using a Context-based Adaptive Binary Arithmetic Coding (CABAC)  bin-string substitution technique, the encryption and data hiding of stereoscopic video are implemented. Experimental implementation reveals that the video stream has the format compatibility and the bit-rate remains unchanged after encryption and data hiding. The video quality degradation is negligible. The algorithm has significant advantages in computational complexity and rate increase.

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