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Published in: Chinese Journal of Electronics

(Volume: 31, Issue: 1, 05 January 2022)

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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Labeled Multi-Bernoulli Maneuvering Target Tracking Algorithm via TSK Iterative Regression Model
WANG Xiaoli, XIE Weixin, LI Liangqun
 doi: 10.1049/cje.2020.00.156
Abstract(92) HTML(43) PDF(12)
Aiming at the problem that the existing labeled multi-Bernoulli (LMB) method has a single and fixed model set, a LMB maneuvering target tracking algorithm via Takagi-Sugeno-Kang (TSK) iterative regression multiple model is proposed. In the TSK iterative regression modeling, the feature information of the targets is analyzed and represented by multiple semantic fuzzy sets. Then the state is expanded to introduce model information, thereby the adaptive multi-model idea is incorporated into the framework of the LMB method to solve the uncertain maneuverability of moving targets. Finally, the simulation results show that the proposed algorithm can effectively achieve maneuvering target tracking in the nonlinear system.
Binary Image Steganalysis Based on Symmetrical Local Residual Patterns
LUO Junwei, YU Mujian, YIN Xiaolin, LU Wei
 doi: 10.1049/cje.2020.00.414
Abstract(4) HTML(2) PDF(0)
Residual computation is an effective method for gray-scale image steganalysis. For binary images, the residual computation calculated by the XOR operation is also employed in the Local residual patterns (LRP) model for steganalysis. In this paper, a binary image steganalytic scheme based on Symmetrical local residual patterns (SLRP) is proposed. The symmetrical relationships among residual patterns are introduced that make the features more compact while reducing the dimensionality of the features set. Multi-scale windows are utilized to construct three SLRP submodels which are further merged to construct the final features set instead of a single model. What's more, SLRPs with higher probability to be modified after embedding are emphasized and selected to construct the feature sets for training the SVM classifier. Finally, experimental results show that the proposed steganalytic scheme is effective for detecting binary image steganography.
Quantum Attacks on Type-3 Generalized Feistel Scheme and Unbalanced Feistel Scheme with Expanding Functions
ZHANG Zhongya, WU Wenling, SUI Han, WANG Bolin
 doi: 10.1049/cje.2021.00.294
Abstract(38) HTML(17) PDF(3)
Quantum algorithms are raising concerns in the field of cryptography all over the world. A growing number of symmetric cryptography algorithms have been attacked in the quantum setting. Type-3 generalized Feistel scheme (GFS) and unbalanced Feistel scheme with expanding functions (UFS-E) are common symmetric cryptography schemes, which are often used in cryptographic analysis and design. We propose quantum attacks on the two Feistel schemes. For $ d $-branch Type-3 GFS and UFS-E, we propose distinguishing attacks on $(d+1)$-round Type-3 GFS and UFS-E in polynomial time in the quantum chosen plaintext attack (qCPA) setting. We propose key recovery by applying Grover's algorithm and Simon's algorithm. For $ r $-round $ d $-branch Type-3 GFS with $ k $-bit length subkey, the complexity is $O({2^{(d - 1)(r - d - 1)k/2}})$ for $r\ge d + 2$. The result is better than that based on exhaustive search by a factor ${2^{({d^2} - 1)k/2}}$. For $ r $-round $ d $-branch UFS-E, the attack complexity is $O({2^{(r - d - 1)(r - d)k/4}})$ for $d + 2 \le r \le 2d$, and $O({2^{(d - 1)(2r - 3d)k/4}})$ for $r > 2d$. The results are better than those based on exhaustive search by factors ${2^{(4rd - {d^2} - d - {r^2} - r)k/4}}$ and ${2^{3(d - 1)dk/4}}$ in the quantum setting, respectively.
Quantum Wolf Pack Evolutionary Algorithm of Weight Decision-Making Based on Fuzzy Control
LU Na, MA Long
 doi: 10.1049/cje.2021.00.217
Abstract(42) HTML(19) PDF(15)
In the traditional quantum wolf pack algorithm, the wolf pack distribution is simplified, and the leader wolf is randomly selected. This leads to the problems that the development and exploration ability of the algorithm is weak and the rate of convergence is slow. Therefore, a quantum wolf pack evolutionary algorithm of weight decision-making based on fuzzy control is proposed in this paper. First, to realize the diversification of wolf pack distribution and the regular selection of the leader wolf, a dual strategy method and sliding mode cross principle are adopted to optimize the selection of the quantum wolf pack initial position and the candidate leader wolf. Second, a new non-linear convergence factor is adopted to improve the leader wolf’s search direction operator to enhance the local search capability of the algorithm. Meanwhile, a weighted decision-making strategy based on fuzzy control and the quantum evolution computation method is used to update the position of the wolf pack and enhance the optimization ability of the algorithm. Then, a functional analysis method is adopted to prove the convergence of the quantum wolf pack algorithm, thus realizing the feasibility of the algorithm’s global convergence. The performance of the quantum wolf pack algorithm of weighted decision-making based on fuzzy control was verified through six standard test functions. The optimization results are compared with the standard wolf pack algorithm and the quantum wolf pack algorithm. Results show that the improved algorithm had a faster rate of convergence, higher convergence precision, and stronger development and exploration ability.
A Novel Robust Online Extreme Learning Machine for the Non-Gaussian Noise
GU Jun, ZOU Quanyi, DENG Changhui, WANG Xiaojun
 doi: 10.1049/cje.2021.00.122
Abstract(74) HTML(35) PDF(7)
Samples collected from most industrial processes have two challenges: one is contaminated by the non-Gaussian noise, and the other is gradually obsolesced. These features can obviously reduce the accuracy and generalization of models. To handle these challenges, a novel method, named the Robust Online Extreme Learning Machine (RO-ELM), is proposed in this paper. In the RO-ELM, the Least Mean $ p $-Power (LMP) criterion is employed as the cost function which is to boost the robustness of the ELM, and the forgetting mechanism is introduced to discard the obsolescence samples. To investigate the performance of the RO-ELM, experiments on artificial and real-world datasets with the non-Gaussian noise are performed, and the datasets are from regression or classification problems. Results show that the RO-ELM is more robust than the ELM, the OS-ELM and the FOS-ELM. The accuracy and generalization of the RO-ELM models are better than those of other models for online learning.
LaTLS: A Lattice-Based TLS Proxy Protocol
ZHANG Xinglong, CHENG Qingfeng, LI Yuting
 doi: 10.1049/cje.2018.00.357
Abstract(56) HTML(28) PDF(5)
The function of the Internet proxy is to check and convert the data exchanged between client and server. In fact, the two-party secure communication protocol with good security is turned into an unsafe multiparty protocol. At present, there are relatively few proxy protocols that can be applied in practice. This paper analyzes the classic agent protocol mcTLS and pointed out the security issues. We focus on the security of TLS 1.3 and proposed a lattice-based multi-party proxy protocol: LaTLS. LaTLS can be proved secure in the eCK model, it can resist key-sharing attacks,counterfeiting attacks, replay attacks, and achieve forward security. Compared with traditional DH and ECDH schemes, LaTLS is more effcient. At the same time, its security is based on the shortest vector problem, there for it has anti-quantum attack properties.
Joint Spectrum Sensing and Spectrum Access for Defending Massive SSDF Attacks: A Novel Defense Framework
XU Zhenyu, SUN Zhiguo, GUO Lili, Muhammad Zahid Hammad, Chintha Tellambura
 doi: 10.1049/cje.2021.00.090
Abstract(75) HTML(36) PDF(17)
Multiple secondary users (SUs) perform collaborative spectrum sensing (CSS) in cognitive radio networks to improve the sensing performance. However, this system severely degrades with spectrum sensing data falsification (SSDF) attacks from a large number of malicious secondary users, i.e., massive SSDF attacks. To mitigate such attacks, we propose a joint spectrum sensing and spectrum access framework. During spectrum sensing, each SU compares the decisions of CSS and Independent spectrum sensing (IndSS), and then the reliable decisions are adopted as its final decisions. Since the transmission slot is divided into several tiny slots, at the stage of spectrum access, each SU is assigned with a specific tiny time slot. In accordance with its independent final spectrum decisions, each node separately accesses the tiny time slot. Simulation results verify effectiveness of the proposed algorithm.
An Improved Navigation Pseudolite Signal Structure Based on the Kasami Sequences and the Pulsing Scheme
TAO Lin, SUN Junren, LI Guangchen, ZHU Bocheng
 doi: 10.1049/cje.2020.00.403
Abstract(147) HTML(62) PDF(11)
Pseudolites (PLs) are ground-based satellites, providing users with navigation solutions. However, implementation of the PL system leads to the near-far problem. In this paper, we proposed an improved navigation PL signal structure of combing kasami sequences and the pulsing scheme to mitigate the near-far effect. The pulse modulation method is adopted to ensure that the PLs transmit signals at different timeslots and reduce the PL signals’ mutual interference. Additionally, we employ the small set of kasami sequences with good cross-correlation properties to improve the anti-interference ability. A simulation test based on software is carried out to evaluate the performance of the proposed signal. The simulation proves that the improved PL signal has an impulsive power spectral density (PSD), makes it a feasible solution to mitigate the near-far effect, and performs better in the capture.
Adaptive Simplified Chicken Swarm Optimization Based on Inverted S-Shaped Inertia Weight
GU Yanchun, LU Haiyan, XIANG Lei, SHEN Wanqiang
 doi: 10.1049/cje.2020.00.233
Abstract(115) HTML(57) PDF(12)
Considering the issues of premature convergence and low solution accuracy in solving high-dimensional problems with the basic chicken swarm optimization algorithm (CSO), an Adaptive simplified chicken swarm optimization algorithm based on inverted S-shaped inertia weight (ASCSO-S) is proposed. Firstly, a simplified chicken swarm optimization algorithm (SCSO) is presented by removing all the chicks from the chicken swarm. Secondly, an inverted S-shaped inertia weight is designed and introduced into the updating process of the roosters and hens to dynamically adjust their moving step size and thus to improve the convergence speed and solution accuracy of the algorithm. Thirdly, in order to enhance the exploration ability of the algorithm, an adaptive updating strategy is added to the updating process of the hens. Simulation experiments on 21 classical test functions show that ASCSO-S is superior to the other comparison algorithms in terms of convergence speed, solution accuracy, and solution stability. In addition, ASCSO-S is applied to the parameter estimation of Richards model, and the test results indicate that ASCSO-S has the best fitting results compared with other three algorithms.
Knowledge Graph Completion Based on GCN of Multi-Information Fusion and High-Dimensional Structure Analysis Weight
NIU Haoran, HE Haitao, FENG Jianzhou, NIE Junlan, ZHANG Yangsen, REN Jiadong
 doi: 10.1049/cje.2021.00.080
Abstract(89) HTML(41) PDF(7)
Knowledge graph completion (KGC) can solve the problem of data sparsity in the knowledge graph. A large number of models for the KGC task have been proposed in recent years. However, the underutilisation of the structure information around nodes is one of the main problems of the previous KGC model, which leads to relatively single encoding information. To this end, a new KGC model that encodes and decodes the feature information is proposed. First, we adopt the subgraph sampling method to extract node structure. Moreover, the graph convolutional network (GCN) introduced the channel attention convolution encode node structure features and represent them in matrix form to fully mine the node feature information. Eventually, the high-dimensional structure analysis weight decodes the encoded matrix embeddings and then constructs the scoring function. The experimental results show that the model performs well on the datasets used.
Cryptanalysis of AEGIS-128
SHI Tairong, HU Bin, GUAN Jie, WANG Senpeng
 doi: 10.1049/cje.2020.00.231
Abstract(82) HTML(39) PDF(9)
AEGIS, an authenticated encryption algorithm designed by Wu and Preneel, is one of the six winners of the CAESAR competition. In this paper, we comprehensively investigate the existence of collision in the initialization of AEGIS-128 and evaluate the number of AES round functions involved in initialization, which reflects the resistance to differential attack. As a result, we find that there are 40 AES round functions, which is less than 50 ones claimed in the design document. We also prove that AEGIS-128 is strong enough to resist adversary who has access to partial state. In particular, we present a collision-based distinguisher and exploit it to recover the key of 4-step and 5-step (out of the full 10) AEGIS-128. The time and memory complexities are about ${2}^{58}$ and ${2}^{26}$ respectively. Specifically, we quantize the attack of 4-step AEGIS-128, in which we solve the technical issue of dealing with the function that does not fulfill Simon's promise. It is noted that the nonce is not reused in our work. Although we present some results of AEGIS-128 that exceed the existed analysis, the security margin of AEGIS-128 remains large.
An Edge-Cloud Collaborative Cross-Domain Identity-Based Authentication Protocol with Privacy Protection
SUN Haipeng, TAN Yu-an, LI Congwu, LEI Lei, ZHANG Qikun, HU Jingjing
 doi: 10.1049/cje.2021.00.269
Abstract(91) HTML(43) PDF(14)
Edge-cloud collaborative computing has a wide range of application scenarios. Resource sharing is one of the key technologies to realize various application scenarios. Identity authentication is an important means to ensure the security of resource sharing in various application scenarios. Because the edge-cloud collaborative application scenario is more complex, it involves collaborative operations among different security domains, frequently access and exit application system of mobile terminals. Traditional identity authentication is no longer suitable for complex application scenarios of edgecloud collaborative computing. Therefore, a cross-domain identity authentication protocol based on privacy protection is proposed. The main advantages of the protocol are as follows. 1) Self-certified key generation algorithm: the public/private key pair of the mobile terminal is generated by the terminal members themselves. The identity registration is realized through the correspondence between the self-authenticating public key and the identity to protect the privacy of the individual. It avoids security risks caused by third-party key distribution and key escrow; 2) Crossdomain identity authentication: the alliance keys are calculated among edge servers through blockchain technology. Each edge server uses the alliance keys to sign the identity information of terminals in its domain. Cross-domain identity authentication is realized through the signature authentication of the alliance domain. The cross-domain authentication process is simple and efficient; 3) Revocability of identity authentication: When the mobile terminal has logged off or exited the system, the legal identity of the terminal in the system will also become invalid immediately, so as to ensure the forward and backward security of accessing system resources. Under the hardness assumption of discrete logarithm problem (DLP) and computational Diffie-Hellman(CDH) problem, the security of the protocol is proven, and the efficiency of the protocol is verified.
Efficient 3D Hilbert Curve Encoding and Decoding Algorithms
JIA Lianyin, LIANG Binbin, LI Mengjuan, LIU Yong, CHEN Yinong, DING Jiaman
 doi: 10.1049/cje.2020.00.171
Abstract(143) HTML(67) PDF(18)
Hilbert curve describes a one-to-one mapping between multidimensional space and 1D space. Most traditional 3D Hilbert encoding and decoding algorithms work on order-wise manner and are not aware of the difference between different input data and spend equivalent computing costs on them, thus resulting in a low efficiency. To solve this problem, in this paper we design efficient 3D state views for fast encoding and decoding. Based on the state views designed, a new encoding algorithm (JFK-3HE) and a new decoding algorithm (JFK-3HD) are proposed. JFK-3HE and JFK-3HD can avoid executing iteratively encoding or decoding each order by skipping the first 0s in input data, thus decreasing the complexity and improving the efficiency. Experimental results show that JFK-3HE and JFK-3HD outperform the state-of-the-arts algorithms for both uniform and skew-distributed data.
A Fine-Grained Flash-Memory Fragment Recognition Approach for Low-Level Data Recovery
ZHANG Li, HAO Shengang, ZHANG Quanxin
 doi: 10.1049/cje.2020.00.206
Abstract(119) HTML(57) PDF(16)
Data recovery from flash memory in the mobile device can effectively reduce the loss caused by data corruption. Type recognition of data fragment is an essential prerequisite to the low-level data recovery. Previous works in this field classify data fragment based on its file type. Still, the classification efficiency is low, especially when the data fragment is a part of a composite file. We propose a fine-grained approach to classifying data fragment from the low-level flash memory to improve the classification accuracy and efficiency. The proposed method redefines flash-memory-page data recognition problem based on the encoding format of the data segment, and applies a hybrid machine learning algorithm to detect the data type of the flash page. The hybrid algorithm can significantly decompose the given data space and reduce the cost of training. The experimental results show that our method achieves better classification accuracy and higher time performance than the existing methods.
Probe Machine Based Computing Model for Maximum Clique Problem
CUI Jianzhong, YIN Zhixiang, TANG Zhen, YANG Jing
 doi: 10.1049/cje.2020.00.293
Abstract(67) HTML(31) PDF(8)
Probe Machine (PM) is a recently reported mathematic model with massive parallelism. Herein, we presented searching the maximum clique of an undirected graph with six vertices. We constructed data library containing n sublibraries, each sublibrary corresponded to a vertex in the given graph. Then, probe library according to the induced subgraph was designed in order to search and generate all maximal cliques. Subsequently, we performed probe operation, and all maximal cliques were generated in parallel. The advantages of the proposed model lie in two aspects. On one hand, solution to NP-complete problem is generated in just one step of probe operation rather than found in vast solution space. On the other hand, the proposed model is highly parallel. The work demonstrates that PM is superior to TM in terms of searching capacity when tackling NP-complete problem.
An Efficient Algebraic Solution for 3-D Moving Source Localization Using Quadruple Hybrid Measurements
DING Ting, ZHAO Yongsheng, ZHAO Yongjun
 doi: 10.1049/cje.2020.00.410
Abstract(109) HTML(51) PDF(25)
In this paper, we address the 3-D moving source localization using time difference of arrival (TDOA), frequency difference of arrival (FDOA), angle of arrival (AOA) and AOA rate measurements, gathered from an array of spatially distributed receivers. The TDOA, FDOA, AOA and AOA rate measurement equations were firstly established according to the space geometric relationship of the source relative to the receivers. Then an efficient closed-form algorithm for moving source localization was proposed, using the quadruple hybrid measurements. The proposed algorithm transforms the nonlinear measurement equations to a set of linear equations, from which the source position and velocity estimate can be obtained by applying weighted least square (WLS) minimization. In contrast to existing two-stage WLS algorithms, the proposed algorithm does not introduce any nuisance parameters and requires only one-stage, which allows for source localization with minimum number of receivers. Theoretical accuracy analysis indicates that the proposed algorithm reaches the Cramer-Rao lower bound, and simulation studies corroborate the efficiency and superiority of the proposed algorithm over existing algorithms.
Secure Beamforming in Downlink MISO Non-orthogonal Multiple Access Symbiotic Radio Systems
LI Yiqing, JIANG Miao
 doi: 10.1049/cje.2020.00.187
Abstract(143) HTML(67) PDF(23)
Symbiotic radio (SR) networks are possible solutions to the future low-power wireless communications for massive Internet of Things devices. In this paper, we investigate a multiple-input-single-output Non-orthogonal multiple access (NOMA) Backscatter device (BD) aided SR network with a potential eavesdropper. In the network, a Base station (BS) broadcasts signals to a central user and a cell-edge user using the NOMA protocol. With ambient backscatter modulation, the BD transmits its own messages to the central user over incident signals from the BS. We propose a constrained concave-convex procedure-based algorithm which maximizes the outage secrecy rate from the BD to the central user under the achievable secrecy rate constraints from the BS to the central and cell-edge users. Simulation results illustrate that our proposed network achieves a much larger secrecy rate region than the orthogonal multiple access network.
Learning to Combine Answer Boundary Detection and Answer Re-ranking for Phrase-Indexed Question Answering
WEN Liang, SHI Haibo, ZHANG Xiaodong, SUN Xin, WEI Xiaochi, WANG Junfeng, CHENG Zhicong, YIN Dawei, WANG Xiaolin, LUO Yingwei, WANG Houfeng
 doi: 10.1049/cje.2021.00.079
Abstract(130) HTML(63) PDF(11)
Phrase-indexed question answering (PIQA) seeks to improve the inference speed of question answering (QA) models by enforcing complete independence of the document encoder from the question encoder, and it shows that the constrained model can achieve significant efficiency at the cost of its accuracy. In this paper, we aim to build a model under the PIQA constraint while reducing its accuracy gap with the unconstrained QA models. We propose a novel framework—AnsDR, which consists of an answer boundary detector (AnsD) and an answer candidate ranker (AnsR). More specifically, AnsD is a QA model under the PIQA architecture and it is designed to identify the rough answer boundaries; and AnsR is a lightweight ranking model to finely re-rank the potential candidates without losing the efficiency. We perform the extensive experiments on public datasets. The experimental results show that the proposed method achieves the state of the art on the PIQA task.
Predicting Microbe-disease Association Based on Heterogeneous Network and Global Graph Feature Learning
WANG Yueyue, LEI Xiujuan, PAN Yi
 doi: 10.1049/cje.2020.00.212
Abstract(122) HTML(60) PDF(27)
Numerous microbes inhabit human body, making a vast difference in human health and disease. Therefore, understanding associations between microbes and diseases is beneficial to disease prevention and treatment. In this study, we develop a prediction method called HNGFL by Learning global graph feature on the heterogeneous network. Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple similarities. For microbe similarity network, on the basis of Gaussian interaction profile (GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, we use an embedding algorithm called GraRep to learn global structural information for this network. According to vector feature of every node, we utilize Support Vector Machine (SVM) classifier to calculate the relevance for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.
Backdoor Attacks on Image Classification Models in Deep Neural Networks
ZHANG Quanxin, MA Wencong, WANG Yajie, ZHANG Yaoyuan, SHI Zhiwei, LI Yuanzhang
 doi: 10.1049/cje.2021.00.126
Abstract(342) HTML(168) PDF(34)
Deep neural network (DNN) is applied widely in many applications and achieves state-of-the-art performance. However, DNN lacks transparency and interpretability for users in structure. Attackers can use this feature to embed trojan horses in the DNN structure, such as inserting a backdoor into the DNN, so that DNN can learn both the normal main task and additional malicious tasks at the same time. Besides, DNN relies on data set for training. Attackers can tamper with training data to interfere with DNN training process, such as attaching a trigger on input data. Because of defects in DNN structure and data, the backdoor attack can be a serious threat to the security of DNN. The DNN attacked by backdoor performs well on benign inputs while it outputs an attacker-specified label on trigger attached inputs. Backdoor attack can be conducted in almost every stage of the machine learning pipeline. Although there are a few researches in the backdoor attack on image classification, a systematic review is still rare in this field. This paper is a comprehensive review of backdoor attacks. According to whether attackers have access to the training data, we divide various backdoor attacks into two types: poisoning-based attacks and non-poisoning-based attacks. We go through the details of each work in the timeline, discussing its contribution and deficiencies. We propose a detailed mathematical backdoor model to summary all kinds of backdoor attacks. In the end, we provide some insights about future studies.
Internet of Brain, Thought, Thinking, and Creation
ZHANG Zhimin, YIN Rui, NING Huansheng
 doi: 10.1049/cje.2021.00.236
Abstract(115) HTML(55) PDF(15)
Thinking space came into being with the emergence of human civilization. With the emergence and development of cyberspace, the interaction between those two spaces began to take place. In the collision of thinking and technology, new changes have taken place in both thinking space and cyberspace. To this end, this paper divides the current integration and development of thinking space and cyberspace into three stages, namely Internet of brain (IoB), Internet of thought (IoTh), and Internet of thinking (IoTk). At each stage, the contents and technologies to achieve convergence and connection of spaces are discussed. Besides, the Internet of creation (IoC) is proposed to represent the future development of thinking space and cyberspace. Finally, a series of open issues are raised, and they will become thorny factors in the development of the IoC stage.
A Comprehensive Study on the Theory of Graphene Solution-Gated Field Effect Transistor: Simulations and Experiments
HU Shihui, ZHANG Jizhao, WANG Zhongrong, JIA Yunfang
 doi: 10.1049/cje.2021.00.032
Abstract(85) HTML(39) PDF(14)
Graphene solution-gated field effect transistors (G-SgFETs) have been widely developed in the field of biosensors, but deficiencies in their theories still exist. A theoretical model for G-SgFET, including the three-terminal equivalent circuit model and the numerically calculating method, is proposed by the comprehensive analyses of the graphene-liquid interface and the FET principle. Not only the applied voltages on the electrode-pairs of gate-source and drain-source, but also the nature of graphene and its derivatives are considered by analysing their influences on the Fermi level, the carriers’ concentration and mobility, which may consequently affect the output drain-source current. To verify whether it is available for G-SgFETs based on different method prepared graphene, three kinds of graphene materials which are liquid-phase exfoliated graphene (LEG), reduced graphene oxide (rGO), and tetra (4-Aminophenyl) porphyrin hybridized rGO (TAP/rGO) are used as examples. The coincidences of calculated output and transfer feature curves with the measured ones are obtained to confirm its adaptivity for simulating the basic G-SgFETs’ electric features, by modulating Fermi level and mobility. Furthermore, the model is exploited to simulate G-SgFETs’ current responding to the biological functionalization with aptamer and the detections for circulating tumor cells, as a proof-of-concept. The calculated curren t changes are compared with the experimental results, to verify the proposed G-SgFETs’ model is also suitable for mimicking the bio-electronic responding, which may give a preview of some conceived G-SgFETs’ biosensors and improve the design efficiency.
Statistical Model on CRAFT
WANG Caibing, GUO Hao, YE Dingfeng, WANG Ping
 doi: 10.1049/cje.2021.00.092
Abstract(234) HTML(119) PDF(17)
Many cryptanalytic techniques for symmetric-key primitives rely on specific statistical analysis to extract some secrete key information from a large number of known or chosen plaintext-ciphertext pairs. For example, there is a standard statistical model for differential cryptanalysis that determines the success probability and complexity of the attack given some predefined configurations of the attack. In this work, we investigate the differential attack proposed by Guo et al. at Fast Software Encryption Conference 2020 and find that in this attack, the statistical behavior of the counters for key candidates deviate from standard scenarios, where both the correct key ${\boldsymbol{k}}$ and ${\boldsymbol{k \oplus XXX}}$ are expected to receive the largest number of votes. Based on this bimodal behavior, we give three different statistical models for truncated differential distinguisher on CRAFT (a cryptographic algorithm proposed by Beierle et al. in IACR Transactions on Symmetric Cryptology in 2019) for bimodal phenomena. Then, we provide the formulas about the success probability and data complexity for different models under the condition of a fixed threshold value. Also, we verify the validity of our models for bimodal phenomena by experiments on round-reduced of the versions distinguishers on CRAFT. We find that the success probability of theory and experiment are close when we fix the data complexity and threshold value. Finally, we compare the three models using the mathematical tool Matlab and conclude that Model 3 has better performance.
Based on Weight and User Feedback: A Novel Trustworthiness Measurement Model
ZHOU Wei, MA Yanfang, PAN Haiyu
 doi: 10.1049/cje.2020.00.391
Abstract(390) HTML(191) PDF(30)
Software trustworthiness is an important criterion for evaluating software quality. In component-based software, different components play different roles and different users give different grades of trustworthiness after using the software. These elements will both affect the trustworthiness of software. When the software quality is evaluated comprehensively, it is necessary to consider the weight of component and user feedback. According to different construction of components, the different trustworthiness measurement models are established based on the weight of components and user feedback. Algorithms of these trustworthiness measurement models are designed in order to obtain the corresponding trustworthiness measurement value automatically. The feasibility of these trustworthiness measurement models is demonstrated by a train ticket purchase system.
WCM-WTrA: A Cross-Project Defect Prediction Method Based on Feature Selection and Distance-Weight Transfer Learning
LEI Tianwei, XUE Jingfeng, WANG Yong, NIU Zequn, SHI Zhiwei, ZHANG Yu
 doi: 10.1049/cje.2021.00.119
Abstract(222) HTML(103) PDF(27)
Cross-project defect prediction is a hot topic in the field of defect prediction. How to reduce the difference between projects and make the model have better accuracy is the core problem. This paper starts from two perspectives: feature selection and distance-weight instance transfer. We reduce the differences between projects from the perspective of feature engineering and introduce the transfer learning technology to construct a cross-project defect prediction model WCM-WtrA and multi-source model Multi-WCM-WTrA. We have tested on AEEEM and ReLink datasets, and the results show that our method has an average improvement of 23% compared with TCA + algorithm on AEEEM datasets, and an average improvement of 5% on ReLink datasets.
Investigation and Comparison of 5G Channel Models: From QuaDRiGa, NYUSIM, and MG5G Perspectives
PANG Lihua, ZHANG Jin, ZHANG Yang, et al.
2022, 31(1): 1-17.   doi: 10.1049/cje.2021.00.103
Abstract(411) HTML(199) PDF(50)
This paper investigates and compares three channel models for the fifth generation (5G) wireless communications: the quasi deterministic radio channel generator (QuaDRiGa), the NYUSIM channel simulator model developed by New York University, and the more general 5G (MG5G) channel model. First, the characteristics of the modeling processes of the three models are introduced from the perspective of model framework. Then, the small-scale parameter modeling strategies of the three models are compared from space/time/frequency domains as well as polarization aspect. In particular, the drifting of small-scale parameters is introduced in detail. Finally, through the simulation results of angular power spectrum, Doppler power spectrum density, temporal autocorrelation function, power delay profile, frequency correlation function, channel capacity, and eigenvalue distribution, the three models are comprehensively investigated. According to the simulation results, we clearly analyze the impact of the modeling strategy on the three channel models and give certain evaluations and suggestion which lay a solid foundation for link and system-level simulations for 5G transmission algorithms.
A 2 Kbits Low Power EEPROM for Passive RFID Tag IC
HU Jianguo, WANG Deming, WU Jing
2022, 31(1): 18-24.   doi: 10.1049/cje.2021.00.044
Abstract(102) HTML(46) PDF(22)
This paper presents a low power consumption and low cost electrically erasable programmable read-only memory (EEPROM) for radio frequency identification (RFID) tag chip. A read-write circuit with parallel input and serial output is proposed. Only one sensitive amplifier is used to read the data in memory, which can effectively reduce the power consumption of the read operation. Because the tag may be read or written while moving, the internal voltage may change in a wide range. Therefore, this paper designs a charge pump and its control circuit with wide voltage working range. The proposed EEPROM is integrated into an RFID tag chip and fabricated using a 180 nm complementary metal-oxide semiconductor (CMOS) process. Experimental results show that the circuit can work in the input voltage range of 1 V to 1.8 V, and the minimum current of read operation and write operation is 0.68 μ A and 30 μ A respectively, which has the characteristic of low power consumption.
A Fast Two-Tone Active Load-Pull Algorithm for Assessing the Non-linearity of RF Devices
SU Jiangtao, CAI Jialing, ZHENG Xing, et al.
2022, 31(1): 25-32.   doi: 10.1049/cje.2020.00.060
Abstract(156) HTML(76) PDF(24)
Radio frequency (RF) devices used in modern wireless systems must meet increasingly complicated spectral constraints while still operating with high power efficiency. A fast real-time two-tone active load-pull algorithm is proposed to assess the relationship between nonlinear performance with associated device load impedance variations. This algorithm employs real time measurement data to extract two-tone local nonlinear behaviour model, which is further used for the prediction of injected signal value in the active real-time load-pull system, therefore minimizing the number of iterations required for load emulation. The proposed method was validated on a real two-tone load-pull measurement bench using off-the-shelf instruments. The result shows that the measurement speed has been greatly increased without sacrifice of the impedance emulation accuracy. This intelligent two-tone load-pull algorithm is expected to be applied in the designing of modern communication system and radar transmitters, as well as the validation of the models of radio frequency transistors.
A 2nd-Order Noise Shaping SAR ADC Realizing NTF Zero-Pole Optimization Based on IIR-FIR Filter
ZHANG Yanbo, LIU Shubin, LIANG Yuhua, et al.
2022, 31(1): 33-39.   doi: 10.1049/cje.2020.00.309
Abstract(141) HTML(62) PDF(30)
This paper reports a successive approximation register (SAR) analog-to-digital converters (ADCs) with the 2nd-order and optimized noise shaping (NS) scheme. Based on the feed-forward structure, both noise transfer function (NTF) zero and pole optimization have been achieved simultaneously utilizing a cascaded IIR-FIR (infinite impulse response and finite impulse response) filter. The analysis shows that the proposed structure provides a better NS efficiency than prior arts. The efficiency of the proposed NS scheme and the ADC performance are verified by simulation achieving a 14.2 effective number of bits with an 8-bit SAR ADC architecture at an over sampling rate of 8 sampled at 100 MS/s. Taking advantage of the NTF zero-pole optimization, a low resolution SAR ADC is allowed under the same quantization noise budget in this structure.
PALES: Optimizing Secure Data Deletion in SSDs via Page Group and Reprogram Speedup
NIE Shiqiang, WU Weiguo, ZHANG Chi, et al.
2022, 31(1): 40-51.   doi: 10.1049/cje.2020.00.379
Abstract(285) HTML(134) PDF(26)
As solid-state drives (SSD) have been widely adopted, secure data deletion becomes an essential component for ensuring user privacy, preventing sensitive data leakage. Due to the erase-before-write property, erasure operation and scrub operation substituting for overwriting technologies are proposed to meet the requirements. However, both methods bring the severe page-copying issue, declining read/write performance, and shortening the lifetime of SSD. In this paper, this issue is alleviated by reserving pages at the page allocation stage to mitigate program disturbance and increasing program step voltage during reprogramming operation to reduce the reprogram latency. The proposed scheme is evaluated by a series of experiments; the results show that the proposed scheme could achieve significant deletion time reduction and alleviate page-copying overhead.
A Programmable Pre-emphasis Technique with Combined RLC Source Degeneration for High-Speed Serial Link Transmitters
WANG Tonghui, ZOU Jiaxuan, QI Huanhuan, et al.
2022, 31(1): 52-58.   doi: 10.1049/cje.2021.00.055
Abstract(99) HTML(44) PDF(9)
This paper presents a clock-less programmable pre-emphasis technique realized by a driver with combined resistive-inductive-capacitive source degeneration for high-speed serial link transmitters. The addition of a series inductive-capacitive resonance network expands the bandwidth of the driver without lowering down the pre-emphasis gain. The driver with the proposed pre-emphasis technique provides adjustable gain from mid-frequency to high-frequency which is controlled by a tunable tail current, offering the capability for the transmitter to adapt to different cable loss from 0 to 6 dB. The driver has been employed in a 2:4 multiplexing and cable driving integrated circuit for 1.65 Gbps high-definition-multimedia-interface and digital-visual-interface application to drive up to 7 m 24-American-wire-gauge cable. Fabricated in 180 nm SiGe BiCMOS technology, the transmitter consumes 68.6 mW for 6 dB pre-emphasis under 3.3 V power supply.
Distinguishing Between Natural and GAN-Generated Face Images by Combining Global and Local Features
CHEN Beijing, TAN Weijin, WANG Yiting, et al.
2022, 31(1): 59-67.   doi: 10.1049/cje.2020.00.372
Abstract(507) HTML(245) PDF(67)
With the development of face image synthesis and generation technology based on generative adversarial networks (GANs), it has become a research hotspot to determine whether a given face image is natural or generated. However, the generalization capability of the existing algorithms is still to be improved. Therefore, this paper proposes a general algorithm. To do so, firstly, the learning on important local areas, containing many face key-points, is strengthened by combining the global and local features. Secondly, metric learning based on the ArcFace loss is applied to extract common and discriminative features. Finally, the extracted features are fed into the classification module to detect GAN-generated faces. The experiments are conducted on two publicly available natural datasets (CelebA and FFHQ) and seven GAN-generated datasets. Experimental results demonstrate that the proposed algorithm achieves a better generalization performance with an average detection accuracy over 0.99 than the state-of-the-art algorithms. Moreover, the proposed algorithm is robust against additional attacks, such as Gaussian blur, and Gaussian noise addition.
Differential Fault Analysis on 3DES Middle Rounds Based on Error Propagation
MA Xiangliang, ZHANG Lizhen, WU Liji, et al.
2022, 31(1): 68-78.   doi: 10.1049/cje.2021.00.117
Abstract(211) HTML(100) PDF(16)
Since differential fault analysis (DFA) was first implemented on data encryption standard (DES), many scholars have improved this attack and extended the limit of the original last two rounds to the earlier rounds. However, the performance of the novel attacks which target middle rounds is not effective, i.e. the number of correct/incorrect ciphertexts required is very large and the recovered result maybe not correct. We address this problem by presenting new DFA methods that can break 3DES when injecting faults at round 12 or 13. By simulating the process of single-bit error propagation, we have built two kinds of error propagation models as well as an intermediate error propagation state table. Then we simplify the intermediate states into state templates that will be further used to locate the injected fault position, which is the main difficulty of implementing fault injection in the middle rounds. Finally, in terms of the idea of error propagation and probability theory, we can recover the last round key only using 2 sets of correct/incorrect ciphertexts when inducting fault in the 13th round and 4 sets of correct/incorrect ciphertexts when inducting fault in the 12th round.
Standard Analysis for Transfer Delay in CTCS-3
CAO Yuan, MA Lianchuan, XIAO Shuo, ZHANG Xia, XU Wei
2017, 26(5): 1057-1063.   doi: 10.1049/cje.2017.08.024
[Abstract](326) [PDF 634KB](368)
According to the standard for the GSM for railway (GSM-R) wireless systems in China train control system level 3 (CTCS-3), the control data transfer delay should be no larger than 500ms with greater than 99% probability. Coverage of both non-redundant networks and intercross redundant networks and cases of single Mobile terminals (MTs) and redundant MTs on one train are considered, and the corresponding vehicle-ground communication models, delay models, and fault models are constructed. The simulation results confirm that the transfer delay can meet the standard requirements under all cases. In particular, the probability is greater than 99.996% for redundant MTs and networks, and the standard of transfer delay in CTCS-3 will be improved inevitably.
A Survey on Emerging Computing Paradigms for Big Data
ZHANG Yaoxue, REN Ju, LIU Jiagang, XU Chugui, GUO Hui, LIU Yaping
2017, 26(1): 1-12.   doi: 10.1049/cje.2016.11.016
[Abstract](405) [PDF 1424KB](2643)
The explosive growth of data volume and the ever-increasing demands of data value extraction have driven us into the era of big data. The "5V" (Variety, Velocity, Volume, Value, and Veracity) characteristics of big data pose great challenges to traditional computing paradigms and motivate the emergence of new solutions. Cloud computing is one of the representative technologies that can perform massive-scale and complex data computing by taking advantages of virtualized resources, parallel processing and data service integration with scalable data storage. However, as we are also experiencing the revolution of Internet-of-things (IoT), the limitations of cloud computing on supporting lightweight end devices significantly impede the flourish of cloud computing at the intersection of big data and IoT era. It also promotes the urgency of proposing new computing paradigms. We provide an overview on the topic of big data, and a comprehensive survey on how cloud computing as well as its related technologies can address the challenges arisen by big data. Then, we analyze the disadvantages of cloud computing when big data encounters IoT, and introduce two promising computing paradigms, including fog computing and transparent computing, to support the big data services of IoT. Finally, some open challenges and future directions are summarized to foster continued research efforts into this evolving field of study.
Clustering by Fast Search and Find of Density Peaks with Data Field
WANG Shuliang, WANG Dakui, LI Caoyuan, LI Yan, DING Gangyi
2016, 25(3): 397-402.   doi: 10.1049/cje.2016.05.001
[Abstract](513) [PDF 6951KB](2517)
A clustering algorithm named "Clustering by fast search and find of density peaks" is for finding the centers of clusters quickly. Its accuracy excessively depended on the threshold, and no efficient way was given to select its suitable value, i.e., the value was suggested be estimated on the basis of empirical experience. A new way is proposed to automatically extract the optimal value of threshold by using the potential entropy of data field from the original dataset. For any dataset to be clustered, the threshold can be calculated from the dataset objectively instead of empirical estimation. The results of comparative experiments have shown the algorithm with the threshold from data field can get better clustering results than with the threshold from empirical experience.
Optimization of Information Interaction Protocols in Cooperative Vehicle-Infrastructure Systems
ZHANG Yuzhuo, CAO Yuan, WEN Yinghong, LIANG Liang, ZOU Feng
2018, 27(2): 439-444.   doi: 10.1049/cje.2017.10.009
[Abstract](155) [PDF 566KB](324)
This research investigate the information interaction protocols for Cooperative vehicleinfrastructure systems (CVIS) safety-related services and optimizes them in three aspects. It puts forward a selfadaptive back-off algorithm. This algorithm considers retransmission times and network busy degree to choose a suitable contention window. A mathematical analysis model is developed to verify its performance improvement. Finally, different scenario models of Vehicle ad hoc network (VANET) are simulated through the network simulation tool and the influences of different access modes on Quality of Service (QoS) are investigated. The simulation results have verified the improvement of the proposed algorithm is obvious and RTS/CTS access mode can sacrifice slight delay for great improvement of packet lost rate when there are large amount of vehicle nodes.
Research on Link Quality Estimation Mechanism for Wireless Sensor Networks Based on Support Vector Machine
SHU Jian, LIU Song, LIU Linlan, ZHAN Liqin, HU Gang
2017, 26(2): 377-384.   doi: 10.1049/cje.2017.01.013
[Abstract](178) [PDF 1133KB](507)
In the application of Wireless sensor networks (WSNs), effective estimation for link quality is a basic issue in guarantying reliable data transmission and upper network protocol performance. A link quality estimation mechanism is proposed, which is based on Support vector machine (SVM) with multi-class classification. Under the analysis of the wireless link characteristics, two physical parameters of communication, Receive signal strength indicator (RSSI) and Link quality indicator (LQI), are chosen as estimation parameters. The link quality is divided into five levels according to Packet reception rate (PRR). A link quality estimation model based on SVM with decision tree is established. The model is built on kernel functions of radial basis and polynomial respectively, in which RSSI, LQI are the input parameters. The experimental results show that the model is reasonable. Compared with the recent published link quality estimation models, our model can estimate the current link quality accurately with a relative small number of probe packets, so that it costs less energy consumption than the one caused by sending a large number of probe packets. So this model which is high efficiency and energy saving can prolong the network life.
Optimal Network Function Virtualization and Service Function Chaining: A Survey
MIRJALILY Ghasem, LUO Zhiquan
2018, 27(4): 704-717.   doi: 10.1049/cje.2018.05.008
[Abstract](406) [PDF 827KB](891)
Network function virtualization (NFV) and Service function chaining (SFC) can fulfill the traditional network functions by simply running special softwares on general-purpose computer servers and switches. This not only provides significantly more agility and flexibility in network service deployment, but can also greatly reduce the capital and operating cost of networks. In this paper, a comprehensive survey on the motivations and state of the art efforts towards implementing the NFV and SFC is provided. In particular, the paper first presents the main concepts of these new emerging technologies; then discusses in details various stages of SFC, including the description, composition, placement and scheduling of service chains. Afterwards, existing approaches to SFC are reviewed according to their application environments, parameters used, and solution strategies. Finally, the paper points out a number of future research directions.
A Text Sentiment Classification Modeling Method Based on Coordinated CNN-LSTM-Attention Model
ZHANG Yangsen, ZHENG Jia, JIANG Yuru, HUANG Gaijuan, CHEN Ruoyu
2019, 28(1): 120-126.   doi: 10.1049/cje.2018.11.004
[Abstract](247) [PDF 1983KB](1051)
The major challenge that text sentiment classification modeling faces is how to capture the intrinsic semantic, emotional dependence information and the key part of the emotional expression of text. To solve this problem, we proposed a Coordinated CNNLSTM-Attention(CCLA) model. We learned the vector representations of sentence with CCLA unit. Semantic and emotional information of sentences and their relations are adaptively encoded to vector representations of document. We used softmax regression classifier to identify the sentiment tendencies in the text. Compared with other methods, the CCLA model can well capture the local and long distance semantic and emotional information. Experimental results demonstrated the effectiveness of CCLA model. It shows superior performances over several state-of-the-art baseline methods.
Performance Evaluation with Improved Receiver Design for Asynchronous Coordinated Multipoint Transmissions
CAO Yuan, WEN Yinghong, MENG Xiangyang, XU Wei
2016, 25(2): 372-378.   doi: 10.1049/cje.2016.03.026
[Abstract](189) [PDF 411KB](1128)
Joint transmission is one of the major transmission schemes in Coordinated multipoint (CoMP) transmission/reception systems for Long term evolutionadvanced (LTE-A). Due to different distances between User equipments (UE) and Base stations (BS), signals are not able to arrive at the receiver with perfect synchronization, which implies that the reception at UE is asynchronous. This paper presents an evaluation on asynchronous UE reception in multi-cell downlink joint transmission systems using our LTE-based CoMP simulator. Then, due to asynchronous reception, we propose an improved reception strategy to mitigate the interference which compensate for Rx timing difference on Joint transmission (JT) CoMP systems. Simulation results show that the per-subband global precoding scheme widely used in the CoMP system is considerably sensitive to asynchronous reception since the performance is dominated by the subcarrier used for precoding vector calculation. It is verified that our proposed solution is able to achieve significant improvements under asynchronous reception.
Study of Sentiment Classification for Chinese Microblog Based on Recurrent Neural Network
ZHANG Yangsen, JIANG Yuru, TONG Yixuan
2016, 25(4): 601-607.   doi: 10.1049/cje.2016.07.002
[Abstract](286) [PDF 583KB](1706)
The sentiment classification of Chinese Microblog is a meaningful topic. Many studies has been done based on the methods of rule and word-bag, and to understand the structure information of a sentence will be the next target. We proposed a sentiment classification method based on Recurrent neural network (RNN). We adopted the technology of distributed word representation to construct a vector for each word in a sentence; then train sentence vectors with fixed dimension for different length sentences with RNN, so that the sentence vectors contain both word semantic features and word sequence features; at last use softmax regression classifier in the output layer to predict each sentence's sentiment orientation. Experiment results revealed that our method can understand the structure information of negative sentence and double negative sentence and achieve better accuracy. The way of calculating sentence vector can help to learn the deep structure of sentence and will be valuable for different research area.
A Fault Diagnosis Method of Power Systems Based on an Improved Adaptive Fuzzy Spiking Neural P Systems and PSO Algorithms
WANG Jun, PENG Hong, TU Min, Pérez-Jiménez J. Mario, SHI Peng
2016, 25(2): 320-327.   doi: 10.1049/cje.2016.03.019
[Abstract](222) [PDF 1027KB](1359)
A new fault diagnosis method based on improved Adaptive fuzzy spiking neural P systems (in short, AFSN P systems) and Particle swarm optimization (PSO) algorithm is presented to improve the efficiency and accuracy of diagnosis for power systems in this paper. AFSN P systems are a novel kind of computing models with parallel computing and learning ability. Based on our previous works, this paper focuses on AFSN P systems inference algorithms and learning algorithms and builds the fault diagnosis model using improved AFSN P systems for diagnosing effectively. The process of diagnosis based on AFSN P systems is expressed by matrix successfully to improve the rate of diagnosis eminently. Furthermore, particle swarm optimization algorithm is introduced into the learning algorithm of AFSN P systems, thus the convergence speed of diagnosis has a big progress. An example of 4-node system is given to verify the effectiveness of this method. Compared with the existing methods, this method has faster diagnosis speed, higher accuracy and strong ability to adapt to the grid topology changes.
Differential Fault Attack on Camellia
ZHOU Yongbin, WU Wenling, XU Nannan, FENG Dengguo
2009, 18(1): 13-19.  
[Abstract](771) [PDF 423KB](91)
Camellia is the final winner of 128-bit blockcipher in NESSIE project, and is also certified as the international IETF standard cipher for SSL/TLS cipher suites.In this study, we present an effcient differential fault attack on Camellia. Ideally, by using our techniques, on average, the complete key of Camellia-128 is recovered with64 faulty ciphertexts while the full keys of Camellia-192and Camellia-256 are retrieved with 96 faulty ciphertexts.Our attack is applicable to generic block ciphers with overall Fiestel structure using a SPN round function.All theseattacks have been successfully put into experimental simulations on a personal computer.
An Ultra Low Steady-State Current Power-on- Reset Circuit in 65nm CMOS Technology
SHAN Weiwei, WANG Xuexiang, LIU Xinning, SUN Huafang
2014, 23(4): 678-681.  
[Abstract](1142) [PDF 832KB](861)
A novel Power-on-reset (POR) circuit is proposed with ultra-low steady-state current consumption. A band-gap voltage comparator is used to generate a stable pull-up voltage. To eliminate the large current consumptions of the analog part, a power switch is adopted to cut the supply of band-gap voltage comparator, which gained ultra-low current consumption in steady-state after the POR rest process completed. The state of POR circuit is maintained through a state latch circuit. The whole circuit was designed and implemented in 65nm CMOS technology with an active area of 120μm*160μm. Experimental results show that it has a steady pull-up voltage of 0.69V and a brown-out voltage of 0.49V under a 1.2V supply voltage rising from 0V, plus its steady-state current is only 9nA. The proposed circuit is suitable to be integrated in system on chip to provide a reliable POR signal.
Face Liveness Detection Based on the Improved CNN with Context and Texture Information
GAO Chenqiang, LI Xindou, ZHOU Fengshun, MU Song
2019, 28(6): 1092-1098.   doi: 10.1049/cje.2019.07.012
[Abstract](822) [PDF 3162KB](127)
Face liveness detection, as a key module of real face recognition systems, is to distinguish a fake face from a real one. In this paper, we propose an improved Convolutional neural network (CNN) architecture with two bypass connections to simultaneously utilize low-level detailed information and high-level semantic information. Considering the importance of the texture information for describing face images, texture features are also adopted under the conventional recognition framework of Support vector machine (SVM). The improved CNN and the texture feature based SVM are fused. Context information which is usually neglected by existing methods is well utilized in this paper. Two widely used datasets are used to test the proposed method. Extensive experiments show that our method outperforms the state-of-the-art methods.
Identity Based Encryption and Biometric Authentication Scheme for Secure Data Access in Cloud Computing
CHENG Hongbing, RONG Chunming, TAN Zhenghua, ZENG Qingkai
2012, 21(2): 254-259.  
[Abstract](1274) [PDF 273KB](152)
Cloud computing will be a main information infrastructure in the future; it consists of many large datacenters which are usually geographically distributed and heterogeneous. How to design a secure data access for cloud computing platform is a big challenge. In this paper, we propose a secure data access scheme based on identity-based encryption and biometric authentication for cloud computing. Firstly, we describe the security concern of cloud computing and then propose an integrated data access scheme for cloud computing, the procedure of the proposed scheme include parameter setup, key distribution, feature template creation, cloud data processing and secure data access control. Finally, we compare the proposed scheme with other schemes through comprehensive analysis and simulation. The results show that the proposed data access scheme is feasible and secure for cloud computing.
A Global K-modes Algorithm for Clustering Categorical Data
BAI Tian, C.A. Kulikowski, GONG Leiguang, YANG Bin, HUANG Lan, ZHOU Chunguang
2012, 21(3): 460-465.  
[Abstract](661) [PDF 334KB](131)
In this paper, a new Global k-modes (GKM) algorithm is proposed for clustering categorical data. The new method randomly selects a sufficiently large number of initial modes to account for the global distribution of the data set, and then progressively eliminates the redundant modes using an iterative optimization process with an elimination criterion function. Systematic experiments were carried out with data from the UCI Machine learning repository. The results and a comparative evaluation show a high performance and consistency of the proposed method, which achieves significant improvement compared to other well-known k-modes-type algorithms in terms of clustering accuracy.
Large Spaceborne Deployable Antennas (LSDAs)-A Comprehensive Summary
DUAN Baoyan
2020, 29(1): 1-15.   doi: 10.1049/cje.2019.09.001
[Abstract](754) [PDF 4261KB](302)
This paper provides a survey of research activities of Large spaceborne deployable antennas (LSDAs) in the past, present and future. Firstly, three main kinds of spaceborne antennas, such as solid reflector, inflatable reflector and mesh reflector, are issued by showing the strengths and weaknesses. Secondly, a detailed research situation of LSDAs with mesh is discussed, for majority of the in-orbit large diameter and high frequency antennas are made in this type of structures. Thirdly, new conception of antenna is proposed as it does have both advantages of large aperture (high gain) and high precision (high frequency). Fourthly, the design theory and approach of LSDAs are concerned. It includes thermal-electromechanical multidisciplinary optimization, shaped beam design technique, performance testing technology and evaluation method, passive intermodulation of mesh, and application of new materials. Finally, the ultra large spaceborne deployable antennas of the next generation are presented, such as the deployable frame and inflatable reflector antennas, space-assembled ultra large antennas, smart array antennas and so on.

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