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Chinese Journal of Electronics

Volume 31, Issue 3

05 May, 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).
Display Method:
A Novel Trustworthiness Measurement Model Based on Weight and User Feedback
ZHOU Wei, MA Yanfang, PAN Haiyu
 doi: 10.1049/cje.2020.00.391
Abstract(555) HTML(249) PDF(35)
Software trustworthiness is an essential 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. The two 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.
Android Malware Detection Method Based on Permission Complement and API Calls
YANG Jiyun, TANG Jiang, YAN Ran, XIANG Tao
 doi: 10.1049/cje.2020.00.217
Abstract(124) HTML(50) PDF(11)
The dynamic code loading mechanism of the Android system allows an application to load executable files externally at runtime. This mechanism makes the development of applications more convenient, but it also brings security issues. Applications that hide malicious behavior in the external file by dynamic code loading are becoming a new challenge for Android malware detection. To overcome this challenge, based on dynamic code loading mechanisms, three types of threat models, i.e. Model I, Model II, and Model III are defined. For the Model I type malware, its malicious behavior occurs in DexCode, so the application programming interface (API) classes were used to characterize the behavior of the DexCode file. For the Model II type and Model III type malwares whose malicious behaviors occur in an external file, the permission complement is defined to characterize the behaviors of the external file. Based on permission complement and API calls, an Android malicious application detection method is proposed, of which feature sets are constructed by improving a feature selection method. Five datasets containing 15,581 samples are used to evaluate the performance of the proposed method. The experimental results show that our detection method achieves accuracy of 99.885% on general dataset, and performes the best on all evaluation metrics on all datasets in all comparison methods.
Lexicon-Augmented Cross-domain Chinese Word Segmentation with Graph Convolutional Network
YU Hao, HUANG Kaiyu, WANG Yu, HUANG Degen
 doi: 10.1049/cje.2021.00.363
Abstract(27) HTML(14) PDF(6)
Existing neural approaches have achieved significant progress for Chinese word segmentation (CWS). The performances of these methods tend to drop dramatically in the cross-domain scenarios due to the data distribution mismatch across domains and the out of vocabulary (OOV) words problem. To address these two issues, proposes a lexicon-augmented graph convolutional network for cross-domain CWS. The novel model can capture the information of word boundaries from all candidate words and utilize domain lexicons to alleviate the distribution gap across domains. Experimental results on the cross-domain CWS datasets (SIGHAN-2010 and TCM) show that the proposed method successfully models information of domain lexicons for neural CWS approaches and helps to achieve competitive performance for cross-domain CWS. The two problems of cross-domain CWS can be effectively solved through various interactions between characters and candidate words based on graphs. Further, experiments on the CWS benchmarks (Bakeoff-2005) also demonstrate the robustness and efficiency of the proposed method.
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(169) HTML(71) PDF(26)
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, reduced graphene oxide (rGO), and tetra (4-aminophenyl) porphyrin hybridized 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 current 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(325) HTML(146) PDF(23)
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 and the correct key xor specific difference 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 name) 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.
A 16-bit, ±10-V Input Range SAR ADC with a 5-V Supply Voltage and Mixed-Signal Nonlinearity Calibration
LUO Hongrui, ZHAO Xianlong, JIAO Zihao, ZHANG Jie, WANG Xiaofei, ZHANG Ruizhi, ZHANG Hong
 doi: 10.1049/cje.2021.00.057
Abstract(135) HTML(58) PDF(16)
This paper presents a high-precision, successive approximation register (SAR) analog-to-digital converter (ADC) with resistive analog front-end for low-voltage and wide input range applications. To suppress the serious nonlinearity brought by the voltage coefficients of analog front-end without deteriorating differential nonlinearity performance, a mixed-signal calibration scheme based on piecewise-linear method with calibration digital-to-analog converter is proposed. A compensation current is designed to sink or source from the reference to keep it independent of input signal, which greatly improves the linearity performance. Fabricated in a 0.5- μ m CMOS process, the proposed ADC achieves 88-dB signal-to-noise-and-distortion ratio and 103-dB spurious free dynamic range with 5-V supply voltage and 2.5-V reference voltage, and the total power consumption is 37.5 mW.
Multipath Suppressing Method Based on Pseudorange Model Using Modified Teaching-Learning Based Optimization Algorithm
CHENG Lan, ZHANG Jing, NI Zihang, YAN Gaowei
 doi: 10.1049/cje.2020.00.168
Abstract(80) HTML(27) PDF(10)
Satellites based positioning has been widely applied to many areas in our daily lives and thus become indispensable, which also leads to increasing demand for high-positioning accuracy. In some complex environments (such as dense urban, valley), multipath interference is one of the main error sources deteriorating positioning accuracy, and it is difficult to eliminate via differential techniques due to its uncertainty of occurrence and irrelevance in different instants. To address this problem, we propose a positioning method for global navigation satellite systems (GNSS) by adopting a modified teaching-learning based optimization (TLBO) algorithm after the positioning problem is formulated as an optimization problem. Experiments are conducted by using actual satellite data. The results show that the proposed positioning algorithm outperforms other algorithms, such as particle swarm optimization (PSO) based positioning algorithm, differential evolution (DE) based positioning algorithm, variable projection (VP) method, and TLBO algorithm, in terms of accuracy and stability.
A Low Complexity Distributed Multitarget Detection and Tracking Algorithm
FAN Jiande, XIE Weixin, LIU Zongxiang
 doi: 10.1049/cje.2021.00.282
Abstract(149) HTML(66) PDF(26)
In this paper, we propose a low complexity distributed approach to address the multitarget detection/tracking problem in the presence of noisy and missing data. The proposed approach consists of two components: a distributed flooding scheme for measurements exchanging among sensors and a sampling-based clustering approach for target detection/tracking from the aggregated measurements. The main advantage of the proposed approach over the prevailing Markov-Bayes-based distributed filters is that it does not require any priori information and all the information required is the measurement set from multiple sensors. A comparison of the proposed approach with the available distributed clustering approaches and the cutting edge distributed multi-Bernoulli filters that are modeled with appropriate parameters confirms the effectiveness and the reliability of the proposed approach.
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(137) HTML(54) PDF(21)
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.
Intelligent Orchestrating of IoT Microservices Based on Reinforcement Learning
WU Yuqin, SHEN Congqi, CHEN Shuhan, WU Chunming, LI Shunbin, Wei Ruan
 doi: 10.1049/cje.2020.00.417
Abstract(123) HTML(47) PDF(16)
With the recent increase in the number of Internet of Things (IoT) services, an intelligent scheduling strategy is needed to manage these services. In this paper, the problem of automatic choreography of microservices in IoT is explored. A type of reinforcement learning (RL) algorithm called TD3 is used to generate the optimal choreography policy under the framework of a softwaredefined network. The optimal policy is gradually reached during the learning procedure to achieve the goal, despite the dynamic characteristics of the network environment. The simulation results show that compared with other methods, the TD3 algorithm converges faster after a certain number of iterations, and it performs better than other non-RL algorithms by obtaining the highest reward. The TD3 algorithm can effciently adjust the traffc transmission path and provide qualified IoT services.
Enhanced Privacy Preserving for Social Networks Relational Data Based on Personalized Differential Privacy
KANG Haiyan, JI Yuanrui, ZHANG Shuxuan
 doi: 10.1049/cje.2021.00.274
Abstract(65) HTML(30) PDF(11)
With the popularization and development of social software, more and more people join the social network, which produces a lot of valuable information, but also contains plenty of sensitive privacy information. To achieve the personalized privacy protection of massive social network relational data, a privacy enhancement method for social networks relational data based on personalized differential privacy is proposed. And a dimensionality reduction segmentation sampling (DRS-S) algorithm is proposed to implement this method. First, in order to solve the problem of inefficiency caused by the excessive amount of data in social networks, dimension reduction and segmentation are carried out to divide the data into groups. According to the privacy protection requirements of different users, we adopt sampling method to protect users with different privacy requirements at different levels, so as to realize personalized different privacy. After that, the noise is added to the protected data to satisfy the privacy budget. Then publish the social network data. Finally, the proposed algorithm is compared with the traditional personalized differential privacy (PDP) algorithm and privacy preserving approach based on clustering and noise (PBCN) in real data set, the experimental results demonstrate that the quality of privacy protection and data availability of DRS-S are better than that of PDP algorithm and PBCN algorithm.
MSK-PK: A Public-Key Encryption Cryptosystem with Multiple Secret-Keys
ZHAI Jiaqi, LIU Jian, CHEN Lusheng, WANG Lingyu
 doi: 10.1049/cje.2020.00.049
Abstract(84) HTML(29) PDF(11)
By allowing intermediate nodes to combine multiple packets before forwarding them, the concept of network coding in multi-cast networks can provide maximum possible information flow. However, this also means traditional encryption methods are less applicable, since the different public-keys of receivers imply different ciphertexts which cannot be easily combined by network coding. While network coding itself may provide confidentiality, its effectiveness heavily depends on the underlying network topology and ability of the eavesdroppers. Finally, broadcast encryption and group key agreement techniques both allow a sender to broadcast the same ciphertext to all the receivers, although they rely on the assumptions of trusted key servers or secure channels. In this paper, we propose a novel public-key encryption concept with a single public-key for encryption and multiple secret keys for decryption (MSK-PK), which has limited ciphertext expansion and does not require trusted key servers or secure channels. To demonstrate the feasibility of this concept, we construct a concrete scheme based on a class of lattice-based multi-trapdoor functions. We prove that those functions satisfy the one-wayness property and can resist the nearest plane algorithm.
A Design and Comparative Investigation of Graded AlxGa1–xN EBL for W-B0.375GaN/ W-B0.45GaN Edge Emitting Laser Diode on AlN Substrate
NIASS Mussaab I., WANG Fang, LIU Yuhuai
 doi: 10.1049/cje.2020.00.178
Abstract(106) HTML(47) PDF(9)
In this paper, we numerically demonstrated the possibility of using wurtzite boron gallium nitride (W-BGaN) as active layers (quantum well and quantum barriers) along with aluminum gallium nitride (AlGaN) to achieve lasing at a deep ultraviolet range at 263 nm for edge emitting laser diode. The laser diode structure simulations were conducted by using the Crosslight-LASTIP software with a self-consistency model for varies quantity calculations. Moreover, multiple designed structures such as full and half have been achieved as well as the study of the effect of grading engineering/techniques at the electron blocking layer for linearly-graded-down and linearly-graded-up grading techniques were also emphasized. As a result, a maximum emitted power of 26 W, a minimum threshold current of 308 mA, a slope efficiency of 2.82 W/A, and a minimum p-type resistivity of 0.228 Ω•cm from the different doping concentrations and geometrical distances were thoroughly observed and jotted down.
W-Band High-Efficiency Waveguide Slot Array Antenna with Low Sidelobe Levels Based on Silicon Micromachining Technology
YAO Shisen, CHENG Yujian, BAI Hang, FAN Yong
 doi: 10.1049/cje.2020.00.315
Abstract(114) HTML(44) PDF(17)
A high-efficiency waveguide slot array antenna with low sidelobe level (SLL) is investigated for W-band applications. The silicon micromachining technology is utilized to realize multilayer antenna architecture by three key steps of selective etching, gold plating and Au-Au bonding. The radiating slot based on this technique becomes thick with a minimum thickness of 0.2 mm and accompanies with the decrease of slot’s radiation ability. To overcome this weakness, a stepped radiation cavity is loaded on the slot. The characteristic of cavity-loaded slot is investigated to synthesize the low-SLL array antenna. The unequal hybrid corporate feeding network is constructed to achieve sidelobe suppression in the E-plane. A pair of 16 × 8 low-SLL and high-effciency slot arrays is fashioned and confirmed experimentally. The bandwidth with the radiation effciency higher than 80 % is 92.3–96.3 GHz. The SLLs in both E- and H-planes are below −19 dB.
Linear Complexity of A Family of Binary p2q2-periodic Sequences From Euler Quotients
LUO Bingyu, ZHANG Jingwei, ZHAO Chang’an
 doi: 10.1049/cje.2020.00.125
Abstract(82) HTML(31) PDF(8)
A family of binary sequences derived from Euler quotients $\psi(\cdot)$ with RSA modulus $pq$ is introduced. Here two primes $p $ and $q $ are distinct and satisfy $\gcd(pq, (p-1)(q-1))=1$. The linear complexities and minimal polynomials of the proposed sequences are determined. Besides, this kind of sequences is shown not to have correlation of order $four$, although there exists the following relation $\psi(t)-\psi(t+p^2q)-\psi(t+q^2p)+\psi(t+(p+q)pq)= $$ 0 \pmod {pq}$ for any integer $t$ by the properties of Euler quotients.
Double-Layer Positional Encoding Embedding Method for Cross-Platform Binary Function Similarity Detection
JIANG Xunzhi, WANG Shen, YU Xiangzhan, GONG Yuxin
 doi: 10.1049/cje.2021.00.139
Abstract(81) HTML(32) PDF(6)
The similarity detection between two cross-platform binary functions has been applied in many fields, such as vulnerability detection, software copyright protection or malware classification. Current advanced methods for binary function similarity detection usually use semantic features, but have certain limitations. For example, practical applications may encounter instructions that have not been seen in training, which may easily cause the out of vocabulary (OOV) problem. In addition, the generalization of the extracted binary semantic features may be poor, resulting in a lower accuracy of the trained model in practical applications. To overcome these limitations, we propose a double-layer positional encoding based transformer model (DP-Transformer). The DP-Transformer’s encoder is used to extract the semantic features of the source instruction set architecture (ISA), which is called the source ISA encoder. Then, the source ISA encoder is fine-tuned by triplet loss while the target ISA encoder is trained. This process is called DP-MIRROR. When facing the same semantic basic block, the embedding vectors of the source and target ISA encoders are similar. Different from the traditional transformer which uses single-layer positional encoding, the double-layer positional encoding embedding can solve the OOV problem while ensuring the separation between instructions, so it is more suitable for the embedding of assembly instructions. Our comparative experiment results show that DP-MIRROR outperforms the state-of-the-art approach, MIRROR, by about 35 % in terms of precision at 1.
Clustering for Topological Interference Management
JIANG Xue, ZHENG Baoyu, WANG Lei, HOU Xiaoyun
 doi: 10.1049/cje.2021.00.277
Abstract(63) HTML(28) PDF(6)
To reduce the overhead and complexity of channel state information (CSI) acquisition in interference alignment (IA), the topological interference management (TIM) was proposed to manage interference, which only relied on the network topology information. The previous research on topological interference management via the low-rank matrix completion approach (LRMC) is known to be NP-hard. This paper considers the clustering method for the topological interference management problem, namely, the low-rank matrix completion for TIM is applied within each cluster. Based on the clustering result, we solve the low-rank matrix completion problem via nuclear norm minimization and Frobenius norm minimization function. Simulation results demonstrate that the proposed clustering method combined with TIM leads to significant gain on the achievable degrees of freedom.
Research on Global Clock Synchronization Mechanism in Software-Defined Control Architecture
LV Shuyu, DAI Xinfa, MA Zhong, GAO Yi, HU Zhekun
 doi: 10.1049/cje.2021.00.059
Abstract(218) HTML(96) PDF(35)
Adopt Software-Definition technology to decouple the functional components of the industrial control system (ICS) in a service-oriented and distributed form is an important way for the industrial internet of things (IIOT) to integrate information technology (IT), communication technology (CT), and operation technology (OT). Therefore, this paper presents the concept of software-defined control architecture (SDCA) and describes the time consistency requirements under the paradigm shift of ICS architecture. By analyzing the physical clock and virtual clock mechanism models, the global clock synchronization space is logically divided into the physical and virtual clock synchronization domains, and a formal description of the global clock synchronization space is proposed. According to the fundamental analysis of the clock state model, the physical clock linear filtering synchronization model is derived, and a distributed observation fusion filtering model is constructed by considering the two observation modes of the virtual clock to realize the time synchronization of the global clock space by way of timestamp layer-by-layer transfer and fusion estimation. Finally, the simulation results show that the proposed model can significantly improve the accuracy and stability of clock synchronization.
Search Algorithm Based on Permutation Group by Quantum Walk on Hypergraphes
JIANG Yaoyao, CHU Pengcheng, MA Yulin, MA Hongyang
 doi: 10.1049/cje.2021.00.125
Abstract(112) HTML(45) PDF(16)
Because a significant number of algorithms in computational science include search challenges and a large number of algorithms that can be transformed into search problems have garnered significant attention, especially the time rate and accuracy of search, a quantum walk search algorithm on hypergraphs, whose aim is to reduce time consumption and increase the readiness and controllability of search, is proposed in this paper. First, the data points are divided into groups and then isomorphic to the permutation set. Second, the element coordinates in the permutation set are adopted to mark the position of the data points. Search the target data by the controllable quantum walk with multiparticle on the ring. By controlling the coin operator of quantum walk, it is determined that search algorithm can increase the accuracy and controllability of search. It is determined that search algorithm can reduce time consumption by increasing the number of search particles. It also provides a new direction for the design of quantum walk algorithms, which may eventually lead to entirely new algorithms.
Non-intrusive Load Monitoring Using Gramian Angular Field Color Encoding in Edge Computing
CHEN Junfeng, WANG Xue
 doi: 10.1049/cje.2020.00.268
Abstract(76) HTML(31) PDF(11)
Non-intrusive load monitoring (NILM) can infer the status of the appliances in operation and their energy consumption by analyzing the energy data collected from monitoring devices. With the rapid increase of electric loads in amount and type, the traditional way of uploading all energy data to cloud is facing enormous challenges. It becomes increasingly significant to construct distinguishable load signatures and build robust classification models for NILM. In this paper, we propose a load signature construction method for load recognition task in home scenarios. The load signature is based on the Gramian angular field encoding theory, which is convenient to construct and significantly reduces the data transmission volume of the network. Moreover, edge computing architecture can reasonably utilize computing resources and relieve the pressure of the server. The experimental results on NILM datasets demonstrate that the proposed method obtains superior performance in the recognition of household appliances under insufficient resources.
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(261) HTML(107) PDF(29)
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.
A Unity-Gain Buffer Assisted Noise-Shaping SAR ADC Based on Error-Feedback Structure
YI Pinyun, ZHU Zhangming, XU Nuo, FANG Liang, HAO Yue
 doi: 10.1049/cje.2020.00.286
Abstract(67) HTML(29) PDF(7)
The passive noise-shaping successive approximation register (NS-SAR) analog-to-digital converter (ADC) demonstrates high performance in resolution improvement, power reduction, and process scaling, while its charge-sharing loss and limited bandwidth weaken the noise-shaping effect. This paper presents a first-order NS-SAR ADC based on error-feedback (EF) structure to realize high-efficiency noise shaping. It employs a lossless EF path by using a set of ping-pong switching capacitors with passive signal-residue summation technique. The proposed first-order EF NS-SAR prototype can be promoted to multi-order structure with the minor modification. Verified by simulation in 65-nm CMOS process, the proposed 9-bit NS-SAR ADC consumes 183.66 μ W when operating at 20 MS/s with the supply voltage of 1.2 V. At the oversampling ratio of 16, it achieves a peak signal-to-noise-and-distortion ratio of 81 dB, yielding Schreier figure of merit (FOM) of 176.32 dB.
Representation of Semantic Word Embeddings Based on SLDA and Word2vec Model
TANG Huanling, ZHU Hui, WEI Hongmin, ZHENG Han, MAO Xueli, LU Mingyu, GUO Jin
 doi: 10.1049/cje.2021.00.113
Abstract(183) HTML(80) PDF(17)
To solve the problem of semantic loss in text representation, this paper proposes a new embedding method of word representation in semantic space called wt2svec based on SLDA(Supervised LDA) and Word2vec. It generates the global topic embedding word vector utilizing SLDA which can discover the global semantic information through the latent topics on the whole document set. It gets the local semantic embedding word vector based on the Word2vec. The new semantic word vector is obtained by combining the global semantic information with the local semantic information. Additionally, the document semantic vector named doc2svec is generated. The experimental results on different datasets show that wt2svec model can obviously promote the accuracy of the semantic similarity of words, and improve the performance of text categorization compared with Word2vec.
Developer Cooperation Relationship and Attribute Similarity Based Community Detection in Software Ecosystem
SHEN Xin, DU Junwei, GONG Dunwei, YAO Xiangjuan
 doi: 10.1049/cje.2021.00.276
Abstract(43) HTML(20) PDF(5)
A software ecosystem (SECO) can be described as a special complex network. Previous complex networks in an SECO have limitations in accurately reflecting the similarity between each pair of nodes. The community structure is critical towards understanding the network topology and function. Many scholars tend to adopt evolutionary optimization methods for community detection. The information adopted in previous optimization models for community detection is incomprehensive and cannot be directly applied to the problem of community detection in an SECO. Based on this, a complex network in SECOs is first built. In the network, the cooperation intensity between developers is accurately calculated, and the attribute contained by each developer is considered. A multi-objective optimization model is formulated. A community detection algorithm based on NSGA-II is employed to solve the above model. Experimental results demonstrate that the proposed method of calculating the developer cooperation intensity and our model are advantageous.
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(354) HTML(155) PDF(48)
Edge-cloud collaborative application scenario is more complex, it involves collaborative operations among different security domains, frequently accessing and exiting application system of mobile terminals. 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. It avoids security risks caused by third-party key distribution and key escrow; 2) Cross-domain identity authentication: the alliance keys are calculated among edge servers through blockchain technology. 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 and computational Diffie-Hellman problem, the security of the protocol is proven, and the efficiency of the protocol is verified.
MalFSM: Feature Subset Selection Method for Malware Family Classification
KONG Zixiao, XUE Jingfeng, WANG Yong, ZHANG Qian, HAN Weijie, ZHU Yufen
 doi: 10.1049/cje.2022.00.038
Abstract(56) HTML(24) PDF(8)
Malware detection has been a hot spot in cyberspace security and academic research. We investigate the correlation between the opcode features of malicious samples and perform feature extraction, selection and fusion by filtering redundant features, thus alleviating the dimensional disaster problem and achieving efficient identification of malware families for proper classification. In the current cyberspace, malware authors use obfuscation technology to generate a large number of malware variants, which imposes a heavy analysis burden on security researchers and consumes a lot of resources in both time and space. To this end, we propose the MalFSM framework. Through the feature selection method, we reduce the 735 opcode features contained in the Kaggle dataset to 16, and then fuse on metadata features (count of file lines and file size) for a total of 18 features, and find that the machine learning classification is efficient and high accuracy. We analyzed the correlation between the opcode features of malicious samples and interpreted the selected features. Our comprehensive experiments show that the highest classification accuracy of MalFSM can reach up to 98.6% and the classification time is only 7.76s on the Kaggle malware dataset of Microsoft. Compared with similar research results, our method outperforms existing algorithms in terms of efficiency. It provides an opcode feature selection strategy for common researchers in the classification of homologous malicious families to reduce the laborious task of data preprocessing, feature selection, and sample classification based on general-purpose computing platforms.
A Novel Plane-Based Control BUS Design with Distributed Registers in 3D NAND Flash Memories
CAO Huamin, WANG Qi, LIU Fei, HUO Zongliang
 doi: 10.1049/cje.2021.00.283
Abstract(86) HTML(29) PDF(5)
This work presents a novel plane-based area-saving control BUS design with distributed registers in 3D NAND flash memory. 99.47% control signal routing wires are reduced compared to the conventional control circuit design. Independent multi-plane read is compatible with the existing read operations thanks to the register addresses are reasonably assigned. Furthermore, power-saving register group address-based plane gating scheme is proposed which saves about 2.9mW BUS toggling power. A four-plane control BUS design with 20K-bits registers has been demonstrated in FPGA tester. The results show that the plane-based control BUS design is beneficial to high-performance 3D NAND flash memory design.
Prediction of Protein Subcellular Localization Based on Microscopic Images via Multi-Task Multi-Instance Learning
ZHANG Pingyue, ZHANG Mengtian, LIU Hui, YANG Yang
 doi: 10.1049/cje.2020.00.330
Abstract(162) HTML(65) PDF(17)
Protein localization information is essential for understanding protein functions and their roles in various biological processes. The image-based prediction methods of protein subcellular localization have emerged in recent years because of the advantages of microscopic images in revealing spatial expression and distribution of proteins in cells. However, the image-based prediction is a very challenging task, due to the multi-instance nature of the task and low quality of images. In this paper, we propose a multi-task learning strategy and mask generation to enhance the prediction performance. Furthermore, we also investigate effective multi-instance learning schemes. We collect a large-scale dataset from the Human Protein Atlas database, and the experimental results show that the proposed multi-task multi-instance learning model outperforms both single-instance learning and common multi-instance learning methods by large margins.
Research on Virtual Coupled Train Control Method Based on GPC & VAPF
CAO Yuan, YANG Yaran, MA Lianchuan, WEN Jiakun
 doi: 10.1049/cje.2021.00.241
Abstract(95) HTML(40) PDF(6)
In rail transit systems, improving transportation efficiency has become a research hotspot. In recent years, a method of train control system based on virtual coupling has attracted the attention of many scholars. And the train operation control method is not only the key to realize the virtual coupling train operation control system but also the key to prevent accidents. Therefore, based on the existing research, a virtual coupled train dynamics model with nonlinear dynamics is established. Then, the recursive least square method based on the train running process data is used to identify the model parameters of the nonlinear dynamics virtual coupling train coupling process, and it is applied to the variable parameter artificial potential field(VAPF) to identify the parameters. A fusion controller based on feature-based generalized model prediction(GPC) and VAPF is used to control the virtual coupled train and prevent collision. Finally, a section of Beijing-Shanghai high-speed railway is taken as the background to verify the effectiveness of the proposed method.
AttentionSplice: An interpretable multi-head self-attention based hybrid deep learning model in splice site prediction
YAN Wenjing, ZHANG Baoyu, ZUO Min, ZHANG Qingchuan, WANG Hong, DA Mao
 doi: 10.1049/cje.2021.00.221
Abstract(157) HTML(66) PDF(14)
Pre-mRNA splicing is an essential procedure for gene transcription. Through the cutting of introns and exons, the DNA sequence can be decoded into different proteins related to different biological functions. The cutting boundaries are defined by the donor and acceptor splice sites. Characterizing the nucleotides patterns in detecting splice sites is sophisticated and challenges the conventional methods. Recently, the deep learning frame has been introduced in predicting splice sites and exhibits high performance. It extracts high dimension features from the DNA sequence automatically rather than infers the splice sites with prior knowledge of the relationships, dependencies, and characteristics of nucleotides in the DNA sequence. This paper proposes the AttentionSplice model, a hybrid construction combined with multi-head self-attention, convolutional neural network (CNN), bidirectional long short-term memory (Bi-LSTM) network. The performance of AttentionSplice is evaluated on the Homo sapiens (Human) and Caenorhabditis Elegans (Worm) datasets. Our model outperforms state-of-the-art models in the classification of splice sites. To provide interpretability of AttentionSplice models, we extract important positions and key motifs which could be essential for splice site detection through the attention learned by the model. Our result could offer novel insights into the underlying biological roles and molecular mechanisms of gene expression.
Time Optimal Trajectory Planning Algorithm for Robotic Manipulator Based on Locally Chaotic Particle Swarm Optimization
DU Yuxiao, CHEN Yihang
 doi: 10.1049/cje.2021.00.373
Abstract(124) HTML(53) PDF(15)
Optimal trajectory planning is a fundamental problem in the area of robotic research. On the time-optimal trajectory planning problem during the motion of a robotic arm, the method based on segmented polynomial interpolation function with a locally chaotic particle swarm optimization (LCPSO) algorithm is proposed in this paper. While completing the convergence in the early or middle part of the search, the algorithm steps forward on the problem of local convergence of traditional particle swarm optimization (PSO) and improved learning factor PSO (IFPSO) algorithms. Finally, simulation experiments are executed in joint space to obtain the optimal time and smooth motion trajectory of each joint, which shows that the method can effectively shorten the running time of the robotic manipulator and ensure the stability of the motion as well.
Design of Pyramidal Horn with Arbitrary E\H Plane Half-Power Beamwidth
ZHANG Wenrui, SHAO Wenyuan, JI Yicai, LI Chao, YANG Guan, LU Wei, FANG Guangyou
 doi: 10.1049/cje.2021.00.212
Abstract(83) HTML(26) PDF(3)
This paper proposed a novel design method for pyramid horns which are under the constraints of 3 dB beamwidth. It is based on the general radiation patterns of E\H planes derived from Huygens’ principle. Through interpolation and fitting techniques, the E\H plane’s maximum aperture error parameter of the pyramid horn is obtained as a function of the angle and aperture electrical size. Firstly, the aperture size of the E (or H) plane is calculated with the help of the optimal gain principle. Secondly, the constraint equation of another plane is derived. Finally, the intersection of constraint equation and interpolation function, which can be solved iteratively, contains all the solution information. The general radiation patterns neglect the influence of the Huygens element factor which makes the error bigger in large design beamwidth. In this paper, through theoretical analysis and simulation experiments, two correction formulas are employed to correct the Huygens element factor’s influence on the E\H planes. Simulation experiments and measurements show that the proposed method has a smaller design error in the range of 0-60 degrees half-power beamwidth.
A Novel Sampling Method Based on Neighborhood Weighted for Imbalanced Datasets
GUANG Mingjian, YAN Chungang, LIU Guanjun, WANG Junli, JIANG Changjun
 doi: 10.1049/cje.2021.00.121
Abstract(153) HTML(60) PDF(21)
The weighted sampling methods based on k-nearest neighbors have been demonstrated to be effective in solving the class imbalance problem. However, they usually ignore the positional relationship between a sample and the heterogeneous samples in its neighborhood when calculating sample weight. This paper proposes a novel neighborhood-weighted based (NWBBagging) sampling method to improve the Bagging algorithm’s performance on imbalanced datasets. It considers the positional relationship between the center sample and the heterogeneous samples in its neighborhood when identifying critical samples. And a parameter reduction method is proposed and combined into the ensemble learning framework, which reduces the parameters and increases the classifier’s diversity. We compare NWBBagging with some stateof-the-art ensemble learning algorithms on 34 imbalanced datasets, and the result shows that NWBBagging achieves better performance.
Gmean Maximum FSVMI Model and Its Application for Carotid Artery Stenosis Risk Prediction
ZHANG Xueying, GUO Yuling, LI Fenglian, WEI Xin, HU Fengyun, HUI Haisheng, JIA Wenhui
 doi: 10.1049/cje.2020.00.185
Abstract(77) HTML(26) PDF(9)
Carotid artery stenosis is a serious medical condition that can lead to stroke. Using machine learning method to construct classifier model, carotid artery stenosis can be diagnosed with transcranial doppler data. We propose an improved fuzzy support vector machine model to predict carotid artery stenosis, with the maximum geometric mean as the optimization target. The fuzzy membership function is obtained by combining information entropy with the normalized class-center distance. Experimental results showed that the proposed model was superior to the benchmark models in sensitivity and geometric mean criteria.
Towards Evaluating the Robustness of Adversarial Attacks Against Image Scaling Transformation
ZHENG Jiamin, ZHANG Yaoyuan, LI Yuanzhang, WU Shangbo, YU Xiao
 doi: 10.1049/cje.2021.00.309
Abstract(146) HTML(58) PDF(20)
The robustness of adversarial examples to image scaling transformation is usually ignored when most existing adversarial attacks are proposed. In contrast, image scaling is often the first step of the model to transfer various sizes of input images into fixed ones. We evaluate the impact of image scaling on the robustness of adversarial examples applied to image classification tasks. We set up an image scaling system to provide a basis for robustness evaluation and conduct experiments in different situations to explore the relationship between image scaling and the robustness of adversarial examples. Experiment results show that various scaling algorithms have a similar impact on the robustness of adversarial examples, but the scaling ratio significantly impacts it.
LBA-ECA Load Balancing Algorithm Based on Weighted Bipartite Graph for Edge Computing
SHAO Sisi, LIU Shangdong, LI Kui, YOU Shuai, QIU Huajie, YAO Xiaoliang, JI Yimu
 doi: 10.1049/cje.2021.00.289
Abstract(154) HTML(57) PDF(15)
Compared with cloud computing environment, edge computing has many choices of service providers due to different deployment environments. The flexibility of edge computing makes the environment more complex. The current edge computing architecture has the problems of scattered computing resources and limited resources of single computing node. When the edge node carries too many task requests, the makespan of the task will be delayed. We propose a load balancing algorithm based on weighted bipartite graph for edge computing (LBA-EC), which makes full use of network edge resources, reduces user delay, and improves user service experience. The algorithm is divided into two phases for task scheduling. In the first phase, the tasks are matched to different edge servers. In the second phase, the tasks are optimally allocated to different containers in the edge server to execute according to the two indicators of energy consumption and completion time. The simulations and experimental results show that our algorithm can effectively map all tasks to available resources with a shorter completion time.
Combination for Conflicting Interval-Valued Belief Structures with CSUI-DST Method
LI Shuangming, GUAN Xin, YI Xiao, SUN Guidong
 doi: 10.1049/cje.2021.00.214
Abstract(106) HTML(36) PDF(16)
Since the basic probability of an interval-valued belief structure (IBS) is assigned as interval number, its combination becomes difficult. Especially, when dealing with highly conflicting IBSs, most of the existing combination methods may cause counter-intuitive results, which can bring extra heavy computational burden due to nonlinear optimization model, and lose the good property of associativity and commutativity in Dempster-Shafer theory (DST). To address these problems, a novel conflicting IBSs combination method named CSUI (conflict, similarity, uncertainty, intuitionistic fuzzy sets)-DST method is proposed by introducing a similarity measurement to measure the degree of conflict among IBSs, and an uncertainty measurement to measure the degree of discord, non-specificity and fuzziness of IBSs. Considering these two measures at the same time, the weight of each IBS is determined according to the modified reliability degree. From the perspective of intuitionistic fuzzy sets, we propose the weighted average IBSs combination rule by the addition and number multiplication operators. The effectiveness and rationality of this combination method are validated with two numerical examples and its application in target recognition.
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(279) HTML(96) PDF(22)
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(143) HTML(61) PDF(20)
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.
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(380) HTML(174) PDF(41)
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.
Internet of Brain, Thought, Thinking, and Creation
ZHANG Zhimin, YIN Rui, NING Huansheng
 doi: 10.1049/cje.2021.00.236
Abstract(304) HTML(135) PDF(41)
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.
Trajectory Optimization and Power Allocation Scheme Based on DRL in Energy Efficient UAV-Aided Communication Networks
WANG Chaowei, CUI Yuling, DENG Danhao, et al.
2022, 31(3): 397-407.   doi: 10.1049/cje.2021.00.314
Abstract(1755) HTML(757) PDF(82)
With flexibility, convenience and mobility, unmanned aerial vehicles (UAVS) can provide wireless communication networks with lower costs, easier deployment, higher network scalability and larger coverage. This paper proposes the deep deterministic policy gradient algorithm to jointly optimize the power allocation and flight trajectory of UAV with constrained effective energy to maximize the downlink throughput to ground users. To validate the proposed algorithm, we compare with the random algorithm, Q-learning algorithm and deep Q network algorithm. The simulation results show that the proposed algorithm can effectively improve the communication quality and significantly extend the service time of UAV. In addition, the downlink throughput increases with the number of ground users.
Demand Learning and Cooperative Deployment of UAV Networks
ZHANG Xiao, WANG Xuehe, XU Xinping, et al.
2022, 31(3): 408-415.   doi: 10.1049/cje.2021.00.278
Abstract(828) HTML(367) PDF(37)
Unmanned aerial vehicle (UAV) as a powerful tool has found its applicability in assisting mobile users to deal with computation-intensive and delay-sensitive applications (e.g., edge computing, high-speed Internet access, and local caching). However, deployment of UAV-aided mobile services (UMS) faces challenges due to the UAV limitation in wireless coverage and energy storage. Aware of such physical limitations, a future UMS system should be intelligent enough to self-plan trajectories and best offer computational capabilities to mobile users. There are important issues regarding the UAV-user interaction, UAV-UAV cooperation for sustainable service provision, and dynamic UMS pricing. These networking and resource management issues are largely overlooked in the literature and this article presents intelligent solutions for cooperative UMS deployment and operation. Mobile users’ locations are generally private information and changing over time. How to learn on-demand users’ truthful location reporting is important for determining optimal UAV deployment in serving all the users fairly. After addressing the truthful UAV-user interaction issue via game theory, we further study the UAV network sustainability for UMS provision by minimizing the energy consumption cost during deployment and seeking UAV-UAV cooperation. Finally, for profit-maximizing purpose, we analyze the cooperative UAVs’ deployment, capacity allocation, and dynamic service pricing.
Navigation for UAV Pair-Supported Relaying in Unknown IoT Systems with Deep Reinforcement Learning
HUANG Fei, LI Guangxia, WANG Haichao, et al.
2022, 31(3): 416-429.   doi: 10.1049/cje.2021.00.305
Abstract(1183) HTML(507) PDF(39)
Unmanned aerial vehicles (UAVs) have recently been regarded as a promising technology in Internet of things (IoT). UAVs functioned as intermediate relay nodes are capable of establishing uninterrupted and high-quality communication links between remotely deployed IoT devices and the destination. Multiple UAVs are required to be deployed due to their limited onboard energy. We study a UAV pair-supported relaying in unknown IoT systems, which consists of transmitter and receiver. Our goal is that transmitter gathers the data from each device then transfers the information to receiver, and receiver finally transmits the information to the destination, while meeting the constraint that the amount of information received from each device reaches a certain threshold. This is an optimization problem with highly coupled variables, such as trajectories of transmitter and receiver. On account of no prior knowledge of the environment, a dueling double deep Q network (dueling DDQN) algorithm is proposed to solve the problem. Whether it is in the phase of transmitter’s receiving information or the phase of transmitter’s forwarding information to receiver, the effectiveness and superiority of the proposed algorithm is demonstrated by extensive simulationsin in comparison to some base schemes under different scenarios.
Time-Varying Channel Estimation Based on Air-Ground Channel Modelling and Modulated Learning Networks
LIU Chunhui, WANG Meilin, DONG Zanliang, et al.
2022, 31(3): 430-441.   doi: 10.1049/cje.2021.00.285
Abstract(1150) HTML(471) PDF(20)
To improve the time-varying channel estimation accuracy of orthogonal frequency division multiplexing air-ground datalink in complex environment, this paper proposes a time-varying air-ground channel estimation algorithm based on the modulated learning networks, termed as MB-ChanEst-TV. The algorithm integrates the modulated convolutional neural networks (MCNN) with the bidirectional long short term memory (Bi-LSTM), where the MCNN subnetworks accomplish channel interpolation in frequency domain and compress the network model while the Bi-LSTM subnetworks achieve channel prediction in time domain. Considering the unique characteristics of airframe shadowing for unmanned aircraft systems, we propose to combine the classical 2-ray channel model with the tapped delay line model and present a more realistic channel impulse response samples generation approach, whose code and dataset have been made publicly available. We demonstrate the effectiveness of our proposed approach on the generated dataset, where experimental results indicate that the MB-ChanEst-TV model outperforms existing state-of-the-art methods with a lower estimation error and better bit error ratio performance under different signal to noise ratio conditions. We also analyze the effect of roll angle of the aircraft and the duration percentage of the airframe shadow on the channel estimation.
Remote Interference Source Localization: A Multi-UAV-Based Cooperative Framework
WU Guangyu, GU Jiangchun
2022, 31(3): 442-455.   doi: 10.1049/cje.2021.00.310
Abstract(1045) HTML(421) PDF(40)
Interference source localization with high accuracy and time efficiency is of crucial importance for protecting spectrum resources. Due to the flexibility of unmanned aerial vehicles (UAVs), exploiting UAVs to locate the interference source has attracted intensive research interests. The off-the-shelf UAV-based interference source localization schemes locate the interference sources by employing the UAV to keep searching until it arrives at the target. This obviously degrades time efficiency of localization. To balance the accuracy and the efficiency of searching and localization, this paper proposes a multi-UAV-based cooperative framework alone with its detailed scheme, where search and remote localization are iteratively performed with a swarm of UAVs. For searching, a low-complexity Q-learning algorithm is proposed to decide the direction of flight in every time interval for each UAV. In the following remote localization phase, a fast Fourier transformation based location prediction algorithm is proposed to estimate the location of the interference source by fusing the searching result of different UAVs in different time intervals. Numerical results reveal that in the proposed scheme outperforms the state-of-the-art schemes, in terms of the accuracy, the robustness and time efficiency of localization.
Blockchain-Empowered Dynamic Spectrum Management for Space-Air-Ground Integrated Network
LI Zuguang, WANG Wei, GUO Jia, et al.
2022, 31(3): 456-466.   doi: 10.1049/cje.2021.00.275
Abstract(1391) HTML(625) PDF(32)
Space-air-ground integrated network is capable of providing seamless and ubiquitous services to cater for the increasing wireless communication demands of emerging applications. However, how to efficiently manage the heterogeneous resources and protect the privacy of connected devices is a very challenging issue, especially under the highly dynamic network topology and multiple trustless network operators. In this paper, we investigate blockchain-empowered dynamic spectrum management by reaping the advantages of blockchain and software defined network (SDN), where operators are incentive to share their resources in a common resourced pool. We first propose a blockchain enabled spectrum management framework for space-air-ground integrated network, with inter-slice spectrum sharing and intra-slice spectrum allocation. Specifically, the inter-slice spectrum sharing is realized through a consortium blockchain formed by the upper-tier SDN controllers, and then a graph coloring based channel assignment algorithm is proposed to manage the intra-slice spectrum assignment. A bilateral confirmation protocol and a consensus mechanism are also proposed for the consortium blockchain. The simulation results prove that our proposed consensus algorithm takes less time than practical Byzantine fault tolerance algorithm to reach a consensus, and the proposed channel assignment algorithm significantly improves the spectrum utilization and outperforms the baseline algorithm in both simulation and real-world scenarios.
A Review of Terahertz Sources Based on Planar Schottky Diodes
KOU Wei, LIANG Shixiong, ZHOU Hongji, et al.
2022, 31(3): 467-487.   doi: 10.1049/cje.2021.00.302
Abstract(2060) HTML(902) PDF(32)
The special position of terahertz wave in the electromagnetic spectrum makes it possess the characteristics of orientation, broadband, penetration and low energy, which promotes the extensive research of terahertz wave in the fields of communication, radar, imaging, sensing, security inspection and so on. The solid-state terahertz sources based on semiconductor devices have attracted extensive attention in the field of terahertz information technology due to their characteristics such as being able to work at room temperature, being small in size, being easy to integrate and having good frequency stability. Terahertz planar Schottky diode is a kind of low parasitic semiconductor device. Its high cutoff frequency makes it work well in the terahertz range. The frequency multiplier based on planar Schottky diode is an important part of terahertz solid state source. In this review, the development of Schottky diodes technology in recent years have been introduced, including the structures and preparation of Schottky diodes. In addition, the current situation and performance of different types of terahertz sources based on Schottky diodes are further introduced, and the future development trend is discussed.
Prospects and Challenges of THz Precoding
GUO Rongbin, TANG Yajie, ZHANG Changming, et al.
2022, 31(3): 488-498.   doi: 10.1049/cje.2021.00.263
Abstract(1311) HTML(624) PDF(22)
Terahertz (THz) communications are considered as very promising for the sixth-generation (6G) ultra-dense wireless networks. However, THz signals suffer from well-known severe path loss, which consequently shortens the coverage of THz communication systems. To deal with this issue, precoding technique is expected to be beneficial to extend the limited coverage by providing directional beams with ultra large number of antenna arrays. In this paper, we overview the state-of-the-art developments of THz precoding techniques such as reconfigurable intelligent surface based precoding, hybrid digital-analog precoding and delay-phase precoding. Based on the survey, we summarize several open issues remaining to be addressed, and discuss the prospects of a few potential research directions on THz precoding, such as one-bit precoding, precoding for hardware impairments and THz security precoding. This overview will be helpful for researchers to study innovative solutions of THz precoding in the future 6G wireless communications.
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](397) [PDF 634KB](376)
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](546) [PDF 1424KB](2653)
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](672) [PDF 6951KB](2540)
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](197) [PDF 566KB](328)
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.
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](572) [PDF 827KB](898)
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.
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](297) [PDF 1133KB](515)
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.
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](482) [PDF 1983KB](1076)
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.
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](393) [PDF 583KB](1710)
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.
MIMO Scheduling Effectiveness Analysis for Bursty Data Service from View of QoE
CHEN Lei, JIANG Dingde, BAO Rong, XIONG Jiping, LIU Fuqiang, BEI Lulu
2017, 26(5): 1079-1085.   doi: 10.1049/cje.2017.07.018
[Abstract](169) [PDF 418KB](315)
In the user selection phrase of the conventional Multiple-input-multiple-output (MIMO) scheduling schemes, the frequent user exchange deteriorates the Quality of user experience (QoE) of the bursty data service. And the channel vector orthogonalization computation results in a high time cost. To address these problems, we propose an inertial scheduling policy to reduce the number of noneffective user exchange, and substitute self-organization policy for channel vector orthogonalization computation to reduce computational complexity. The relationship between the scheduling effectiveness and the inertia of objective function is observed in the simulation. The simulation results show that the inertial scheduling policy effectively reduce the number of potential noneffective scheduling which is inversely proportional to the Mean opinion score (MOS) that quantifies the QoE. Our proposed scheduling scheme provides significant improvement in QoE performance in the simulation. Although the proposed scheduling scheme does not consider the channel vector orthogonalization in the user selection phrase, its throughput approaches the level of the throughput-oriented scheme because of its selforganization scheduling policy.
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](219) [PDF 411KB](1130)
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.
Differential Fault Attack on Camellia
ZHOU Yongbin, WU Wenling, XU Nannan, FENG Dengguo
2009, 18(1): 13-19.  
[Abstract](834) [PDF 423KB](111)
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](1332) [PDF 832KB](934)
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](936) [PDF 3162KB](165)
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](1354) [PDF 273KB](174)
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](746) [PDF 334KB](157)
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](916) [PDF 4261KB](356)
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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