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Chinese Journal of Electronics

Volume 31, Issue 2

05 March, 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 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(232) HTML(95) 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 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(86) HTML(38) PDF(7)
In this paper, we numerically demonstrated the possibility of using wurtzite Boron Gallium Nitride BGaN as active layers (quantum well and quantum barriers) along with Aluminum Gallium Nitride 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 LASTIP-Crosslight 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 the result, a maximum emitted power of 26 W, a minimum threshold current of 308 mA, a slope efficiency of 2.82 W/A, and minimum p-type resistivity of 0.228 Ω.cm from different doping concentrations and geometrical distances were thoroughly observed and jotted down.
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(59) HTML(18) PDF(8)
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.
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(89) HTML(34) 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.
Design and Fabrication of Reflective Phase Shifter for Two-Dimensional Terahertz Beam-Scanning Reflectarray
YANG Jun, YIN Mingjun, GAO Haoyu, YIN Zhiping, YE Yang, DENG Guangsheng, LU Hongbo
 doi: 10.1049/cje.2021.00.201
Abstract(144) HTML(62) PDF(26)
A reflective phase shifter is proposed to realize the two-dimensional beam-scanning reflectarray. The reflectarray is composed of double-dipole resonant elements in which the phase shift is implemented by applying a driving voltage to the liquid crystal (LC). A 30 × 30 phase shifter sample is fabricated on a quartz substrate with a 480 μ m thickness and a 4 cm × 4 cm area. According to the experimental results, 0 to 360° phase shift can be achieved within the bandwidth of 5 GHz. In order to accomplish two-dimensional beam-scanning and reduce the negative impact of driving lines on the reflectarray, a new LC driving method is developed. To verify the accuracy of the proposed approach, three samples of different driving line numbers are designed using the above-mentioned phase shifter, which all have achieved a 0−360° phase shift in the bandwidth of 4 GHz. Considering the influence of LC anisotropy and inhomogeneity, an improved calculation result is obtained and compared with experimental data.
220 GHz Multi Circuit Integrated Front End Based on Solid-State Circuits for High Speed Communication System
NIU Zhongqian, ZHANG Bo, DAI Bingli, ZHANG Jicong, SHEN Fang, HU Yi, FAN Yong, ZHANG Yihan
 doi: 10.1049/cje.2021.00.295
Abstract(66) HTML(23) PDF(7)
This paper presents the research on a 220 GHz multi circuit inte-grated front end based on solid-state circuits. This integrated front end integrates a 220 GHz subharmonic mixer, a 110 GHz tripler, a 110 GHz 8 dB hybrid coupler and a 220 GHz waveguide bandpass filter (BPF) in one single block. Compared to the traditional transceivers which usually use cascade connection of the independent mixers and multipliers, the size of the proposed multi circuit integrated front-end block is ${\boldsymbol{25}}\,{\bf{mm}}{\boldsymbol{\times}}{\boldsymbol{20}}\,{\bf{mm}}{\boldsymbol{\times}}{\boldsymbol{20}}\,{\bf{mm}}$, 10 times smaller than the cascading transceiver. In order to check the tripler’s output power, a modified compact 110 GHz 8 dB hybrid coupler is set between mixer and tripler. Due to the characteristics of the hybrid coupler, the deterioration of cascading transceiver’s performance caused by mismatch has also been improved. In addition, to achieve single sideband (SSB) communication, a 220 GHz BPF with high selectivity is integrated in the circuit. The measured conversion loss of the fabricated multi circuit integrated front end is less than 11 dB, where the LO and RF frequency are 37 and 210–220 GHz. Based on this front-end, a 220 GHz high speed communication system has been setup and it can achieve 10 Gbps data transmission using 16QAM modulation.
Integrated Intelligent Electromagnetic Radiator Design for Future THz Communication: A Review
LU Xuyang, Venkatesh Suresh, Saeidi Hooman, Sengupta Kaushik
 doi: 10.1049/cje.2021.00.324
Abstract(187) HTML(82) PDF(34)
Advances in 6G communication changes how machines and humans interact. The blossom of new applications demands significantly higher data bandwidth while preserving the mobility and sustainability of electronic wireless communication systems. It also demands an integrable system that allows convenient interactions between communication units and signal processing units. A review of CMOS-based THz communication system solutions is presented, with a focus on novel systematic EM-circuit co-design philosophy. This review starts with a review of THz power generation, followed by the discussion of THz localization and THz beamforming for efficient high-throughput communication.
Prospects and Challenges of THz Precoding
GUO Rongbin, TANG Yajie, ZHANG Changming, LIU Shanyun, ZHAO Zhifeng
 doi: 10.1049/cje.2021.00.263
Abstract(139) HTML(58) PDF(14)
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.
A Review of Terahertz Sources Based on Planar Schottky Diodes
KOU Wei, LIANG Shixiong, ZHOU Hongji, DONG Yazhou, GONG Sen, YANG Ziqiang, ZENG Hongxin
 doi: 10.1049/cje.2021.00.302
Abstract(106) HTML(45) PDF(19)
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.
Remote Interference Source Localization: A Multi-UAV-Based Cooperative Framework
WU Guangyu, GU Jiangchun
 doi: 10.1049/cje.2021.00.310
Abstract(70) HTML(25) PDF(23)
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.
Time-Varying Channel Estimation Based on Air-Ground Channel Modelling and Modulated Learning Networks
LIU Chunhui, WANG Meilin, DONG Zanliang, WANG Pei
 doi: 10.1049/cje.2021.00.285
Abstract(60) HTML(19) PDF(10)
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.
Trajectory Optimization and Power Allocation Scheme Based on DRL in Energy Efficient UAV-Aided Communication Networks
WANG Chaowei, CUI Yuling, DENG Danhao, WANG Weidong, JIANG Fan
 doi: 10.1049/cje.2021.00.314
Abstract(126) HTML(51) PDF(23)
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.
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(526) HTML(236) PDF(33)
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.
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(62) HTML(18) 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 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(49) HTML(16) PDF(5)
This paper presents a 16-bit, 200-KSPS and ±10-V input range single-ended successive approximation register (SAR) analog-to- digital converter (ADC), which adopts a resistive scaling front-end to convert the ±10-V single-ended input signal to 0~2.5V swing with the ADC core operating under a 5-V power supply. A mixed-signal calibration scheme based on piecewise-linear (PWL) method is proposed to suppress the serious nonlinearity brought by the voltage coefficients of the scaling resistors, furthermore, the nonlinearity due to the limited load regulation (LR) of the reference buffer is eliminated with a current compensation circuit, which greatly improves the linearity performance. Fabricated in a 0.5-μm CMOS process, the proposed ADC achieves 88-dB signal-to-noise-and-distortion ratio (SNDR) and 103-dB spurious free dynamic range (SFDR) with 5-V supply voltage and 2.5-V reference voltage, and the total power consumption is 37.5 mW.
Design and Realization of Broadband Active Inductor Based Band Pass Filter
Aysu Belen, Mehmet A. Belen, Merih Palandöken, Peyman Mahouti, Özlem Tari
 doi: 10.1049/cje.2021.00.322
Abstract(38) HTML(15) PDF(4)
With the latest developments in the wireless communication systems, the alternative design methodologies are required for the broadband design of microwave components. In this paper, a compact broad band pass filter (BPF) design is introduced through the microwave design technique based on the active inductor (AIN) with the numerical computation and experimental measurement studies. The proposed AIN based BPF has operating frequency band extending from 0.8 GHz to 2.7 GHz in compact size with high selectivity in comparison to conventional LC based BPF. The experimental measurement results agree well with the numerical computation results. The proposed AIN based BPF design has technical capability to be conveniently tuned to operate at different frequency bands.
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(40) HTML(13) PDF(2)
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.
Radiation Principle and Spatial Direct Modulation Method of a Low Frequency Antenna Based on Rotating Permanent Magnet
LIU Wenyi, ZHANG Feng, SUN Faxiao, GONG Zhaoqian, LIU Xiaojun, FANG guangyou
 doi: 10.1049/cje.2020.00.130
Abstract(141) HTML(54) PDF(29)
The theory of mechanical antenna is still in its infancy at present, and its radiation mechanism, field distribution, modulation methods and other basic theories need to be explored and improved. The radiation mechanism of a Rotating-magnet Based Mechanical Antenna (RMBMA) is explored. An equivalent radiation model of the mechanical antenna is established. The field formula of mechanical antenna is derived using this model and rotation matrix. The spatial direct modulation method of mechanical antenna is also investigated. Two prototype antennas are fabricated using DC/AC servo motors and NdFeB magnets, and experiments are carried out to verify the correctness of the derivation and analysis. The measured and simulated results are in consistent with each other. By precisely controlling the moving parameters of an AC servo motor, signal of Binary Amplitude Shift Keying (BASK) is generated, and the original code sequence is recovered by demodulation.
High-Gain Dual Circularly Polarized Antenna for Air-to-Ground Wireless Link
JIANG Hang, YAO Yuan, XIU Tao, CHENG Xiaohe, YU Junsheng, CHEN Xiaodong
 doi: 10.1049/cje.2021.00.257
Abstract(100) HTML(37) PDF(11)
This paper presents an E-band reflector antenna fed by dual circularly polarized feed system. Axial displaced ellipse reflector is adopted for high gain and low blockage mechanisms. The feed system mainly composed of orthomode transducer, iris polarizer and horn antenna, and possesses dual circular polarization. The orthomode transducer offers high isolation performance in broadband by double symmetry and the iris polarizer achieves phase shift by introducing discontinuities. In addition, the ridge and iris in the feed are optimized to be wide enough to fabricate and maintain good mechanical properties in higher frequency band. The entire antenna is simulated and fabricated. The measured gain of 45 ± 1.2 dBi corresponds to about 52 % efficiency. The measured results of reflection coefficients less than −18 dB, isolation over 27 dB, axial-ratio value less than 2.3 dB are achieved from 71 to 86 GHz.
Research on Silicon-Based Terahertz Communication Integrated Circuits
ZHOU Peigen, CHEN Jixin, TANG Siyuan, YU Jiayang, WANG Chen, LI Huanbo, LU Haiyan, YAN Pinpin, HOU Debin, CHEN Zhe, HONG Wei
 doi: 10.1049/cje.2021.00.253
Abstract(157) HTML(53) PDF(14)
With the increasing number of users and emerging new applications, the demand for mobile data traffic is growing rapidly. The limited spectrum resources of the traditional microwave and millimeter-wave frequency bands can no longer support the future wireless communication systems with higher system capacity and data throughput. The terahertz (THz) frequency bands have abundant spectrum resources, which can provide sufficient bandwidth to expand channel capacity and increase transmission data rate. In addition, with the rapid development of silicon-based semiconductor technology, its characteristic size keeps decreasing, and the radio frequency performance of active devices is gradually approaching the performance of III-V semiconductor technology. The realization of THz communication systems based on low-cost, high-stability, and easy-to-integrate silicon-based process has become a feasible solution. This review summarizes the reported silicon-based THz communication systems, as well as the key sub-circuit chips in these systems, including the local oscillator, power amplifier, low noise amplifier, on-chip antenna and transceiver chip, etc.
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(75) HTML(28) PDF(8)
A family of binary sequences derived from Euler quotients $\psi(\cdot)$ with RSA modulus $pq$ is introduced. Here $p$ and $q$ are two distinct odd primes and satisfy $\gcd(pq, (p-1)(q-1))=1$. The minimal polynomials and linear complexities 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.
A 220 GHz Orthogonal Modulator Based on Subharmonic Mixers Using Anti-Paralleled Schottky Diodes
HE Yue, LIU Ge, LIU Juan, LIN Changxing, SU Wei
 doi: 10.1049/cje.2021.00.270
Abstract(111) HTML(37) PDF(16)
In this paper, A 220 GHz orthogonal modulator based on two symmetrical subharmonic mixers is designed, analyzed and measured, where the mixers are implemented basde on antiparallel Schottky diodes. The orthogonal modulator consists of a 90° phase shifted 220 GHz 3 dB coupler, two sub-harmonically pumped mixers and an in-phase 110 GHz 3 dB power divider. The IF output signals are in-phase (I) branch and quadrature (Q) branch operating from DC±3.5GHZ. In the back to back experimentaltests, it is shown that the conversion loss of the two 220 GHz IQ mixers is less than 30 dB when the IF operating frequency is DC−3.5 GHz, IF input power is −10 dBm and the LO power is around 6 mW. Based on floating-point simulation, the amplitude and phase imbalance of the IQ mixer are less than 0.2 dB and 2 degree respectively. When the 220 GHz modulator and demodulator are set in a back to back configuration,a signal-to-noise ratio of 21 dB can be obtained using the 16QAM modulation type.
High Power 170 GHz Frequency Doubler Based on GaAs Schottky Diodes
TIAN Yaoling, HE Yue, HUANG Kun, JIANG Jun, LIN Changxing, ZHANG Jian
 doi: 10.1049/cje.2021.00.248
Abstract(94) HTML(36) PDF(12)
The research on high power 170 GHz frequency doubler based on the GaAs Schottky diodes is proposed in this paper. This basic doubler cell is developed with a 50- μ m-thick, 600- μ m-wide, and 2-mm-long AlN substrate with high thermal conductivity to reduce the thermal effect. Besides, power combined frequency doubler has been fabricated to improve the power capacity by a factor of two. Great agreement has been achieved between the simulated results based on electro-thermal model and measured performances. At room temperature, the 3 dB bandwidth of the single doubler based on GaAs Schottky diodes is 11.8 % over the frequency range from 160 to 180 GHz with pumping power of 150−330 mW. And the peak efficiency of the doubler is measured to be 33.1 % , while the maximum output power is 101.7 mW at 174.08 GHz. As for power combined circuit, the best efficiency is 30.1 % with a related output power of 204.6 mW. The proposed methods of developing high power multipliers can be applied in higher frequency band in the future.
Digital Signal Processing for High-Speed THz Communications
YU Jianjun, WEI Yi
 doi: 10.1049/cje.2021.00.258
Abstract(131) HTML(54) PDF(22)
The use of advanced digital signal processing (DSP) technology in the high-speed terahertz (THz) communication can effectively compensate the linear and nonlinear effects of the system and further improve the transmission performance of the system. This paper introduces the principle and application of advanced DSP algorithms such as probability shaping (PS) technology, Volterra series nonlinear compensation algorithm, Kramers-Kronig receiver, look-up table (LUT) pre-distortion compensation technology, pre-equalization and decision-directed least-mean-square equalization algorithm. Combined with DSP algorithms such as PS and LUT pre-distortion, using photon-assisted technology to successfully realize the wireless transmission of vector THz signals higher than 1Tbit/s on the sub-THz band (D-band) 4×4 multiple input multiple output system.
Blockchain-Empowered Dynamic Spectrum Management for Space-Air-Ground Integrated Network
LI Zuguang, WANG Wei, GUO Jia, ZHU Youwen, HAN Lu, WU Qihui
 doi: 10.1049/cje.2021.00.275
Abstract(118) HTML(43) PDF(17)
Space-air-ground integrated network (SAGIN) 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 ena-bled spectrum management framework for SAGIN, with inter-slice spectrum sharing and intra-slice spectrum allo-cation. Specifically, the inter-slice spectrum sharing is realized through a consortium blockchain formed by 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. 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.
Navigation for UAV Pair-Supported Relaying in Unknown IoT Systems with Deep Reinforcement Learning
HUANG Fei, LI Guangxia, WANG Haichao, TIAN Shiwei, YANG Yang, CHANG Jinghui
 doi: 10.1049/cje.2021.00.305
Abstract(84) HTML(34) PDF(15)
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 (Tx) and receiver (Rx). Our goal is that UAV-Tx gathers the data from each device then transfers the information to UAV-Rx, and UAV-Rx 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 UAV-Tx and UAV-Rx. 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’s in the phase of UAV-Tx’s receiving information or the phase of UAV-Tx’s forwarding information to UAV-Rx, the effectiveness and superiority of the proposed algorithm is demonstrated by extensive simulationsin in comparison to some base schemes under different scenarios.
Demand Learning and Cooperative Deployment of UAV Networks
ZHANG Xiao, WANG Xuehe, XU Xinping, ZHAO Yingchao
 doi: 10.1049/cje.2021.00.278
Abstract(51) HTML(17) PDF(12)
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.
Phase Noise Effects on the Performance of High-Order Digital Modulation Terahertz Communication System
DENG Xianjin, YANG Hao, WU Qiuyu, JIANG Jun, LIN Changxing
 doi: 10.1049/cje.2021.00.321
Abstract(79) HTML(30) PDF(15)
As the carrier frequency goes into the terahertz band, the phase noise of the signal source has increasing impacts on the performance of the communication system. Considering a 16QAM high-order digital modulation terahertz communication system (DMTCS), by comparing the influence of two kinds of local oscillator signal sources with different phase noise characteristics on the bit error rate (BER) performance of the system based on theoretical analysis and experimental research, it is found that the near-end phase noise of local oscillator signal source has a great influence on the BER performance of the DMTCS. The suppression of phase noise of the local oscillator signal source based on choosing loop bandwidth of the digital phase-locked loop (DPLL) at the receiving end is also discussed. It is found that the negative impacts of phase noise on BER performance of the system can be efficiently decreased by reasonably selecting the loop bandwidth of the DPLL.
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(64) HTML(23) PDF(13)
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. However, 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. Next, the characteristic of cavity-loaded slot is investigated to synthesize the low-SLL array antenna. Then, the unequal hybrid corporate feeding network is constructed to achieve sidelobe suppression in the E-plane. Finally, a pair of 16×8 low-SLL and high-effciency slot arrays is fashioned and confirmed experimentally. The bandwidth for the radiation effciency higher than 80% is 92.3~96.3 GHz. The SLLs in both E- and H-planes are below −19 dB.
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(69) HTML(22) 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(118) HTML(46) PDF(14)
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(53) HTML(18) PDF(2)
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(107) HTML(43) PDF(9)
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(101) HTML(43) PDF(14)
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(41) HTML(8) PDF(1)
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(129) HTML(50) 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(63) HTML(20) PDF(8)
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(123) HTML(48) PDF(17)
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.
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(143) HTML(58) PDF(13)
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. Meanwhile, it gets the local semantic embedding word vector based on the Word2vec. Therefore, 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.
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(129) HTML(47) PDF(14)
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(94) HTML(31) PDF(13)
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(246) HTML(81) 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(115) HTML(48) PDF(17)
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(123) HTML(39) PDF(19)
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.
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(320) HTML(139) PDF(45)
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.
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(342) HTML(155) PDF(37)
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(270) HTML(121) PDF(37)
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(154) HTML(64) PDF(25)
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(310) HTML(139) PDF(21)
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.
Backdoor Attacks on Image Classification Models in Deep Neural Networks
ZHANG Quanxin, MA Wencong, WANG Yajie, et al.
2022, 31(2): 199-212.   doi: 10.1049/cje.2021.00.126
Abstract(1179) HTML(513) PDF(239)
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.
Predicting the Power Spectrum of Amplified OFDM Signals Using Higher-Order Intercept Points
YAN Siyuan, YANG Xianzhen, WANG Xiaoru, et al.
2022, 31(2): 213-219.   doi: 10.1049/cje.2020.00.299
Abstract(260) HTML(114) PDF(62)
Orthogonal frequency-division multiplexing (OFDM) has been developed into a popular modulation scheme for wireless communication systems, used in applications such as LTE and 5G. In wireless communication systems, nonlinearity caused by radio frequency (RF) amplifiers will generate distortions to both passband and adjacent channels such that the transmission quality is degraded. The study of this article aims to predict the power spectrum for OFDM based signals at the output of an RF amplifier due to the nonlinearity. In this article, based on Taylor polynomial coefficients, a power spectrum expression for amplified OFDM signals in terms of intercept points (up to ${\boldsymbol{n}} $th-order) is derived. This model is useful to RF engineers in choosing and testing RF amplifiers with appropriate specifications, such as intercept points and gain, to meet the requirements of wireless standards. Measurements are carried out to confirm the results of the proposed model.
An Improved Navigation Pseudolite Signal Structure Based on the Kasami Sequences and the Pulsing Scheme
TAO Lin, SUN Junren, LI Guangchen, et al.
2022, 31(2): 220-226.   doi: 10.1049/cje.2020.00.403
Abstract(319) HTML(127) PDF(60)
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, makes it a feasible solution to mitigate the near-far effect, and performs better in the capture.
Labeled Multi-Bernoulli Maneuvering Target Tracking Algorithm via TSK Iterative Regression Model
WANG Xiaoli, XIE Weixin, LI Liangqun
2022, 31(2): 227-239.   doi: 10.1049/cje.2020.00.156
Abstract(171) HTML(72) PDF(29)
Aiming at the problem that the existing labeled multi-Bernoulli (LMB) method has a single and fixed model set, an 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.
Joint Spectrum Sensing and Spectrum Access for Defending Massive SSDF Attacks: A Novel Defense Framework
XU Zhenyu, SUN Zhiguo, GUO Lili, et al.
2022, 31(2): 240-254.   doi: 10.1049/cje.2021.00.090
Abstract(211) HTML(84) PDF(49)
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 Efficient Algebraic Solution for Moving Source Localization from Quadruple Hybrid Measurements
DING Ting, ZHAO Yongsheng, ZHAO Yongjun
2022, 31(2): 255-265.   doi: 10.1049/cje.2020.00.410
Abstract(228) HTML(93) PDF(55)
This paper deals with 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 a set 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 source position and velocity estimation from the quadruple hybrid measurements was proposed. The proposed algorithm converts the nonlinear measurement equations into a linear set of equations, which can then be used to estimate the source position and velocity 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 merely one-stage, which enables for source localization with the fewest receivers necessary. Theoretical accuracy analysis shows that the proposed algorithm reaches the Cramer-Rao lower bound, and simulation studies corroborate the efficiency and superiority of the proposed algorithm over other algorithms.
Multi-Traffic Targets Tracking Based on an Improved Structural Sparse Representation with Spatial-Temporal Constraint
YANG Honghong, SHANG Junchao, LI Jingjing, et al.
2022, 31(2): 266-276.   doi: 10.1049/cje.2020.00.007
Abstract(224) HTML(92) PDF(37)
Vehicles or pedestrians tracking is an important task in intelligent transportation system. In this paper, we propose an online multi-object tracking for intelligent traffic platform that employs improved sparse representation and structural constraint. We first build the spatial-temporal constraint via the geometric relations and appearance of tracked objects, then we construct a robust appearance model by incorporating the discriminative sparse representation with weight constraint and local sparse appearance with occlusion analysis. Finally, we complete data association by using maximum a posteriori in a Bayesian framework in the pursuit for the optimal detection estimation. Experimental results in two challenging vehicle tracking benchmark datasets show that the proposed method has a good tracking performance.
Efficient 3D Hilbert Curve Encoding and Decoding Algorithms
JIA Lianyin, LIANG Binbin, LI Mengjuan, et al.
2022, 31(2): 277-284.   doi: 10.1049/cje.2020.00.171
Abstract(309) HTML(130) PDF(59)
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.
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](382) [PDF 634KB](374)
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](524) [PDF 1424KB](2650)
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](649) [PDF 6951KB](2537)
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](190) [PDF 566KB](326)
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](547) [PDF 827KB](897)
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](278) [PDF 1133KB](514)
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](438) [PDF 1983KB](1073)
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](374) [PDF 583KB](1709)
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](154) [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](213) [PDF 411KB](1129)
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](814) [PDF 423KB](104)
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](1294) [PDF 832KB](917)
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](906) [PDF 3162KB](152)
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](1333) [PDF 273KB](168)
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](731) [PDF 334KB](151)
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](873) [PDF 4261KB](337)
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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