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2022, Volume 31,  Issue 1

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Investigation and Comparison of 5G Channel Models: From QuaDRiGa, NYUSIM, and MG5G Perspectives
PANG Lihua, ZHANG Jin, ZHANG Yang, HUANG Xinyi, CHEN Yijian, LI Jiandong
2022, 31(1): 1-17. doi: 10.1049/cje.2021.00.103
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Abstract:
This paper investigates and compares three channel models for the fifth generation (5G) wireless communications: the quasi deterministic radio channel generator (QuaDRiGa), the NYUSIM channel simulator model developed by New York University, and the more general 5G (MG5G) channel model. First, the characteristics of the modeling processes of the three models are introduced from the perspective of model framework. Then, the small-scale parameter modeling strategies of the three models are compared from space/time/frequency domains as well as polarization aspect. In particular, the drifting of small-scale parameters is introduced in detail. Finally, through the simulation results of angular power spectrum, Doppler power spectrum density, temporal autocorrelation function, power delay profile, frequency correlation function, channel capacity, and eigenvalue distribution, the three models are comprehensively investigated. According to the simulation results, we clearly analyze the impact of the modeling strategy on the three channel models and give certain evaluations and suggestion which lay a solid foundation for link and system-level simulations for 5G transmission algorithms.
A 2 Kbits Low Power EEPROM for Passive RFID Tag IC
HU Jianguo, WANG Deming, WU Jing
2022, 31(1): 18-24. doi: 10.1049/cje.2021.00.044
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This paper presents a low power consumption and low cost electrically erasable programmable read-only memory (EEPROM) for radio frequency identification (RFID) tag chip. A read-write circuit with parallel input and serial output is proposed. Only one sensitive amplifier is used to read the data in memory, which can effectively reduce the power consumption of the read operation. Because the tag may be read or written while moving, the internal voltage may change in a wide range. Therefore, this paper designs a charge pump and its control circuit with wide voltage working range. The proposed EEPROM is integrated into an RFID tag chip and fabricated using a 180 nm complementary metal-oxide semiconductor (CMOS) process. Experimental results show that the circuit can work in the input voltage range of 1 V to 1.8 V, and the minimum current of read operation and write operation is 0.68 μ A and 30 μ A respectively, which has the characteristic of low power consumption.
A Fast Two-Tone Active Load-Pull Algorithm for Assessing the Non-linearity of RF Devices
SU Jiangtao, CAI Jialing, ZHENG Xing, SUN Lingling
2022, 31(1): 25-32. doi: 10.1049/cje.2020.00.060
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Abstract:
Radio frequency (RF) devices used in modern wireless systems must meet increasingly complicated spectral constraints while still operating with high power efficiency. A fast real-time two-tone active load-pull algorithm is proposed to assess the relationship between nonlinear performance with associated device load impedance variations. This algorithm employs real time measurement data to extract two-tone local nonlinear behaviour model, which is further used for the prediction of injected signal value in the active real-time load-pull system, therefore minimizing the number of iterations required for load emulation. The proposed method was validated on a real two-tone load-pull measurement bench using off-the-shelf instruments. The result shows that the measurement speed has been greatly increased without sacrifice of the impedance emulation accuracy. This intelligent two-tone load-pull algorithm is expected to be applied in the designing of modern communication system and radar transmitters, as well as the validation of the models of radio frequency transistors.
A 2nd-Order Noise Shaping SAR ADC Realizing NTF Zero-Pole Optimization Based on IIR-FIR Filter
ZHANG Yanbo, LIU Shubin, LIANG Yuhua, ZHU Zhangming
2022, 31(1): 33-39. doi: 10.1049/cje.2020.00.309
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Abstract:
This paper reports a successive approximation register (SAR) analog-to-digital converters (ADCs) with the 2nd-order and optimized noise shaping (NS) scheme. Based on the feed-forward structure, both noise transfer function (NTF) zero and pole optimization have been achieved simultaneously utilizing a cascaded IIR-FIR (infinite impulse response and finite impulse response) filter. The analysis shows that the proposed structure provides a better NS efficiency than prior arts. The efficiency of the proposed NS scheme and the ADC performance are verified by simulation achieving a 14.2 effective number of bits with an 8-bit SAR ADC architecture at an over sampling rate of 8 sampled at 100 MS/s. Taking advantage of the NTF zero-pole optimization, a low resolution SAR ADC is allowed under the same quantization noise budget in this structure.
PALES: Optimizing Secure Data Deletion in SSDs via Page Group and Reprogram Speedup
NIE Shiqiang, WU Weiguo, ZHANG Chi, ZHANG Chen
2022, 31(1): 40-51. doi: 10.1049/cje.2020.00.379
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Abstract:
As solid-state drives (SSD) have been widely adopted, secure data deletion becomes an essential component for ensuring user privacy, preventing sensitive data leakage. Due to the erase-before-write property, erasure operation and scrub operation substituting for overwriting technologies are proposed to meet the requirements. However, both methods bring the severe page-copying issue, declining read/write performance, and shortening the lifetime of SSD. In this paper, this issue is alleviated by reserving pages at the page allocation stage to mitigate program disturbance and increasing program step voltage during reprogramming operation to reduce the reprogram latency. The proposed scheme is evaluated by a series of experiments; the results show that the proposed scheme could achieve significant deletion time reduction and alleviate page-copying overhead.
A Programmable Pre-emphasis Technique with Combined RLC Source Degeneration for High-Speed Serial Link Transmitters
WANG Tonghui, ZOU Jiaxuan, QI Huanhuan, WANG Xi, WANG Jingbo, ZHANG Hong
2022, 31(1): 52-58. doi: 10.1049/cje.2021.00.055
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Abstract:
This paper presents a clock-less programmable pre-emphasis technique realized by a driver with combined resistive-inductive-capacitive source degeneration for high-speed serial link transmitters. The addition of a series inductive-capacitive resonance network expands the bandwidth of the driver without lowering down the pre-emphasis gain. The driver with the proposed pre-emphasis technique provides adjustable gain from mid-frequency to high-frequency which is controlled by a tunable tail current, offering the capability for the transmitter to adapt to different cable loss from 0 to 6 dB. The driver has been employed in a 2:4 multiplexing and cable driving integrated circuit for 1.65 Gbps high-definition-multimedia-interface and digital-visual-interface application to drive up to 7 m 24-American-wire-gauge cable. Fabricated in 180 nm SiGe BiCMOS technology, the transmitter consumes 68.6 mW for 6 dB pre-emphasis under 3.3 V power supply.
Distinguishing Between Natural and GAN-Generated Face Images by Combining Global and Local Features
CHEN Beijing, TAN Weijin, WANG Yiting, ZHAO Guoying
2022, 31(1): 59-67. doi: 10.1049/cje.2020.00.372
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Abstract:
With the development of face image synthesis and generation technology based on generative adversarial networks (GANs), it has become a research hotspot to determine whether a given face image is natural or generated. However, the generalization capability of the existing algorithms is still to be improved. Therefore, this paper proposes a general algorithm. To do so, firstly, the learning on important local areas, containing many face key-points, is strengthened by combining the global and local features. Secondly, metric learning based on the ArcFace loss is applied to extract common and discriminative features. Finally, the extracted features are fed into the classification module to detect GAN-generated faces. The experiments are conducted on two publicly available natural datasets (CelebA and FFHQ) and seven GAN-generated datasets. Experimental results demonstrate that the proposed algorithm achieves a better generalization performance with an average detection accuracy over 0.99 than the state-of-the-art algorithms. Moreover, the proposed algorithm is robust against additional attacks, such as Gaussian blur, and Gaussian noise addition.
Differential Fault Analysis on 3DES Middle Rounds Based on Error Propagation
MA Xiangliang, ZHANG Lizhen, WU Liji, LI Xia, ZHANG Xiangmin, LI Bing, LIU Yuling
2022, 31(1): 68-78. doi: 10.1049/cje.2021.00.117
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Abstract:
Since differential fault analysis (DFA) was first implemented on data encryption standard (DES), many scholars have improved this attack and extended the limit of the original last two rounds to the earlier rounds. However, the performance of the novel attacks which target middle rounds is not effective, i.e. the number of correct/incorrect ciphertexts required is very large and the recovered result maybe not correct. We address this problem by presenting new DFA methods that can break 3DES when injecting faults at round 12 or 13. By simulating the process of single-bit error propagation, we have built two kinds of error propagation models as well as an intermediate error propagation state table. Then we simplify the intermediate states into state templates that will be further used to locate the injected fault position, which is the main difficulty of implementing fault injection in the middle rounds. Finally, in terms of the idea of error propagation and probability theory, we can recover the last round key only using 2 sets of correct/incorrect ciphertexts when inducting fault in the 13th round and 4 sets of correct/incorrect ciphertexts when inducting fault in the 12th round.
Proving Mutual Authentication Property of RCIA Protocol in RFID Based on Logic of Events
ZHONG Xiaomei, XIAO Meihua, ZHANG Tong, YANG Ke, LUO Yunxian
2022, 31(1): 79-88. doi: 10.1049/cje.2021.00.101
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The increasing commercialization and massive deployment of radio frequency identification (RFID) systems has raised many security related issues which in return evokes the need of security protocols. Logic of events theory (LoET) is a formal method for constructing and reasoning about distributed systems and protocols that involve concepts of security. We propose fresh ciphertext and ciphertext release lemmas to extend LoET for analyzing and proving the security of authentication protocols that use symmetric key cryptography more than just digital signature. Based on the extended LoET we formally analyze and prove the authentication property of RCIA protocol, which provides mutual authentication between Tag and Reader in RFID system. Our proof approach based on extended LoET could be applied to the design and analysis of such ultralightweight RFID mutual authentication protocols.
MILP-Based Linear Attacks on Round-Reduced GIFT
CUI Yaxin, XU Hong, QI Wenfeng
2022, 31(1): 89-98. doi: 10.1049/cje.2020.00.113
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GIFT is a lightweight block cipher with an substitution-permutation-network (SPN) structure proposed in CHES 2017. It has two different versions whose block sizes are 64 and 128 respectively. In RSA 2019, Zhu et al. found some differential characteristics of GIFT with mixed integer linear programming (MILP) method and presented corresponding differential attacks. In this paper, we further find some linear characteristics with MILP method. For GIFT-64, we find two 11-round linear characteristics with correlation ${\boldsymbol{2^{-29}}}$, and use one of them to present a 16-round linear attack on GIFT-64 by adding 4 rounds before and one round after the linear characteristic. For GIFT-128, we find a 16-round linear characteristic with correlation ${\boldsymbol{2^{-62}}}$. As far as we know, it is the longest linear characteristic found for GIFT-128. Using the 16-round linear characteristic, we present a 20-round linear attack on GIFT-128 by adding 2 rounds before and 2 rounds after the linear characteristic.
Tumor Classification of Gene Expression Data by Fuzzy Hybrid Twin SVM
DUAN Hua, FENG Tong, LIU Songning, ZHANG Yulin, SU Jionglong
2022, 31(1): 99-106. doi: 10.1049/cje.2020.00.260
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A new classification model, the fuzzy hybrid twin support vector machine (TWSVM), namely FHTWSVM, is proposed by combining the fuzzy TWSVM and the hypersphere support vector machine (SVM). The hypersphere SVM is utilized for generating the hyperspheres for the positive and negative class with the smallest possible radius, so that the hyperspheres can contain as many samples as possible. The samples which the hyperspheres cover form a new sample set. Furthermore a distance-based fuzzy function is utilized to calculate the fuzzy factors for the samples. Finally FHTWSVM is used to train all samples with the parameters optimized by grid search. This method can maximize intra-class clustering for noise removal and reduce the influence of outliers. To demonstrate the superiority of the performance of FHTWSVM over other classifiers, e.g., KNN, RF, Bayesian, TWSVM, AdaBoost and XGBoost, a series of experiments is conducted using eight gene expression datasets. The evaluation results show that the proposed approach can improve the classification performance as well as reduce prediction errors for the datasets.
A Decision-Making Method for Air Combat Maneuver Based on Hybrid Deep Learning Network
LI Bo, LIANG Shiyang, CHEN Daqing, LI Xitong
2022, 31(1): 107-115. doi: 10.1049/cje.2020.00.075
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In this paper, a hybrid deep learning network-based model is proposed and implemented for maneuver decision-making in an air combat environment. The model consists of stacked sparse auto-encoder network for dimensionality reduction of high-dimensional, dynamic time series combat-related data and long short-term memory network for capturing the quantitative relationship between maneuver control variables and the time series combat-related data after dimensionality reduction. This model features: using time series data as the basis of decision-making, which is more in line with the actual decision-making process; using stacked sparse auto-encoder network to reduce the dimension of time series data to predict the result more accurately; in addition, taking the maneuver control variables as the output to control the maneuver, which makes the maneuver process more flexible. The relevant experiments have demonstrated that the proposed model can effectively improve the prediction accuracy and convergence rate in the prediction of maneuver control variables.
Domain Adaptive Learning with Multi-Granularity Features for Unsupervised Person Re-identification
FU Lihua, DU Yubin, DING Yu, WANG Dan, JIANG Hanxu, ZHANG Haitao
2022, 31(1): 116-128. doi: 10.1049/cje.2020.00.072
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Unsupervised person re-identification (Re-ID) aims to improve the model’s scalability and obtain better Re-ID results in the unlabeled data domain. In this paper, we propose an unsupervised person Re-ID method based on multi-granularity feature representation and domain adaptive learning, which can effectively improve the performance of unsupervised person re-identification. The multi-granularity feature extraction module integrates global and local information of different granularity to obtain the multi-granularity person feature representation with rich discriminative information. The source domain classification module learns the labeled source dataset classification and obtains the person’s discriminative knowledge in the source domain. On this basis, the domain adaptive module further considers the difference between the target domain and the source domain to learn adaptively for the model. Experiments on multiple public datasets show that the proposed method can achieve a competitive performance among other state-of-the-art unsupervised Re-ID methods.
A Hypernetwork Based Model for Emergency Response System
WANG Wei, LIU Shufen, LI Bing
2022, 31(1): 129-136. doi: 10.1049/cje.2020.00.335
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Current emergency response systems are facing several challenges, including complex emergency network structure definition and inefficient emergency scheduling. For these problems, the paper analyzes the characteristics of emergency networks, and abstracts them into multiple interconnected, interdependent and interactive networks according to the characteristics of hierarchy, attribute and function, and then proposes a hypernetwork based model and its constraint conditions. Furthermore, the paper proposes an emergency scheduling method. This method fully considers the psychological factors of people and the rescue cost factors in disasters in order to balance the interests among different levels of network during rescue. The experiment results show that the model and the method proposed in this paper can not only better reveal the composition and structure of emergency response system, but also effectively balance the cost and the satisfaction in rescue.
Multi-Matching Nested Languages
LIU Jin, DUAN Zhenhua, TIAN Cong
2022, 31(1): 137-145. doi: 10.1049/cje.2020.00.228
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The data with both a linear ordering and a hierarchically nested one-to-one matching of items is ubiquitous, including parenthesis matching languages and hypertext markup language/extensive markup language (HTML/XML) documents. There exist some real-world problems which are beyond one-to-one matching. They have a multi-matching structure including one-to-n or n-to-one matching relation. Multiple threads can simultaneously read the same file, one block of memory can be referenced by multiple pointers in programs. We propose a new model of multi-matching nested relations consisting of a sequence of linearly ordered call, return and internal positions and augmented with one-to-one, one-to-n or n-to-one matching nested edges from calls to returns. Via linear encoding by introducing tagged letters, multi-matching nested words are obtained over a tagged alphabet. We put forward multi-matching nested traceable automata and the accepted languages are called multi-matching nested languages. Multi-matching nested grammars are presented which have the same expressive power as the proposed automata. An application is displayed to illustrate how the automata work.
Hyperspectral Image Classification Based on Capsule Network
MA Qiaoyu, ZHANG Xin, ZHANG Chunlei, ZHOU Heng
2022, 31(1): 146-154. doi: 10.1049/cje.2021.00.056
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The conventional convolutional neural network performs not well enough in the ground objects classification because of its insufficient ability in maintaining sensitive spectral information and characterizing the covariance of spatial structure, resulting from the narrow sensitive frequency band and complex spatial structure with diversity of hyperspectral remote sensing data which caused more serious phenomena of “same material, different spectra” and “different material, same spectra”. Therefore, an improved capsule network is proposed and introduced into hyperspectral image target recognition. A convolution structure combining shallow features and multi-scale depth features is put forward to reduce the phenomena of “different material, same spectra” firstly, and then the diversity of the spatial structure is expressed by the capsule vector and sub-capsule division in channel wise, so that the averaging effect of the convolution process is weakened in the spectral domain and the spatial domain to reduce the phenomena of “same material, different spectra”. By comparing the experimental results on the hyperspectral data sets such as Indian Pines, Salinas, Tea Tree and Xiongan, the capsule network shows strong spatial structure expression ability, flexible deep and shallow feature fusion ability in multi-scale, and its accuracy in target recognition is better than that of conventional convolutional neural networks, so it is suitable for the recognition of complex targets in hyperspectral images.
Multi-Distributed Speech Emotion Recognition Based on Mel Frequency Cepstogram and Parameter Transfer
LIN Long, TAN Liang
2022, 31(1): 155-167. doi: 10.1049/cje.2020.00.080
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Speech emotion recognition (SER) is the use of speech signals to estimate the state of emotion. At present, machine learning is one of the main research methods of SER, the test and training dataS of traditional machine learning all have the same distribution and feature space, but the data of speech is accessed from different environments and devices, with different distribution characteristics in real life. Thus, the traditional machine learning method is applied to the poor performance of SER. This paper proposes a multi-distributed SER method based on Mel frequency cepstogram (MFCC) and parameter transfer. The method is based on single-layer long short-term memory (LSTM), pre-trained inception-v3 network and multi-distribution corpus. The speech pre-processed MFCC is taken as the input of single-layer LSTM, and input to the pre-trained inception-v3 network. The features are extracted through the pre-trained inception-v3 model. Then the features are sent to the newly defined the fully connected layer and classification layer, let the parameters of the fully connected layer be fine-tuned, finally get the classification result. The experiment proves that the method can effectively complete the classification of multi-distribution speech emotions and is more effective than the traditional machine learning framework of SER.
Action Status Based Novel Relative Feature Representations for Interaction Recognition
LI Yanshan, GUO Tianyu, LIU Xing, LUO Wenhan, XIE Weixin
2022, 31(1): 168-180. doi: 10.1049/cje.2020.00.088
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Skeleton-based action recognition has always been an important research topic in computer vision. Most of the researchers in this field currently pay more attention to actions performed by a single person while there is very little work dedicated to the identification of interactions between two people. However, the practical application of interaction recognition is actually more critical in our society considering that actions are often performed by multiple people. How to design an effective scheme to learn discriminative spatial and temporal representations for skeleton-based interaction recognition is still a challenging problem. Focusing on the characteristics of skeleton data for interactions, we first define the moving distance to distinguish the action status of the participants. Then some view-invariant relative features are proposed to fully represent the spatial and temporal relationship of the skeleton sequence. Further, a new coding method is proposed to obtain the novel relative feature representations. Finally, we design a three-stream CNN model to learn deep features for interaction recognition. We evaluate our method on SBU dataset, NTU RGB+D 60 dataset and NTU RGB+D 120 dataset. The experimental results also verify that our method is effective and exhibits great robustness compared with current state-of-the-art methods.
Parameter Estimation of Multiple Mono-Pulse Radar Signals Intercepted by Nyquist Folding Receiver Using Periodic Chirp Local Oscillator Based on Periodic Fractional Autocorrelation
QIU Zhaoyang, LI Tingpeng, WANG Yiming
2022, 31(1): 181-189. doi: 10.1049/cje.2020.00.305
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The frequency space of modern radar system has been extended to a wide range. To intercept the modern radar signals with high interception probability, the wideband receiving is of significance to the radar reconnaissance receiver. The Nyquist folding receiver (NYFR) is a novel ultra-wideband receiving architecture, which requires a small amount of equipment. Because the mono-pulse radar signal is widely used in radar systems, the multiple mono-pulse radar signals will be intercepted by the NYFR. The parameter estimation of multiple simultaneous arrival mono-pulse radar signals intercepted by the NYFR using periodic chirp local oscillator is considered. The definition of periodic fractional autocorrelation (PFA) is proposed. Based on the PFA, a novel fast parameter estimation method is given. The proposed estimation approach can estimate the multiple NYFR outputs under frequency aliasing condition and has low computational complexity. Simulation results show the effectiveness of the proposed method.
Maximum Correntropy High-Order Extended Kalman Filter
SUN Xiaohui, WEN Chenglin, WEN Tao
2022, 31(1): 190-198. doi: 10.1049/cje.2020.00.334
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In this paper, a novel maximum correntropy high-order extended Kalman filter (H-MCEKF) is proposed for a class of nonlinear non-Gaussian systems presented by polynomial form. All high-order polynomial terms in the state model are defined as implicit variables and regarded as parameter variables; the original state model is equivalently formulated into a pseudo-linear form with original variables and parameter variables; the dynamic relationship between each implicit variable and all variables is modeled, then an augmented linear state model appears by combing with pseudo-linear state model; similarly, the nonlinear measurement model can be equivalently rewritten into linear form; once again, the statistical characteristics of non-Gaussian modeling error are described by mean value and variance based on their finite samples; combing original measurement model with predicted value regarded as added state measurement, a cost function to solve the state estimation based on maximum correntropy criterion (MCC) is constructed; on the basis of this cost function, the state estimation problem can be equivalently converted into a recursive solution problem in the form of Kalman filter, in which the filter gain matrix is solved by numerical iteration though its fixed-point equation; illustration examples are presented to demonstrate the effectiveness of the new algorithm.