Chinese Journal of Electronics

A 2 Kbits Low Power EEPROM for Passive RFID Tag IC

HU Jianguo, WANG Deming, WU Jing

, Available online , doi: 10.1049/cje.2021.00.044

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Abstract:
This paper presents a low power and low cost EEPROM memory for 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 write 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 memory is integrated into an RFID tag chip and fabricated using a 180 nm 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 are 0.68 μ A and 30 μ A respectively, which has the characteristics of low power consumption.

Hyperspectral Image Classification Based on Capsule Network

MA Qiaoyu, ZHANG Xin, ZHANG Chunlei, ZHOU Heng

, Available online , 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.

Proving Mutual Authentication Property of RCIA Protocolin RFID Based on Logic of Events

ZHONG Xiaomei, XIAO Meihua, ZHANG Tong, YANG Ke, LUO Yunxian

, Available online , 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.

Domain Adaptive Learning with Multi-granularity Features for Unsupervised Person Re-identification

FU Lihua, DU Yubin, DING Yu, WANG Dan, JIANG Hanxu, ZHANG Haitao

, Available online , 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 Combined Countermeasure Against Side-Channel and Fault Attack with Threshold Implementation Technique

JIAO Zhipeng, CHEN Hua, FENG Jingyi, KUANG Xiaoyun, YANG Yiwei, LI Haoyuan, FAN Limin

, Available online , doi: 10.1049/cje.2021.00.089

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Side-channel attack (SCA) and Fault attack (FA) are two classical physical attacks against cryptographic implementation. In order to resist them, we present a combined countermeasure scheme which can resist both SCA and FA. The scheme combines the Threshold implementation (TI) and duplication-based exchange technique. The exchange technique can confuse the fault propagation path and randomize the faulty values. The TI technique can ensure a provable security against SCA. Moreover, it can also help to resist the FA by its incomplete property and random numbers. Compared with other methods, the proposed scheme has simple structure, which can be easily implemented in hardware and result in a low implementation cost. Finally, we present a detailed design for the block cipher LED and implement it. The hardware cost evaluation shows our scheme has the minimum overhead factor.

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

, Available online , doi: 10.1049/cje.2021.00.113

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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.

Timely Data Delivery for Energy-harvesting IoT Devices

LU Wenwei, GONG Siliang, ZHU Yihua

, Available online , doi: 10.1049/cje.2021.00.005

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The devices in the Internet of Things (IoT) gain capability of sustainable operation when they harvest energy from ambient sources. Fluctuation in the harvested energy may cause the energy-harvesting IoT devices to suffer from frequent energy shortage, which may bring in intolerable packet delay or packet discarding. It is important to design a low-delay packet delivery scheme that adapts to variation in the harvested energy. In this paper, we present the Timely Data Delivery (TDD) scheme for the IoT devices. Using Markov chain, we develop a probability model for the TDD scheme, which leads to the expected number of packets delivered in an operation cycle, the expected numbers of packets waiting in the data buffer in an operation cycle and an energy-harvesting cycle, and the expected packet delay. Additionally, we formulate the optimization problem that minimizes the packet delay in the TDD scheme, and the solution to the optimization problem yields the optimal parameters for the IoT devices to determine when to harvest energy and when to deliver data under the TDD scheme. The simulation results show that the proposed TDD scheme outperforms the existing schemes in terms of packet delay.

Maximum Correntropy High-order Extended Kalman Filter

SUN Xiaohui, WEN Chenglin, WEN Tao

, Available online , 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.

Statistical Model on CRAFT

WANG Caibing, GUO Hao, YE Dingfeng, WANG Ping

, Available online , doi: 10.1049/cje.2021.00.092

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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.

Multi-matching Nested Languages

LIU Jin, DUAN Zhenhua, TIAN Cong

, Available online , doi: 10.1049/cje.2020.00.028

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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 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.

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

, Available online , doi: 10.1049/cje.2021.00.103

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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 model developed by New York University (NYU), 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 2nd-Order Noise Shaping SAR ADC Realizing NTF Zero-Pole Optimization Based on IIR-FIR Filter

ZHANG Yanbo, LIU Shubin, LIANG Yuhua, ZHU Zhangming

, Available online , doi: 10.1049/cje.2020.00.309

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This brief reports a successive approximation register (SAR) analog-to-digital converters (ADCs) with the 2^nd-order and optimized noise shaping (NS) scheme. Based on the feed-forward structure, both NTF zero and pole optimization have been achieved simultaneously utilizing a cascaded IIR-FIR filter. The analysis shows that the proposed structure provides a better noise shaping (NS) efficiency than prior arts. The efficiency of the proposed NS scheme and the performance of the ADC are verified by simulation achieving a 14.2 effective number of bits with an 8-bit SAR ADC architecture at an over sampling rate (OSR) 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.

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

, Available online , doi: 10.1049/cje.2021.00.117

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Since Differential Fault Analysis (DFA) was first implemented on 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. In this paper, 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.

PALES: Optimizing Secure Data Deletion in SSDs via Page Group and Reprogram Speedup

NIE Shiqiang, WU Weiguo, ZHANG Chi, ZHANG Chen

, Available online , doi: 10.1049/cje.2020.00.379

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As SSDs 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 SSDs. 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.

Distinguishing Between Natural and GAN-Generated Face Images by Combining Global and Local Features

CHEN Beijing, TAN Weijin, WANG Yiting, ZHAO Guoying

, Available online , doi: 10.1049/cje.2020.00.372

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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.

A Fine-Grained Flash-Memory Fragment Recognition Approach for Low-Level Data Recovery

ZHANG Li, HAO Shengang, ZHANG Quanxin

, Available online , doi: 10.1049/cje.2020.00.206

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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.

An Improved Navigation Pseudolite Signal Structure Based on the Kasami Sequences and the Pulsing Scheme

TAO Lin, SUN Junren, LI Guangchen, ZHU Bocheng

, Available online , doi: 10.1049/cje.2020.00.403

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Pseudolites (PLs) are ground-based satellites, providing users with navigation solutions. However, implementation of the PL system leads to the near-far problem. In this paper, we proposed an improved navigation PL signal structure of combing kasami sequences and the pulsing scheme to mitigate the near-far effect. The pulse modulation method is adopted to ensure that the PLs transmit signals at different timeslots and reduce the PL signals’ mutual interference. Additionally, we employ the small set of kasami sequences with good cross-correlation properties to improve the anti-interference ability. A simulation test based on software is carried out to evaluate the performance of the proposed signal. The simulation proves that the improved PL signal has an impulsive power spectral density (PSD), makes it a feasible solution to mitigate the near-far effect, and performs better in the capture.

A Decision-Making Method for Air Combat Maneuver Based on Hybrid Deep Learning Network

LI Bo, LIANG Shiyang, CHEN Daqing, LI Xitong

, Available online , 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: 1) time series data is used as the basis of decision-making, which is more in line with the actual decision-making process. 2) using stacked sparse auto-encoder network to reduce the dimension of time series data to predict the result more accurately. 3) the model takes the maneuver control variables as the output to control the maneuver, making 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.

A Fast Two-tone Active Load-Pull Algorithm for Assessing the Non-linearity of RF Devices

SU Jiangtao, CAI Jialing, ZHENG Xing, SUN Lingling

, Available online , doi: 10.1049/cje.2020.00.060

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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 for the first time 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 results 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 the models of radio frequency transistors.

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Chinese Journal of Electronics