Online First

Online First Papers are peer-reviewed and accepted for publication. Note that the papers under this directory, they are posted online prior to technical editing and author proofing. Please use with caution.
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Dispersed Computing Resource Discovery Model and Algorithm for Polymorphic Migration Network Architecture
ZHOU Chengcheng, ZHANG Lukai, ZENG Guangping, LIN Fuhong
, Available online  , doi: 10.23919/cje.2022.00.305
Abstract(62) HTML (31) PDF(8)
Dynamic resource discovery in a network of dispersed computing resources is an open problem. The establishment and maintenance of resource pool information are critical, which involves both the polymorphic migration of the network and the time and energy costs resulting from node selection and frequent interactions of information between nodes. The resource discovery problem for dispersed computing can be considered a dynamic multi-level decision problem. A bi-level programming model of dispersed computing resource discovery is developed, which is driven by time cost, energy consumption and accuracy of information acquisition. The upper-level model is to design a reasonable network structure of resource discovery, and the lower-level model is to explore an effective discovery mode. Complex network topology features are used for the first time to analyze the polymorphic migration characteristics of resource discovery networks. We propose an integrated calibration method for energy consumption parameters based on the two discovery modes. A symmetric trust region based heuristic algorithm (STRA) is proposed for solving the system model. The numerical simulation is performed in a dispersed computing network with multiple modes and topological states, which proves the feasibility of the model and the effectiveness of the algorithm.
Asymptotically Optimal Golay-ZCZ Sequence Sets with Flexible Length
GU Zhi, ZHOU Zhengchun, Adhikary Avik Ranjan, FENG Yanghe, FAN Pingzhi
, Available online  , doi: 10.23919/cje.2022.00.266
Abstract(37) HTML (18) PDF(8)
Zero correlation zone (ZCZ) sequences and Golay complementary sequences are two kinds of sequences with different preferable correlation properties. Golay-ZCZ sequences are special kinds of complementary sequences which also possess a large ZCZ and are good candidates for pilots in OFDM systems. Known Golay-ZCZ sequences reported in the literature have a limitation in the length which is the form of a power of 2. In this paper, we propose two constructions of Golay-ZCZ sequence sets with new parameters which generalize the constructions of Gong et al. (IEEE Transaction on Communications 61(9), 2013) and Chen et al (IEEE Transaction on Communications 61(9), 2018). Notably, one of the constructions results in optimal binary Golay-ZCZ sequences, while the other results in asymptotically optimal polyphase Golay-ZCZ sequences as the number of sequences increases. We also show, through numerical simulations, the applicability of the proposed Golay-ZCZ sequences in ISI channel estimation. Interestingly, in certain application scenarios, the proposed Golay-ZCZ sequences performs better as compared to the existing state-of-the-art sequences.
Zero-Cerd: A Self-Blindable Anonymous Authentication System Based on Blockchain
YANG Kunwei, YANG Bo, WANG Tao, ZHOU Yanwei
, Available online  , doi: 10.23919/cje.2022.00.047
Abstract(228) HTML (114) PDF(26)
While the internet of things brings convenience to people’s lives, it will also bring people hidden worries about data security. As an important barrier to protect data security, identity authentication is widely used in the internet of things. However, it is necessary to protect users’ identity privacy while authenticating their identity. Anonymous authentication technology is often used to solve the contradiction between legitimacy and privacy in the authentication process. The existing anonymous authentication scheme has many problems in practical application such as the inability to achieve complete anonymity, the high computational complexity of the algorithm, and the corruption of the central authority. Aiming at the privacy of authentication, we propose Zero-Cerd, a self-blindable anonymous authentication system based on blockchain and dynamic accumulator. The self-blinding properties of the credential enable the users themselves to generate a new validly pseudonymous credential. With the help of zero-knowledge proof technology, users can prove the validity of their credentials without disclosing any information. Security analysis shows that our scheme has achieved the expected security objectives. Compared with the existing schemes, our scheme has the advantages of complete anonymity and high efficiency, and is more suitable for IoT applications with privacy protection requirements.
Recover the Secret Components in a ForkCipher
HOU Tao, ZHANG Jiyan, CUI Ting
, Available online  , doi: 10.23919/cje.2021.00.368
Abstract(198) HTML (103) PDF(16)
Recently, a new cryptographic primitive has been proposed called ForkCiphers. This paper aims at proposing new generic cryptanalysis against such constructions. We give a generic method to apply existing decompositions againt the underlying block cipher ${{\cal{{E}}}}^{\boldsymbol{r}}$ on the forking variant Fork${{\cal{E}}}$-(r−1)-r0-(r+1−r0). As application, we consider the security of ForkSPN and ForkFN with secret inner functions. We provide a generic attack against ForkSPN-2-r0-(4−r0) based on the decomposition of SASAS. And also we extend the decomposition of Biryukov et al. against Feistel networks in SAC 2015 to get all the unknown round functions in ForkFN-r-r0-r1 for r$\leq$6 and r0+r1$\leq$8. Therefore, compared with the original block cipher, the forking version requires more iteration rounds to resist the recovery attack.
A New Edge Perturbation Mechanism for Privacy-Preserving Data Collection in IOT
CHEN Qiuling, YE Ayong, ZHANG Qiang, HUANG Chuan
, Available online  , doi: 10.23919/cje.2021.00.411
Abstract(377) HTML (187) PDF(23)
A growing amount of data containing the sensitive information of users is being collected by emerging smart connected devices to the center server in Internet of things (IoT) era, which raises serious privacy concerns for millions of users. However, existing perturbation methods are not effective because of increased disclosure risk and reduced data utility, especially for small data sets. To overcome this issue, we propose a new edge perturbation mechanism based on the concept of global sensitivity to protect the sensitive information in IoT data collection. The edge server is used to mask users’ sensitive data, which can not only avoid the data leakage caused by centralized perturbation, but also achieve better data utility than local perturbation. In addition, we present a global noise generation algorithm based on edge perturbation. Each edge server utilizes the global noise generated by the center server to perturb users’ sensitive data. It can minimize the disclosure risk while ensuring that the results of commonly performed statistical analyses are identical and equal for both the raw and the perturbed data. Finally, theoretical and experimental evaluations indicate that the proposed mechanism is private and accurate for small data sets.
A Semi-shared Hierarchical Joint Model for Sequence Labeling
LIU Gongshen, DU Wei, ZHOU Jie, LI Jing, CHENG Jie
, Available online  , doi: 10.23919/cje.2020.00.363
Abstract(336) HTML (165) PDF(14)
Multi-task learning is an essential yet practical mechanism for improving overall performance in various machine learning fields. Owing to the linguistic hierarchy, the hierarchical joint model is a common architecture used in natural language processing. However, in the state-of-the-art hierarchical joint models, higher-level tasks only share bottom layers or latent representations with lower-level tasks thus ignoring correlations between tasks at different levels, i.e., lower-level tasks cannot be instructed by the higher features. This paper investigates how to advance the correlations among various tasks supervised at different layers in an end-to-end hierarchical joint learning model. We propose a semi-shared hierarchical model that contains cross-layer shared modules and layer-specific modules. To fully leverage the mutual information between various tasks at different levels, we design four different dataflows of latent representations between the shared and layer-specific modules. Extensive experiments on CTB-7 and CONLL-2009 show that our semi-shared approach outperforms basic hierarchical joint models on sequence tagging while having much fewer parameters. It inspires us that the proper implementation of the cross-layer sharing mechanism and residual shortcuts is promising to improve the performance of hierarchical joint natural language processing models while reducing the model complexity.
Necessary Condition for the Success of Synchronous GNSS Spoofing
WANG Yiwei, KOU Yanhong, HUANG Zhigang
, Available online  , doi: 10.23919/cje.2021.00.307
Abstract(201) HTML (99) PDF(27)
A synchronous GNSS generator spoofer aims at directly taking over the tracking loops of the receiver with the lowest possible spoofing to signal ratio (SSR) without forcing it to lose lock. This paper investigates the factors that affect spoofing success and their relationships. The necessary conditions for successful spoofing are obtained by deriving the code tracking error in the presence of spoofing and analyzing the effects of SSR, spoofing synchronization errors, and receiver settings on the S-curve ambiguity and code tracking trajectory. The minimum SSRs for a successful spoofing calculated from the theoretical formulation agree with Monte Carlo simulations at digital intermediate frequency signal level within 1 dB when the spoofer pulls the code phase in the same direction as the code phase synchronization error, and the required SSRs can be much lower when pulling in the opposite direction. The maximum spoofing code phase error for a successful spoofing is tested by using TEXBAT datasets, which coincides with the theoretical results within 0.1 chip. This study reveals the mechanism of covert spoofing and can play a constructive role in the future development of spoofing and anti-spoofing methods.
Convolution Theorem Associated with the QWFRFT
MEI Yinyin, FENG Qiang, GAO Xiuxiu, ZHAO Yanbo
, Available online  , doi: 10.23919/cje.2021.00.225
Abstract(348) HTML (178) PDF(52)
The quaternion windowed fractional Fourier transform (QWFRFT) is a generalized form of the quaternion fractional Fourier transform (QFRFT), it plays a crucial role in signal processing for the analysis of multidimensional signals. In this paper, we first give the definition of the two-sided QWFRFT and some fundamental properties. Then, the quaternion convolution is proposed, and the relation between the quaternion convolution and the classical convolution is also given. Based on the quaternion convolution of the QWFRFT, relevant convolution theorems for the QWFRFT are studied. Moreover, the fast algorithm for QWFRFT is discussed. Finally, the complexity of QWFRFT and the quaternion windowed fractional convolution are given.
No Reference Image Sharpness Assessment Based on Global Color Difference Variation
SHI Chenyang, LIN Yandan
, Available online  , doi: 10.23919/cje.2022.00.058
Abstract(131) HTML (64) PDF(11)
Image quality assessment (IQA) model is designed to measure the image quality in consistent with subjective ratings by computational models. In this research, a valid no reference IQA (NR-IQA) model for color image sharpness assessment is proposed based on local color difference map in a color space. In the proposed model, the absolute color difference variation and relative color difference variation are combined to evaluate sharpness in YIQ color space (a color coordinate system for the development of the United States color television system). The difference between sharpest and blurriest spot of an image is represented by the absolute color difference variation, and relative color difference variation expresses the variation in the image content. Extensive experiments are performed on five publicly available benchmark synthetic blur databases and two real blur databases, and the results prove that the proposed model work better than the other state-of-the-art and latest NR-IQA models for the prediction accuracy on blurry images. Besides, the model maintains the lowest computational complexity.
Track-oriented Marginal Poisson Multi-Bernoulli Mixture Filter for Extended Target Tracking
DU Haocui, XIE Weixin, LIU Zongxiang, LI Liangqun
, Available online  , doi: 10.23919/cje.2021.00.194
Abstract(319) HTML (164) PDF(36)

In this paper, we derive and propose a track-oriented marginal Poisson multi-Bernoulli mixture (TO-MPMBM) filter to address the problem that the standard random finite set (RFS) filters cannot build continuous trajectories for multiple extended targets. Firstly, the Poisson point process (PPP) model and the multi-Bernoulli mixture (MBM) model are used to establish the set of birth trajectories and the set of existing trajectories, respectively. Secondly, the proposed filter recursively propagates the marginal association distributions and the Poisson multi-Bernoulli mixture (PMBM) density over the set of alive trajectories. Finally, after pruning and merging process, the trajectories with existence probability greater than the given threshold are extracted as the estimated target trajectories. A comparison of the proposed filter with the existing trajectory filters in two classical scenarios confirms the validity and reliability of the TO-MPMBM filter.

A Low Complexity Distributed Multitarget Detection and Tracking Algorithm
FAN Jiande, XIE Weixin, LIU Zongxiang
, Available online  , doi: 10.23919/cje.2021.00.282
Abstract(443) HTML (212) PDF(42)

In this paper, we propose a low complexity distributed approach to address the multitarget detection/tracking problem in the presence of noisy and missing data. The proposed approach consists of two components: a distributed flooding scheme for measurements exchanging among sensors and a sampling-based clustering approach for target detection/tracking from the aggregated measurements. The main advantage of the proposed approach over the prevailing Markov-Bayes-based distributed filters is that it does not require any priori information and all the information required is the measurement set from multiple sensors. A comparison of the proposed approach with the available distributed clustering approaches and the cutting edge distributed multi-Bernoulli filters that are modeled with appropriate parameters confirms the effectiveness and the reliability of the proposed approach.

AI Empowered Technologies in Railway Systems
Research on Health Stage Division of Switch Machine Based on Bray-Curtis and Fisher
WU Xiaochun, WEN Xin
, Available online  , doi: 10.23919/cje.2022.00.250
Abstract(72) HTML (35) PDF(9)
In order to reasonably and accurately evaluate the health status of the switch machine, a health stage division method of switch machine combining Bray-Curtis distance and Fisher optimal segmentation is proposed. Firstly, the power curve of switch machine is divided into five sections, and eight time-domain characteristic parameters of each section are extracted. The characteristic parameters with the largest correlation between 15 dimensions and state of the switch machine are selected by using the Holder coefficient method as input of the Bray-Curtis distance algorithm; Using Bray-Curtis distance to calculate HI (health index), which represents health state of switch machine; Finally, HI curve is divided by Fisher optimal segmentation method, and the optimal number of health stages of switch machine is determined to be 3, and HI interval and threshold of each health stage are obtained. The effectiveness of this method is verified by 4382 sets of on-site switch machine data experiments. The experimental results show that the health index curve calculated by Bray-Curtis distance can accurately represent the health status of the switch machine. Compared with Frechet distance and European distance, this method has better performance in Trendiness、Robustness and runtime. Combining with Fisher optimal segmentation method, it can reasonably and effectively divide the health stage of the switch machine, providing some support for the on-site judgment of the health status of the switch machine.
Formal Verification of Data Modifications in Cloud Block Storage Based on Separation Logic
ZHANG Bowen, JIN Zhao, WANG Hanpin, CAO Yongzhi
, Available online  , doi: 10.23919/cje.2022.00.116
Abstract(126) HTML (63) PDF(18)
Cloud storage is now widely used, but its reliability has always been a major concern. Cloud block storage (CBS) is a famous type of cloud storage. It has the closest architecture to the underlying storage and can provide interfaces for other types. Data modifications in CBS have potential risks such as null reference or data loss. Formal verification of these operations can improve the reliability of CBS to some extent. Although separation logic is a mainstream approach to verifying program correctness, the complex architecture of CBS creates some challenges for verifications. This paper develops a proof system based on separation logic for verifying the CBS data modifications. The proof system can represent the CBS architecture, describe the properties of the CBS system state, and specify the behavior of CBS data modifications. Using the interactive verification approach from Coq, the proof system is implemented as a verification tool. With this tool, the paper builds machine-checked proofs for the functional correctness of CBS data modifications. This work can thus analyze the reliability of cloud storage from a formal perspective.
Colour Variation Minimization Retinex Decomposition and Enhancement with a Multi-branch Decomposition Network
DENG Jiawei, YU Zhenming, PANG Guangyao
, Available online  , doi: 10.23919/cje.2021.00.350
Abstract(38) HTML (19) PDF(4)
This paper proposes a colour variation minimization retinex decomposition and enhancement with a multi-branch decomposition network (CvmD-net) to remove single image darkness. The network overcomes the problem that retinex deep learning model relies on matching bright images to process dark images. Specifically, our method takes two stages to light up the darkness in initial images: image decomposition and brightness optimization. We propose an input constant feature prior mechanism (ICFP) based on reflection constant features. The mechanism extracts structure and colour from the input images and constrains the reflected images output from the decomposition model to reduce color distortion and artifacts. The noise amplification during decomposition is addressed by a multi-branch decomposition network. Sub-networks with different structures are employed to focus on different prediction tasks. This paper proposes a reference mechanism for input brightness. This mechanism optimizes the output brightness distribution by calculating the reference brightness of the dark images. Experimental results on the two benchmark datasets, namely, LOL and ZeroDCE, demonstrate that the proposed method can better balance dense noise interference and colour restoration. For the evaluation on real images, we collect Skynet images at night to verify the performance of the proposed approach. Compared with the state-of-the-art non-reference retinex decomposition-enhancement models, this paper has the best brightness optimization.
Self-adaptive Discrete Cuckoo Search Algorithm for the Service Routing Problem with Time Windows and Stochastic Service Time
OU Xianfeng, WU Meng, LI Wujing, ZHANG Guoyun, XIE Wenwu
, Available online  , doi: 10.23919/cje.2022.00.072
Abstract(104) HTML (52) PDF(15)
Making house calls is very crucial to deal with the competitive pressures of the service business and to improve service quality. We design a model called service routing problem with time windows and stochastic service time (SRPTW-SST) that is based on vehicle routing problem with time windows (VRPTW). A self-adaptive discrete Cuckoo Search Algorithm with genetic mechanism (sDCS-GM) is proposed for the SRPTW-SST. We design a selection mechanism to improve the logicality of the algorithm based on the strong randomness of the Lévy flight. We introduce a genetic mechanism and design a neighborhood search mechanism for improving the robustness of the algorithm. An adaptive parameter adjustment method is designed to eliminate the impact of fixed parameters. The experimental results show that the sDCS-GM algorithm is more robust and effective than the state-of-the-art methods.
A verifiable multi-secret sharing scheme based on short integer solution
LI Fulin, YAN Jiayun, ZHU Shixin, HU Hang
, Available online  , doi: 10.23919/cje.2021.00.062
Abstract(115) HTML (55) PDF(17)
The threshold secret sharing scheme plays a very important role in cloud computing and group communication. With the possible birth of the quantum computer, traditional secret sharing schemes have been unable to meet security requirements. We proposed a new verifiable multi-secret sharing scheme based on the short integer solution problem. By utilizing a symmetric binary polynomial, $ k $ secrets and secret shares can be generated, and then we convert the secret shares into binary string on $ \mathbb{Z}_q $, which can be identified by one-way anti-collision hash function on the lattice, so that multiple secrets can be reconstructed safely. The advantages mainly focus on verifiability without interaction in the distribution phase and less memory requirement. In a secret sharing scheme, verifiability prevents the dealer to share the wrong shares and forces the participants to submit their shares correctly. Meanwhile, the interaction between dealer and participant (participant and participant) can be reduced, which means the security is improved. In a multi-secret sharing scheme, releasing the public values is inevitable, this paper has less public values and less size of shares per secret size to reduce the pressure of memory consumption in the proper parameters. In the end, based on the short integer solution problem, this paper can also effectively resist the quantum attack.
Improved Cross-Corpus Speech Emotion Recognition Using Deep Local Domain Adaptation
ZHAO Huijuan, YE Ning, WANG Ruchuan
, Available online  , doi: 10.23919/cje.2021.00.196
Abstract(535) HTML (262) PDF(73)
As the labeled speech emotion data is scarce, using transfer learning to recognize emotion is usually a natural way. However, the complexity of emotion and its certain degree of ambiguity make transfer learning-based speech emotion recognition more challenging. Domain adaptation based on maximum mean discrepancy considers the marginal alignment of source domain and target domain, but not paying regard to the class prior distribution in both domains, which results in the reduction of transfer efficiency. In order to solve the problem, we propose a novel cross-corpus speech emotion recognition framework based on local domain adaption. In this paper, a category-grained discrepancy is used to evaluate the distance between the two relevant domains. The research findings show that the generalization ability of the model is enhanced by using the local adaptive method. Compared with global adaptive and non-adaptive methods, the performance of cross-corpus speech emotion recognition is significantly improved.
Rating Prediction Model Based on Causal Inference Debiasing Method in Recommendation
NAN Jiangang, WANG Yajun, WANG Chengcheng
, Available online  , doi: 10.23919/cje.2022.00.076
Abstract(144) HTML (72) PDF(17)
The rating prediction task plays an important role in the recommendation model. Most existing methods predict ratings by extracting user and items characteristics from historical review data. However, the recommended strategies in historical review data are often based on partial observational data, which has the problems of unbalanced distribution, lack of robustness, and inability to obtain unbiased prediction results. Therefore, a novel rating prediction model based on causal inference debiasing method (CID) is proposed. The model can mitigate the negative effects of context bias and improve the robustness by studying the causal relationship between review information and user ratings. The proposed CID rating prediction model is plug-and-play and is not limited to one baseline prediction method. The proposed method is tested on four open datasets. The results show that the proposed method is feasible. Compared with the most advanced models, the prediction accuracy of the CID rating prediction model has been further improved. The experimental results show the debiasing effectiveness of the CID rating prediction model.
Adaptive Tensor Rank Approximation for Multi-view Subspace Clustering
SUN Xiaoli, HAI Yang, ZHANG Xiujun, XU Chen
, Available online  , doi: 10.23919/cje.2022.00.180
Abstract(182) HTML (93) PDF(31)
Multi-view subspace clustering under a tensor framework remains a challenging problem, which can be potentially applied to image classification, impainting, denoising, etc. There are some existing tensor-based multi-view subspace clustering models mainly making use of the consistency in different views through tensor nuclear norm (TNN). The diversity which means the intrinsic difference in individual view is always ignored. In this paper, a new tensorial multi-view subspace clustering model is proposed, which jointly exploits both the consistency and diversity in each view. The view representation is decomposed into view-consistent part (low-rank part) and view-specific part (diverse part). A tensor adaptive log-determinant regularization (TALR) is imposed on the low-rank part to better relax the tensor multi-rank, and a view-specific sparsity regularization is applied on the diverse part to ensure connectedness property. Although the TALR minimization is not convex, it has a closed-form analytical solution and its convergency is validated mathematically. Extensive evaluations on six widely used clustering datasets are executed and our model is demonstrated the superior performance.
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: 10.23919/cje.2021.00.113
Abstract(429) HTML (199) PDF(34)

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 supervised latent Dirichlet allocation (SLDA) and Word2vec. It generates the global topic embedding word vector utilizing SLDA which can discover the global semantic information through the latent topics on the whole document set. It gets the local semantic embedding word vector based on the Word2vec.  The new semantic word vector is obtained by combining the global semantic information with the local semantic information. Additionally, the document semantic vector named doc2svec is generated. The experimental results on different datasets show that wt2svec model can obviously promote the accuracy of the semantic similarity of words, and improve the performance of text categorization compared with Word2vec.

Sensing Matrix Optimization for Random Stepped-Frequency Signal Based on Two-Dimensional Ambiguity Function
LYU Mingjiu, CHEN Hao, YANG Jun, WU Xia, ZHOU Ming, MA Xiaoyan
, Available online  , doi: 10.23919/cje.2022.00.046
Abstract(73) HTML (36) PDF(6)
Compressive sensing technique has been widely applied to achieve range-Doppler reconstruction of high frequency radar by utilizing sparse random stepped-frequency (SRSF) signal, which can suppress the complex electromagnetic interference and greatly reduce the coherent processing interval. An important way to improve the performance of sparse signal reconstruction is to optimize the sensing matrix (SM). However, the existing research on the SM optimization needs to design a measurement matrix with superior performance, which needs a large amount of computation and does not consider the influence of the waveform parameters design. In order to improve the superior reconstruction performance, a novel SM optimization approach for SRSF signal is proposed by using two-dimensional ambiguity function (TDAF) in this paper. Firstly, based on the two-dimensional sparse reconstruction model of the SRSFs, the internal relationship between the waveform parameters and the SM was derived. Secondly, the SM optimization problem was directly transformed into the waveform design of SRSFs. Furthermore, on the basis of analyzing the relationship between the mutual coherence matrix of SM and the TDAF matrix of SRSFs, the purpose of optimizing the SM can be achieved by designing the TDAF of the SRSFs. Based on this analysis, a sparse waveform optimization method with joint constraints of maximum and mean sidelobes of the TDAF by using the genetic algorithm was derived. Compared with the traditional SM optimization method, our method not only avoids generating a new measurement matrix, but also further reduces the complexity of the waveform optimization. Simulation experiments verified the effectiveness of the proposed method.
A Dynamic Hysteresis Model of Piezoelectric Ceramic Actuators
DONG Ruili, TAN Yonghong, XIE Yingjie, LI Xiaoli
, Available online  , doi: 10.23919/cje.2021.00.273
Abstract(145) HTML (72) PDF(25)

A modified Prandtl-Ishlinskii (PI) model with rate-dependent thresholds for describing the hysteresis characteristics of piezoelectric actuators is proposed. Based on the classical PI model, a novel threshold depending on the input rate is constructed. With the novel rate-dependent threshold, the play operator has the capability to track the frequency variation of the input signal. Thus, the proposed modified PI model can be used to depict the rate-dependent hysteresis of piezoelectric actuators. Finally, experimental results are presented to show the model validation results of the proposed modeling method.

A Directly Readable Halftone Multifunctional Color QR Code
HUANG Yuan, CAO Peng, LV Guangwu
, Available online  , doi: 10.23919/cje.2021.00.366
Abstract(295) HTML (148) PDF(27)

Color quick response (QR) code is an important direction for the future development of QR code, which has become a research hotspot due to the additional functional characteristics of its colors as the wide application of QR code technology. The existing color QR code has solved the problem of information storage capacity, but it requires an enormous hardware and software support system, making how to achieve its direct readability an urgent issue. This paper proposes a novel color QR code that combines multiple types of different identification information. This code combines multiplexing and color-coding technology to present the publicly encoded information (such as advertisements, public query information) as plain code, and traceability, blockchain, anti-counterfeiting authentication and other information concealed in the form of hidden code. We elaborate the basic principle of this code, construct its mathematical model and supply a set of algorithm design processes, which breakthrough key technology of halftone printout. The experimental results show that the proposed color QR code realizes the multi-code integration and can be read directly without special scanning equipment, which has unique advantages in the field of printing anti-counterfeiting labels.

An Adaptive Interactive Multiple-Model Algorithm Based on End-to-End Learning
ZHU Hongfeng, XIONG Wei, CUI Yaqi
, Available online  , doi: 10.23919/cje.2021.00.442
Abstract(247) HTML (121) PDF(20)

The interactive multiple-model (IMM) is a popular choice for target tracking. However, to design transition probability matrices (TPMs) for IMMs is a considerable challenge with less prior knowledge, and the TPM is one of the fundamental factors influencing IMM performance. IMMs with inaccurate TPMs can make it difficult to monitor target maneuvers and bring poor tracking results. To address this challenge, we propose an adaptive IMM algorithm based on end-to-end learning. In our method, the neural network is utilized to estimate TPMs in real-time based on partial parameters of IMM in each time step, resulting in a generalized recurrent neural network. Through end-to-end learning in the tracking task, the dataset cost of the proposed algorithm is smaller and the generalizability is stronger. Simulation and automatic dependent surveillance-broadcast (ADS-B) tracking experiment results show that the proposed algorithm has better tracking accuracy and robustness with less prior knowledge.

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
, Available online  , doi: 10.23919/cje.2020.00.185
Abstract(253) HTML (112) PDF(13)

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 (FSVMI) model to predict carotid artery stenosis, with the maximum geometric mean (Gmean) 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.

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
, Available online  , doi: 10.23919/cje.2021.00.212
Abstract(417) HTML (185) PDF(12)

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.

Electromagnetic & Microwave
Design and Realization of Broadband Active Inductor Based Band Pass Filter
Aysu Belen, Mehmet A. Belen, Merih Palandöken, Peyman Mahouti, Özlem Tari
, Available online  , doi: 10.23919/cje.2021.00.322
Abstract(209) HTML (101) PDF(22)

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.