## Just Accepted

Just Accepted Papers are peer-reviewed and accepted for publication. They will soon (normally in 1–3 weeks) transform into Typeset Proofs when initial checks such as language editing and reference cross-validation are completed and typesettings of the papers are done. Note that for both types of papers under this directory, they are posted online prior to technical editing and author proofing. Please use with caution.
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, Available online  , doi: 10.1049/cje.2021.00.417
Abstract(34) HTML (17) PDF(7)
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A further characterization of the bent-negabent functions is presented. Based on the concept of complete mapping polynomial, we provide a necessary and sufficient condition for a class of quadratic Boolean functions to be bent-negabent. A new characterization of negabent functions can be described by using the parity of Hamming weight. We further generalize the classical convolution theorem and give the nega-Hadamard transform of the composition of a Boolean function and a vectorial Boolean function. The nega-Hadamard transform of a generalized indirect sum is calculated by this composition method.
, Available online  , doi: 10.1049/cje.2021.00.347
Abstract(62) HTML (31) PDF(9)
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Feature-based (FB) algorithms are widely used in modulation classification due to their low complexity. As a prerequisite step of FB, feature selection can reduce the computational complexity without significant performance loss. In this paper, according to the linear separability of cumulant features, the hyperplane of the support vector machine is used to classify modulation types, and the contribution of different features is ranked through the weight vector. Then, cumulant features are selected using recursive feature elimination (RFE) to identify the modulation type employed at the transmitter. We compare the performance of the proposed algorithm with existing feature selection algorithms and analyze the complexity of all the mentioned algorithms. Simulation results verify that the proposed RFE algorithm can optimize the selection of the features to realize modulation recognition and improve identification efficiency.
, Available online  , doi: 10.1049/cje.2019.00.028
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For more effective image sampling, Compressive Sensing (CS) imaging methods based on image saliency have been proposed in recent years. Those methods assign higher measurement rates to salient regions, but lower measurement rate to non-salient regions to improve the performance of CS imaging. However, those methods are block-based, which are difficult to apply to actual CS sampling, as each photodiode should strictly correspond to a block of the scene. In our work, we propose a non-uniform CS imaging method based on image saliency, which assigns higher measurement density to salient regions and lower density to non-salient regions, where measurement density is the number of pixels measured in a unit size. As the dimension of the signal is reduced, the quality of reconstructed image will be improved theoretically, which is confirmed by our experiments. Since the scene is sampled as a whole, our method can be easily applied to actual CS sampling. To verify the feasibility of our approach, we design and implement a hardware sampling system, which can apply our non-uniform sampling method to obtain measurements and reconstruct the images. To our best knowledge, this is the first CS hardware sampling system based on image saliency.
, Available online  , doi: 10.1049/cje.2021.00.149
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Soluble sugar is an important index to determine the quality of jujube, and also an important factor to influence the taste of jujube. The soluble sugar content of jujube mainly depends on manual chemical measurement, which is time-consuming and labor-intensive. In this study, the feasibility of multi-spectral imaging combined with deep learning for rapid nondestructive testing of fruit internal quality was analyzed. Support vector machine regression model, partial least squares regression model and convolutional neural networks (CNNs) model were established by multi-spectral imaging method to predict the soluble sugar content of the whole jujube fruit, and the optimal model was selected to predict the content of three kinds of soluble sugar. The study showed that the sucrose prediction model of the whole jujube had the best performance after CNNs training, and the correlation coefficient of verification set was 0.88, which proved the feasibility of using CNNs for prediction of the soluble sugar content of jujube fruits.
, Available online  , doi: 10.1049/cje.2021.00.213
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In this paper, we mainly study the discrete approximation about average differential entropy of continuous bounded space-time random field. The estimation of differential entropy on random variable is a classic problem, and there are many related studies. Space-time random field is a theoretical extension of adding random variables to space-time parameters, but studies on discrete estimation of entropy on space-time random field are relatively few. The differential entropy forms of continuous bounded space-time random field and discrete estimations are discussed, and three estimation forms of differential entropy in the case of random variables are generated in this paper. Furthermore, it is concluded that under the condition that the entropy estimation formula after space-time segmentation converges with probability 1, the average entropy in the bounded space-time region can also converge with probability 1, and three generalized entropies are verified respectively. In addition, we also carried out numerical experiments on the convergence of average entropy estimation based on parameters, and the numerical results are consistent with the theoretical results, which indicting further study of the average entropy estimation problem of space-time random fields is significant in the future.
, Available online  , doi: 10.1049/cje.2021.00.372
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This paper presents a Ku-Band fully differential 4-element phased-array transceiver using a standard 180-nm CMOS process. Each transceiver is integrated with a 5-bit phase shifter and 4-bit attenuator for high-resolution radiation manipulation. The front-end system adopts time-division mode, and hence two low-loss T/R switches are included in each channel. At room temperature, the measured root-mean-square (RMS) phase error is less than 5.5°. Furthermore, the temperature influence on passive switched phase shifters is analyzed. Meanwhile, an extra phase-shifting cell is developed to calibrate phase error varying with the operating temperatures. With the calibration, the RMS phase error is reduced by 7° at −45 ℃, and 5.4° at 85 ℃. The RMS amplitude error is less than 0.92 dB at 15~18 GHz. In the RX mode, the tested gain is 9.6±1.1 dB at 16.5 GHz with a noise figure of 10.9 dB, and the input P1dB is −15 dBm, while the single-channel’s gain and output P1dB in the TX mode are 11.3 ± 0.4 dB and 9.4 dBm at 16.1 GHz, respectively. The whole chip occupies an area of 5 × 4.2 mm2 and the measured isolation between each two adjacent channels is lower than −23.1 dB.
, Available online  , doi: 10.1049/cje.2021.00.219
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In this article, a design approach to a radial-resonant wide beamwidth circular sector patch antenna is advanced. As properly evolved from a U-shaped dipole, a prototype magnetic dipole can be fit in the radial direction of a circular sector patch radiator, with its length set as the positive odd-integer multiples of one-quarter wavelength. In this way, multiple TM0m (m = 1, 2, …) modes resonant circular sector patch antenna with short-circuited circumference and widened E-plane beamwidth can be realized by proper excitation and perturbations. Prototype antennas are then designed and fabricated to validate the design approach. Experimental results reveal that the E-plane beamwidth of a dual-resonant antenna fabricated on air/Teflon substrate can be effectively broadened to 128°/120°, with an impedance bandwidth of 17.4%/7.1%, respectively. In both cases, the antenna heights are strictly limited to no more than 0.03-guided wavelength. It is evidently validated that the proposed approach can effectively enhance the operational bandwidth and beamwidth of a microstrip patch antenna while maintaining its inherent low profile merit.
, Available online  , doi: 10.1049/cje.2021.00.093
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Few-Shot learning (FSL) is a new machine learning method that applies the prior knowledge from some different domains tasks. The existing FSL models of metric-based learning have some drawbacks, such as the extracted features cannot reflect the true data distribution and the generalization ability is weak. In order to solve the problem in the present, we develop a model, named COOPERATE (CrOss mOdal adaPtive fEw-shot leaRning bAsed on Task dEpendence). Firstly, a feature extraction and task representation method based on task condition network and auxiliary co-training is proposed. Secondly, semantic representation is added to each task by combining both visual and textual features. Finally, the measurement scale is adjusted to change the property of parameter update of the algorithm. The experimental results show that the COOPERATE has the better performance comparing with all approaches of the monomode and modal alignment FSL.
, Available online  , doi: 10.1049/cje.2021.00.174
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The triangular geometry is the basis of near field array accurate distance estimation algorithms. The fisher expression of traditional distance estimation is derived by utilizing the Taylor series. To improve convergence rate and estimation accuracy, a novel iterative distance estimation algorithm is proposed with differential equations based on the triangular geometry. Firstly, its convergence performance is analysed in detail. Secondly, the selection of the initial value and the number of iterations are respectively studied. Thirdly, compared with the traditional estimation algorithms by utilizing the fisher approximation, the proposed algorithm has a higher convergence rate and estimation accuracy. Moreover, its pseudocode is presented. Finally, the experiment results and performance analysis are provided to verify the effectiveness of the proposed algorithm.
, Available online  , doi: 10.1049/cje.2021.00.198
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Speech keyword spotting system is a critical component of human-computer interfaces. And Connectionist temporal classifier (CTC) has been proven to be an effective tool for that task. However, the standard training process of speech keyword spotting faces a data imbalance issue where positive samples are usually far less than negative samples. Numerous easy-training negative examples overwhelm the training, resulting in a degenerated model. To deal with it, this paper tries to reshape the standard CTC loss and proposes a novel re-weighted CTC loss. It evaluates the sample importance by its number of detection errors during training and automatically down-weights the contribution of easy examples, the majorities of which are negatives, making the training focus on samples deserving more training. The proposed method can alleviate the imbalance naturally and make use of all available data efficiently. Evaluation on several sets of keywords selected from AISHELL-1 and AISHELL-2 achieves 16%—38% relative reductions in false rejection rates over standard CTC loss at 0.5 false alarms per keyword per hour in experiments.
, Available online  , doi: 10.1049/cje.2021.00.327
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Although millimeter-wave (mmWave) aerial base station (mAeBS) gains rich wireless capacity, it is technically difficult for deploying several mAeBSs to solve the surge of data traffic in hotspots when considering the amount of interference from neighboring mAeBS. This paper introduces coordinated multiple points transmission (CoMP) into the mAeBS-assisted network for capacity enhancement and designs a two-timescale approach for 3D deployment and user association of cooperative mAeBSs. Specially, an affinity propagation clustering (APC)-based mAeBS-user cooperative association scheme is conducted on a large timescale followed by modeling the capacity evaluation, and a deployment algorithm based on multi-agent deep deterministic policy gradient (MADDPG) is designed on the small timescale to obtain the 3D position of mAeBS in a distributed manner. Simulation results demonstrate that the proposed approach has significant throughput gains over conventional schemes without CoMP, and the MADDPG is more efficient than centralized DRL algorithms in deriving the solution.
, Available online  , doi: 10.1049/cje.2021.00.196
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Due to insufficient data and high cost of data annotation, it is usually necessary to use knowledge transfer to recognize speech emotion. However, the uncertainty and subjectivity of emotion make speech emotion recognition based on transfer learning more challenging. Domain adaptation based on maximum mean discrepancy considers the marginal alignment of source domain and target domain, but without paying regard to the class prior distribution in both domains, which reduces the transfer efficiency. To solve this problem, a novel cross-corpus speech emotion recognition framework based on local domain adaption is proposed, in which a local weighted maximum mean discrepancy is used to evaluate the distance between different emotion datasets. Experimental results show that the cross-corpus speech emotion recognition has been improved when compared with other cross-corpus methods including global domain adaptation and cross-corpus speech emotion recognition directly.
, Available online  , doi: 10.1049/cje.2021.00.209
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${\textsf{Magpie}}$ is a lightweight block cipher proposed by Li et al. at Acta Electronica Sinica 2017. It adopts an SPN structure with a block size of 64 bits and the key size of 96 bits, respectively. To achieve the consistency of the encryption and decryption, which is both hardware and software friendly, 16 bits of the key are used as control signals to select S-boxes and another 16 bits of the key are used to determine the order of the operations. As the designers claimed, the security might be improved as different keys generate different ciphers. This paper analyzes the security of ${\textsf{Magpie}}$, studies the difference propagation of ${\textsf{Magpie}}$, and finally finds that the cipher has a set of $2^{80}$ weak keys which makes the full-round encryption weak, and corrects the lower bound of the number of active S-boxes to 10 instead of 25 proposed by the designers. In the weak key model, the security of the cipher is reduced by the claimed $2^{80}$ to only $4\times2^{16}$.
, Available online  , doi: 10.1049/cje.2021.00.368
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Recently, a new cryptographic primitive has been proposed called $\texttt{Forkciphers}$. 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 ${\cal{{E}}}^r$ on the forking variant $\texttt{Fork}{\cal{E}}$-(r-1)-r$_0$-(r+1-r$_0$). As application, we consider the security of $\texttt{ForkSPN}$ and $\texttt{ForkFN}$ with secret inner functions. We provide a generic attack against $\texttt{ForkSPN}$-2-r$_0$-(4-r$_0$), which is based on the decomposition of $\texttt{SASAS}$. Also we extend the decomposition of Biryukov et al. against Feistel networks to get all the unknown round functions in $\texttt{ForkFN}$-r-r$_0$-r$_1$ for r$\leq$6 and r$_0$+r$_1$$\leq$8. Therefore, compared with the original block cipher, the forking version requires more iteration rounds to resist the recovery attack.
, Available online  , doi: 10.1049/cje.2021.00.130
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Recently, many deep learning models have shown excellent performance in hyperspectral image classification. Among them, networks with multiple convolution kernels of different sizes have been proved to achieve richer receptive fields and extract more representative features than those with a single convolution kernel. However, in most networks, different-sized convolution kernels are usually used directly on multi-branch structures, and the image features extracted from them are fused directly and simply. In this paper, to fully and adaptively explore the multiscale information in both spectral and spatail domains of HSI, a novel multi-scale weighted kernel network (MSWKNet) based on an adaptive receptive field is proposed. First, the original HSI cubic patches are transformed to the input features by combining the principal component analysis and one-dimensional (1D) spectral convolution. Then, a three-branch network with different convolution kernels is designed to convolve the input features, and adaptively adjust the size of the receptive field through the attention mechanism of each branch. Finally, the features extracted from each branch are fused together for the task of classification. Experiments on three well-known hyperspectral data sets show that MSWKNet outperforms many deep learning networks in HSI classification.
, Available online  , doi: 10.1049/cje.2020.00.326
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A common but critical task in biological ontologies data analysis is to compare the difference between ontologies. There have been numerous ontology-based semantic-similarity measures proposed in specific ontology domain, but it still remains a challenge for cross-domain ontologies comparison. An ontology contains the scientific natural language description for the corresponding biological aspect. Therefore, we develop a new method based on natural language processing (NLP) representation model Bidirectional Encoder Representations from Transformers (BERT) for cross-domain semantic representation of biological ontologies. This article uses the BERT model to represent the word-level of the ontologies as a set of vectors, facilitating the semantic analysis or comparing the biomedical entities named in an ontology or associated with ontology terms. We evaluated the ability of our method in two experiments: calculating similarities of pair-wise Disease Ontology (DO) and Human Phenotype Ontology (HPO) terms and predicting the pair-wise of proteins interaction. The experimental results demonstrated the comparative performance. This gives promise to the development of NLP methods in biological data analysis.
, Available online  , doi: 10.1049/cje.2021.00.058
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Type-I Generalized Feistel Networks (GFN) are widely used frameworks in symmetric-key primitive designs such as CAST-256 and Lesamnta. Different from the extensive studies focusing on specific block cipher instances, the analysis against Type-I GFN structures gives generic security evaluation of the basic frameworks and concentrates more on the effect of linear transformation. Currently, works in this field mainly evaluate the security against impossible differential attack, zero-correlation linear attack, meet-in-the-middle attack and yoyo game attack, while its security evaluation against rectangle attack is still missing. In this paper, we filled this gap and gave the first structural analytical results of Type-I GFN against rectangle attack. We proved there exists a $(b^2-b)$ round boomerang switch for the first time, which is independent of the round functions when the GFN has $b$ branches of $m$ bits. Then we proposed a new rectangle attack model and turned the boomerang switch into chosen plaintext setting. By appending 1 more round in the beginning of the boomerang switch, we constructed a $(b^2-b+1)$ round rectangle distinguisher with probability $2^{-2(b-1)m}$, whose advantage over random permutation is $2^{m}$. Using this distinguisher, a $b^2$ round key recovery attack is performed with $2^{\frac{bm}{2}+1}$ chosen plaintexts and $(2b^{-2})2^{bm}$ encryptions.
, Available online  , doi: 10.1049/cje.2019.00.102
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We consider the general problem of the approximate joint diagonalization of a set of non-Hermitian matrices. This problem mainly arises in the data model of the joint blind source separation for two datasets. Based on a special parameterization of the two diagonalizing matrices and on adapted approximations of the classical cost function, we establish two Jacobi-like algorithms. They may serve for the canonical polyadic decomposition (CPD) of a third-order tensor, and in some scenarios they can outperform traditional CPD methods. Simulation results demonstrate the competitive performance of the proposed algorithms.
, Available online  , doi: 10.1049/cje.2021.00.108
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With the development of deep learning, Graph Neural Networks(GNNs) have yielded substantial results in various application fields. GNNs mainly consider the pair-wise connections and deal with graph-structured data. In many real-world networks, the relations between objects are complex and go beyond pairwise. Hypergraph is a flexible modeling tool to describe intricate and higher-order correlations. Therefore, the researchers have been concerned how to develop hypergraph-based neural network model. The existing hypergraph neural networks show better performance in node classification tasks and so on, while they are shallow network because of over-smoothing, over-fitting and gradient vanishment. To tackle these issues, we present a novel Deep hypergraph neural network (DeepHGNN). We design DeepHGNN by using the technologies of residual connection, identity mapping and sampling hyperedge, residual connection and identity mapping bring from GCNs. We evaluate DeepHGNN on two visual object datasets. The experiments show the positive effects of DeepHGNN, and it works better in visual object classification tasks.

, 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.
, Available online  , doi: 10.1049/cje.2021.00.081
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Constrained by physics, the spatial resolution of hyperspectral images (HSI) is low. Hyperspectral image super-resolution (HSI SR) is a task to obtain high-resolution hyperspectral images (HR HSI) from low-resolution hyperspectral images (LR HSI). Existing algorithms have the problem of losing important spectral information while improving spatial resolution. To handle this problem, a spatial-spectral feature extraction network (SSFEN) for HSI SR is proposed in this paper. It enhances the spatial resolution of the HSI while preserving the spectral information. The SSFEN is composed of three parts: spatial-spectral mapping network (SSMN), spatial reconstruction network (SRN), and spatial-spectral fusing network (SSFN). And a joint loss function with spatial and spectral constraints is designed to guide the training of the SSFEN. Experiment results show that the proposed method improves the spatial resolution of the HSI and effectively preserves the spectral information coinstantaneously.
, Available online  , doi: 10.1049/cje.2021.00.225
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The quaternion windowed fractional Fourier transform (QWFRFT) is a generalized form of the quaternion fractional Fourier transform (QFRFT), which plays an important role in signal processing for the analysis of higher-dimensional signals. In this paper, we firstly introduce the two-sided quaternion windowed fractional Fourier transform (QWFRFT), and give some fundamental properties for QWFRFT. Secondly, the quaternion convolution is proposed, the relationship between the quaternion convolution and the classical convolution is also given. Based on the quaternion convolution of the QWFRFT, convolution theorems associated with the QWFRFT are studied. Thirdly, fast algorithm for QWFRFT is discussed. The complexity of QWFRFT and the quaternion windowed fractional convolution are given.
, Available online  , doi: 10.1049/cje.2020.00.367
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By solving the existing expectation-signal-to-noise ratio (expectation-SNR) based inequality model of the closed-form instantaneous cross-correlation function type of Choi-Williams distribution (CICFCWD), the linear canonical transform (LCT) free parameters selection strategies obtained are usually unsatisfactory. Since the second-order moment variance outperforms the first-order moment expectation in accurately characterizing output SNRs, this paper uses the variance analysis technique to improve parameters selection strategies. The CICFCWD’s average variance of deterministic signals embedded in additive zero-mean stationary circular Gaussian noise processes is first obtained. Then the so-called variance-SNRs are defined and applied to model a variance-SNR based inequality. A stronger inequalities system is also formulated by integrating expectation-SNR and variance-SNR based inequality models. Finally, a direct application of the system in noisy one-component and bi-component linear frequency-modulated signals detection is studied. Analytical algebraic constraints on LCT free parameters newly derived seem more accurate than the existing ones, achieving better noise suppression effects. Our methods have potential applications in optical, radar, communication and medical signal processing.
, Available online  , doi: 10.1049/cje.2021.00.411
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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.
, Available online  , doi: 10.1049/cje.2021.00.194
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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.
, Available online  , doi: 10.1049/cje.2020.00.363
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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 & CONLL-09 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 NLP models while reducing the model complexity.
, Available online  , doi: 10.1049/cje.2021.00.415
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Differential cryptanalysis is one of the most critical analysis methods to evaluate the security strength of cryptographic algorithms. This paper first applies the genetic algorithm to search for differential characteristics in differential cryptanalysis. A new algorithm is proposed as the fitness function to generate a high-probability differential characteristic from a given input difference. Based on the differential of the differential characteristic found by genetic algorithm, Boolean satisfiability (SAT) is used to search all its differential characteristics to calculate the exact differential probability. In addition, a penalty-like function is also proposed to guide the search direction for the application of the stochastic algorithm to differential cryptanalysis. Our new automated cryptanalysis method is applied to SPECK32 and SPECK48. As a result, the 10-round differential probability of SPECK32 is improved to 2−30.34, and a 12-round differential of SPECK48 with differential probability 2−46.78 is achieved. Furthermore, the corresponding differential attacks are also performed. The experimental results show our method’s validity and outstanding performance in differential cryptanalysis.
, Available online  , doi: 10.1049/cje.2021.00.124
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Vehicles equipped with 5th Generation(5G) wireless communication devices can exchange information with infrastructure(Vehicle to Infrastructure, V2I) to improve positioning accuracy. Vehicle location has great research value due to the problems of multipath environment and lack of Global Navigation Satellite System(GNSS) signals. This paper proposes a multi-frequency ranging method and positioning algorithm for 5G Orthogonal Frequency Division Multiplexing(OFDM) communication system. It selects specific subcarriers in the OFDM communication system to be used for transmitting ranging frames and delay observations without affecting other subcarriers used for communication. With almost no impact on communication capacity, several specific subcarriers of OFDM are used for ranging and positioning. It introduces the ranging subcarriers’ selection method and the format of the ranging frame carried by the subcarriers. The Cramero Lower Bound(CRLB) of this ranging positioning system is proved. Ranging positioning accuracy meets the requirements of vehicle location applications. The experimental simulation compares the performance with other positioning methods and proves the superiority of this system. The theory proves and simulates the relationship between ranging accuracy and channel parameters in a multipath environment. The simulation results show that the positioning accuracy about 5 cm can be achieved under the conditions of 5 GHz frequency and high signal-to-noise ratio(SNR).
, Available online  , doi: 10.1049/cje.2021.00.233
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An innovative design of bandpass (BP) negative group delay (NGD) passive circuit based on defect ground structure (DGS) is developed in the present paper. The NGD DGS topology is originally built with notched cells associated with self-matched substrate waveguide elements. The DGS design method is introduced as a function of the geometrical notched and substrate integrated waveguide via elements. Then, parametric analyses based on full wave 3-D electromagnetic S-parameter simulations were considered to investigate the influence of DGS physical size effects. The design method feasibility study is validated with fully distributed microstrip circuit prototype. Significant BP NGD function performances were validated with 3-D simulations and measurements with −1.69 ns NGD value around 2 GHz center frequency over 33.7 MHz NGD bandwidth with insertion loss better than 4 dB and reflection loss better than 40 dB.
, Available online  , doi: 10.1049/cje.2021.00.170
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With the recent advances in the field of deep learning, an increasing number of deep neural networks have been applied to business process prediction tasks, remaining time prediction, to obtain more accurate predictive results. However, existing time prediction methods based on deep learning have poor interpretability, an explainable business process remaining time prediction method is proposed using reachability graph, which consists of prediction model construction and visualization. For prediction models, a Petri net is mined and the reachability graph is constructed to obtain the transition occurrence vector. Then, prefixes and corresponding suffixes are generated to cluster into different transition partitions according to transition occurrence vector. Next, the bidirectional recurrent neural network with attention is applied to each transition partition to encode the (trace) prefixes, and the deep transfer learning between different transition partitions is performed. For the visualization of prediction models, the evaluation values are added to the sub-processes of Petri net to realize the visualization of the prediction models. Finally, the proposed method is validated by publicly available event logs.
, Available online  , doi: 10.1049/cje.2021.00.230
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Weakly supervised semantic segmentation using just image-level labels is critical since it alleviates the need for expensive pixel-level labels. Most cutting-edge methods adopt two-step solutions that learn to produce pseudo-ground-truth using only image-level labels and then train off-the-shelf fully supervised semantic segmentation network with these pseudo labels. Although these methods have made significant progress, they also increase the complexity of the model and training. In this paper, we propose a one-step approach for weakly supervised image semantic segmentation—Attention guided enhancement network (AGEN), which produces pseudo-pixel-level labels under the supervision of image-level labels and trains the network to generate segmentation masks in an end-to-end manner. Particularly, we employ Class activation maps (CAM) produced by different layers of the classification branch to guide the segmentation branch to learn spatial and semantic information. However, the CAM produced by the lower layer can capture the complete object region but with many noises. Thus, the self-attention module is proposed to enhance object regions adaptively and suppress irrelevant object regions, further boosting the segmentation performance. Experiments on the Pascal VOC 2012 dataset show that the performance of AGEN outperforms other state-of-art weakly supervised semantic segmentation with only image-level labels.
, Available online  , doi: 10.1049/cje.2021.00.401
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Abstract:
The shuffle operations are the bottleneck when mapping the FFT-like algorithms to the vector SIMD architectures. We propose six (three pairs) innovative vector memory-access shuffle fused instructions,which have been proved mathematically. Together with the proposed modified binary-exchange method,the innovative instructions can efficiently address the bottleneck problem for DIF/DIT radix-2/4 FFT-like algorithms,reach a performance improvement by 17.9%~111.2% and reduce the code size by 5.4%~39.8%.Besides,the proposed instructions fit some hybrid-radix FFTs and are suitable for the terms of the initial or result data placement for general algorithms. The software and hardware cost of the proposed instructions is moderate.
, Available online  , doi: 10.1049/cje.2021.00.140
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Abstract:
Assuming unknown knowledge of Target impulse response (TIR), this paper deals with the joint design of Multiple-input multiple-output (MIMO) Space-time transmit code (STTC) and Space-time receive filter (STRF) for the detection of extended targets in the presence of signal-dependent interference. To enhance the detection performance of extended targets for MIMO radar, we consider transmit-receive system optimization to maximize the worst-case Signal to interference plus noise ratio (SINR) at the output of the STRF array. The problem is formulated in terms of a non-convex max-min quadratic fractional optimization program. Relying on an appropriate reformulation, we present an alternate optimization technique which monotonically increases the SINR value and converges to a stationary point. All iterations of the procedure, involve both a convex and a max-min quadratic fractional programming problem which is globally solved resorting to the generalized Dinkelbachos process with a polynomial computational complexity. In addition, resorting to several mathematical manipulations, the original problem is transformed into an equivalent convex problem, which can also be globally solved via interior-point methods. Finally, the effectiveness of two optimization design procedures is demonstrated through experimental results, underlining the performance enhancement offered by robust joint design methods.