## 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.2021.00.316
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Satellite communication has become a popular study topic owing to its inherent advantages of high capacity, large coverage, and no terrain restrictions. Hence, it can be combined with terrestrial communication to overcome the shortcomings of current wireless communication, such as limited coverage and high destructibility. Over recent years, the integrated satellite-unmanned aerial vehicle-terrestrial networks (IS-UAV-TNs) have aroused tremendous interests to effectively reduce the transmission latency and enhance quality-of-service (QoS) with improved spectrum efficiency. However, the rapidly growing access demands and conventional spectrum allocation scheme lead to the shortage of spectrum resources. To tackle the mentioned challenge, the non-orthogonal multiple access (NOMA) scheme and cognitive radio technique are utilized in IS-UAV-TN, which can improve spectrum utilization. In our paper, the transmission capacity of a NOMA-enabled IS-UAV-TN under overlay mode is discussed, specifically, we derive the closed-form expressions of ergodic capacity for both primary and secondary networks. Besides, simulation results are provided to demonstrate the validity of the mathematical derivations and indicate the influences of critical system parameters on transmission performance. Furthermore, the orthogonal multiple access (OMA)-based scheme is compared with our NOMA-based scheme as a benchmark, which illustrates that our proposed scheme has better performance.
, Available online  , doi: 10.1049/cje.2022.00.075
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As one of the most important railway signaling equipment, railway point machines undertake the major task of ensuring train operation safety. Thus fault diagnosis for railway point machines becomes a hot topic. Considering the advantage of the anti-interference characteristics of vibration signals, this paper proposes an novel intelligent fault diagnosis method for railway point machines based on vibration signals. A feature extraction method combining variational mode decomposition (VMD) and multiscale fluctuation-based dispersion entropy (MFDE) is developed, which is verified a more effective tool for feature selection. Then, a two-stage feature selection method based on Fisher discrimination and ReliefF is proposed, which is validated more powerful than signle feature selection methods. Finally, support vector machine (SVM) is utilized for fault diagnosis. Experiment comparisons show that the proposed method performs best. The diagnosis accuracies of normal-reverse and reverse-normal switching processes reach 100% and 96.57% respectively. Especially, it is a try to use new means for fault diagnosis on railway point machines, which can also provide references for similar fields.
, Available online  , doi: 10.1049/cje.2021.00.215
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Based on an arbitrarily-chosen binary Golay complementary pair (BGCP) $({\boldsymbol{c}},{\boldsymbol{d}})$ of even length ${\boldsymbol{N}}$, first of all, construct quadriphase sequences ${\boldsymbol{a}}$ and ${\boldsymbol{b}}$ of length ${\boldsymbol{N}}$ by weighting addition and difference of the aforementioned pair with different weights, respectively. Secondly, new quadriphase sequence ${\boldsymbol{u}}$ is given by interleaving three sequences ${\boldsymbol{d}}$, ${\boldsymbol{a}}$, and $-{\boldsymbol{c}}$, and similarly, the sequence ${\boldsymbol{v}}$ is acquired from three sequences ${\boldsymbol{d}}$, ${\boldsymbol{b}}$, and ${\boldsymbol{c}}$. Thus, the resultant pair $({\boldsymbol{u}},{\boldsymbol{v}})$ is the quadriphase Golay complementary pair (QGCP) of length ${\boldsymbol{3N}}$. The QGCPs play a fairly important role in communications, radar, and so on.
, Available online  , doi: 10.1049/cje.2021.00.451
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The development of smart mobile devices not only brings convenience to people’s lives but also provides a breeding ground for Android malware. The sharp increasing malware poses a disastrous threat to personal privacy in the information age. Based on the fact that malware heavily resorts to system APIs to perform its malicious actions, there has been a variety of API-based detection approaches. Most of them do not consider the relationship between APIs. We contribute a new approach based on Enhanced API Order for Android malware detection, named EAODroid. EAODroid learns the similarity of system APIs from a large number of API sequences and groups similar APIs into clusters. The extracted API clusters are further used to enhance the original API calls executed by an app to characterize behaviors and perform classification. We perform multi-dimensional experiments to evaluate EAODroid on three datasets with ground truth. We compare with many state-of-the-art works, showing that EAODroid achieves effective performance in Android malware detection.
, Available online  , doi: 10.1049/cje.2021.00.272
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In recent years, converting environmental energy into electrical energy to meet the needs of modern society for clean and sustainable energy has become a research hotspot. Electrostatic energy is a pollution-free environmental energy source. The use of electrostatic conversion devices to convert electrostatic energy into electrical energy has been proven to be a feasible solution to meet sustainable development. This paper proposes a light-controlled electrostatic conversion device (LCECD). When static electricity comes, an avalanche breakdown occurs inside the LCECD and a low resistance path is generated to clamp the voltage, thereby outputting a smooth square wave of voltage and current. Experiments have proved that LCECD can convert 30kV electrostatic pulses into usable electrical energy for the normal operation of the back-end LED lights. In addition, the LCECD will change the parasitic capacitance after being exposed to light. For different wavelengths of light, the parasitic capacitance exhibited by the device will also be different. The smaller the parasitic capacitance of the LCECD, the higher the efficiency of its electrostatic conversion. This is of great significance to the design of electrostatic conversion devices in the future.
, Available online  , doi: 10.1049/cje.2021.00.429
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A novel Wilkinson pulse combiner(WPC) is proposed for the combination of Gaussian pulse signals. The WPC requires a very wide bandwidth, small size and high port isolation. To improve the operating bandwidth, the design adopts the form of eight-section WPC. Eight capacitors are connected in series with the isolating resistors of each section. After capacitive loading, isolation between WPC input ports is significantly improved at low frequency. Consequently, the operating bandwidth of WPC has been increased from 13:1 to 31:1. Compared with the conventional Wilkinson combiner with the same bandwidth, the proposed WPC reduces the size by 40%. In addition, all the ports are well impedance matched and the insertion loss in the operating frequency band is less than 0.5dB. To verify the feasibility of the design, a prototype was fabricated and measured. Experiment shows that the novel WPC is more advantageous to generate dual-Gaussian pulse signals.
, Available online  , doi: 10.1049/cje.2020.00.377
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A conjunction can be viewed as a pattern-matching with wildcards. An input string of length n matches a pattern of the same length if and only if it is same as the pattern for all non-wildcard positions. Since 2013, there are abundant works of conjunction obfuscations which are based on Generic Group Model, LWE assumption, LPN assumption, et al. After obfuscation, any adversary can not find the pattern or a accepting input from the obfuscated program. In this work, we propose a conjunction obfuscation from the General Decoding Problem. In addition to satisfying the distributional virtual black-box security, our obfuscation also achieve the strong functionality preservation which solves the open problem in the work of Bartusek et al. It means that we construct a conjunction obfuscation with simultaneous correct from a standard assumption. The conjunction obfuscation can resist the information set decoding attack and the structured error attack with some parameter constraints.
, Available online  , doi: 10.1049/cje.2022.00.096
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We present graph Hilbert neural network (GHNN), a novel framework of graph neural networks based on graph signal processing (GSP) theory, which is different from the previous method based on convolution theorem. Graph Hilbert transform (GHT) can explain the emergence of complex eigenvalues and complex eigenvectors, which provides a theoretical basis for convolution operation on digraphs. GHT can be expressed by the form of polynomial filter because of its linear shift invariant (LSI) property and the definition in spectral domain, which is applied to construct the layers of graph neural network. The graph Laplacian matrix is adopted as the graph shift operator in the function of LSI filter to realize the property of localization. To make better use of both low-frequency and high-frequency information, we design a two-channel filter bank to perform low-pass filtering and high-pass filtering. Experiments on three benchmark datasets show that the proposed GHNN outperforms previous spectral graph CNNs on the task of graph-based semi-supervised classification.
, Available online  , doi: 10.1049/cje.2021.00.383
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The strength of any cryptographic algorithm is mostly based on the difficulty of its encryption key.However, the larger size of the shared key the more computational operations and processing time for cryptographic algorithms. To avoid increasing the key size and keep its secrecy, we must hide it. The authors proposed a stream cipher algorithm that can hide the symmetric key[1] through hashing and splitting techniques. This paper aims to measure security analysis and performance assessment for this algorithm. This algorithm is compared with three of the commonly used stream cipher algorithms: RC4, Rabbit, and Salsa20 in terms of execution time and throughput. This comparison has been conducted with different data types as audio, image, text, docs, and pdf. Experiments proved the superiority of SCKHA algorithm over both Salsa20 and Rabbit algorithms. Also, results proved the difficulty to recover the secret key for SCKHA algorithm. Although RC4 has a lower encryption time than SCKHA, it is not recommended for use because of its vulnerabilities. Security factors that affect the performance as avalanche effect, correlation analysis, histogram analysis, and Shannon information entropy are highlighted. Also, the ciphertext format of the algorithm gives it the ability to search over encrypted data.
, Available online  , doi: 10.1049/cje.2021.00.393
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Parallel simulation generally needs efficient, reliable and order-preserving communication. In this article, a zero-copy, reliable and order-preserving intra-node message passing approach ZeROshm is proposed, and it partitions shared memory into segments assigned to processes for receiving messages. Each segment consists of two levels of index L1 and L2 that recordes the order of messages in the host segment, and the processes also read from and write to the segments directly according to the indexes, thereby eliminating allocating and copying buffers. As experimental results show, ZeROshm exhibits nearly equivalent performance to MPI for small message and superior performance for large message - ZeROshm costs less time by 43%, 40% and 55% respectively in pure communication, communication with contention and real Phold simulation within a single node. In hybrid environment, the combination of ZeROshm and MPI also shorten the execution time of Phold simulation by about 42% compared to pure MPI.
, Available online  , doi: 10.1049/cje.2021.00.326
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Unmanned aerial vehicles (UAVs) can be effectively used as serving stations in emergency communications because of their free movements, strong flexibility, and dynamic coverage. In this paper, we propose a coordinated multiple points (CoMP) based UAV deployment framework to improve system average ergodic rate, by using the fuzzy C-means (FCM) algorithm to cluster the ground users and considering exclusive forest channel models for the two cases, i.e., associated with a broken base station (BS) or an available one. In addition, we derive the upper bound of the average ergodic rate to reduce computational complexity. Since deep reinforcement learning (DRL) can deal with the complex forest environment while the large action and state space of UAVs leads to slow convergence, we use a ratio cut method to divide UAVs into groups and propose a hierarchical clustering DRL (HC-DRL) approach with quick convergence to optimize the UAV deployment. Simulation results show that the proposed framework can effectively reduce the complexity, and outperforms the counterparts in accelerating the convergence speed.
, Available online  , doi: 10.1049/cje.2022.00.047
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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.
, Available online  , doi: 10.1049/cje.2022.00.100
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In the past three decades, a large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems. While most variation operators in existing metaheuristics are empirically designed, this paper aims to design new operators automatically, which are expected to be search space independent and thus exhibit robust performance on different problems. For this purpose, this work first investigates the influence of translation invariance, scale invariance, and rotation invariance on the search behavior and performance of some representative operators. We deduce the generic form of translation, scale, and rotation invariant operators. A principled approach is proposed for the automated design of operators, which searches for high-performance operators based on the deduced generic form. The experimental results demonstrate that the operators generated by the proposed approach outperform state-of-the-art ones on a variety of problems with complex landscapes and up to 1000 decision variables.
, Available online  , doi: 10.1049/cje.2020.00.340
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Through-silicon via (TSV) provides vertical interconnectivity among the stacked dies in three-dimensional integrated circuits (3D ICs) and is a promising option to minimize 3D solenoid inductors for on-chip radio-frequency applications. In this paper, a rigorous analytical inductance model of 3D solenoid inductor is proposed based on the concept of loop and partial inductance. And a series of 3D samples are fabricated on 12" high-resistivity silicon wafer using low-cost standard CMOS-compatible process. The results of the proposed model match very well with those obtained by simulation and measurement. With this model, the inductance can be estimated accurately and efficiently over a wide range of inductor windings, TSV height, space, and pitch.
, Available online  , doi: 10.1049/cje.2021.00.195
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Compared with the traditional short-term object tracking task based on temporal-spatial consistency, the long-term object tracking task faces the challenges of object disappearance, dramatic changes in object scale, and object appearance. To address these challenges and problems, in this paper we propose a Multi-scale Global Retrieval and Temporal-Spatial Consistency Matching based long-term Tracking Network (MTTNet). MTTNet regards the long-term tracking task as a single sample object detection task and takes full advantage of the temporal-spatial consistency assumption between adjacent video frames to improve the tracking accuracy. MTTNet utilizes the information of single sample as guidance to perform full-image multi-scale retrieval on any instance and does not require online learning and trajectory refinement. Any type of error generated during the detection process will not affect its performance on subsequent video frames. This can overcome the accumulation of errors in the tracking process of traditional object tracking networks. We introduce Atrous Spatial Pyramid Pooling to address the challenge of dramatic changes in the scale and the appearance of the object. On the experimental results, MTTNet can achieve better performance than composite processing methods on two large datasets.
, Available online  , doi: 10.1049/cje.2022.00.031
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Collaborative Inference (co-inference) accelerates deep neural network inference via extracting representations at the device and making predictions at the edge server, which however might disclose the sensitive information about private attributes of users (e.g., race). Although many privacy-preserving mechanisms on co-inference have been proposed to eliminate privacy concerns, privacy leakage of sensitive attributes might still happen during inference. In this paper, we explore privacy leakage against privacy-preserving co-inference by decoding the uploaded representations into a vulnerable form. We propose a novel attack framework AttrLeaks, which consists of the shadow model of feature extractor (FE), the susceptibility reconstruction decoder, and the private attribute classifier. Based on our observation that values in inner layers of FE (internal representation) are more sensitive to attack, the shadow model is proposed to simulate the FE of the victim in the black-box scenario and generates the internal representations. Then, the susceptibility reconstruction decoder is designed to transform the uploaded representations of the victim into the vulnerable form, which enables the malicious classifier to easily predict the private attributes. Extensive experimental results demonstrate that AttrLeaks outperforms the state-of-the-art in terms of attack success rate.
, Available online  , doi: 10.1049/cje.2021.00.211
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Real-time 6D object pose estimation is essential for many real-world applications, such as robotic grasping and augmented reality. To achieve an accurate object pose estimation from RGB images in real-time, we propose an effective and lightweight model, namely High-Resolution 6D Pose Estimation Network (HRPose). We adopt the efficient and small HRNetV2-W18 as a feature extractor to reduce computational burdens while generating accurate 6D poses. With only 33% of the model size and lower computational costs, our HRPose achieves comparable performance compared with state-of-the-art models. Moreover, by transferring knowledge from a large model to our proposed HRPose through output and feature-similarity distillations, the performance of our HRPose is improved in effectiveness and efficiency. Numerical experiments on the widely-used benchmark LINEMOD demonstrate the superiority of our proposed HRPose against state-of-the-art methods.
, Available online  , doi: 10.1049/cje.2021.00.254
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In this paper, intelligent reflecting surface (IRS) is introduced to enhance the performance of cognitive radio (CR) systems. The robust beamforming is designed based on combined bounded channel state information (CSI) error for primary user (PU) related channels. The transmit precoding at the secondary user (SU) transmitter and phase shifts at the IRS are jointly optimized to minimize the SU's total transmit power subject to the quality of service of SUs, the limited interference imposed on the PU and unit-modulus of the reflective beamforming. Simulation results verify the efficiency of the proposed algorithm and reveal that the number of phase shifts at IRS should be carefully chosen to obtain a tradeoff between the total minimum transmit power and the feasibility rate of the optimization problem.
, Available online  , doi: 10.1049/cje.2021.00.338
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There are many kinds of linear canonical transform (LCT)-based Wigner distributions (WDs), which are both very effective in detecting noisy linear frequency-modulated (LFM) signals. Among WDs in LCT domains, the instantaneous cross-correlation function type of Wigner distribution (ICFWD) attracts much attention from scholars, because it achieves not only low computational complexity but also good detection performance. However, the existing LCT free parameters selection strategy, a solution of the expectation-based output signal-to-noise ratio (SNR) optimization model, is not unique. In this paper, by introducing the variance-based output SNR optimization model, a multiobjective optimization model is established. Then the existence and uniqueness of the optimal parameters of ICFWD are investigated. The solution of the multiobjective optimization model with respect to one-component LFM signal added with zero-mean stationary circular Gaussian noise is derived. A comparison of the unique parameters selection strategy and the previous one is carried out. The theoretical results are also verified by numerical simulations.
, Available online  , doi: 10.1049/cje.2022.00.008
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In adaptive optics (AO) systems, the bad spot detected by the wavefront detector affects the wavefront reconstruction accuracy. A convolutional neural network (CNN) model is established to estimate the missing information on bad points, reduce the reconstruction error of the distorted wavefront. By training 10,000 groups of spot array images and the corresponding 30th order Zernike coefficient samples, learns the relationship between the light intensity image and the Zernike coefficient, and predicts the Zernike mode coefficient based on the spot array image to restore the wavefront. Following the wavefront restoration of 1000 groups of test set samples, the root mean square (RMS) error between the predicted value and the real value was maintained at approximately 0.2 μ m. Field wavefront correction experiments were carried out on three links of 600 m, 1.3 km and 10 km. The wavefront peak-to-valley (PV) values corrected by the CNN decreased from 12.964 µ m, 13.958 µ m, and 31.310 µ m to 0.425, 3.061, and 11.156 µ m, respectively, and the RMS values decreased from 2.156 µ m, 9.158 µ m, and 12.949 µ m to approximately 0.166 µ m, 0.852 µ m, and 6.963 µ m, respectively. The results show that the CNN method predicts the missing wavefront information of the sub-aperture from the bad spot image, reduces the wavefront restoration error, and improves the wavefront correction performance.
, Available online  , doi: 10.1049/cje.2021.00.452
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In this paper, a 4.2-7.2 GHz (52.6%) beam-steering microstrip antenna was proposed. The proposed antenna consists of three tapered slots and feeds. The three radiation directions of the antenna on the plane are independent of each other, and the three feeds correspond to the three radiation structures. Symmetry isolation trenches are introduced to improve isolation between different ports. Radiation pattern simulation and measurement show horizontal beam steering at the sampled frequencies of 4.2, 5, 6, and 7.2 GHz. The results shows that the overlapped beam of the three ports in the E-plane and H-plane can cover more than 200 degrees and 60 degrees, respectively. Apart from the capability of beam-steering, high isolation (> 28 dB) of the proposed antenna in the operating band is obtained.
, Available online  , doi: 10.1049/cje.2021.00.280
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Phase filtering is an essential step in interferometric synthetic aperture radar (InSAR). For interferograms with complicated and changeable terrain, the increasing resolution of InSAR images makes it even more difficult. In this paper, a novel adaptive InSAR phase filtering method based on complexity factors is proposed. Firstly, three complexity factors based on the noise distribution and terrain slope information of the interferogram are selected. The complexity indicator composed of three complexity factors is used to guide the adaptive selection of the most suitable and effective filtering strategies for different areas. Then, the complexity scalar is calculated, which can guide the adaptive local fringe frequency (LFF) estimation and adaptive parameters calculation in different filter methods. Finally, validations are performed on the simulated and real data. The performance comparison between the other three representative phase filtering method and the proposed method have validated the effectiveness and superiority of the proposed method.
, Available online  , doi: 10.1049/cje.2021.00.312
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, Available online  , doi: 10.1049/cje.2022.00.032
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In this paper, we propose a terahertz radiation source over the R-band (220-325 GHz) based on metamaterials (MTMs) structure and parallel multiple beams. The effective permittivity and permeability of the slow-wave structure (SWS) can be obtained through the S-parameter retrieval approach, using numerical simulation. Additionally, the electromagnetic properties of the MTMs structure are analyzed, including the dispersion and the coupling impedance. Furthermore, we simulate the beam-wave interaction of the backward oscillator (BWO) with MTMs structure and parallel multiple beams by 3-D particle-in-cell (PIC) code. It is observed that parallel multiple beams can highly enhance the beam-wave interaction and greatly enlarge the output power. These results indicate that the saturated (peak) output power is approximately 63W with the efficiency of roughly 6% at the operating frequency of 231 GHz, under the beam voltage of 35 kV and total current of 30 mA (6-beam) respectively. Meanwhile, the BWO can generate power of 10 W-80 W in the tunable frequency of 220 GHz-240 GHz.
, Available online  , doi: 10.1049/cje.2021.00.447
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In advanced CMOS technology, process, voltage, and temperature (PVT) variations increase the paths’ latency in digital circuits, especially when operating at a low supply voltage. The fan-out-of-4 inverter chain (FO4 chain) metric has been proven to be a good metric to estimate the path’s delay variability, whereas the previous work ignored the non-independent characteristic between the adjacent cells in a path. In this study, an improved model of path delay variability is established to describe the relationship between the paths’ max-delay variability and a FO4 chain, which is based on multilevel FO4 metric and circuit-level parameters knobs (i.e., cell topology and driving strength) of the first few cells. We take the slew and load into account to improve the accuracy of this framework. Examples of 28 nm and 40 nm digital circuits show that our model conforms with Monte Carlo simulations as well as fabricated chips’ measurements. It is able to model the delay variability effectively to speed up the design process with limited accuracy loss. It also provides a deeper understanding and quick estimation of the path delay variability from the near-threshold to nominal voltages.
, Available online  , doi: 10.1049/cje.2021.00.084
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The key to multi-sensor image fusion is the fusion of infrared and visible images. Fusion of infrared and visible images with generative adversarial network (GAN) has great advantages in automatic feature extraction and subjective vision improvement. Due to different principle between infrared and visible imaging, the blur phenomenon of edge and texture is caused in the fusion result of GAN. For this purpose, this paper conducts a novel generative adversarial network with blur suppression. Specifically, the generator uses the residual-in-residual dense block with switchable normalization layer (RRDB+SN) as the elemental network block to retain the infrared intensity and the fused image textural details and avoid fusion artifacts. Furthermore, we design an anti-blur loss function based on weber local descriptor (WLD). Finally, numerous experiments are performed qualitatively and quantitatively on public datasets. Results justify that the proposed method can be used to produce a fusion image with sharp edge and clear texture.
, Available online  , doi: 10.1049/cje.2020.00.314
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As an emerging technology, cloud-assisted Wireless Body Area Networks (WBANs) provide more convenient services to users. Recently, many remote data auditing (RDA) protocols have been proposed to ensure the data integrity and authenticity when data owners outsourced their data to the cloud. However, most of them cannot check data integrity periodically according to the pay-as-you-go business model. These protocols also need high tag generation computation overhead, which brings a heavy burden for data owners. Therefore, we construct a lightweight remote data auditing protocol to overcome all above drawbacks. Our work can be deployed in the public environment without secret channels. It makes use of certificate-based cryptography which gets rid of certificate management problems, key escrow problems, and secret channels. The security analysis illustrates that the proposed protocol is secure. Moreover, the performance evaluation implies that our work is available in cutting down computation and communication overheads.
, Available online  , doi: 10.1049/cje.2021.00.118
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In this paper, a 3.6 mm gate width GaN HEMT with 0.35 μm gate length process and input and output matching circuits of Nanjing Electronic Devices Institute are used for broadband design respectively, and a novel high-power and high-efficiency self-bias S-band broadband continuous wave GaN power amplifier is realized. Under the working conditions of 2.2 GHz to 2.6 GHz and 32 V drain power supply, the continuous wave output power of the amplifier is more than 20 W, the power gain is more than 15 dB, and the max power added efficiency is more than 65%. The self-bias amplifier simplifies the circuit structure and realizes excellent circuit performance.
, Available online  , doi: 10.1049/cje.2021.00.347
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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.2021.00.198
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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.2021.00.209
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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.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.2021.00.089
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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.2022.00.034
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To solve the rating prediction problems of low accuracy and data sparsity on different datasets, we propose an interactive perception method based collaborative rating prediction algorithm (DCAE-MF), by fusing dual convolutional autoencoder (Dual-CAE) and probability matrix factorization (PMF). Deep latent representations of users and items are captured simultaneously by Dual-CAE and are deeply integrated with PMF to collaboratively make rating predictions based on the known rating history of users. A global multi-angle collaborative optimization learning method is developed to effectively optimize all the parameters of DCAE-MF. Extensive experiments are performed on seven real-world datasets to demonstrate the superiority of DCAE-MF on the key rating accuracy metrics of RMSE and MAE.
, Available online  , doi: 10.1049/cje.2022.00.077
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Shui manuscripts are part of the national intangible cultural heritage of China. Owing to the particularity of text reading, the level of informatization and intelligence in the protection of Shui manuscript culture is not adequate. To address this issue, this study created Shuishu_C, the largest image dataset of Shui manuscript characters that has been reported. Furthermore, after extensive experimental validation, we proposed ShuiNet-A, a lightweight artificial neural network model based on the attention mechanism, which combines channel and spatial dimensions to extract key features and finally recognize Shui manuscript characters. The effectiveness and stability of ShuiNet-A were verified through multiple sets of experiments. Our results showed that, on the Shui manuscript dataset with 113 categories, the accuracy of ShuiNet-A was 99.8%, which is 1.5% higher than those of similar studies. The proposed model could contribute to the classification accuracy and protection of ancient Shui manuscript characters.
, Available online  , doi: 10.1049/cje.2022.00.044
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Currently, many advanced object detection algorithms are mainly based on natural scenes object and rarely dedicated to fine-grained objects. This seriously limits the application of these advanced detection algorithms in remote sensing object detection. Therefore, how to apply horizontal detection in remote sensing images has important research significance. The mainstream remote sensing object detection algorithms achieve this task by angle regression, but the periodicity of angle leads to very large losses in this regression method, which increases the difficulty of model learning. Circular smooth label(CSL) solved this problem well by transforming the regression of angle into a classification form. Yolov5 combines many excellent modules and methods in recent years, which greatly improves the detection accuracy of small objects. Therefore, we use yolov5 as a baseline and combine the CSL method to learn the angle of arbitrarily oriented targets, and distinguish the fine-grained between instance classes by adding an attention mechanism module to accomplish the fine-grained target detection task for remote sensing images. Finally, our improved model achieves an average category accuracy of 39.2 on the FAIR1M dataset. Although our method does not achieve satisfactory results, this approach is very efficient and simple, reducing the hardware requirements of the model.
, 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.2022.00.053
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Essential proteins with biological functions are necessary for the survival of organisms. Computational recognition methods of essential proteins can reduce the workload and provide candidate proteins for biologists. However, existing methods fail to efficiently identify essential proteins, and generally do not fully use amino acid sequence information to improve the performance of essential protein recognition. In this work, we proposed an end-to-end deep contextual representation learning framework called DeepIEP to automatically learn biological discriminative features without prior knowledge based on protein network heterogeneous information. Specifically, the model attaches amino acid sequences as the attributes of each protein node in the protein interaction network, and then automatically learns topological features from protein interaction networks by graph embedding algorithms. Next, multi-scale convolutions and gated recurrent unit networks are used to extract contextual features from gene expression profiles. The extensive experiments confirm that our DeepIEP is an effective and efficient feature learning framework for identifying essential proteins and contextual features of protein sequences can improve the recognition performance of essential proteins.
, Available online  , doi: 10.1049/cje.2021.00.148
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The GPS system is a navigation satellite system with high precision, all-weather service, and global coverage, whose main purpose is to provide real-time and continuous global navigation services for the US military, and whose signal interference in wartime is a heavy blow to the US military. Its existing interference measures are classified into two types: blanket jamming and deception jamming, with the latter having better interference effects due to its imperceptibility. Frame synchronization, as the foundation of deception jamming, is a focus of current research on navigation countermeasures. This paper discusses the frame synchronization of CNAV-2 messages in GPS L1C signals and proposes a frame synchronization algorithm based on association rules. It analyzes the structural characteristics of CNAV-1 message data, reveals the hidden mapping relationships in the BCH code sequence of the first sub-frame by applying association rules, and achieves a blind synchronization of navigation messages by counting the types of mapping relationships and calculating the confidence levels. The simulation test results show that the proposed algorithm displays high error resilience and correct recognition rates and demonstrates certain values in engineering applications.
, 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.2021.00.081
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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.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.2021.00.233
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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.2021.00.415
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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.2021.00.401
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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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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.