Chinese Journal of Electronics

Cellular V2X-Based Integrated Sensing and Communication System: Feasibility and Performance Analysis

LI Yibo, ZHAO Junhui, LIAO Jieyu, HU Fajin

, Available online , doi: 10.23919/cje.2022.00.340

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Communication and sensing are basically required in intelligent transportation. The combination of two functions can provide a viable way in alleviating concerns about resource limitations. To achieve this, we propose an integrated sensing and communication (ISAC) system based on cellular vehicle-to-everything (C-V2X). We first analyze the feasibility of new radio (NR) waveform for ISAC system. We discuss the possibility of reusing NR waveform for sensing based on current NR-V2X standards. Ambiguity function is calculated to investigate the sensing performance limitation of NR waveform. A C-V2X-based ISAC system is then designed to realize the two tasks in vehicular network simultaneously. We formulate an integrated framework of vehicular communication and automotive sensing using the already-existing NR-V2X network. Based on the proposed ISAC framework, we develop a receiver algorithm for target detection/estimation and communication with minor modifications. We evaluate the performance of the proposed ISAC system with communication throughput, detection probability and range/velocity estimation accuracy. Simulations show that the proposed system achieves high reliability communication with 99.9999% throughput and high accuracy sensing with errors below 1m and 1m/s in vehicle scenarios.

Deep Guided Attention Network for Joint Denoising and Demosaicing in Real Image

ZHANG Tao, FU Ying, ZHANG Jun

, Available online , doi: 10.23919/cje.2022.00.414

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Denoising (DN) and demosaicing (DM) are the first crucial stages in the image signal processing pipeline. Recently, researches pay more attention to solve DN and DM in a joint manner, which is an extremely undetermined inverse problem. Existing deep learning methods learn the desired prior on synthetic dataset, which limits the generalization of learned network to the real world data. Moreover, existing methods mainly focus on the raw data property of high green information sampling rate for DM, but occasionally exploit the high intensity and signal-to-noise (SNR) of green channel. In this work, a deep guided attention network (DGAN) is presented for real image joint DN and DM (JDD), which considers both high SNR and high sampling rate of green information for DN and DM, respectively. To ease the training and fully exploit the data property of green channel, we first train DN and DM sub-networks sequentially and then learn them jointly, which can alleviate the error accumulation. Besides, in order to support the real image JDD, we collect paired raw clean RGB and noisy mosaic images to conduct a realistic dataset. The experimental results on real JDD dataset show the presented approach performs better than the state-of-the-art methods, in terms of both quantitative metrics and qualitative visualization.

An Algorithm of Deformation Image Correction Based on Spatial Mapping

DENG Xiangyu, ZHANG Aijia, YE Jinhong

, Available online , doi: 10.23919/cje.2022.00.443

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The original image undergoes geometric deformation in terms of position, shape, size, and orientation due to the shooting angle or capturing process during image acquisition. This brings about inconveniences and significant challenges in various image processing fields such as image fusion, denoising, recognition, and segmentation. To enhance the processing ability and recognition accuracy of deformation images, an adaptive image deformity correction algorithm is proposed for quadrilaterals and triangles. The deformation image undergoes preprocessing, and the contour of the image edge is extracted. Discrete points on the image edge are identified to accurately locate the edges. The deformation of the quadrilateral or triangle is transformed into a standard rectangular or equilateral triangular image using the proposed three-dimensional homography transformation algorithm. This effectively completes the conversion from an irregular image to a regular image in an adaptive manner. Numerous experiments demonstrate that the proposed algorithm surpasses traditional methods like Hough transform and Radon transform. It improves the effectiveness of correcting deformation in images, effectively addresses the issue of geometric deformation, and provides a new technical method for processing deformation images.

Correlation-aware Multi-dimensional Service Quality Prediction and Recommendation with Privacy-preservation in IoT

QI Lianyong, ZHONG Weiyi, HU Chunhua, ZHOU Xiaokang, WANG Fan, LIU Yuwen, YAN Chao

, Available online , doi: 10.23919/cje.2023.00.112

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Benefiting from the low data transmission requirements from user clients to remote cloud centers, edge computing has emerged as a lightweight and cost-effective solution for various data-intensive IoT applications, including intelligent transportation and smart healthcare. However, integrating distributed IoT data from multiple edge servers to provide better services poses practical and valuable research challenges. First, data redundancy is possible in each edge server, which reduces IoT data processing and transmission efficiency significantly. Second, user privacy is probably breached when the IoT data stored in different edge servers are integrated together for comprehensive data analysis and mining. Third, IoT data are often multi-dimensional and correlated with each other, which places an obstacle to scientific and accurate data analysis and decision-making. To solve these challenges, we propose a multi-dimensional and correlation-aware service quality prediction and recommendation approach with privacy preservation for edge-assisted IoT applications, named TLTM. Specifically, our approach employs Truncated Singular Value Decomposition (TSVD) to remove data redundancy in each edge server, Locality-Sensitive Hashing (LSH) to secure user privacy during multi-source data integration, and Mahalanobis distance to minimize correlation among different data dimensions. Finally, the feasibility of our proposal is validated through experiments conducted on the well-known WS-DREAM dataset.

New Algebraic Attacks on Grendel with the Strategy of Bypassing SPN Steps

QIAO Wenxiao, SUN Siwei, HU Lei

, Available online , doi: 10.23919/cje.2023.00.127

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Recently, the rapid development of modern cryptographic applications such as Zero-Knowledge (ZK), Secure Multi-Party Computation (MPC), Fully Homomorphic Encryption (FHE) has motivated the design of new so-called Arithmetization-Oriented (AO) symmetric primitives. As designing ciphers in this domain is relatively new and not well-understood, the security of these new ciphers remains to be completely assessed. In this paper, we revisit the security analysis of AO cipher Grendel. Grendel uses the Legendre symbol as a component, which is tailored specifically for the use in zero-knowledge and efficiently-varifiable proof systems. At FSE 2022, the first preimage attack on some original full GrendelHash instances was proposed. As a countermeasure, the designer adds this attack into the security analysis and updates the formula to derive the secure number of rounds. In our work, we present new algebraic attacks on GrendelHash. For the preimage attack, we can reduce the complexity or attack one more round than previous attacks for some instances. In addition, we present the first collision attack on some round-reduced instances by solving the CICO problem for the underlying permutations.

FGM-SPCL: Open-Set Recognition Network for Medical Images based on Fine-Grained data Mixture and Spatial Position Constraint Loss

ZHANG Ruru, E Haihong, YUAN Lifei, WANG Yanhui, WANG Lifei, SONG Meina

, Available online , doi: 10.23919/cje.2023.00.081

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The current intelligent auxiliary diagnosis models all follow the closed-set recognition setting. After the model is deployed online, the input data is often not completely controlled. Diagnosing an untrained disease as a known category would lead to serious medical malpractice. Therefore, realizing the open set recognition is significant to the safe operation of the intelligent auxiliary diagnosis model. Currently, most open-set recognition models are studied for natural images, and it is very challenging to obtain clear and concise decision boundaries be-tween known and unknown classes when applied to fine-grained medical images. Therefore, we propose an Open-Set Recognition Network for Medical Images based on Fine-Grained data Mixture and Spatial Position Constraint Loss (FGM-SPCL) in this work. First, considering the fine graininess of medical images and the diversity of unknown samples, we propose a fine-grained data Mixture (FGM) method to simulate unknown data by performing a mixing operation on known data to expand the coverage of unknown data difficulty levels. Secondly, in order to obtain a concise and clear decision boundary, we propose a Spatial Position Constraint Loss (SPCL) to control the position distribution of prototypes and samples in the feature space and maximize the distance between known classes and unknown classes. Finally, we validate on a private ophthalmic OCT dataset, and extensive experiments and analyses demonstrate that FGM-SPCL outperforms state-of-the-art models.

Method of Single Event Effects Radiation Hardened Design for DC-DC Converter Based Load Transient Detection

GUO Zhongjie, LIU Nan, LU Hu, LI Mengli, QIU Ziyi

, Available online , doi: 10.23919/cje.2022.00.442

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Aiming at the impact of load current change on single-event transient, the essential difference between single-event transient and load transient of DC-DC converter is deeply studied. A hardened circuit based on load transient detection is proposed. The circuit detects the load transient information in time and outputs a control signal to control the single event hardened circuit, thereby realizing the improvement of the transient characteristics of the system under dynamic conditions. Based on the 180nm BCD process, the design and physical verification of a Boost converter are completed. The experimental results show that the input voltage range is 2.9~4.5V, the output voltage range is 5.8~7.9V, and the load current is 0~55 mA. During load transients, the load detection circuit turns off the hardened circuit in time, avoiding system oscillation and widening the dynamic range of the hardening circuit. Under the single event transient, the output voltage fluctuation of the system does not exceed the maximum ripple voltage, and the single event transient suppression ability reaches more than 86%, the system can work well with linear energy transient of about 100 MeV·cm²/mg.

A Task Scheduling Algorithm based on Clustering Pre-processing in Space-based Information Network

WANG Yufei, LIU Jun, ZHANG Shengnan, XU Sai, WANG Jingyi

, Available online , doi: 10.23919/cje.2022.00.114

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With the diversification of space-based information network task requirements and the dramatic increase in demand, the efficient scheduling of various tasks in space-based information network becomes a new challenge. To address the problems of a limited number of resources and resource heterogeneiry in the space-based information network, we propose a bilateral pre-processing model for tasks and resources in the scheduling pre-processing stage. We use an improved fuzzy clustering method to cluster tasks and resources and design coding rules and matching methods to match similar categories to improve the clustering effect. We propose a space-based information network task scheduling strategy based on an ant colony simulated annealing algorithm for the problems of high latency of space-based information network communication and high resource dynamics. The strategy can efficiently complete the task and resource matching and improve the task scheduling performance. The experimental results show that our proposed task scheduling strategy has less task execution time and higher resource utilization than other algorithms under the same experimental conditions. It has significantly improved scheduling performance.

Hybrid ITÖ Algorithm for Large-scale Colored Traveling Salesman Problem

DONG Xueshi

, Available online , doi: 10.23919/cje.2023.00.040

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In the fields of intelligent transportation and multi-task cooperation, many practical problems can be modeled by colored traveling salesman problem (CTSP). However, when solving large-scale CTSP with a scale of more than 1000 dimensions, their convergence speed and the quality of their solutions are limited. Therefore, this paper proposes a new hybrid ITÖ (HITÖ) algorithm, which integrates two new strategies, crossover operator and mutation strategy, into the standard ITÖ. In the iteration process of HITÖ, the feasible solution of CTSP is represented by the double chromosome coding, and the random drift and wave operators are used to explore and develop new unknown regions. In this process, the drift operator is executed by the improved crossover operator, and the wave operator is performed by the optimized mutation strategy. Experiments show that HITÖ is superior to the known comparison algorithms in term of the quality solution.

Long Short-Term Memory Spiking Neural Networks for Classification of Snoring and Non-Snoring Sound Events

ZHANG Rulin, LI Ruixue, LIANG Jiakai, YUE Keqiang, LI Wenjun, LI Yilin

, Available online , doi: 10.23919/cje.2022.00.210

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Snoring is a widespread occurrence that impacts human sleep quality. It is also one of the earliest symptoms of many sleep disorders. Snoring is accurately detected, making further screening and diagnosis of sleep problems easier. However, snoring is frequently ignored because of its underrated and costly detection costs. As a result, this research offered an alternative method for snoring detection based on a Long Short-Term Memory based Spiking Neural Network (LSTM-SNN) that is appropriate for large-scale home detection for snoring. In this paper, We designed acquisition equipment to collect the sleep recordings of 54 subjects and constructed the sleep sound database in the home environment. And Mel Frequency Cepstral Coefficients (MFCCs) were extracted from these sound signals and encoded into spike trains by a threshold encoding approach. Then, they were classified automatically as non-snoring or snoring sounds by our LSTM-SNN model. We used the backpropagation algorithm based on an alternative gradient in the LSTM-SNN to complete the parameter update. The categorization percentage reached an impressive 93.4%, accompanied by a remarkable 36.9% reduction in computer power compared to the regular LSTM model.

Towards Semi-supervised Classification of Abnormal Spectrum Signals based on Deep Learning

JIANG Tao, CHEN Wanqing, ZHOU Hangping, HE Jinyang, QI Peihan

, Available online , doi: 10.23919/cje.2022.00.395

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In order to cope with the heavy labor cost challenge of the manual abnormal spectrum classification and improve the effectiveness of existing machine learning schemes for spectral datasets with Interference-to-Signal Ratios (ISR), we proposes a Semi-Supervised Classification of Abnormal Spectrum Signals (SSC-ASS), aimed at addressing some of the challenges in Abnormal Spectrum Signal (ASS) classification tasks. A significant advantage of SSC-ASS is that it does not require manual labeling of every abnormal data, but instead achieves high-precision classification of ASSs using only a small number of labeled data. Furthermore, the method can to some extent avoid the introduction of erroneous information resulting from the complex and variable nature of abnormal signals, thereby improving classification accuracy. Specifically, SSC-ASS uses a Memory AutoEncoder (MAE) module to efficiently extract features from abnormal spectrum signals by learning from the reconstruction error. Additionally, SSC-ASS combines Convolutional Neural Network (CNN) and the K-means using a DeepCluster framework to fully utilize the unlabeled data. Furthermore, SSC-ASS also utilizes pre-training, category mean memory module and replaces pseudo-labels to further improve the classification accuracy of ASSs. And we verify the classification effectiveness of SSC-ASS on synthetic spectrum datasets and real on-air spectrum dataset.

A Secure Communicating while Jamming Approach for End-to-End Multi-hop Wireless Communication Network

MA Xiao, LI Dan, WANG Liang, HAN Weijia, ZHAO Nan

, Available online , doi: 10.23919/cje.2022.00.448

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With the rapid development of wireless communications, cellular communication and distributed wireless network is fragile to eavesdropping due to distributed users and transparent communication. However, to adopt bigger transmit power at a given area to interfere potential eavesdroppers not only incurs huge energy waste but also may suppresses regular communication in this area. To this end, we focus on secure communication in multi-hop wireless communication network, propose two communicating while jamming schemes for secure communication in presence of potential eavesdroppers for the narrow band and broad band point-to-point (P2P) systems respectively with the aid of artificial noise transmitted by a chosen cooperative interferer. Furthermore, to achieve the end-to-end (E2E) multi-hop secure communication, we devise the secure network topology discovering scheme via constructing a proper network topology with at least one proper node as the cooperative interferer in each hop, and then propose the secure transmission path planning scheme to find an E2E secure transmission route from source to destination, respectively. Experiments on the WARP platform demonstrate the feasibility of the proposed schemes. Besides, simulations results validate that the proposed schemes can achieve better performance compared with existing methods in both the P2P communication case and E2E multi-hop communication network scenario.

DeepLogic: Priority Testing of Deep Learning through Interpretable Logic Units

LIN Chenhao, ZHANG Xingliang, SHEN Chao

, Available online , doi: 10.23919/cje.2022.00.451

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With the increasing deployment of deep learning-based systems in various scenes, it is becoming important to conduct sufficient testing and evaluation of deep learning models to improve their interpretability and robustness. Recent studies have proposed different testing criteria and strategies for deep neural network (DNN) testing. However, they rarely conduct effective testing on the robustness of DNN models and lack interpretability. This paper proposes priority testing criteria called DeepLogic, to analyze the robustness of the DNN models from the perspective of model interpretability. Specifically, we first define the neural units in DNN with the highest average activation probability as “interpretable logic units.” Then we analyze the changes in these units to evaluate the model's robustness by conducting adversarial attacks. After that, the interpretable logic units of the inputs are taken as context attributes, and the probability distribution of the softmax layer in the model is taken as internal attributes to establish a comprehensive test prioritization framework. Finally, the weight fusion of context and internal factors is carried out, and the test cases are sorted according to this priority. The experimental results on 4 popular DNN models using 8 testing metrics show that our DeepLogic significantly outperforms existing state-of-the-art methods.

High Speed Column Level ADC Design of Full Parallel Two-Step Nested TDC for CMOS Image Sensor

GUO Zhongjie, WANG Yangle, XU Ruiming, YU Ningmei, WU Longsheng

, Available online , doi: 10.23919/cje.2022.00.270

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A high-speed column-level ADC for CMOS image sensors is proposed in this paper, which uses a fully parallel two-step structure combined with TDC. After performing the coarse and fine conversions process, the clock signal is used to constrain the comparator’s output to produce a time difference value during the last clock cycle of the conversion. TDC is used to convert the difference to the corresponding digital code and difference with the conversion result of the ADC proposed in this paper. High-precision A/D conversion is achieved, which significantly improves the conversion speed of the ADC. The proposed circuit is designed and verified based on 55 nm CMOS process. Under the design environment of 3.3 V analog voltage, 1.2 V digital voltage, 250 MHz clock frequency and 1.5 V input signal range, the signal-to-noise-distortion ratio (SNDR) of the 12-bit ADC reaches 68.272 dB, the differential nonlinearity (DNL) is +0.8/−0.8 LSB, and the integral nonlinearity (INL) is +1.47/−1.74 LSB. The power consumption of the column circuit is 72 μW and the switching time is 320 ns. It provides an efficient ADC design scheme for high frame rate and large area array CMOS image sensors.

Troy: Efficient Service Deployment for Windows Systems

ZHANG Deyu, XIE Yu, XU Mucong, CHENG En, KUI Xiaoyan, HE Bangwen, LI Yunhao

, Available online , doi: 10.23919/cje.2022.00.405

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The modern university computer lab or K-12 classrooms require a centralized solution to efficiently manage a large number of desktops. The existing solutions either bring virtualization overhead in runtime or requires loading a large image over 30 GB leading to an unacceptable network latency. In this work, we propose Troy which takes advantage of the differencing virtual hard disk techniques in Windows systems. As such, Troy only loads the modifications made on one machine to all other machines. Troy consists of two modules that are responsible to generate an initial image and merge a differencing image with its parent image, respectively. Specifically, we identify the key fields in the virtual hard disk image that links the differencing image and the parent image and find the modified blocks in the differencing images that should be used to replace the blocks in the parent image. We further design a lazy copy solution to reduce the I/O burden in image merging. We have implemented Troy on bare metal machines. The evaluation results show that the performance of Troy is comparable to the native implementation in Windows, without requiring the Windows environment.

A Wideband High-Gain Sawtooth Slot Array Antenna with Frequency-Scanning at Lower Frequency and Fixed-Beam at Higher Frequency

SUN Qiang, BAN Yongling, HU Jun

, Available online , doi: 10.23919/cje.2022.00.332

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Exploring the multifunctional array antenna suitable for complex communication environment has very important research value. In this paper, a millimeter wave (mm-wave) double longitudinal sawtooth slot array antenna (DLSA) based on substrate integrated waveguide (SIW) technology is proposed, which has wideband, high-gain and novel beam characteristics. Two irregular longitudinal slots are etched on the SIW conductor surface as the radiation structure. To improve the bandwidth of the DLSA, the metallized vias are added at the transition point of the slots to construct multiple resonant frequency points. By adjusting the size of the slots and the position of the metallized vias, the phase constant can be regulated, and the unusual beam characteristics can be obtained. That is to say, in the lower frequency band, the beam pointing angle increases with frequency. In the higher frequency band, the beam pointing angle is maintained at a fixed angle. Finally, to improve the antenna gain, a 16 × 8 DLSA is fabricated, measured and discussed. The proposed antenna has an impedance matching bandwidth close to 9.7 GHz. From 27 to 33 GHz, the beam pointing angle changes from 8° to 29°. From 33 to 37 GHz, the beam pointing angle is fixed at 29°, and the pointing angle error range is ±1°. In addition, the measured maximum gain is 22.9 dBi at 32 GHz.

Enhanced Privacy-Preserving WiFi Fingerprint Localization from CL Encryption

WANG Zhiwei, ZHU Qiuchi, ZHANG Zhenqi

, Available online , doi: 10.23919/cje.2022.00.257

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The WiFi fingerprint-based localization method is considered one of the most popular techniques for indoor localization. In INFOCOM'14, Li et al. proposed a WiFi fingerprint localization system based on Paillier encryption, which is claimed to protect both client $C$’s location privacy and service provider $S$’s database privacy. However, Yang et al. presented a practical data privacy attack in INFOCOM'18, which allows a polynomial time attacker to obtain $S$’s database. In this paper, we propose a novel WiFi fingerprint localization system based on CL encryption, which has a trustless setup and is efficient due to the excellent properties of CL encryption. To prevent Yang et al.’s attack, the system requires that $S$ selects only the locations from its database that can receive the nonzero signals from all the available APs in $C$’s nonzero fingerprint in order to determine $C$’s location. Security analysis shows that our scheme is secure under Li et al.’s threat model. Furthermore, to enhance the security level of PriWFLCL, we propose a secure and efficient zero-knowledge proof protocol for the discrete logarithm relations in $C$’s encrypted localization queries.

Joint Communication-Caching-Computing Resource Allocation for Bidirectional Data Computation in IRS-Assisted Hybrid UAV-Terrestrial Network

LIAO Yangzhe, LIU Lin, SONG Yuanyan, XU Ning

, Available online , doi: 10.23919/cje.2023.00.089

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Joint communication-caching-computing resource allocation in wireless inland waterway communications enables resource-constrained unmanned surface vehicles (USVs) to provision computation-intensive and latency-sensitive tasks forward B5G and 6G era. However, the power of such resource allocation cannot be fully studied unless bidirectional data computation is properly managed. In this paper, a novel IRS-assisted hybrid UAV-terrestrial network architecture is proposed with bidirectional tasks. The sum of uplink and downlink bandwidth minimization problem is formulated by jointly considering link quality, task execution mode selection, UAVs trajectory and task execution latency constraints. A heuristic algorithm is proposed to solve the formulated challenging problem. We divide the original challenging problem into two subproblems, i.e., the joint optimization problem of USVs offloading decision, caching decision and task execution mode selection, and the joint optimization problem of UAVs trajectory and IRS phase shift-vector design. The Karush–Kuhn–Tucker conditions are utilized to solve the first subproblem and the enhanced differential evolution algorithm is proposed to solve the latter one. The results show that the proposed solution can significantly decrease bandwidth consumption in comparison with the selected advanced algorithms. The results also prove that the sum of bandwidth can be remarkably decreased by implementing a higher number of IRS elements.

The Choice of Mesh Size and Integration Points Number for the Electrostatically Controlled Membrane Antenna Structural-electromagnetic Coupling Model

GU Yongzhen, ZHANG Qinggang, YU Xiao, LI Guixu

, Available online , doi: 10.23919/cje.2022.00.424

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It is of great significance for the improvement of the computational efficiency of the electrostatically controlled membrane antenna (ECMA) structural-electromagnetic coupling (SEC) model through the choice of appropriate mesh size and integration points number. In this paper, the physical optics (PO) formulation is used to analyze the radiation pattern of the ECMA surface and the finite element method is applied to the electrostatic-structural coupling analysis. An expression for the relation between the mesh size, the focal length of the parabolic antenna, and the wavelength is developed based on the discretization error analysis of the triangular mesh approximating the parabolic surface. Moreover, the integration points number in each triangular mesh is determined by the numerical evaluation of the PO integral. Numerical results show that the proposed method improves the computing efficiency by about 87% compared with the referenced method.

ZHANG Yi, ZHANG Kai, CUI Ting

, Available online , doi: 10.23919/cje.2022.00.419

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Related-key model is a favourable approach to improve attacks on block ciphers with a simple key schedule. However, to the best of our knowledge, there are a few results in which zero-correlation linear attacks take advantage of the related-key model. We ascribe this phenomenon to the lack of consideration of the key input in zero-correlation linear attacks. In this paper, concentrating on the linear key schedule of a block cipher, we generalize the zero-correlation linear attack by using a related-key setting. Specifically, we propose the creation of generalized linear hulls (GLHs) when the key input is involved; moreover, we indicate the links between GLHs and conventional linear hulls (CLHs). Then, we prove that the existence of zero-correlation GLHs is completely determined by the corresponding CLHs and the linear key schedule. In addition, we introduce a method to construct zero-correlation GLHs by CLHs and transform them into an integral distinguisher. The correctness is verified by applying it to SIMON16/16, a SIMON-like toy cipher. Based on our method, we find 12/13/14/15/15/17/20/22-round related-key zero-correlation linear distinguishers of SIMON32/64, SIMON48/72, SIMON48/96, SIMON64/96, SIMON64/128, SIMON96/144, SIMON128/192 and SIMON128/256, respectively. As far as we know, these distinguishers are one, two, or three rounds longer than current best zero-correlation linear distinguishers of SIMON.

LIU Jiamei, TAN Lin, XU Hong

, Available online , doi: 10.23919/cje.2022.00.383

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Deoxys-BC is the primitive tweakable block cipher of Deoxys family of authenticated encryption schemes. Based on the existing related-tweakey boomerang distinguishers, this paper improves the boomerang attacks on 11-round Deoxys-BC-256 and 13-round Deoxys-BC-384 by the optimized key guessing and the precomputation technique. It transfers a part of subtweakey guess in the key-recovery phase to the precomputation resulting in a significant reduction of the overall time complexity. For 11-round Deoxys-BC-256, we give a related-tweakey boomerang attack with time/data/memory complexities of $2^{218.6}/2^{125.7}/2^{125.7}$, and give another attack with the less time complexity of $2^{215.8}$ and memory complexity of $2^{120}$ when the adversary has access to the full codebook. For 13-round Deoxys-BC-384, we give a related-tweakey boomerang attack with time/data/memory complexities of $2^{k-96}+2^{157.5}/2^{120.4}/2^{113}$. For the key size $k=256$, it reduces the time complexity by a factor of $2^{31}$ compared with the previous 13-round boomerang attack. In addition, we present two new related-tweakey boomerang distinguishers on 11-round Deoxys-BC-384 with the same probability as the best previous distinguisher.

XPull: A Relay-based Blockchain Intercommunication Framework Achieving Cross-chain State Pulling

LIANG Xinyu, CHEN Jing, DU Ruiying

, Available online , doi: 10.23919/cje.2023.00.004

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Cross-chain technology, which enables different blockchains to intercommunicate with one another, is challenging. Many existing cross-chain platforms, such as Polkadot and Cosmos, generally adopt a relay-based scheme: a relaychain (relay blockchain) receives and records the state information from every parachain (parallel blockchain), and publish the information on the platform, by which parachains are able to efficiently acquire the state information from one another. However, in the condition when parachain is consortium blockchain, the cross-chain platform cannot work properly. On the one hand, whether state information is submitted to relaychain is completely decided by the internal decision of parachain. The timeliness of state information cannot be guaranteed. On the other hand, the transfer of state information will be interrupted due to the failure of parachain or relaychain-parachain connection. In this paper, we propose a relay-based blockchain intercommunication framework, called XPull. Specifically, to ensure the timeliness of state information, we propose a cross-chain state pulling scheme based on cosigned state pulling agreement. To solve the interruption of state transfer, we propose a random scheduling scheme to resume the transfer, or confirm the failure of parachain. The security analysis and experimental results demonstrate that XPull is secure and efficient.

Global Ramp Uniformity Correction Method for Super-large Array CMOS Image Sensors

XU Ruiming, GUO Zhongjie, LIU Suiyang, YU Ningmei

, Available online , doi: 10.23919/cje.2022.00.397

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Aiming at the problem of the non-uniformity of the ramp signal in the super-large array CMOS image sensors(CIS), a ramp uniformity correction method for CMOS image sensors is proposed in this paper. Based on the error storage technique, the ramp non-uniformity error is stored. And the input ramp signal of each column is shifted by level-shifting technique to eliminate the ramp non-uniformity error. Based on the 55 nm-1P4M CMOS process, this paper has completed the detailed circuit design and comprehensive simulation verification of the proposed method. Under the design conditions that the voltage range of the ramp signal is 1.4 V, the slope of the ramp signal is 71.908 V/ms, the number of pixels is 8192(H)×8192(V), and a single pixel size is 10 μm, the correction method proposed in this paper reduces the ramp non-uniformity error from 7.89 mV to 36 μV. The differential non-linearity (DNL) of the ramp signal is +0.0013/−0.004 LSB and the integral non-linearity (INL) is +0.045/-0.021 LSB. The ramp uniformity correction method proposed in this paper reduces the ramp non-uniformity error by 99.54% on the basis of ensuring the high linearity of the ramp signal, without significantly increasing the chip area and without introducing additional power consumption. The column FPN is reduced from 1.9% to 0.01%. It provides theoretical support for the design of high-precision CMOS image sensors.

Compressed Classification of Brain Tumor Images Using Novel Chaotic Map and Improved Squeezenet Architecture

Ashwini K, Mathivanan P, Sharon Femi P, Kala A

, Available online , doi: 10.23919/cje.2022.00.330

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Presented in this paper is a novel Brain tumor image classification algorithm in a compressed domain with the aid of a new chaotic map and improved squeezenet architecture. The tumor images for classification are compressed using compressive sensing theory. An unique chaotic map is proposed that is quite responsive to its initial conditions. The measurement matrix for compressing the images in the CS paradigm is constructed based on the presented chaotic map. The compressed images are then given to an improved squeezenet architecture for classification. Images are classified within the compressed domain itself, obviating the use of pricey decompression algorithms. The presented method is tested on the MRI brain images of four different classes obtained from Kaggle repository. Through simulation results it has been verified that the computational time required for classification of compressed images using neural networks has been greatly reduced. Around 91% classification accuracy is achieved, which is just 2% less than the accuracy achieved for images classified without compression. A number of other categorization metrics that were calculated to demonstrate the efficacy of the suggested strategy also support the same.

The Establishment and Analysis of the Structural-ElectromagnetiC Coupling Model of the Electrostatically Controlled Deployable Membrane Antenna

ZHANG Shunji, GU Yongzhen, ZHONG Wang, ZHANG Qinggang

, Available online , doi: 10.23919/cje.2022.00.328

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A new structural-electromagnetic coupling (SEC) analysis based on quadratic elements is proposed to solve the mismatch problem between structural elements and electromagnetic grids of the electrostatically controlled deployable membrane antenna (ECDMA). Firstly, the ECDMA reflector surface is meshed and redefined by a series of quadratic elements. Without grid transformation, the calculating formulas for the far-field pattern of ECDMA are derived by the Physical-Optics (PO) method. Then the structural deformation of ECDMA is analyzed and the far-field pattern calculating formulas including deformation errors are developed. Simulation and experiment results show that the quadratic elements are effective and efficient in SEC analysis of the ECDMA, moreover, the electromagnetic grid size demand and the grid discretization error are reduced greatly.

Miniaturized, Wide Stopband Filter Based on Shielded Capacitively Loaded SIW Resonators

ZHENG Yan, TIAN Hanyu, DONG Yuandan

, Available online , doi: 10.23919/cje.2023.00.057

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Based on the miniaturized shielded half mode capacitively loaded substrate integrated waveguide (S-HMCSIW) cavities and full mode capacitively loaded SIW resonator, a novel compact high- performance filter is proposed. The footprint of the half-mode SIW (HMSIW) is further reduced due to the application of the capacitive-loading technique. By applying cross coupling, the proposed SIW filter’s transmission zero (TZ) enhances the stopband rejection and shows excellent selectivity. For the bandpass filter (BPF), the measured |S₂₁| and |S₁₁| are better than 1.09 dB and −14 dB, respectively. And a 3 dB fractional bandwidth of 9.14-10.76 GHz (FBW=16.2%) is also observed. The filter achieves a wide stopband with a −20 dB out-of-band rejection up to 2.69 f₀ (f₀ = 10 GHz), with a size of 0.39λ_g×0.51λ_g only. Good agreement between measurement and simulation is obtained.

An Effective Power Optimization Approach Based on Whale Optimization Algorithm with Two-Populations and Mutation Strategies

HE Juncai, HE Zhenxue, LIU Jia, ZHANG Yan, ZHANG Fan, LIANG Fangfang, WANG Tao, XIAO Limin, WANG Xiang

, Available online , doi: 10.23919/cje.2022.00.358

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Power is an issue that must be considered in the design of logic circuits. Power optimization is a combinatorial optimization problem, since it is necessary to search for a logical expression that consumes the least amount of power from a large number of logical Reed-Muller (RM) logical expressions. The existing approach for optimizing the power of multi-output mixed polarity RM (MPRM) logic circuits suffer from poor optimization results. To solve this problem, we propose a whale optimization algorithm with two-populations strategy and mutation strategy (TMWOA). The two-populations strategy speeds up the convergence of the algorithm by exchanging information about the two-populations. The mutation strategy enhances the ability of the algorithm to jump out of the local optimal solutions by using the information of the current optimal solution. Based on the TMWOA, we propose a multi-output MPRM logic circuits power optimization approach(TMMPOA). Experiments based on the Microelectronics Center of North Carolina (MCNC) benchmark circuits validate the effectiveness and superiority of the proposed TMMPOA.

Friendship Inference Based on Interest Trajectory Similarity and Co-occurrence

TIAN Junfeng, HOU Zhengqi

, Available online , doi: 10.23919/cje.2022.00.363

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Most of the current research on user friendship speculation based on location-based social networks is based on the co-occurrence characteristics of users, and statistics find that co-occurrence is not common among all users, and most of the existing work focuses on mining more features to improve the accuracy ignoring the time complexity in practical applications. Based on this, a friendship inference model (ITSIC) is proposed based on the similarity of user interest tracks and joint user location co-occurrence. Based on the Meanshift clustering algorithm, ITSIC clustered and filtered user check-ins and divided the dataset into interesting, abnormal, and noise check-ins. User interest trajectories were constructed using interest check-in, which allows ITSIC to work efficiently even for users without co-occurrences. At the same time, based on clustering, the single-moment multi-interest trajectory was further proposed, which increased the richness of the meaning of the trajectory moment. Finally, extensive experiments on two real online social network datasets show that ITSIC outperforms existing methods in terms of AUC score and time efficiency compared to existing methods.

A Lattice-Based Method for Recovering the Unknown Parameters of Truncated Multiple Recursive Generators with Constant

YU Hanbing, ZHENG Qunxiong

, Available online , doi: 10.23919/cje.2022.00.387

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Multiple recursive generators with constant, the high-order extension of linear congruence generators, are an important class of pseudorandom number generators that are widely used in cryptography. The predictability of truncated sequences output by multiple recursive generators with constant that predicting the whole sequences by the truncated high-order bits of the sequences is a cryptographically crucial problem. This paper studies the predictability of truncated multiple recursive generators with constant. Given a few truncated digits of high-order bits output by a multiple recursive generator with constant, we first convert the multiple recursive generator with constant to multiple recursive generator and then adopt the method we proposed recently to recover the modulus, the coefficients, and the differences of initial state. In particular, we give an estimation of the number of truncated digits required for recovering the differences of initial state by using the expected norm of target vector. Moreover, we prove by exponential sums that the number of truncated digits required for uniquely determining both the initial state and the constant is finite and give an upper bound. Extensive experiments confirm the correctness of our method.

Method and Practice of Trusted Embedded Computing and Data Transmission Protection Architecture Based on Android

WANG Yichuan, GAO Wen, HEI Xinhong, DU Yanning

, Available online , doi: 10.23919/cje.2022.00.196

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In recent years, the rapid development of Internet technology has constantly enriched people's daily life and gradually changed from the traditional computer terminal to the mobile terminal. But with it comes the security problems brought by the mobile terminal. Especially for Android system, due to its open source nature, malicious applications continue to emerge, which greatly threatens the data security of users. Therefore, this paper proposes a method of trusted embedded static measurement and data transmission protection architecture based on Android to reduce the risk of data leakage in the process of terminal storage and transmission. We conducted detailed data and feasibility analysis of the proposed method from the aspects of time consumption, storage overhead and security. The experimental results show that this method can detect Android system layer attacks such as self-booting of the malicious module and improve the security of data encryption and transmission process effectively. Compared with the native system, the additional performance overhead is small.

A Secure Mutual Authentication Protocol Based on Visual Cryptography Technique for IoT-Cloud

Ehui Brou Bernard, CHEN Chen, WANG Shirui, GUO Hua, LIU Jianwei

, Available online , doi: 10.23919/cje.2022.00.339

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Because of the increasing number of threats in the IoT cloud, an advanced security mechanism is needed to guard data against hacking or attacks. A user authentication mechanism is also required to authenticate the user accessing the cloud services. The conventional cryptographic algorithms used to provide security mechanisms in cloud networks are often vulnerable to various cyber-attacks and inefficient against new attacks. Therefore, developing new solutions based on different mechanisms from traditional cryptography methods is required to protect data and users’ privacy from attacks. In view different from the conventional cryptography method, we suggest a secure mutual authentication protocol based on the visual cryptography technique in this paper. We use visual cryptography to encrypt and decrypt the secret images. The mutual authentication is based on two secret images and tickets. The user requests the ticket from the AS (Authentication Server), granting him permission for accessing the cloud services. Three shared secret keys are used for encrypting and decrypting the authentication process. We analyze the protocol using the Barrows-Abadi-Needham (BAN)-logic method and the results show that the protocol is robust and can protect the user against various attacks. Also, it can provide a secure mutual authentication mechanism.

Multi-time-scale Variational Mode Decomposition-based Robust Fault Diagnosis of Railway Point Machines under Multiple Noises

LIU Junqi, WEN Tao, XIE Guo, CAO Yuan, Roberts Clive

, Available online , doi: 10.23919/cje.2022.00.234

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Fault diagnostics of railway point machines (RPMs) have attracted engineers’ and researchers’ attention. However, seldom have studies considered diverse noises along the track. To fulfill this aspect, a multi-time-scale variational mode decomposition (MTSVMD) is proposed in this paper to realize the accurate and robust fault diagnosis of RPMs under multiple noises. MTSVMD decomposes condition monitoring signals after coarse-grained processing in varying degrees. In this manner, the information contained in the signal components at multiple time scales can construct a more abundant feature space than at a single scale. In the experimental validation, a random position, random type, random number, and random length (4R) noise-adding algorithm helps to verify the robustness of the approach. The adequate experimental results demonstrate the superiority of the proposed MTSVMD-based fault diagnosis.

An Efficient Task Scheduling Algorithm in the Cloud and Edge Collaborative Environment

LONG Saiqin, WANG Cong, LONG Weifan, LIU Haolin, DENG Qingyong, LI Zhetao

, Available online , doi: 10.23919/cje.2022.00.223

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With the advent of the 5G era and the accelerated development of edge computing and Internet of Things technologies, the number of tasks to be processed by mobile devices continues to increase. Edge nodes become incapable of facing massive tasks due to their own limited computing capabilities, and thus the cloud and edge collaborative environment is produced. In order to complete as many tasks as possible while meeting the deadline constraints, we consider the task scheduling problem in the cloud-edge and edge-edge collaboration scenarios. As the number of tasks on edge nodes increases, the solution space becomes larger. Considering that each edge node has its own communication range, we design an edge node based clustering algorithm (ENCA), which can reduce the feasible region while dividing the edge node set. Subsequently, we transform the edge nodes inside the cluster into a bipartite graph, and then propose a task scheduling algorithm based on maximum matching (SAMM). Finally, our ENCA and SAMM are used to solve the task scheduling problem. Compared with the other benchmark algorithms, experimental results show that our algorithms increase the number of tasks which can be completed and that meet the latest deadline constraints by 32%-47.2% under high load conditions.

FSCIL-EACA: Few-Shot Class-Incremental Learning Network Based on Embedding Augmentation and Classifier Adaptation for Image Classification

ZHANG Ruru, E Haihong, SONG Meina

, Available online , doi: 10.23919/cje.2022.00.396

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The ability to learn incrementally is critical to the long-term operation of AI systems. Benefiting from the power of Few-shot Class-Incremental Learning, deep learning models can continuously recognize new classes with only a few samples. The difficulty is that limited instances of new classes will lead to overfitting and exacerbate the catastrophic forgetting of the old classes. Most previous works alleviate the above problems by imposing strong constraints on the model structure or parameters. However, embedding network transferability and classifier adaptation are often ignored, failing to guarantee the efficient utilization of visual features and establishing relationships between old and new classes. This paper proposes a simple and novel approach from two perspectives, embedding bias, and classifier bias. On the one hand, an Embedding Augmented Network with cross-class transfer and classspecific discrimination abilities is learned based on self-supervised learning and modulated attention to alleviate embedding bias. On the other hand, an Adaptive Incremental Classifier learning scheme is proposed, which captures the dependencies between different classes through a Hybrid relation projection module to guide adaptive update of prototypes and embeddings to alleviate classifier bias. At the same time, we propose a Pseudo-incremental Episode Selection module, which enables Hybrid relation projection module learning to have more transferable meta-knowledge information through meta-training to realize incremental learning skills. We conduct comparative and ablation experiments on two popular natural image datasets (CUB-200 and MiniImageNet) and two medical datasets (HyperKvasir and SKIN-7), showing that our method is significantly better than the baseline and achieves state-of-the-art results.

Research on Modeling and Parameter Identification of Comprehensive Load Model of Distribution Network in Industrial Park

WANG Tingling, YAN Xiaohe

, Available online , doi: 10.23919/cje.2022.00.024

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With the rapid development of industrial parks, its load model research has become a hot spot. In order to study the load of power system in industrial park, based on the characteristics of the industrial park load, a comprehensive load admittance static model with full voltage range adaptability is considered, and a comprehensive load model of distribution network of the industrial park is established. A complete parameter identification of the model is carried out through chaos particle swarm optimization. The simulation results show that the model can effectively describe the load characteristics of the distribution network in industrial parks.

Fast Cross-Platform Binary Code Similarity Detection Framework based on CFGs Taking Advantage of NLP and Inductive GNN

PENG Jinxue, WANG Yong, XUE Jingfeng, LIU Zhenyan

, Available online , doi: 10.23919/cje.2022.00.228

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Cross-platform binary code similarity detection aims at detecting whether two or more pieces of binary code, e.g., basic blocks, functions, or whole programs compiled on different platforms are similar or not. It has a wide range of applications in plagiarism detection, malware detection, vulnerability search, etc. Since different compilers and optimization levels may lead to huge differences in syntax and structure even for the same source code after assembling. Existing approaches that combine Control Flow Graphs (CFGs)-based function representation and Graph Convolutional Network (GCN)-based similarity analysis are the best-performing ones. However, due to a large amount of convolutional computation and the loss of structural information, the use of convolution networks will inevitably bring problems such as high overhead and sometimes inaccuracy. To address these issues, we propose a fast cross-platform binary code similarity detection framework that takes advantage of Natural Language Processing (NLP) and inductive Graph Neural Network (GNN) for basic blocks embedding and function representation respectively by simulating extracting structural features and temporal features. GNN’s node-centric and small batch is a suitable training way for large CFGs, it can greatly reduce computational overhead. Various NLP basic block embedding models and GNNs are evaluated. Experimental results show that the scheme with Long Short Term Memory (LSTM) for basic blocks embedding and inductive learning-based GraphSAGE(GAE) for function representation outperforms the state-of-the-art works. In our framework, we can take only 45% overhead. Improve efficiency significantly with a small performance trade-off.

Multi-sensor Fusion Adaptive Estimation for Nonlinear Under-observed System with Multiplicative Noise

CUI Yongpeng, SUN Xiaojun

, Available online , doi: 10.23919/cje.2022.00.364

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The adaptive fusion estimation problem was studied for the multi-sensor nonlinear under-observed systems with multiplicative noise. A one-step predictor with state update equations was designed for the virtual state with virtual noise first of all. An extended incremental Kalman filter (EIKF) was then proposed for the nonlinear under-observed systems. Furthermore, an adaptive filtering method was given for optimization. The fusion adaptive incremental Kalman filter weighted by scalar was finally proposed. The comparison analysis was made to verify the optimization of the state estimation using adaptive filtering method in the filtering process.

The Squeeze & Excitation Normalization based nnU-Net for Segmenting Head & Neck Tumors

XIE Juanying, PENG Ying, WANG Mingzhao

, Available online , doi: 10.23919/cje.2022.00.306

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Head and neck cancer is one of the most common malignancies in the world. We propose SE-nnU-Net by adapting SE (Squeeze and Excitation) normalization into nnU-Net, so as to segment head and neck tumors in PET/CT images by combining advantages of SE capturing features of interest regions and nnU-Net configuring itself for a specific task. The basic module referred to convolution-ReLU-SE is designed for SE-nnU-Net. In the encoder it is combined with residual structure while in the decoder without residual structure. The loss function combines Dice loss and Focal loss. The specific data preprocessing and augmentation techniques are developed, and specific network architecture is designed. Furthermore, the deep supervised mechanism is introduced to calculate the loss function using the last four layers of the decoder of SE-nnU-Net. This SE-nnU-net is applied to HECKTOR 2020 and HECKTOR 2021 challenges, respectively, using different experimental design. The experimental results show that SE-nnU-Net for HECKTOR 2020 obtained 0.745, 0.821, and 0.725 in terms of Dice, Precision, and Recall, respectively, while the SE-nnU-Net for HECKTOR 2021 obtains 0.778 and 3.088 in terms of Dice and median HD95, respectively. This SE-nnU-Net for segmenting head and neck tumors can provide auxiliary opinions for doctors’ diagnoses.

FlowGANAnomaly: Flow-based Anomaly Network Intrusion Detection with Adversarial Learning

LI Zeyi, WANG Pan, WANG Zixuan

, Available online , doi: 10.23919/cje.2022.00.173

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In recent years, low recall rates and high dependencies on data labelling have become the biggest obstacle to developing DAD techniques. Inspired by the success of generative adversarial networks (GANs) in detecting anomalies in computer vision and imaging, we propose an anomaly detection model called FlowGANAnomaly for detecting anomalous traffic in NIDS. Unlike traditional GAN-based approaches, the architecture consists of a generator (G) and a discriminator (D), which are composed of a flow encoder, a convolutional encoder-decoder-encoder, a flow decoder, and a convolutional encoder, respectively. FlowGANAnomaly maps the different types of traffic feature data from separate datasets to a uniform feature space, thus can capture the normality of network traffic data more accurately in an adversarial manner to mitigate the problem of the high dependence on data labeling. Moreover, instead of simply detecting the anomalies by the output of D, we proposed a new anomaly scoring method that integrates the deviation between the output of two G's convolutional encoders with the outputs of D as weighted scores to improve the low recall rate of anomaly detection. We conducted several experiments comparing existing machine learning algorithms (e.g., One-Class SVM, LOF, Isolation Forest, and PCA) and existing deep learning methods (AutoEncoder and VAE) on four public datasets (NSL-KDD, CIC-IDS2017, CIC-DDoS2019, and UNSW-NB15). The evaluation results show that FlowGANAnomaly can significantly improve the performance of anomaly-based NIDS.

A Survey on Graph Neural Network Acceleration: A Hardware Perspective

CHEN Shi, LIU Jingyu, SHEN Li

, Available online , doi: 10.23919/cje.2023.00.135

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Graph neural networks (GNNs) have emerged as powerful approaches to learn knowledge about graphs and vertices. The rapid employment of GNNs poses requirements for processing efficiency. Due to incompatibility of general platforms, dedicated hardware devices and platforms are developed to efficiently accelerate training and inference of GNNs. In the article, we conduct a survey on hardware acceleration for GNNs. We first include and introduce recent advances of the domain, and provide a methodology of categorization to classify existing works into three categories. Secondly, we discuss optimization techniques adopted at different levels. Finally, we propose suggestions on future directions to facilitate further works.

A Review of Intelligent Configuration and Its Security for Complex Networks

ZHAO Yue, YANG Bin, TENG Fei, NIU Xianhua, HU Ning, TIAN Bo

, Available online , doi: 10.23919/cje.2023.00.001

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Complex networks are becoming more complex because of the use of many components with diverse technologies. In fact, manual configuration that makes each component interoperable has breed latent danger to system security. However, there is still no comprehensive review of these studies and prospects for further research. According to the complexity of component configuration and difficulty of security assurance in typical complex networks, this paper systematically reviews the abstract models and formal analysis methods required for intelligent configuration of complex networks, specifically analyzes, and compares the current key technologies such as configuration semantic awareness, automatic generation of security configuration, dynamic deployment, and verification evaluation, and so on. These technologies can effectively improve the security of complex networks intelligent configuration and reduce the complexity of operation and maintenance. This paper also summarizes the mainstream construction methods of complex networks configuration and its security test environment and detection index system, which lays a theoretical foundation for the formation of the comprehensive effectiveness verification capability of configuration security. The whole lifecycle management system of configuration security process proposed in this paper provides an important technical reference for reducing the complexity of network operation and maintenance and improving network security.

Review of GAN-Based Research on Chinese Character Font Generation

WANG Xuanhong, LI Cong, SUN Zengguo, HUI Luying

, Available online , doi: 10.23919/cje.2022.00.402

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With the rapid development of deep learning, Generative Adversarial Network (GAN) has become a research hotspot in the field of computer vision. GAN has a wide range of applications in image generation. Inspired by GAN, a series of models of Chinese character font generation have been proposed in recent years. In this paper, the latest research progress of Chinese character font generation is analyzed and summarized. Firstly, GAN and its development history are summarized. Secondly, GAN-based methods for Chinese character font generation are clarified as well as their improvements, based on whether the specific elements of Chinese characters are considered. Then, the public datasets used for font generation are summarized in detail, and various application scenarios of font generation are provided. Finally, the evaluation metrics of font generation are systematically summarized from both qualitative and quantitative aspects. This paper contributes to the in-depth research on Chinese character font generation and has a positive effect on the inheritance and development of Chinese culture with Chinese characters as its carrier.

A Multi-channel CMOS Analog Front-End Interface IC with 157.8 dB Current Detection Dynamic Range

WANG Kunyu, XU Wenjing, ZHANG Chengbin, YANG Yanjun, Law Man-Kay, ZHOU Li, CHEN Jie, CHEN Ming

, Available online , doi: 10.23919/cje.2022.00.137

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A high dynamic range, low-noise CMOS front-end interface IC with multi-channel detection is presented in this paper. Two different current detection channels, composed of a trans-impedance amplifier (TIA) and an integrator-differentiator (I-D) TIA, are used to boost the current detection range. A capacitance-coupled instrument amplifier (CCIA) is also included to realize high precision voltage detection. A fourth-order sigma-delta modulator using a second-order loop filter and a second-order noise shaping integral quantizer is adopted to realize ENOB above 16 bit. The presented interface IC is implemented in 0.18-μm CMOS process with supply voltage of 3.3 V, and a proto-type electrochemical sensor platform with miniaturized sensor array is developed to verify the functionality of the interface IC. Measurements results indicate that the designed interface IC achieves 157.8 dB current detection dynamic range, and the measured input-referred current noise and voltage noise floor are 1.04 pA and 58.4 nV within 10 KHz integration bandwidths, respectively.

Investigating the Effects of V₂C MXene on Improving the Switching Stability and Reducing the Operation Voltages of TiO₂-Based Memristors

HE Nan, WANG Lei, TONG Yi

, Available online , doi: 10.23919/cje.2022.00.327

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Three-atoms-type V₂C MXene, an emerging class of transition metal carbides, has attracted tremendous attention in the fabrication of advanced memristive devices due to its excellent electrochemical properties. However, the inserted and behind physical effects of inserting V₂C on traditional TiO₂-based memristors have not been clearly explored. In this work, exhaustive electrical characterizations of the V₂C/TiO₂-based devices exhibit enhanced performance (e.g., improved switching stability and lower operating voltages) compared to the TiO₂-based counterparts. In addition, the advantaged influences of the inserted V₂C have also been studied by means of first-principles calculations, confirming that V₂C MXene enables controllable internal ionic process and facilitated formation mechanism of the Ag conductive filaments. This work demonstrates a way to combine experimental and theoretical investigations to reveal the positive effects of introducing V₂C MXene on memristor, which is beneficial for fabricating performance-enhanced memristors.

Optimal Tuning of FOPID Controller for Hybrid AC/DC Microgrids with PV-Wind-Battery System using novel Optimization Algorithm

S. Shobana, B. K. Gnanavel

, Available online , doi: 10.23919/cje.2022.00.308

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Hybrid AC/DC microgrids have emerged as a promising method for improving interconnection between various types of distributed generators that are essentially AC or DC electric sources. The hybrid AC/DC Microgrid is made up of AC and DC to power the AC and DC loads, respectively. The potential of such systems to transfer power responsibly allows for a reduction in total microgrid prices. To make this study more realistic, a stochastic methodology is proposed to adequately design the uncertainty impacts of photovoltaic (PV) power, wind turbine power, and load profile. This paper proposes a new optimization-based coordinated control of a fractional-order proportional-integral-derivative (FOPID) controller for hybrid AC/DC microgrids with PV, wind, and Battery Energy storage systems (BESS). The settings of the FOPID controller are optimally adjusted using the unique Sooty Tern-based Wild Horse Optimization (STWHO) method, which inherits features from both the sooty term bird and the wild horse. During the first phase, a coordinated control method for distributed converters was provided, in which an MPPVC technique for DC/AC interlinking converters have been used to ensure optimal power transmission between AC and DC sub grids. The proposed technique's effectiveness is assessed in terms of grid voltage and load voltage measures, and the findings are compared to the existing techniques to demonstrate the robustness of the proposed method.

IP-Pealing: A Robust Network Flow Watermarking Method based on IP Packet Sequence

FENG Wangxin, LUO Xiangyang, LI Tengyao, YANG Chunfang

, Available online , doi: 10.23919/cje.2022.00.366

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Network flow watermarking (NFW) is usually used for flow correlation. By actively modulating some features of the carrier traffic, NFW can establish the correspondence between different network nodes. However, in the face of strict demands of network traffic tracing, current watermarking methods cannot work efficiently due to the dependence on specific protocols, demand for large quantities of packets, weakness on resisting network channel interferences and so on. To this end, we propose a robust network flow watermarking method based on IP packet sequence, called as IP-Pealing. It is designed to utilize the packet sequence as watermark carrier with IP identification field which is insensitive to time jitter and suitable for all IP based traffic. To enhance the robustness against packet loss and packet reordering, the detection sequence set is constructed in terms of the variation range of packet sequence, correcting the possible errors caused by the network transmission. To improve the detection accuracy, the long watermark information is divided into several short sequences to embed in turn and assembled during detection. By a large number of experiments on the Internet, the overall detection rate and accuracy of IP-Pealing reach 99.91% and 99.42% respectively. In comparison with the classical network flow watermarking methods, such as PROFW, IBW, ICBW, WBIPD and SBTT, the accuracy of IP-Pealing is increased by 13.70% to 54.00%.

Sharper Hardy Uncertainty Relations on Signal Concentration in terms of Linear Canonical Transform

XU Xiaogang, XU Guanlei, WANG Xiaotong

, Available online , doi: 10.23919/cje.2023.00.096

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Linear canonical transform is of much significance to optics and information science. Hardy uncertainty principle, like Heisenberg uncertainty principle, plays an important role in various fields. In this paper, four new sharper Hardy uncertainty relations on linear canonical transform are derived. These new derived uncertainty relations are connected with the linear canonical transform parameters and indicate new insights for signal energy concentration. Especially, for certain transform parameters, e.g. b=0, these new proposed uncertainty relations break the traditional counterparts in signal energy concentration, as will result in new physical interpretation in terms of uncertainty principle. Theoretical analysis and numerical examples are given to show the efficiency of these new relations.

Review on Security Defense Technology Research in Edge Computing Environment

SHANG Ke, HE Weizhen, ZHANG Shuai

, Available online , doi: 10.23919/cje.2022.00.170

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Edge computing, which achieves quick data processing by sinking data computing and storage to the network edge, has grown rapidly along with the Internet of Things. However, the new network architecture of edge computing brings new security challenges. Based on this, this paper investigates the edge computing security literature published in recent years and summarizes and analyzes research work on edge computing security from different attack surfaces. In this paper, we start with the definition and architecture of edge computing. Then, from the attack surface between device and edge server, as well as on edge servers, the research describes the security threats and defense methods of edge computing. In addition, the cause of the attack and the pros and cons of defense methods is introduced. Finally, the challenges and future research directions of edge computing are given.

QARF: A Novel Malicious Traffic Detection Approach via Online Active Learning for Evolving Traffic Streams

NIU Zequn, XUE Jingfeng, WANG Yong, LEI Tianwei, HAN Weijie, GAO Xianwei

, Available online , doi: 10.23919/cje.2022.00.360

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In practical abnormal traffic detection scenarios, traffic often appears as drift, imbalanced and rare labeled streams, and how to effectively identify malicious traffic in such complex situations has become a challenge for malicious traffic detection. Researchers have extensive studies on malicious traffic detection with single challenge, but the detection of complex traffic has not been widely noticed. In this paper, Queried Adaptive Random Forests (QARF) is proposed to detect traffic streams with concept drift, imbalance and lack of labeled instances. QARF is an online active learning based approach which combines Adaptive Random Forests method and adaptive margin sampling strategy. QARF achieves querying a small number of instances from unlabeled traffic streams to obtain effective training. We conduct experiments using the NSL-KDD dataset to evaluate the performance of QARF. Meanwhile, QARF is compared with other state-of-the-art methods. The experimental results show that QARF obtains 98.20% accuracy on the NSL-KDD dataset. In addition, QARF performs better than other state-of-the-art methods in comparisons.

SWIFTTHEFT: A Time-efficient Model Extraction Attack Framework against Cloud-based Deep Neural Networks

YANG Wenbin, GONG Xueluan, CHEN Yanjiao, WANG Qian, DONG Jianshuo

, Available online , doi: 10.23919/cje.2022.00.377

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With the rise of artificial intelligence (AI) and cloud computing, machine-learning-as-a-service (MLaaS) platforms, such as Google, Amazon, and IBM, have emerged to provide sophisticated tasks for cloud applications. However, these proprietary models are vulnerable to model extraction attacks due to their commercial value. In this paper, we propose a time-efficient model extraction attack framework called SWIFTTHEFT that aims to steal the func-tionality of cloud-based DNN models. We distinguish SWIFTTHEFT from the existing works with a novel distribution estimation algorithm and reference model settings, finding the most informative query samples without querying the victim model. Moreover, the selected query samples can be applied to various cloud models with a one-time selection. We evaluate our proposed method through extensive experiments on three victim models and six datasets, with up to 16 models for each dataset. Compared to the existing attacks, SWIFTTHEFT increases agreement by 8% while consuming 98% less selecting time.

Echo State Network Based on Improved Knowledge Distillation for Edge Intelligence

ZHOU Jian, JIANG Yuwen, XU Lijie, ZHAO Lu, XIAO Fu

, Available online , doi: 10.23919/cje.2022.00.292

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Echo state network (ESN) as a novel artificial neural network has drawn much attention from time series prediction in edge intelligence. However, ESN is slightly insufficient in long-term memory, thereby impacting the prediction performance. Meanwhile, it suffers from a higher computational overhead when deploying on edge devices. In this paper, we firstly introduce the knowledge distillation into the reservoir structure optimization, and then propose the echo state network based on improved knowledge distillation (ESN-IKD) for edge intelligence to improve the prediction performance and reduce the computational overhead. First, the model of ESN-IKD is constructed with the classic ESN as a student network, the long and short-term memory network as a teacher network, and the ESN with double loop reservoir structure as an assistant network. In particular, the student network learns the long-term memory capability of the teacher network with the help of the assistant network. Second, the training algorithm of ESN-IKD is proposed to correct the learning direction through the assistant network and eliminate the redundant knowledge through the iterative pruning. It can solve the problems of error learning and redundant learning in the traditional knowledge distillation process. Finally, extensive experimental simulation shows that ESN-IKD has a good time series prediction performance in both long-term and short-term memory, and achieves a lower computational overhead.

Dispersion Compensation and Demultiplexing Using a Cascaded CFBG Structure in a 150 km Long DWDM Optical Network

Baseerat Gul, Faroze Ahmad

, Available online , doi: 10.23919/cje.2022.00.416

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Abstract:
This paper proposes the design of a 150 km dense wavelength division multiplexed (DWDM) optical network with a capacity of 8×10 Gbps. To mitigate system dispersion, a cost-effective hybrid dispersion compensator is implemented using chirped fiber Bragg gratings (CFBG) and a pair of 5 km long dispersion compensation fibers (DCF). The novelty of the work is the use of CFBG for multiple functions, including operating as a demultiplexer and providing dispersion compensation. The proposed network design uses 140 km long conventional single-mode fiber (CSMF) and a 10 km long DCF in a symmetrical compensation mode. Without the CFBG structure, a 33 km long DCF would be needed to compensate for total channel dispersion, costing around 3$/m. However, by adding the CFBG structure, the design only requires a 10 km long DCF, reducing the DCF length by more than 65% and lowering the system cost. The CFBG integration also eliminates the need for an additional demultiplexer in the receiver section, reducing system complexity and cost. The system performance is evaluated analytically in terms of Q-factor, bit-error rate (BER), eye-diagram, and optical signal-to-noise ratio (OSNR). The average Q-factor and BER values achieved per channel are 16.5 and 8.38×10⁻⁵⁶, respectively, and for all receiver channels, the eye-openings are good enough with commendable OSNR values. The proposed design achieves good performance characteristics despite using shorter-length DCF when compared with previously reported works.

QoS-Aware Computation Offloading in LEO Satellite Edge Computing for IoT: A Game-Theoretical Approach

CHEN Ying, HU Jintao, ZHAO Jie, MIN Geyong

, Available online , doi: 10.23919/cje.2022.00.412

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Abstract:
Low earth orbit (LEO) satellite edge computing can overcome communication difficulties in harsh environments, which lack the support of terrestrial communication infrastructure. It is an indispensable option for achieving worldwide wireless communication coverage in the future. To improve the Quality-of-Service (QoS) for IoT devices, we combine LEO satellite edge computing and ground communication systems to provide network services for IoT devices in harsh environments. We study the QoS-aware computation offloading (QCO) problem for IoT devices in LEO satellite edge computing. Then we investigate the computation offloading strategy for IoT devices that can minimize the total QoS cost of all devices while satisfying multiple constraints, such as the computing resource constraint, delay constraint, and energy consumption constraint. We formulate the QoS-aware computation offloading problem as a game model named QCO game based on the non-cooperative competition game among IoT devices. We analyze the finite improvement property of the QCO game and prove that there is a Nash equilibrium for the QCO game. Finally, we propose a distributed QoS-aware computation offloading (DQCO) algorithm for the QCO game. Experimental results show that the DQCO algorithm can effectively reduce the total QoS cost of IoT devices.

Robust Regularization Design of Graph Neural Networks against Adversarial Attacks Based on Lyapunov Theory

YAN Wenjie, LI Ziqi, QI Yongjun

, Available online , doi: 10.23919/cje.2022.00.342

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Abstract:
The robustness of graph neural networks (GNNs) is a critical research topic in deep learning. Many researchers have enhanced the robustness of neural networks by designing regularization methods, but there is lack of the theoretical analyses on the principle of robustness. In order to tackle the weakness of current robustness designing methods, this paper gives new insights into how to guarantee the robustness of GNNs. In particular, a novel regularization strategy(Lya-Reg) is designed to guarantee the robustness of GNNs by Lyapunov theory. Our results give new insights into how regularization can mitigate the various adversarial effects on different graph signals. Moreover, extensive experiments on various public datasets demonstrate that the proposed regularization method is more robust than the state-of-the-art methods $ L_1 $-norm, $ L_2 $-norm, $ L_{21} $-norm, Pro-GNN, PA-GNN and GARNET against various types of graph adversarial attacks.

Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

WANG Hao, WANG Jinwei, HU Xuelong, HU Bingtao, YIN Qilin, LUO Xiangyang, MA Bin, SUN Jinsheng

, Available online , doi: 10.23919/cje.2022.00.179

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Detection of color images that have undergone double compression is a critical aspect of digital image forensics. Despite the existence of various methods capable of detecting double Joint Photographic Experts Group (JPEG) compression, they are unable to address the issue of mixed double compression resulting from the use of different compression standards. In particular, the implementation of Joint Photographic Experts Group 2000 (JPEG2000) as the secondary compression standard can result in a decline or complete loss of performance in existing methods. To tackle this challenge of JPEG+ JPEG2000 compression, a detection method based on Quaternion Convolutional Neural Networks (QCNN) is proposed. The QCNN processes the data as a quaternion, transforming the components of a traditional Convolutional Neural Network (CNN) into a quaternion representation. The relationships between the color channels of the image are preserved, and the utilization of color information is optimized. Additionally, the method includes a feature conversion module that converts the extracted features into quaternion statistical features, thereby amplifying the evidence of double compression. Experimental results indicate that the proposed QCNN-based method improves, on average, by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

Real-time 3D Ultrasound Imaging System Based on a Hybrid Reconstruction Algorithm

LYU Yifei, SHEN Yu, ZHANG Mingbo, WANG Junchen

, Available online , doi: 10.23919/cje.2023.00.002

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Abstract:
As a safe and convenient imaging technology in clinical routine diagnosis, ultrasound imaging can provide real-time 2D images of internal tissues and organs. To realize real-time 3D image reconstruction, pixel nearest neighbor interpolation (PNN) reconstruction algorithm and Bezier interpolation algorithm are combined into a hybrid reconstruction algorithm. On this basis, a real-time interactive 3D ultrasound imaging system is developed. Through temporal calibration and spatial calibration, the six degrees of freedom poses of 2D ultrasound images can be accurately collected by an optical measurement system. The 3D volume reconstructed by the proposed 3D reconstruction algorithm using collected data is visualized by volume rendering. A multi-thread software system allows parallel operation of data acquisition, 3D reconstruction, volume visualization and other functions. Interactive functions enable clinical practitioners to freely select regions of interest and segment or adjust images. 3D imaging experiments on a 3D printing femur model, a neck phantom and the neck of human volunteers were performed for systematic evaluation. The reconstruction error was defined as the registration error between the reconstruction result and the actual geometry. When the reconstruction voxel size was set to be (0.5³ mm³, 1.0³ mm³, 1.5³ mm³), the reconstruction errors of the femur and trachea model were (0.23 mm, 0.31 mm, 0.56 mm) and (0.62 mm, 0.88 mm, 1.41 mm), respectively. Clinical feasibility was demonstrated by application of the 3D ultrasound imaging on the neck of human volunteers.

A Novel Approach of Electromagnetic Compatibility Conducted Susceptibility Analysis Based on Gaussian Even Pulse Signal

MENG Youwei, PENG Yanhua, ZHANG Haoyang, LI lilin, SU Donglin

, Available online , doi: 10.23919/cje.2022.00.298

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Abstract:
The purpose of the electromagnetic compatibility conducted susceptibility test for interconnected cables in the system is to evaluate its ability to operate acceptably when subjected to interference. In this paper, we propose a novel conducted susceptibility analysis approach: by injecting the Gaussian even pulse signal, we find that the susceptibility threshold of the system shows two different patterns with the change of signal parameters. Then we locate the cause of the susceptibility of the device by analyzing the threshold level curves (TLC). The effectiveness of the proposed approach is verified by testing with devices containing digital modules such as navigation receivers. The proposed approach facilitates a deeper understanding of the susceptibility mechanism of systems and their appropriate electromagnetic compatibility design.

RFID-Based WSN Communication System with ESPAR Array Antenna for SIR Improvement

Md. Moklesur RAHMAN, Heung-Gyoon RYU

, Available online , doi: 10.23919/cje.2022.00.213

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To improve the received signal strength (RSS) and signal-to-interference and noise ratio (SINR), electronically steerable parasitic array radiator (ESPAR) array antennas are designed for the ultra-high frequency (UHF) radio frequency identification (RFID) communication systems that can provide very low power consumption in sensor tag edge. Higher gain, appropriate radiation pattern, and most power-efficient array antennas are completely essential in these sensor network systems. As a result, it is suggested that ESPAR array antennas be used on the RFID reader side to reduce interference, multipath fading, and extend communication range. Additionally, a system architecture for UHF- RFID wireless sensor network (WSN) communication is put forth in order to prevent interference from antenna nulling technology, in which ESPAR array antennas could be capable of generating nulls. The array antennas within the system demonstrate high efficiency, appropriate radiation patterns, and gains (9.63 dBi, 10.2 dBi, and 12 dBi) from one array to other arrays. The nulling technique using the proposed array antennas also provides better SINR values (31.63 dB, 33.2 dB, and 36 dB). Finally, the nulling space matrix is studied in relation to the channel modeling. Therefore, the suggested approach might offer better communications in sensor networking systems.

A Polarization Control Operator for Polarized Electromagnetic Wave Designing

CUI Shuo, LI Yaoyao, ZHANG Shijian, CHEN Ling, CAO Cheng, SU Donglin

, Available online , doi: 10.23919/cje.2022.00.410

Abstract(145) HTML (71) PDF(17)

Abstract:
To describe and control the polarization state of electromagnetic waves, a polarization control operator of the complex vector form is proposed. Distinct from traditional descriptors, the proposed operator employs an angle parameter to configure the polarization state of the polarized wave. By setting the parameter in the proposed operator, the amplitude of the field components can be modified, resulting in changes in the magnitude and direction of the field vector, and thus realizing control of the polarization state of the electromagnetic wave. The physical meaning, orthogonal decomposition, and discrete property of the proposed operator are demonstrated through mathematical derivation. In the simulation examples, the polarization control operator with the fixed and time-varying parameters are applied to the circularly polarized wave. The propagation waveform, the trajectory projection and the waveform cross section in different reception directions of the new electromagnetic waves are observed. The results show that complex electromagnetic waves with more flexible polarization states can be obtained with the aid of the polarization operator.

Levy Flight Adopted Particle Swarm Optimization-based Resource Allocation Strategy in Fog Computing

Sharmila Patil(Karpe), Brahmananda S H

, Available online , doi: 10.23919/cje.2022.00.212

Abstract(266) HTML (132) PDF(26)

Abstract:
The prevalence of the Internet of Things (IoT) is unsteady in the context of cloud computing, it is difficult to identify fog and cloud resource scheduling policies that will satisfy users’ QoS need. As a result, it increases the efficiency of resource usage and boosts user and resource supplier profit. This research intends to introduce a novel strategy for computing fog via emergency-oriented resource allotment, which aims and determines the effective process under different parameters. The modeling of a non-linear functionality that is subjected to an objective function and incorporates needs or factors like Service response rate, Execution efficiency, and Reboot rate allows for the resource allocation of cloud to fog computing in this work. Apart from this, the proposed system considers the resource allocation in emergency priority situations that must cope-up with the immediate resource allocation as well. Security in resource allocation is also taken into consideration with this strategy. Thus the multi-objective function considers 3 objectives such as Service response rate, Execution efficiency, and Reboot rate. All these strategies in resource allocation are fulfilled by Levy Flight adopted Particle Swarm Optimization (LF-PSO). Finally, the evaluation is performed to determine whether the developed strategy is superior to numerous traditional schemes. However, the cost function attained by the adopted technique is 120, which is 19.17%, 5%, and 2.5% greater than the conventional schemes like GWSO, EHO, and PSO, when the number of iterations is 50.

Link Prediction Method Fusion with Local Structural Entropy for Directed Network

LIU Shuxin, CHEN Hong-chang, WU Lan, WANG Kai, LI Xing

, Available online , doi: 10.23919/cje.2022.00.166

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Abstract:
Link prediction utilizes accessible network information to complement or predict the network links. Similarity is an important prerequisite for link prediction which means links more likely occurs between two similar nodes. Existing methods utilize the similarity of nodes but neglect of network structure. However the link direction leads to a far more complex structure and contains more information useful than the undirected networks. Most classic methods are difficult to depict the distribution of the network structure with incidental direction so the similarity characteristics of the network structure itself are lost. In this respect, a new method of local structure entropy is proposed to depict the directed structural distribution characteristics, which can be evaluate the degree of local structural similarity of nodes and then applied to link prediction methods. Experimental results on 8 real directed network show that this method is effective for both AUC and Ranking-Score measures and improved predictive capacity of the baseline methodology.

SAT-Based Automatic Searching for Differential and Linear Trails: Applying to CRAX

HAN Yiyi, WANG Caibing, NIU Zhongfeng, HU Lei

, Available online , doi: 10.23919/cje.2022.00.313

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Abstract:
Boolean satisfiability problem (SAT) is now widely applied in differential cryptanalysis and linear cryptanalysis for various cipher algorithms. It generated many excellent results for some ciphers, for example, Salsa20. In this research, we study the differential and linear propagations through the operations of addition, rotation and XOR (ARX), and construct the SAT models. Then we apply the models to CRAX to search differential trails and linear trails automatically. In this sense, our contribution can be broadly divided into two parts. Firstly, we give the bounds for differential and linear cryptanalysis of Alzette both up to 12 steps, by which we present a 3-round differential attack and a 3-round linear attack for CRAX. Secondly, we construct a 4-round key-recovery attack for CRAX with time complexity $ 2^{89} $ times of 4-round encryption and data complexity $ 2^{25} $.

Constructing the Impossible Differential of Type-II GFN with Boolean Function and its Application to $\mathtt{WARP}$

SHI Jiali, LIU Guoqiang, LI Chao

, Available online , doi: 10.23919/cje.2022.00.132

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Type-II generalized Feistel network (GFN) has attracted a lot of attention for its simplicity and high parallelism. Impossible differential attack is one of the powerful cryptanalytic approaches for word-oriented block ciphers such as Feistel-like ciphers. In this paper, we deduce the impossible differential of Type-II GFN by analyzing the Boolean function in the middle round. The main idea is to investigate the expression with the variable representing the plaintext (ciphertext) difference words for the internal state words. By adopting the miss-in-the-middle approach, we can construct the impossible differential of Type-II GFN. As an illustration, we apply this approach to $\mathtt{WARP}$. The structure of $\mathtt{WARP}$ is a 32-branch Type-II GFN. Therefore, we find two 21-round truncated impossible differentials and implement a 32-round key recovery attack on $\mathtt{WARP}$. For the 32-round key recovery attack on $\mathtt{WARP}$, some observations are used to mount an effective attack. Taking the advantage of the early abort technique, the data, time, and memory complexities are $2^{125.69}$ chosen plaintexts, $2^{126.68}$ 32-round encryptions, and $2^{100}$-bit. To the best of our knowledge, this is the best attack on $\mathtt{WARP}$ in the single-key scenario.

The investigation of data voting algorithm for train air-braking system based on multi-classification SVM and ANFIS

WANG Juhan, GAO Ying, CAO Yuan, TANG Tao

, Available online , doi: 10.23919/cje.2021.00.428

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The pressure data of the train air braking system is of great significance to accurately evaluate its operation state. In order to overcome the influence of sensor fault on the pressure data of train air braking system, it is necessary to design a set of sensor fault-tolerant voting mechanism to ensure that in the case of a pressure sensor fault, the system can accurately identify and locate the position of the faulty sensor, and estimate the fault data according to other normal data. In this paper, a fault-tolerant mechanism based on multi classification support vector machine (MSVM) and adaptive network-based fuzzy inference system (ANFIS) is introduced. Specifically, MSVM is used to identify and locate the system fault state, and ANFIS is used to estimate the real data of the fault sensor. After estimation, the system will compare the real data of the fault sensor with the ANFIS estimated data. If it is similar, the system will recognize that there is a false alarm and record it. Then the paper tests the whole mechanism based on the real data. The test shows that the system can identify the fault samples and reduce the occurrence of false alarms.

Analytical Models of On-chip Hardware Trojan Detection based on Radiated Emission Characteristics

ZHANG Fan, ZHANG Dongrong, REN Qiang, CHEN Aixin, SU Donglin

, Available online , doi: 10.23919/cje.2022.00.310

Abstract(240) HTML (119) PDF(27)

Abstract:
Nowadays, since the many third parties involved in IC manufacturing, hardware Trojans (HT) malicious implantation have become a threat to the integrated circuit (IC) industry. Therefore, varieties of reliable hardware Trojan detection methods are need. Since electromagnetic radiation is an inherent phenomenon of electronic devices, there are significant differences in the electromagnetic radiated characteristics for circuits with different structures and operating states. In this paper, a novel hardware Trojan detection method is proposed, which considers the electromagnetic radiation differences caused by hardware Trojan implantation. Experiments of detecting hardware Trojan in FPGA show that the proposed method can effectively distinguish the ICs with Trojan from the ones without Trojan by the radiated emission.

Rapid Phase Ambiguity Elimination Methods for DOA Estimator via Hybrid Massive MIMO Receive Array

ZHAN Xichao, SUN Zhongwen, SHU Feng, CHEN Yiwen, CHENG Xin, WU Yuanyuan, ZHANG Qi, LI Yifan, ZHANG Peng

, Available online , doi: 10.23919/cje.2022.00.112

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Abstract:
For a sub-connected hybrid multiple-input multiple-output (MIMO) receiver with $ K $ subarrays and $ N $ antennas, there exists a challenging problem of how to rapidly remove phase ambiguity in only single time-slot. First, a DOA estimator of maximizing received power (Max-RP) is proposed to find the maximum value of $ K $-subarray output powers, where each subarray is in charge of one sector, and the center angle of the sector corresponding to the maximum output is the estimated true DOA. To make an enhancement on precision, Max-RP plus quadratic interpolation (Max-RP-QI) method is designed. In the proposed Max-RP-QI, a quadratic interpolation scheme is adopted to interpolate the three DOA values corresponding to the largest three receive powers of Max-RP. Finally, to achieve the CRLB, a Root-MUSIC plus Max-RP-QI scheme is developed. Simulation results show that the proposed three methods eliminate the phase ambiguity during one time-slot and also show low-computational-complexities. In particular, the proposed Root-MUSIC plus Max-RP-QI scheme can reach the CRLB, and the proposed Max-RP and Max-RP-QI are still some performance losses 2dB~4dB compared to the CRLB.

An Optimized Fractional order PI Controller for Enhancing the Power Quality of Three-phase Solar PV, BESS and Wind Integrated UPQC

Shravan Kumar Yadav, Krishna Bihari Yadav

, Available online , doi: 10.23919/cje.2022.00.079

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This paper focuses on an optimized Fractional Order Proportional Integral (FOPI) Controller for enhancing the power quality of three-phase hybrid energy storage system integrated with Unified Power Quality Conditioner (UPQC). With a view to providing continuous electricity, Renewable Energy Sources (RES) like Photovoltaic (PV) array, Battery Energy Storage System (BESS) and wind energy are modeled. To ease the grid's power quality issues and the harmonics injected by non-linear loads there endures the UPQC model with series and shunt active filter compensator. Furthermore, PV, wind, and BESS integrated UPQC are capable of solving power quality issues in the event of long voltage interruptions. The shunt compensator of UPQC extracts the power from the hybrid energy systems whereas the load is protected by the series compensator from the grid related power quality issues. Hence, to regulate the voltage of the DC link at the desired level, this paper intends to develop a FOPI controller that exhibits iso-damping properties. Particularly, the gain of the FOPI controller is optimally tuned by a novel hybrid algorithm known as Enhanced Seagull with Rooster Update (ES-RU) algorithm that hybrid the concepts of Seagull Optimization Algorithm (SOA) and Chicken Swarm Optimization (CSO). At last, the proposed method was validated during voltage sag/swell, concerning the total harmonic distortion.

Privacy Preserving Algorithm for Spectrum Sensing in Cognitive Vehicle Networks

LI Hongning, HU Tonghui, CHEN Jiexiong, WU Xiuqiang, PEI Qingqi

, Available online , doi: 10.23919/cje.2022.00.007

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Abstract:

The spectrum resources are scarce to support the increasing throughput demands in vehicular networks. It is urgent to make full use of spectrum bands in mobile network. To get the availability of spectrum bands, users should sense wireless channels and cooperate with others. The spectrum sensing data are always related to users’ privacy, such as location. In this paper, we first introduce sensing trajectory inference attack (STIA) in cognitive vehicular networks (CVN), and then propose a data confusion-based privacy-preserving algorithm (DCPPA) and a cryptonym array-based privacy-preserving aggregation (CAPPA) scheme for spectrum sensing in CVN. Unlike existing methods, the proposed schemes transmit confused data during aggregation process. It is almost impossible to infer users’ location from the data transmitted. Analysis demonstrates that the proposed scheme are resilient to STIA

Study on Coded Permutation Entropy of Finite Length Gaussian White Noise Time Series

SUN Huihui, ZHANG Xiaofeng

, Available online , doi: 10.23919/cje.2022.00.209

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Abstract:

As an extension of PE, coded permutation entropy (CPE) improves the performance of PE by making a secondary division for ordinal patterns defined in PE. In this study, we provide an exploration of the statistical properties of CPE using a finite length Gaussian white noise time series theoretically. By means of the Taylor series expansion, the approximate expressions of the expected value and variance of CPE are deduced and the Cramér-Rao Low Bound (CRLB) is obtained to evaluate the performance of the CPE estimator. The results indicate that CPE is a biased estimator, but the bias only depends on relevant parameters of CPE and it can be easily corrected for an arbitrary time series. The variance of CPE is related to the encoding patterns distribution, and the value converges to the CRLB of the CPE estimator when the time series length is large enough. For a finite-length Gaussian white noise time series model, the predicted values can match well with the actual values, which further validates the statistic theory of CPE. Using the theoretical expressions of CPE, it is possible to better understand the behavior of CPE for most of the time series.

Security Analysis for SCKHA Algorithm: Stream Cipher Algorithm Based on Key Hashing Technique

Souror Samia, El-Fishawy Nawal, Badawy Mohammed

, Available online , doi: 10.23919/cje.2021.00.383

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Abstract:

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.

Multi-scale Global Retrieval and Temporal-Spatial Consistency Matching based long-term Tracking Network

SANG Haifeng, LI Gongming, ZHAO Ziyu

, Available online , doi: 10.23919/cje.2021.00.195

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Abstract:

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.

Robust Beamforming Design for IRS-Aided Cognitive Radio Networks with Bounded CSI Errors

ZHANG Lei, WANG Yu, SHANG Yulong, TIAN Jianjie, JIA Ziyan

, Available online , doi: 10.23919/cje.2021.00.254

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Abstract:

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.

Design and Implementation of a Novel Self-bias S-band Broadband GaN Power Amplifier

ZHANG Luchuan, ZHONG Shichang, CHEN Yue

, Available online , doi: 10.23919/cje.2021.00.118

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Abstract:

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.

Two Jacobi-like algorithms for the general joint diagonalization problem with applications to blind source separation

CHENG Guanghui, MIAO Jifei, LI Wenrui

, Available online , doi: 10.23919/cje.2019.00.102

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Abstract:

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.

High-Efficiency Wideband Transmitarray Antenna Using Polarization-Rotating Elements with Parasitic Stubs

WANG Xi, DONG Yuandan

, Available online , doi: 10.23919/cje.2023.00.094

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Abstract:
A high-efficiency polarization-rotating transmitarray antenna (TA) using wideband elements is proposed for millimeter-wave applications. The polarization-rotating element consists of three metallic layers and two substrates. Orthogonal polarizers are employed on the top and bottom of the element, respectively. And the split ring with a parasitic stub on the middle layer is symmetric about the diagonal, performing the polarization rotation and phase compensation simultaneously. A parasitic stub is designed to decrease transmission loss and broaden the bandwidth. The periodicity of the element is only 1/4 wavelength at 30 GHz. A prototype TA with 28×28 elements is designed, fabricated, and measured. The measured peak gain reaches 27.5 dBi at 37.8 GHz. The 1-dB gain drop bandwidth is 30.8 GHz – 40 GHz (26.0%). The aperture efficiency reaches as high as 71% at 31.5 GHz. Within the bandwidth of 26.5 GHz – 38.8 GHz (37.7%), the aperture efficiency is higher than 50%. The proposed polarization-rotating TA features wide bandwidth and high efficiency, demonstrating great application potential for 5G millimeter-wave communication.

Wide Stopband Substrate Integrated Waveguide Filter Using Bisection and Trisection Coupling in Multilayer

CHU Peng, FENG Jianguo, ZHU Peng, GUO Lei, ZHANG Long, LIU Leilei, LUO Guoqing, WU Ke

, Available online , doi: 10.23919/cje.2023.00.027

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Abstract:
This article presents a highly efficient method for substrate integrated waveguide (SIW) filters to achieve very wide stopbands. By employing the proposed trisection slots in addition to the bisection slots as the inter-coupling structures, all spurious modes below TE₅₀₅ of a SIW filter working in the fundamental mode TE₁₀₁ (f₀) can be eliminated without requiring additional structure or complex theoretical analysis, without affecting the design of the fundamental passbands, and without degrading the performance of the filters. For verification, two prototype filters are designed, fabricated, and measured with wide stopbands up to 4.15 f₀ and 4.83 f₀, respectively. The proposed technique could facilitate the development of high-performance wide-stopband SIW filters for microwave/wireless circuits and systems.

An Encoding-Decoding Framework Based on CNN for circRNA-RBP Binding Sites Prediction

GUO Yajing, LEI Xiujuan, PAN Yi

, Available online , doi: 10.23919/cje.2022.00.361

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Abstract:
Predicting RNA binding protein (RBP) binding sites on circular RNAs (circRNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a Convolutional Neural Network (CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. To make it easier to extract relevant information during training, we preprocess them using CNN. To capture the feature dependencies, we utilize Temporal Convolutional Network (TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. All deep learning methods used in this framework are based on CNN. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.

A 5 mW 1-to-5 GHz Multiband Ladder CMOS Mixer Employing Transconductance Tuning Mechanism Achieving IIP3 of 27 dBm

PRAVINAH Shasidharan, SELVAKUMAR Mariappan, JIA XIN Lim, JAGADHESWARAN Rajendran, NARENDRA KUMAR Aridas

, Available online , doi: 10.23919/cje.2022.00.028

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Abstract:
This paper presents a CMOS mixer employing a transconductance tuning (TCT) mechanism to achieve wideband, low power, high gain, and high linearity. The ladder CMOS mixer consists of one current source, one differential amplifier and two differential low noise switching stage. The TCT technique optimizes the optimum drain current requirement and the output voltage at the voltage control oscillator node and the RF output node, thus producing a balance linearity performance with low power consumption for 4 GHz operating bandwidth. The wideband linearity performance is achieved without inductors, thus reducing the size of the chip significantly to 0.5 mm². Designed in 180-nm CMOS, the TCT mixer operates from 1 GHz to 5 GHz with a 1.2 V supply voltage, resulting in a highest measured result performances of the third-order input intercept point (IIP3) of 35.97 dBm across the local oscillator (LO) input power and 27.2 dBm across the RF input power. The highest measured conversion gain (CG) encapsulated around 29.17 dB under RF input power whereas 22.27 dB across the LO input power at center frequency of 3 GHz. The TCT mixer provides full mixing operation which achieves the measured noise figure (NF) below 5 dB across the IF output frequency. Moreover, the port-to-port isolation less than −30 dB has also been achieved across the RF operating bandwidth. The total power consumption, P_DC of the TCT chip is 5 mW. The operating bandwidth of the TCT mixer qualifies it to be integrated into a multiband 5G New Radio receiver system.

Blind Signal Reception in Downlink Generalized Spatial Modulation Multiuser MIMO System Based on Minimum Output Energy

WU Wei-Chiang

, Available online , doi: 10.23919/cje.2022.00.113

Abstract(243) HTML (120) PDF(19)

Abstract:
This paper considers downlink multiuser MIMO (MU-MIMO) system with parallel spatial modulation (PSM) scheme, in which base station transmitter (BSTx) antennas are separated into K groups corresponding to K user terminals (UT). Generalized spatial modulation (GSM) is employed, where a particular subset of transmit antennas in each group is activated and the activation pattern itself conveys spatial information symbols. Different from the existing precoding-based algorithms, we develop a two-stage detection scheme at each UT: In the pre-processing stage, a “Minimax” algorithm is proposed to identify the indices of active antennas, where the key idea is that that the minimum output energy of the detector is maximized. A constrained Minimum Output Energy (MOE) algorithm is proposed in the post-processing stage to mitigate multiuser interference (MUI) and extract temporal symbols. Compared with existing precoding-based algorithms, the complexity is significantly reduced. Moreover, the proposed algorithm is semi-blind in that only a small subset of channel state information (CSI) is required to identify active antennas as well as eliminate MUI. Simulation results demonstrate that the proposed algorithm is near-far resistant and the capacity is extensively increased compared to the conventional spatial modulation (SM) scheme.

Some Results on Optimal Ternary Cyclic Codes with Minimal Distance Four

LI Lanqiang, LIU Li

, Available online , doi: 10.23919/cje.2022.00.317

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Abstract:
Cyclic codes over fnite fields have been studied for decades due to they have wide applications in communication systems, consumer electronics and data storage systems. In this paper, we investigate a family of ternary cyclic codes generated by a product of two distinct minimal polynomials. We proposed a sufficient and necessary condition such that such code has minimum distance 4 and is optimal. Based on this, four classes of optimal ternary cyclic codes are presented. Finally, our codes are compared with the previous work to make sure that they all are generated by different cyclotomic cosets and thus represent different codes.

Graph Signal Reconstruction from Low-Resolution Multi-Bit Observations

LIU Zhaoting, YU Chen, WANG Yafeng, LIU Shuchen

, Available online , doi: 10.23919/cje.2022.00.272

Abstract(130) HTML (63) PDF(13)

Abstract:
Low hardware cost and power consumption in information transmission, processing and storage is an urgent demand for many big data problems, in which the high-dimensional data often be modelled as graph signals. This paper considers the problem of recovering a smooth graph signal by using its low-resolution multi-bit quantized observations. The underlying problem is formulated as a regularized maximum-likelihood optimization and is solved via an expectation maximization scheme. With this scheme, the multi-bit graph signal recovery (MB-GSR) is efficiently implemented by using the quantized observations collected from random subsets of graph nodes. The simulation results show that increasing the sampling resolution to 2 or 3 bits per sample leads to a considerable performance improvement, while the energy consumption and implementation costs remain much lower compared to the implementation of high resolution sampling.

Visible Light Indoor Positioning System Based on Pisarenko Harmonic Decomposition and Neural Network

ZHAO Li, REN Yi, WANG Qi, DENG Lange, ZHANG Feng

, Available online , doi: 10.23919/cje.2022.00.161

Abstract(187) HTML (90) PDF(10)

Abstract:
Visible-light indoor positioning is a new generation of positioning technology that can be integrated into smart lighting and optical communications. The current received signal strength (RSS)-based visible-light positioning systems struggle to overcome the interferences of background and indoor-reflected noise. Meanwhile, when ensuring the lighting, it is impossible to use the superposition of each light source to accurately distinguish light source information; furthermore, it is difficult to achieve accurate positioning in complex indoor environments. This study proposes an indoor positioning method based on a combination of power spectral density (PSD) detection and a neural network. The system integrates the mechanism for visible-light radiation detection with RSS theory, to build a back propagation neural network model fitting for multiple reflection channels. Different frequency signals are loaded to different light sources at the beacon end, and the characteristic frequency and power vectors are obtained at the location end using the Pisarenko harmonic decomposition method. Then, a complete fingerprint database is established to train the neural network model and conduct location tests. Finally, the location effectiveness of the proposed algorithm is verified via actual positioning experiments. The simulation results show that, when four groups of sinusoidal waves with different frequencies are superimposed with white noise, the maximum frequency error is 0.104 Hz and the maximum power error is 0.0362 W. For the measured positioning stage, a 0.8 m × 0.8 m × 0.8 m solid wood stereoscopic positioning model is constructed, and the average error is 4.28 cm. This study provides an effective method for separating multi-source signal energies, overcoming background noise, and improving indoor visible-light positioning accuracies.

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Chinese Journal of Electronics