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Research Article
Model Parameter Extraction for InGaN/GaN Multiple Quantum Well-based Solar Cells using Dynamic Programming
SHAN Hengsheng, LI Chengke, LI Xiaoya, LI Minghui, SONG Yifan, MA Shufang, XU Bingshe
, Available online  , doi: 10.23919/cje.2023.00.337
Abstract(6) HTML (3) PDF(3)
A dynamic programming (DP) algorithm is proposed for parameter extraction of the single-diode model (SDM). Five parameters of SDM are extracted from current-voltage (I-V) curves of InGaN/GaN Multi-quantum wells solar cells (SCs) under AM1.5 standard sunlight conditions, with indium (In) compositions of 7% and 18%. Firstly, the range of series resistance (Rs) of the device is adaptively selected and its value is randomly determined. Next, after the series resistance and the range of ideal factors are planned the parameters of SDM are iteratively solved using the root mean square error (RMSE) of the I-V curve and the photoelectric conversion efficiency (η). Due to this approach, the proposed algorithm is fast and accurate compared with other conventional algorithms. Additionally, the obtained RMSE value is controlled within 1.2e-5, and the calculated fill factor (FF) and η are consistent with the measured values. This study provides a reference for power optimization of advanced semiconductor photovoltaic cell systems.
Research on Object Detection and Combination Clustering for Railway Switch Machine Gap Detection
FENG Qingsheng, XIAO Shuai, LIU Wangyang, LI Hong
, Available online  , doi: 10.23919/cje.2023.00.268
Abstract(6) HTML (3) PDF(5)
Turnouts and switch machines play a crucial role in facilitating train line operations and establishing routes, making them vital for ensuring the safety and efficiency of railway transportation. Through the gap detection system of switch machines, the real-time working status of turnouts and switch machines on railway sites can be quickly known. However, due to the challenging working environment and demanding conversion tasks of switch machines, the current gap detection system has often experienced the issues of fault detection. To address this, this study proposes an automatic gap detection method for railway switch machines based on object detection and combination clustering. Firstly, a lightweight object detection network, specifically the MobileNetV3-YOLOv5s model, is used to accurately locate and extract the focal area. Subsequently, the extracted image undergoes preprocessing and is then fed into a combination clustering algorithm (SLIC-Canopy-KFCM) to achieve precise segmentation of the gap area and background. Finally, the Fisher optimal segmentation criterion is utilized to divide the data sequence of pixel values, determine the classification nodes and calculate the gap size. The experimental results obtained from switch machine gap images captured in various scenes demonstrate that the proposed method is capable of accurately locating focal areas, efficiently completing gap image segmentation with a segmentation accuracy of 93.55%, and swiftly calculating the gap size with a correct rate of 98.57%. Notably, the method achieves precise detection of gap sizes even after slight deflection of the acquisition camera, aligning it more closely with the actual conditions encountered on railway sites.
Multimodal Cross-Attention Mechanism-Based Algorithm for Elderly Behavior Monitoring and Recognition
LIU Hao, FENG Zhiquan, GUO Qingbei
, Available online  , doi: 10.23919/cje.2023.00.263
Abstract(12) HTML (6) PDF(1)
In contrast to the general population, behavior recognition among the elderly poses increased specificity and difficulty, rendering the reliability and usability aspects of safety monitoring systems for the elderly more challenging. Hence, this study proposes a multi-modal perception-based solution for an elderly safety monitoring recognition system. The proposed approach introduces a recognition algorithm based on multi-modal cross-attention mechanism, innovatively incorporating complex information such as scene context and voice to achieve more accurate behavior recognition. By fusing four modalities, namely image, skeleton, sensor data, and audio, we further enhance the accuracy of recognition. Additionally, we introduce a novel human-robot interaction mode, where the system associates directly recognized intentions with robotic actions without explicit commands, delivering a more natural and efficient elderly assistance paradigm. This mode not only elevates the level of safety monitoring for the elderly but also facilitates a more natural and efficient caregiving approach. Experimental results demonstrate significant improvement in recognition accuracy for 11 typical elderly behaviors compared to existing methods.
AOYOLO Algorithm Oriented Vehicle and Pedestrian Detection in Foggy Weather
SU Jian, MAO Shiang, ZHUANG Wei
, Available online  , doi: 10.23919/cje.2023.00.280
Abstract(35) HTML (17) PDF(2)
In the context of complex foggy environments, the acquired images often suffer from low visibility, high noise, and loss of detailed information. The direct application of general object detection methods fails to achieve satisfactory results. To address these issues, this paper proposes a foggy object detection method based on YOLOv8n, named AOYOLO. The AOD-Net, a lightweight defogging network, is employed for data augmentation. Additionally, the ResCNet module is introduced in the backbone to better extract features from low-illumination images. The GACSP module is proposed in the neck to capture multi-scale features and effectively utilize them, thereby generating discriminative features with different scales. The detection head is improved using WiseIoU, which enhances the accuracy of object localization. Experimental evaluations are conducted on the publicly available datasets RTTS and synthetic foggy KITTI dataset. The results demonstrate that the proposed AOYOLO algorithm outperforms the original YOLOv8n algorithm with an average mean average precision (mAP) improvement of 3.3% and 4.6% on the RTTS and KITTI datasets, respectively. The AOYOLO method effectively enhances the performance of object detection in foggy scenes. Due to its improved performance and stronger robustness, this experimental model provides a new perspective for foggy object detection.
Comparative Analysis of Noise Margin between Pure SET-SET and Hybrid SET-PMOS Inverters
ZHANG Fan, LIU Yi, WANG Yibo, WU Minghu, HU Sheng, DONG Youli
, Available online  , doi: 10.23919/cje.2023.00.287
Abstract(20) HTML (11) PDF(1)
Single-electron transistor (SET) is considered as one of the promising candidates for future electronic devices due to its advantages of low power consumption and high integration. In this paper, the comparative analysis of SET-based inverters, especially the noise margin, is carried out. Pure SET-SET and hybrid SET-PMOS inverters are designed for investigation. The effects of SET supply voltage, junction resistance and junction capacitance on noise tolerance and power consumption of inverters are studied. For hybrid SET-PMOS inverters, the Noise Margin High (NMH) is less than 60 mV under various conditions, which may become the bottleneck of its application. For pure SET-SET inverters, both NMH and NML could reach 300 mV at a supply voltage of 0.8 V. The minimum power consumption of pure SET-SET and hybrid SET-PMOS inverters is 2.85 nw and 58 nw, respectively. Therefore, the pure SET-SET inverters have greater noise tolerance and lower power consumption, which is more conducive to large-scale integration. In particular, when junction capacitance $ C_J=0.0273aF $ and junction resistance $ R_T \ge 1M $ in SET-SET inverters at a supply voltage of 0.8 V, the NMH and NML are not significantly affected by the junction resistance and the noise margin fluctuates at 300 mV.
Self-Decoupled Square Patch Antenna Arrays by Exciting and Using Mixed Electric/Magnetic Coupling between Adjacent Radiators
LIU Qianwen, ZHU Lei, LU Wenjun
, Available online  , doi: 10.23919/cje.2023.00.222
Abstract(12) HTML (6) PDF(1)
This article presents and develops a simple decoupling method for the planar square patch antenna arrays by virtue of mixed electric and magnetic coupling property. Since the resonant modes of TM10 and TM01 are a pair of degenerate modes in the square patch radiator which are intrinsically orthogonal, a superposed mode of them can be generated to possess consistent field distributions along all the four sides of the patch by adjusting the feeding position. By employing such superposed mode, the mutual coupling between two horizontally adjacent patch elements will become identical to that between two vertical ones, indicating an expected possibility that the complex 2-D decoupling problem in a large-scale antenna patch array can be effectively facilitated and simplified to a 1-D one. Subsequently, metallic pins and connecting strip are properly loaded in each square patch resonator, such that appropriate electric and magnetic coupling strengths can be readily achieved and thus the mutual coupling can get highly decreased. A 1×2 antenna array with an edge-to-edge separation of 1mm, which corresponding to 0.0117λ0, is firstly discussed, simulated, and fabricated. The measured results show that the isolation can be highly improved from 4 dB to 17 dB across the entire passband. In final, 1×3, 2×2, and 4×4 antenna array prototypes are constructed and studied for verification of the expansibility and feasibility of the proposed decoupling method to both linear and 2-D antenna arrays.
Wideband Millimeter Wave Antenna with Cavity Backed Slotted Patch and Magneto-Electric Dipole
CHENG Yang, DONG Yuandan
, Available online  , doi: 10.23919/cje.2023.00.064
Abstract(9) HTML (4) PDF(1)
This paper proposes a wideband cavity-backed slotted patch antenna, loaded with a magneto-electric (ME) dipole and fed by a microstrip line, for millimeter wave (mm-Wave) applications. The coupled-feed cavity-backed slotted patch antenna is loaded with the ME-dipole. The slotted patch antenna serves both as a radiator and a ground for the ME-dipole. The combination of the ME-dipole antenna and the slotted patch antenna realizes a -10dB impedance bandwidth covering over 22.86-44.35GHz (63.9%). The pattern of the antenna element remains stable throughout this bandwidth. The proposed broadband antenna unit not only realizes single linearly polarized (LP) radiation but also can be designed for dual-LP radiation. The dual-polarized radiation can be achieved by changing the slot of the patch antenna to a crossed slot and altering the ME-dipole antenna to a dual-polarization form. A 2×2 dual-polarized array has been designed, fabricated, and tested. A novel dual-polarized feeding network is proposed. To achieve higher isolation, broadband in-phase feed and differential feed are adopted, respectively. A low-loss single to the differential structure is proposed for differential feeding. The simulated isolation of the array is higher than 40 dB. Measured results show that the dual-polarized 2×2 array has an overlapping bandwidth of 52.3% (|S11|<−10 dB and |S21|<−30 dB) with a peak gain of 14 dBi. The proposed antenna, featuring a wide overall bandwidth, low cost, and good radiation performance, is well suited for mm-Wave applications.
TE101 Substrate Integrated Waveguide Filter With Wide Stopband Up to TE10(2n-1) and Coplanar Ports
CHU Peng, FENG Jianguo, GUO Lei, ZHU Fang, KONG Wei-Bin, LIU Leilei, LUO Guo Qing, WU Ke
, Available online  , doi: 10.23919/cje.2023.00.225
Abstract(12) HTML (6) PDF(0)
This article presents a new method for substrate integrated waveguide (SIW) filters to achieve wide stopbands. Using the proposed staggered inter-coupling structures, double-layer SIW filters working at the fundamental mode TE101 (f0) can have wide stopbands up to TE10(2n-1), where n is the order of the filter. They can break the upper limit of the stopband extension and have coplanar ports suitable for planar circuits and systems in comparison to their multilayer counterparts, and they can further extend the stopbands and have shielding structures suitable for high-performance and high-frequency applications in comparison to their hybrid counterparts. Three examples are provided. The measured results show that they respectively achieve wide stopbands up to 3.97 f0, 5.22 f0, and 6.53 f0. The proposed technique should be effective for developing wide stopband SIW filters for microwave circuits and systems.
Realization of Complete Boolean Logic and Combinational Logic Functionalities on A Memristor-based Universal Logic Circuit
LIAN Xiaojuan, SUN Chuanyang, TAO Zeheng, WAN Xiang, CAI Zhikuang, WANG Lei
, Available online  , doi: 10.23919/cje.2023.00.091
Abstract(11) HTML (6) PDF(0)
Memristors are a promising solution for building an advanced computing system due to their excellent characteristics, including small energy consumption, high integration density, fast write/read speed, great endurance and so on. In this work, we firstly design three basis logic XNOR1, XNOR2 and XOR gates by virtue of memristor ratioed logic (MRL), and further construct 1-bit numerical comparators, 2-bit numerical comparators and full adder 1 based on the above XNOR1, XNOR2 and XOR gates. Furthermore, we propose and design a universal logic circuit that can realize four different kinds of logic functions (AND, OR, XOR, XNOR) at the same time. Subsequently, a full adder 2 is built using XOR function of this universal logic circuit. Compared with the traditional CMOS circuits, the universal logic circuit designed in this work exhibits several merits such as fewer components, less power, and lower delay. This work demonstrates that memristors can be used as a potential solution for building a novel computing architecture.
An Improved YOLOv7-tiny Algorithm for Vehicle and Pedestrian Detection with Occlusion in Autonomous Driving
SU Jian, WANG Fang, ZHUANG Wei
, Available online  , doi: 10.23919/cje.2023.00.256
Abstract(33) HTML (17) PDF(9)
Future transportation is advancing in the direction of intelligent transportation systems (ITS), where an essential part is vehicle and pedestrian detection. However, due to the complex urban traffic environment, vehicles and pedestrians in road monitoring have different forms of occlusion problems, resulting in the missed detection of objects. In this paper, we design an improved YOLOv7-tiny algorithm for vehicle and pedestrian detection under occlusion, with the following four main improvements. Firstly, in order to locate the object more accurately, 1$ \times $1 convolution and identity connection are added to the 3$ \times $3 convolution, and convolution reparameterization is used to enhance the inference speed of the network model. Secondly, in view of the complex road background and more interference, the Coordinate Attention was added to the connection part of backbone and neck to enhance the network’s capacity to detect the object and lessen interference from other targets. At the same time, before being sent to the detection head, Global Attention Mechanism is added to improve the accuracy of model detection by capturing three-dimensional features. Finally, considering the issue of imbalanced training samples, we propose Focal CIOU Loss instead of CIOU Loss to become the bounding box regression loss, so that the regression process attention to high-quality anchor boxes. Experiments show that the improved YOLOv7-tiny algorithm achieves 82.2% map@0.5 in PASCAL VOC dataset, which is 2.8% higher than before the improvement. The performance of map@0.5:0.95 is 5.2% better than the previous improvement. Therefore, the proposed improved algorithm can availably to detect partial occlusion objects.
A Distributed Self-tallying Electronic Voting System Using the Smart Contract
YAO Jingyu, WANG Tao, YANG Bo, ZHANG Wenzheng
, Available online  , doi: 10.23919/cje.2023.00.233
Abstract(14) HTML (7) PDF(6)
For electronic voting(e-voting) with a trusted authority, the ballots may be discarded or tampered, so it is attractive to eliminate the dependence on the trusted party. An e-voting protocol, where the final voting result can be calculated by any entity, is known as self-tallying e-voting protocol. To the best of our knowledge, addressing both abortive issue and adaptive issue simultaneously is still an open problem in self-tallying e-voting protocols. In this paper, combining Ethereum blockchain with cryptographic technologies, we present a decentralized self-tallying e-voting protocol. We solve the above problem efficiently by utilizing optimized Group Encryption Scheme and standard Exponential ElGamal Cryptosystem. In addition, we use zero-knowledge proof and homomorphic encryption to protect votes’ secrecy and achieve self-tallying. All ballots can be verified by anyone and the final voting result can be calculated by any entity. In addition, using the paradigm of score voting and "1-out-of-K" proof, our e-voting system is suitable for a wide range of application scenarios. Experiments show that our protocol is more competitive and more suitable for large-scale voting.
Sparse Homogeneous Learning: A New Approach for Sparse Learning
SHI Jiajie, YANG Zhi, LIU Jiafeng, SHI Hongli
, Available online  , doi: 10.23919/cje.2023.00.130
Abstract(4) HTML (2) PDF(0)
Many sparse representation problems boil down to address the underdetermined systems of linear equations subject to solution sparsity restriction. Many approaches have been proposed such as sparse Bayesian learning. In order to improve solution sparsity and effectiveness in a more intuitive way, a new approach is proposed, which starts from the general solution of the linear equation system. The general solution is decomposed into the particular and homogeneous solutions, where the homogeneous solution is designed to counteract as many elements of particular solution as possible to make the general solution sparse. First, construct a special system of linear equations to link the homogeneous solution with particular solution, which typically is an inconsistent system. Second, the largest consistent sub-system are extracted from the system so that as many corresponding elements of two solutions as possible cancel each other out. By improving implementation efficiency, the procedure can be accomplished with moderate computational time. The results of extensive experiments for sparse signal recovery and image reconstruction demonstrate the superiority of the proposed approach in terms of sparseness or recovery accuracy with acceptable computational burden.
YOLO-Drone: A Scale-Aware Detector for Drone Vision
LI Yutong, MA Miao, LIU Shichang, YAO Chao, GUO Longjiang
, Available online  , doi: 10.23919/cje.2023.00.254
Abstract(9) HTML (4) PDF(2)
Object detection is an important task in drone vision. However, since the number of objects and their scales always vary greatly in the drone-captured video, small object-oriented feature becomes the bottleneck of model performance, and most existing object detectors tend to underperform in drone-vision scenes. To solve these problems, we propose a novel detector named YOLO-Drone in this paper. In the proposed detector, the backbone of YOLO is firstly replaced with ConvNeXt, which is the state-of-the-art one to extract more discriminative features. Then, a novel Scale-Aware Attention (SAA) module is designed in detection head to solve the large disparity scale problem. Finally, a Scale-Sensitive Loss (SSL) is also introduced to put more emphasis on object scale to enhance the discriminative ability of the proposed detector. Experimental results on the latest VisDrone 2022 test-challenge dataset (detection track) show that our detector can achieve an AP of 39.43%, which is tied with the previous SOTA, meanwhile, reducing 39.8% of the computational cost.
Study on Static Deflection Model of MEMS Capacitive Microwave Power Sensors
, Available online  , doi: 10.23919/cje.2023.00.087
Abstract(24) HTML (12) PDF(6)
In this paper, a static deflection model of MEMS cantilever beam is proposed, which can better study the force deformation of MEMS cantilever beam and the output characteristics of capacitive microwave power sensor. The deflection curve is used to describe the deformation of the cantilever beam and then the overload power and sensitivity of this power sensor is derived. It is found that the overload power decreases with the beam length, and increases with the initial height of beam. The sensitivity increases with the beam length, and has a linear growth relationship with the measuring electrode width. A MEMS dual-channel microwave power sensor is designed, fabricated and measured. At a microwave signal frequency of 10 GHz, the sensitivity of the sensor is measured to be 0.11 V/W for the thermoelectric detection channel and 65.17 fF/W for the capacitive detection channel. The sensitivity calculated by the lumped model is 92.93 fF/W, by the pivot model is 50.88 fF/W, by the deflection model proposed in this work is 75.21 fF/W. Therefore, the theoretical result of the static deflection model is more consistent with the measured result and has better accuracy than the traditional lumped model and pivot model.
A Microstrip Leaky-Wave Antenna with Scanning Beams Horizontal to the Antenna Plane
Henghui WANG, Peiyao CHEN, Sheng SUN
, Available online  , doi: 10.23919/cje.2023.00.033
Abstract(34) HTML (17) PDF(8)
A leaky-wave antenna with horizontal scanning beams and broadside radiation is presented on the periodically modulated microstrip. The horizontal radiation is realized by periodically etching a set of resonant open-ended slots on the ground plane. Dispersion diagrams and Bloch impedance are first analyzed to investigate the propagation and radiation characteristics of the periodic structure. Subsequently, shunt matching stubs are installed aiming to obtain seamless beam scanning property through the broadside. Finally, a prototype is implemented as verification of the presented antenna. Results of the simulations and measurements agree well with each other, indicating the elimination of the open-stop band effect and the horizontal radiation beams. The fabricated antenna exhibits a beam range from −62° to +34°, and provides a maximum measured gain about 14.6 dBi at 10 GHz.
An Intelligent Privacy Protection Scheme for Efficient Edge Computation Offloading in IoV
YAO Liang, XU Xiaolong, DOU Wanchun, Bilal Muhammad
, Available online  , doi: 10.23919/cje.2023.00.111
Abstract(18) HTML (9) PDF(2)
As a pivotal enabler of Intelligent Transportation System (ITS), Internet of Vehicles (IoV) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive and privacy-aware vehicular applications in IoV result in the transformation from cloud computing to edge computing, which enables tasks to be offloaded to edge nodes (ENs) closer to vehicles for efficient execution. In ITS environment, however, due to dynamic and stochastic computation offloading requests, it is challenging to efficiently orchestrate offloading decisions for application requirements. How to accomplish complex computation offloading of vehicles while ensuring data privacy remains challenging. In this paper, we propose an intelligent computation offloading with privacy protection scheme, named COPP. In particular, an Advanced Encryption Standard-based encryption method is utilized to implement privacy protection. Furthermore, an online offloading scheme is proposed to find optimal offloading policies. Finally, experimental results demonstrate that COPP significantly outperforms benchmark schemes in the performance of both delay and energy consumption.
A General Authentication and Key Agreement Framework for Industrial Control System
Gao Shan, Chen Junjie, Zhang Bingsheng, Ren Kui, Ye Xiaohua, Shen Yongsheng
, Available online  , doi: 10.23919/cje.2023.00.192
Abstract(35) HTML (17) PDF(2)
In modern Industrial Control Systems (ICSs), when user retrieving the data stored in field device like smart sensor, There are two main problems. Firstly, the identification of user and field device should be verified. Secondly, to protect the privacy of sensitive data transmitted over the network, user and field device should exchange a key to encrypt data. In this study, we propose a comprehensive authentication and key agreement framework that enables all connected devices in an ICS to mutually authenticate each other and establish a peer-to-peer session key. The framework combines two types of protocols for authentication and session key agreement: the first one is an asymmetric cryptographic key agreement protocol based on TLS handshake protocol used for Internet access, while the second one is a newly designed lightweight symmetric cryptographic key agreement protocol specifically for field devices. This proposed lightweight protocol imposes very light computational load and merely employs simple operations like one-way hash function and exclusive-or (XOR) operation. In comparison to other lightweight protocols, our protocol requires the field device to perform fewer computational operations during the authentication phase. The simulation results obtained using OpenSSL demonstrates that each authentication and key agreement process in the lightweight protocol requires only 0.005ms. Additionally, our lightweight key agreement protocol satisfies several essential security features, including session key secrecy, identity anonymity, untraceability, integrity, forward secrecy, and mutual authentication. Furthermore, it is capable of resisting impersonation, Man-in-the-Middle (MitM), and replay attacks. We have employed the GNY logic and AVISPA tool to verify the security of our symmetric cryptographic key agreement protocol.
BAD-FM: Backdoor Attacks Against Factorization-Machine Based Neural Network for Tabular Data Prediction
MENG Lingshuo, GONG Xueluan, CHEN Yanjiao
, Available online  , doi: 10.23919/cje.2023.00.041
Abstract(16) HTML (8) PDF(1)
Backdoor attacks pose great threats to deep neural network (DNN) models. However, all existing backdoor attacks are designed for unstructured data (image, voice, and text), but not structured tabular data, which has wide real-world applications, e.g., recommendation systems, fraud detection, and click-through rate (CTR) prediction. To bridge this research gap, we make the first attempt to design a backdoor attack framework, named BAD-FM, for tabular data prediction models. Unlike images or voice samples composed of homogeneous pixels or signals with continuous values, tabular data samples contain well-defined heterogeneous fields that are usually sparse and discrete. Moreover, tabular data prediction models do not solely rely on deep networks but combine shallow components (e.g., factorization machine, FM) with deep components to capture sophisticated feature interactions among fields. To tailor the backdoor attack framework to tabular data models, we carefully design field selection and trigger formation algorithms to intensify the influence of the trigger on the backdoored model. We evaluate BAD-FM with extensive experiments on four datasets, i.e., HUAWEI, Criteo, Avazu, and KDD. The results show that BAD-FM can achieve an attack success rate as high as 100% at a poison ratio of 0.001%, outperforming baselines adapted from existing backdoor attacks against unstructured data models. As tabular data prediction models are widely adopted in finance and commerce, our work may raise alarms on the potential risks of these models and spur future research on defenses.
Model Checking Computation Tree Logic over Multi-valued Decision Processes and Its Reduction Techniques
LIU Wuniu, WANG Junmei, HE Qing, LI Yongming
, Available online  , doi: 10.23919/cje.2021.00.333
Abstract(54) HTML (27) PDF(4)
Model checking computation tree logic (CTL) based on multi-valued possibility measures has been studied by Li et al. in 2019. However, the previous work did not consider the nondeterministic choices inherent in systems represented by multi-valued Kripke structures (MvKSs). This nondeterminism is crucial for accurate system modeling, decision making, and control capabilities. To address this limitation, we draw inspiration from the generalization of Markov chains (MCs) to Markov decision processes (MDPs) in probabilistic systems. By integrating nondeterminism into MvKS, we introduce multi-valued decision processes (MvDPs) as a framework for modeling MvKSs with nondeterministic choices. Additionally, we investigate the challenges of model checking over MvDPs. In our approach, verifying properties are expressed by using multi-valued computation tree logic (MvCTL) based on schedulers. Our primary objective is to leverage fixpoint techniques to determine the maximum and minimum possibilities of the system satisfying temporal properties. This allows us to identify the optimal or worst-case schedulers for decision making or control purposes. Furthermore, we aim to develop reduction techniques that enhance the efficiency of model checking, thereby reducing the associated time complexity.
A Deep Deterministic Policy Gradient-based Method for Enforcing Service Fault-tolerance in MEC
LONG Tingyan, CHEN Peng, XIA Yunni, MA Yong, SUN Xiaoning, ZHAO Jiale, LYU Yifei
, Available online  , doi: 10.23919/cje.2023.00.105
Abstract(36) HTML (17) PDF(8)
Mobile edge computing (MEC) provides edge services to users in a distributed and on-demand way. Due to the heterogeneity of edge applications, it is a key challenge for service providers to deploy latency and resource-intensive applications on resource-constrained devices. This is especially true when underlying edge infrastructures are fault and error-prone. In this paper, we propose a fault tolerance approach named DFGP, for enforcing mobile service fault-tolerance in MEC. It synthesizes a Generative Optimization Network (GON) model for predicting resource failure and a Deep Deterministic Policy Gradient (DDPG) model for yielding preemptive migration decisions. We show through extensive simulation experiments that DFGP is more effective in fault detection and guaranteeing quality of service (QoS), in terms of fault detection accuracy, migration efficiency, task migration time, task scheduling time, and energy consumption than other existing method.
Cooperative Self-Learning: A Framework for Few-Shot Jamming Identification
SHI Yuxin, LU Xinjin, SUN Yifu, AN Kang, LI Yusheng
, Available online  , doi: 10.23919/cje.2023.00.229
Abstract(16) HTML (9) PDF(1)
Jamming identification is the key objective behind effective anti-jamming methods. Due to the requirement of low-complexity and the condition of few labeled shots for a real jamming identification, it is very challenging to identify jamming patterns with high accuracy. To this end, we first propose a general framework of cooperative jamming identification among multiple nodes. Moreover, we further propose a novel fusion center (FC) aided self-learning scheme, which uses the guidance of the FC to improve the effectiveness of the identification. Simulations show that the proposed framework of the cooperative jamming identification can significantly enhance the average accuracy with low-complexity. It is also demonstrated that the proposed FC aided self-learning scheme has the superior average accuracy compared with other identification schemes, which is very effective especially in the few labeled jamming shots scenarios.
Efficient nonnegative tensor decomposition using alternating direction proximal method of multipliers
WANG Deqing, HU Guoqiang
, Available online  , doi: 10.23919/cje.2023.00.035
Abstract(222) HTML (110) PDF(36)
Nonnegative CANDECOMP/PARAFAC (NCP) tensor decomposition is a powerful tool for multiway signal processing. The optimization algorithm alternating direction method of multipliers (ADMM) has become increasingly popular for solving tensor decomposition problems in the block coordinate descent framework. However, the ADMM-based NCP suffers from rank deficiency and slow convergence for some large-scale and highly sparse tensor data. The proximal algorithm is preferred to enhance optimization algorithms and improve convergence properties. In this study, we propose a novel NCP algorithm using the alternating direction proximal method of multipliers (ADPMM) that consists of the proximal algorithm. The proposed NCP algorithm can guarantee convergence and overcome the rank deficiency. Moreover, we implement the proposed NCP using an inexact scheme that alternatively optimizes the subproblems. Each subproblem is optimized by a finite number of inner iterations yielding fast computation speed. Our NCP algorithm is a hybrid of alternating optimization and ADPMM and is named A2DPMM. The experimental results on synthetic and real-world tensors demonstrate the effectiveness and efficiency of our proposed algorithm.
Wideband Circularly Polarized Substrate-Integrated Waveguide Aperture-Coupled Metasurface Antenna Array for Millimeter-Wave Applications
LIAN Jiwei, GENG Chun, LU Xue, DING Dazhi
, Available online  , doi: 10.23919/cje.2023.00.029
Abstract(78) HTML (39) PDF(9)
A wideband circularly polarized (CP) aperture-coupled metasurface antenna operating at millimeter-wave frequency spectrum in substrate-integrated waveguide (SIW) technology is proposed. Such a proposed metasurface antenna is composed of two substrates. The first substrate contains an end-shorted SIW section with a slot etched. The introduced metasurface is printed on the top of the second substrate. The metasurface is comprised of 3 × 3 unit cells, each of which contains two interconnected patches and two parasitic patches. The working mechanism of the proposed metasurface antenna is illustrated in details. The proposed metasurface antenna has wide impedance bandwidth and axial ratio (AR) bandwidth, which are 66.7% and 40%, respectively. Using the proposed metasurface antenna, a 4 × 4 CP metasurface antenna array with an impedance bandwidth of 24%, an AR bandwidth of 30%, and a peak gain of 18.7 dBic in simulation is developed in this paper for millimeter-wave applications.
Poisson Multi-Bernoulli Mixture Filter for Heavy-tailed Process and Measurement Noises
ZHU Jiangbo, XIE Wexin, LIU Zongxiang, WANG Xiaoli
, Available online  , doi: 10.23919/cje.2022.00.325
Abstract(84) HTML (42) PDF(13)
A novel Poisson multi-Bernoulli mixture (PMBM) filter is proposed to track multiple targets in the presence of heavy-tailed process and measurement noises. Unlike the standard PMBM filter that requires the Gaussian process and measurement noises, the proposed filter uses the Student’s t distribution to model the heavy-tailed noise feature. It propagates Student’s t-based PMBM posterior in the closed-form recursion. The introduction of the moment matching method enables the proposed filter to deal with the process and measurement noises with different heavy-tailed degrees to some extent. Simulation results demonstrate that the overall performance of the proposed filter is better than the existing heavy-tailed noise filters in various scenarios.
High Power GaN Doubler with High Duty Cycle Pulse Based on Local Non-Reflection Design
DONG Yazhou, ZHOU Tianchi, LIANG Shixiong, GU Guodong, ZHOU Hongji, YU Jianghua, GUO Hailong, ZHANG Yaxin
, Available online  , doi: 10.23919/cje.2023.00.179
Abstract(68) HTML (34) PDF(9)
The study focuses on the development of gallium nitride (GaN) Schottky barrier diode (SBD) frequency doublers for terahertz technology. The low conversion efficiency of these doublers limits their practical applications. To address this challenge, the paper proposes a multi-objective local no-reflection design method based on a three-dimensional electromagnetic structure. The method aims to improve the coupling efficiency of input power and reduce the reflection of power output. Experimental results indicate that the proposed method significantly improves the performance of GaN SBD frequency doublers, achieving an efficiency of 16.9% and a peak output power of 160 mW at 175 GHz. These results suggest that the method can contribute to the further development of GaN SBD frequency doublers for terahertz technology.
Weighted linear loss large margin distribution machine for pattern classification
LIU Ling, CHU Maoxiang, GONG Rongfen, LIU Liming, YANG Yonghui
, Available online  , doi: 10.23919/cje.2022.00.156
Abstract(97) HTML (48) PDF(18)
Compared with support vector machine (SVM), large margin distribution machine (LDM) has better generalization performance. The central idea of LDM is to maximize the margin mean and minimize the margin variance simultaneously. But the computational complexity of LDM is high. In order to reduce the computational complexity of LDM, a weighted linear loss LDM (WLLDM) is proposed in this paper. The framework of WLLDM is built based on LDM and the weighted linear loss. Firstly, the weighted linear loss is adopted instead of the hinge loss in WLLDM. This modification can transform the quadratic programming problem (QPP) into a simple linear equation, resulting in lower computational complexity. Thus, WLLDM has the potential to deal with large-scale datasets. Secondly, the WLLDM is similar in principle to the LDM algorithm, which can optimize the margin distribution and achieve better generalization performance. Finally, the WLLDM algorithm is compared with other models by conducting experiments on different datasets. The experimental results show that the proposed WLLDM has better generalization performance and faster training speed.
FMR-GNet: Forward Mix-hop spatial-temporal Residual Graph Network for 3D Pose estimation
YANG Honghong, LIU Hongxi, ZHANG Yumei, WU Xiaojun
, Available online  , doi: 10.23919/cje.2022.00.365
Abstract(126) HTML (61) PDF(24)
With the powerful representative ability of learning spatial-temporal information from skeleton data, the spatial-temporal graph convolution network (ST-GCN) has been a popular baseline for 3D human pose estimation (HPE). However, how to comprehensively model coherent spatial-temporal joints information of skeleton is still a challenging task. Existing methods have limitations in performing graph convolutions only on the one-hop neighbors of each node, simply deploy interleaving spatial graph convolution network (S-GCN) only or temporal graph convolution network (T-GCN) only modules, and traditional graph convolution network (GCN) is single-pass feedforward network. To address the above issues, a forward mix-hop spatial-temporal residual graph convolutional network (FMR-GNet) is devised for 3D HPE in this paper. Firstly, a mix-hop spatial temporal attention graph convolution layer is designed to effectively gather the neighbor features in a weighted way from large spatial-temporal receptive field. With the attention mechanism introduced into the mix-hop feature aggregation, the attention weighted neighbor matrix is computed at each layer instead of sharing same adjacency matrix for all GCN layers. Then, a cross-domain spatial-temporal residual connection block was devised to fuse the multi-scale spatial-temporal convolution features in a residual connection manner, which directly models cross-spacetime joint dependencies. Finally, a forward dense connection block is introduced to transmit the spatial-temporal features from different layers of FMR-GNet, enabling the proposed model to transmit high-level semantic skeleton connectivity information to its features in low-level layers. Two challenging 3D human pose datasets are used for evaluating the effectiveness of the proposed model. Experimental results show that FMR-GNet achieves the state-of-the-art (SOTA) performance.
An Efficient and Fast Area Optimization Approach for Mixed Polarity Reed-Muller Logic Circuits
Yuhao ZHOU, Zhenxue HE, Jianhui JIANG, Xiaojun ZHAO, Fan ZHANG, Limin XIAO, Xiang WANG
, Available online  , doi: 10.23919/cje.2022.00.407
Abstract(135) HTML (67) PDF(19)
At present, area has become one of the main bottlenecks restricting the development of integrated circuits. The area optimization approaches of existing XNOR/OR-based mixed polarity Reed-Muller (MPRM) circuits have poor optimization effect and efficiency. Since the area optimization of MPRM logic circuits is a combinatorial optimization problem, we propose a whole annealing adaptive bacterial foraging algorithm (WAA-BFA), which includes individual evolution based on Markov chain and Metropolis acceptance criteria, and individual mutation based on adaptive probability. In addition, we propose a fast polarity conversion algorithm (FPCA) due to the low conversion efficiency of existing polarity conversion approaches. Finally, we propose an MPRM circuits area optimization approach (MAOA), which uses the FPCA and WAA-BFA to search for the best polarity corresponding to the minimum circuits area. The experimental results show that MAOA is effective and can be used as a promising EDA tool.
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
Abstract(120) HTML (59) PDF(21)
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
Abstract(131) HTML (68) PDF(22)
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
Abstract(159) HTML (79) PDF(32)
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
Abstract(155) HTML (77) PDF(12)
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.
Hybrid ITÖ Algorithm for Large-scale Colored Traveling Salesman Problem
DONG Xueshi
, Available online  , doi: 10.23919/cje.2023.00.040
Abstract(241) HTML (119) PDF(19)
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
Abstract(154) HTML (75) PDF(21)
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
Abstract(143) HTML (69) PDF(33)
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
Abstract(100) HTML (51) PDF(16)
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
Abstract(148) HTML (75) PDF(17)
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.
Enhanced Privacy-Preserving WiFi Fingerprint Localization from CL Encryption
WANG Zhiwei, ZHU Qiuchi, ZHANG Zhenqi
, Available online  , doi: 10.23919/cje.2022.00.257
Abstract(180) HTML (90) PDF(13)
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
Abstract(441) HTML (216) PDF(54)
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.
Related-Key Zero-Correlation Linear Attacks on Block Ciphers with Linear Key Schedules
, Available online  , doi: 10.23919/cje.2022.00.419
Abstract(205) HTML (104) PDF(17)
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.
New Related-Tweakey Boomerang Attacks and Distinguishers on Deoxys-BC
LIU Jiamei, TAN Lin, XU Hong
, Available online  , doi: 10.23919/cje.2022.00.383
Abstract(177) HTML (83) PDF(20)
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
Abstract(134) HTML (67) PDF(26)
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.
Friendship Inference Based on Interest Trajectory Similarity and Co-occurrence
TIAN Junfeng, HOU Zhengqi
, Available online  , doi: 10.23919/cje.2022.00.363
Abstract(117) HTML (58) PDF(6)
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
Abstract(181) HTML (90) PDF(16)
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
Abstract(176) HTML (84) PDF(8)
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.
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
Abstract(179) HTML (87) PDF(17)
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
Abstract(199) HTML (100) PDF(24)
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.
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
Abstract(310) HTML (153) PDF(23)
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.
Accepted Research Article for Publication
Enhanced Acceleration for Generalized Nonconvex Low-Rank Matrix Learning
ZHANG Hengmin, YANG Jian, DU Wenli, ZHANG Bob, ZHA Zhiyuan, WEN Bihan
, Available online  , doi: 10.23919/cje.2023.00.340
Abstract(0) HTML (0) PDF(0)
Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion (RMC), low-rank representation (LRR), and robust matrix regression (RMR). We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the $\ell_0$-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers (ADMM), backed by rigorous theoretical analysis for complexity and convergence. This algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition (RSVD) technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.
Review Article
A Review of Terahertz Solid-state Electronic/Optoelectronic Devices and Communication Systems
LI Wenbo, ZENG Hongxin, HUANG Lin, GONG Sen, CAO Haoyi, WANG Weipeng, WANG Zheng, ZHOU Hongji, LIANG Shixiong, YANG Ziqiang, ZHANG Yaxin
, Available online  , doi: 10.23919/cje.2023.00.282
Abstract(46) HTML (23) PDF(4)
With the rapid development of modern communication technology, spectrum resources have become non-renewable and precious resources, and the terahertz frequency band has entered people’s vision. Nowadays, terahertz communication technology has become one of the core technologies for future high-capacity and high-rate communication. This paper discusses and analyzes the core technologies related to the field of terahertz communication. We introduce the characteristics, domestic and international comparisons and development trends of the core devices for terahertz communication, and also introduce and discuss the terahertz solid-state frequency mixing communication system, terahertz direct modulation communication system, and terahertz optoelectronic communication system. Finally, we summarize the development of terahertz communication technology and the outlook of future applications.
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
Abstract(216) HTML (107) PDF(35)
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
Abstract(202) HTML (101) PDF(94)
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
Abstract(403) HTML (201) PDF(48)
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.
Extracting Integrated Features of Electronic Medical Records Big Data for Mortality and Phenotype Prediction
LI Fei, CHEN Yiqiang, GU Yang, WANG Yaowei
, Available online  , doi: 10.23919/cje.2023.00.181
Abstract(122) HTML (60) PDF(30)
The key to synthesizing the features of electronic medical records (EMR) big data and using them for specific medical purposes, such as mortality and phenotype prediction, is to integrate the individual medical event and the overall multivariate time series feature extraction automatically, as well as to alleviate data imbalance problems. This paper provides a general feature extraction method to reduce manual intervention and automatically process large-scale data. The processing uses two variational auto-encoder (VAEs) to automatically extract individual and global features. It avoids the well-known posterior collapse problem of Transformer VAE through a uniquely designed “proportional and stabilizing” mechanism and forms a unique means to alleviate the data imbalance problem. We conducted experiments using ICU-STAY patients’ data from the MIMIC-III database and compared them with mainstream EMR time series processing methods. The results show that the method extracts visible and comprehensive features, alleviates data imbalance problems and improves the accuracy in specific predicting tasks.
Dual-Mode Resonant Sectorial Monopole Antenna with Stable Backfire Gain
JI Feiyan, ZHANG Heng, XING Xiuqiong, LU Wenjun, ZHU Lei
, Available online  , doi: 10.23919/cje.2023.00.032
Abstract(85) HTML (43) PDF(8)
A novel design approach to wideband, dual-mode resonant monopole antenna with stable, enhanced backfire gain is advanced. The sectorial monopole evolves from a linear, 0.75-wavelength electric prototype monopole under wideband dual-mode resonant operation. As theoretically predicted by the two resonant modes TE3/5,1 and TE9/5,1 within a 150° radiator, the operation principle is revealed at first. As have been numerically demonstrated and experimentally validated at 2.4-GHz band, the designed antenna exhibits a wide impedance bandwidth over 90.1%(i.e., 2.06-5.44 GHz), in which the stable gain bandwidth in the backfire, -$ x $-direction ($ \theta $ = 90°, $ \varphi $ = 180°) with peak value of 3.2 dBi and fluctuation less than 3 dB is up to 45.3% (i.e., 3.74-5.44 GHz). It is concluded that the stable wideband backfire gain frequency response should be owing to the high-order resonant mode in the unique sectorial monopole antennas.
Expression Complementary Disentanglement Network for Facial Expression Recognition
WANG Shanmin, SHUAI Hui, ZHU Lei, LIU Qingshan
, Available online  , doi: 10.23919/cje.2022.00.351
Abstract(157) HTML (78) PDF(19)
Disentangling facial expressions from other disturbing facial attributes in face images is an essential topic for facial expression recognition (FER). However, previous methods only care about facial expression disentanglement (FED) itself, ignoring the negative effects of other facial attributes. Moreover, due to the annotations on limited facial attributes, it is difficult for existing FED solutions to disentangle all disturbance from the input face. To solve this issue, we propose an Expression Complementary Disentanglement Network (ECDNet). On the one hand, ECDNet proposes to finish the FED task during a face reconstruction process, so as to address all facial attributes during disentanglement. Different from traditional reconstruction models, ECDNet reconstructs face images by progressively generating and combing facial appearance and matching geometry. On the other hand, ECDNet designs the Expression Incentive (EIE) and Expression inhibition (EIN) mechanisms, inducing the model to characterize the disentangled expression and complementary parts precisely. Specifically, facial geometry and appearance, generated in the reconstructed process, are dealt with to represent facial expressions and complementary parts, respectively. The combination of distinctive reconstruction model, EIE, and EIN mechanisms significantly ensures the completeness and exactness of the FED task. Experimental results on RAF-DB, AffectNet, and CAER-S datasets have proven the effectiveness and superiority of ECDNet.
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
Abstract(213) HTML (105) PDF(33)
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%.
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
Abstract(123) HTML (63) PDF(17)
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.
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
Abstract(139) HTML (67) PDF(19)
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
Abstract(662) HTML (328) PDF(107)
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
Abstract(162) HTML (83) PDF(22)
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.
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
Abstract(362) HTML (178) PDF(42)
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.
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(372) HTML (186) PDF(38)
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.
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
Abstract(1245) HTML (618) PDF(46)

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
Abstract(345) HTML (174) PDF(24)

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
Abstract(417) HTML (208) PDF(40)

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
Abstract(398) HTML (198) PDF(37)

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
Abstract(617) HTML (293) PDF(36)

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.

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
Abstract(266) HTML (131) PDF(37)
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.
A 5 mW 1-to-5 GHz Multiband Ladder CMOS Mixer Employing Transconductance Tuning Mechanism Achieving IIP3 of 27 dBm
, Available online  , doi: 10.23919/cje.2022.00.028
Abstract(191) HTML (97) PDF(14)
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, PDC 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.
Some Results on Optimal Ternary Cyclic Codes with Minimal Distance Four
LI Lanqiang, LIU Li
, Available online  , doi: 10.23919/cje.2022.00.317
Abstract(387) HTML (190) PDF(33)
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.