Chinese Journal of Electronics最新文献

Persistent-Fault Based Differential Analysis and Applications to Masking and Fault Countermeasures 基于持续故障的差分分析及其在屏蔽和故障对策中的应用

IF 1.6 4区计算机科学

Chinese Journal of Electronics Pub Date : 2025-03-01 DOI: 10.23919/cje.2023.00.381

Shihui Zheng;Shoujin Zang;Ruihao Xing;Jiayu Zhang;Changhai Ou

{"title":"Persistent-Fault Based Differential Analysis and Applications to Masking and Fault Countermeasures","authors":"Shihui Zheng;Shoujin Zang;Ruihao Xing;Jiayu Zhang;Changhai Ou","doi":"10.23919/cje.2023.00.381","DOIUrl":"https://doi.org/10.23919/cje.2023.00.381","url":null,"abstract":"A persistent fault analysis (PFA) can break implementations of the advanced encryption standard (AES) secured by fault attack countermeasures that prevent differential analyses based on transient faults (DFA). When the AES implementation is protected by some higher-order masking countermeasure, the number of required ciphertexts may increase exponentially with the growth of the number of shares. We present a persistent-fault-based differential analysis (PFDA) against AES implementations. Two error patterns are detected by ciphertext pairs. Namely, only one error occurs at a SubBytes operation in round 10, and only one error occurs at a SubBytes operation in round 9. The latter is used to derive a differential characteristic (DC) for the key recovery, and the former is explored to deduce the input difference of the DC. Thus, the computational complexity is reduced compared to DFA. Encrypting a fixed plaintext many times to tolerate errors is utilized in PFDA against RP countermeasures. The number of required encryptions increases linearly with the growth of the number of shares. The simulation results show that PFDA can break unprotected AES implementations and implementations secured by fault attack counter-measures or the above higher-order masking countermeasures. Compared to other analyses based on persistent fault, the required number of ciphertexts of PFDA is the lowest.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 2","pages":"548-562"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10982070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Type GNSS User Classification Using RANSAC-K-Means Clustering 基于RANSAC-K-Means聚类的多类型GNSS用户分类

IF 1.6 4区计算机科学

Chinese Journal of Electronics Pub Date : 2025-03-01 DOI: 10.23919/cje.2024.00.041

Kun Li;Shuailong Chen;Shuaiyong Zheng;Xuanwen Wang;Jixi Liu;Peng Yang;Mengzhi Gao;Xiaoqin Jin

{"title":"Multi-Type GNSS User Classification Using RANSAC-K-Means Clustering","authors":"Kun Li;Shuailong Chen;Shuaiyong Zheng;Xuanwen Wang;Jixi Liu;Peng Yang;Mengzhi Gao;Xiaoqin Jin","doi":"10.23919/cje.2024.00.041","DOIUrl":"https://doi.org/10.23919/cje.2024.00.041","url":null,"abstract":"The BeiDou Navigation Satellite System (BDS-3) has provided positioning, navigation and timing (PNT) services to global users across land, maritime, and aviation. However, how to classify these three types of users with complex movement patterns poses great challenges to the work of monitoring and evaluating the PNT system. To accurately classify multi-type global navigation satellite system (GNSS) users, this paper proposes a method that combines random sample consensus (RANSAC) and K-means clustering to track the movements of massive users and classify them based on their dynamic characteristics in different areas, which is noted as RANSAC-K-means. The simulated massive user data show that the recognition rate of the proposed algorithm exceeds 83.22%, and compared with the conventional method, the proposed RANSAC-K-means method improved the recognition rate by 11.16%. The RANSAC-K-means method can provide more accurate clustering results under the situations where multi-type users present dynamic characteristics with significant differences, showing significant stability and robustness. The proposed method is more suitable for monitoring and evaluating the service performance of satellite navigation systems.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 2","pages":"730-738"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10982099","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved YOLOv8 for High-Precision Detection of Rail Surface Defects on Heavy-Haul Railways 改进的YOLOv8用于重载铁路轨道表面缺陷的高精度检测

IF 1.6 4区计算机科学

Chinese Journal of Electronics Pub Date : 2025-03-01 DOI: 10.23919/cje.2024.00.200

Yuan Cao;Long Ma;Yongkui Sun;Feng Wang;Shuai Su

{"title":"Improved YOLOv8 for High-Precision Detection of Rail Surface Defects on Heavy-Haul Railways","authors":"Yuan Cao;Long Ma;Yongkui Sun;Feng Wang;Shuai Su","doi":"10.23919/cje.2024.00.200","DOIUrl":"https://doi.org/10.23919/cje.2024.00.200","url":null,"abstract":"The complex infrastructure and harsh conditions of heavy-haul railways result in frequently and rapidly deteriorating rail surface defects. Accurate detection of these defects is essential. To solve the problem of low detection precision caused by complex background interference, significant variation in defect scales, and similar features between different types of defects, a high-precision rail surface defect detection method for heavy-haul railways based on an improved YOLOv8 is proposed. First, the original grayscale images are preprocessed to reduce background noise interference. Then, the designed scale variation adaptation module is introduced to mitigate the impact of significant scale variations in the target defects. Additionally, a bidirectional feature pyramid network is incorporated to enhance feature fusion effectiveness. Furthermore, a small target detection head is introduced to improve the detection performance of small-scale defects. Lastly, network performance is optimized by replacing the original loss function with wise-intersection over union. Experimental results demonstrate that the improved model achieves a mean average precision at 50% intersection over union (mAP50) value of 0.975, representing a 4.13% improvement in precision and a 7.75% increase in recall compared to the baseline model. The improved model effectively detects typical defects such as spalling, shelling, and corrugation, providing valuable technical support for field maintenance personnel.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 3","pages":"802-815"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11060014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Characteristic Mode Based Multiport Antennas for MIMO Base Station Applications 基于特征模式的多端口天线在MIMO基站中的应用

IF 1.6 4区计算机科学

Chinese Journal of Electronics Pub Date : 2025-03-01 DOI: 10.23919/cje.2024.00.115

Xuan Deng;Yikai Chen;Shiwen Yang

{"title":"A Characteristic Mode Based Multiport Antennas for MIMO Base Station Applications","authors":"Xuan Deng;Yikai Chen;Shiwen Yang","doi":"10.23919/cje.2024.00.115","DOIUrl":"https://doi.org/10.23919/cje.2024.00.115","url":null,"abstract":"A novel multiport multi-input multi-output (MIMO) antenna for 4G/5G base station application is developed based on the theory of characteristic modes (TCM). The solved characteristic modes illustrate that it is possible to excite five modes simultaneously with a properly designed patch in the 1.7-2.7 GHz frequency band. The characteristic modes also provide clear guidelines for selecting feeding places and designing excitation for the characteristic modes. The natural orthogonality property of characteristic modes guarantees high isolations among the many ports in a MIMO antenna system. The developed MIMO antenna is comprised of two layers. The top layer is the radiating aperture, while the bottom layer is the feeding circuit. The working frequency band of the developed MIMO antenna is 1.64-2.74 GHz, which can be used in 4G Long-Term Evolution and sub-6 GHz 5G networks. By using TCM and orthogonality of characteristic modes, a compact four-port MIMO antenna is designed with at least 20 dB isolations. The four ports excite different shapes of radiation patterns with diverse polarizations and radiation patterns. The envelope correlation coefficient is extremely low between the four ports, which makes it attractive for MIMO diversity systems. Both simulated and measured results are presented to demonstrate the proposed design. In addition, the ergodic channel capacity is also evaluated to demonstrate the enhanced capability in communication system applications.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 3","pages":"755-765"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11060053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sparse Homogeneous Learning: A New Approach for Sparse Learning 稀疏同构学习：稀疏学习的一种新方法

IF 1.6 4区计算机科学

Chinese Journal of Electronics Pub Date : 2025-03-01 DOI: 10.23919/cje.2023.00.130

Jiajie Shi;Zhi Yang;Jiafeng Liu;Hongli Shi

{"title":"Sparse Homogeneous Learning: A New Approach for Sparse Learning","authors":"Jiajie Shi;Zhi Yang;Jiafeng Liu;Hongli Shi","doi":"10.23919/cje.2023.00.130","DOIUrl":"https://doi.org/10.23919/cje.2023.00.130","url":null,"abstract":"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.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 2","pages":"712-721"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10982098","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Combination Model of Periodic and Non-periodic Events for Temporal Knowledge Graph Event Prediction 时间知识图事件预测的周期与非周期事件组合模型

IF 1.6 4区计算机科学

Chinese Journal of Electronics Pub Date : 2025-03-01 DOI: 10.23919/cje.2024.00.182

Yue Chen;Yongzhong Huang

{"title":"A Combination Model of Periodic and Non-periodic Events for Temporal Knowledge Graph Event Prediction","authors":"Yue Chen;Yongzhong Huang","doi":"10.23919/cje.2024.00.182","DOIUrl":"https://doi.org/10.23919/cje.2024.00.182","url":null,"abstract":"Temporal knowledge graph (TKG) reasoning aims to predict missing facts or future events at given timestamps and has attracted more and more attention in recent years. Existing TKG reasoning methods mainly focus on the interactions between entities and ignore the associations between events where the entities involve. In addition, the characteristics of different types of events have not been studied and exploited, which reduces the performance of event prediction. To address these problems, this paper proposes a combination model of periodic and non-periodic events (CM-PNP). Specifically, there are two basic components designed to process different types of events. The periodic component of CM-PNP learns the recurrent pattern of periodic events and encodes the temporal information in the manner of timespan to prevent the unseen timestamp issue. The non-periodic component of CM-PNP introduces extra information (e.g., entity attributes) to represent non-periodic events, and predicts this type of events according to the related historical events. A combination model of multiple sub-models that focus on encoding different parts of the event is used to improve the performance of single model. The periodic and non-periodic components are combined by a gate block. The experimental results on three real-world datasets demonstrate that CM-PNP outperforms the existing baselines.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 3","pages":"952-961"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11060050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DCUGAN: Dual Contrastive Learning GAN for Unsupervised Underwater Image Enhancement 用于无监督水下图像增强的双对比学习GAN

IF 1.6 4区计算机科学

Chinese Journal of Electronics Pub Date : 2025-03-01 DOI: 10.23919/cje.2023.00.257

Baodi Liu;Jing Tian;Zhenlong Wang;Weifeng Liu;Xinan Yuan;Wei Li

{"title":"DCUGAN: Dual Contrastive Learning GAN for Unsupervised Underwater Image Enhancement","authors":"Baodi Liu;Jing Tian;Zhenlong Wang;Weifeng Liu;Xinan Yuan;Wei Li","doi":"10.23919/cje.2023.00.257","DOIUrl":"https://doi.org/10.23919/cje.2023.00.257","url":null,"abstract":"Most existing deep learning-based underwater image enhancement methods rely heavily on synthetic paired underwater images, which limits their practicality and generalization. Unsupervised underwater image enhancement methods can be trained on unpaired data, overcoming the reliance on paired data. However, existing unsupervised methods suffer from poor color correction capability, artifacts, and blurry details in the generated images. Therefore, this paper proposes a dual generative adversarial network (GAN) with contrastive learning constraints to achieve unsupervised underwater image enhancement. Firstly, we construct a dual GAN network for image transformation. Secondly, we utilize patch-based learning to maximize the mutual information between inputs and outputs, eliminating the reliance on paired data. Thirdly, we use image gradient difference loss to mitigate artifacts in the generated images. Lastly, to address the problem of blurry details, we incorporate channel attention in the generator network to focus on more important content and improve the quality of the generated images. Extensive experiments demonstrate that the enhanced results of our method show amelioration in visual quality.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 3","pages":"906-916"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11060021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AOYOLO Algorithm Oriented Vehicle and Pedestrian Detection in Foggy Weather 面向AOYOLO算法的雾天车辆和行人检测

IF 1.6 4区计算机科学

Chinese Journal of Electronics Pub Date : 2025-03-01 DOI: 10.23919/cje.2023.00.280

Jian Su;Shiang Mao;Wei Zhuang

{"title":"AOYOLO Algorithm Oriented Vehicle and Pedestrian Detection in Foggy Weather","authors":"Jian Su;Shiang Mao;Wei Zhuang","doi":"10.23919/cje.2023.00.280","DOIUrl":"https://doi.org/10.23919/cje.2023.00.280","url":null,"abstract":"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 all-in-one dehazing network, 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: the annotated real-world task-driven testing set (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.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 2","pages":"661-672"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10982094","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TE101 Substrate Integrated Waveguide Filter with Wide Stopband Up to TE10(2n-l) and Coplanar Ports TE101基片集成波导滤波器，宽阻带高达TE10（2n- 1）和共面端口

IF 1.6 4区计算机科学

Chinese Journal of Electronics Pub Date : 2025-03-01 DOI: 10.23919/cje.2023.00.225

Peng Chu;Jianguo Feng;Lei Guo;Fang Zhu;Weibin Kong;Leilei Liu;Guoqing Luo;Ke Wu

引用次数: 0

Development of GCC Optimizations to Speed up CPUBench Integer Benchmarks on ARMv8.2 在ARMv8.2上开发GCC优化以加速CPUBench整数基准测试

IF 1.6 4区计算机科学

Chinese Journal of Electronics Pub Date : 2025-03-01 DOI: 10.23919/cje.2024.00.105

Viacheslav Chernonog;Andrey Dobrov;Ilia Diachkov;Alexander Pronin;Egor Melnichenko;Emin Gadzhiev

引用次数: 0