Chinese Journal of Electronics最新文献_第2页

3DCFAR-Net: A Coherent Accumulation Network for Target Information Mining on Millimeter-Wave Radar 3DCFAR-Net：毫米波雷达目标信息挖掘的相干积累网络

IF 3 4区计算机科学

Chinese Journal of Electronics Pub Date : 2026-01-01 Epub Date: 2026-04-13 DOI: 10.23919/cje.2024.00.026

Jiang Liu;Yunxuan Wang;Yan Huang;Lvhongkang Lan;Kaihuang Zheng;Hui Zhang;Wei Hong

引用次数: 0

Robust and Economic Digital Rights Management Solution Based on White-Box Encryption Scheme 基于白盒加密方案的鲁棒经济数字版权管理方案

IF 3 4区计算机科学

Chinese Journal of Electronics Pub Date : 2026-01-01 Epub Date: 2026-04-13 DOI: 10.23919/cje.2025.00.091

Jun Liu;Dachao Wang;Feng Zhu;Bo Yang

{"title":"Robust and Economic Digital Rights Management Solution Based on White-Box Encryption Scheme","authors":"Jun Liu;Dachao Wang;Feng Zhu;Bo Yang","doi":"10.23919/cje.2025.00.091","DOIUrl":"https://doi.org/10.23919/cje.2025.00.091","url":null,"abstract":"Digital rights management (DRM) is crucial in safeguarding digital content in today's digital era. However, with the advancement of attack techniques, DRM systems face significant threats from key exposure, leading to increased piracy. To counter this, various DRM solutions utilizing white-box encryption schemes have been developed to prevent key exposure. Unfortunately, many existing solutions exhibit security vulnerabilities, compromising their robustness. Other solutions, while secure, suffer from limited efficiency and high memory costs, rendering them unsuitable for managing the vast amounts of digital content prevalent today. In this paper, we propose a robust and cost-effective DRM solution based on a novel white-box encryption scheme. Our theoretical analysis demonstrates that the proposed solution effectively resists key recovery attacks on the server side, as well as key extraction and code lifting attacks on the client side. Experimental validations further reveal that our solution surpasses existing methods in processing efficiency and memory usage. Notably, decryption achieves speed-ups of up to 831 times and 400 times on Intel central processing units compared to SPACE-32 (CCS'15) and HOUND-32 (ASIACRYPT'16), respectively.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"35 1","pages":"293-306"},"PeriodicalIF":3.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11480047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665544","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

Volume 34 (2025) Index 第34卷（2025）索引

IF 3 4区计算机科学

Chinese Journal of Electronics Pub Date : 2025-11-01 DOI: 10.23919/CJE.2025.11322816

引用次数: 0

Egyptian Mosquito Optimization Algorithm: A Novel Swarm-Based Metaheuristic Algorithm to Solve Electronic and Industrial Problems 埃及蚊子优化算法：一种新的基于群体的元启发式算法，用于解决电子和工业问题

IF 3 4区计算机科学

Chinese Journal of Electronics Pub Date : 2025-11-01 DOI: 10.23919/cje.2024.00.223

Ruiyang Sun;Wenjuan Cheng;Xiao Liang;Hao Tang;Yan Xiong

引用次数: 0

WaveTimes: Short-Term Load Forecast Based on Wavelet Decomposition and Improved TimesNet 波时：基于小波分解和改进时间网的短期负荷预测

IF 3 4区计算机科学

Chinese Journal of Electronics Pub Date : 2025-11-01 DOI: 10.23919/cje.2024.00.212

Rongheng Lin;Shuo Chen;Budan Wu;Xin Zhao;Qiushuang Li

{"title":"WaveTimes: Short-Term Load Forecast Based on Wavelet Decomposition and Improved TimesNet","authors":"Rongheng Lin;Shuo Chen;Budan Wu;Xin Zhao;Qiushuang Li","doi":"10.23919/cje.2024.00.212","DOIUrl":"https://doi.org/10.23919/cje.2024.00.212","url":null,"abstract":"Short-term load forecasting (STLF) is an essential component of smart grids, enabling power departments to anticipate grid operations in advance, thereby controlling electricity usage in a timely manner, allocating power resources reasonably, and ensuring the quality and reliability of grid services. In the field of electric load forecasting, existing prediction models perform poorly on load data with frequent fluctuations and have issues with prediction lag. To address these issues, this paper proposes an improved TimesNet model based on wavelet decomposition, called WaveTimes. Firstly, the model uses wavelet decomposition to decompose load data, obtaining sequences with smaller mutation amplitudes and weaker autocorrelation. Secondly, a feature extraction module is introduced, which uses Fourier transformation and computer vision models for periodic analysis, effectively capturing periodic changes in load data across different frequency domains. Finally, a residual connection and prediction module are used to complete the forecasting task. This paper evaluates the proposed method on public datasets and load data from a certain province in China, showing significant improvements compared to the baseline. The model can more effectively support relevant departments in load control and demand response, contributing to the construction of smart grids.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 6","pages":"1911-1923"},"PeriodicalIF":3.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11322771","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886549","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

Inequality Methods and Global Exponential Stability for Higher-Order Neural Networks with Time-Varying Leakage Delays and S-Type Distributed Delays 具有时变泄漏时滞和s型分布时滞的高阶神经网络不等式方法及全局指数稳定性

IF 3 4区计算机科学

Chinese Journal of Electronics Pub Date : 2025-09-01 DOI: 10.23919/cje.2024.00.250

Puchen Liu;Ming Yang;Qiankun Li

{"title":"Inequality Methods and Global Exponential Stability for Higher-Order Neural Networks with Time-Varying Leakage Delays and S-Type Distributed Delays","authors":"Puchen Liu;Ming Yang;Qiankun Li","doi":"10.23919/cje.2024.00.250","DOIUrl":"https://doi.org/10.23919/cje.2024.00.250","url":null,"abstract":"The higher-order neural network system with both leakage delays and S-type distributed transmission delays is introduced. By means of topological degree theory, properties of M-matrices and inequality tricks, the exis-tence of the equilibrium point for the system is deduced. In existing work, authors mainly employed the Lyapunov functional or multiple integral inequalities or matrix inequalities, which usually required more computations. On the other hand, in treating the infiniteness of the distributed delays, most scholars had explored the intercept method or the generalized Halanay differential inequality. Instead of constructing complicated Lyapunov functionals, the principle of reductio ad absurdum and simple algebra inequality strategy are explored to prove the global exponential stability of the system, which has largely reduced the computational complexity. Finally, two examples, their simulations and the related remark are demonstrated for illustrating the effectiveness and generality of the theoretical results. Our model is more general and the sufficient results are easily verifiable and have wider adaptiveness.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 5","pages":"1652-1660"},"PeriodicalIF":3.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11232555","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145456079","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

Path Planning for Unmanned Aerial Vehicle Swarm Based on Electromagnetic Environment Sensing 基于电磁环境感知的无人机群路径规划

IF 3 4区计算机科学

Chinese Journal of Electronics Pub Date : 2025-07-01 DOI: 10.23919/cje.2024.00.088

Tong Li;Zhuangzhuang Ma;Jinliang Shao;Yuan Zhao;Xilin Zhang;Yuhua Cheng

{"title":"Path Planning for Unmanned Aerial Vehicle Swarm Based on Electromagnetic Environment Sensing","authors":"Tong Li;Zhuangzhuang Ma;Jinliang Shao;Yuan Zhao;Xilin Zhang;Yuhua Cheng","doi":"10.23919/cje.2024.00.088","DOIUrl":"https://doi.org/10.23919/cje.2024.00.088","url":null,"abstract":"Unmanned aerial vehicle (UAV) swarm is widely used in tasks such as post-disaster rescue and battle-field monitoring. These tasks are often executed in unknown or complex environments, necessitating the programming of safe and efficient paths for UAV swarm to ensure the completion of missions. To address the path planning problem for UAV swarm in unknown electromagnetic environment, we propose a multi-agent deep deterministic policy gradient algorithm based on environment sensing where a safe learning mechanism is designed by using control barrier function. Additionally, a weakly supervised learning-based generative adversarial network algorithm is employed to construct an electromagnetic environment sensing module. By using the algrothim we propose, UAV swarm can avoid zones with strong electromagnetic interference and guarantee inter-UAV collisions avoidance during task execution. Compared to reinforcement learning algorithm without environment sensing module and safe learning mechanism, the algorithm we propose reduces convergence time by approximately 2.5 times. Simultaneously, it prevents individual trial-and-error learning process from violating safety constraints, ensuring the safety of UAV swarm in unknown environment. Finally, we verified the effectiveness of our algorithm on the experimental platform which is constructed by using universal software radio peripherals and quadcopter UAVs.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 4","pages":"1156-1171"},"PeriodicalIF":3.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11151183","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990278","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

Mode Composite Coplanar Waveguide Duplexing Power Divider with Large Frequency Ratio for 5G Multiband System Application 5G多波段系统大频率比模复合共面波导双工功率分配器

IF 3 4区计算机科学

Chinese Journal of Electronics Pub Date : 2025-07-01 DOI: 10.23919/cje.2024.00.085

Yihong Su;Mingtang Li;Xianqi Lin;Yong Fan

{"title":"Mode Composite Coplanar Waveguide Duplexing Power Divider with Large Frequency Ratio for 5G Multiband System Application","authors":"Yihong Su;Mingtang Li;Xianqi Lin;Yong Fan","doi":"10.23919/cje.2024.00.085","DOIUrl":"https://doi.org/10.23919/cje.2024.00.085","url":null,"abstract":"This paper proposes a duplexing power divider (DPD) with a large frequency ratio based on the mode composite coplanar waveguide (MCCPW). The DPD is a five-port network circuit. It integrates the function of the duplexer and power divider within a compact size. The overall DPD can be regarded as a combination of two filtering power dividers and a frequency splitter. The low-band channel circuit is a filtering power divider with harmonic suppression, the bandwidth ranges from 2 GHz to 6 GHz, covering some of the typical frequencies (2.4 GHz, 3.5 GHz, and 5.8 GHz) in sub-6 GHz. The high-frequency band filtering power divider works in substrate integrated waveguide (SIW)-TE<inf>10</inf> mode. The bandwidth ranges from 27 GHz to 29 GHz targeting 5G millimeter wave application. The two bands of the duplexer can be designed independently with very high freedom due to the mode separation used in the MCCPW. The DPD can be fabricated using a single PCB process and assembled by the fixing screws. The measured results of the DPD have good agreement with its simulation. The MCCPW-based DPD has the benefits of multi-function, large frequency ratio, high design freedom, high isolation, and high mid-band suppression at the same time with compact size. It is a promising candidate in the multiband system with large frequency ratios such as 5G.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 4","pages":"1064-1077"},"PeriodicalIF":3.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11151246","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989945","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

Joint Activity and Data Detection for Asynchronous Grant-Free Access in NTN NTN中异步无授权访问的联合活动和数据检测

IF 3 4区计算机科学

Chinese Journal of Electronics Pub Date : 2025-07-01 DOI: 10.23919/cje.2024.00.317

Jiaqi Huang;Lixia Xiao;Shuo Li;Jiaxi Zhou;Tao Jiang

引用次数: 0

A Time-Frequency Representation Method Based on ETF-MDNet for Radar Target Micro-Motion Features 基于ETF-MDNet的雷达目标微运动特征时频表示方法

IF 3 4区计算机科学

Chinese Journal of Electronics Pub Date : 2025-07-01 DOI: 10.23919/cje.2024.00.233

Jinhao Wang;Xiaolong Chen;Jian Guan;Ningyuan Su;Wang Yuan

{"title":"A Time-Frequency Representation Method Based on ETF-MDNet for Radar Target Micro-Motion Features","authors":"Jinhao Wang;Xiaolong Chen;Jian Guan;Ningyuan Su;Wang Yuan","doi":"10.23919/cje.2024.00.233","DOIUrl":"https://doi.org/10.23919/cje.2024.00.233","url":null,"abstract":"This paper proposes a deep learning-based time-frequency representation approach that employs the enhanced time-frequency micro-Doppler network (ETF-MDNet) model to improve the characterization of micro-Doppler features for radar targets, particularly “low, slow, and small” ones. The ETF-MDNet model consists of four key components: the micro-Doppler target signal input module, the basis function selection module, the feature aggregation module, and the energy concentration module. A notable characteristic of this method is its utilization of the inherent adaptive learning capabilities of deep learning, which are combined with an attention mechanism to enhance the aggregation of time-frequency energy. This integration optimizes the method's capacity to represent micro-motion features across both channel and spatial dimensions. Consequently, this approach effectively captures the micro-motion information of the target while suppressing extraneous noise. In comparison to traditional short time Fourier transform, generalized warblet and reassigned spectrogram analysis methods, the proposed method achieves an average enhancement of 31.5% in time-frequency energy concentration, higher time-frequency energy aggregation, and the ability to reveal micro-motion feature details not captured by traditional methods.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 4","pages":"1199-1208"},"PeriodicalIF":3.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11151189","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990276","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