Pub Date : 2025-03-01DOI: 10.23919/cje.2024.00.050
Yang Yang;Zhen Wang;Daniyal M Alghazzawi;Li Cheng;Gaoyang Liu;Chen Wang;Cheng Zeng;Yuying Li
In the digital era, escalating concerns over personal privacy and social security have led to the advocacy for the “right to be forgotten”, a principle that empowers individuals to request the deletion of their personal data from online platforms. Consequently, machine unlearning (MU) has been proposed as a method for targeted data deletion within machine learning models. However, MU encounters difficulties in distributed learning environments, such as federated learning (FL), where direct access to data is restricted. Federated unlearning (FU) has been developed in response, aiming to facilitate the process of data deletion requests from clients within FL frameworks. Despite advancements, FU methods based on approximate unlearning present a risk of potential data breaches, while methods reliant on retraining necessitate either complete or repeated retraining of clients, which is inefficient. Addressing these challenges, we introduce the federated cluster slicing algorithm (FedCSA), a novel FU strategy that achieves precision and efficiency in data unlearning. FedCSA organizes clients into distinct slices based on model deviation values, facilitating targeted retraining of local models upon unlearning requests. This method not only ensures consistency in the independent and identically distributed degree across slices but also improves unlearning efficiency and maintains global model accuracy. Moreover, FedCSA features an adaptive clustering mechanism that autonomously determines the optimal number of slices, optimizing the unlearning process. Our empirical analysis, conducted across the MNIST, Fashion-MNIST, and CIFAR-10 datasets, underscores FedCSA's superior performance. FedCSA exhibits a fourfold increase in unlearning efficiency compared to traditional retraining methods. Furthermore, when juxtaposed with the sharded, isolated, sliced, and aggregated technique, FedCSA demonstrates a 4%–5% enhancement in global model accuracy. These findings corroborate the efficacy of FedCSA.
{"title":"FedCSA: Enhancing Federated Unlearning Efficiency Through Adaptive Clustering Under Data Heterogeneity","authors":"Yang Yang;Zhen Wang;Daniyal M Alghazzawi;Li Cheng;Gaoyang Liu;Chen Wang;Cheng Zeng;Yuying Li","doi":"10.23919/cje.2024.00.050","DOIUrl":"https://doi.org/10.23919/cje.2024.00.050","url":null,"abstract":"In the digital era, escalating concerns over personal privacy and social security have led to the advocacy for the “right to be forgotten”, a principle that empowers individuals to request the deletion of their personal data from online platforms. Consequently, machine unlearning (MU) has been proposed as a method for targeted data deletion within machine learning models. However, MU encounters difficulties in distributed learning environments, such as federated learning (FL), where direct access to data is restricted. Federated unlearning (FU) has been developed in response, aiming to facilitate the process of data deletion requests from clients within FL frameworks. Despite advancements, FU methods based on approximate unlearning present a risk of potential data breaches, while methods reliant on retraining necessitate either complete or repeated retraining of clients, which is inefficient. Addressing these challenges, we introduce the federated cluster slicing algorithm (FedCSA), a novel FU strategy that achieves precision and efficiency in data unlearning. FedCSA organizes clients into distinct slices based on model deviation values, facilitating targeted retraining of local models upon unlearning requests. This method not only ensures consistency in the independent and identically distributed degree across slices but also improves unlearning efficiency and maintains global model accuracy. Moreover, FedCSA features an adaptive clustering mechanism that autonomously determines the optimal number of slices, optimizing the unlearning process. Our empirical analysis, conducted across the MNIST, Fashion-MNIST, and CIFAR-10 datasets, underscores FedCSA's superior performance. FedCSA exhibits a fourfold increase in unlearning efficiency compared to traditional retraining methods. Furthermore, when juxtaposed with the sharded, isolated, sliced, and aggregated technique, FedCSA demonstrates a 4%–5% enhancement in global model accuracy. These findings corroborate the efficacy of FedCSA.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 3","pages":"970-979"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11060051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519222","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}
Pub Date : 2025-03-01DOI: 10.23919/cje.2024.00.109
Yihong Su;Zuxing Wu;Yulei Yang;Xianqi Lin;Xinlian Liang;Yong Fan
In this paper, a theory formulated by S-parameter analysis is proposed for dual-band leaky-wave antenna (LWA) featuring open stopband (OSB) suppression and high-aperture efficiency at two different bands. For continuous scanning, this theory is used for quantitative analysis of unit cells, leading to OSB suppression. To achieve high aperture efficiency, S-parameter analysis is set to help modulate the leakage rate to realize balanced radiation in dual frequency bands. Several types of unit cells and their LWA are studied as a demonstration of the design principle. A dual-band LWA with a shared aperture based on mode composite ridged waveguide (MCRW) is derivated as an example to validate the S-parameter analysis. The MCRW is a sort of mode composite structure made of substrate-integrated waveguide (SIW) and substrate-integrated ridged waveguide (SIRW). Through the S-parameter analysis and the use of MCRW, the proposed dual-band aperture-shared LWA has shown numerous advantages such as compact size, high channel isolation, large continuous scanning angle, high aperture and radiation efficiency, and improved gain performance. The measured results agree well with the simulation counterparts, which verifies the effectiveness of the S-parameter analysis method.
{"title":"Theory and Demonstration of High Aperture Efficiency Dual-Band Leaky-Wave Antenna with Open Stopband Suppression","authors":"Yihong Su;Zuxing Wu;Yulei Yang;Xianqi Lin;Xinlian Liang;Yong Fan","doi":"10.23919/cje.2024.00.109","DOIUrl":"https://doi.org/10.23919/cje.2024.00.109","url":null,"abstract":"In this paper, a theory formulated by S-parameter analysis is proposed for dual-band leaky-wave antenna (LWA) featuring open stopband (OSB) suppression and high-aperture efficiency at two different bands. For continuous scanning, this theory is used for quantitative analysis of unit cells, leading to OSB suppression. To achieve high aperture efficiency, S-parameter analysis is set to help modulate the leakage rate to realize balanced radiation in dual frequency bands. Several types of unit cells and their LWA are studied as a demonstration of the design principle. A dual-band LWA with a shared aperture based on mode composite ridged waveguide (MCRW) is derivated as an example to validate the S-parameter analysis. The MCRW is a sort of mode composite structure made of substrate-integrated waveguide (SIW) and substrate-integrated ridged waveguide (SIRW). Through the S-parameter analysis and the use of MCRW, the proposed dual-band aperture-shared LWA has shown numerous advantages such as compact size, high channel isolation, large continuous scanning angle, high aperture and radiation efficiency, and improved gain performance. The measured results agree well with the simulation counterparts, which verifies the effectiveness of the S-parameter analysis method.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 3","pages":"937-951"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11060055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519309","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}
Pub Date : 2025-03-01DOI: 10.23919/cje.2023.00.417
Jundong Chen;Honglei Zhang;Haoxuan Li;Yidong Li
Learning recommender models from vast amounts of behavioral data has become a mainstream paradigm in recent information systems. Conversely, with privacy awareness grown, there has been increasing attention to the removal of sensitive or outlier data from well-trained recommendation models (known as recommendation unlearning). However, current unlearning methods primarily focus on fully/partially retraining the entire model. Despite considerable performance, it inevitably introduces significant efficiency bottlenecks, which is impractical for latency-sensitive streaming services. While recent efforts exploit efficient unlearning in point-wise recommender tasks, these approaches overlook the partial order relationships between items, resulting in suboptimal performance in both recommendation and unlearning capabilities. In light of this, we explore learning to unlearn for Bayesian personalized ranking via influence function, which relies on a pair-wise ranking loss to model user preferences and item characteristics, making unlearning more challenging than in point-wise settings. Specifically, we propose an influence function-guided unlearning framework tailored for pair-wise ranking models to efficiently perform unlearning requests, which involves unlearning partial order relationships while handling negative samples appropriately during the unlearning process. Besides, we prove that our proposed method can theoretically match the performance of retraining counter-parts. Finally, we conduct extensive experiments to validate the effectiveness and efficiency of our model.
{"title":"Learning to Unlearn for Bayesian Personalized Ranking via Influence Function","authors":"Jundong Chen;Honglei Zhang;Haoxuan Li;Yidong Li","doi":"10.23919/cje.2023.00.417","DOIUrl":"https://doi.org/10.23919/cje.2023.00.417","url":null,"abstract":"Learning recommender models from vast amounts of behavioral data has become a mainstream paradigm in recent information systems. Conversely, with privacy awareness grown, there has been increasing attention to the removal of sensitive or outlier data from well-trained recommendation models (known as recommendation unlearning). However, current unlearning methods primarily focus on fully/partially retraining the entire model. Despite considerable performance, it inevitably introduces significant efficiency bottlenecks, which is impractical for latency-sensitive streaming services. While recent efforts exploit efficient unlearning in point-wise recommender tasks, these approaches overlook the partial order relationships between items, resulting in suboptimal performance in both recommendation and unlearning capabilities. In light of this, we explore learning to unlearn for Bayesian personalized ranking via influence function, which relies on a pair-wise ranking loss to model user preferences and item characteristics, making unlearning more challenging than in point-wise settings. Specifically, we propose an influence function-guided unlearning framework tailored for pair-wise ranking models to efficiently perform unlearning requests, which involves unlearning partial order relationships while handling negative samples appropriately during the unlearning process. Besides, we prove that our proposed method can theoretically match the performance of retraining counter-parts. Finally, we conduct extensive experiments to validate the effectiveness and efficiency of our model.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 3","pages":"990-1001"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11060023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519342","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}
Terahertz communication is anticipated to play a pivotal role in applications like super-capacity data retrieval, ultra-high-speed short-distance transmission, holographic communication, and micro-sized communication. Emerging scenarios such as the sixth generation (6G), integrated sensing and communication, the metaverse, and autonomous agent networking are also poised to benefit. Additionally, it promises high-precision positioning and high-resolution perceptual imaging for networks and terminal devices. This paper provides a comprehensive overview of the current performance, developmental trends, and measurement techniques associated with solid-state terahertz circuits and communication systems. Regarding circuits, the research and development of single-function circuits in the terahertz band have reached maturity. Traditional single-function circuits continue to evolve towards higher frequency bands (exceeding 1 THz), with reduced loss and improved efficiency. Concurrently, building upon these traditional circuits, researchers have introduced innovative integrated circuit designs and layout techniques to minimize system volume. Solid-state terahertz communication systems are also progressing towards elevated carrier frequencies, faster communication rates, phased arrays, and full-duplex communication. Through collaborative efforts, the global academic and industrial communities are intensifying their focus on terahertz key technologies and prototype system validation, aiming to bolster industrial growth and ecosystem development.
{"title":"Solid-State Terahertz Circuits for 6G: A Review","authors":"Zhongqian Niu;Bo Zhang;Yihan Zhang;Yinian Feng;Zhi Chen;Yihong Su;Yong Fan;Yongxin Guo","doi":"10.23919/cje.2023.00.279","DOIUrl":"https://doi.org/10.23919/cje.2023.00.279","url":null,"abstract":"Terahertz communication is anticipated to play a pivotal role in applications like super-capacity data retrieval, ultra-high-speed short-distance transmission, holographic communication, and micro-sized communication. Emerging scenarios such as the sixth generation (6G), integrated sensing and communication, the metaverse, and autonomous agent networking are also poised to benefit. Additionally, it promises high-precision positioning and high-resolution perceptual imaging for networks and terminal devices. This paper provides a comprehensive overview of the current performance, developmental trends, and measurement techniques associated with solid-state terahertz circuits and communication systems. Regarding circuits, the research and development of single-function circuits in the terahertz band have reached maturity. Traditional single-function circuits continue to evolve towards higher frequency bands (exceeding 1 THz), with reduced loss and improved efficiency. Concurrently, building upon these traditional circuits, researchers have introduced innovative integrated circuit designs and layout techniques to minimize system volume. Solid-state terahertz communication systems are also progressing towards elevated carrier frequencies, faster communication rates, phased arrays, and full-duplex communication. Through collaborative efforts, the global academic and industrial communities are intensifying their focus on terahertz key technologies and prototype system validation, aiming to bolster industrial growth and ecosystem development.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 2","pages":"373-400"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10982497","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900630","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}
Pub Date : 2025-03-01DOI: 10.23919/cje.2023.00.322
Shuai Wang;Aimin Zhou;Yi Zhang
In recent years, multiobjective differential evolution (DE) algorithms have gained significant attention due to their effective search capabilities for multiobjective optimization problems. The differential mutations of DE operators distinguish them from other generators. However, the efficiency of DE operators heavily relies on the selection of parents used to generate differential perturbation vectors. To address this challenge, this work proposes a novel algorithm, called perturbation estimation strategy based DE algorithm (PESDE), for multiobjective optimization. In PESDE, at each iteration, it utilizes a clustering approach to partition the population, and then constructs a probability model to estimate the distributions of differential perturbation vectors of the solutions within a cluster. Specifically, the differential perturbation vectors of solutions are regarded as trial points in building a probability model in the proposed approach. In this way, perturbation vectors are sampled from the built probability model, and then embedded in the solutions to generate new trial solutions. Empirical experimental studies are conducted to investigate the performance of PESDE by comparing it with five representative multiobjective evolutionary algorithms on several test instances with complicated Pareto set and front shapes. The results demonstrated the advantages of the proposed algorithm over other approaches.
{"title":"Differential Evolution with Perturbation Estimation Strategy for Multiobjective Optimization","authors":"Shuai Wang;Aimin Zhou;Yi Zhang","doi":"10.23919/cje.2023.00.322","DOIUrl":"https://doi.org/10.23919/cje.2023.00.322","url":null,"abstract":"In recent years, multiobjective differential evolution (DE) algorithms have gained significant attention due to their effective search capabilities for multiobjective optimization problems. The differential mutations of DE operators distinguish them from other generators. However, the efficiency of DE operators heavily relies on the selection of parents used to generate differential perturbation vectors. To address this challenge, this work proposes a novel algorithm, called perturbation estimation strategy based DE algorithm (PESDE), for multiobjective optimization. In PESDE, at each iteration, it utilizes a clustering approach to partition the population, and then constructs a probability model to estimate the distributions of differential perturbation vectors of the solutions within a cluster. Specifically, the differential perturbation vectors of solutions are regarded as trial points in building a probability model in the proposed approach. In this way, perturbation vectors are sampled from the built probability model, and then embedded in the solutions to generate new trial solutions. Empirical experimental studies are conducted to investigate the performance of PESDE by comparing it with five representative multiobjective evolutionary algorithms on several test instances with complicated Pareto set and front shapes. The results demonstrated the advantages of the proposed algorithm over other approaches.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 3","pages":"871-880"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11060013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519336","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}
Pub Date : 2025-03-01DOI: 10.23919/cje.2024.00.090
Junfeng Zhao;Shen Wang;Fanghui Sun
Adversarial image steganography can fool the targeted convolutional neural network (CNN)-based steg-analyzers, thereby improving the security performance. Despite the fact that existing works have achieved great success, there are still some limitations that make it difficult to exploit their potentiality, including the issue that selecting a final stego from the candidate stegos cannot perfectly help them fool the targeted steganalyzers. Since the trade-off between gradient and embedding cost has not been thoroughly investigated, this may simplify the design of more effective methods. In this article, we design a new model to score each image element in a cover by utilizing this trade-off, and a saliency map is constructed to represent the scores of the image. Based on the above, a simple and efficient scheme called SAL is presented. It selects the elements from the map according to the amplitudes of the scores, and their costs are updated based on the signs of the corresponding gradients. Finally, data embedding is accomplished with the new costs to generate an adversarial stego. Extensive experiments illustrate that SAL can achieve better security performance than state-of-the-art methods under different targeted CNN-based steganalyzers in both spatial and JPEG domains.
{"title":"Saliency Map Construction for Adversarial Image Steganography","authors":"Junfeng Zhao;Shen Wang;Fanghui Sun","doi":"10.23919/cje.2024.00.090","DOIUrl":"https://doi.org/10.23919/cje.2024.00.090","url":null,"abstract":"Adversarial image steganography can fool the targeted convolutional neural network (CNN)-based steg-analyzers, thereby improving the security performance. Despite the fact that existing works have achieved great success, there are still some limitations that make it difficult to exploit their potentiality, including the issue that selecting a final stego from the candidate stegos cannot perfectly help them fool the targeted steganalyzers. Since the trade-off between gradient and embedding cost has not been thoroughly investigated, this may simplify the design of more effective methods. In this article, we design a new model to score each image element in a cover by utilizing this trade-off, and a saliency map is constructed to represent the scores of the image. Based on the above, a simple and efficient scheme called SAL is presented. It selects the elements from the map according to the amplitudes of the scores, and their costs are updated based on the signs of the corresponding gradients. Finally, data embedding is accomplished with the new costs to generate an adversarial stego. Extensive experiments illustrate that SAL can achieve better security performance than state-of-the-art methods under different targeted CNN-based steganalyzers in both spatial and JPEG domains.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 3","pages":"816-827"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11060012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519348","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}
Pub Date : 2025-03-01DOI: 10.23919/cje.2023.00.424
Ilyas Bayanbayev;Hongjian Shi;Ruhui Ma
Dear Editor, Federated learning (FL) has emerged as a pivotal approach in distributed machine learning, allowing models to be trained across decentralized data sources while maintaining privacy [1], [2]. However, FL faces significant challenges, particularly in balancing personalization, privacy, and computational efficiency, especially when deployed in heterogeneous environments with varied client capabilities [3]. To address these challenges, we introduce FedBW, a novel framework that integrates FL with blockwise knowledge distillation.
{"title":"Balancing Efficiency and Personalization in Federated Learning via Blockwise Knowledge Distillation","authors":"Ilyas Bayanbayev;Hongjian Shi;Ruhui Ma","doi":"10.23919/cje.2023.00.424","DOIUrl":"https://doi.org/10.23919/cje.2023.00.424","url":null,"abstract":"Dear Editor, Federated learning (FL) has emerged as a pivotal approach in distributed machine learning, allowing models to be trained across decentralized data sources while maintaining privacy [1], [2]. However, FL faces significant challenges, particularly in balancing personalization, privacy, and computational efficiency, especially when deployed in heterogeneous environments with varied client capabilities [3]. To address these challenges, we introduce FedBW, a novel framework that integrates FL with blockwise knowledge distillation.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 3","pages":"1006-1008"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11060024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519447","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}
Aiming at the problems of the communication inefficiency and high energy consumption in vehicular networks, the platoon service recommendation systems (PSRS) are presented. Many schemes for evaluating the reputation of platoon head vehicles have been proposed to obtain and recommend reliable platoon head vehicles. These trustworthiness evaluation protocols for PSRS fail to achieve both reliability and fairness. We first provide a reliable trustworthiness evaluation method to ensure that the reputation level of platoon head vehicle can be calculated by cloud service provider (CSP) with the help of key agreement mechanism and truth discovery technology. The semi-trusted entity CSP may maliciously tamper with the reputation level of the platoon head vehicle. We also provide a reputation level confirmation method to ensure the fairness of trustworthiness evaluation. Formal security proof and security analysis are provided to show that our trustworthiness evaluation protocol can achieve the goals of privacy protection, reliability, fairness and resistance to several security attacks. Experiments demonstrate that this protocol can save execution time and achieve reliable and fair trustworthiness evaluation for PSRS.
{"title":"Reliable and Fair Trustworthiness Evaluation Protocol for Platoon Service Recommendation System","authors":"Hongyuan Cheng;Yining Liu;Fei Zhou;Zhiyuan Tan;Xianchao Zhang","doi":"10.23919/cje.2023.00.012","DOIUrl":"https://doi.org/10.23919/cje.2023.00.012","url":null,"abstract":"Aiming at the problems of the communication inefficiency and high energy consumption in vehicular networks, the platoon service recommendation systems (PSRS) are presented. Many schemes for evaluating the reputation of platoon head vehicles have been proposed to obtain and recommend reliable platoon head vehicles. These trustworthiness evaluation protocols for PSRS fail to achieve both reliability and fairness. We first provide a reliable trustworthiness evaluation method to ensure that the reputation level of platoon head vehicle can be calculated by cloud service provider (CSP) with the help of key agreement mechanism and truth discovery technology. The semi-trusted entity CSP may maliciously tamper with the reputation level of the platoon head vehicle. We also provide a reputation level confirmation method to ensure the fairness of trustworthiness evaluation. Formal security proof and security analysis are provided to show that our trustworthiness evaluation protocol can achieve the goals of privacy protection, reliability, fairness and resistance to several security attacks. Experiments demonstrate that this protocol can save execution time and achieve reliable and fair trustworthiness evaluation for PSRS.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 2","pages":"563-575"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10982072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900522","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}
Pub Date : 2025-03-01DOI: 10.23919/cje.2023.00.132
Na Li;Yuyu Shan;Jianqiang Bao;Hongzhang Feng;Yiqun Zhang;Guo Liu
In low-frequency cross-media communication systems, traditional mechanical antennas have problems such as limiting the upper limit of operating frequency due to motor speed, waveform distortion, and limiting transmission rate due to modulation methods. Thus, we have designed a new magnetic shutter type mechanical antenna. It is designed based on the radiation equation of a rotating magnetic dipole, combined with the principle of relative motion between the magnetic dipole and the high permeability shutter material. Relying upon the rotation of the shutter structure, the magnetic field of the spherical permanent magnet array is intermittently shielded, generating a low-frequency magnetic induction signal that is multiplied by the motor speed. The entire antenna system uses a cross array of spherical permanent magnets with two evenly distributed magnetic poles and a two-dimensional signal modulation method that combines frequency modulation and amplitude modulation, so it has high radiation intensity and transmission rate in the ultra-low frequency band. Experimental results show that when the motor speed is $n$ r/s (rad per second), the operating frequency of the mechanical antenna can reach 4n Hz, and the signal amplitude measured at 5 m is 50 mV, which is about 3.5 nT. Compared with the current mechanical antenna of the same volume, its signal radiation intensity is stronger.
{"title":"Magnetic Shutter Mechanical Antenna for Cross-Media Communication","authors":"Na Li;Yuyu Shan;Jianqiang Bao;Hongzhang Feng;Yiqun Zhang;Guo Liu","doi":"10.23919/cje.2023.00.132","DOIUrl":"https://doi.org/10.23919/cje.2023.00.132","url":null,"abstract":"In low-frequency cross-media communication systems, traditional mechanical antennas have problems such as limiting the upper limit of operating frequency due to motor speed, waveform distortion, and limiting transmission rate due to modulation methods. Thus, we have designed a new magnetic shutter type mechanical antenna. It is designed based on the radiation equation of a rotating magnetic dipole, combined with the principle of relative motion between the magnetic dipole and the high permeability shutter material. Relying upon the rotation of the shutter structure, the magnetic field of the spherical permanent magnet array is intermittently shielded, generating a low-frequency magnetic induction signal that is multiplied by the motor speed. The entire antenna system uses a cross array of spherical permanent magnets with two evenly distributed magnetic poles and a two-dimensional signal modulation method that combines frequency modulation and amplitude modulation, so it has high radiation intensity and transmission rate in the ultra-low frequency band. Experimental results show that when the motor speed is <tex>$n$</tex> r/s (rad per second), the operating frequency of the mechanical antenna can reach 4n Hz, and the signal amplitude measured at 5 m is 50 mV, which is about 3.5 nT. Compared with the current mechanical antenna of the same volume, its signal radiation intensity is stronger.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 2","pages":"464-474"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10982049","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900523","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}
Pub Date : 2025-03-01DOI: 10.23919/cje.2023.00.398
Jiancheng Liu;Jingtao Zhang;Tian Li;Yu Zhou;Yanpeng Wang
Considering the non-spread spectrum interference in overlapping frequency domain for direct-sequence spread spectrum (DSSS) satellite communication, the unintended strong interference suppression method based on adaptive cancellation with decision feedback is proposed. Firstly, the model of adaptive cancellation with decision feedback is built by making the best of interference modulation mode and bandwidth. Secondly, the adaptive cancellation based on polynomial nonlinear reconstruction model is analyzed in theory, and the mathematical expression of interference suppression ratio is derived. Finally, the proposed method is simulated under different parameters. The theory analysis and simulation results both show that this method can achieve more than 4.5 dB transmission link processing gain to efficiently suppress the overlapping unintended interference.
{"title":"Unintended Interference Suppression Based on Decision Feedback Adaptive Cancellation for DSSS Satellite Communication","authors":"Jiancheng Liu;Jingtao Zhang;Tian Li;Yu Zhou;Yanpeng Wang","doi":"10.23919/cje.2023.00.398","DOIUrl":"https://doi.org/10.23919/cje.2023.00.398","url":null,"abstract":"Considering the non-spread spectrum interference in overlapping frequency domain for direct-sequence spread spectrum (DSSS) satellite communication, the unintended strong interference suppression method based on adaptive cancellation with decision feedback is proposed. Firstly, the model of adaptive cancellation with decision feedback is built by making the best of interference modulation mode and bandwidth. Secondly, the adaptive cancellation based on polynomial nonlinear reconstruction model is analyzed in theory, and the mathematical expression of interference suppression ratio is derived. Finally, the proposed method is simulated under different parameters. The theory analysis and simulation results both show that this method can achieve more than 4.5 dB transmission link processing gain to efficiently suppress the overlapping unintended interference.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 2","pages":"673-682"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10982095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900613","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}
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