Chinese Journal of Electronics最新文献

{"title":"Unlearning Recently Learned Data to Preserve Historical Learning for Dynamic Data Stream Classification","authors":"Yimin Wen;Xingzhi Zhou;Xiang Liu;Yun Xue;Chenzhong Bin","doi":"10.23919/cje.2024.00.219","DOIUrl":"https://doi.org/10.23919/cje.2024.00.219","url":null,"abstract":"At present, dynamic data stream classification has achieved many successful results through concept drift detection and ensemble learning. However, generally, due to delay in concept drift detection, the active classifier may further learn data belonging to a new concept. This will ultimately degrade the generalization capability of this active classifier on its corresponding concept. Thus, how can a classifier corresponding to one concept unlearns the learned data belonging to another concept? Two unlearning algorithms are proposed to address this problem. The first one based on the passive-aggressive (PA) algorithm adopts the least squares method to reversely update the already-trained model, achieving the effect of approximately unlearning, while another based on a modified PA algorithm achieves complete unlearning by modifying the loss function of the PA algorithm. The comprehensive experiments illustrated the effectiveness of these proposed methods.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 3","pages":"849-860"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11060016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519338","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}

{"title":"Model Parameter Extraction for InGaN/GaN Multiple Quantum Well-Based Solar Cells Using Dynamic Programming","authors":"Hengsheng Shan;Chengke Li;Xiaoya Li;Minghui Li;Yifan Song;Shufang Ma;Bingshe Xu","doi":"10.23919/cje.2023.00.377","DOIUrl":"https://doi.org/10.23919/cje.2023.00.377","url":null,"abstract":"A dynamic programming algorithm is proposed for parameter extraction of the single-diode model (SDM). Five parameters of SDM are extracted from current-voltage curves of InGaN/GaN multi-quantum wells solar cells under AM1.5 standard sunlight conditions, with indium compositions of 7% and 18%. The range of series resistance of the device is adaptively selected and its value is randomly determined. 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 current-voltage curve and the photoelectric conversion efficiency. Based on this parameter extraction approach, the proposed algorithm is faster and more accurate compared to other conventional algorithms. Additionally, the obtained RMSE value is controlled within 1.2E-5, and the calculated fill factor and photoelectric conversion efficiency are consistent with the measured values. This study provides a reference for power optimization of advanced semiconductor photovoltaic cell systems.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 2","pages":"412-421"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10982082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900611","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}

{"title":"Congestion Control Method for Campus Opportunity Network Based on Ant Colony Algorithm","authors":"Peng Li;Yumei Cao;Huan Jia;Xiaoming Wang;Xiaojun Wu","doi":"10.23919/cje.2024.00.019","DOIUrl":"https://doi.org/10.23919/cje.2024.00.019","url":null,"abstract":"Due to the limited storage resources of portable devices, congestion control has become a hot direction in opportunity networks. To address the issue of heavy loads on certain nodes, which can impact routing efficiency and overall network performance, this paper proposes a load-balancing opportunity routing algorithm based on ant colony optimization (LBOR) in a campus environment. The congestion status is represented by the ratio of message drop receptions within a certain period and the occupancy of the cache. Path selection is based on the concentration of pheromones and the pheromones on the path are updated when a data transmission is completed. In the event of congestion, the algorithm prevents a large amount of data from entering the node and unloads the data to other nodes, even if they are not the optimal relay nodes. Experimental results demonstrate that the proposed algorithm effectively improves data transmission success rates, reduces network loads, and decreases the number of packet losses, especially under low latency conditions.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 2","pages":"576-585"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10982071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900612","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}

{"title":"New Coefficient Grouping for Complex Affine Layers","authors":"Wenxiao Qiao;Siwei Sun;Ying Chen;Lei Hu","doi":"10.23919/cje.2023.00.373","DOIUrl":"https://doi.org/10.23919/cje.2023.00.373","url":null,"abstract":"Recently, designing symmetric primitives for applications in cryptographic protocols including multi-party computation, fully homomorphic encryption, and zero-knowledge proofs has become an important research topic. Among many such new symmetric schemes, a power function over a large finite field <tex>$mathbb{F}_{q}$</tex> is commonly used. In this paper, we revisit the algebraic degree's growth for a substitution-permutation network (SPN) cipher over <tex>$mathbb{F}_{2^{n}}(ngeq 3)$</tex>, whose S-box is defined as a composition of a power function <tex>$P(x)=x^{2^{d}+1}$</tex> where <tex>$dgeq 1$</tex> with a polynomial <tex>$A(x)=a_{0}+ sumlimits_{w=1}^{W}a_{w}x^{2^{beta_{w}}}$</tex> where <tex>$a_{i}in mathbb{F}_{2^{n}}$</tex> for <tex>$0leq ileq W$</tex> and <tex>$a_{w}neq 0$</tex> for <tex>$1leq wleq W$</tex>. We propose a new coefficient grouping technique, which is based on our new description of the monomials that will probably appear in the state. Specifically, we propose a new measure to find proper <tex>$(beta_{1},beta_{2}, ldots,beta_{W})$</tex> for the algebraic degree's fastest growth and a new method to compute the algebraic degree's upper bound for arbitrary <tex>$A(x)$</tex>. Especially for Chaghri, which was presented at ACM CCS 2022, we obtained a tighter upper bound on the algebraic degree.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 2","pages":"520-532"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10982055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900633","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}

{"title":"Word2State: Modeling Word Representations as States with Density Matrices","authors":"Chenchen Zhang;Qiuchi Li;Zhan Su;Dawei Song","doi":"10.23919/cje.2023.00.336","DOIUrl":"https://doi.org/10.23919/cje.2023.00.336","url":null,"abstract":"Polysemy is a common phenomenon in linguistics. Quantum-inspired complex word embeddings based on Semantic Hilbert Space play an important role in natural language processing, which may accurately define a genuine probability distribution over the word space. The existing quantum-inspired works manipulate on the real-valued vectors to compose the complex-valued word embeddings, which lack direct complex-valued pre-trained word representations. Motivated by quantum-inspired complex word embeddings, we propose a complex-valued pre-trained word embedding based on density matrices, called Word2State. Unlike the existing static word embeddings, our proposed model can provide non-linear semantic composition in the form of amplitude and phase, which also defines an authentic probabilistic distribution. We evaluate this model on twelve datasets from the word similarity task and six datasets from the relevant downstream tasks. The experimental results on different tasks demonstrate that our proposed pre-trained word embedding can capture richer semantic information and exhibit greater flexibility in expressing uncertainty.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 2","pages":"649-660"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10982096","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900642","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}

{"title":"A Millimeter-Wave Sensor and Differential Filter-Paper-Based Measurement Method for Cancer Cell Detections","authors":"Yi Le;Hao Liu;Guodong Su;Jun Liu;Xiang Wang;Lingling Sun","doi":"10.23919/cje.2024.00.047","DOIUrl":"https://doi.org/10.23919/cje.2024.00.047","url":null,"abstract":"This paper introduces a novel, easily-designed millimeter-wave sensor and an innovative liquid sensing method, both suitable for biological sample detection and cancer cell discrimination. The sensor, composed of coplanar waveguides with load resonators, features a centrally symmetric stepped-impedance resonator that creates a detection region, capable of achieving multiple transmission poles and zeros. This resonator is responsive to the equivalent dielectric constant of the surrounding space, mirroring the electromagnetic properties of the tested sample via the resonant frequency and notch depth. The proposed sensing method uses filter paper to characterize a liquid's electromagnetic properties by comparing the S-parameters of dry and wet filter paper loaded onto the sensor. This method, an alternative to traditional microfluidic channels, allows all planar microwave/millimeter-wave solid dielectric constant sensors to robustly detect liquid materials. Applied to biomedicine, the design enables the sensor to generate multiple transmission peaks in the 20–60 GHz range, thereby facilitating discrimination of various cancer cell culture media and suspensions. Compared to traditional biochemical methods, this approach significantly reduces cancer detection costs and offers new avenues for label-free, real-time detection.","PeriodicalId":50701,"journal":{"name":"Chinese Journal of Electronics","volume":"34 2","pages":"401-411"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10982078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900634","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}

{"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}

{"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}

{"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}

{"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}