Electronics Letters最新文献_第7页

Time-Based Wireless Synchronous Detection of Bioimpedance Signals

IF 0.7 4区工程技术

Electronics Letters Pub Date : 2025-02-19 DOI: 10.1049/ell2.70181

Virgilio Valente, Hanieh Ashrafirad

引用次数: 0

Non-Linearity Incremental Ensemble Learning Based Depth Explorations of Latent Individual Contributions

IF 0.7 4区工程技术

Electronics Letters Pub Date : 2025-02-19 DOI: 10.1049/ell2.70178

Xiaozheng Deng, Yuanyuan Zhang, Shasha Mao, Peng Liu

{"title":"Non-Linearity Incremental Ensemble Learning Based Depth Explorations of Latent Individual Contributions","authors":"Xiaozheng Deng, Yuanyuan Zhang, Shasha Mao, Peng Liu","doi":"10.1049/ell2.70178","DOIUrl":"https://doi.org/10.1049/ell2.70178","url":null,"abstract":"In ensemble learning, individual accuracy and diversities are two key factors for improving the ensemble performance, and most existing methods have been designed from the view of boosting individual diversities. Whereas stronger individual diversities inevitably result in decreasing accuracies of partial individuals, it brings a great challenge for improving the ensemble performance. In fact, we observe that the contribution of individuals should be a crucial factor for ensemble learning, which can effectively alleviate the problem of balancing diversity and accuracy, but it is ignored in most existing studies. Based on this, we propose a new incremental non-linearity deep ensemble learning method that effectively combines multiple individuals based on the exploration of individual contributions by utilizing deep learning. In the proposed method, a characterization matrix is first constructed to represent the original individuals. Then, a deep ensemble network is constructed to explore the potential contribution of individuals in conjunction with the optimization objective of ensemble learning. Interestingly, a special layer is designed to achieve the minimization of ensemble error. Finally, experimental results on public datasets illustrate that the proposed method achieves the average 0.66–4.8% performance improvements compared to existing typical ensemble methods.","PeriodicalId":11556,"journal":{"name":"Electronics Letters","volume":"61 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.70178","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research and Improvement of Muller C Element

IF 0.7 4区工程技术

Electronics Letters Pub Date : 2025-02-19 DOI: 10.1049/ell2.70184

Zhizhi Chen, Qian Wang, Houpeng Chen, Xi Li, Xiaogang Chen, Sannian Song, Zhitang Song

引用次数: 0

Dual-Loop Antennas With a Branched Feed-Line for Broadband Circular Polarisation

IF 0.7 4区工程技术

Electronics Letters Pub Date : 2025-02-19 DOI: 10.1049/ell2.70180

Kazuhide Hirose, Shintaro Mita, Yuya Urushibata, Hisamatsu Nakano

引用次数: 0

Diode Laser-Based Generation of Mode-Locked Pulses at 1067 Nm (290 fs, 69 W Peak Power)

IF 0.7 4区工程技术

Electronics Letters Pub Date : 2025-02-17 DOI: 10.1049/ell2.70176

Paul Haas, Nils Surkamp, Navina Kleemann, Heike Christopher, Andrea Knigge, Carsten Brenner, Martin R. Hofmann

引用次数: 0

Cigarette defect detection based on independent feature extraction constraints

IF 0.7 4区工程技术

Electronics Letters Pub Date : 2025-02-17 DOI: 10.1049/ell2.70163

Zhendong Su, Jiang Li, Guoyun Huang, Zhanheng Tang, Honghan Qin, Liren Huang, Jian Zhou, Benxue Liu

{"title":"Cigarette defect detection based on independent feature extraction constraints","authors":"Zhendong Su, Jiang Li, Guoyun Huang, Zhanheng Tang, Honghan Qin, Liren Huang, Jian Zhou, Benxue Liu","doi":"10.1049/ell2.70163","DOIUrl":"https://doi.org/10.1049/ell2.70163","url":null,"abstract":"The detection of appearance defects in cigarettes is crucial in the field of industrial defect detection. Most existing detection methods achieve defect detection by utilizing deep learning to learn feature representations of various types of defects. However, due to the complexity and randomness of the production and processing process, the differences between defects in different cigarettes are not significant, which greatly affects the detection performance. Therefore, to address this issue, this article proposes a cigarette defect detection method based on independent feature extraction constraints, called IFEC. The core idea of this method is to extract independent features of different defect categories to increase the differences between features of different categories, enhance the distinguishability of features, and achieve accurate cigarette defect detection. In IFEC, an independence feature extraction constraint module is proposed that constrains the network to extract highly independent defect features of different categories through feature decoupling and feature decorrelation. The sufficient experimental results indicate that the proposed IFEC has superior detection performance compared to existing detection methods.","PeriodicalId":11556,"journal":{"name":"Electronics Letters","volume":"61 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.70163","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143424148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CSS-UNet: Convolution-State Space Enhanced UNet for Cardiac MRI Segmentation

IF 0.7 4区工程技术

Electronics Letters Pub Date : 2025-02-17 DOI: 10.1049/ell2.70175

Yaliang Tong, Kun Liu, Yuquan He, Ping Yang

{"title":"CSS-UNet: Convolution-State Space Enhanced UNet for Cardiac MRI Segmentation","authors":"Yaliang Tong, Kun Liu, Yuquan He, Ping Yang","doi":"10.1049/ell2.70175","DOIUrl":"https://doi.org/10.1049/ell2.70175","url":null,"abstract":"Cardiac magnetic resonance imaging (CMR) is widely adopted in clinic for the assessment of cardiac anatomical structures and functions, and accurately segmenting left ventricular, myocardium and right ventricle from CMR plays an important role in clinical practice. Convolutional neural networks (CNNs), for example, U-Net, have been widely used in CMR segmentation. However, current CNN-based models focus on extracting local features via convolution modules, which cannot well understand the long-range dependencies within images, leading to the sub-optimal solution for CMR segmentation task. Inspired by Mamba that efficiently captures global context information using state space model, we propose a novel CMR segmentation model, called CSS-UNet, that can capture both local features and global contexts simultaneously by fusing features from convolution block and visual state space block. Our new model follows the design of U-Net architecture that contains encoder and decoder with skip connections, where a proposed feature fusion module, called spatial-state space, is seamlessly integrated into our model. By using the spatial-state space module, the low-level and high-level features can be extracted and fused for capturing both global and local information, enhancing the capability of feature extraction of CSS-UNet. We evaluate our proposed model on two public CMR datasets, and the experimental results reveal that our proposed model outperforms the most widely-used UNet, demonstrating the effectiveness of our model.","PeriodicalId":11556,"journal":{"name":"Electronics Letters","volume":"61 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.70175","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143424147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Small target detection with decoupling and decorrelation of confusion features

IF 0.7 4区工程技术

Electronics Letters Pub Date : 2025-02-15 DOI: 10.1049/ell2.70158

Yonghua Zhang, Hui Wang, He Tang, Xingze Liu, Benxue Liu, Siyuan Sun

{"title":"Small target detection with decoupling and decorrelation of confusion features","authors":"Yonghua Zhang, Hui Wang, He Tang, Xingze Liu, Benxue Liu, Siyuan Sun","doi":"10.1049/ell2.70158","DOIUrl":"https://doi.org/10.1049/ell2.70158","url":null,"abstract":"Small target detection has important application value in military, cigarette defect detection, monitoring and remote sensing fields. However, due to the complex background interference of infrared images, the background and target features are confused with each other, which makes it difficult for existing detection methods to effectively distinguish the features of the background and the target, resulting in poor detection effect. Therefore, to solve this problem, this paper proposes a small target detection method based on confusion feature decoupling and decorrelation, aiming to achieve accurate detection of small targets in complex environments by extracting robust small target features. Specifically, in confusion feature decoupling and decorrelation, we propose a confusion feature decoupling and decorrelation module. By introducing a feature decoupling mechanism, the features of the input image are decomposed into independent background features and target features, and feature decorrelation is used to achieve independence between the target and the background. The target features after decoupling and decorrelation are purer, which helps to reduce background interference and thus improve detection performance. Systematic experimental results show that the detection performance of the proposed method on public small target detection datasets is much better than that of existing advanced detection methods.","PeriodicalId":11556,"journal":{"name":"Electronics Letters","volume":"61 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.70158","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implementation of an Ultra-Low Profile High-Purity OAM Generator

IF 0.7 4区工程技术

Electronics Letters Pub Date : 2025-02-14 DOI: 10.1049/ell2.70174

Songtao Peng, Youlei Pu

{"title":"Implementation of an Ultra-Low Profile High-Purity OAM Generator","authors":"Songtao Peng, Youlei Pu","doi":"10.1049/ell2.70174","DOIUrl":"https://doi.org/10.1049/ell2.70174","url":null,"abstract":"This letter presents an orbital angular momentum (OAM) generator based on a uniform circular array (UCA) micro-integrated structure that produces ultra-high purity OAM vortex beams at Ka-band while maintaining a low profile. Utilizing cut-angle microstrip patch elements, the UCA excites circularly polarized waves and generates OAM modes with (<math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>l</mi>\u0000 <mo>=</mo>\u0000 <mo>±</mo>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 <annotation>$l=pm 1$</annotation>\u0000 </semantics></math>) through specific amplitude and phase signal inputs. A key feature of this design is the integration of a substrate-integrated waveguide (SIW) radial feed network with the UCA, significantly lowering fabrication costs and reducing the overall thickness to only 1.1 mm (0.11<math>\u0000 <semantics>\u0000 <msub>\u0000 <mi>λ</mi>\u0000 <mn>0</mn>\u0000 </msub>\u0000 <annotation>$lambda _{0}$</annotation>\u0000 </semantics></math> at 30.0 GHz). Measurements of the Ka-band prototype reveal an OAM mode purity of 93% for (<math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>l</mi>\u0000 <mo>=</mo>\u0000 <mo>+</mo>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 <annotation>$l=+1$</annotation>\u0000 </semantics></math>) and a 10 dB bandwidth of approximately 1.8 GHz.","PeriodicalId":11556,"journal":{"name":"Electronics Letters","volume":"61 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.70174","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","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 Novel D-InSAR and Offset Tracking Fusion Algorithm Based on Variance-Masked Mean Filtering

IF 0.7 4区工程技术

Electronics Letters Pub Date : 2025-02-13 DOI: 10.1049/ell2.70173

Yifan Wang, Gen Li, Zihan Hu, Chuanxu Sun, Zhe Li

引用次数: 0