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Transistors With MoS$_{2}$ Subnanometer Channels Embedded in 2D WSe$_{2}$

IF 2.1 4区工程技术

IEEE Transactions on Nanotechnology Pub Date : 2025-03-11 DOI: 10.1109/TNANO.2025.3549522

T. Cusati;D. Marian;A. Toral-Lopez;E. G. Marin;G. Iannaccone;G. Fiori

引用次数: 0

Retraction Notice: A Proposed MIMO Antenna Prototype for Frequency Identification

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-03-06 DOI: 10.1109/JSEN.2025.3546793

Amin H. Al Ka’bi

{"title":"Retraction Notice: A Proposed MIMO Antenna Prototype for Frequency Identification","authors":"Amin H. Al Ka’bi","doi":"10.1109/JSEN.2025.3546793","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3546793","url":null,"abstract":"Frequency Identification in Cognitive Radio (CR) networks is the key step to find the unused frequencies, so CR networks use less bandwidth and energy. The MIMO antenna system which is proposed for spectrum sensing in CR systems is a small super-wideband (SWB) design, which includes three band-notched diversity antennas. There are four identical semi-elliptical monopole antennas, directed perpendicularly with feed lines gently widened CWG type, which constitute a MIMO antenna. Every SWB characteristic has an antenna that has the cross-slot carved through its bottom just like a radiator. The antenna radiator is composed of two linked slits that replicate the image of the split ring resonator and also have a backward-S shaped slit to ensure that there is no negative impact on SWB. The antenna has a bandwidth ratio of 36:1mm and 0.2-43mm waves. In addition, 18dB of isolation and an envelope correlation coefficient of less than 0.01 have been implemented in a resonant frequency band for the MIMO antenna that has orthogonally placed antenna elements. On a frequency of 3.5GHz, 5.5GHz, and 8.5GHz, the gain level drops leading to a maximum gain of 4 dBi for the antenna. The proposed antenna has higher bandwidth ratio and hence incorporates easily into an existing RF equipment. In this manner, this SWB, MIMO antenna demonstrates superiority over those mentioned in the literature with a multi-notched band. In the same manner, we obtain three small super-wideband (SWB), which have not been filtered, so, the design and implementation of the antenna is feasible.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 6","pages":"10512-10512"},"PeriodicalIF":4.3,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10916580","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Sensors Council

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-03-05 DOI: 10.1109/JSEN.2025.3542672

引用次数: 0

Corrections to “Generating Multiple Distinct Feasible Solutions for MEMS Accelerometers Using Deep Learning”

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-03-05 DOI: 10.1109/JSEN.2025.3528277

Xiong Cheng;Zhixiang Zhai;Pengfei Zhang;Yiqi Zhou;Rui Wang;Wenhua Gu;Xiaodong Huang;Daying Sun

引用次数: 0

Corrections to “Improved XGBoost and GM UWB/MEME IMU Positioning Methods for Non-Line-of-Sight Environments”

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-03-05 DOI: 10.1109/JSEN.2024.3524872

Xin Sui;Bangwen Liao;Changqiang Wang;Zhengxu Shi

引用次数: 0

Guest Editorial Special Issue on Energy-Efficient Embedded Intelligent Sensor Systems (S1)

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-03-05 DOI: 10.1109/JSEN.2025.3537212

Michele Magno;Daniela de Venuto;Giuseppe Ferri;Seonyeong Heo

引用次数: 0

Polynomial Formal Verification of a RISC-V Processor

IF 2.1 4区工程技术

IEEE Transactions on Nanotechnology Pub Date : 2025-03-05 DOI: 10.1109/TNANO.2025.3548265

Lennart Weingarten;Kamalika Datta;Rolf Drechsler

引用次数: 0

MFSonar: Multiscale Frequency-Domain Contextual Denoising for Forward-Looking Sonar Image Semantic Segmentation

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-03-04 DOI: 10.1109/JSEN.2025.3545146

Jiayuan Li;Zhen Wang;ShenAo Yuan;Zhu-Hong You

{"title":"MFSonar: Multiscale Frequency-Domain Contextual Denoising for Forward-Looking Sonar Image Semantic Segmentation","authors":"Jiayuan Li;Zhen Wang;ShenAo Yuan;Zhu-Hong You","doi":"10.1109/JSEN.2025.3545146","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3545146","url":null,"abstract":"Semantic segmentation of forward-looking sonar (FLS) images is crucial for enhancing situational awareness in marine environments. However, FLS images are often degraded by environmental noise, similarity noise, and shading effects, which result in low resolution, poor signal-to-noise ratio, and suboptimal image quality. These issues significantly hinder the accuracy of semantic segmentation in FLS images. To address these challenges, we propose a novel method called MFSonar, which is based on the Transformer-Mamba architecture. MFSonar incorporates a context channel denoising module (CCDM) that exploits the similarity characteristics of local and global features to effectively suppress similarity noise and enhance target features. Additionally, the Multiscale Frequency-Domain Decoding Module integrates multiscale frequency-domain convolution with visual state-space (VSS) blocks, leveraging frequency-domain characteristics to mitigate environmental noise and occlusion shadows. Furthermore, our approach prioritizes local features before global features to achieve effective fusion and enhancement of global semantic features and multiscale local visual information. Extensive comparative experiments across multiple datasets demonstrate that MFSonar achieves state-of-the-art performance. Moreover, ablation studies and visual comparisons on a primary dataset validate the superiority, effectiveness, and uniqueness of our approach. Our implementation is available at <uri>https://github.com/NWPUFranklee/PVSonar.git</uri>.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"11792-11808"},"PeriodicalIF":4.3,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved Principal Component Analysis for Magnetic Gradient Signal Detection Using Dual Three-Axis Magnetometers

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-03-04 DOI: 10.1109/JSEN.2025.3544707

Hongyi Yang;Jianying Zheng;Qinglei Hu;Yong Cui

{"title":"Improved Principal Component Analysis for Magnetic Gradient Signal Detection Using Dual Three-Axis Magnetometers","authors":"Hongyi Yang;Jianying Zheng;Qinglei Hu;Yong Cui","doi":"10.1109/JSEN.2025.3544707","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3544707","url":null,"abstract":"Magnetic anomaly detection (MAD) is an effective technique for detecting ferromagnetic materials in the presence of a geomagnetic field. In this article, by integrating and enhancing the orthogonal basis function (OBF) and principal component analysis (PCA), a high-sensitivity PCA (HSPCA) method is proposed, achieving both low computational complexity and high detection accuracy in MAD tasks. We first employ the PCA method to decompose the target signal used in the full magnetic gradient OBF (FMG-OBF) method for detection, referred to as the PCA baseline method, which achieves rapid and automated decomposition. It achieves the detection accuracy of FMG-OBF while reducing the real-time processing time by 61.1%. Furthermore, we propose the HSPCA method as an improvement by modifying the form of target signal to enhance detection sensitivity, establishing a mapping to eliminate the influence of redundant sampling parameters, and applying weights to the basis functions to balance their relative contributions. Ultimately, the computational time of this improved method is only 1.3% of that of the PCA baseline method, and its detection rate is increased by 53.1% compared to FMG-OBF at a noise level of −20 dB with a false alarm rate of 4%. Field experiments are conducted to validate the convenience and practicality of the proposed methods.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"10922-10933"},"PeriodicalIF":4.3,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TinyFL_HKD: Enhancing Edge AI Federated Learning With Hierarchical Knowledge Distillation Framework

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-03-03 DOI: 10.1109/JSEN.2025.3544861

Chung-Wen Hung;Cheng-Yu Tsai;Chun-Chieh Wang;Ching-Hung Lee

{"title":"TinyFL_HKD: Enhancing Edge AI Federated Learning With Hierarchical Knowledge Distillation Framework","authors":"Chung-Wen Hung;Cheng-Yu Tsai;Chun-Chieh Wang;Ching-Hung Lee","doi":"10.1109/JSEN.2025.3544861","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3544861","url":null,"abstract":"With the rapid evolution of artificial intelligence (AI) and the Internet of Things (IoT), machine learning is increasingly being integrated into embedded systems, bringing computational capabilities closer to where data are generated. This article introduces a tiny federated learning framework, which concerns privacy, personalized training, and the constrained computational resources of edge platforms by introducing a novel hierarchical knowledge distillation (HKD), called TinyFL_HKD. The HKD leverages hierarchical learning and advanced encryption security (AES) schemes to ensure data privacy and security. It employs knowledge distillation to reduce model complexity for implementation in edge devices and enhance personalization. The performance of TinyFL_HKD is introduced by using two datasets: the tool wear dataset and the PHM 2010 Data Challenge dataset. Experimental results indicate that the HKD framework surpasses traditional federated averaging (FedAvg) and personalized federated learning (PFL) algorithms in both model accuracy and computational efficiency. This establishes HKD as a resilient solution for edge AI applications.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"12038-12047"},"PeriodicalIF":4.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0