IEEE Sensors Letters最新文献_第3页

In-House Developed Graphene-Based Leaf Wetness Sensor With Enhanced Stability 内部开发的基于石墨烯的叶片湿度传感器具有增强的稳定性

IF 2.2

IEEE Sensors Letters Pub Date : 2025-04-23 DOI: 10.1109/LSENS.2025.3563696

Kamlesh Patle;Pooja Yogi;Devkaran Maru;Yash Agrawal;Vinay S. Palaparthy;Kambiz Moez

{"title":"In-House Developed Graphene-Based Leaf Wetness Sensor With Enhanced Stability","authors":"Kamlesh Patle;Pooja Yogi;Devkaran Maru;Yash Agrawal;Vinay S. Palaparthy;Kambiz Moez","doi":"10.1109/LSENS.2025.3563696","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3563696","url":null,"abstract":"Prolonged leaf wetness is a critical factor influencing the development and spread of plant diseases, particularly fungal pathogens, which thrive in moist environments. These pathogens negatively impact crop health, photosynthesis, nutrient absorption, and agricultural productivity. Accurately measuring leaf wetness duration (LWD) is essential for early disease detection and mitigation strategies. Leaf wetness sensors (LWS) are designed to detect wetness on leaf surfaces. Traditional LWS, fabricated using printed circuit boards, have been extensively studied and are commercially available such as the PHYTOS-31. However, flexible LWS are preferred due to their ability to conform to the natural shape of leaves, improving accuracy, better contact resistance, and durability under field conditions. However, these sensors exhibited limitations such as electrode oxidation and peeling, reducing stability and wetness sensitivity over time. To overcome these challenges, this study investigates replacing conventional metal-based interdigitated electrodes (IDEs) with graphene-based IDEs, leveraging graphene's superior electrical, mechanical, and thermal properties. The fabricated flexible graphene LWS has been evaluated for its sensitivity, response time, hysteresis, temperature response, and stability, which are about ≈26,000%, ≈35 s, ≈5%, ≈6.79%, and 5 months, respectively. Benchmarking against the commercially available PHYTOS-31 LWS demonstrated an absolute error of ±3%, confirming the reliability and accuracy. These results validate the potential of graphene-based flexible LWS for accurate and long-term monitoring of LWD in agricultural and ecological applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 6","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On-Chip $mathbf{G}{{mathbf{e}}_{1 - {bm{x}}}}mathbf{S}{{mathbf{n}}_{bm{x}}}$ Slot Optical Waveguides-Based Highly Sensitive Mid-Infrared Biochemical Sensors for Room Temperature Applications 片上$mathbf{G}{{mathbf{e}}_{1 - {bm{x}}}}mathbf{S}{{mathbf{n}}_{bm{x}}}$基于槽位光波导的高灵敏度室温中红外生化传感器

IF 2.2

IEEE Sensors Letters Pub Date : 2025-04-23 DOI: 10.1109/LSENS.2025.3563778

Harshvardhan Kumar;Jagrati Yadav;Neha Soni

{"title":"On-Chip $mathbf{G}{{mathbf{e}}_{1 - {bm{x}}}}mathbf{S}{{mathbf{n}}_{bm{x}}}$ Slot Optical Waveguides-Based Highly Sensitive Mid-Infrared Biochemical Sensors for Room Temperature Applications","authors":"Harshvardhan Kumar;Jagrati Yadav;Neha Soni","doi":"10.1109/LSENS.2025.3563778","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3563778","url":null,"abstract":"In this work, we present the first proof of complementary metal-oxide-semiconductor-compatible GeSn slot optical waveguides (WGs)-based highly sensitive biochemical sensors for mid-infrared (MIR) applications. Moreover, proposed WGs are designed to achieve high sensitivity values in the MIR region, specifically at 3.67 μm for lipids detection. The simulation indicates that GeSn core height and width affect the confinement factor significantly in both the sensing and slot regions. In an optimized WG geometry (H = W = 300 nm), the proposed cross-slot waveguide (CS-WG) demonstrates the highest confinement factors of 43% and 50% in the slot and sensing regions, respectively, notably higher than the values obtained for the designed vertical-slot-WG and horizontal-slot-WG. Subsequently, the WG sensitivity is determined by taking into account the impact of changes in the thickness of the sensing layer. The results indicate that a biochemical sensor utilizing a cross-slot WG demonstrates the highest sensitivity compared to biochemical sensors based on either horizontal-slot or vertical-slot WGs. Furthermore, the CS-WG MIR sensor we propose demonstrates the sensitivity value of <inline-formula><tex-math>$2.8 times {{10}^{ - 3}} mathrm{n}{{mathrm{m}}^{ - 1}}$</tex-math></inline-formula>, which is one order of magnitude higher than the sensitivity value of <inline-formula><tex-math>$4 times {{10}^{ - 4}} mathrm{n}{{mathrm{m}}^{ - 1}}$</tex-math></inline-formula> achieved by the earlier reported Si slot SWIR WG sensor. This comparison highlights the efficacy of our proposed biochemical sensors for MIR sensing applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 5","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EEG Artifact Removal At the Edge Using AI Hardware 基于AI硬件的边缘脑电信号伪影去除

IF 2.2

IEEE Sensors Letters Pub Date : 2025-04-22 DOI: 10.1109/LSENS.2025.3563390

Mahdi Saleh;Le Xing;Alexander J. Casson

引用次数: 0

Leveraging Trend-Aware Attention in Transformers for Lithium-Ion Battery Capacity Prediction 利用变压器趋势感知注意力进行锂离子电池容量预测

IF 2.2

IEEE Sensors Letters Pub Date : 2025-04-21 DOI: 10.1109/LSENS.2025.3562870

Chuang Chen;Yuheng Wu;Jiantao Shi;Dongdong Yue;Hongtian Chen

{"title":"Leveraging Trend-Aware Attention in Transformers for Lithium-Ion Battery Capacity Prediction","authors":"Chuang Chen;Yuheng Wu;Jiantao Shi;Dongdong Yue;Hongtian Chen","doi":"10.1109/LSENS.2025.3562870","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3562870","url":null,"abstract":"The prediction of lithium-ion battery capacity plays an essential role in ensuring the reliability and safety of modern electronic devices. To effectively capture the local trend information inherent in lithium-ion batteries and enhance the accuracy of capacity forecasts, this letter presents an innovative Transformer model that incorporates a specialized trend-aware attention mechanism. This novel model synergistically combines the strengths of trend-aware attention and the Transformer encoder. It introduces 1-D convolution within the trend-aware attention framework, thereby replacing the traditional linear projections of queries and keys found in conventional self-attention mechanisms. This strategic enhancement enables the model to more adeptly and efficiently capture both local trends and global features, surpassing the performance of standard self-attention approaches. Extensive validation using the NASA and CALCE lithium-ion battery datasets reveals that the proposed model significantly outperforms existing state-of-the-art models across a variety of evaluative metrics. This noteworthy performance underscores the model's advantages in effectively managing the complexities of time-series data for accurate battery capacity prediction.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 6","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Sensors Letters Publication Information IEEE传感器通讯出版信息

IF 2.2

IEEE Sensors Letters Pub Date : 2025-04-18 DOI: 10.1109/LSENS.2025.3560520

引用次数: 0

Inductive Pressure Sensors Using 3D-Printed Structures With Tunable Stiffness 使用具有可调刚度的3d打印结构的感应压力传感器

IF 2.2

IEEE Sensors Letters Pub Date : 2025-04-18 DOI: 10.1109/LSENS.2025.3562455

Rahul Bhaumik;Thomas Preindl;Alexandra Ion;Camilo Ayala-Garcia;Nitzan Cohen;Michael Haller;Niko Münzenrieder

引用次数: 0

IEEE Sensors Letters Subject Categories for Article Numbering Information 用于物品编号信息的IEEE传感器字母主题分类

IF 2.2

IEEE Sensors Letters Pub Date : 2025-04-18 DOI: 10.1109/LSENS.2025.3560524

引用次数: 0

FPGA-Based In-Vehicle Occupancy Detection Using mmWave Radar With Mexican Hat Wavelet Transform 基于fpga的墨西哥帽小波变换毫米波雷达车载乘员检测

IF 2.2

IEEE Sensors Letters Pub Date : 2025-04-17 DOI: 10.1109/LSENS.2025.3562097

Anand Mohan;Hemant Kumar Meena;Mohd Wajid;Abhishek Srivastava

{"title":"FPGA-Based In-Vehicle Occupancy Detection Using mmWave Radar With Mexican Hat Wavelet Transform","authors":"Anand Mohan;Hemant Kumar Meena;Mohd Wajid;Abhishek Srivastava","doi":"10.1109/LSENS.2025.3562097","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3562097","url":null,"abstract":"A demonstration of the implementation of vehicle occupancy detection on hardware-software is shown in this letter. For the purpose of validating applications for vehicle occupancy detection, a hardware field programmable gate array (FPGA) platform, also known as Python productivity for zynq ultrascale+ MPSoC (PYNQ-ZU), is a feasible embedded architecture. Automatic in-car occupancy monitoring is an important technology in modern transportation, with major implications for safety, energy efficiency, and smart vehicle management. One of the primary benefits of millimeter wave (mmWave) radar is its ability to accurately detect the number and location of vehicle occupants, mmWave radar ensures robust detection under all lighting and weather conditions. In our research, the proposed approach was applied to point cloud images. Following the generation of 3-D point cloud images, two filters, top-view (TV), and front-view (FV), were used to improve vehicle occupancy detection. These filters transformed 3-D images into 2-D ones. TV filter was found to be more effective than the FV filter. After filtering the 2-D images, Mexican Hat Wavelet Transform (MHWT) was used to extract features from them. Four machine learning methods were then used to determine vehicle seat occupancy, with logistic regression (LR) and support vector machine producing the highest results, with an accuracy of 98%. In comparison to existing methods, the proposed approach, which utilizes mmWave radar, TV Filter, MHWT, FPGA (PYNQ-ZU), and LR, was determined to significantly improve the accuracy of vehicle occupancy detection.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 5","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multistream LSTM for Artifact Detection in Impedance Cardiography 多流LSTM在阻抗心电图伪影检测中的应用

IF 2.2

IEEE Sensors Letters Pub Date : 2025-04-16 DOI: 10.1109/LSENS.2025.3561688

Maryam Hosseini;Massimiliano de Zambotti;Fiona C. Baker;Mohamad Forouzanfar

{"title":"Multistream LSTM for Artifact Detection in Impedance Cardiography","authors":"Maryam Hosseini;Massimiliano de Zambotti;Fiona C. Baker;Mohamad Forouzanfar","doi":"10.1109/LSENS.2025.3561688","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3561688","url":null,"abstract":"Monitoring cardiac hemodynamic parameters, such as cardiac output and pre-ejection period, is critical for assessing cardiovascular function, particularly in critically ill patients. Impedance cardiography (ICG) offers a noninvasive approach to measuring these parameters; however, its utility is often compromised by motion artifacts and electrode displacement. Many traditional artifact detection methods rely on rigid waveform templates, which may struggle to adapt to individual variations in ICG morphology, potentially resulting in limited generalization and higher misclassification rates in certain scenarios. In this study, we propose a deep learning-based framework that combines a multistream long short-term memory (LSTM) network, attention mechanisms, and ensemble learning to automatically detect corrupted ICG cycles. The model concurrently processes raw ICG signals and their derivatives to capture both temporal dynamics and morphological transitions. Attention layers highlight diagnostically relevant regions, while data augmentation and ensemble postprocessing improve generalization and robustness. The proposed method was validated on a dataset of 2000 ICG cycles from 20 individuals, achieving an accuracy of 96.42% against human expert visual detection, significantly outperforming traditional methods and single-stream LSTM models. This method enhances artifact detection and supports more reliable noninvasive cardiac monitoring.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 5","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Natural Cellulose-Based Flexible Bioimpedance Electrodes for AgriFood Applications 农业食品应用的天然纤维素基柔性生物阻抗电极

IF 2.2

IEEE Sensors Letters Pub Date : 2025-04-16 DOI: 10.1109/LSENS.2025.3561689

Ahmed Rasheed;Sundus Riaz;Pietro Ibba;Giulia Elli;Angelo Zanella;Luisa Petti;Paolo Lugli

{"title":"Natural Cellulose-Based Flexible Bioimpedance Electrodes for AgriFood Applications","authors":"Ahmed Rasheed;Sundus Riaz;Pietro Ibba;Giulia Elli;Angelo Zanella;Luisa Petti;Paolo Lugli","doi":"10.1109/LSENS.2025.3561689","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3561689","url":null,"abstract":"This letter presents a monolayer onion epidermis as an ultrathin substrate for the development of dry electrodes used in electrical impedance spectroscopy (EIS), offering biocompatibility, flexibility, and high fidelity. The dry electrode is fabricated by sputtering a 100 nm layer of molybdenum onto the hydrophilic side of the epidermis membrane. Optical characterization confirms the structural integrity and quality of the thin metal film. Electrical characterization reveals a low sheet resistance of 151.28 <inline-formula><tex-math>$pm$</tex-math></inline-formula> 0.08 <inline-formula><tex-math>$Omega$</tex-math></inline-formula>/m<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>, and experimental EIS studies demonstrate the electrode's enhanced electrical characteristics and capability to acquire impedance spectra from various fruits. In addition, durability tests over six months show comparable performance to commercial electrodes. This biocompatible electrode offrs a promising, cost-effective, and sustainable solution with reduced resource utilization, ideal for agrifood and wearable electronics applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 5","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0