IEEE Sensors Letters最新文献

Fast Monocular Visual-Inertial Odometry With Point-Line Features and Vanishing Point Constraints 基于点-线特征和消失点约束的快速单目视觉惯性里程计

IF 2.2

IEEE Sensors Letters Pub Date : 2025-04-29 DOI: 10.1109/LSENS.2025.3565321

Jingyi Sun;Rui Wang

{"title":"Fast Monocular Visual-Inertial Odometry With Point-Line Features and Vanishing Point Constraints","authors":"Jingyi Sun;Rui Wang","doi":"10.1109/LSENS.2025.3565321","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3565321","url":null,"abstract":"Visual-inertial odometry (VIO) is widely used in autonomous driving, robots, and drones in global navigation satellite system (GNSS)-denied environments. Current VIO methods primarily rely on point features for tracking in the visual front end. However, point-feature-based methods often perform poorly in challenging scenes, such as illumination changes and low textures. Extracting line features as a supplement from images can alleviate the above issues, but it introduces considerable computational overhead. Furthermore, degeneracy is more likely to occur with line features. To address these, we propose a fast monocular VIO that integrates point-line features and vanishing point constraints. We improve the EDLines algorithm by adaptively adjusting the gradient threshold and designing a short-line merging algorithm, which reduces computational time and yields higher quality line features. In addition, we introduce a parallel line filtering algorithm and estimate vanishing points based on it to solve the problem of degeneracy. Experiment results show that the line feature extraction of our method is much faster than that of visual–inertial odometry using point and line features (PL-VIO), and visual–inertial SLAM with point and line features (PL-VINS), and our method also demonstrates better localization accuracy and robustness in challenging scenes.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 6","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073255","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

Geometrically Informed Graph Neural Networks for Distributed Speech Enhancement in Challenging Acoustic Environments 几何信息图神经网络在具有挑战性的声环境中的分布式语音增强

IF 2.2

IEEE Sensors Letters Pub Date : 2025-04-28 DOI: 10.1109/LSENS.2025.3564309

Shih-Hau Fang;Po-Han Li;Hau-Hsiang Jung;Syu-Siang Wang

{"title":"Geometrically Informed Graph Neural Networks for Distributed Speech Enhancement in Challenging Acoustic Environments","authors":"Shih-Hau Fang;Po-Han Li;Hau-Hsiang Jung;Syu-Siang Wang","doi":"10.1109/LSENS.2025.3564309","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3564309","url":null,"abstract":"Speech signals face challenges in the presence of ambient sounds, encompassing factors, such as reverberation and diffuse noise, leading to compromises in clarity and intelligibility. Taking inspiration from the effectiveness of human binaural hearing, researchers have delved into the exploration of distributed speech enhancement (DSE) processors on the distributed microphone system. Despite the achievements of previous approaches, challenges remain, especially in reducing noise from speech signals and improving model interpretability. This letter introduces an innovative geometrically informed graph neural network (GIGNN) designed for DSE tasks. The distinct advantage of GIGNN lies in the capability of graph neural networks (GNNs) to visualize structured data, providing effective in parameterizing a wide array of spatiotemporal interactions and accordingly enhancing the model interpretability. In addition, we assess the effectiveness of geometrically informed spatial matrices within GNNs in our evaluation. Experimental validation in varying signal-to-noise ratios in real-life scenarios underscores the potential of GIGNN.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 6","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949225","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

Medical PPG Sensor for Cardiovascular Disease Diagnosis Using Personalized Hemodynamics Model and Pulse Wave Analysis 基于个性化血流动力学模型和脉搏波分析的医用PPG传感器用于心血管疾病诊断

IF 2.2

IEEE Sensors Letters Pub Date : 2025-04-25 DOI: 10.1109/LSENS.2025.3564573

Yi-Cheng Lai;Yu-Hsiang Lin;Ching-Hao Yu;Shih-Ping Huang;Chong-Yi Liou;Yen-Wen Wu;Shau-Gang Mao

{"title":"Medical PPG Sensor for Cardiovascular Disease Diagnosis Using Personalized Hemodynamics Model and Pulse Wave Analysis","authors":"Yi-Cheng Lai;Yu-Hsiang Lin;Ching-Hao Yu;Shih-Ping Huang;Chong-Yi Liou;Yen-Wen Wu;Shau-Gang Mao","doi":"10.1109/LSENS.2025.3564573","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3564573","url":null,"abstract":"Utilizing the medical photoplethysmography (PPG) sensor, this study presents the development of a hemodynamic model of the human body to diagnose cardiovascular disease. Through advanced signal processing techniques, the real-time PPG and pulse data are analyzed to achieve noninvasive physiological signal measurement. By considering both the cardiovascular pressure output and venous return systems, the model aims to accurately simulate peripheral blood circulation, enabling the creation of a hemodynamic model in physiological data and integrating the relationship between PPG waveforms and pulse signals. The hemodynamic model is established by dividing the human body into four major systems: the heart, the large arteries and medium-sized arteries, the arterioles and capillaries, and the venous system. The real-time PPG and pulse data are collected from the subject under normal conditions and then integrated with the hemodynamic model to generate output data and further refined through systematic computational modeling, resulting in a personalized hemodynamic model corresponding to the individual's PPG and pulse characteristics. By evaluating the results from the personalized node models under normal conditions, real-time, accurate, and effective results between the personalized model outputs and pulse waveforms are achieved. This process allows for the adjustment of the hemodynamic model parameters, thereby leading to the development of an optimized, individualized hemodynamic model tailored to the specific physiological conditions of the person.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 5","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929711","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

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